Dr. Debendra Das Sharma
Intel Senior Fellow and co-GM Memory and I/O Technologies,
Keynote: PCI-Express: Continued Journey of an Open Innovation Slot in all Computing Platforms Spanning Decades
Dr. Debendra Das Sharma is an Intel Senior Fellow and co-GM of Memory and I/O Technologies in the Data Platforms and Artificial Intelligence Group at Intel Corporation. He is a leading expert on I/O subsystem and interface architecture.
Dr. Das Sharma is a member of the Board of Directors for the PCI Special Interest Group (PCI-SIG) and a lead contributor to PCIe specifications since its inception. He is a co-inventor and founding member of the CXL consortium, co-leads the CXL Board Technical Task Force, and is a leading contributor to CXL specifications. He co-invented the chiplet interconnect standard UCIe and is the chair of the UCIe consortium.
Dr. Das Sharma has a bachelor’s in technology (with honors) degree in Computer Science and Engineering from the Indian Institute of Technology, Kharagpur and a Ph.D. in Computer Engineering from the University of Massachusetts, Amherst. He holds 180+ US patents and 450+ patents world-wide. He is a frequent keynote speaker, plenary speaker, distinguished lecturer, invited speaker, and panelist at the IEEE Hot Interconnects, IEEE Cool Chips, IEEE 3DIC, SNIA SDC, PCI-SIG Developers Conference, CXL consortium, Open Server Summit, Open Fabrics Alliance, Flash Memory Summit, Intel Innovation, and Universities (CMU, Texas A&M, Georgia Tech, UIUC, UC Irvine). He has been awarded the Distinguished Alumnus Award from Indian Institute of Technology, Kharagpur in 2019, the IEEE Region 6 Outstanding Engineer Award in 2021, the first PCI-SIG Lifetime Contribution Award in 2022, and the IEEE Circuits and Systems Industrial Pioneer Award in 2022.
Dr. Rajeev Rastogi
Vice President of Machine Learning
Keynote: Recommendation systems: Challenges and solutions
Abstract: In this talk, he will present Machine Learning solutions for three specific recommendation system challenges in the real world –
- Node recommendations in directed graphs: Given a directed graph, the problem is to recommend the top-k nodes with the highest likelihood of a link from a query node. We enhance GNNs with dual embeddings and propose adaptive neighborhood sampling techniques to handle asymmetric recommendations.
- Delayed feedback: The problem is to train an ML model in the presence of target labels that may change over time due to delayed feedback of user actions. We employ an important sampling strategy to deal with delayed feedback – the strategy corrects the bias in both target labels and feature computation and leverages pre-conversion signals such as clicks.
- Uncertainty in model predictions: For binary classification problems, we show that we can leverage uncertainty estimates for model predictions to improve accuracy. Specifically, we propose algorithms to select decision boundaries with multiple threshold values on model scores, one per uncertainty level, to increase recall without hurting precision.
Bio: Rajeev Rastogi is the Vice President of Machine Learning (ML) for Amazon’s International Stores business. He leads the development of ML solutions in the areas of Search, Advertising, Deals, Catalog Quality, Payments, Forecasting, Question Answering, Grocery Grading, etc. Previously, he was Vice President of Yahoo! Labs Bangalore and the founding Director of the Bell Labs Research Center in Bangalore, India. Rajeev is an ACM Fellow and a Bell Labs Fellow. He currently serves on the editorial board of the CACM and has been an Associate Editor for IEEE Transactions on Knowledge and Data Engineering in the past. Rajeev received his B. Tech degree from IIT Bombay, and a PhD degree in Computer Science from the University of Texas, Austin.
Nagaraju N. Kodalapura
Lead Offensive Security Researcher
Title of Tutorial Talk: Embedded Systems Security
Abstract: In this tutorial, we will provide a quick introduction to embedded systems security and the market needs. Then we shall discuss about the role of secure boot in enabling embedded system security, its associated properties, and a brief introduction to technology offering by Intel corporation in this space. Then we shall focus on some of the emerging threats, attacker motivations and potential attack scenarios in this area by taking TOCTOU (Time of Check and Time of Use) attack as an example, followed by Q & A.
Bio: Nagaraju N Kodalapura is the Lead Offensive Security Researcher within IPAS (Intel Product Assurance and Security) organization of Intel Technology India Private Limited. He has been working with Intel Corporation for about 20+ years and has been working in the security research space for more than 14 years. He received his M.S. degree in Digital Design and Embedded Systems from Manipal University, India.
Nagaraju is an IEEE Senior Member publishing his security research work in security conferences like IEEE, Black Hat and other renowned venues and he holds 5 Intel patents.
Nagaraju leads a team of offensive security researchers focusing on Confidential Computing and Virtualization technologies targeting cloud/datacentric platforms and his primary area of research includes Hardware, Firmware and Software Security analysis and research. He is passionate about spirituality, technology and hardware security.