Seminars

Friday, October 4, 2024

10:20 – 11:10 a.m.  (CST)
ETB 1020           

Dr. Li-C. Wang
Professor in Electrical and Computer Engineering
University of California, Santa Barbara

Title: “Harnessing the Power of Large Language Models” 

Abstract
The emergence of Large Language Models (LLMs) has transformed how we apply Machine Learning (ML) in the field of semiconductor test. Recent advancements in LLMs showcase their impressive ability to engage in meaningful dialogue across a wide range of topics, answer complex questions, and even generate code.

In this talk, I will share our experience in harnessing the power of LLMs to develop an AI agent specifically for semiconductor test data analytics. Our approach centers on the integration of a Knowledge Graph (KG) advocate for an end-to-end methodology that positions the KG as a critical component. I will introduce a novel paradigm, Decision-Support ML (DSML), and explain its implementation in common test data analytics workflows. Using wafermap analytics as a case study, we have demonstrated how we built IEA-Plot, an LLM-assisted AI solution, by leveraging typical LLM functionalities. I will show the real-world application of IEA-Plot on test data collected from a recent production line.

Biography
Li-C. Wang is a professor in the ECE department at the University of CA, Santa Barbara. He received his PhD in 1996 from the University of Texas at Austin and was previously with Motorola PowerPC Design Center.

Starting from 2003, his research has focused on investigating how machine learning could be utilized in design and test flows, where he had published more than 100 papers and supervised 22 PhD thesis on the related subjects. Prior to that, his research spanned across multiple topics in EDA and test including microprocessor test and verification, statistical timing analysis, defect-oriented testing, and SAT solvers. He received 10 Best Paper Awards and 2 Honorable Mentions paper awards from major conferences including recent best paper awards from ITC 2022, ITC 2020, VTS 2016, and VLSI-DAT 2019. He is the recipient of the 2010 Technical Excellence Award from Semiconductor Research Corporation (SRC) for his research contributions in data mining for test and validation. He is the recipient of the 2017 IEEE-TTTC Bob Madge Innovation Award. He is an IEEE fellow and served as the General Chair of the International Test Conference (ITC) in 2017, 2018, and 2023.

To learn more about Dr. Wang go to https://iea.ece.ucsb.edu/.

Join us on Friday, 10/4/24 at 10:20 a.m. in ETB 1020 to learn more about Dr. Wang’s approach!

Friday, September 20, 2024

10:20 – 11:10 a.m.  (CST)
ETB 1020           

Dr. Haris Pozids
Scientist & Manager of Infrastructure AIOPS
IBM Research Europe – Zurich, Switzerland

Title: “Using Generative AI Technology to Transform Customer Support Services” 

Abstract
Agent Assist is a software tool, that enables remote technical support agents solve clients’ problems faster and helps newly hired support agents increase their productivity and reduce the time required to learn and resolve cases. Agent Assist integrates tightly with the IBM Cognitive Support Platform (CSP), the tool that IBM support agents use to troubleshoot customer cases. It receives a real-time notification from CSP every time a new case is opened and pushes back to CSP recommendations for resolving that case, based on analysis of past, resolved, similar cases and relevant product documentation. Agent Assist leverages watsonx.ai for retrieving similar historical cases and for past case resolution extraction and summarization, using generative large language models (LLMs). It achieves fast response of less than 60 seconds end-to-end by performing most time-consuming LLM and indexing operations offline.

The transformation of customer support service into a more automated and efficient business by the use of AI is strategic for many businesses, promising higher efficiency, lower cost of operation and improved customer satisfaction. The Agent Assist technology is enabling and accelerating this transformation by harnessing the power of generative AI and by scaling across multiple Support products and functions.

Biography
Haris Pozidis manages the AI for Infrastructure group at IBM Research in Zurich, Switzerland, which focuses on the design of algorithms for scalable and accelerated machine learning, on the development of Flash memory controllers and Computational Storage, and on AI-infused systems for improving cloud resiliency and operations.

