Lina Bertling Tjernberg is a Professor at the Department of Electrical Engineering, KTH Royal Institute of Technology, and Deputy Head of the School of Electrical Engineering and Computer Science (EECS) with responsibility for research conditions and impact. She served as Director of KTH's Energy Platform during 2018-2024 and holds memberships in IVA (Swedish Royal Academy of Engineering Sciences) and the IEEE Power & Energy Society. Research Focus: Applying mathematics (statistics, optimization, life cycle assessment) to enhance reliability and predictive maintenance in electric power systems, with emphasis on future electricity grids integrating microgrids, battery storage, HVDC, nuclear/pumped/hydro/wind/solar power, hydrogen, and electrified transport. Collaborations: Engaged with Comillas Pontifical University (Madrid), Addis Ababa University, Norwegian University of Science and Technology (NTNU), and IEA Wind. Key Research Trends: Recent articles highlight advancements in microgrid control (2025), SMR nuclear energy integration (2025), AI-driven asset management (2024), hydrogen sector coupling (2024), and renewable forecasting techniques (2024). Awards: 2021 Power Woman of the Year 2022 Energy Power List (Sweden’s top 20 energy influencers) Leadership Roles: Swedish Electromobility Center (SEC) board Chair of Swedish Electrical Standards (SEK Svensk Elstandard) Member, IEEE PES ISGT Europe steering committee
Geng Yuan is an Assistant Professor at the University of Georgia's School of Computing, specializing in AI systems, energy-efficient deep learning, and hardware-software co-design. His work bridges machine learning algorithms with emerging hardware technologies like superconducting circuits and ReRAM. He holds a Ph.D. in Computer Engineering from Northeastern University (2023) and a Master's in Electrical & Computer Engineering from Syracuse University (2016). Doctor of Philosophy (Ph.D.) in Computer Engineering, Northeastern University (2023) Master of Science (M.S.) in Electrical & Computer Engineering, Syracuse University (2016) Bachelor of Science (B.S.) in Electrical Engineering, Beijing University of Technology (2014) His research focuses on optimizing deep learning systems for edge computing and mobile platforms through techniques like model compression, sparse training, and hardware-aware neural architecture search. Recent projects include adapting large language models via hybrid-grained pruning and developing ultra-low-power AQFP circuits for binary networks. Geng Yuan's publications span top venues like NeurIPS, CVPR, ICML, ICLR, ISCA, and DAC, with notable awards including a Best Paper Award at ICLR Workshop 2021, Spotlight Papers at ICLR 2023 and NeurIPS 2021, and a Design Contest 1st Place at ISLPED 2020. Best Paper Award (ICLR Workshop'21) Spotlight Paper Award (ICLR'23, NeurIPS'21) Design Contest 1st Place (ISLPED'20) Best Paper Nomination (DATE'21, ISQED'18) He actively recruits Ph.D., Master's students, and interns to his research group, focusing on advancing AI systems through interdisciplinary approaches combining machine learning, computer architecture, and electronic design automation.
Dr. Can Bayram is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. He serves as a resident faculty member at the Nick Holonyak, Jr. Micro and Nanotechnology Laboratory and leads the Innovative Compound Semiconductor Laboratory (ICORLAB). His research focuses on semiconductor technologies, particularly cubic GaN and diamond-based devices for next-generation electronics and photonics. Current research includes cubic GaN quantum wells for green LEDs Diamond semiconductor devices for high-power applications Photonic technologies and nanoscale device fabrication Investigation of efficiency cliff phenomena in micro-LEDs Recent Article Trends : His work spans photonic devices, power electronics, and nanoscale systems, with particular emphasis on diamond photoconductive switches and cubic GaN efficiency improvements. Notable contributions include record-breaking diamond diodes and efficiency droop mitigation in green LEDs. Scientific Awards : IEEE Electron Devices Society Early Career Award NSF CAREER Award AFOSR Young Investigator Award Dean's Award for Early Innovation (2024) SPIE Fellow (2025) Editor's Pick & Front Cover Article, Applied Physics Letters (2024) Dr. Bayram teaches courses in electronics, including ECE 110: Introduction to Electronics, ECE 443: LEDs and Solar Cells, and ECE 500: ECE Colloquium.
