

PhD
 M. Saravanan (2008)

Thesis title: OntologyBased Retrieval and Automatic Summarization of Legal Judgments
 Ericsson R &D, Chennai
 ABSTRACT

In the current scenario of information overload, the most challenging task
before the computing society is to devise methods for efficient retrieval and
timely delivery of relevant information in short and precise segments to the
end user. The increasing availability of legal judgments in digital form
creates opportunities and challenges for both the legal community and for
information technology researchers. While digitized documents facilitate easy
access to a large number of documents, finding all documents that are relevant
to the task at hand and comprehending a vast number of them are nontrivial
tasks. In this thesis we address the issues of legal judgment retrieval and of
aiding in rapid comprehension of the retrieved documents. The usual practice of
the legal community is that of reading the summaries (headnotes) instead of
reading the entire judgments. Moreover, the legal community is also in search
of an endtoend legal information retrieval system which can give a solution
for their day today activities. In our thesis, a system has been proposed and
tested for creating headnotes automatically from the retrieved legal judgments.
Rhetorical roles are used to represent the collection of sentences under
common titles, according to their function in a narrative. Imposing a structure
on a document using such roles greatly aids in comprehension. In our study,
seven rhetorical roles namely identifying the case, establishing the facts of
the case, arguing the case, history of the case, arguments, ratio decidendi and
final decision have been identified for this purpose. We pose the problem of
performing genre analysis for identifying these roles present in a judgment as
one of automatic segmentation of a document. Conditional Random Fields, a
discriminative graphical model has been employed in our work for text
segmentation and the resulting labels are used to structure the judgments and
the corresponding headnotes to aid in better comprehension.
The other major problem we address is that of retrieval of judgments
relevant to the cases a legal user is currently involved in. To facilitate this
we have developed a novel framework for constructing a legal knowledge base in
the form of a legal ontology. Ontologies ensure efficient retrieval of
resources by enabling inferences based on domain knowledge. The knowledge base
developed is used to enhance the query given by the user in order to retrieve
more relevant judgments. The retrieved judgments are considered for generating
a final summary.
We have used term distribution model to extract important sentences
from the legal judgments for the automatic summarization task. Another major
issue to be handled in our study is to generate an "userfriendly"
summary at the end. The rhetorical roles identified in the earlier phase have
been used to improve the final summary. That is, we can significantly improve
extractionbased summarization to generate coherent and concise outputs that
incorporate important features of the legal judgment, namely the ratio
decidendi and final decision.
The documents considered for study in this thesis are from three
different subdomains viz. rent control, income tax and sales tax related to
civil court judgments. The proposed model was evaluated on a specific data
collection spanning three legal sub domains. The performances of our system and
other autosummarizers available in the public domain were compared with the
summary generated by a set of human subjects. It is found that, at different
stages, our systemgenerated output is close to the outputs generated by human
subjects, and it is better than the other tools considered in the study. Thus,
the present work comprises almost all the aspects of finding relevant
information in the document space for helping the legal communities in their
information needs.
 Pragathi Priyadarshini B. (2015)(BT, Jointly with Prof. V. Srinivasa
Chakravarthy)
 Thesis title: A Risk Based Decision Making Model Combining the
Functions of Dopamine and Serotonin in the Basal Ganglia
 Predoctoral fellow, IIT Madras
 ABSTRACT

The research work presented in this thesis proposes a computational model that
reconciles the various functions of neuromodulators dopamine (DA) and serotonin
(5HT) in the basal ganglia (BG), viz., risk sensitivity, time scale of reward
punishment prediction, and rewardpunishment sensitivity. A utilitybased
approach is proposed to be more suitable to model the actions of DA and 5HT in
the BG, compared to the purely valuebased approaches adopted in existing
literature. The value function represents the expectation of the sampled reward
outcomes, while the utility function is a combination of value and risk
function that captures the variance associated with the observed reward
samples. The thesis begins with an abstract, utilitybased model that
reconciles three divergent functions of DA and 5HT in BGmediated decision
making processes. This is further developed into a network model representation
of the BG in the later chapters of the thesis.
Basal Ganglia (BG) is a group of subcortical nuclei involved in wide ranging
functions such as cognition and decision making, voluntary motor control,
timing, procedural memory and emotions. The diverse functions of the BG are
coordinated by key neuromodulators including DA and 5HT. Loss of dopaminergic
cells in Substantia Nigra pars compacta, a mesencephalic nucleus, is the
primary etiology for Parkinson's disease (PD), a neurodegenerative
disorder. There is evidence that, in addition to DA deficiency, PD is
characterized by serotonergic changes. Models of the BG often aim to explain
functions of BG in both control and PD conditions. The series of models
presented in this thesis also seek to explain the BG functions in control and
pathological conditions.
A large body of modeling literature has grown around the idea that the BG
system is a Reinforcement Learning engine. A quantity known as temporal
difference (TD) error is thought to be analogous to dopamine signal, while
another parameter called the discount factor or time scale of prediction, is
related to 5HT. The first computational model of the BG presented in this
thesis (Chapter 4), applies these ideas to explain impairments in Parkinsonian
gait (Muralidharan et al., 2014). We then introduce the utility function, as a
preparation to the full abstract model presented later in Chapter 5, and
explain features of precision grip performance in control and PD conditions
(Gupta et al., 2013).
Although empirical studies show that 5HT plays many functional roles in
riskrewardpunishment learning, computational models mostly focus on its role
in behavioral inhibition or time scale of prediction. Then presented is a
abstract, RLbased model of DA and 5HT function in the BG, a model that
reconciles some of the diverse roles of 5HT. The model uses the concept of the
utility function  a weighted sum of the traditional value function
expressing the expected sum of the rewards, and a risk function expressing the
variance observed in reward outcomes. Serotonin is represented by a weight
parameter, used in this combination of value and risk functions, while the
neuromodulator dopamine (DA) is represented as reward prediction error as in
the classical models. The proposed 5HTDA abstract model is applied to data
from different experimental paradigms used to study the role of 5HT: 1)
Risksensitive decision making, where 5HT controls the risk sensitivity; 2)
Temporal reward prediction, where 5HT controls timescale of reward prediction,
and 3) Reward/Punishment Sensitivity, where punishment prediction error depends
on 5HT levels. Thus this abstract and extended RL model explains the three
diverse roles of 5HT in a single framework. The model is also shown to be
efficient in explaining the effects of medications on reward/punishment
learning in PD patients (Balasubramani et al., 2014).
Little is known about the neural correlates of risk computation in the
subcortical BG system. The later part of the thesis deals with a network model
that is conservatively built from the earlier described abstract model. At the
core of the proposed network model is the following insight regarding cellular
correlates of value and risk computation. Just as the DA D1 receptor (D1R)
expressing medium spiny neurons (MSNs) of the striatum are thought to be neural
substrates for value computation, we propose that DA D1R and D2R coexpressing
MSNs that occupy a substantial proportion of the striatum, are capable of
computing risk. This is the firstofitskind model to account for the
significant computational possibilities of these co expressing D1RD2R MSNs,
and describes how the DA5HT mediated activity in these classes of neurons
(D1R, D2R, D1RD2R MSNs) contribute to the BG dynamics. Firstly the network
model is shown for consistently explaining all the results emerging out of the
earlier abstract model. This includes reconciling the multifarious functioning
of the DA5HT in the BG through the network modelrisk sensitivity,
timescale of reward prediction and punishment sensitivity. Furthermore, the
network model is also shown to capture the PD patients' behavior in a
probabilistic learning paradigm. The model predicts that optimizing 5HT levels
along with DA medication might be quintessential for improving the patients'
rewardpunishment learning (Balasubramani et al., submitted).
All the above experiments tested the accuracy in the action selection. Finally
a study to investigate the efficiency of the developed network model in a task
analyzing the reaction times of subjects, is presented. This task also employs
a probabilistic learning paradigm tested on healthy controls and PD patients
with and without Impulse Control Disorder (ICD). Impulsivity involves
irresistibility in execution of actions and is prominent in ON medication
condition of PD patients. Therefore, four kinds of subject groupshealthy
controls, ON medication PD patients with ICD (PDON ICD) and without ICD
(PDON nonICD), OFF medication PD patients (PDOFF)are tested. The
proposed network model is able to infer the neural circuitry responsible for
displaying ICD in PD condition. Significant experimental results are increased
reward sensitivity in PDON ICD patients, and increased punishment sensitivity
in PDOFF patients. The PDON ICD subjects had lower reaction times (RT)
compared to that of the PDON nonICD patients. The models for PDOFF and PDON
are found to have lower risk sensitivity, while that of the PDON also has
lower punishment sensitivity especially in ICD condition. The model for healthy
controls shows comparatively higher risk sensitivity (Balasubramani et al.,
accepted).

