Invited Speakers

Bert de Jong  (LBNL)

Dr. Bert de Jong leads the Applied Computing for Scientific Discovery Group, which advances scientific computing by developing and enhancing applications in key disciplines, as well as developing tools and libraries for addressing general problems in computational science. He currently serves as the Department Head for Computational Sciences. de Jong is the Director of the Quantum Systems Accelerator, which is part of the National Quantum Initiative. In addition, de Jong is the Team Director of the Accelerated Research for Quantum Computing (ARQC) Team AIDE-QC, funded by DOE ASCR, focused on developing software stacks, algorithms, and computer science and applied mathematics solutions for chemical sciences and other fields on near-term quantum computing devices. He is also a co-PI on the ARQC team FAR-QC (led out of Sandia). He is the LBNL lead for the BES QIS project (led out of PNNL), where he is focusing on new approaches for encoding wave functions and embedding quantum systems. Prior to this, de Jong was the Director of the LBNL Quantum Algorithms Team QAT4Chem. de Jong is a co-PI within the DOE ASCR Exascale Computing Project (ECP) as the LBNL lead for the NWChemEx effort, contributing to the development of an exascale computational chemistry code. He is the LBNL lead for the Basic Energy Sciences SPEC Computational Chemistry Center (led out of PNNL), where he is working on reduced scaling MCSCF and beyond GW approaches for molecules.  de Jong is a co-PI on a DOE BES Rare Earth Project and a DOE BES carbon capture from air project, where he focuses on using machine learning and computational chemistry to discover new materials for rare earth separation, and designing new molecular crystals for carbon dioxide adsorption. He is also a co-PI on the recently funded RESTOR-C Earthshot. de Jong leads an effort on machine learning for chemical sciences, focused on developing deep learning networks (GANs and autoencoders) for the prediction of structure-function relationships and is developing approaches for inverse design. As part of this effort, his team developed the ML4Chem Python package. In 2020 de Jong was elected as a Fellow of the AAAS.  Prior to joining Berkeley Lab, de Jong was with the Pacific Northwest National Laboratory (PNNL). There he led the High-Performance Software Development Group responsible for NWChem at the Environmental Molecular Sciences Laboratory (EMSL), a national scientific user facility providing integrated experimental and computational resources for environmental molecular science research. de Jong earned his doctorate in theoretical chemistry in 1998 from the University of Groningen in the Netherlands. He earned his master’s in chemistry from the University of Groningen in 1993 and his bachelor’s degree in chemical engineering from the Technical College of Leeuwarden, the Netherlands, in 1990. He was a postdoctoral fellow at PNNL before transitioning to a staff member in 2000.

Siddharth Garg  (NYU)

Dr. Siddharth Garg is currently an Institute Associate Professor of ECE at NYU. He received his Ph.D. degree in Electrical and Computer Engineering from Carnegie Mellon University in 2009, and a B.Tech. degree in Electrical Engineering from the Indian Institute of Technology Madras. He joined NYU in Fall 2014 as an Assistant Professor, and prior to that, was an Assistant Professor at the University of Waterloo from 2010-2014. His research interests are in machine learning, cyber-security, and computer hardware design. He is a member of NYU Center for Cybersecurity and NYU WIRELESS. In 2016, he was listed in Popular Science Magazine’s annual list of “Brilliant 10” researchers. He has received the NSF CAREER Award (2015), best paper awards at the IEEE Symposium on Security and Privacy (S&P) 2016 and the USENIX Security Symposium 2013. His NDSS 2015 paper was selected as a “Top Picks” in Hardware Security in 2019. He also received the Angel G. Jordan Award from ECE department of Carnegie Mellon University.

Tinoosh Mohsenin  (JHU)

Tinoosh Mohsenin is an associate professor of electrical and computer engineering in the Whiting School of Engineering and an affiliate faculty member of the Johns Hopkins Institute for Assured Autonomy (IAA). She is also the director of the Energy Efficient High Performance Computing Lab. Before joining Johns Hopkins, she spent 11 years at the University of Maryland, Baltimore County’s Department of Computer Science and Electrical Engineering. Her research focus is on energy efficient computing for signal processing and machine learning used in multi-agent aerial and ground autonomous systems, human and machine teaming, wearable smart health monitoring, and cyber physical systems. She has also directed more than $10 million in research funding from NSF, LPS, ARL, DARPA, and various health institutes and industrial sponsors. She received her master’s degree from Rice University in 2004 and her PhD in electrical and computer engineering from University of California, Davis in 2010.

