Speakers

Ashwin Srinivasan

Ashwin Srinivasan has worked in the field of Machine Learning and Logic for more than 30 years at places like the Turing institute, Oxford University and IBM Research. He is a Senior Professor at BITS Goa. He also heads Anuradha and Prashanth Palakurthi Centre for AI Research (APPCAIR) Lab at BITS Goa.


Jayashree Kalpathy-Cramer

Jayashree Kalpathy-Cramer is the Director of the QTIM lab and the Center for Machine Learning at the Athinoula A. Martinos Center for Biomedical Imaging and an Associate Professor of Radiology at MGH/Harvard Medical School. Dr. Kalpathy-Cramer is also Scientific Director at MGB Center for Clinical Data Science and a Senior Scientist at the American College of Radiology Data Science Institute. She is an electrical engineer by training, having received a B.Tech in EE from IIT Bombay and a PhD in EE from Rensselaer Polytechnic Institute.

Her research interests include medical image analysis, machine learning and artificial intelligence for applications in radiology, oncology and ophthalmology. The work in her lab spans the spectrum from novel algorithm development to clinical deployment. She is passionate about the potential that these techniques have to improve access to healthcare in the US and worldwide. Dr. Kalpathy-Cramer has authored over 100 peer-reviewed publications and has written over a dozen book chapters.

She is a Deputy Editor for the Radiology-AI journal, a journal from the Radiological Society of North America focused on the applications of AI in Radiology. This year, she was elected to the Council of Distinguished Investigators of the Academy for Radiology & Biomedical Imaging Research.

Manik Varma

Manik Varma is researcher at Microsoft Research India and an adjunct professor of computer science at the Indian Institute of Technology (IIT) Delhi. His research interests lie in the areas of machine learning, computational advertising and computer vision. He has served as an area chair for machine learning and computer vision conferences such as ACCV, CVPR, ICCV, ICML, ICVGIP, IJCAI and NIPS. Classifiers that he has designed are running live on millions of devices around the world protecting them from viruses and malware. He has been awarded the Microsoft Gold Star award and won the PASCAL VOC Object Detection Challenge. He is also a physicist (BSc St. Stephen's College, David Raja Ram Prize), theoretician (BA Oxford, Rhodes Scholar), engineer (DPhil Oxford, University Scholar) and mathematician (MSRI Berkeley, Post-doctoral Fellow).

Manish Gupta

Manish Gupta is the Director of Google Research India, a new AI research lab recently announced by Google. He holds an additional appointment as Infosys Foundation Chair Professor at IIIT Bangalore. Previously, Manish has led VideoKen, a video technology startup, and the research centers for Xerox and IBM in India. As a Senior Manager at the IBM T.J. Watson Research Center in Yorktown Heights, New York, Manish led the team developing system software for the Blue Gene/L supercomputer. IBM was awarded a National Medal of Technology and Innovation for Blue Gene by US President Barack Obama in 2009. Manish holds a Ph.D. in Computer Science from the University of Illinois at Urbana Champaign. He has co-authored about 75 papers, with more than 7,000 citations in Google Scholar (and an h-index of 45), and has been granted 19 US patents. While at IBM, Manish received two Outstanding Technical Achievement Awards, an Outstanding Innovation Award and the Lou Gerstner Team Award for Client Excellence. Manish is a Fellow of ACM and the Indian National Academy of Engineering, and a recipient of a Distinguished Alumnus Award from IIT Delhi.

Ross D. King

Ross D. King obtained a B.Sc. Hons. Microbiology from the University of Aberdeen, and a Ph.D. in Computer Science from the Turing Institute. He was formerly Professor of Machine Intelligence at the University of Manchester. His main research interests are in the interface between computer science and biology/chemistry. The research achievement he is most proud of is originating the idea of a “Robot Scientist”: using laboratory robotics to physically implement a closed-loop scientific discovery system.

