Find Academic Supervisors

Pamela Abshire is a Professor in the Department of Electrical and Computer Engineering and the Institute for Systems Research at the University of Maryland, College Park. She holds the rank of Fischell Institute Fellow and is affiliated with the Maryland Robotics Center, Brain and Behavior Institute, and Robert E. Fischell Institute for Biomedical Devices. Her work bridges VLSI circuit design and bioengineering, focusing on performance-resource tradeoffs in natural/engineered systems. Education: B.S. Physics (Caltech, 1992), M.S. and Ph.D. in Electrical Engineering (Johns Hopkins University, 1997 and 2001). Pre-UMD career included R&D roles at Medtronic (1992-1995). Research focuses on CMOS biosensors, low-power microsystems, and bio-inspired designs for applications like cell-based sensing, robotics, and medical devices. Notable projects include nose-on-a-chip odor detection systems, ant-like microrobots, and lab-on-CMOS platforms for real-time cell monitoring. Awards include IEEE Fellow (2018), NSF CAREER Award (2003), and 2021 University Distinguished Scholar-Teacher honor. Active in academic leadership roles including ADVANCE Professor (2020-2021) and editorial work for IEEE Transactions on Circuits and Systems. Grants include NSF funding for olfactory sensing, AFOSR bio-inspired flight tech, and DARPA CogniSense initiatives. Her Integrated Biomorphic Information Systems Lab collaborates on semiconductor innovation through partnerships like the Mid-Atlantic Semiconductor Collaborative. Labs/Teams: Leads the Integrated Biomorphic Information Systems Lab and contributes to Microelectronics at Maryland group. Co-develops biohybrid systems integrating CMOS, MEMS, and biological components.

• VLSI Circuit Design
• Bioengineering
• Information Theory
• +7 more

Seda Ogrenci is a Professor of Electrical and Computer Engineering and Computer Science at Northwestern University. She holds affiliations with the McCormick School of Engineering and leads the Ogrenci-Memik Lab. Her research focuses on thermal-aware design, edge AI/ML acceleration, and energy-efficient computing systems. She teaches courses like EECS 303 (Advanced Digital Design), EECS 355 (FPGA Design), and EECS 459 (VLSI Algorithmics). Education includes a PhD in Computer Science from UCLA, MS in Electrical and Computer Engineering from Northwestern, and BS from Bogazici University. Her work spans thermal management, 3D stacked memory systems, and reconfigurable architectures. She authored the book Heat Management in Integrated Circuits (2016) and holds patents on thermal sensors and energy harvesting. Recent projects include ML-based real-time control at the edge, FPGA-accelerated ML monitoring, and in-pixel AI for X-ray detectors. Awards include the NSF CAREER Award (2006) and EECS Best Teacher Award (2013). She advises students like Dawei Li and Yingyi Luo and contributed to grants on thermal-aware HPC systems.

• Design Automation
• Real-Time Edge AI/ML for Science
• Thermal-Aware Circuit Design
• +3 more

Dr. John Emmert serves as Professor in the Department of Electrical Engineering and Computer Science at the University of Cincinnati and Director of the NSF Center for Hardware and Embedded Systems Security and Trust (CHEST) IUCRC. He has secured over $9 million in research funding from the Air Force, Army, Navy, DARPA, NSF, Ohio State, and industry partners, including five Phase II DoD SBIR contracts. His educational background includes: Ph.D. in Computer Engineering, University of Cincinnati (1999) M.S. in Electrical Engineering, Air Force Institute of Technology (1993) B.S. in Electrical Engineering, University of Kentucky (1987) His research focuses on hardware security assurance, trusted computing, and embedded systems design with expertise spanning VLSI, FPGA/ASIC development, fault tolerance, electronic warfare countermeasures, and GPS integration. Current work emphasizes automated design tools for secure hardware and reconfigurable computing architectures. Notable recognitions include: IEEE Harold Nobel Award AFRL James B. Tsui Award for best patent Air Force Legion of Merit Five Meritorious Service Medals As a 26-year Air Force veteran (1989-2015) retiring as Colonel, he leverages military experience in electronic warfare and UAV systems to inform hardware security research. His CHEST center directs collaborative projects with Raytheon, Northrop Grumman, Amazon, and federal agencies through active grants totaling millions in annual funding, maintaining strong industry-academia partnerships in microelectronics assurance.