Joseph Bentsman is a Professor in the Department of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign's Grainger College of Engineering. He also holds affiliate appointments in the Department of Aerospace Engineering (since 2015) and the Department of Electrical and Computer Engineering (since 2018). His academic journey began with an M.S. from Byelorussian Polytechnic Institute in Minsk, USSR (1979), followed by a Ph.D. in Electrical Engineering from Illinois Institute of Technology (1984). Professor Bentsman's research focuses on control of nonlinear and distributed parameter systems, nonlinear oscillations, network control, stability theory, and stochastic multiscale methods. He pioneered a new class of dynamical systems with active singularities that admit control actions during singular phases of motion, which represent a novel category of hybrid systems characterized by impulsively controlled discrete transitions. His recent work has expanded into biomedical applications, particularly thermophysical modeling of tissue during electrosurgery and control of phase change processes. His recent publications (2021-2024) reveal a strong trend toward biomedical applications of control theory, particularly in modeling heat conduction in biological tissues, electrosurgical processes, and phase change phenomena. Approximately 60% of his recent work focuses on biomedical applications, while the remainder continues his foundational work on nonlinear control systems, distributed parameter systems, and systems with active singularities. Key subfields include Stefan problems, enthalpy-based control, telegraph equation modeling, and PDE-based control of complex physical processes. NSF Presidential Young Investigator Award (1989) Life Fellow of American Society of Mechanical Engineers Life Senior Member of IEEE IEEE Control Systems Society Technical Committee Chair on Power Generation (2015-2019) International Society of Automation POWID Achievement Award (2014) 2018 AIST Computer Applications Best Paper Award Featured in 'People in Control', IEEE Control Systems Magazine (2018) Professor Bentsman has been instrumental in developing educational approaches that integrate signal processing, instrumentation, control, and machine learning, as evidenced by his two textbooks. His work on the steel continuous casting process, particularly the mold oscillation system, has led to practical industrial applications. He has also made significant contributions to power plant control systems and boiler/turbine control. His research group appears to focus on both theoretical control systems development and practical implementation in industrial and biomedical settings, with strong connections to steel manufacturing, power generation, and medical device industries.
André DeHon is the Oliver C. Boileau Jr. and Nan Eleze Boileau Professor of Electrical Engineering at the University of Pennsylvania, with affiliations in the Electrical and Systems Engineering (ESE) and Computer and Information Science (CIS) departments. He founded the Penn Computer Engineering (CMPE) program and directs the DARPA-sponsored CyberSavvy Security Center. His research spans reconfigurable computing, FPGA architecture, interconnect design, hardware security, and energy efficiency, with a focus on adapting to fabrication imperfections and runtime variability. His work explores programmable substrates (e.g., CMOS VLSI, molecular electronics) and algorithmic mappings to create resilient, efficient systems. Current projects include runtime feedback mechanisms (REFINE), partial reconfiguration (ExHiPR), and security frameworks like SCALPEL and μSCOPE. Recent publications highlight advancements in FPGA compilation (PLD, HiPR), security (SCALPEL, μSCOPE), and interconnect optimization (Asymmetry in NoC). These studies intersect computer architecture, security, and embedded systems. Scientific awards and recognitions: 2023 IEEE Fellow 2020 IEEE Mary Kenneth Keller Teaching Award 2018 ACM Fellow TCFPGA Hall of Fame (2019) Best Paper at ICFPT 2015 Academic leadership: He chairs the ACM/SIGDA Technical Committee on FPGAs and Reconfigurable Computing, previously held faculty roles at Caltech (1999–2006), and was a postdoc at UC Berkeley (1996–1999). Education includes MIT SB (1990), SM (1993), and PhD (1996) in Electrical Engineering and Computer Science.
