I am currently a third-year PhD student at Université de Montréal (MILA) advised by Prof. Aaron Courville. My interests lie in applications of machine learning/deep learning in natural language processing. I am currently working towards approaches where NLP models have better generalization capabilities and hence are more robust in practical environments. Previously, I had been working in the natural language generation (NLG) project in IBM Research India for three years and have published papers in top conferences and journals like NeurIPS, ACL, NAACL, INTERSPEECH, and Computational Linguistics. Our work on NLG was successfully deployed to provide match insights and commentary in US Open 2020 Tennis Tournament. At IBM, I also contributed towards the IBM Project Debater, which received wide news coverage worldwide because of a LIVE machine vs human debate. Prior to joining IBM, I held positions like Applied Scientist at Microsoft Bing Ads (2013-2015) and SDE at Amazon.com (2010-2011).

I am a Research Staff Member at IBM Research AI in the AI Composition Lab, Cambridge (MA). My research inter ests include human-AI interaction, especially planning and collaborative decision-making with humans in the loop, with applications in human-agent teaming and decision support.

Vineeth N Balasubramanian is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology, Hyderabad (IIT-H), India, and currently serves as the Head of the Department of Artificial Intelligence at IIT-H. His research interests include deep learning, machine learning, and computer vision. His research has resulted in over 100 peer-reviewed publications at various international venues, including top-tier venues such as ICML, CVPR, NeurIPS, ICCV, KDD, ICDM, and IEEE TPAMI. His PhD dissertation at Arizona State University on the Conformal Predictions framework was nominated for the Outstanding PhD Dissertation at the Department of Computer Science.

He serves as a Senior PC/Area Chair for conferences such as CVPR, ICCV, AAAI, IJCAI, and is an Associate Editor for the Pattern Recognition journal with recent awards as Outstanding Reviewer at ICLR 2021, CVPR 2019, ECCV 2020, etc. He is also a recipient of the Teaching Excellence Award at IIT-H (2017) and a Google Research Scholar Award - earlier known as Google Research Faculty award (2020). His research is funded by various organizations including DST, MeiTY, DRDO, Microsoft Research, Google Research, Adobe, Intel, KLA and Honeywell. He currently serves as the Secretary of the AAAI India Chapter. For more details, please see his profile page.

"Karthikeyan Shanmugam is a Research Staff Member with the IBM Research AI group in NY in the Trustworthy AI Department since 2017. Previously, he was a Herman Goldstine Postdoctoral Fellow in the Mathematical Sciences Division at IBM Research, NY. He obtained his Ph.D. from UT Austin in 2016, MS degree from USC in 2012 and B.Tech, M.Tech degrees in Electrical Engineering from IIT Madras in 2010. His research interests broadly lie in Statistical Machine Learning (ML), Optimization and Information Theory. In ML, his focus is on causal inference, online learning and explainable ML. He has won several awards in IBM for his contributions to Trustworthy AI projects including the Corporate Technical Award in 2021."

Vijay Arya is a Senior Researcher at IBM Research India and part of IBM Research AI group where he works on problems related to Trusted AI. Vijay has 15 years of combined experience in research and software development. His research work spans Machine learning, Energy & smart grids, network measurements & modeling, wireless networks, algorithms, and optimization. His work has received Outstanding Technical Achievement Awards, Research Division awards, & Invention Plateau Awards at IBM, and has been deployed by power utilities in USA. Before joining IBM, Vijay worked as a researcher at National ICT Australia (NICTA) and received his PhD in Computer Science from INRIA, France, and a Masters from Indian Institute of Technology (IIT) Delhi. He has served on the program committees of IEEE, ACM, and IFIP conferences, he is a senior member of IEEE & ACM, and has more than 60 conference & journal publications and patents.

Amit has always been interested in understanding things, even before working on explainable AI his research focused on understanding different AI methods in terms of their statistical behavior. He has worked on projects spanning multiple industries such as Semi-conductor manufacturing, Oil and Gas, Procurement, Retail, Utilities, Airline, Health Care. His current research includes proposing various methods for enhancing trust in systems by developing methods that try to explain or understand their behaviors. His recent work was featured in Forbes and PC magazine with corresponding technical contribution in leading AI research venues such NeurIPS. His work has helped uncover interesting insights in fields such as Olfaction with papers in reputed journals such as Science and Nature Communications with extensive media coverage (Quartz, New Yorker, Atlantic, Biological Scene, Science News). His research also has received the AAAI deployed application award as well as being selected as Best of ICDM twice. For his work on explainability he was invited to attend a Schloss Dagstuhl seminar in 2019 and 2021. He also Co-led the creation of the AI Explainability 360 open source toolkit. Besides research impact, his work has also gone into IBM product and he has received Outstanding Technical Achievement as well as IBM Corporate award. He has been an Area Chair and PC member for top AI conferences as well as has served on National Science Foundation (NSF) panels for the small business innovative research (SBIR) program. He also serves on the invention disclosure committee (IDT) in IBM Research.

Shalmali Joshi is a Postdoctoral Fellow at the Center for Research on Computation and Society at Harvard University. Previously, she was a Postdoctoral Fellow at the Vector Institute. She received her Ph.D. from the University of Texas at Austin (UT Austin). Shalmali's research expertise is on developing reliable Machine Learning (ML) methods for clinical healthcare. She uses techniques from probabilistic modeling and causal inference for explainability, algorithmic recourse and domain generalization for clinical decision-making. She has also contributed to interdisciplinary venues on ethical challenges of deploying ML and explainability tools in clinical healthcare.

Harish Guruprasad is currently an assistant professor at the computer science and engineering (CSE) department of IIT Madras. His primary areas of interest are in machine learning, statistical learning theory and optimization. He was previously a research scientist at IBM research labs and a post-doc at the University of Michigan. Harish completed his PhD at the Computer Science and Automation (CSA) department of the Indian Institute of Science (IISc), Bangalore advised by Prof. Shivani Agarwal and has been fortunate to work with Profs. Ambuj Tewari and Clayton Scott during his PhD and postdoc. Earlier, he finished his M.E. under the supervision of Prof. Chiranjib Bhattacharyya.

Abhijeet did his undergrad in Computer Science and Engineering from IIT (BHU). Worked earlier as an Analyst with Goldman Sachs. Currently, working as a Research Project Associate at IIT Madras. In the past, he has worked in the applications of Deep Learning to Vision and NLP problems. Currently, he is working on ensuring Fairness in Machine Learning models.