Summer Seminar Series
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Vivek Srikumar
Presenting August 14, 2020 @ 12:00 pm MDT
Venue: Zoom Video Call - Subscribe to our Mailing List to receive the link via email
Title: Fads, Fallacies and Fantasies in the Name of Machine Learning. Click here for more info.
Vivek Srikumar is associate assistant professor in the School of Computing at the University of Utah. His research lies in the areas of natural learning processing and machine learning and has primarily been driven by questions arising from the need to reason about textual data with limited explicit supervision and to scale NLP to large problems. His work has been published in various AI, NLP and machine learning venues and has been recognized by paper awards from EMNLP and CoNLL. His work has been supported by awards from NSF, BSF, and NIH, and also from several companies.. He obtained his Ph.D. from the University of Illinois at Urbana-Champaign in 2013 and was a post-doctoral scholar at Stanford University.
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Jeff Phillips
Presenting August 7, 2020 @ 12:00 pm MDT
Venue: Zoom Video Call - Subscribe to our Mailing List to receive the link via email
Title: A Primer on the Geometry in Machine Learning. Click here for more info.
Jeff M. Phillips is an Associate Professor in the School of Computing at the University of Utah, and is the director of the Utah Center for Data Science. His research focuses on algorithms for data science, a burgeoning area that includes data mining, machine learning, data management, visualization, statistics, and in his view computational geometry. He has particular interest in specialized topics including matrix sketching, coresets, kernel density estimates, and the geometry of word vector embeddings. His work is supported by an NSF CAREER Award and several other grants from NSF. He earned his PhD in Computer Science at Duke University in 2009, and a BS in Computer Science and BA in Mathematics from Rice University in 2003. At the Utah School of Computing he serves as the Director of the Data Science Program which oversees most educational programs related to computational aspects of data science at the university. As part of this role, he helped create the Data Management and Analysis track, a graduate certificate in data science, a Bachelors in Data Science, and more similar offerings on the way.
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Daniel Preotiuc-Pietro
Presenting July 31, 2020 @ 12:00 pm MDT
Venue: Zoom Video Call - Subscribe to our Mailing List to receive the link via email
Title: Making sense of Twitter @ Bloomberg. Click here for more info.
Daniel Preotiuc-Pietro is a Senior Research Scientist and team lead at Bloomberg LP, where he works on analyzing and building models for real-world large scale news mining and information extraction. His research interests are focused on understanding the social and temporal aspects of text, especially from social media, with applications in domains such as Social Psychology, Law, Political Science and Journalism. Several of his research studies were featured in popular press including the Washington Post, BBC, New Scientist, Scientific American or FiveThirtyEight. He is a co-organizer of the Natural Legal Language Processing workshop series. Prior to joining Bloomberg LP, Daniel was a postdoctoral researcher at the University of Pennsylvania with the interdisciplinary World Well Being Project and obtained his PhD in Natural Language Processing and Machine Learning at the University of Sheffield, UK.
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Shirley Wu
Presenting July 23, 2020 @ 12:00 pm MDT
Venue: Zoom Video Call - Subscribe to our Mailing List to receive the link via email
Title: Data Driven Art & Visualization. Click here for more info.
Shirley Wu is an award-winning creative focused on data-driven art and visualizations. She has worked with clients such as Google, The Guardian, SFMOMA, and NBC Universal to develop custom, highly interactive data visualizations. She combines her love of art, math, and code into colorful, compelling narratives that push the boundaries of the web. Her work can be found at sxywu.com.
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Satwik Kottur
Presenting July 17, 2020 @ 12:00 pm MDT
Venue: Zoom Video Call - Subscribe to our Mailing List to receive the link via email
Title: Visual Dialog - Towards Communicative Visual Agents. Click here for more info.
Satwik Kottur is a research scientist at Facebook AI Applied Research (FAIAR), Menlo Park. He recently obtained his PhD degree (2019) from the Department of Electrical and Computer Engineering at Carnegie Mellon University, advised by Prof. José M. F. Moura; and his undergraduate degree from the Indian Institute of Technology Bombay, India (2014).
His research interests are in solving high-level multimodal AI problems, specifically at the intersection of language and vision. His recent works explore communicative visual agents, which interact with humans in natural language about visual content. In the past, he has worked on problems related to video surveillance, scalable machine learning, and learning grounded word representations.
He has been the recipient of the Snap Inc. Fellowship (2018), best paper award (EMNLP 2017, short paper), best reviewer award (NeurIPS 2017), and Carnegie Institute of Technology (CIT) Dean’s Fellowship (2014).
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Unnat Jain
Presenting July 10, 2020 @ 11:00 am MDT
Venue: Zoom Video Call - Subscribe to our Mailing List to receive the link via email
Title: Collaborative Embodied Agents . Click here for more info.
Unnat is a Ph.D. student in Computer Science at UIUC working with Alex Schwing and Svetlana Lazebnik. Within computer vision and machine learning, he is currently looking into developing communicative embodied agents and interpreting their communication. He spent summers at Allen Institute for AI and Facebook AI Research as a research intern.
Previously, he completed his MS in Computer Science at UIUC with the best thesis award and featured in the Siebel Scholars Class of 2018. Prior to joining Illinois, Unnat graduated with the Director’s Gold Medal from the Indian Institute of Technology, Kanpur. He was also awarded the Cadence Gold Medal for the best research thesis across all engineering departments. Learn more about Unnat at https://unnat.github.io/.