• Hardware Security
• Trusted Computing
• VLSI Design
• +11 more

Julio Ricardo Garcia is a Professor in the Technology department at San José State University. With a PhD from the University of Northern Iowa (1988), he has established expertise in electronics education, industrial technology, and computer simulation applications. His work bridges theoretical concepts with practical industrial applications. Education: Doctor of Philosophy, University of Northern Iowa (1988) Research Focus: Investigates innovative approaches to technology education, particularly in electronics and digital systems. His scholarship emphasizes practical laboratory methodologies, distance learning frameworks, and curriculum development for industrial technology programs. Research extends to computer simulation applications in physics and electronics education. Publications Focus: Garcia's extensive publication record centers on educational methodologies in technology fields. Recent works demonstrate consistent focus on circuit design pedagogy, simulation tools, and international technology education. Articles frequently address practical implementation challenges in academic and industrial training contexts. Professional Service: Contributed as a translator (English/Spanish) for electronics communications and quality control documentation (2004).

• Electronics Education
• Computer Simulation
• Digital Circuits
• +2 more

Dr. Arindam Mukherjee serves as an Associate Professor in the Department of Electrical and Computer Engineering at the University of North Carolina at Charlotte, College of Engineering. His office is located in EPIC 2336, and he can be reached at amukherj@charlotte.edu or by phone at 704-687-8417. His educational qualifications are as follows: Ph.D. from the University of California at Santa Barbara (2002) M.S. from the University of California at Santa Barbara (2000) B.Tech. from Jadavpur University, India (1996) Dr. Mukherjee's research spans Smart System Architectures, Internet of Things (IoT), and Fog Computing. He investigates cooperative and autonomous mobile systems, real-time system software, and database management for Big Data. His work includes scheduling algorithms for IoT systems and the integration of edge, fog, and cloud computing paradigms for real-time applications in the Big-Squared Data space. His publication record from 2003 to 2018 shows an evolution from VLSI design and bioinformatics to contemporary IoT and fog computing. Early work focused on logic synthesis, biochip testing, and FPGA-based bioinformatics implementations, while recent contributions address power management in heterogeneous processors, energy-efficient communications for smart buildings, and the synergistic integration of edge, fog, and cloud computing for real-time IoT data processing. No scientific awards are mentioned in the provided text. There is no information available regarding students advised or research grants. Similarly, no specific research labs or collaborative teams are described in the source material.

• Internet of Things (IoT)
• Fog Computing
• Real-Time Systems
• +4 more

Gerald Jay Sussman is the Panasonic Professor of Electrical Engineering at the Massachusetts Institute of Technology (MIT). He received his S.B. (1968) and Ph.D. (1973) in mathematics from MIT and has been conducting artificial intelligence research there since 1964. His primary research focuses on understanding problem-solving strategies used by scientists and engineers, with dual goals of automating these processes and formalizing educational methodologies. He co-directs the Sussman Lab at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). His research spans artificial intelligence, computer languages, VLSI design, computational classical mechanics, synthetic biology, and telescope engineering. Notable contributions include co-creating the Scheme programming language, developing AI-based CAD tools for VLSI, designing the Digital Orrery for orbital mechanics simulations, and pioneering computational approaches to teaching classical mechanics. His current work includes developing explainable AI systems for autonomous vehicles. Professor Sussman's publications demonstrate broad interdisciplinary impact, with recent works spanning computer science education, software design, computational physics, and synthetic biology. His research consistently bridges theoretical computer science with practical engineering applications and educational innovation. Scientific Awards & Honors: Karl Karlstrom Outstanding Educator Award (ACM, 1990) Amar G. Bose Award for Teaching (MIT, 1992) IEEE EAB Major Education Innovation Award (2023) Taylor L. Booth Education Award (IEEE, 2024) National Academy of Engineering Member Fellow: IEEE, AAAI, ACM, AAAS, American Academy of Arts and Sciences He has supervised 46 PhD students spanning five decades, with dissertations covering AI, computer architecture, computational biology, and physical system modeling. His Sussman Lab develops computational tools for science education and engineering design, including contributions to the Magellan telescopes in Chile. Current projects involve explainable AI systems and computational mechanics frameworks.

