The Technical Committee on Very Large Scale Integration (TCVLSI) of the IEEE Computer Society (IEEE-CS) addresses the interactions among the various aspects of VLSI design including system-level design, logic-level design, circuit-level design, and semiconductor processes. It also covers the computer-aided design techniques to facilitate the VLSI design process. The VLSI may include digital circuits and systems, analog circuits, as well as mixed-signal circuits and systems.
Meet the Team
Dr. Himanshu Thapliyal, Chair TCVLSI
Inaugural Walden and Paula Rhines Endowed Quantum Informatics Professor, Lyle School of Engineering, Southern Methodist University (SMU), Dallas, Texas, USA
Dr. Himanshu Thapliyal is the Inaugural Walden and Paula Rhines Endowed Quantum Informatics Professor in the Lyle School of Engineering at Southern Methodist University (SMU), Dallas, Texas, USA. He is a Full Professor with joint appointments in the Department of Electrical and Computer Engineering (ECE) and the Department of Computer Science (CS) at SMU. Dr. Thapliyal leads a research team that was among the winners of the NIH Quantum Computing Biomedical Research Innovation Lab. Previously, at the University of Tennessee, Knoxville (UTK), he spearheaded the institution’s designation as a National Center of Academic Excellence in Cyber Research (CAE-R) and developed new programs in cybersecurity, including a minor, concentration, and certification track. He has been ranked in the top 20 most-cited scientists worldwide in the field of Computer Hardware and Architecture (Stanford/Elsevier dataset). His recognitions include the NSF CAREER Award (2019), the IEEE-CS TCVLSI Mid-Career Research Achievement Award (2020), the IEEE Computer Society Distinguished Contributor Award (2022), and his selection as an IEEE Computer Society Distinguished Visitor (2025). Dr. Thapliyal has authored 200+ journal and conference publications, holds 3 U.S. patents, and has received multiple Best Paper/Poster Awards at leading venues such as ISVLSI, GLSVLSI, ICCE, and the IEEE World Forum on IoT. His work has been cited more than 7,700 times with an h-index of 53. He has held key leadership roles in premier conferences, serving on steering committees and as General Chair of the IEEE ISVLSI, ACM GLSVLSI, and the upcoming 19th IEEE Dallas Circuits and Systems Conference. He also co-founded the IEEE International Workshop on Quantum Computing, served as Quantum Computing Track Chair at the 2025 Design Automation Conference (DAC), and is currently the Section Editor for Quantum Computing at Springer Nature. He has also edited a Springer book on Quantum Computing.
Dr. Md Saif Hassan Onim, Webmaster TCVLSI
Postdoctoral Fellow, Department of ECE, Southern Methodist University (SMU), Dallas, Texas, USA
Dr. Onim is a Post Doctoral Fellow at the Southern Methodist University, Dallas, Texas. He did his PhD in Computer Engineering in the Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville, as a UT-ORII GATE Fellow. His research sits at the intersection of machine learning, quantum machine learning, and digital health, with a strong focus on applying hybrid quantum-classical models to real-world problems in healthcare, cyber-physical systems, and wearable sensing. His work spans computer vision, deep learning, time-series modeling, and physiological signal analysis, with applications including stress and emotion detection, Alzheimer’s-related behavioral analytics, and anomaly detection. He also has experience building end-to-end research pipelines, from data acquisition and preprocessing to model development, evaluation, and deployment, often leveraging HPC environments and emerging quantum computing frameworks. Before his PhD, he served as a Lecturer in the Department of Electrical, Electronic and Communication Engineering at the Military Institute of Science and Technology (MIST), Bangladesh. He enjoys interdisciplinary collaboration and is always open to research partnerships, academic discussions, and industry-academia opportunities aligned with machine learning, quantum computing, and digital health.