Mohsenin has more than 150 peer-reviewed journal and conference publications and is the recipient of NSF CAREER award in 2017, the best paper award in the ACM Great Lakes VLSI conference 2016, and the best paper honorable award in the IEEE Circuits and Systems Symposium 2017 for developing processors in biomedical and deep learning. She was a recipient of the ISSCC 2020 Evening Session Award for co-organizing a session titled “The Smartest Designer in The Universe.” She was the invited moderator for the ISSCC 2022 panel “The Bright and Dark Side of AI” and an invited keynote speaker for the IEEE AI Circuits and Systems Conference (AICAS), 14th IEEE Dallas Circuits and Systems Conference (DCAS), and 27th IEEE International Conference on Electronics Circuits and Systems (ICECS) in 2020.

Danella Zhao  (NSF)

Danella Zhao is an associate professor of electrical and computer engineering at the University of Arizona and a program director at NSF CCF. Between 2016-2022, she was a graduate program director and associate professor of computer science at Old Dominion University. Before joining ODU, she was a Lockheed Martin Corporation/BORSF Endowed Associate Professor at the Center for Advanced Computer Studies, the University of Louisiana at Lafayette.

Dr. Zhao's research focuses on the broad theme of high-performance secure and intelligent computing, spanning such topics as multicore/many-core computing and on-chip networking, Internet-of-Things (IoT) intelligence & security, hardware security, autonomous computing, and machine learning. She is one of the pioneers in the research of wireless Network-on-Chip, a new communication paradigm for building energy-efficient many-core chips. Based on this research, she received the prestigious NSF CAREER award. Her research was recognized by various awards such as the University of Louisiana at Lafayette Research Excellence Award, Louisiana Board of Regents Commendation for Teacher-Scholar, and Japan Society for Promotion of Science (JSPS) Research Fellowship Award. 

Callie Hao (Gatech)

Dr. Cong (Callie) Hao is an assistant professor in ECE at Georgia Tech, where she currently holds the Sutterfield Family Early Career Professorship. She was a postdoctoral fellow in the School from 2020-2021 and also worked as a postdoctoral researcher in ECE at the University of Illinois at Urbana-Champaign from 2018-2020. She received the Ph.D. degree in Electrical Engineering from Waseda University in 2017, and the M.S. and B.S. degrees in Computer Science and Engineering from Shanghai Jiao Tong University. Dr. Hao believes in the power of software/hardware co-design. Her primary research interests lie in the joint area of efficient hardware design and machine learning algorithms. She is passionate about reconfigurable and high-efficiency computing and building useful electronic design automation tools. Dr. Hao is a big fan of outdoor activities, especially mountain climbing, long distance hiking, cycling, and running. She is also a passionate but amateur Judo player.

Deliang Fan (JHU)

Dr. Deliang Fan is an associate professor in the Department of Electrical and Computer Engineering (ECE). He received his master’s and PhD in electrical and computer engineering from Purdue University in 2012 and 2015, respectively. Fan’s primary research interest includes energy efficient and high performance in-memory computing circuit, architecture, and algorithm cross-layer co-design, with applications in deep neural network, data encryption, graph processing and bioinformatics; artificial intelligence and machine learning systems hardware and software co-design, trustworthy AI systems, and brain-inspired (neuromorphic) computing. He has authored more than 160 peer-reviewed international journal and conference research papers. He is also the recipient of a National Science Foundation CAREER Award, the 2019 ACM Great Lakes Symposium’s Best Paper Award on VLSI, 2018 IEEE Computer Society Annual Symposium’s Best Paper Award on VLSI (ISVLSI), the 2017 IEEE Best Paper Award (ISVLSI), and the 2022 Design Automation and Test in Europe’s Best IP Paper Award. His research works were also nominated as best paper candidate in the 2019 Asia and South Pacific Design Automation Conference (ASPDAC) and the 2019 International Symposium on Quality Electronic Design (ISQED).

Tsung-Wei (TW) Huang (UW-Madison)

Dr. Tsung-Wei (TW) Huang is an Assistant Professor in the ECE Department at the University of Wisconsin at Madison. He was an Assistant Professor at the University of Utah from 2019 to 2023. He received his PhD from the ECE Department at the University of Illinois at Urbana-Champaign in 2017 and BS/MS from the CS Department at Taiwan’s National Cheng Kung University in 2011. He has been creating software systems to simplify the building of performance-critical applications, such as computer-aided design, machine learning systems, and quantum computing. These software systems are being used by thousands of people in industry and academia.

