Seminars

Friday, October 18, 2024
10:20 – 11:10 a.m.  (CST)
ETB 1020           

Dr. Prabhakar R. Pagilla
Professor, Mechanical Engineering Department
Texas A&M University

Title: “Planning and Control Problems in Robotics for Manufacturing Operations” 

Abstract
There has been a significant growth in the use of articulated robots for automation of manufacturing operations across many industrial sectors, including aerospace, transportation, construction, electronics, etc., with applications ranging from assembly, surface finishing, to material handling and delivery. For example, mechanical surface finishing operations, such as grinding, sanding, polishing, chamfering, etc., are widely employed in many industrial sectors to remove part anomalies and achieve a desired surface finish. Surface finishing has predominantly been a manual operation that is highly labor intensive and requires skilled operators. The key benefits of integrating robots into these environments include consistent surface quality, improved productivity, preventing hazardous exposure to vibration and particulate, significant flexibility for small as well as large batch manufacturing of finished parts, and the potential for re-purposing the robot quickly to adapt to a new part.

One can find many recent research and technology development activities focusing on the technical challenges of integrating robots into these operations in various environments. This talk will provide an overview of some of the essential elements, challenges, and recent advances in integrating robots to improve manufacturing operations, including registration of the workpiece in the robot workspace, path planning and control. A portion of the talk will also discuss some recent work on the benefits and challenges of V2V communication in connected and autonomous vehicles.

Biography
Prabhakar R. Pagilla is a Professor in the Mechanical Engineering Department at Texas A&M University. His formal background and research interests are in dynamic systems and control with applications in robotics, manufacturing, and autonomy. His current research focuses on robot motion planning and control problems in manufacturing, modeling and control of transport behavior of flexible materials in roll-to-roll manufacturing, and cooperative adaptive cruise control systems in connected and autonomous vehicles. He teaches undergraduate and graduate courses in dynamic systems, control, and robotics.

Please join us on Friday, 10/18/24 at 10:20 a.m. in ETB 1020 to learn more and meet Dr. Pagilla.

For more on Dr. Pagilla visit his website at https://pagilla.engr.tamu.edu.

Joseph Jean Boutros
TAMU Qatar
Professor of Electrical & Computer Engineering

Thursday, October 10, 2024
&
Tuesday, October 15, 2024

11:10 a.m. – 12:25 p.m.
@ the WEB 333’s Fishbowl

Title: “Algebra of Codes, Goppa Codes, and Applications to Post-Quantum Cryptography”

Abstract
We introduce linear codes over finite fields. Then, we study Generalized Reed-Solomon (RS) codes. Goppa codes are defined as subfield subcodes of RS codes. Finally, we describe the McEliece cryptosystem based on Goppa codes for asymmetric post-quantum cryptography.

Biography
Joseph Jean Boutros received the M.S. degree in electrical engineering in 1992 and the Ph.D. degree in 1996, both from Ecole Nationale Superieure des Telecommunications (ENST, Telecom ParisTech), Paris, France. From 1996 to 2006, he was with the Communications and Electronics Department at ENST as an Associate Professor. Also, Dr Boutros was a member of the research unit UMR-5141 of the French National Scientific Research Center (CNRS) in Paris. In July 2007, Doctor Boutros joined Texas A&M University at Qatar (TAMUQ) as a full Professor in the electrical engineering program.

Prof. Boutros’ fields of research are codes on graphs, lattice sphere packings, iterative decoding, joint source-channel coding, compressive sensing, space-time coding, physical-layer security, and physical-layer network coding.

Friday, October 11, 2024
10:20 – 11:10 a.m.  (CST)
ETB 1020           

Dr. Nina Taft
Principal Scientist/Director at Google

Title: “Leveraging Deep Learning to Understand Users’ Views about Privacy” 

Abstract
Privacy nudges can offer developers suggestions to improve the privacy of their apps.  We design a multi-stage methodology that leverages recent advances in NLP and LLMs to automatically extract privacy insights from smartphone app reviews.  Our analysis pipeline includes a privacy classifier, automated issue tagging for thematic clusters, a classifier to attach emotions to reviews, and extracts temporal and geographic trends.  We apply this methodology to publicly visible app reviews on the Google Play store that span a 10-year period and uncover 12 million instances of privacy-relevant reviews.  We’ll summarize users’ perspectives about smartphone app privacy along multiple dimensions – across a decade of time, from over 200 countries, and across a diversity of app types and privacy topics.  This approach complements traditional user studies by providing developers with actionable feedback from a vast and diverse user base.

Biography
Nina Taft is a Principal Scientist/Director at Google where she leads the Applied Privacy Research group. Nina received her PhD from UC Berkeley and has worked in industrial research labs since then – at SRI, Sprint Labs, Intel Berkeley Labs, and Technicolor Research before joining Google.  For many years, Nina worked in the field of networking, focused on Internet traffic modeling, traffic matrix estimation, and intrusion detection. In 2017, she received the top-10 women in networking IEEE N2Women award. In the last decade, she has been working on privacy enhancing technologies with a focus on applications of machine learning for privacy. She has been chair of the SIGCOMM, IMC and PAM conferences, has published over 100 papers, and holds 10 patents.

Please join us on Friday, 10/11/24 at 10:20 a.m. in ETB 1020!

Friday, October 25, 2024
10:20 – 11:10 a.m.  (CST)
ETB 1020           

 

Dr. Vijay Narayanan
Pennsylvania State University
Professor of Computer Science & Engineering and Electrical Engineering
Associate Dean of Innovation
Director of the Penn State Center for AI Intelligence Foundations and Engineering Systems

Title: “Designing Emerging Computing Systems with Ferroelectric Devices” 

Abstract
This talk will present a brief overview of advances in ferroelectric devices and their integration into computing systems to provide novel functionality and energy efficiency in various data intensive applications. The talk will emphasize on cross-stack design opportunities in designing stacked intelligent 3D memory systems.

Biography
Vijaykrishnan Narayanan is an Evan Pugh University Professor and Robert Noll A. Chair Professor of Computer Science and Engineering and Electrical Engineering at Pennsylvania State University.
He is a Fellow of ACM, IEEE, AAAS and the National Academy of Inventors. He serves as associate director of DoE 3DFeM center, thrust lead for DARPA/SRC PRISM center, associate Editor-in-Chief of IEEE Micro, the academic coordinator for the India-US Defense Acceleration Ecosystem and Associate Executive Director of AI for the GeoEd Foundation.

To learn more about Dr. Narayanan go to https://sites.psu.edu/vijaykrishnannarayanan.

Join us on Friday, 10/25/24 at 10:20 a.m. in ETB 1020 to hear Dr. Narayanan present in-person. 

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.