MS
 Sriram Raghavan (2007)
 Thesis title: Distributed Algorithms for Hierarchical Area Coverage Using Teams of Homogeneous Robots
 PhD, QUT
 ABSTRACT

Covering a given area using a team of mobile robots poses several challenges. One such challenge lies in guaranteeing efficient coverage in
the absence of complete information. The robots (agents) must cover the area in a manner that the overlap (repeated coverage) in coverage across
all the robots is minimized.
In this thesis, we introduce “overlap_ratio” to explicitly measure the overlap in coverage for various algorithms. This measure has been shown
to adequately represent the performance of any coverage system as it effectively captures the resource usage as well as the time required for
coverage. We show systematically how the area coverage algorithms, which perform complete coverage with minimum overlap, can be designed
for a team of mobile robots. We begin by understanding the behavior of robots in a randomwalk model and successively refine the model to
provide the robots with additional capabilities and minimize the overlap.
We gradually transition from random decisionmaking to deterministic decisionmaking and suggest appropriate algorithms to suit various
application needs and capabilities. We also prove that arbitrarily large areas can be covered with simple and elegant coverage algorithms by
hierarchically composing it using smaller areas called primitives. An associated theorem called the H2C theorem that provides a linear scaling
of overlap ratio with exponential increase in area has been proved.
Further, this theorem is applicable across arbitrary number of levels in hierarchy. We demonstrate the same experimentally through simulation.
Performance of such multirobot (agent) applications critically depends on the communication architecture that facilitates coordination. A generic
architecture often turns out to be burdensome on the robot (agent) due to overhead. With significant increase in the number of applications, a lightweight architecture that supports reliable and robust delivery of coordination messages is the need of the hour. We explain the design of a
MultiRobot Coordination Architecture, called Pseudonet, based on Bluetooth, to develop area coverage algorithms through successive
refinement. Pseudonet supports a wide variety of multiagent applications in which communication is the primary mode of coordination. We
illustrate the use of Pseudonet architecture in the multirobot area coverage application.
 B. H. Sreenivasa Sarma (2007)
 Thesis title: Intelligent Tutoring System using Reinforcement Learning
 LSI Technologies, Bangalore

ABSTRACT
Intelligent Tutoring System (ITS) is an interactive and effective way of teaching students. ITS gives instructions about a topic to a student, who is using it. The student has to learn the topic from an ITS by solving problems. The system gives a problem and compares the solution it has with that of the student’s and then it evaluates the student based on the differences. The system continuously updates a student model by interacting with the student. Usually, ITS uses artificial intelligence techniques to customize its instructions according to the student’s need. For this purpose the system should have the knowledge of the student (student model) and the set of pedagogical rules. Disadvantages of these methods are, it is hard to encode all pedagogical rules, it is difficult to incorporate the knowledge that human teachers use and it is difficult to maintain a student model and also these systems are not adaptive to new student’s behavior. To overcome these disadvantages we use Reinforcement Learning (RL) to take teaching actions. RL learns to teach, given only the state description of the student and reward. Here, the student model is just the state description of the student, which reduces the cost and time of designing ITS. The RL agent uses the state description as the input to a function approximator, and uses the output of the function approximator and reward, to take a teaching action. To validate and to improve the ITS we have experimented with teaching a simulated student using the ITS. We have considered issues in teaching a special case of students suffering from autism. Autism is a disorder of communication, socialization and imagination. An Artificial Neural Network (ANN) with backpropagation is selected as simulated student, that shares formal qualitative similarities with the selective attention and generalization deficits seen in people with autism. Our experiments show that an autistic student can be taught as effectively as a normal student using the proposed ITS.
 Vimal Mathew (2007)
 Thesis title: Automated SpatioTemporal Abstraction in Reinforcement Learning
 PhD, UMass Amherst

ABSTRACT
Reinforcement learning is a machine learning framework in which an agent manipulates its environment through a series of actions, receiving a scalar feedback signal or reward after each action. The agent learns to select actions so as to maximize the expected total reward. The nature of the rewards and their distribution across the environment decides the task that the agent learns to perform. Consider a generic agent solving multiple tasks in a common environment. The effectiveness of such an agent increases with its ability to reuse past experience in new tasks. Identifying similar representations
between tasks paves the way for a possible transfer of knowledge between such tasks. A sufficiently broad definition of similarity and an ability to effectively store and reuse previous experience can greatly increase the longterm performance of a multipurpose agent.
This thesis introduces an automated taskindependent abstraction method that generalizes across tasks sharing the same statespace topology and similar dynamics. New tasks performed on this common environment are able to reuse skills acquired over previous tasks. Pruning techniques are identified for specific situations to identify relevant skills. The interaction of the agent with the environment is modeled as a dynamical system. Identification of metastable regions in the agent dynamics leads to a partitioning of the state space. Transitions between metastable regions, which normally are events of low probability during a randomwalk on the state space, are identified as useful skills.
 Shravan Matthur Narayanamurthy (2007)
 Thesis title: Abstraction using Symmetries in Markov Decision Processes
 Yahoo Labs, Bangalore

ABSTRACT
Current approaches to solving Markov Decision Processes (MDPs), the defacto standard for modeling stochastic sequential decision problems, scale poorly with the size of the MDP. When used to model realworld problems though, MDPs exhibit considerable implicit redundancy, especially in the form of symmetries. However, existing model minimization approaches do not efficiently exploit this redundancy due to symmetries. These approaches involve constructing a reduced model first and then solving them. Hence we term these as “explicit minimization” approaches. In the first part of this work, we address the question of finding symmetries of a given MDP. We show that the problem is Isomorphism Complete, that is, the problem is polynomially equivalent to verifying whether two graphs are isomorphic. Apart from the theoretical importance of this result it has an important practical application. The reduction presented can be used together with any offtheshelf Graph Isomorphism solver, which performs well in the average case, to find symmetries of an MDP. In fact, we present results of using NAutY (the best Graph Isomorphism solver currently available), to find symmetries of MDPs. We next address the issue of exploiting the symmetries of a given MDP. We propose the use of an explicit model minimization algorithm called the Greduced image algorithm that exploits symmetries in a time efficient manner. We present an analysis of the algorithm and corroborate it with empirical results on a probabilistic GridWorld domain and a single player GridWorld Soccer domain. We also present results of integrating the symmetry finding with the explicit model minimization approach to illustrate an endtoend approach for “Abstraction using Symmetries in MDPs”. We then note some of the problems associated with this explicit scheme and as a solution present an approach wherein we integrate the symmetries into the solution technique implicitly. However, we should select a suitable solution technique so that the overheads due to integration do not outweigh the improvements. We validate this approach by modifying the Real Time Dynamic Programming (RTDP) algorithm and empirically demonstrate significantly faster learning and reduced
overall execution time on several domains.
 Aakanksha Gagrani (2007) (Jointly with Prof. Koshy Varghese, CE, IITM)
 Thesis title: Image Modeling using Hierarchical Conditional Random Fields
 Oracle, Hyderabad

