Stephen Boyd is a Professor in the Department of Electrical Engineering within Stanford University's College of Engineering. His current research focuses on convex optimization applications across multiple domains. His primary research interests include: Convex Optimization as a foundational methodology Control systems theory and implementation Signal processing algorithms and architectures Machine learning model optimization Analog/digital circuit design techniques Professor Boyd teaches core courses including EE 364A (Convex Optimization I) and ENGR 108 (Introduction to Matrix Methods), alongside extensive independent study supervision across 21 graduate/undergraduate research categories spanning Electrical Engineering, Computer Science, and Computational Mathematics.
Matthew W. Buczynski is an Assistant Professor at the School of Neuroscience , part of the College of Science at Virginia Tech . Holding a Ph.D. in Biochemistry from the University of California San Diego (2008) and postdoctoral training at The Scripps Research Institute (2009-2016), he joined Virginia Tech in August 2016 after completing his postdoctoral fellowship. Education: B.S. in Chemistry, University of Michigan , 2001 Ph.D. in Biochemistry, University of California San Diego , 2008 Postdoctoral Training, The Scripps Research Institute , 2009-2016 Dr. Buczynski’s research program focuses on identifying novel druggable targets for addiction and neurological disorders through mass spectrometry and behavioral pharmacology . His work integrates chemical biology , molecular pharmacology , and in vivo microdialysis to study molecular changes in the brain during chronic drug exposure. Key areas include nicotine dependence , ethanol withdrawal , and cross-talk between pain and addiction mechanisms. His recent publications highlight endocannabinoid system modulation , TRPV1/TRPA1 receptor activation in pain, and diacylglycerol lipase (DAGL) mechanisms in nicotine withdrawal. He employs both self-administration and forced exposure models to validate therapeutic targets. Prospective students can contact him directly through his lab’s website .
Jonathan Fan is an Associate Professor at Stanford University in the Department of Electrical Engineering. His teaching portfolio includes graduate and undergraduate courses in electromagnetics, integrated circuit fabrication, and specialized studies across all quarters. EE 242: Electromagnetic Waves (Autumn) EE 312: Integrated Circuit Fabrication Laboratory (Winter) ENGR 42/EE 42: Electromagnetics and Applications (Spring) 11 independent studies and thesis courses (EE 190, EE 191, EE 300, etc.) His research focuses on nanophotonics and metasurface engineering , with particular emphasis on inverse design methodologies, machine learning -driven photonic optimization, and machine learning in electromagnetic simulation. His recent publications demonstrate a strong trend toward deep learning-enabled photonic design and high-speed optimization of complex optical systems. His work spans metamaterial fabrication , nonlocal effects in metasurfaces, and multi-functional optical devices such as spaceplates for aberration correction. Key technical contributions include physics-augmented neural networks , reparameterization techniques for design constraints, and topology-optimized metasurfaces .
Dr. King Man Siu is an Assistant Professor in the Department of Electrical Engineering at the University of North Texas, College of Engineering. He established the Power Electronics and Renewable Energy (PERE) Lab in February 2022, focusing on power electronics technologies for renewable energy, smart grids, and electric vehicle applications. University: University of North Texas School: College of Engineering Department: Electrical Engineering Research Interests: Dr. Siu specializes in power electronics, renewable energy systems, and smart grid technologies. His work addresses challenges in: Efficient energy conversion for solar and battery systems Grid integration of electric vehicles and renewable sources Advanced inverter design for residential and industrial applications Reduction of magnetic components in power converters Reactive power management and circuit breaker development Modular solutions for DC distribution and rural electrification Publication Trends: His research emphasizes optimizing power electronics through innovative topologies (e.g., Manitoba inverters, interleaved totem-pole converters) and materials (e.g., SiC MOSFETs). Key areas include energy efficiency in photovoltaic systems, smart grid stability, and DC microgrid interconnection strategies. Contact: Email: Kingman.Siu@unt.edu Office: Discovery Park B233
Daniele Ielmini is a Professor at the Department of Electronics, Information and Bioengineering at Politecnico di Milano, Italy, where he leads research in non-volatile memory technologies and neuromorphic computing. He received his Laurea (with merit) and Ph.D. in Nuclear Engineering from Politecnico di Milano in 1995 and 2000, respectively, and has held visiting positions at Intel Corporation (2006), Stanford University (2006), and the University of Illinois at Urbana-Champaign (2010). His research focuses on the modeling and characterization of non-volatile memories, including nanocrystal memory, charge trap memory, phase change memory (PCM), resistive switching memory (RRAM), and spin-transfer torque magnetic memory (STT-MRAM). He has co-edited the book 'Resistive switching – from fundamental redox-processes to device applications' and published over 300 