Sony Varghese

Dr. Sony Varghese is Senior Director of strategic marketing for memory in the Semiconductor Products Group at Applied Materials. In this role, he is involved in identifying challenges to scaling and future key inflections in the memory industry. Prior to Applied Materials, he worked on developing various memory technologies within the R&D organization at Micron Technologies. Dr. Varghese has over 25 U.S. patents issued or pending in the area of semiconductor processing and integration. He holds a Ph.D. in Mechanical and Materials Engineering from The Oklahoma State University, USA.

Nuwan Jayasena

Nuwan Jayasena is a Fellow at AMD Research, and leads a team exploring hardware support, software enablement, and application adaptation for processing in memory. His broader interests include memory system architecture, accelerator-based computing, and machine learning. Nuwan holds an M.S. and a Ph.D. in Electrical Engineering from Stanford University and a B.S. from the University of Southern California. He is an inventor of over 70 US patents, an author of over 30 peer-reviewed publications, and a Senior Member of the IEEE. Prior to AMD, Nuwan was a processor architect at Nvidia Corp. and at Stream Processors, Inc.

Puja Das

Dr. Puja Das, leads the Personalization team at Warner Brothers Discovery (WBD) which includes offerings on Max, HBO, Discovery+ and many more.

Prior to WBD, she led a team of Applied ML researchers at Apple, who focused on building large scale recommendation systems to serve personalized content on the App Store, Arcade and Apple Books. Her areas of expertise include user modeling, content modeling, recommendation systems, multi-task learning, sequential learning and online convex optimization. She also led the Ads prediction team at Twitter (now X), where she focused on relevance modeling to improve App Ads personalization and monetization across all of Twitter surfaces.

She obtained her Ph.D from University of Minnesota in Machine Learning, where the focus of her dissertation was online learning algorithms, which work on streaming data. Her dissertation was the recipient of the prestigious IBM Ph D. Fellowship Award.

She is active in the research community and part of the program committee at ML and recommendation system conferences. Shas mentored several undergrad and grad students and participated in various round table discussions through Grace Hopper Conference, Women in Machine Learning Program colocated with NeurIPS, AAAI and Computing Research Association- Women’s chapter.

Sandeep Singh

Tom Sheffler

Tom earned his PhD from Carnegie Mellon in Computer Engineering with a focus on parallel computing architectures and prrogramming models.  His interest in high-performance computing took him to NASA Ames, and then to Rambus where he worked on accelerated memory interfaces for providing high bandwidth.  Following that, he co-founded the cloud video analytics company, Sensr.net, that applied scalable cloud computing to analyzing large streams of video data.  He later joined Roche to work on next-generation sequencing and scalable genomics analysis platforms.  Throughout his career, Tom has focused on the application of high performance computer systems to real world problems.

Uri Rosenberg

Uri Rosenberg is the Specialist Technical Manager of AI & ML services within enterprise support at Amazon Web Services (AWS) EMEA. Uri works to empower enterprise customers on all things ML: from underwater computer vision models that monitor fish to training models on satellite images in space; from optimizing costs to strategic discussions on deep learning and ethics. Uri brings his extensive experience to drive success of customers at all stages of ML adoption.

Before AWS, Uri led the ML projects at AT&T innovation center in Israel, working on deep learning models with extreme security and privacy constraints.

Uri is also an AWS certified Lead Machine learning subject matter expert and holds an MsC in Computer Science from Tel-Aviv Academic College, where his research focused on large scale deep learning models.