Dr. Pozidis holds over 160 US patents in areas ranging from scalable machine learning systems to storage systems and solid-state memory technology. He has co-authored more than 120 journal and conference publications in the above areas. He is a Senior Member of the IEEE and a Principal Research Scientist at IBM Research.

 

To learn more about Dr. Pozids go to https://research.ibm.com/people/haris-pozidis.

You are welcome to join us on Friday, 9/20/24 at 10:20 a.m. in ETB 1020!

Friday, September 13. 2024

10:20 – 11:10 a.m.  (CST)
ETB 1020           

Dr. Wen-mei W. Hwu
Senior Director of Research at NVIDIA
Professor Emeritus l Electrical & Computer Engineering
University of Illinois, Urbana-Champaign

Title: “BaM: System Architecture and Software Stack for Accelerating Compute-Directed Access to Massive Datasets”

Abstract
Compute Devices have traditionally relied on OS services to bring data into the memory in bulk before performing algorithmic computation on the individual data-structure elements. For example, Graphics Processing Units (GPUs) have relied on the host CPU services to bring chunks of storage data into its device memory for use by compute kernels. This approach is well-suited for GPU applications with known data access patterns that enable partitioning of their dataset to be processed in a pipelined fashion in the GPU. However, emerging applications such as graph and data analytics, recommender systems, and graph neural networks, require fine-grained, data-dependent and sparse access to vast feature vectors and embedding datasets. CPU services are unsuitable for these applications due to high CPU-GPU synchronization overheads, I/O traffic amplification, and low CPU software throughput. GPU-initiated access avoids these overheads by removing the CPU from the storage control path and, thus, can potentially support these applications at much higher speed. However, there is a lack of systems architecture and software stack that enable efficient GPU-initiated storage access for applications today. I will present a vision for enabling fast, compute-directed sparse access to massive datasets, the BaM system architecture to realize this vision, and the BaM software stack that efficiently supports emerging applications on existing and upcoming GPUs.

Biography
Wen-mei W. Hwu is a Senior Distinguished Research Scientist and Senior Director of Research at NVIDIA. He is also a Professor Emeritus and the Sanders-AMD Endowed Chair Emeritus of ECE at the University of Illinois at Urbana-Champaign. His research is in the architecture, algorithms, and infrastructure software for data intensive and computational intelligence applications. He served as the Illinois director of the IBM-Illinois Center for Cognitive Computing Systems Research Center (c3sr.com) from 2016 to 2020. He was a PI of the NSF Blue Waters supercomputer project. He received the ACM SigArch Eckert-Mauchly Award, ACM/IEEE Maurice Wilkes Award, the ACM Grace Murray Hopper Award, the IEEE Computer Society Charles Babbage Award, the ISCA Influential Paper Award, the MICRO Test-of-Time Award, the IEEE Computer Society B. R. Rau Award, the CGO Test-of-Time Award, numerous best paper awards, numerous teaching awards, and the Distinguished Alumni Award in CS of the University of California, Berkeley. He is a Fellow of IEEE and ACM.

To learn more about Dr. Hwu: https://research.nvidia.com/person/wen-mei-hwu

Please join us on Friday, 9/13/24 at 10:20 a.m. in ETB 1020.

Friday, September 6, 2024

10:20 – 11:10 a.m.  (CST)
ETB 1020           

Dr. Srinivas Shakkottai
Professor, Electrical & Computer Engineering, Texas A&M University                  

Title: “Structured Reinforcement Learning in NextG Cellular Networks”