Dr. Huang receives several awards for his research contributions, including ACM SIGDA Outstanding PhD Dissertation Award, NSF CAREER Award, Humboldt Research Fellowship Award, and ACM SIGDA Outstanding New Faculty Award.

Gushu Li (UPenn)

Dr. Gushu Li is an Assistant Professor at the Department of Computer and Information Science, University of Pennsylvania. His research interest lies in the emerging quantum computer system and spans the quantum programming language, quantum compiler, and quantum computer architecture. He received the NSF CAREER award this year. His research has been recognized by the ACM SIGPLAN Distinguished Paper Award at OOPSLA 2020 and an NSF Quantum Information Science and Engineering Network Fellow Grant Award. His research outputs have been adopted by several industry/academia quantum software frameworks, including IBM’s Qiskit, Quantinuum’s TKET, Oak Ridge National Lab’s qcor, etc.

Xiaolin Xu (Northeastern University)

Dr. Xiaolin Xu is an Assistant Professor in the Electrical and Computer Engineering Department at Northeastern University. He received B.S. and M.S. degrees from the University of Electronic Science and Technology of China and his Ph.D. in electrical and computer engineering from UMass Amherst. He was a Post-Doctoral Fellow with the Florida Institute for Cybersecurity Research Center. Dr. Xu's research interests span security, efficient machine learning, FPGA, computer architecture, and VLSI. He has authored and co-authored over 80+ peer-reviewed international conference/journal papers in these research areas. His research works received the Rookie Author of the Year (RAY) Award from ACM Transactions on Design Automation of Electronic Systems (TODAES), Best Paper nomination from Embedded Systems Week (ESWEEK) 2022, and Best Paper nomination from the International Conference on Computer-Aided Design (ICCAD) 2022. He is a recipient of the National Science Foundation Early CAREER award. He was the program co-chair of 2024 New England Hardware Security Day (NEHWS), the Ph.D. Competition co-chairs for IEEE HOST 24/23 and the Tutorial Chair for ISQED'23. He is the PI/Co-PI of research projects sponsored by NSF, ONR, ARL, MathWorks, Cisco, AMD-Xilinx, etc. Please refer to https://www.xiaolinxu.com for more details.

Helen Li (Duke) 

Dr. Hai “Helen” Li is the Clare Boothe Luce Professor and Department Chair of the Electrical and Computer Engineering Department at Duke University. She received her B.S and M.S. from Tsinghua University and Ph.D. from Purdue University. Her research interests include neuromorphic circuit and system for brain-inspired computing, machine learning acceleration and trustworthy AI, conventional and emerging memory design and architecture, and software and hardware co-design. Dr. Li served/serves as the Associate Editor for multiple IEEE and ACM journals. She was the General Chair or Technical Program Chair of multiple IEEE/ACM conferences and the Technical Program Committee members of over 30 international conference series. Dr. Li is a Distinguished Lecturer of the IEEE CAS society (2018-2019) and a distinguished speaker of ACM (2017-2020). Dr. Li is a recipient of the NSF Career Award, DARPA Young Faculty Award, TUM-IAS Hans Fischer Fellowship from Germany, ELATE Fellowship, nine best paper awards and another nine best paper nominations. Dr. Li is a fellow of ACM and IEEE.

Sachin S. Sapatnekar (University of Minnesota)

Dr. Sachin S. Sapatnekar is the Henle Chair in ECE and Distinguished McKnight University Professor at the University of Minnesota. His current research interests include design automation methods for analog and digital circuits, circuit reliability, and algorithms and architectures for machine learning. He is a recipient of the NSF Career Award, the SRC Technical Excellence Award, the Semiconductor Industry Association’s University Research Award, and 12 Best Paper awards. He is a Fellow of the IEEE and the ACM.

David Z. Pan (UT Austin)

David Z. Pan is a Professor in the Department of Electrical & Computer Engineering at The University of Texas at Austin and holds the Silicon Laboratories Endowed Chair in Electrical Engineering. He received his B.S. degree from Peking University, and his M.S./Ph.D. degrees from University of California at Los Angeles (UCLA). From 2000 to 2003, he was a Research Staff Member with the IBM T. J. Watson Research Center, Yorktown Heights, NY. His research is mainly focused on electronic design automation, synergistic AI/IC co-optimizations, domain-specific accelerators, design for manufacturing, hardware security, and design/CAD for analog/mixed-signal and emerging technologies. He has published over 450 technical papers in refereed journals and conferences, and is the holder of 8 U.S. patents. He has held various advisory, consulting, or visiting positions in academia and industry, such as MIT and Google. He has graduated over 40 PhD/postdoc students at UT Austin who are now holding key academic and industry positions.

