Abstract: (coming soon)

Bio: Vaneet Aggarwal received the BTech degree from the Indian Institute of Technology Kanpur, Kanpur, India, in 2005 and the MA and PhD degrees from Princeton University, Princeton, NJ, USA, in 2007 and 2010, respectively, all in Electrical Engineering. He is currently a Professor and University Faculty Scholar in the School of Industrial Engineering, the School of Electrical and Computer Engineering (by courtesy), and the Department of Computer Science (by courtesy) at Purdue University, where he has been since Jan 2015. He is also affiliated with Purdue Institute of Cancer Research, Purdue Quantum Science and Engineering Institute, Purdue Computational Science and Engineering Interdisciplinary Program (CS&E), Purdue Center for Education and Research in Information Assurance and Security (CERIAS), Quantum Collaborative, Midwest Quantum Collaboratory, Purdue Institute for Control, Optimization and Networks, and Purdue Center for Resilient Infrastructures, Systems, and Processes (CRISP). His research interests are in machine learning, reinforcement learning, and quantum computing.

Prior to joining Purdue, he was a Senior Member of the Technical Staff - Research for five years with AT&T Labs Research, Bedminster, NJ, USA (2010-2014). Dr. Aggarwal has been Adjunct Assistant Professor in the Department of Electrical Engineering at Columbia University (2013-2014), a VAJRA Adjunct Professor in the Department of Electrical Communications Engineering at IISc Bangalore (2018-2019), Visiting Professor at Plaksha University (2022-2023), Adjunct Professor in CS at IIIT Delhi (2022-2023), and Visiting Professor in CS Program at KAUST, Saudi Arabia (2022-2023). Dr. Aggarwal received the Princeton University's Porter Ogden Jacobus Honorific Fellowship in 2009, AT&T Vice President Excellence Award in 2013, AT&T Senior Vice President Excellence Award in 2014, and Purdue University's Most Impactful Faculty Innovator award in 2020. In addition, he received IEEE Jack Neubauer Memorial Award in 2017 (recognizing the best systems paper published in the IEEE Transactions on Vehicular Technology), IEEE Infocom Workshop Best paper award in 2018, Neurips Workshop Best paper award in 2021, and IEEE William R. Bennett Prize in 2024 (recognizing outstanding original papers published in IEEE/ACM Transactions on Networking​). He was an Associate Editor for the IEEE Transactions on Green Communications and Networking (2017-2020) and IEEE Transactions on Communications (2017-2022).

He is a senior member of IEEE since 2015. He is currently serving on the Editorial Board of the IEEE/ACM Transactions on Networking (2019-current) and the IEEE Transactions on Network Science and Engineering (2024-current), and is co-Editor-in-Chief of the ACM Journal on Transportation Systems (2022-current). He is 2024-2025 IEEE Comsoc Distinguished Lecturer. He has Erdos Number 3, Hawking Number 3, and Einstein Number 4. 

Abstract: Can the $n$-party broadcast channel, where any symbol sent by one party is received by all, be made resilient to noise with low overhead? Namely, is it possible to construct interactive error-correcting codes that convert any protocol designed for the noiseless broadcast channel into one that works over the noisy broadcast channel and is not much longer than the original protocol?

[EKS18, STOC 2018] showed that such interactive codes with constant multiplicative overhead are possible under the assumption that the noiseless protocol being simulated is non-adaptive, meaning that it is restricted to have a pre-determined order of turns. Their noise resilient simulating protocols, however, require adaptivity, where each party can decide whether or not to broadcast given all the information available to them, including their input and received transcript. The question of whether such a simulation is possible for general, potentially adaptive, noiseless protocols was left open.

We resolve this question negatively, proving that any interactive code that converts adaptive noiseless broadcast protocols into adaptive broadcast protocols resilient to stochastic errors must incur a multiplicative overhead of $\Omega(\log n / \log \log n)$, which is nearly tight.

Bio: Raghuvansh Raj Saxena is a Reader at the School of Technology and Computer Science at the Tata Institute of Fundamental Research, Mumbai. His primary research interest is communication complexity and its applications to other areas of theoretical computer science, such as coding theory, algorithmic game theory, streaming algorithms, and distributed systems. Other topics he likes to think about are computational complexity, information theory, and things you can convince him to think about.

Before joining TIFR, Raghuvansh received his Ph.D. from Princeton University under the amazing supervision of Prof. Gillat Kol and his Bachelor's degree in Computer Science and Engineering from IIT Delhi.

Abstract:  In this talk, we consider the scenario where multiple, noisy observations of a sequence intended to be transmitted/stored are obtained at the receiver. The noisy observations are obtained via corruptions by independent copies of a common channel; we call the induced channel between the input sequence and the received noisy outputs as a "multi-view" channel. Such a setting arises naturally in several applications in communications, including in packet-switched communication and wireless information transfer, and in storage, particularly in magneto-optical media and in modern, DNA-based data storage. 

We shall review some of our work on the asymptotics of information rates over, and channel capacity and dispersion of, multi-view channels. This will then lead to a discussion on recent work on using such asymptotic estimates to derive achievable rates (and a coding scheme) over a noisy DNA nanopore channel model. These works were carried out jointly with Nir Weinberger of the Technion - Israel Institute of Technology.

Bio: Arvind is an Assistant Professor at the Department of Electrical Engineering, Indian Institute of Technology Madras, India. He received the B.E. (Hons.) degree in Electronics and Communication Engineering from BITS Pilani University, India, in 2018, and the Ph.D. degree in Electrical Communication Engineering from the Indian Institute of Science (IISc), Bengaluru, in 2023, specializing in error-control coding. He then spent a couple of years as a Research Fellow at the Centre of Data for Public Good (CDPG), IISc, where he worked on differential privacy.

Arvind was a recipient of the 2023 IEEE Jack Keil Wolf ISIT Student Paper Award. His papers have also won paper awards at the ACM IKDD CODS, NCC, and the IEEE SPCOM. Arvind's Ph.D. thesis was awarded the Seshagiri Kaikini Medal for the best Ph.D. thesis from the ECE Department at IISc, for the year 2023–24.