Abstract
NextG cellular networks face increasing demands for intelligent control, especially with the advent of softwarized Open Radio Access Networks (O-RAN) and diverse user applications. We present EdgeRIC, a real-time RAN Intelligent Controller (RIC) co-located with the Distributed Unit (DU) in the O-RAN architecture, enabling sub-millisecond AI-optimized decision-making. We propose a constrained reinforcement learning (CRL) approach for developing such real-time strategies, showing that these algorithms can be trained with only a logarithmic increase in complexity compared to traditional RL. We introduce structured learning using threshold and Whittle index-based policies, which provides low-complexity learning and scalable, real-time inference for optimizing resource allocation and enhancing user experience. For media streaming, we prove the optimality of a threshold policy and develop a soft-threshold natural policy gradient (NPG) algorithm that prioritizes clients based on video buffer length, achieving inference times of about 10μs and improving user quality of experience by over 30%. Additionally, we leverage Whittle indexability to simplify resource allocation, ensuring service guarantees such as ultra-low latency or high throughput by training neural networks to compute constrained Whittle indices. Our Whittle index approach, implemented on EdgeRIC, achieves allocation decisions within 20μs per user, enhancing service guarantees across standardized 3GPP service classes, making a case for structured, scalable reinforcement learning for real-time control of NextG networks.

Biography
Srinivas Shakkottai received his PhD in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign in 2007, after which he was a postdoctoral scholar in Management Science and Engineering at Stanford University.  He joined Texas A&M University in 2008, where he is currently a professor at the Dept. of Electrical and Computer Engineering and at the Dept. of Computer Science and Engineering (by courtesy).  His research interests include multi-agent learning and game theory, reinforcement learning, communication and information networks, networked markets, as well as data collection and analytics.  He co-directs the Learning and Emerging Network Systems (LENS) Laboratory and the RELLIS Spectrum Innovation Laboratory (RSIL).  He has served as an Associate editor of IEEE/ACM Transactions on Networking and the IEEE Transactions on Wireless Communications.  Srinivas is the recipient of the Defense Threat Reduction Agency (DTRA) Young Investigator Award and the NSF CAREER Award, as well as research awards from Cisco and Google. His work has received honors at fora such as ACM MobiHoc, ACM eEnergy and the International Conference on Learning Representations.  He has also received an Outstanding Professor Award, the Select Young Faculty Fellowship, and the Engineering Genesis Award (twice) at Texas A&M University.

For more on Dr. Shakkottai and his work, go to Srinivas Shakkottai (tamu.edu).

Please join us on Friday, 9/6/24 at 10:20 a.m. in ETB 1020.

Friday, August 31, 2024

10:20 – 11:10 a.m.  (CST)
ETB 1020           

Dr. Bobak Mortazavi
Associate Professor, Computer Science & Engineering, Texas A&M University                  

Title: “Sensing and Modeling for Personalized Cardiovascular Digital Health”

Abstract
Recent sensing and modeling techniques have led to advancements in clinical modeling, resulting in advancements in interpretation, evaluation, and risk prediction in cardiovascular outcomes. Large datasets, a focus on integrating electronic health records and clinical trials data has enabled improvements in cardiovascular care. However, there still remain significant gaps in patient care in personalized and remote setting in both diagnosis and recovery. In this talk we explore sensing and modeling for remote health for personalized cardiovascular care, moving towards continuous monitoring of clinical biomarker signals in remote settings for clinical diagnosis.

Biography
Bobak Mortazavi, PhD, is an Associate Professor of Computer Science & Engineering and part of the Center for Remote Health Technologies and Systems at Texas A&M University and holds an affiliation with the Yale University School of Medicine’s Center for Outcomes Research and Evaluation. His research focuses on the intersection of wearable technology, machine learning, and cardiovascular-focused clinical outcomes research, to develop longitudinal, personalized models of health. He has made important contributions in enabling wearable sensing technologies for personal health monitoring and integrating machine learning modeling for improving the use of this data in the context of clinical outcomes, with his work supported by awards from DARPA, the NSF, and the NIH.

For more on Dr. Mortazavi and his work, go to https://engineering.tamu.edu/cse/profiles/mortazavi-bobak.html) & STMI Lab – Home

Please join us on Friday, 8/31/24 at 10:20 a.m. in ETB 1020.