papers with more than 10,000 citations and an H-index of 69 (Scopus, September 2023). Prof. Ielmini's recent publications demonstrate a strong trend toward in-memory computing and neuromorphic applications, with particular emphasis on closed-loop analog computing architectures, reservoir computing with 2D materials, and hardware security implementations using emerging memory technologies. His work bridges fundamental device physics with practical computing applications, especially for energy-efficient AI acceleration. Intel Outstanding Researcher Award (2013) ERC Consolidator Grant (2014) IEEE-EDS Paul Rappaport Award (2015) Fellow of the IEEE Prof. Ielmini leads multiple ERC-funded projects including SHANNON (Secure Hardware with Advanced Nonvolatile memories), NEURO2D (neuromorphic systems based on reservoir computing in MoS2), and ANIMATE (closed-loop in-memory computing). His research group includes post-doctoral researchers, PhD students, and M.Sc. students working on various aspects of emerging memory technologies and their applications. He serves as Associate Editor for IEEE Trans. Nanotechnology and Semiconductor Science and Technology (IOP), and has served in several Technical Subcommittees of international conferences including IEEE-IEDM, IEEE-IRPS, and IEEE-ISCAS. His laboratory at Politecnico di Milano is equipped with advanced semiconductor device testing equipment including probe-stations, semiconductor parameter analyzers, high-speed waveform generators, and other specialized instruments for nano-electronic research. The lab collaborates with major semiconductor companies including Micron Technology Inc. and STMicroelectronics, as well as participating in national and international research projects.
Robert E. (Rob) Kass is the Maurice Falk University Professor of Statistics and Computational Neuroscience at Carnegie Mellon University, holding joint appointments in the Department of Statistics & Data Science, Machine Learning Department, and Neuroscience Institute. His research spans Bayesian statistics, neural data analysis, and computational neuroscience. Kass earned a B.A. in Mathematics from Antioch College, a Ph.D. in Statistics from the University of Chicago, and has been at CMU since 1981. He has served as Department Head of Statistics (1995–2004) and Interim Co-Director of the CNBC (2015–2018). His work focuses on statistical methods for neuroscience, particularly analyzing spike train data and identifying cross-brain interactions. Notable contributions include co-authoring Analysis of Neural Data and foundational articles on Bayesian inference. Kass has received prestigious awards such as the National Academy of Sciences membership and COPSS Distinguished Achievement Award. Research interests include computational neuroscience, statistical modeling of neural systems, and interdisciplinary education. He has advised numerous students and co-organized major workshops like the Statistical Analysis of Neuronal Data series. Kass’s work emphasizes the interplay between statistical rigor and scientific insight, bridging theoretical and applied domains. Education: B.A. in Mathematics, Antioch College Ph.D. in Statistics, University of Chicago Postdoctoral Fellow, Princeton University Scientific contributions include advancements in spike train analysis, Bayesian model assessment, and statistical methods for brain connectivity. His work on neural synchrony and population coding has influenced both theoretical and applied neuroscience.
Christian Enz is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he serves as Director of the Institute of Microengineering and Head of the Integrated Circuits Laboratory. With M.S. and Ph.D. degrees in electrical engineering from EPFL (1984 and 1989), he has established himself as a leading researcher in low-power analog circuit design and semiconductor device modeling. His research interests focus on very low-power analog and RF IC design , semiconductor device modeling , and increasingly on cryogenic electronics for quantum computing applications . Professor Enz is particularly known for his work on FDSOI MOSFET behavior at cryogenic temperatures, developing comprehensive models that address challenges in subthreshold swing saturation, threshold voltage shifts, and self-heating effects. As a Life Fellow of IEEE with 282 publications and over 7,400 citations, Professor Enz has made significant contributions to the field. His recent work demonstrates how the $G_{m}/I_{D}$ design methodology remains effective in advanced technology nodes and can be extended to cryogenic temperature operation. His research bridges fundamental semiconductor physics with practical circuit design considerations for quantum computing interfaces. Life Fellow, IEEE Director of the Institute of Microengineering, EPFL Head of the Integrated Circuits Laboratory 282 publications with 7,400+ citations Specialist in cryogenic CMOS for quantum computing Professor Enz's work on cryogenic electronics addresses critical challenges for quantum computing scalability. By developing accurate models for transistor behavior at temperatures as low as 3.3K, his research enables the design of specialized control electronics that can operate inside dilution refrigerators, potentially solving major wiring constraints that currently limit quantum computer scaling. His laboratory continues to advance the understanding of semiconductor device physics at cryogenic temperatures while developing practical circuit design methodologies for this emerging application domain.