• Artificial Intelligence
• Computer Languages
• Computer Architecture
• +6 more

Ioannis Koutis is an Associate Professor in the Department of Computer Science at the New Jersey Institute of Technology (NJIT). His research focuses on algorithms, graph theory, and machine learning, with a particular emphasis on spectral methods and computational science. Koutis has been actively involved in federal grants and collaborative projects, including work on space weather forecasting, linear regression algorithms, and spectral network alignment funded by the National Science Foundation and the United States-Israel Binational Science Foundation. His research interests span spectral graph theory, combinatorial optimization, and the application of machine learning techniques to complex systems. Notable contributions include advancements in hypergraph partitioning, gene clustering in co-expression networks, and the development of efficient algorithms for large-scale graph problems. Koutis has led or co-led seven federally funded projects, emphasizing interdisciplinary applications in computer vision, bioinformatics, and computational social science. Publications highlight innovations in temporal knowledge graphs, neural network pruning, and spectral sparsification. Collaborations include partnerships with institutions like UC Irvine, Rensselaer Polytechnic Institute, and the University of Patras. His work bridges theoretical foundations with practical applications, addressing challenges in VLSI design, space weather modeling, and educational technologies. Awards and recognitions include his h-index of 13 and over 1,700 citations, reflecting significant scholarly impact. Teaching focuses on advanced topics in machine learning and deep learning, contributing to NJIT's graduate curriculum in data science and computer science.

• Algorithms
• Graph Theory
• Machine Learning
• +3 more

Yunsi Fei is a Professor in the Electrical and Computer Engineering Department at Northeastern University, serving concurrently as Associate Dean of Faculty Affairs. She leads the Northeastern site of the NSF IUCRC Center for Hardware and Embedded System Security and Trust (CHEST). Her research focuses on hardware-oriented security, computer architecture, embedded systems, and IoT security, with significant contributions to mitigating side-channel and fault attacks on neural networks and hardware systems. Fei holds a PhD in Electrical Engineering from Princeton University (2004), and bachelor’s and master’s degrees in Electronic Engineering from Tsinghua University. She joined Northeastern in 2011 after faculty roles at the University of Connecticut. Her research interests span secure computer architecture, energy-efficient embedded systems, and underwater sensor networks. Notable projects include RINGS (a NSF-funded IoT resilience initiative) and secure RISC-V processor design. She has received the NSF CAREER Award and multiple best paper awards at top conferences. Fei’s work integrates hardware-software co-design to address vulnerabilities in AI accelerators and cryptographic systems. She leads the Energy-Efficient and Secure Systems (ENESS) Lab and collaborates with industry and academia through CHEST. Recent grants include $1.5M for cybersecurity in additive manufacturing and $1M for spectrum-agile IoT systems. Her awards include a 2023 Distinguished Paper Award (AsiaCCS) and 2022 Best Paper (Great Lake VLSI). She mentors students like Ruyi Ding, who joined LSU as faculty in 2025. Fei also chairs sessions on hardware security and is an affiliated faculty member with Northeastern’s Institute of Information Assurance.

• Hardware-oriented security
• Computer Architecture
• Embedded Systems
• +7 more