Friday, August 23, 2024
10:20 a.m.  (CST)
ETB 1020           

Dr. Srikanth Saripalli
Professor, Department of Mechanical Engineering, Texas A&M University                  

Title: “Autonomy in the Wild: Perception and Control for Off-Road Autonomous Vehicles”

Abstract
This talk focuses on perception and planning algorithms for autonomous vehicles in off-road situations. A particular emphasis is on why off-road vehicles are different than on-road vehicles and how can we solve autonomy in the off-road domain. A major portion of the talk will be on applications of the above algorithms to real vehicles and the lessons that we have learned i.e. what worked and what didn’t and how we should go about building such systems.

Biography
Srikanth Saripalli is a Professor in the Mechanical Engineering Department and the Director for Center for Autonomous Vehicles and Sensor Systems (CANVASS) at Texas A&M University. He holds the J. Mike Walker ’66 Professorship. His research focuses on robotic systems: particularly in air, water and ground vehicles and necessary foundations in perception, planning, control and system integration for this domain. He is currently leading several efforts in off-road autonomous ground vehicles. He has also led several long-term (> 6 month) on-road deployments of autonomous 18-wheeler trucks and slow-moving shuttles in Texas. He is currently interested in developing and deploying Autonomous Shuttles on campus and in cities. He is also interested in developing such autonomous shuttles for mobility challenged and para transit applications.

For more on Dr. Saripalli and his work, go to https://unmanned.tamu.edu.

Please join us on Friday, 8/23/24 at 10:20 a.m. in ETB 1020.

Monday, May 13, 2024

10:15 – 11:15 a.m.  (CST)
WEB 236C

Dr. Naehyuck Chang
Executive Vice President | Samsung SDI America

Title: “The Challenges and Opportunities in the Mobility Electrification“

Abstract
The electrification of mobilities is essential for sustainability, and governments are pushing hard to expedite electric vehicle penetration. As a result, many electric vehicles on the road are present today. Nevertheless, the battery industry faces significant challenges starting in late 2023, which will continue for years. In this talk, we will introduce industry perspective challenges and opportunities for mobility electrification in both the technical and business aspects. The technical aspects to be covered are automotive battery requirements such as energy density, charging time, lifetime, cost, and safety. We will talk about the infrastructure issues for electric vehicle charging. As for the business aspects, we will discuss the vehicle electrification roadmap, government support, marketing challenges, cost demands, battery raw material costs, etc. This talk is the first to introduce battery technical evolution in the context of “Battery Technology Scaling,” analogous to semiconductor technology scaling. We will also summarize the expected academic contributions to the electrification of mobilities.

Biography
Naehyuck Chang is an Executive Vice President at Samsung SDI America. He was the Head of Development at Samsung SDI Headquarters from 2021 to 2023. Dr. Chang was in charge of all automotive and energy-storage product developments, from cells to systems. He is the Founder of EMVcon, Inc., Irvine, CA, a vehicle electrification company funded by Samsung. Dr. Chang’s research interests include low-power computing, cyber-physical systems, and Design Automation of Things, such as systematic design and optimization of mobility electrification, energy storage systems, and energy harvesting. From 1997 to 2014, Dr. Chang was a professor at the Department of Computer Science and Engineering at Seoul National University. Since 2014, he has been a professor at the Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea. Dr. Chang is a Fellow of the ACM and a Fellow of IEEE for his contributions to low-power design and computing. Dr. Chang is a member of the National Academy of Engineering of Korea. Dr. Chang was the Chair and the Past Chair of the ACM Special Interest Group on Design Automation. Dr. Chang was the TPC Co-Chair of DAC 2016, ASP-DAC 2015, ICCD 2014, CODES+ISSS 2012, and ISLPED 2009, and the General Co-Chair of VLSI-SoC 2015, ICCD 2015 and 2014, and ISLPED 2011. Dr. Chang was the Editor-in-Chief of the ACM Transactions on Design Automation of Electronics Systems.

Please join on Monday, 5/13/24 at 10:15 a.m. in WEB 236C