He has received the 2013 SRC Technical Excellence Award, DAC Top 10 Author in Fifth Decade, DAC Prolific Author Award, ASP-DAC Frequently Cited Author Award, 20 Best Paper Awards (TCAD 2021, ISPD 2020, ASP-DAC 2020, DAC 2019, GLSVLSI 2018, VLSI Integration 2018, HOST 2017, SPIE-AL 2016, ISPD 2014, ICCAD 2013, ASPDAC 2012, ISPD 2011, IBM Research Pat Goldberg Memorial Best Paper Award 2010 in CS/EE/Math, ASPDAC 2010, DATE 2009, ICICDT 2009, SRC Techcon 2015, 2012, 2007 and 1998), Communications of the ACM Research Highlights (2014), ACM/SIGDA Outstanding New Faculty Award (2005), NSF CAREER Award (2007), UCLA Engineering Distinguished Young Alumnus Award (2009), UT Austin RAISE Faculty Excellence Award (2014), IBM Faculty Award four times, SRC Inventor Recognition Award three times, Cadence Academic Collaboration Award (2019), and a number of international CAD contest awards, among others.  He is a Fellow of ACM, IEEE and SPIE.

Benjamin Carrion Schaefer (UT Dallas)

Dr. Benjamin Carrion Schaefer completed his Ph.D. at the University of Birmingham, U.K. in 2003. He then worked in the Computer Science Department at the University of California Los Angeles (UCLA) as a Postdoctoral Researcher from 2003 to 2004 and joined the School of Electronic Engineering and Computer Science at Seoul National University, Korea, as a Visiting Research Scholar from 2005 to 2007. From 2007 until September 2012, he was a researcher at the System IP Core Department, Central R&D Centre, NEC Corporation, Kawasaki, Japan. From 2012 to 2016, he worked as an assistant professor at the Department of Electronic and Information Engineering (EIE) at the Hong Kong Polytechnic University, where he established the Design Automation and Reconfigurable Computing Laboratory (DARClab). Since 2016, he works as at the Department of Electrical and Computer Engineering at the University of Texas at Dallas where he is an associate Professor. He is the recipient of the Early Career Scheme from the Research Grants Council, Hong Kong.

Dr. Carrion Schafer has been engaged in the research and development of VLSI systems, reconfigurable computing, thermal-aware VLSI design and High-Level Synthesis (HLS). He has over 30 publications as first author in international scientific journals, conferences and books. He has served on the TPC of most EDA and FPGA conferences including ASP-DAC, DATE, DAC, FPL, ICCAD and ICCD. He is the recipient of the Best Paper Award from the 2022 ACM Great Lake Symposium of VLSI (GLSVLSI’22) conference. He was also a member of Accellera’s SystemC synthesizable user group committee, leading the effort to standardize a synthesizable subset of SystemC.

Yingyan (Celine) Lin (Gatech)

Dr. Yingyan (Celine) Lin is currently an Associate Professor in the School of Computer Science and a member of the Machine Learning Center at the Georgia Institute of Technology. She leads the Efficient and Intelligent Computing (EIC) Lab, which focuses on developing efficient machine learning techniques via cross-layer innovations, spanning from efficient artificial intelligence (AI) algorithms to AI hardware accelerators and AI chip design, and aims to foster green AI and ubiquitous AI-powered intelligence. She received a Ph.D. degree in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign in 2017, and was an assistant professor at Rice University from 2017 to 2022, where she was a finalist for the Dean's Teaching and Research Excellence Award in Spring 2022. Prof. Lin is a Facebook Research Award (2020), NSF CAREER Award (2021), IBM Faculty Award (2021), and Meta Faculty Research Award (2022) recipient, and received the ACM SIGDA Outstanding Young Faculty Award in 2022. She was selected as a Rising Star in EECS by the 2017 Academic Career Workshop for Women at Stanford University. She received a Best Student Paper Award at the 2016 IEEE International Workshop on Signal Processing Systems (SiPS 2016), and the 2016 Robert T. Chien Memorial Award for Excellence in Research at UIUC. Prof. Lin has served as the lead PI on multiple multi-university projects and is currently a co-PI on the $30-million JUMP 2.0 center called COCOSYS, and her group has been funded by NSF, NIH, DARPA, SRC, ONR, Qualcomm, Intel, HP, IBM, and Meta. Her group's research won first place in both the ACM SIGDA University Demonstration at DAC 2022 and the ACM/IEEE TinyML Design Contest at ICCAD 2022, and was selected as an IEEE Micro Top Pick of 2023.