His Robot Scientist “Adam” was the first machine to hypothesise and experimentally confirm scientific knowledge. His new robot “Eve” is searching for drugs against neglected tropical diseases. His work on this subject has been published in the top scientific journals, Science and Nature, and has received wide publicity. He is also very interested in building nondeterministic universal Turing machines using DNA, computational aesthetics, and computational economics.

Balaraman Ravindran

Balaraman Ravindran is the Mindtree Faculty Fellow, professor at the Department of Computer Science and Engineering and head of the Robert Bosch Centre for Data Science and AI at the Indian Institute of Technology Madras. He completed his PhD from the University of Massachusetts, Amherst during which he worked on an algebraic framework for abstraction in reinforcement learning with Prof. Andrew Barto. More recently, his research interests have ranged from Spatio-temporal Abstractions in Reinforcement Learning to social network analysis and Data/Text Mining.

Having co-authored over 200 conference and journal publications, Prof. Ravindran is widely seen as India's foremost reinforcement learning expert. He is the recipient of the Mindtree Faculty Fellowship and the Yahoo faculty award among many others. He is also an academic editor for PLOS ONE and an associate editor for the "Machine Learning and Artificial Intelligence" section for Frontiers in Big Data.


Vijay Janapa Reddi

Vijay Janapa Reddi is an associate professor in the John A. Paulson School of Engineering and Applied Sciences (SEAS) at Harvard University. He is also the inference chair of MLPerf - A broad community focused on benchmarking ML with over 70 participating organisations. He is also a consultant in the Facebook Oculus VR division.

His research is centered on mobile and edge-centric computing systems with a rare taste for cloud computing aspects, mostly as it pertains to edge computing. He directs the Edge Computing Lab. He believes in solving computing problems, rather than associating himself with a particular domain or field of computing (i.e., hardware or software).

Aleksandra Faust

Aleksandra Faust is a Staff Research Scientist at Google Brain Research, specializing in reinforcement learning and motion planning. Previously, Aleksandra led Task and Motion Planning research in Robotics at Google, machine learning for self-driving car planning and controls in Waymo, and was a researcher in Sandia National Laboratories. She earned a Ph.D. in Computer Science at the University of New Mexico (with distinction), and a Master's in Computer Science from the University of Illinois at Urbana-Champaign. Her research interests include learning for safe and scalable reinforcement learning, learning to learn, motion planning, decision-making, and robot behavior. Aleksandra won IEEE RAS Early Career Award for Industry, the Tom L. Popejoy Award for the best doctoral dissertation at the University of New Mexico in Engineering, Mathematics, and Sciences in the period of 2011-2014, and was named Distinguished Alumna by the University of New Mexico School of Engineering. Her work has been featured in the New York Times, PC Magazine, ZdNet, and ​was awarded Best Paper in Service Robotics at ICRA 2018 and Best Paper in Reinforcement Learning for Real Life (RL4RL) at ICML.

Carsen Stringer

Carsen Stringer is the Head of Stringer lab at Howard Hughes Medical Institute Janelia Research Campus. She completed her graduate studies at the University College London in the Gatsby Computational Neuroscience Unit under the mentorship of Kenneth D. Harris. She combined her experience in mathematical modelling with her skills and knowledge in neuroscience to explore how multi-neuron recordings can be used to understand the population dynamics that reflect internal state and representations of external stimuli in the brain. Her recordings were performed in the rodent visual cortex and she used a variety of machine learning and dimensionality reduction techniques to explore the network level mechanisms that give rise to neural dynamics. She also helped to develop the Suite2p software which has revolutionized the ability to process videos and computationally analyze the video recordings from in vivo calcium imaging.