Boris Murmann is Professor at Stanford University, specializing in integrated circuit design, mixed-signal computing, and energy-efficient AI hardware. His research advances neural interface technologies, analog design automation, and tinyML systems. Recent work develops ultra-low-power neural recording ICs for brain-computer interfaces, RRAM-based memory systems, and open-source semiconductor design frameworks. Publications demonstrate innovations in compressive sensing for neural data, hardware-algorithm co-design, and reinforcement learning for analog circuit synthesis. Significant contributions include Medusa (TinyML processor), EMBER (RRAM macro), and methodologies for coarsely-quantized computer vision and analog design automation.
Jennifer Pfeifer is a Professor at the University of Oregon and co-Director of the Center for Translational Neuroscience within the College of Arts and Sciences, Department of Psychology. Her research spans developmental cognitive neuroscience, focusing on adolescence, puberty, self-concept, social cognition, emotion, motivation, and mental health. Her work integrates neuroimaging with behavioral and hormonal data to examine normative and atypical brain development, particularly how social processes and early adversity influence neurobiological models of adolescence. She has secured funding from NIMH, NICHD, NIDA, NSF, and other institutions. Recent publications analyze adolescent social reorientation, pubertal timing, self-disclosure mechanisms, and affective reactivity. Awards and student lists are not explicitly mentioned in the provided text. Her lab emphasizes translational neuroscience applications for mental health prevention and well-being promotion across the lifespan.
Elizabeth Phelps is the Pershing Square Professor of Human Neuroscience in the Department of Psychology at Harvard University's Faculty of Arts and Sciences. She directs the Phelps Lab, which investigates how emotions influence learning, memory, and decision-making using multidisciplinary approaches including behavioral studies, neuroimaging (fMRI), physiological measurements, and computational modeling. The lab collaborates widely across psychology, neuroscience, economics, and clinical disciplines. Her research examines: Human neuroscience of affect and cognition interactions Emotional modulation of learning and memory systems Neural mechanisms of decision-making under uncertainty Impact of emotion on social cognition and behavior Translational applications for psychological disorders Contact information: Email: phelps@fas.harvard.edu Lab email: phelpslab@fas.harvard.edu Address: Northwest Lab Building, 52 Oxford Street, Cambridge, MA 02138 The lab welcomes study participants and research assistant applicants, emphasizing diversity and inclusion in research.
Professor Eva Kosek holds dual academic positions as Professor of Clinical Pain Research at Karolinska Institutet (since 2015) and Uppsala University (since 2020). She is affiliated with the Department of Clinical Neuroscience and leads the Mechanisms of Pain and Treatment Research Group . Her roles include senior consultant at Uppsala University Hospital's Pain Center. Education: MD from Uppsala University (1986), PhD from Karolinska Institutet (1996). Specializations: Rehabilitation Medicine (1998), Pain Relief (2001). Academic promotions: Associate Professor at Karolinska (2004), Full Professor (2015). Research Focus: Chronic pain mechanisms, neuroimmune interactions, fibromyalgia, and autoimmune pathways. Key projects include the RAFT trial (Rituximab for fibromyalgia autoantibody therapy) and the BACPAP consortium for low back pain phenotyping. She chairs IASP's Terminology Task Force, introducing the 'nociplastic pain' concept. Key Contributions: Discovered anti-satellite glial cell IgG antibodies in fibromyalgia patients, linked to symptom severity. Pioneered neuroimaging studies on pain modulation circuits. Authored over 300 publications, with recent work on cerebrospinal fluid biomarkers and lipid metabolite profiles. Awards: 2024 Roland Melzack Lecture Award from IASP. Recognized for redefining pain taxonomy and translational research. Grants: Active funding includes Swedish Research Council grants on fibromyalgia autoimmunity, environmental exposures, and neuroinflammation. Total grants exceed SEK 100M over her career. Labs/Teams: Leads a multidisciplinary team at Karolinska's Pain Research Center, collaborating internationally on translational pain studies. Active in biobanking initiatives like the Swedish Chronic Pain Biobank.
Dr. Mahesh Tripunitara is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, serving as Associate Chair for Undergraduate Studies. He holds a PhD (2005) and Master's (1995) in Computer Science from Purdue University, along with a BSc (1993) in Computer Science from Dalhousie University. His research focuses on information security, authorization mechanisms, cryptographic key management, and hardware security, with industry experience at Motorola's R&D labs and Silicon Valley. His work spans theoretical advancements like access control policy analysis and practical applications such as secure payments systems and IoT device reliability. Notable awards include the Best Student Paper at Usenix Security 2013 and Best Paper at ACM SACMAT 2013. He actively serves on program committees for major security conferences including CCS, CODASPY, and SACMAT. Recent publications highlight innovations in cellular security (SUCI-Catchers defense), role-mining optimization, and blockchain smart contract auditing. Teaching includes advanced algorithm design courses (ECE 406/606) and digital computation (BME 121). His research emphasizes balancing security rigor with usability in authorization systems and hardware protection mechanisms.