ABSTRACT
Image modeling methods broadly fall under two categories, (a) Methods which consider bottom up cues , these methods take pixel statistics into consideration
for classification. (b) Methods which consider top down cues as well, these methods utilize the context information as well for the modeling task. Natural images exhibit strong contextual dependencies in the form of spatial interactions among components. For example, neighboring pixels tend to have similar class labels, and different parts of an object are related through geometric constraints. Going beyond these, different regions e.g., objects such as, monitor and keyboard appear in restricted spatial configurations. Modeling these interactions is crucial for achieving good classification accuracy.
In this thesis, we present a method based on Hierarchical Conditional Random Fields that can handle both bottom up and top down cues simultaneously. This offers tremendous advantages over existing methods which use either bottom up cues, or model only limited contextual information. The Tree Structured Conditional Random Field (TCRF) as proposed, is capable of capturing long range correlations at various levels of scales. TCRF has non loopy graph structure whichallows us to perform inference in time linear in number of nodes. The model is generic enough to be applied to several challenging computer vision tasks, such as object detection and image labeling; seamlessly within a single, unified framework.
 Dinakar Jayarajan (2009)(Jointly with Dr. Dipti Deodhare, CAIRDRDO)
 Thesis title: Using Semantics in Document Representation: A Lexical Chains Approach
 PhD, USC

ABSTRACT
Automatic classification and clustering are two of the most common operations
performed on text documents. Numerous algorithms have been proposed for this
and invariably, all of these algorithms use some variation of the vector space
model to represent the documents. Traditionally, the Bag of Words (BoW)
representation is used to model the documents in a vector space. The BoW scheme
is a simple and popular scheme, but it suffers from numerous drawbacks. The
chief among them is that the feature vectors generated using BoW results in
very large dimensional vectors. This creates problems with most machine
learning algorithms where the high dimensionality severely affects the
performance. This fact is manifested in the current thinking in the machine
learning community, that some sort of a dimensionality reduction is a
beneficial preprocessing step for applying machine learning algorithms on
textual data. BoW also fails to capture the semantics contained in the
documents properly.
In this thesis, we present a new representation for documents based on
lexical chains. This representation addresses both the problems with BoW  it
achieves a significant reduction in the dimensionality and captures some of the
semantics present in the data. We present an improved algorithm to compute
lexical chains and generate feature vectors using these chains. We evaluate our
approach using datasets derived from the 20 Newsgroups corpus. This corpus is a
collection of 18,941 documents across 20 Usenet groups related to topics such
as computers, sports, vehicles, religion, etc. We compare the performance of
the lexical chain based document features against the BoW features on two
machine learning tasks  clustering and classification. We also present a
preliminary algorithm for soft clustering using the lexical chains based
representation, to facilitate topic detection from a document corpus.
 A. Yousuf (2011)
 Thesis title: Visual Object detection using Frequent Pattern Mining
 Nvidia, Bengaluru

ABSTRACT
Visual object detection is the problem of identifying an object from a visual
scene or more generally, addressing object queries like, What are the objects
present in the scene ? How many objects are there in the scene ?, etc.
Performing reliable object detection is an important task as it is one of the
key requirements for realizing a fully autonomous, general purpose robotic
agent. Activities like playing a game, navigating a road, looking for a book in
library, etc., are examples involving object detection. Humans perform these
tasks incredibly well. But this is a very hard problem for a learning agent.
Part of the difficulties arise from the visual processing involved as the
objects can appear in different scale, lighting conditions, rotation, occlusion
etc. Vision researchers have come up with representations which are robust to
these visual variations and can reliably detect objects even in the case of
noise. Apart from the visual challenges, there are machine learning challenges
of generalization, discrimination and incremental addition. From the training
samples the agent has to learn representations which can generalize well over
objects within the class and discriminate between objects of different classes.
For example, the representation learned for class cup should generalize various
cups of different size, and shape model and should distinguish it from other
classes say ball. As the agent interacts with the environment, it may encounter
new objects. It needs to learn and update its knowledge base so that it can
detect the new object class. This incremental update of knowledge base is very
desirable for a vision system and the relearning involved should be minimal.
In this work we propose a system which addresses the learning challenges
explained above. The key idea here is to learn those visual properties which
are common among the objects of the same class so that these properties can be
later used for detecting the class. Shape, color, size, texture, etc., are
examples of visual properties. A combination of these properties can represent
each class and the learning is done in three phases  pattern mining phase,
scoring phase and rescoring phase. In the pattern mining phase, training image
of each class is processed and the various visual properties are extracted.
From these visual properties, those patterns that frequently occur are learned
using frequent pattern mining. Since we are considering the frequency alone, it
might happen that the operators learned will include those which covers more
background pixels than the foreground ones or whose coverage is superseded by
other operators. The scoring phase is used to eliminate such erroneous or extra
operators. To handle discrimination and incremental addition, we have the
rescoring phase, which re scores the operator sets learned based on how
popular the operator sets are among other classes. Generally those operators
which are common among many classes are considered as bad choices for
discriminating the classes. Rescoring phase tries to remove such operators by
switching them with other alternatives. The system is empirically evaluated
with the Caltech101 data set and the results are presented.
 Abhishek Ghose (2011)
 Thesis title: Supervised Lexical Chaining
 Adobe, Bengaluru

ABSTRACT
Lexical chaining is a method of grouping semantically related words in a
document. Groups thus obtained are known as lexical chains. Lexical chains
provide a rich representation of text, and have been used in various tasks like
discourse analysis, summarization, corrections of malapropisms, amongst many
others, with reasonable success. However, despite the general applicability of
the method, its use is limited by the fact that chaining algorithms often group
weakly related or unrelated words together. The large amount of time required
for mining chains from a document also make it unsuitable for certain tasks. In
our research, we look at applying supervised learning methods to address these
drawbacks.
We propose two supervised algorithms as viable alternatives to classical
algorithms. These algorithms rely on certain probabilistic properties of usage
of words in text. We empirically establish the relevance of these properties to
rapid construction of high quality lexical chains through experiments we have
performed. Using lexical chains formed over sense tagged documents as training
data, along with a knowledge of these properties, our algorithms are shown to
be capable of reliably constructing chains on a test set of documents.
Although, we believe that exploring supervised learning for chaining is a
worthy investigation in its own right, we provide certain encouraging results
in defence of the approach. We compare our algorithms to a classical chaining
algorithm and report a 44% improvement in quality and 55 times improvement in
speed. Our experiments were performed on the SemCor 3.0 dataset.
 R. Vijayasarathy (2012) (External, SETS)
 Thesis title: A Systems Approach to Network Modelling for DDoS Attack Detection using Naive Bayes Classifier

ABSTRACT
Denial of Service attacks are a major threat to the modern electronic
society. Carefully crafted attacks of large magnitude, better referred to as
Distributed Denial of Service attacks (DDoS) have the ability to cause havoc at
the highest level, national information infrastructure. The ease at which such
attacks can be performed today is startling given the number of free online
tools available in the open. An ideal situation would be to differentiate
between good and bad packets as generally attempted and accomplished in the
case of intrusion attempts, which in the case of DoS attacks is extremely hard
as only the intent differs between a genuine user and an attacker.
There are multiple perspectives of dealing with the DoS attack problem, in
order to mitigate, detect and cope with detected attack. Techniques dealing
with each of these aspects can be integrated to make a foolproof system. An
important such perspective in terms of detecting DoS attacks is to view the
problem as that of a classification problem on network state (and not on
individual packets or other units) by modelling normal and attack traffic and
classifying the current state of the network as good or bad, thereby detecting
attacks when they happen. Classical machine learning algorithms are used to
solve the problem.
In this work, the approach to a carefully engineered, practically realisable
system to detect DoS attacks based on a Naive Bayesian classifier is
described. The system is designed to be near the target. The focus of the
system is on two transport layer protocols  TCP and UDP, both of which
work on a common framework of mechanisms, although with different input
parameters.
For the TCP protocol, TCP flags are taken as the feature, and the probability
space is engineered taking into account hardware implementability and
operability at line speeds. Simple independence assumptions are made inside a
window of packets, and the probability of a window is determined as a function
of the probabilities of flag combinations observed inside the window. Threshold
probability is determined by cross validation  a technique commonly used
in data mining. For the UDP protocol, the feature is the Windows Arrival Time
(WAT), the time taken for a window of packets to arrive at the mitigation
device. Techniques similar to TCP are used.
Experiments are carried out with both tagged and untagged datasets, and the
results are found to be promising. In order to assert the efficiency and
usefulness of our system, the system was compared with a oneclass SVM
classifier, and found to be more suitable for hardware implementation.
 S. Shivashankar(2012)
 Thesis title: Multiview based Approaches for Collective Learning and Retrieval Tasks
 Ericsson R & D, Chennai

