Abstract
Topic: Human In the Loop AI for Modern Crisis Response
In this talk I will discuss our decade plus adventure working on crisis response with a focus on human-in-the-loop explainable AI. I will present some of the broad challenges in this space touching upon specific work on how such ideas can be leveraged during crises for crowdsourced remote sensing, flood mapping during hurricane response, the role of misinformation during a crisis and the importance of responsible and grounded AI in mitigating the impact of such harms. As part of this effort, I will also spend some time discussing the importance of handling anomalies and biases inherent to such data and how effectively modeling such information can lead to better performance and outcomes.
This research was conducted with many collaborators but I want to highlight in particular that a lot of this work involves joint work with my former PhD students and postdocs - Albert Liang, Nikhita Vedula, Pranav Maneriker, Mark Susmann and Goonmeet Bajaj.
Speaker Biography:
Dr.Srinivasan Parthasarathy received his PhD in Computer Science from the University of Rochester, New York, USA. He is a Professor and University Distinguished Scholar in the Computer Science and Engineering Department at the Ohio State University (OSU). He directs the data mining research laboratory at OSU and co-directs the university-wide undergraduate program in Data analytics. His research interests are broadly in the areas of Data Mining, Databases, Bioinformatics and High Performance Computing. He is a recipient of an Ameritech Faculty fellowship in 2001, an NSF CAREER award in 2003, a DOE Early Career Award in 2004, and multiple grants or fellowships from IBM, Google and Microsoft. His papers have received sixteen best paper awards or similar honors from leading conferences in the field, including ones at SIAM international conference on data mining (SDM), IEEE international conference on data mining (ICDM), the Very Large Databases Conference (VLDB) ACM Knowledge Discovery and Data Mining (SIGKDD), ACM Web Search and Data Mining, The Web Conference, the ACM Bioinformatics Conference and the Intelligent Systems and Molecular Biology conference. Many of his works have transitioned to practice (commercial implementations of Eclat, MLR-MCL, graph sparsification are used by many companies; and his work on the use of Zernike modeling for the early detection of Keratoconus is widely used in clinics). He has served on the program committees of leading conferences in the fields of data mining, databases, and high performance computing. He currently serves on the editorial boards of several journals including Data Mining and Knowledge Discovery: An International Journal. He just completed his final term as chair (elected) of the steering committee for the SIAM data mining conference series. He is a Fellow of the IEEE, the Risk Institute, the AAIA and the Robert Bosch Center for Data Science and AI.