The Speakers

Shayan Srinivasa Garani

Shayan Srinivasa Garani is a Professor in Division of EECS at IISc, where he directs the Physical Nano-memories Signal and Information Processing Laboratory. Prior to joining IISc, he was leading various research activities, managing and directing research and external university research programs within Western Digital. He was the Chairman for signal processing for the IDEMA-ASTC and a co-chair for the overall technological committee. He was the past Chair for IEEE Data Storage Technical Committee (2018–2020). He was a lead architect for read channels architectures and pioneered advanced coding and signal processing solutions for magnetic recording and flash memories that were put to practice in hard disk drives (HDDs) and solid-state drives (SSDs). He holds more than 16 U.S. patents in the area of data storage, some of them in disruptive technologies. His research interests include all aspects of physical data storage, quantum information and computation, artificial intelligence and music science and technology. Outside academics, he is a Carnatic classical vocalist.

Talk title: Magnetic Data Storage: The Journey of a Bit

Abstract: The story behind magnetic storage dates back to more than 140 years. From a 5 MB commercial IBM hard disk drive, we are today beyond 25 TB over multiple platters with areal densities beyond 2Tb/in^2. In this talk, I will first begin with an overview of the physics behind this storage technology along with technological advancements. From there, I will describe how several innovations from coding theory, information theory, signal processing along with systems engineering are continuing to shape the existence magnetic hard disk drives to remain competitive within the storage world. I will also try to draw parallels to how many advanced techniques developed for magnetic storage are applicable to other communication systems.

Bireswar Das

Bireswar received his PhD from IMSc Chennai in 2010. He was a visiting researcher at Laboratoire de Recherche en Informatique, France, and later he held a visiting faculty position at National Institute of Science Education and Research, Bhubaneswar. He then joined IIT Gandhinagar, where he is a faculty member in the CSE department. His research interests span algorithms and complexity theory. His current research focus is on studying the computational complexity of group theoretic problems.

Talk title: Mathematics of Juggling

Abstract: Juggling patterns can be described using numerical sequences. In this talk we will explore how jugglers use sequences of numbers to describe and analyze juggling patterns. However, not all numerical sequences translate to valid juggling sequences. Interestingly, the validity of a sequence is linked to a combinatorial problem in group theory. In addition to this connection, we will discuss other combinatorial results on juggling sequences.

Krishna Bharadwaj

After obtaining his PhD from the Center for Nanoscience and Engineering, Indian Institute of Science, Krishna joined as a post-doctoral fellow at the ultrafast quantum optics lab in electrical engineering department of Technion – Israel Institute of Technology. He was an assistant professor at the Electrical Engineering department at IIT – Kanpur for an year and currently associated with the department of material science and engineering at IIT - Delhi. His research is focused on the interfaces of superconductors with low dimensional materials for interesting opto-electronic properties. He also works on high-temperature superconducting devices such as single photon detectors, nano-inductors and tunnel/super-Schottky diodes.

Talk title and Abstract: TBA

Sumit Soman

Sumit Soman is currently a Senior Data Scientist with the Global AI Accelerator at Ericsson R&D. Prior to this, he worked with the Health Informatics Group at the Center for Development of Advanced Computing (CDAC) since 2011. He holds a Ph.D. in Machine Learning from IIT Delhi and is a Senior Member of the IEEE. He has published several research papers in leading conferences and journals, and is also an inventor on 14 patents. He was awarded the DG-CDAC Young Innovator Award in 2019. He served as the chair of the IEEE Sensors Council Bangalore Chapter and is also a voting member of the IEEE Standards Committee Working Group on Reporting Standards for in-Vivo Neural Interface Research and the IEEE Industry Connections group for Neurotechnologies for Brain Computer Interfaces. His areas of interest are machine learning, health informatics and AI use-cases in the telecom domain.

Talk title:Research in Industry: Focus on Telecom AI

Abstract: Doing research in industry has its own share of challenges, especially in fast-paced domains where developments are difficult to keep track of. Moving from grounded training in academia to the industry often requires adapting thought processes aligned with industry trends, while leveraging fundamentals pursued in formal academic courses. In this talk, I discuss my research work, starting from model complexity in machine learning to working on research problems in the industry, with a focus on the telecom domain. Takeaways from the talk would include approaches to develop for problem solving under practical constraints, and publishing research work through various fora.

Tejas Indulal Dhamecha

Tejas has close to 9 years of experience in industry research and applied science. Prior to joining Adobe, he was Principal Data and Applied Scientist and manager at Microsoft, Turing team in Bengaluru where he led the applied science team for generating "follow up" questions/suggestions in Microsoft Copilot. Earlier, he was Senior Research Scientist and a manager at IBM Research Lab where he played an instrumental role to strengthen the core capabilities for Indic languages at IBM and built NLP solutions for the education domain. He is the recipient of three Outstanding Technical Achievement Awards at IBM. He has co-authored three patents in the domain of Education. Prior to joining IBM, he received Ph.D. in Computer Science from IIIT-Delhi. His Ph.D. work revolved around the field of Machine Learning and Biometrics for improving face recognition techniques. One of his research work received the Highest Impact Award at CVPR Biometrics workshop. His research works have received more than 1.5k citations and he has an h-index of 17. He has served as guest faculty at IIT-Jodhpur where he taught MLOps for 3 years.

In his free time, he enjoys spending time with his family and cycling.

Talk title:The Evolution and Future of NLP: From Feature Engineering to Multimodal Models

Abstract: This talk explores the rapid advancements in Natural Language Processing (NLP), tracing the journey from traditional feature engineering to the latest large language models. We will delve into the transformative impact of transformers, the rise of AI assistants, and the ethical challenges faced by the field. Looking ahead, we will discuss the convergence towards multimodal models that integrate text, image, and audio, promising a more comprehensive and intelligent AI future. Join us to understand the current state and future directions of NLP research.

Siddhartha Asthana

Dr. Siddhartha presently holds the position of Director of Data Science and leads both the Product Council and Research Council at AI Garage, Mastercard. Possessing expertise in Machine Learning, he has previously contributed to esteemed organizations including UHG, Accenture, Mahindra, and the Delhi Government. Prior to his industry engagements, he earned his Ph.D. from the Computer Science Department at IIIT-Delhi.

During his doctoral studies, Dr. Siddhartha made significant contributions, publishing papers in renowned computer science conferences such as ACM CHI, CKIM, SIGKDD, DEV, ICTD, among others. His outstanding academic achievements were recognized with a prestigious Ph.D. fellowship from TCS research. Notably, he has been honored with various awards and travel grants from esteemed institutions like Google India, Microsoft Research India, ACM SIGSOFT, and more for his impactful work.

Talk title: Research as a Career and Insights into Graph ML

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