ABSTRACT
Machine learning techniques can be used to build systems to perform various
tasks such as classification, clustering, retrieval, etc. In traditional
machine learning algorithms, the data points are represented in a
ddimensional space which defines a view of the data. But many realworld
datasets possess rich additional information that can be leveraged to improve
the system's performance, reduce labeling cost, etc. For example, in
webpage classification the content of the webpage and text on hyperlink
pointing to this page can be used as two different views. Multiview learning
is a technique which allows the use of multiple views where a model built on
one of the views can aid learning the models built on other views. It has been
shown that multiview approaches are more effective than singleview approaches
for various domains. In this work, we propose multiview based approaches for
the following two tasks:
 Linked Data Classification: In general, all the machine learning
paradigms assume the data points to be independent and identically
distributed (i.i.d). But many realworld datasets such as webpages, images and
protein interaction networks possess additional link information which
provides local label smoothness. For example, in webpage classification, links
between webpages convey that there is a strong correlation between labels of
the linked pages. This can be exploited to provide regularization and thereby
improve the performance. Collective classification is one of the popular
approaches that can handle this type of network data. It combines traditional
machine learning and link based classification in an iterative procedure. This
involves two important steps: 1) collective learning  training the base
classifier on the appended content and link information with the given
(partially) labeled network data; and 2) collective inference  applying
the trained base classifier on the partially labeled network data and
labeling it completely. Most of the collective classification frameworks assume
that sufficient labeled data is available for training the base classifier. The
trained base classifier is applied on the test data iteratively to perform
collective inference. But, there has been little attention paid towards
collective learning on partially labeled network data. We propose a multiview
learning based algorithm which utilizes both content and link views
effectively to learn from the unlabeled data also. We empirically evaluate the
effectiveness of the proposed framework by showing that it performs better than
traditional single view approaches on sentiment analysis and standard linked
datasets such as Cora, Citeseer and WebKB. We also performed sentiment analysis
on automobile reviews.
 Similar Protein Structures Retrieval: With the rapid expansion of
protein structure databases, the task of efficiently retrieving structures
similar to a given protein has gained importance. It involves two major issues:
(i) an effective protein structure representation that captures inherent
relationship between fragments and facilitates efficient comparison between
structures, (ii) an effective framework to accommodate different retrieval
requirements. Recently, researchers proposed a vector space model of proteins
using bag of fragments representation (FragBag), which corresponds to a basic
information retrieval model. In this work, first, we propose an improved
representation of protein structures using Latent Dirichlet Allocation (LDA)
topic model. Second, we handle the most important requirement which is to
retrieve proteins, whether they are close or intermediate or remote homologs.
Close, remote and intermediate homologs are defined based on Structural
Alignment Score (SAS). The SAS thresholds typically are 2, 3.5 and 5 for close,
intermediate and remote homologs respectively. In order to meet diverse
objectives, we propose a multiview based framework that combines multiple
representations and retrieval techniques. We experimentally show that the
proposed multiview based retrieval framework performs better than
stateoftheart filter and match techniques across different retrieval
requirements. The experiments are performed on FragBag dataset, which
contains 2,930 sequence nonredundant structures selected from CATH version
2.4.
 Priya Anna Mani (2012)
 Thesis title: A Hierarchical Markov Logic Based Framework for
Reasoning with Incomplete Visual Evidence
 Arista Networks, Bengaluru

ABSTRACT
Visual perception is a key function for an embodied agent to interact with its
environment for complex object manipulation tasks. The theory of visual
routines suggests a framework for employing perception to solve highlevel
vision tasks in a cognitively oriented way. But a major challenge in building
vision systems for embodied agents is that the evidence obtained from sensors
is uncertain and incomplete, i.e., the results of operation of visual routines
are not completely reliable. This is due to the inherent limitations of the
equipment in terms of fieldofview and resolution of the camera, which causes
the input image to be of low fidelity. Moreover, the application of visual
operators may yield spurious or imprecise evidence and choosing the right
parameters is hard. We propose a novel approach for inference over uncertain
and incomplete evidence, using Markov Logic Networks (MLN) and active vision
in a hierarchical framework and evaluate it using an object categorization
task.
Markov Logic Networks belong to the class of Statistical Relational Learning
(SRL) methods that combine the expressiveness of firstorder logic and the
ability of probability theory to handle uncertainty. MLNs extend Markov
networks to a relational setting by expressing the knowledge as a set of
weighted formulas. We propose a layered MLN design which performs stagewise
inference to allow for reasoning at multiple levels and at varying levels of
uncertainty. Given that the information is incomplete, active vision is a
mechanism for focused gathering of additional information. Our framework
integrates active vision with the layered MLN model to gather missing evidence,
facilitating reliable and tractable inference. Inspired by the ideas of active
vision, in the event of missing evidence, our framework restricts the selective
visual processing to specific regions of the input image and further inference
is carried out incorporating the new evidence.
We present a cognitively motivated, complete endtoend system for object
categorization in a SRL framework. We use three different datasets for
experimental evaluation: synthetic images generated using OpenCV library,
images obtained from the iCub humanoid simulator and real images taken from
Microsoft's Kinect Xbox (R). The system is evaluated with different
levels of incompleteness and noise on these datasets and empirically prove its
applicability to detect objects of complex structures.
 K. V. N. Pradyot (2013)
 Thesis title: Beyond Rewards: Learning from Richer Supervision
 Hitech Robotics Systemz Ltd., Gurgaon

ABSTRACT
Robots have captivated human imagination for a long time. Numerous science
fictions have promised that the era of robots is beckoning and the day androids
walk shoulder to shoulder with us is not too far away. If this were to happen,
the intelligent systems that would populate our environment will have to adapt
and learn fast, possibly from humans too. For such systems, human teachers
present a narrow but critical learning environment. Even between humans, the
interactive teacherstudent relationship is known to be effective. Endowing
robots with humanlike learning capabilities would facilitate a similar human
machine interaction, resulting in effective utilization of human knowledge by
the robot. We model a subset of these interactions as instructions and propose
a framework that enables humans to instruct robots and robots to exploit these
supervisory inputs. In all of our experiments, the systems are based on
Reinforcement Learning (RL).
In RL, rewards have been considered the most important feedback in
understanding environments. However, recently there have been interesting
forays into other modes such as sporadic human instructions. We utilize these
instructions to identify structural regularities in the environment that can
aid in taking behavioural decisions in unfamiliar situations. An important
aspect of working with instructions is their proper interpretation. Our
approach accommodates multiple interpretations of instructions and provides a
handle to choose the best. In this regard, we have tested our approach on
several domains and implemented it on a real robotic system.
 Karthick Rangadurai (2014)
 Thesis title: Sequence Modelling to Detect Network Intrusions
 Cisco Systems, Bengaluru