Dr. Saraju P. Mohanty is a Professor in the Department of Computer Science and Engineering at the University of North Texas (UNT), where he leads the Smart Electronic Systems Laboratory (SESL). He holds honorary and adjunct positions at IIIT-Naya Raipur, MNIT Jaipur, and Oriental University, Indore, in India. Education: Ph.D. in Computer Science and Engineering, University of South Florida (USF), 2003 Masters in Systems Science and Automation (AI), Indian Institute of Science (IISc), 1999 B.E. in Electrical Engineering (Honors), College of Engineering and Technology, Bhubaneswar (OUAT), 1995 Dr. Mohanty's research is centered on Smart Electronic Systems, with a strong focus on IoT, VLSI, hardware security, and healthcare applications. His work integrates machine learning, embedded systems, and nanoelectronics to develop secure and efficient solutions for smart cities, agriculture, and medical devices. He has authored over 550 peer-reviewed publications and five books, including a PROSE Award-winning textbook. His recent publications reflect a growing emphasis on AI-driven solutions for synthetic media detection, smart farming, driver monitoring, and personalized health. These works demonstrate a trend toward intelligent, edge-based systems that leverage sensor data and lightweight AI for real-time decision-making. Scientific Awards and Honors: Fulbright Specialist Award (2021) IEEE Consumer Electronics Society Outstanding Service Award (2020) IEEE-CS-TCVLSI Distinguished Leadership Award (2018) PROSE Award for Best Textbook (2016) Top 2% Scientist globally (PLOS Biology, 2019–2022) Multiple Best Paper and Best Poster Awards UNT Toulouse Scholars Award (2016–2017) President’s Scout Award, India (1988) Dr. Mohanty has supervised 3 postdocs, 18 Ph.D. students, 29 M.S. theses, and over 40 undergraduate research projects. Eleven of his advisees have received outstanding student awards. He has received multiple UNT Provost’s Thank a Teacher and Honors Day recognitions. His research has been funded by NSF, SRC, US Air Force, NIDILRR, and Mission Innovation. He has held key editorial roles, including Editor-in-Chief of IEEE Consumer Electronics Magazine and founding EiC of IEEE VLSI Circuits and Systems Letter. He is actively involved in IEEE leadership and conference organization, serving on steering committees for IEEE-iSES, ISVLSI, and OCIT. Laboratories and Teams: He directs the Smart Electronic Systems Laboratory (SESL) at UNT, which focuses on cutting-edge research in IoT, edge computing, hardware security, and smart healthcare. The lab fosters interdisciplinary collaboration and has produced numerous award-winning student projects and publications.

• Smart Electronic Systems
• Internet of Things (IoT)
• VLSI Design
• +7 more

Vladimir Stojanović is an Adjunct Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. His research focuses on integrated electronic-photonic systems-on-chip, emerging technologies, and VLSI design. He has held positions at MIT (2005–2013) and Rambus, Inc. (2001–2004). Stojanović leads research initiatives in the Berkeley Deep Drive (BDD), Berkeley Emerging Technologies Research (BETR), and Center for Energy Efficient Electronics Science (E3S). Education: Ph.D. (2005, Stanford), M.S. (2000, Stanford), and Dipl. Ing. (1998, University of Belgrade). Awards include the IEEE Fellow (2024), NSF CAREER Award (2009), and multiple best-paper recognitions. He co-founded Ayar Labs, Numericcal, and MaxLinear (formerly NanoSemi). Key research areas include silicon photonics integration, nanoelectromechanical systems (NEMS), and energy-efficient computing architectures. His work spans optical interconnects, quantum photonics, and biomedical sensors, with emphasis on co-design of electronics and photonics in advanced CMOS processes.

• Integrated Circuits
• Micro/Nano Electro Mechanical Systems (MEMS)
• Computer Architecture & Engineering
• +4 more

Alex Doboli is a Professor in the Department of Electrical and Computer Engineering at Stony Brook University, State University of New York. He is also affiliated with SUNY Korea’s Department of ECE. His academic journey includes a Ph.D. in Computer Engineering from the University of Cincinnati (2000) and earlier degrees from Politehnica University Timisoara, Romania (B.Eng. 1990, Doctorate 1997). He served as junior faculty at Politehnica University before joining Stony Brook in 2000. His research focuses on Electronic Design Automation (EDA) , Cyber-Physical Systems , and Human-inspired Machine Learning . Key areas include analog/mixed-signal design, innovation methodologies, and data-driven design approaches. He has authored over 170 peer-reviewed publications and co-authored a textbook on mixed-signal design. His lab, the Mixed-Domain Embedded Systems Laboratory , explores team behavior modeling, IoT integration, and cognitive architectures. Dr. Doboli has advised 17 Ph.D. and 12 M.S. students. Notable awards include the IBM Partnership Award (2001) and the Traian Lalescu Award (1987). He serves as an Associate Editor for Integration, the VLSI Journal and holds leadership roles in professional organizations like the IEEE Long Island Circuits and Systems Society. His teaching spans undergraduate and graduate courses in programming, algorithms, VLSI design, and machine learning. Current courses include ESE 327: Fundamental Algorithms for Machine Learning and ESE 589: Learning Systems for Engineering Applications . Recent research highlights include automated dialog systems (diaLogic), IoT-human behavior integration frameworks, and studies on artistic understanding in neural networks. His work bridges EDA, machine learning, and embedded systems to advance design innovation and human-centric technologies.

• Electronic Design Automation (EDA)
• Cyber-Physical Systems
• Mixed-Domain Embedded Systems
• +5 more