An emerging trend is to leverage an under-explored and richly heterogeneous pool of human knowledge inside machine algorithms, a practice popularly termed as human-in-the-loop (HIL) process. A wide variety of applications, starting from query processing to text translation, feature engineering, or even human decision making in complex uncertain environments stand to benefit from such synergistic man-machine collaboration. This talk will give an overview of our ongoing research projects, recent research results, and impacts. Following that, the talk will delve deeper to one recent research result, i.e., how to aggregate ranked preferences coming from multiple workers and create a single ranked order that satisfies a group fairness criteria, namely, the proportionate or p-fairness. The talk will conclude by summarizing some additional related research results on this space and our ongoing work.
Bio: Senjuti Basu Roy is the Panasonic Chair in Sustainability and an Associate Professor in the Department of Computer Science at the New Jersey Institute of Technology. Her research focus lies on the intersection of data management, data exploration, and AI, especially enabling human-machine analytics in scale. Senjuti has published more than 80 research papers in high impact data management and data mining conferences and journals. She is the track chair of Economics, Online Markets, and Human Computation track of The ACM Web Conference 2024, an Associate Editor of ICDE 2024 and SIGMOD 2023, tutorial co-chair of VLDB 2023, The Web Conference 2022, has served as the Mentorship co-chair of SIGMOD 2018, PhD workshop co-chair of VLDB 2018, and has been involved in organizing several international workshops and meetings. She is a recipient of the NSF CAREER Award, a PECASE nominee, and one of the 100 invited early career engineers to attend the National Academy of Engineering’s 2021 US Frontiers of Engineering Symposium.