ABSTRACT
Any activity aimed at disrupting a service or making a resource unavailable
or gaining unauthorized access can be termed as an intrusion. Examples
include buffer overflow attacks, flooding attacks, system breakins, etc.
Intrusion Detection Systems (IDS) play a key role in detecting such malicious
activities and enable administrators in securing network systems. Two key
criteria should be met by an IDS for it to be effective: (a) ability to detect
unknown attack types (b) having very less misclassification rate.
Over the last decade, using Machine Learning (ML) techniques to address the
problem of detecting network intrusions has become prevalent. The ability to
detect novel intrusions sets apart ML based systems from standard signature
based intrusion detection methods. In this work, the problem of detecting
network intrusions is posed as a classification problem. The classifier
built should be able to separate good/legitimate traffic from bad/anomalous
traffic. Machine Learning (ML) based classfiers are used to build our IDS.
It profiles clean traffic traces and uses the model built to classify
unseen traffic.
In this work, we describe a two stage network intrusion detection system
(NIDS) that can operate at high packet rates. In the first stage, which is
inline with the network, we use Naïve Bayesian based probabilistic
classifier to detect potential anomalies in the trafic. In the second
stage, a cascaded Hidden Markov Model (HMM) is used to pin down the potential
IP address of misbehaving connections. Various design choices that were made to
make this system practical and the difficulties faced while integrating the
system with existing models are also described. Experiments were conducted to
evaluate the performance and to validate the proposed model. We show that this
system achieves good performance empirically.
 Vishnu Sankar (2015)
 Thesis title: Scalable Community Detection and Centrality Algorithms for Network Analysis
 VMWare, Bengaluru

ABSTRACT
Communication has become a lot easier with the advent of easy and cheap means
of reaching people across the globe. This has allowed the development of large
networked communities and, with the technology available to track them, has
opened up the study of social networks at unprecedented scales. This has
necessitated the scaling up of various network analysis algorithms that have
been proposed earlier in the literature. While some algorithms can be readily
adapted to large networks, in many cases the adaptation is not trivial.
Realworld networks typically exhibit nonuniform edge densities with there
being a higher concentration of edges within modules or communities. Various
scoring functions have been proposed to quantify the quality of such
communities. The popu lar scoring functions suffer from certain limitations.
This thesis identifies the necessary features that a scoring function should
incorporate in order to characterize good community structure and propose a new
scoring function, CEIL (Community detection using External and Internal scores
in Large networks), which conforms closely with the characterization. It also
demonstrates experimentally the superiority of CEIL score over the existing
scoring functions. Modularity, a very popular scoring function, exhibits a
resolution limit, i.e., one cannot find communities that are much smaller in
size compared to the size of the network. In many real world networks,
community size does not grow in proportion to the network size. This implies
that resolution limit is a serious problem in large networks. Still modularity
is very popular since it offers many advantages such as fast algorithms for
maximizing the score, and nontrivial community structures corresponding to the
maxima. This thesis shows analytically that the CEIL score does not suffer from
resolution limit. It also modifies the Louvain method, one of the fastest
greedy algorithms for maximizing modularity, to maximize the CEIL score. The
modified algorithm, known as CEIL algorithm, gives the expected communities in
synthetic networks as opposed to maximizing modularity. The community labels
given by CEIL algorithm matches closely with the groundtruth community labels
in real world networks. It is on par with Louvain method in computation time
and hence scales well to large networks.
This thesis also explores the scaling up of a class of node centrality
algorithms based on cooperative game theory which were proposed earlier as
efficient alternatives to traditional measure of information diffusion
centrality. It presents the distributed versions of these algorithms in a
MapReduce framework, currently the most popular distributed computing
paradigm. The scaling behavior of the distributed version of algorithms on
large synthetic networks is demonstrated empirically thereby establishing the
utility of these methods in settings such as online social networks.
 Pratik Gupte (2015)
 Thesis title: Epsilon Equitable Partitions based Approach for Role
& Positional Analysis of Social Networks
 Indix.com, Chennai

ABSTRACT
In social network analysis, the fundamental idea behind the notion of position
is to discover actors who have similar structural signatures. Positional
analysis of social networks involves partitioning the actors into disjoint sets
using a notion of equivalence which captures the structure of relationships
among actors. Classical approaches to Positional Analysis, such as Regular
equivalence and Equitable Partitions, are too strict in grouping actors and
often lead to trivial partitioning of actors in real world networks. An
epsilonEquitable Partition (eEP) of a graph is an useful relaxation to the
notion of structural equivalence which results in meaningful partitioning of
actors. All these methods assume a single role per actor, actors in real world
tend to perform multiple roles. For example, a Professor can possibly be in a
role of "Advisor" to his PhD students, but in a role of "Colleague" to other
Professors in his department.
In this thesis we propose epsilonequitable partitions based approaches to
perform scalable positional analysis and to discover positions performing
multiple roles. First, we propose and implement a new scalable distributed
algorithm based on MapReduce methodology to find eEP of a graph. Empirical
studies on random powerlaw graphs show that our algorithm is highly scalable
for sparse graphs, thereby giving us the ability to study positional analysis
on very large scale networks.
Second, we propose a new notion of equivalence for performing positional
analysis of networks using multiple epsilonequitable partitions. These
multiple partitions give us a better bound on identifying equivalent actor
"positions" performing multiple "roles".
Evaluation of our methods on multirole groundtruth networks and
time evolving snapshots of real world social graphs show the importance of
epsilon equitable partitions for discovering positions performing multiple
roles and in studying the evolution of actors and their ties.
 A. P. Sarath Chandar (2015)
 Thesis title: Correlational Neural Networks for Common
Representation Learning
 PhD, University of Montreal, Canada

ABSTRACT
Classical Machine Learning (ML) algorithms learn to do a specific task, say
classification by using the available training data and classify the test
data which is unknown during the training. However, all these algorithms
assume that the data is represented using a set of features, which are mostly
handcrafted by human experts. Learning useful features or representations from
the raw data, also known as Representation Learning is one of the hardest
problems in Machine Learning. Deep Learning is an emerging field in Machine
Learning which solves the problem of representation learning using Deep Neural
Networks (DNNs). These representations learned using Deep Neural Networks, when
coupled with simple classification algorithms, perform significantly better
than the complex stateoftheart procedures in text, image and speech data.
Common Representation Learning (CRL), wherein different descriptions (or views)
of the data are embedded in a common subspace, is receiving a lot of attention
recently. Two popular paradigms in CRL world are Canonical Correlation
Analysis (CCA) based approaches and Autoencoder (AE) based approaches. CCA
based approaches learn a joint representation by maximizing correlation of the
views when projected on the common subspace. AE based methods learn a common
representation by minimizing the error of reconstructing the two views. Each of
these approaches has its own advantages and disadvantages. For example, though
CCA based approaches outperform AE based approaches for the task of transfer
learning, they are not as scalable as the latter.
In this thesis, we propose Correlational Neural Networks (CorrNet), a class of
neural networks that can learn common representation for two different views.
CorrNet is an AE based approach, that explicitly maximizes correlation among
the views when projected on the common subspace. The model can be efficiently
trained with batch gradient descent and is thus scalable to big data. Apart
from learning common representation for different views, the model also
learns to predict the features in one view given the features in the other
view, which has several interesting applications. CorrNet, unlike CCA, can be
easily extended to more than two views. Experiments show that CorrNet
outperforms CCA in learning common representation. Thus scalability is gained
without compromising performance. The CorrNets come with another advantage,
that is, it can make use of single view data when there are less parallel data
and this is not possible in CCA.
CorrNet has been successfully applied in Natural Language Processing (NLP) to
several cross lingual tasks where a learner has to learn from one language and
perform in a different language. In the task of Cross Language Document
Classification (CLDC), CorrNet based bilingual word representation learning
algorithm performs significantly better than the current state of the art
procedures and a strong Machine Translation baseline. The model is also applied
in Transliteration Mining. It has been tested with several language pairs and
works well even if the languages differ in scripts, since the model itself is
not language dependent.
CorrNet can be considered as a variant of autoencoder to handle multiview
data. In this thesis, we also propose ways to make the CorrNet deep. One of
the deep versions of the CorrNet performs much better than several strong
baselines in the task of Cross Language Sentiment Analysis (CLSA). All the
mentioned studies leads us to believe that CorrNet is a promising tool from
common representation learning.
 Saket Gurukar (2015) (Jointly with Sayan Ranu, CSE)
 Thesis title: A Scalable Approach to Mining Communication Motifs
from Dynamic Networks
 CodeNation, Bengaluru

