Congratulations on your acceptance to the UMass Amherst Manning College of Information and Computer Sciences PhD program! We look forward to showcasing our research, faculty, and students at our March 3 & 4, 2022 virtual event!
The link to our events will be emailed to all candidates on March 3.
Candidate Friday 2022
March 3 and 4, 2022
Virtual via Gather.Town (Zoom capability required)
All times are in US Eastern Standard Time (EST).
Dean, Laura Haas
Chair of the Faculty, James Allan
Mohit Iyyer, Natural Language Processing at UMass
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This talk will consist of a brief overview of the different types of research going on within the UMass NLP group, which includes topics such as computational social science, information extraction, neural language modeling and representation learning, question answering, and digital humanities. Iyyer also discusses some of the lab's projects on controllable text generation, covering models that answer complex questions, produce paraphrases of sentences, and generate stories.
Prashant Shenoy, Computing & Sustainability
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An overview of how computing techniques can be used to address sustainability problems.
Various faculty research groups present their work.
Open Labs will have current PhD students available to discuss their research and lab life.
Topic Rooms to include:
Donghyun Kim, Dynamic and Autonomous Robotic Systems
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To accomplish human- and animal-level agility in robotic systems, we must holistically understand robot hardware, real-time controls, dynamics, perception, and motion planning. This talk will explain the challenges related to classical control and high-level planning. Strategies for tackling the changes and an exploration of the experimental results of legged robots will also be presented.
Francine Berman, Public Interest Technology at UMass
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Public Interest Technology (PIT) focuses on socially responsible solutions to today's challenges in a tech-driven world. Fran Berman will describe UMass Amherst’s new PIT initiative and the new program developing around it across campus.
Phil Thomas, Safe and Fair Machine Learning
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An exploration of the problem of off-policy evaluation, a discussion of example use-cases for ensuring safety and fairness, and a discussion of recent developments from the Autonomous Learning Laboratory.
Hava Siegelmann, Lifelong Learning Machines
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Lifelong Learning is the cutting edge of artificial intelligence and machine learning - encompassing computational methods that allow systems to learn in runtime and thus not to rely solely on the training sets given in advance. Such systems will have awareness of their environment and would combine recent and old learning for use in new, unanticipated situations. Until recently, this sort of computation has been found exclusively in nature; thus, Lifelong Learning looks to nature for its underlying principles and mechanisms and then transfer them to this new technology.
