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Maruna

The team envisions creating a robust taskbot with customer-focused design at its core.

By University of Massachusetts Amherst
3 min read
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We know that robustness and advanced response generation capabilities will lead to a good conversation, but a careful customer-focused design will make a great system that customers will love to use.

Team Maruna (2022)
Team Maruna (2022)

Chris Samarinas - Team leader

Samarinas is a second year Computer Science PhD student at UMass Amherst. His recent work is on conversational information seeking, representation learning and ranking for information retrieval. In the past, he has solved challenging NLP problems at 3 AI start-ups (adtech, fintech, biotech), in academia (NUS) and as an intern at Adobe Research. My long-term goal is to improve multimodal content organization & discovery.

Rohan Lekhwani

Lekhwani is a first year Computer Science MS student at UMass Amherst and is interested in building and scaling user-facing products. As a software engineer at Gojek, he led an infrastructure as code project built to onboard 1300+ internal applications. He was a Google Summer of Code student and mentor at Rocket.Chat where he built an app currently used on 1500+ servers. Prior to that, he worked as a research intern at DRDO to build a Keras model to predict landslides using 1 million geospatial data samples. He is currently interested in building and scaling conversational information retrieval systems for end users.

Rahul Seetharaman

Seetharaman is a first year Masters student at UMass Amherst and is interested in Natural Language Processing and its applications to Information Retrieval and Conversational AI. Prior to joining UMass, he was a Research Engineer in the Singularity AI project at Microsoft Research India, where he worked on novel mechanisms for GPU timesharing in the Pytorch Distributed Data Parallel ecosystem.

Pracha Promthaw

Promthaw is at junior year Undergraduate student at UMass Amherst. He is interested in Natural Language Processing and its applications in Education and conversational AI. He is a activity leader in their Machine Learning club. He has participated in competitive programming for 6 years, as a silver medalist in Thailand Olympiad of Informatics. His long term goal is to improve the education system in the most productive way with conversational AI and personalized learning

Sheshera Mysore

Mysore is a Ph.D. candidate in computer science at UMass Amherst, jointly advised by Hamed Zamani and Andrew McCallum. Her current interests are in the development of resources and methods in interactive machine learning and natural language processing to facilitate greater user control and transparency in search and recommendation. She has previously interned with the Allen Institute of Artificial Intelligence and IBM Research and worked on information retrieval and information extraction problems. She obtained his Bachelor's degree in Electronics & Telecommunication Engineering from Savitribai Phule Pune University, India, and a Masters's Degree in Computer Science at UMass Amherst, USA.

Hansi Zeng

Zeng is a second year PhD student at UMass Amherst with interest in Information Retrieval and Natural Language Processing. His current research project focuses on applying large pre-trained language models to several search and recommendation tasks, including dense retrieval. He won the best short paper award at SIGIR 2022.

Hamed Zamani - Faculty advisor

Zamani is an Assistant Professor of Computer Science at the University of Massachusetts Amherst, where he also serve as the Associate Director of the Center for Intelligent Information Retrieval (CIIR). His research focuses on designing and evaluating statistical and machine learning models with applications to (interactive) information access systems, including conversational systems. He is currently focusing on Neural Information Retrieval and Conversational AI. He received an NSF CAREER Award in 2022 for research on Conversational IR. Prior to UMass, he was a Researcher at Microsoft, working on a wide range of problems related to search engines.

About the Author
University of Massachusetts Amherst

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