ABSTRACT
Social networks have become an effective means of communication among people.
Recently, there is a trend to analyze these social networks to infer the
dynamics of human interaction. A fundamental problem in behavioral analysis of
human interactions is to understand how communications unfold. In this thesis,
we propose and solve the problem of mining Communication motifs from dynamic
interaction networks. Simply stated, a communication motif is a recurring
subgraph that has a similar sequence of information flow. Communication motifs
provide a powerful mechanism to capture the dynamics of human interactions.
Existing work show that communication motifs reveal how the functional
behavioral patterns evolve with time, how the structures of these patterns
change with the social network, and finally, how the social network influences
the speed and amount of information exchanged in communications between
individuals. However, no technique is proposed for mining these motifs in a
scalable manner. Mining communication motifs requires us to explore the
exponential subgraph search space where existing techniques fail to scale. To
tackle this scalability bottleneck, we develop a technique called COMMIT.
COMMIT converts a dynamic graph into a database of sequences. Through careful
analysis in the sequence space, only a small portion of the exponential search
space is accessed to identify regions embedding communication motifs. We also
store the pointers to these regions as a result costly subgraph enumeration
step is avoided.
We perform extensive experiments on three real world datasets and evaluate the
proposed COMMIT based on accuracy and scalabilty. We find that COMMIT is up to
two orders of magnitude faster than baseline techniques. COMMIT can also mine
large size communication motifs where existing algorithms fails. Furthermore,
qualitative analysis demonstrate communication motifs to be effective in
characterizing the recurring patterns of interactions while also revealing the
role that the underlying social network plays in shaping human behavior.
 S. S. Manimaran (2016)
 Thesis title: Towards an Autonomous Human Inclusive Robot Learning
Framework
 Epic, Wisconsin, USA

ABSTRACT
Building intelligent robots has been one of the motivating problems in
Artificial Intelligence research. Choosing what actions to perform is a
fundamental problem every robot must solve. The aptness of the actions depends
on the robot's understanding of its environment and the dynamics
involved. This understanding is an everchanging model based on the robot's
observations and experiences. In this thesis, we propose a robotlearning
architecture and discuss two key modules  a continuous space path planning
algorithm and an activity recognition framework.
Path planning in continuous spaces is a challenging problem for which a
variety of solutions have been proposed. The performance of sampling based path
planning techniques relies on identifying a good approximation to the
costtogo distance metric, in the case of systems with complex dynamics. We
propose a technique that uses reinforcement learning to learn this distance
metric on the fly from samples and combine it with existing sampling based
planners to produce near optimal solutions. The resulting algorithm  Policy
Iteration on Rapdily exploring Random Trees (RRTPI) can solve problems with
complex dynamics in a sampleefficient manner while preserving asymptotic
guarantees. We provide experimental evaluation of this technique on domains
with underactuated and underpowered dynamics such as the mountain car domain,
the acrobot domain and a variant of the octopus arm domain.
Human activity recognition is a key component in enabling social robots to
naturally interact with humans. While humans can easily distinguish whether
an activity corresponds to a form of communication or an act of daily living,
robots are yet not that insightful. This thesis addresses the problem of
enabling a social robot to understand an activity as well as classify it as an
action corresponding to a human instruction or an act of daily living. We
address this problem by presenting a simple yet effective approach of modeling
pose trajectories in terms of directions taken by skeletal joints over the
duration of an activity. Specifically, we propose a novel activity
descriptor: Histogram of direction vectors (HODV). The descriptor is
computationally efficient, scale and speed invariant and provides state of
the art classification accuracies using off the shelf classification algorithms
on multiple datasets such as the Cornell Activity Dataset (CAD60), UTKinect
Dataset as well as on our novel RGBD dataset.
 Avijit Saha (2016)
 Thesis title: Scalable Bayesian Factorization Models for Recommender
Systems
 Flipkart, Bengaluru

ABSTRACT
In recent years, Recommender Systems (RSs) have become ubiquitous.
Factorization models are a common approach to solve RSs problems, due to their
simplicity, prediction quality and scalability. The idea behind such models
is that preferences of a user are determined by a small number of unobserved
latent factors. One of the most well studied factorization model is matrix
factorization using the Frobenius norm as the loss function. In more
challenging prediction scenarios where additional "sideinformation" is
available and/or features capturing higher order may be needed, new challenges
due to feature engineering needs arise. The sideinformation may include user
specific features such as age, gender, demographics, and network information
and item specific information such as product descriptions. While
interactions are typically desired in such scenarios, the number of such
features grows very quickly. This dilemma is cleverly addressed by the
Factorization Machine (FM), which combines high prediction quality of
factorization models with the flexibility of feature engineering.
Interestingly, the framework of FM subsumes many successful factorization
models like matrix factorization, SVD++, TimeSVD++, Pairwise Interaction Tensor
Factorization (PITF), and factorized personalized Markov chains (FPMC). Also,
due to the availability of large data, several sophisticated probabilistic
factorization models have been developed. However, in spite of having a vast
literature on factorization models, several problems exist with different
factorization model algorithms.
In this thesis, we take a probabilistic approach to develop several
factorization models. We adopt a fully Bayesian treatment of these models and
develop scalable approximate inference algorithms for them.
Bayesian Probabilistic Matrix Factorization (BPMF), which is a Markov chain
Monte Carlo (MCMC) based Gibbs sampling inference algorithm for matrix
factorization, pro vides stateoftheart performance. BPMF uses multivariate
Gaussian prior on latent factor vector which leads to cubic time complexity
with respect to the dimension of latent space. To avoid this cubic time
complexity, we develop the Scalable Bayesian Matrix Factorization (SBMF)
which considers independent univariate Gaussian prior over latent factors.
SBMF, which is a MCMC based Gibbs sampling inference algorithm for matrix
factorization, has linear time complexity with respect to the dimension of
latent space and linear space complexity with respect to the number of nonzero
observations.
We then develop the Variational Bayesian Factorization Machine (VBFM) which
is a batch scalable variational Bayesian inference algorithm for FM. VBFM
converges faster than the existing stateoftheart MCMC based inference
algorithm for FM while providing similar performance. Additionally for large
scale learning, we develop the Online Variational Bayesian Factorization
Machine (OVBFM) which utilizes stochastic gradient descent to optimize the
lower bound in variational approximation. OVBFM outperforms existing online
algorithms for FM as validated by extensive experiments performed on numerous
largescale real world data.
Finally, the existing inference algorithm for FM assumes that the data is
generated from a Gaussian distribution which may not be the best assumption for
count data such as integervalued ratings. Also, to get the best performance,
one needs to crossvalidate over the number of latent factors used for modeling
the pairwise interaction in FM, a process that is computationally intensive. To
overcome these problems, we develop the Nonparametric Poisson Factorization
Machine (NPFM), which models count data using the Poisson distribution,
provides both modeling and computational advantages for sparse data. The ideal
number of latent factors is estimated from the data itself. We also consider a
special case of NPFM, the Parametric Poisson Factorization Machine (PPFM), that
considers a fixed number of latent factors. Both PPFM and NPFM have linear time
and space com plexity with respect to the number of nonzero observations.
Using extensive experiments, we show that our methods outperform two strong
baseline methods by large margins.
 Prasanna Parthasarathy (2017)
 Thesis title: Understanding Exploration Strategies in Model Based
Reinforcement Learning
 PhD, McGill University, Montreal, Canada

