ICTS

Probability and Optimization are two of the core areas that underpin much of data science and machine learning. The current workshop, Data Science: Probabilistic and Optimization Methods, will focus on this field with a special focus on shedding light on the core principles that enable both current successes and future breakthroughs in data science and machine learning. The program begins with a bootcamp covering foundational topics in probability, statistics, and optimization, followed by tutorials and research talks highlighting innovative ideas and open challenges. The topics covered will include new theoretical developments in some of the areas likely to be key in upcoming data science research such as reinforcement learning, generative modelling, causal inference and advanced probability and optimization. Through these sessions, participants will see how rigorous theory can inform robust, adaptable systems—and have opportunities to propose fresh lines of inquiry.

A centerpiece of the event is the Infosys-ICTS Turing lectures, delivered by Andrea Montanari (Stanford University), whose work spans several areas including probability, statistical physics, statistics, theoretical computer science, information theory and machine learning. We warmly invite researchers, students, and practitioners from all backgrounds to join this collaborative exploration of data science’s evolving theoretical landscape—and help shape its next wave of discoveries.

Accommodation will be provided for outstation participants at our on campus guest house.

Eligibility criteria: Candidate should be at least in their third year of a STEM undergraduate or be enrolled in or possess a STEM masters OR Candidate should be working in a STEM role as a PhD student, postdoc, faculty, engineer or scientist. Candidate should demonstrate interest and ability in working in theoretical aspects of machine learning by means of a statement of purpose, a CV and/or referee letters.

ICTS is committed to building an environment that is inclusive, non-discriminatory and welcoming of diverse individuals. We especially encourage the participation of women and other under-represented groups.