Prasad Krishnan is with the Signal Processing and Communications Research Centre, International Institute of Information Technology Hyderabad (IIIT Hyderabad) as a faculty member. He received the B.E. degree from the College of Engineering at Guindy, Anna University, in 2007, and the Ph.D. degree from the Department of Electrical Communication Engineering, Indian Institute of Science, Bangalore, in 2014. His research has been supported through generous grants from the Govt. of India (SERB/ANRF), as well as private agencies like Qualcomm. His current research interests include but is not limited to Algebraic Coding Theory, Private Information Retrieval, and Information-theoretic limits of DNA data storage. 

Abstract: In Private Information Retrieval, a client seeks to download a desired file privately from a library stored in a distributed fashion across multiple databases (or servers). Characterisation of the capacity (i.e., the largest possible rate) of PIR under perfect information-theoretic privacy, for various typical distributed storage settings, has been the primary focus of the last decade in IT-PIR. This session will present results regarding some recent shades of IT-PIR, including PIR under relaxed notions of privacy, PIR under special storage schemes, with constraints on required file-sub-packetization, etc. These explorations lead to newer questions that are often simple to state, but perhaps not so simple to solve.

Parthe Pandit is the Thakur Family Chair Assistant Professor with the Center for Machine Intelligence and Data Science (C-MInDS) at IIT Bombay. He was a Simons Postdoctoral Fellow at UCSD, and obtained his PhD in ECE, MS in Statistics both from UCLA, and his undergraduate degrees from IIT Bombay. He is a recipient of the Schmidt Sciences AI2050 Early Career Fellowship (2024), and the Jack K Wolf ISIT student paper award (2019).

Abstract: Modern machine learning involves design choices about the architecture of the model, optimization procedures used for training, and amount of data. These choices interact in a complex manner. However, to build *good* ML systems, it is essential to understand their effect on the performance of the system. Through this tutorial, we will build a framework to analyze some of these design choices. We will focus on addressing issues arising out of the models being overparameterized and overfitted, which have challenged classical statistical learning theory based on uniform convergence and have demanded the development of a revised theory of statistical ML. While this is a fast growing area with many new tools introduced to pose and answer important questions, we will focus on some techniques based on spectral analysis of kernel methods, and nonlinear statistical inverse problems in high dimensions. We will conclude by outlining some important open questions in the field.

Emina Soljanin is a Distinguished Professor of Electrical and Computer Engineering at Rutgers. Before moving to Rutgers in January 2016, she was a (Distinguished) Member of Technical Staff for 21 years in the Mathematical Sciences Research Center of Bell Labs. Her interests and expertise are broad, currently spanning distributed computing and quantum information science. She is an IEEE Fellow, an outstanding alumnus of the Texas A&M School of Engineering, the 2011 Padovani Lecturer, a 2016/17 Distinguished Lecturer, and the 2019 IEEE Information Theory Society President. In 2023, Emina received the IEEE Information Theory Society Aaron D. Wyner Distinguished Service Award and Mrs. Urmila Agrawal Distinguished Visiting Chair Professorship at the Indian Institute of Science.

Abstract: Quantum information science is a rapidly advancing and inherently interdisciplinary field, making it both compelling and challenging for newcomers. This tutorial provides an accessible introduction to the essentials of quantum information processing by first addressing three foundational questions: How is quantum information represented? How is it processed? How is classical information extracted from quantum states? We then present several fundamental quantum algorithms and protocols that illustrate the distinctive power of quantum computing and conclude with examples that highlight the role of quantum correlations. The tutorial emphasizes reliability and security topics, including quantum error correction, quantum key distribution, the quantum one-time pad, quantum money, and quantum games. The only prerequisites are undergraduate probability and algebra.