ABSTRACT
Reinforcement Learning (RL) is a collection of learning approaches that solve
for a behavior of an agent in a world maximizing some notion of payoff. Under
certain circumstances the agent's world, described by a set of parameters, is
essential to be learnt for it to discover an optimal behavior. This paradigm of
problems is studied under the broad topic of modelbased RL, and the class of
algorithms that help an agent in learning the model parameters are known as
model learning algorithms. In this thesis, we discuss exploration strategies in
modelbased RL from two different perspectives  asymptotic agent, where early
convergence to an optimal solution is desirable but not a necessity, and
finiteepisode agent, which has a constraint on the number of episodes to
converge to an optimal behavior.
The first part of this thesis proposes a novel uncertainty based exploration
strategy, Thompson Sampling with Exploration Bonus (TSEB), for the asymptotic
agent case. This work draws insights from Thompson Sampling, a Bayesian
approach to modelbased RL, and the homomorphism literature in RL. The proposed
algorithm helps the agent learn close to true model parameters of the world
with lesser sample complexity than the existing model learning algorithms. TSEB
algorithm trades off the proposed exploration bonus with the sampled reward
estimate in Thompson Sampling algorithm. This strategy, with the elegance of
Bayesian approach, provides a better way to learn model parameters and is
validated theoretically and empirically in this thesis.
Despite having sound strategies for the asymptotic case, the finiteepisode
case in RL has been seldom looked into. The other part of this thesis studies
the exploration strategies of finiteepisode agents and proposes a novel
framework, Structuring FiniteEpisode Exploration (SFEE) in Markov Decision
Processes, that adapts an asymptotic exploration strategy for finiteepisode
setting. This work justifies the need for such a framework by showing that the
asymptotic exploration strategies don't suit the finiteepisode setting which
can be because of them not being conscious of the agent's lifetime. Aiming to
make good use of the sound strategies of asymptotic learning algorithms, the
proposed framework uses the policy of an asymptotic algorithm plugged into the
framework for a certain number of episodes and then appropriately switches to a
greedy policy. Further, this thesis shows, under some mild assumptions, that
this is indeed the optimal way to explore in a finiteepisode setting using
that exploration algorithm. The framework also enables exporting theoretical
guarantees from the plugged asymptotic algorithm to the proposed framework.
M.Tech
 Ankit Malpani (Dual Degree) (Jointly with Prof. Hema A. Murthy)
 Thesis title:Personalized Intelligent Tutoring System
 MSIDC, Hyderabad

ABSTRACT
Throughout the educational system, teaching has traditionally followed a
onesizefitsall approach to learning, with a single set of instructions
provided identically to everybody in a given class,regardless of differences in
aptitude or interest.
In recent years,a growing appreciation of individual preferences and
aptitudes has led towards Personalized Intelligent Tutoring System where
instructions are presented to students based on theisr needs, adapting to their
learning behaviour to make learning both interesting and challenging. The US
National Academy of Engineers identified it as one of the fourteen grand
challenges for engineering in the 21st century. The system becomes even more
relevant in the current Indian Educational scenario. In rural arears skilled
and inspiring teaches are few and far to come by. An Intelligent Tutoring
system can go a long way in bringing in good students from the marginalized
sections of society into the main stream.
Most of the present tutoring systems use extensive expert knowledge to
either explicitly store teaching rules or have a finely labelled dataset
specifying skills extending such a Tutoring System to newer domains difficult
specially in India with numerous education boards and poorly labelled
datasets.
In this project, we propose a system that works on coarsely labelled
datasets and uses Reinforcement Learning techniques to implicitly learn
teaching rules. We use Student Models to simulate student's learning behaviour
and show that our system helps in speeding up students learning process. As
interactions with the student proceed the system also learns to identify
between different types of students and presents instructions to suit
individual student's need.
 Kiran Kate
 Thesis title: Positional Analysis of Social Networks
 IBMIRL, Bangalore

ABSTRACT
Social network analysis is an emerging area of research for computer
scientists. It employs different concepts from graph theory, probability, and
statistics to solve problems in a wide range of disciplines. Social network
analysis can be performed on many real world networks from different
domains.There are a number of social network analysis methods designed for
various tasks and positional analysis is one of them.
It involves partitioning of the set of actors into subsets such that
actors in a subset are similar in their structural relationships with other
actors. Traditional methods of positional analysis are either too strict or too
loose. Their applicability to real world large graphs is severely limited as
they result into almost trival partitions. We propose a useful relaxation to
the concept of equitable partition called as eequitable partition. A variant
of epsilon equitable partition called maximal eequitable partition is also
proposed and formulated as an optimization problem. Various heuristics to
compute a maximal eequitable partition of a graph were tried and finally,a
fast algorith is proposed.
We also present the results of performing positional analysis on a
number of networks and demonstrate empirically that positional analysis with
our notion gives raise to nontrivial and meaningful partitions. Along with the
static network anaysis with respect to positions, we have also studied the
impact of positional analysis on the evolution of networks. Our results show
that positional analysis indeed plays an important role in the evolution.
 Rachit Arora
 Thesis title: Latent Dirichlet Allocation and Singular Value Decomposition based MultiDocument Summarization
 Microsoft, Redmond

ABSTRACT
Mixture models like Latent Dirichlet Allocation are used to represent documents
as a mathematical model with a given probability distribution. We look at a
novel way of applying this to one of the most challenging task in Statistical
Natural Language Processing multidocument summarization. Representing the
documents as a probability distribution using LDA can effectively capture and
represent the topics of the corpus. We also look at the methods of improving
the preexisting algorithm for estimation and interference in LDA and give
certain workarounds for the limitations in the LDA Model which can be used in
other topic models which are extension of the basic LDA model.
Singular Value Decomposition is an effective tool of Principal
Component Analysis used in the area of Text Retrieval. It is used to find
orthogonal dimensions for the terms and we exploit this ability to find
orthogonal dimensions to extract sentences orthogonal to each other and form a
summary. In PCA one of the problems being looked into is a weighting scheme for
the terms of the termdocument matrix. The current weighting mechanisms dont
capture the underlying structure that the documents represent as a set. they
are based on the assumption that the documents are independant of each other.
We show that improvements can be made by making the assumption that is central
to the LDA theme that documents share a certain set of underlying latent
topics. Using the probality distribution of the LDA model we see a way of
weighting the terms of the documents and show its application by computing
multidocument summary using this combined LDASVD model.
LDA based topic models are very effective in capturing the correlations
among the terms at the document level. Howvere many tasks in SNLP deal with the
documents at the level of the sentences. The terms have stronger correlation
among them at the sentence level and lesser at the document level. Thus we have
come up with a new topic model, which we call Latent Dirichlet Segment and word
allocation that captures the correlation among the terms of the documents both
at the level of sentences or paragraphs and at the level of the documents. Thus
we represent the documents of a corpus by capturing there underlying structure
of latent topics, which is the theme of LDA and capturing the structure of the
documents which is represented by the sentences of the documents.
Finally we look at a News Aggregator in which we attempt to gather the
common news articles from various websites and present them to the user under
one heading. Thus a user can read the same news event of his favorite new
websites and also compare them at one place. Together with news articles we can
provide the user even more facilities like timeline and summary for the entire
news event or the individual articles.
 Simarjit Singh
 Thesis title:Policy Learning in Partially Observable Environment
 PGP, IIM Lucknow

