We learn from our alumni in this interaction series, often technically, sometimes semi-technically.

Revanth Gangi Reddy Google DeepMind

Revanth Gangi Reddy recently finished his Ph.D. in Computer Science at the University of Illinois Urbana-Champaign, where he was advised by Prof. Heng Ji. His research interests lie in knowledge-driven natural language processing, focusing on agentic search, embedding and reranking models, and retrieval-augmented generation. Revanth’s work has been published in leading conferences such as ICLR, ACL, AAAI, EMNLP, NAACL, and SIGIR, and he also presented a tutorial on Open-Retrieval Question Answering at IJCAI 2023. Revanth has previously done research internships at Salesforce Research, Apple, AI2, IBM Research, and Amazon Science, and was the team lead for UIUC at the Alexa SocialBot Grand Challenge 5. Revanth holds a Bachelor's degree in Computer Science from the Indian Institute of Technology Madras and is a Siebel Scholar (class of 2022).

He will be joining Google DeepMind soon.

The vast expansion of online information has shifted the challenge from simply locating data to efficiently filtering and aggregating relevant content from diverse sources. This talk introduces innovative methodologies aimed at enhancing automated information seeking capabilities within intelligent systems. I will present a modular, agent-based framework that decomposes the information-seeking process into navigation, extraction, and aggregation components, enabling exploratory behaviors that outperform current web agents. I will then discuss the application of this framework to the novel challenge of automatically updating Wikipedia. The second part of the talk focuses on enabling information-seeking over code repositories, wherein I will discuss our work on building state-of-the-art code embedding and ranking models directed at issue localization for addressing software problems.

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