Keynote Speakers

Christopher Matthew De Sa

Cornell University, USA


Christopher Matthew De Sa is currently an Assistant Professor in the Computer Science department at Cornell University. research interests include algorithmic, software, and hardware techniques for high-performance machine learning, with a focus on relaxed-consistency variants of stochastic algorithms such as asynchronous and low-precision stochastic gradient descent (SGD) and Markov chain Monte Carlo. He has authored numerous papers in leading journals and conferences in Machine Learning. He holds BS and MS degrees, both in Electrical Engineering, from Stanford University, and completed his PhD from the same department at Stanford University. For more information about his work please see

Milind Tambe

Google Research, Harvard University, USA


AI for social impact: Results from deployments for public health and conservation


For more than 15 years, my team and I have been focused on AI for social impact, deploying end-to-end systems in areas of public health, conservation and public safety. In this talk, I will highlight the results from our deployments for social impact in public health and conservation, as well as the core AI and multiagent systems innovations that were necessary. First in terms of public health, I will present recent results from our work in India with the world’s two largest mobile health programs for maternal and child care that have served millions of beneficiaries. Additionally, I will highlight results from earlier projects on HIV prevention and others. In terms of conservation, I will highlight efforts for protecting endangered wildlife in national parks around the globe. The key challenge in all of these applications is one of optimizing limited intervention resources. To address this challenge, I will present methodological advances in restless multi-armed bandits, decision-focused learning for predict-then-optimize systems, influence maximization in social networks and green security games. In pushing this research agenda, our ultimate goal is to facilitate local communities and non-profits to directly benefit from advances in AI tools and techniques.

Prof. Milind Tambe is Gordon McKay Professor of Computer Science and Director of Center for Research in Computation and Society at Harvard University; concurrently, he is also Principal Scientist and Director “AI for Social Good” at Google Research. He is recipient of the IJCAI (International Joint Conference on Artificial Intelligence) John McCarthy Award, AAAI (Association for Advancement of Artificial Intelligence) Feigenbaum Prize, AAAI Robert S. Engelmore Memorial Lecture Award, AAMAS ACM (Association for Computing Machinery) Autonomous Agents Research Award, INFORMS ( Institute for Operations Research and the Management Sciences) Wagner prize for excellence in Operations Research practice and Rist Prize from MORS (Military Operations Research Society). He is a fellow of AAAI and ACM. For his work on AI and public safety, he has received Columbus Fellowship Foundation Homeland security award and commendations and certificates of appreciation from the US Coast Guard, the Federal Air Marshals Service and airport police at the city of Los Angeles.

Svetha Venkatesh

Alfred Deakin Professor
Co-Director of Applied AI Institute, Deakin University, Australia.


From Guglielmo Marconi who developed the radio telegraph to the Wright brothers who invented flying machines, curiosity driven experimentation has powered human innovation. Such experimental optimisation remains an integral part of the Scientific Method. This time-honoured method needs a step change to accelerate scientific innovation because this iterative method quickly hits limits.

To speed-up innovation, it is imperative to expand the capability of experimental optimisation and improve its efficiency. This talk will demonstrate how sample efficient AI can be used to deliver this acceleration in experimental design. I will discuss how the methods can be applied widely, focusing on health and advanced manufacturing particularly in settings where data is scare and experimentation is expensive. In healthcare, I show how these methods can accelerate the design of clinical/health trials to efficiently determine the optimal strategy. In advanced manufacturing, I will show how it can be applied broadly from inventing new materials and alloys to accelerating industrial processes.

The second part of the talk will focus on the new machine learning innovations that have been formulated and solved to advance experimental design. These include incorporating experimental design constraints such as process constraints, transferring knowledge from pervious experiments or experimenter “hunches”, and high dimensional Bayesian optimisation so that the number of experimental control variables can be increased.


Svetha Venkatesh is an Alfred Deakin Professor and a Co- Director of Applied Artificial Intelligence Institute (A2I2) at Deakin University. She was elected a Fellow of the International Association of Pattern Recognition in 2004 for contributions to formulation and extraction of semantics in multimedia data, a Fellow of the Australian Academy of Technological Sciences and Engineering in 2006, and a Fellow of the Australian Academy of Science in 2021 for ground-breaking research and contributions that have had clear impact. In 2017, Professor Venkatesh was appointed an Australian Laureate Fellow, the highest individual award the Australian Research Council can bestow.

Professor Venkatesh and her team have tackled a wide range of problems of societal significance, including the critical areas of autism, security and aged care. The outcomes have impacted the community and evolved into publications, patents, tools and spin-off companies.

Prof. Sanghamitra Bandyopadhyay

Indian Statistical Institute, India

Prof. Sanghamitra Bandyopadhyay did her B Tech, M Tech and Ph.D. in Computer Science from Calcutta University, IIT Kharagpur and Indian Statistical Institute respectively. She then joined the Indian Statistical Institute as a faculty member, and became the Director in 2015. Since 2020 she is continuing in her second tenure as the Director of the Institute. Her research interests include computational biology, soft and evolutionary computation, artificial intelligence and machine learning. She has authored/co-authored several books and numerous articles in journals, book chapters, and conference proceedings and has a citation h-index of more than 60. She is the recipient of several awards including the Shanti Swarup Bhatnagar Prize in Engineering Science, TWAS Prize, Infosys Prize, JC Bose Fellowship, Swarnajayanti fellowship, INAE Silver Jubilee award, INAE Woman Engineer of the Year award (academia), IIT Kharagpur Distinguished Alumni Award, Humboldt Fellowship from Germany, Senior Associateship of ICTP, Italy, young engineer/scientist awards from INSA, INAE and ISCA, and Dr. Shanker Dayal Sharma Gold Medal and Institute Silver from IIT, Kharagpur, India. She is a Fellow of the Indian National Science Academy (INSA), National Academy of Sciences, India (NASI), Indian National Academy of Engineers (INAE), Indian Academy of Sciences (IASc), Institute of Electrical and Electronic Engineers (IEEE), The World Academy of Sciences (TWAS), International Association for Pattern Recognition (IAPR) and West Bengal Academy of Science and Technology. She serves as a member of the Science, Technology and Innovation Advisory Council of the Prime Minister of India (PM-STIAC). In 2022, she received the Padma Shri award, the fourth highest civilian award of the Government of India.