Snehanshu Saha

Snehanshu Saha holds Masters Degree in Mathematical and Computational Sciences at Clemson University and Ph.D. from the Department of Applied Mathematics at the University of Texas at Arlington in 2008. He was the recipient of the prestigious Dean's Fellowship during PhD. After working briefly at his Alma matter, Snehanshu moved to the University of Texas El Paso as a regular full-time faculty in the Department of Mathematical Sciences. He is an Associate Professor of CS&IS and Anuradha and Prashanth Palakurthi Centre for Artificial Intelligence Research (APPCAIR), BITS PILANI K K Birla Goa Campus and heads the Center for AstroInformatics Modeling and Simulation (CAMS). He is also a visiting Professor at the department of Statistics, University of Georgia, USA. He has published 90 peer-reviewed articles in International journals and conferences. Dr. Saha is an IEEE Senior member, ACM Senior Member, Vice Chair-International Astrostatistics Association and Former Chair, IEEE Computer Society Bangalore Chapter. Dr. Saha is the Editor of Journal of Scientometric Research, a peer-reviewed SCI/SCOPUS indexed journal. He's an associate fellow of the Inter University Consortium of Astronomy & Astrophysics and a Fellow of IETE. Dr. Saha received distinguished researcher award, PEACE in AstroInformatics and Machine Learning in 2019. Snehanshu's current and future research interests lie in Data Science, Theory of Machine Learning and Astronomy.

Lovekesh Vig

Lovekesh Vig is a senior scientist at TCS Research. He leads the Deep Learning and AI research area at TCS Research, New Delhi where their aim is to strive to develop and improve deep learning solutions to real world problems in a wide variety of domains ranging from insurance, banking and finance to healthcare, manufacturing and retail.

Hima Lakkaraju

Hima Lakkaraju is an assistant professor at Harvard University with appointments in the both the business school and the Department of Computer Science, SEAS. Her research interests lie within the broad area of trustworthy machine learning. More specifically, her research spans explainable ML, fairness, adversarial robustness, reinforcement learning, and causal inference.

She develops machine learning tools and techniques which enable human decision makers to make better decisions. More specifically, her research addresses the following fundamental questions pertaining to human and algorithmic decision-making:

  1. How can one build fair and interpretable models that can aid human decision-making?

  2. How can one ensure that models and their explanations are robust to adversarial attacks?

  3. How can one evaluate the effectiveness of algorithmic predictions in the presence of missing counterfactuals?

  4. How can one detect and correct underlying biases in human decisions and algorithmic predictions?

These questions have far-reaching implications in domains involving high-stakes decisions such as criminal justice, health care, public policy, business, and education.


Student Speakers

Bhargav Srinivasa Desikan

Bhargav is a Research Fellow at the Knowledge Lab at the University of Chicago, where he previously received an MA in Computational Social Science. He researches at the intersection of artificial intelligence and the social sciences/humanities, with a focus on structures of knowledge production and society. He is the author of an NLP book, and has co-authored articles published in the Journal of Machine Learning Research and Cognition. He has previously worked at INRIA and the Turing Institute, is a past Google Summer of Code student, maintains multiple python scientific computing packages, and has spoken about python and computational social science at over 15 conferences across the world.

Yaman Kumar

Yaman Kumar is a Ph.D. student in Computer Science at MIDAS-Lab in IIIT-D, advised by Prof. Rajiv Ratn Shah. He attended his masters at Georgia Tech and his bachelors from the University of Delhi (NSIT).

Yaman has published 15 articles in peer reviewed conferences. His research revolves around Adversarial Networks, Meta-learning, QA and Speech. He is particularly interested in the business and public policy side of these things. He is also an Artificial Intelligence research scholar for companies like Benesse Corp and Berlitz Inc.

Vikram Voleti

Vikram Voleti is a PhD student at Mila, University of Montreal, supervised by Prof. Christopher Pal. He is also a Visiting Researcher at the University of Guelph with Prof. Graham Taylor. His current research interests broadly involve generative modelling of images and video. In Fall 2019, he worked as a Research Intern at Google, where he worked on active speaker detection in videos. Vikram has prior research experience at IIIT Hyderabad, India in video synthesis, and lip reading. He has also worked as a consultant on semantic segmentation for autonomous driving, and as an engineer at an autonomous robotics company. Vikram graduated from IIT Kharagpur in 2014 with a Bachelors and Masters in Electrical Engineering.