ABSTRACT
Agents that act in real environments,whether physical or virtual, rarely have
complete information about the state of environment in which they are working.
Robots and complex systems have limitations in sensor capabilities as well as
bottle necks in computational power,these limitations further restrict the real
time information available to agent for decisionmaking. Predictive State
Representation (PSRs) is a recently introduced class of models for discrete
time dynamical systems with partial observability of the actual state of
system. PSRs replace the traditional notion of state with a set of statistics
about future actions and observations. PSRs have been extended to encompass
continuous domains,since a lot of real world signals are continous. But the
problem is the quality of the predicitions are dependant on the core test cases
chosen, and in continous domains,there is not a good criterion yat, like linear
independant in discrete domains. We have extended the existing literature in
PSR to address the problems of number of test cases and test case length. We
have also provided a firm theoretical foundation using Schwarz information
theoretic criteria for test case selection. By modeling problems from field of
economics in RL domain, we have shown our criterion for selection of test case
length and count indeed provide a sufficient statistic, at the same time we
have established the applicability of PSR to such complex stateless domains
that are mainly addressed using time series analysis or chaos theory.
Availability of long range real time data for macroeconomics indicators, as
well as complex and continous nature of state make economics domain as obvious
for experimentation. Moreover Economic Systems are huge systems, it is not
possible to find complete state and other machine learning techniques have been
employed with only limited success. We found that relatively established fields
in RL, Markov decision process (MDP) and Partially Observable Markov Decision
Process (POMDP) are not suitable to address problems in economics domain.
However we have shown that PSR(Predictive State Representation) along with
Artificial Neural Networks (ANNs) can be efficiently employed with very high
accuracy in prediction of future interest rate and industrial production
average. We have further shown that Support Vector Machine (SVMs) regression
can address the problem of prediction of future interest rate with higher
accuracy than ANN.
 P. Swaminathan
 Thesis title:CoSOFTClusteringAn Informatic Theoretic Approach To Obtain Overlapping Clusters From Cooccurrence Data
 Yahoo Research, Bangalore

ABSTRACT
Coclustering exploits cooccurrence information, from contingency tables to
cluster both rows and columns simultaneously. It has been established that
coclustering produces a better clustering structure as compared to
conventional methods of clustering. So far, coclustering has only been used as
a technique for producing hard clusters. In this papaer, we present an
algorithm using the information theoretic approach to generate overlapping
(soft) clusters. The algorithm maintains probability membership for every
instance to each of the possible clusters and iteratively tunes these
membership values. The theoretical formulation of the criterion function is
presented first, followed by the actual algorithm. We evalute the algorithm
over document/word cooccurance information and present experimental results.
We discuss the algorithm's applicability for polysemy detection experimental
verification. We also discuss directions for future research.
B.Tech
 S. Shonima
 Thesis title:Models for Prediction of Angle Closure Glaucoma
 Strand Life Sciences, Bangalore

ABSTRACT
Angleclosure glaucoma is a condition in which the iris in the eye shifts and
blocks the exit passegeway of the aqueous humor,the fluid that fills the eye.
This fluid breakup causes a rapid buildup of pressure in the eye. Angle
closure glaucoma is an emergency condition that requires immediate medical
treatment to preserve vision. Predicting the suspects of the various stages of
the angle closure glaucoma at an early stage is important to avoid the
progressive loss of vision in both the suspected eye and the other eye.
The first part of the work deals with building classification models
for prediction of angle closure glaucoma in the right eye. A variety of
classification algorithms including Support Vector Machines, Random forests,
Maximum Margin Classifiers with specified maximum false positive and false
negative error rates and Layered Hierarchial Committee of Trees were used to
bulid the classification models. In oder to address imbalance, methods like
sampling, down sampling and modification of misclassification costs were
employed to achieve a high classification accuracy.
The second part of the work is a risk factor analysis of angle closure
glaucoma where a subset of the features that are good predictors of angle
closure glaucoma in the right eye are identified.
 Deepthi Cheboli
 Thesis title:Models For Detection Of Feratoconus
 MS, Univ. of Minnesota, Minneapolis

ABSTRACT
Keratoconus, is a noninflammatory disorder of the eye in which structural
changes within the cornea cause it to thin and change to a more conical shape
which leads to substantial distortion of vision. Detection of Keratoconus is
important to avoid unpredictable results in refractive surgery. This surgery on
a Keratoconic eye might lead to even more thinning of the cornea.
Current methods for the detection of the disease mainly consistes of Neural
Networks. We propose a new model, which integrates Manifold learning techniques
with SemiSupervised learning,to maximise the utility of unlabeled data.
Through the empirical results,we show that highly accurate models can be bulit
using these principles.
 Aniket Ray
 Thesis title:Models For Prediction Of Retinopathy Of Prematurity
 Adobe, Mumbai

ABSTRACT
Retinopathy of Prematurity(ROP) is a major condition affecting prematurely born
children in the developing world. In this work, we have attempted to use
machine learning appraches to learn models for the prediction of Retinopathy of
Prematurity.
We conduct a study on several kinds of classification methods and the
empirical results for them have been outlined.
We propose a hierarchical random committee of trees model to closely
model the disease. It has been shown through empirical results that a high
level of accuracy is achieved while using this model.
 Pranjal Awasthi
 Thesis title:Image Segmentation using Multiscale Conditional Random Fields
 PhD, CMU

ABSTRACT
This work is an attempt to study probablistic graphical models, specifically
for image segmentation. The problem of image segmentation is of fundamental
importance in computer vision. It is one of the priliminary tasks that are to
be performed before any high level processing of the image can be done.
Needless to say, good segmentation is crucial for the success of other vision
tasks such as object detection and tracking, video sequence analysis etc. In
recent years, a lot of research has focused on the use of graphical models for
segmenting images. Graphical models combine ideas from graph theory and
probability theory in an elegant manner to represent complex systems. Efficient
training and interference procedures make them a popular choice for many
problem domains.
Markov random fields (MRFs) are the most popular form of graphical
models used for image segmentation. They represent a generative framework by
modeling the joint probability of the image pixels and the corresponding
labels. There have been a lot of improvements suggested over the basic MRF
framework. Some of them include hierarchical MRFs which try to capture
information over a range of scales and multiscale random fields (MSRFs) which
try to simplify the parameter estimation procedures. In parallel, work has also
been done on the use of directed models such as tree structures belief
networks(TSBNs) and dynamic trees (DTs). Recently with the introduction of
conditional random fields (CRFs), there has been a shift towards the use of
conditional models for image segementation. CRFs provide a lot of advantage
over generative models.
Multiscale conditional random field (mCRF) is a recently proposed conditional model for image segmentation. It belongs to the family of the latent state models which are able to capture long range association among pixel labels. After a comprehensive survey of the existing techniquies, we study the mCRF framework in detail and provide an efficient implementation of it. We also provide two possible extensions to the basic model and successfully demonstrate the increase in the performance which they achieve.
 E. Siva Sowmya
 Thesis title:Placement And Routing Of 3DFPGA using Reinforcement Learning and Support Vector Machines
 PhD, UPenn

ABSTRACT
The primary advantage of using 3DFPGA over 2DFPGA is that the vertical
stacking of active layers reduce the Manhattan distance between the components
in 3DFPGA than when placed on 2DFPGA. This results in a considerable
reduction in total interconnect length. Reduced wire length eventually leads to
reduction in delay and hence improved performance and speed. Design of an
efficient placement and routing algorithm for 3DFPGA that fully exploits the
abovementioned advantage is a problem of deep research and commercial
interest. In this work, an efficient placement and routing algorithm is
proposed for 3DFPGAs which yields better results in terms of
totalinterconnect length and channelwidth. The proposed algorithm employs two
important techniques, namely, Reinforcement Learning(RL) and Support Vector
Machines (SVMs), to perform the placement. The proposed algorithm is
implemented and tested on standard benchmark circuits and the results obtained
are encouraging. This is one of the very few instances where, reinforcement
learning is used for solving a problem in the area of VLSI.