Zhonghai Lu is a Professor of Electronic Systems Design (specializing in Dependable and Autonomous Systems) at KTH Royal Institute of Technology, part of the Department of Electrical Engineering in the School of Electrical Engineering and Computer Science (EECS). He serves as Program Director for KTH's Embedded Systems master's program and Director of Studies at the Division of Electronics and Embedded Systems. His research focuses on Network-on-Chip (NoC), computer architecture, embedded systems, and Prognostics and Health Management (PHM) of power electronics. He leads a research group exploring in-network processing and embedded intelligence, transforming passive networks into active computational frameworks. Lu holds a BSc from Beijing Normal University (1989), MSc and PhD from KTH (2002, 2007), and an MBA in Innovation and Growth from the University of Turku (2012). He has authored over 240 scientific papers, including journal articles and peer-reviewed conferences, with notable recognitions such as Best Paper Awards at NOCS’2015 and EU HiPEAC, and a Featured Paper in IEEE Transactions on Computers (2020). He serves as Associate Editor for ACM Transactions on Architecture and Code Optimization (TACO) and has chaired major conferences like HiPEAC’2017 and NOCS’2018. His research group’s recent work includes integrating AI into hardware acceleration, fault-tolerant neural networks, and RUL estimation for power electronics using recurrent neural networks. Lu has secured grants from the Swedish Research Council and Intel Corporation and developed courses like IL2230 (Hardware Architectures for Deep Learning) and IL2233 (Embedded Intelligence), pioneering embedded AI education at KTH. Education: BSc (Beijing Normal University), MSc/PhD (KTH), MBA (University of Turku) Awards: Best Paper Awards (NOCS, EU HiPEAC), Swedish Research Council Grants, Intel Research Gifts Labs/Teams: Research Group on In-Network Processing and Embedded Intelligence
Josiah Hester is Associate Professor of Interactive Computing and Computer Science at Georgia Tech's College of Computing, where he also directs the Center for Advancing Responsible Computing and the Ka Moamoa – Ubiquitous and Mobile Computing Lab . His research focuses on sustainable, battery-free computing systems for health, conservation, and education, informed by his Native Hawaiian heritage. Education: Ph.D. in Computer Science, Clemson University, 2017 B.S. in Computer Science, Clemson University Honors College, 2011 Research Interests: Hester’s group pioneers intermittent computing —devices that harvest ambient energy (solar, radio, thermal, microbial) and operate reliably despite frequent power loss. Applications span: Large-scale conservation sensing with Indigenous communities Battery-free health wearables (smart masks, implantables) Educational tools for sustainable design Carbon-aware IoT and recyclable robotics Recent Work & Trends: His 2024–2025 publications unveil soil-powered sensors, recyclable electronics, and culturally-grounded CS education tools. A consistent theme is co-design with underserved communities and embedding sustainability metrics at every layer—from hardware to curriculum. Scientific Awards: Presidential Early Career Award for Scientists and Engineers (PECASE) – 2025 Alfred P. Sloan Fellowship – 2022 NSF CAREER Award – 2022 Popular Science “Brilliant 10” – 2021 3M Non-Tenured Faculty Award – 2021 Multiple best-paper & best-presentation awards Grants & Teams: Hester’s lab is supported by >$50 million in active funding from NSF, NIH (ARPA-H), DARPA, DoD, Sloan, VMware, and 3M. He leads interdisciplinary teams on projects such as: $35 million ARPA-H bioelectronics initiative for diabetes and cancer $5 million NSF “Coastlines and People” Hub for Manoomin conservation $2 million NSF-DELPHI sustainable edge device design Laboratory & Mentorship: The Ka Moamoa Lab (named with Hawaiian meaning “lamp of the moamoa fish”) hosts graduate, undergraduate, and post-doctoral researchers. Hester actively recruits Native and Indigenous students and has mentored award-winning advisees such as Nivedita Arora (ACM Doctoral Dissertation Award 2024).
Noorbakhsh Amiri Golilarz is an Assistant Professor in the Department of Computer Science at The University of Alabama, College of Engineering. He has established himself as a prominent researcher in artificial intelligence, particularly in computer vision, deep learning, and image processing. His educational background includes: Postdoctoral Research Fellow, Computer Science, Boston College (2023) Ph.D., Electrical and Computer Engineering, Southern Illinois University Carbondale (2023) D. Eng., Computer Science and Technology, University of Electronic Science and Technology of China (2021) M.S., Electrical and Electronic Engineering, Eastern Mediterranean University (2017) B.S., Electrical Engineering, University of Guilan (2012) Dr. Golilarz's research spans multiple domains of artificial intelligence with a particular focus on computer vision, deep learning, and image processing applications. His work addresses challenges in medical imaging, satellite imagery, and cognitive neuroscience. He has made significant contributions to image denoising techniques, control chart pattern recognition, and AI applications in healthcare. His recent work has expanded into generative AI, large language models, and secure machine learning operations. His publication portfolio demonstrates consistent productivity with over 2500 citations and an h-index of 25. His most impactful work includes applications of blockchain and federated learning for COVID-19 detection, optimized support vector machines for medical diagnosis, and innovative image denoising techniques using metaheuristic optimization algorithms. Among his professional achievements: Co-founded AI Letters journal in 2024, serving as Associate Editor-in-Chief Served as Lead Guest Editor and Topic Editor for several SCI-indexed journals Held the role of Conference Program Chair Dr. Golilarz has supervised numerous graduate students and research projects, with his work spanning theoretical advancements in AI algorithms to practical applications in healthcare, energy systems, and cybersecurity. His research group has established collaborations with institutions including Boston College and Mississippi State University.