Patrick R. Haspel (Synopsys)

Dr. Patrick Haspel is passionate about connecting universities and academics with industry and creating mutually beneficial opportunities for one another. Patrick joined Synopsys in 2020 as the Global Director of the Synopsys Academic and Research Alliances (SARA), to improve Synopsys’ relationships with academics beyond the University Software Program. Patrick and his team foster university partnerships and create programs that support students, educators, and researchers. SARA’s portfolio of projects range from enabling access to the latest and greatest products through technology deployment, to targeted research engagements that address the ever-evolving challenges of the semiconductor industry.

Prior to Synopsys, Patrick worked at Cadence for 15 years, developing and building the Cadence Academic Network, leading the global expansion of the program from Cadence’s headquarters in San Jose, California. Patrick started his career at the University of Mannheim where he gave lectures on computer architecture, digital design and verification and the front-to-back synthesis, place & route flow and researched low-latency communication networks for high performance compute clusters. Patrick received his diploma in Computer Engineering and his Ph.D. from the University of Mannheim, Germany.

Ji Liu  (Argonne)

Dr. Ji Liu is an Assistant Computer Scientist under the supervision of Dr. Paul Hovland. He received his Ph.D. from North Carolina State University, Raleigh, NC, where he majored in computer engineering under the supervision of Prof. Huiyang Zhou. Prior to joining Argonne, he was a summer intern at IBM in 2020, where he developed topology-aware quantum circuit optimizations. Personal website: https://​pro​gram​in​quan​tum​.com

Liu’s research focuses on Quantum Computing, Computer Architecture, and Compiler Optimization with a special interest in improving the programmability, debuggability, and reliability of quantum computers. His work has appeared at top venues in Computer Architecture (ASPLOS’20, HPCA’21, HPCA’22, ISCA’24), Quantum Computing(QCE’23), Compiler Optimization(CGO’21), Super Computing(ICS’20), and Workload Characterization(IISWC’20). Currently, he is working on noise-aware compilers for NISQ computers. He is also interested in quantum algorithms including quantum machine learning and quantum combinatorial algorithms such as VQE and the Quantum Approximate Optimization Algorithm.

Zichang He (JPMC)

Dr. Zichang He is a senior research associate at the Global Technology Applied Research center at JPMorgan Chase. He received the PhD degree in Electrical and Computer Engineering at UC Santa Barbara in 2023. Zichang’s research primarily focuses on the quantum computing and its design automation. His work has appeared in quantum information conferences & journals, design automation conferences & journals, covering the topics of quantum optimization algorithm, simulation of quantum computations, and circuit optimization. Zichang is the receipt of IEE Excellent in Research Fellowship in 2021 at UCSB and two best student paper awards in IEEE EPEPS 2020 and IEEE HPEC 2022. 

Serge Leef (Microsoft)

Mr. Serge Leef is the Head of Azure for Secure Microelectronics at Microsoft.  Prior to joining Microsoft, he was a program manager in the Microsystems Technology Office (MTO) at DARPA. His research interests include computer architecture, simulation, synthesis, semiconductor intellectual property (IP), cyber-physical modeling, distributed systems, secure design flows, and supply chain management. He is also interested in the facilitation of startup ecosystems and business aspects of technology. Leef came to DARPA from Mentor, a Siemens Business where from 2010 until 2018 he was a Vice President of New Ventures, responsible for identifying and developing technology and business opportunities in systems-oriented markets. Additionally, from 1999 to 2018, he served as a division General Manager, responsible for defining strategies and building successful businesses around design automation products in the areas of hardware/software co-design, multi-physics simulation, IP integration, SoC optimization, design data management, automotive/aerospace networking, cloud-based electronic design, Internet of Things (IoT) infrastructure, and hardware cybersecurity. Prior to joining Mentor, he was responsible for design automation at Silicon Graphics, where he and his team created revolutionary, high-speed simulation tools to enable the design of high-speed 3D graphics chips, which defined the state-of-the-art in visualization, imaging, gaming, and special effects for a decade. Prior to that, he managed a CAE/CAD organization at Microchip and developed functional and physical design and verification tools for major 8- and 16-bit microcontroller and microprocessor programs at Intel. Leef received his Bachelor of Science degree in electrical engineering and Master of Science degree in computer science from Arizona State University. He has served on corporate, state, and academic advisory boards, delivered numerous public speeches, and holds two patents.