Bertan Bakkaloglu is the On Semiconductor Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University (ASU), where he has been since 2004. Prior to ASU, he worked at Texas Instruments focusing on analog, mixed-signal, and RF SoC development for communication transceivers. His research expertise spans RF and mixed-signal IC design, wireless/wireline communication systems, and broadband communication systems. Education: Ph.D. in Electrical Engineering, Oregon State University (1995) M.S.C. in Electrical Engineering, University of Houston (1992) Research Interests: RF and mixed-signal integrated circuits Power management ICs (including LDO regulators and DC-DC converters) High-efficiency power delivery systems Radiation-hardened electronics for space applications MEMS-based sensor systems Biomedical circuits for implantable devices Grants & Collaborations: Over 40+ funded research projects with institutions like NASA/JPL, BAE Systems, and NSF, focusing on power electronics, space systems, and biomedical applications Key projects include radiation-hardened converters, self-calibrating DACs, and implantable medical device circuits Industry partnerships with Texas Instruments, Space Micro, and FLIR Professional Activities: Technical committee member for IEEE Radio Frequency Integrated Circuits Conference Founding chair of IEEE Solid-State Circuits Society Phoenix Chapter
Eby G. Friedman is a Professor in the Department of Electrical and Computer Engineering at the University of Rochester, where he has served since 1991 as director of the High Performance VLSI/IC Design and Analysis Laboratory. He concurrently holds a visiting professor position at the Technion - Israel Institute of Technology, directing the Technion Advanced Circuits Research Center (ACRC). His research specializes in high performance synchronous digital and mixed-signal microelectronic design with applications in high-speed portable processors and low-power wireless communications. Core focus areas include CMOS circuit design, clock/power distribution networks, interconnect synthesis, substrate noise mitigation, pipelining techniques, and 3-D integration methodologies. His work bridges theoretical frameworks with practical implementations in VLSI systems. Scientific Awards and Honors IEEE Fellow Distinguished Lecturer of the IEEE CAS Society Dr. Friedman has authored or edited 16 books and nearly 500 publications, establishing foundational contributions in clock distribution and power delivery networks. He maintains significant editorial leadership as Editor-in-Chief of the Microelectronics Journal, past Editor-in-Chief of IEEE Transactions on VLSI Systems, and serves on the editorial boards of the Journal of Low Power Electronics, Journal of VLSI Signal Processing, Journal of Low Power Application and Circuits, and Proceedings of the IEEE. His laboratory directs cutting-edge research in high-speed circuit design and 3-D integration architectures.
Prof. Dr.-Ing. Hans-Georg Herzog is a Professor of Energy Conversion Technology at the Technical University of Munich (TUM), School of Engineering and Design. He has headed the Energy Conversion Technology group at TUM since 2002 and is a Senior Member of IEEE and member of VDE and VDI professional organizations. His research focuses on energy-efficient electromechanical drives and related technologies critical for modern electric and hybrid vehicles. Prof. Herzog's research interests encompass energy-efficient electromechanical drives, with key expertise in design and optimization of hybrid-electric and battery-electric powertrains, automated design methods for electromechanical actuators, energy and power management systems, and analysis of loss mechanisms in soft magnetic materials. His work bridges fundamental electromagnetic theory with practical automotive applications, particularly in fault-tolerant systems and reliability engineering for electric propulsion. His recent publication trends show a strong focus on vehicular power systems, with particular emphasis on electronic fuses, fault diagnosis in multiphase machines, wireless power transfer, and reliability analysis of electric aircraft propulsion systems. The research spans from fundamental electromagnetic modeling to practical automotive applications, with increasing attention to autonomous driving power requirements and next-generation vehicle electrical architectures. Prize for Good Teaching of the Free State of Bavaria (2010) Prof. Herzog leads a substantial research team including doctoral candidates and postdoctoral researchers who contribute to his extensive publication record. His research group collaborates with automotive industry partners on various grants focused on electric vehicle technology, power system reliability, and advanced electromagnetic systems. The team regularly develops novel methodologies for machine design, fault tolerance analysis, and power system optimization. The research is conducted within TUM's Energy Technology Workshop with specialized facilities for electrical machine testing, power electronics development, and automotive power system simulation. The team maintains strong connections with industry partners in the automotive and aerospace sectors, facilitating technology transfer from academic research to practical applications.
