Time: 10:00 AM to 10:40 AM (Eastern Time)
Other Time Zones: 7:30 PM to 8:10 PM (IST) and 3:00 PM to 3:40 PM (BST)
Place:
Physical Location: Room 233, Information Technology and Engineering (ITE) Building, University of Maryland Baltimore County Campus
Virtual location through WebEx: https://umbc.webex.com/meet/manas
Speaker: Divy Thakkar, Program Manager, Google Research India
Contact: dthakkar@google.com
Abstract of the Talk:
The talk will discuss the role of human-centred research in the effective use and deployment of AI systems in highstakes domains such as public health. I will discuss the design and learnings from a real-world deployment of a large-scale AI system that aims to improve the efficacy of (maternal and neonatal) public health information delivery in marginalized communities in India. I will enrich the discussion through our qualitative work that discusses the role of AI as an actor that generates human-human collaboration. I will also discuss the tensions in the data supply chain that impact data quality and how we can build greater accountability in not just ML models but also datasets.
Bio:
Divy a Program Manager lead at Google Research where I drive Strategy and Operations for several critical functions such as academic collaborations, research investments, AI for Social Good etc. I am one of the founding members of Google Research India. My research in HCI is centered examining Human-AI interactions in low-resource and/or high–stakes environments. My research has been published at top-tier HCI venues such as CHI.
Time: 10:00 AM to 11:00 AM (Eastern Time)
Place:
Physical Location: Room 233, Information Technology and Engineering (ITE) Building, University of Maryland Baltimore County Campus
Virtual location through WebEx: https://umbc.webex.com/meet/manas
Speaker: Kaushik Roy, Researcher, AI Institute, University of South Carolina
Contact: kaushikr@email.sc.edu
Title of the Talk: Interpretable Machine Learning
Bio:
Kaushik a Ph.D. student at the Artificial Intelligence Institute South Carolina (AIISC). He completed his master’s in computer science at Indiana University Bloomington and have worked at UT Dallas’s Starling lab. His research interests include statistical relational artificial intelligence, sequential decision making, knowledge graphs, and reinforcement learning. His work is published in reputed conferences in IEEE, KR, AAAI, AAMAS, and ECML. He is the creator Knowledge-infused Reinforcement Learning and Knowledge-infused Policy Gradient algorithms that allow healthcare chatbotto make sequential decisions on patient-chatbot interactions using patient data and external knowledge.