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Shruti Rijhwani
Presenting July 2, 2020 @ 3:00 pm MDT
Venue: Zoom Video Call - Subscribe to our Mailing List to receive the link via email
Title: Entity Linking for Low-Resource Languages . Click here for more info.
Shruti Rijhwani is a PhD student at Carnegie Mellon University. Her primary research interest is multilingual natural language processing, with a focus on low-resource and endangered languages. Her research is supported by a Bloomberg Data Science Ph.D. Fellowship. Much of her published work focuses on improving named entity recognition and entity linking for low-resource languages and domains.
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Sambaran Bandyopadhyay
Presenting June 26, 2020 @ 12:00 pm MDT
Venue: Zoom Video Call - Subscribe to our Mailing List to receive the link via email
Title: Graph Representation Learning in the Presence of Outliers. Click here for more info.
Sambaran Bandyopadhyay is an Advisory Research Engineer at IBM Research, India. He is also a final year PhD student at the department of Computer Science and Automation at Indian Institute of Science, Bangalore. Sambaran has published his research works in top tier AI conferences such as AAAI, IJCAI, KDD, WSDM, ICAPS, ECAI etc. and also has 8 patents filed to USPTO. He has been a member of the technical program committee of several AI conferences such as NeurIPS, ICML, AAAI, ECML-PKDD, etc.
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Ayan Mukhopadhyay
Presenting June 19, 2020 @ 11:30 am MDT
Venue: Zoom Video Call - Subscribe to our Mailing List to receive the link via email
Title: Robust Incident Forecasting and Response. Click here for more info.
Ayan Mukhopadhyay is a Post-Doctoral Research Fellow at the Stanford Intelligent Systems Lab at Stanford University, USA. His research interests include multi-agent systems, robust machine learning and decision-making under uncertainty. He was awarded the 2019 CARS post-doctoral fellowship by the Center of Automotive Research at Stanford (CARS). Before joining Stanford, he finished his PhD at Vanderbilt University’s Computational Economics Research Lab. His doctoral thesis was nominated for the Victor Lesser Distinguished Dissertation Award 2020. His work on urban emergency response management has been covered in the Government Technology Magazine, Financial Times, multiple global smart city summits, and received a best paper award at ICLR’s AI for Social Good Workshop.
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Arpita Biswas
Presenting June 12, 2020 @ 11:30 am MDT
Venue: Zoom Video Call - Subscribe to our Mailing List to receive the link via email
Title: Two-Sided Fairness Guarantees for Recommendation Systems (arXiv). Click here for more info.
Arpita Biswas is a Google Ph.D. Fellow at the Department of Computer Science and Automation, Indian Institute of Science. Her broad areas of interest include Game Theory, Optimization, and Machine Learning. She is presently looking at problems in Computational Social Choice Theory and Fairness in Machine Learning. Prior to her Ph.D., she worked as a Research Engineer at Xerox Research Centre India. Her work spans several research spaces such as multi-agent learning, incentive mechanisms, facility location, planning and scheduling, etc. Thus far, she has worked on problems arising from real-world scenarios like online crowd-sourcing, resource allocation, dynamic pricing in transportation, and ride-sharing. More details about her can be obtained at https://sites.google.com/view/arpitabiswas/.
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Eunice Jun
Presenting May 29, 2020 @ 12:00 pm MDT
Venue: Zoom Video Call - Subscribe to our Mailing List to receive the link via email
Title: Tea: A High-level Language and Runtime System for Automating Statistical Analysis. Click here for more info.
Eunice Jun is a PhD student in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. Her research focuses on developing new tools and methods for conducting valid and reproducible statistical analyses. She hopes to make conducting valid data analyses easy (and fun) for end-users. She incorporates methods and techniques from human-computer interaction, programming languages, software engineering, and data science. Her work has been supported by a UW CSE Wilma Bradley Fellowship and an NSF Graduate Research Fellowship.
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Anirbit Mukherjee
Presenting May 21, 2020 @ 12:00 pm MDT
Venue: Zoom Video Call - Subscribe to our Mailing List to receive the link via email
Title: Understanding training of depth 2 nets under minimal distributional assumptions and data poisoning attacks. Click here for more info.
Anirbit, is a (final) year Ph.D. student in applied mathematics at the Johns Hopkins University advised by Prof. Amitabh Basu. He specializes in deep-learning theory and has been awarded 2 fellowships from JHU for this research, "Walter L.Robb Fellowship" and the inaugural "Mathematical Institute for Data Science Fellowship." Earlier, he was a researcher in Quantum Field Theory, while doing his undergrad in physics at the Chennai Mathematical Institute (CMI) and masters in theoretical physics at the Tata Institute of Fundamental Research (TIFR). For more details, you can visit his website.
Nitish Gupta
Presenting May 14, 2020 @ 12:00 pm MDT
Venue: Zoom Video Call - Subscribe to our Mailing List to receive the link via email
Title: Neural Module Networks for Reasoning over Text. Click here for more info.
Nitish is a senior PhD student in Computer Science at the University of Pennsylvania pursuing research in NLP. He is advised by Prof. Dan Roth and co-advised by Prof. Sameer Singh, UCI. Nitish's research focuses on developing structured models for grounded language understanding, primarily in the context of making machines understand and answer questions against text. He is particularly interested in models that perform reasoning by understanding the compositional nature of language, and are able to provide a formal semantic parse and an explanation about their predictions. For more details you can visit https://nitishgupta.github.io/.