Dr. Hiren Patel is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. He holds a Doctorate in Computer Engineering from Virginia Tech and previously worked as a postdoctoral fellow at UC Berkeley under Edward A. Lee. His research focuses on real-time embedded systems, computer architecture, machine learning hardware, and cybersecurity. He teaches courses like ECE 150 (Programming), ECE 320/429 (Computer Architecture), and ECE 327 (Digital Systems). Research Interests: Cyber-physical systems and hybrid architectures Hardware/software co-design methodologies Predictable cache coherence protocols IoT and edge computing systems Security in embedded and real-time systems Recent work emphasizes cache coherence solutions for safety-critical systems and GPU acceleration strategies. His publications address challenges in multicore predictability, FPGA bandwidth optimization, and autonomous robotics orchestration. No specific awards are listed, though his extensive publication record indicates significant contributions to embedded systems research. He currently oversees graduate student applications focusing on his core research areas.
Yan Liu is a full professor in the Thomas Lord Department of Computer Science at the University of Southern California (USC), serving as Director of the USC Machine Learning Center within the Viterbi School of Engineering. He holds courtesy appointments in the Ming Hsieh Department of Electrical Engineering and the Quantitative and Computational Biology Department. Before joining USC in 2010, he was a research staff member at IBM's T.J. Watson Research Center. He earned his M.S. and Ph.D. from Carnegie Mellon University. His research focuses on machine learning for time series, physics-informed AI, and interpretable models, with applications in healthcare, sustainability, and social media. Notable projects include developing AI for surgical training, analyzing misinformation on social platforms, and predicting cancer treatment outcomes. He has held leadership roles in top conferences like ICLR and ACM KDD, and serves as Associate Editor-in-Chief of TPAMI and Board Member of ICLR. Education: Ph.D., Carnegie Mellon University Affiliations: USC Machine Learning Center, Viterbi School of Engineering Service: General Chair (ICLR 2023, ACM KDD 2020), Program Chair roles across multiple conferences His lab, the Melady Group, emphasizes foundational ML advancements and interdisciplinary applications. Recent work includes physics-aware neural networks and time-series foundation models.
Luca Peretti is an Associate Professor in Electric Machines and Drives at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and the Department of Electrical Engineering, Division of Electric Power and Energy Systems. He works as a researcher in the EMD (Electric Machines and Drives) group and serves as Partner Director for KTH's strategic partnership with ABB. Education: M.Sc. in Electronic Engineering (2005) from University of Udine, Ph.D. from University of Padova (2008) Professional Experience: Postdoc at University of Padova (2009-2010), Principal Scientist at ABB Corporate Research (2010-2018), Associate Professor at KTH (2018-present) His research focuses on: Automatic parameter estimation in electric machines Multiphase drive systems Sensorless control algorithms Loss segregation in drive systems Condition monitoring of industrial and transportation applications Recent publications demonstrate expertise in variable phase-pole machines, harmonic plane decomposition, predictive control algorithms, and advanced modeling of permanent magnet motors. Key application areas include transportation electrification, wind energy systems, and industrial drive technologies. Scientific roles include: Associate Editor, IET Electric Power Applications Journal (2019-present) Theme Co-Leader, Swedish Electromobility Center (2020-present) Member, IEEE (2021-present) and IET (2006-present) He leads the strategic partnership with ABB and contributes to doctoral program committees at University of Padova.
Judith Driscoll is Professor of Materials Science at the University of Cambridge in the Department of Materials Science & Metallurgy. She holds the prestigious Royal Academy of Engineering Chair in Emerging Technologies and serves as a Visiting Staff Member at Los Alamos National Laboratory. As the founding Editor-in-Chief of APL Materials, she has significantly contributed to the materials science community. Dr. Driscoll's research focuses on Energy Efficient Oxide Materials for Information and Communications Technologies and energy devices. Her work spans the development of non-volatile memory, resistive switching devices, and ferroelectric materials for neuromorphic computing applications. She investigates oxide thin films for applications ranging from data storage to energy generation and conversion, with particular emphasis on creating more energy-efficient device technologies to handle the exponential growth of data-centric applications. Her recent publications demonstrate strong trends in developing novel oxide-based memory devices with improved energy efficiency, particularly for AI applications. The work shows significant progress in hafnium-zirconium oxide ferroelectrics, resistive switching mechanisms, and vertically aligned nanocomposite structures for enhanced device performance. These innovations address critical challenges in reducing the unsustainable energy demands of modern computing, particularly for artificial intelligence systems. Fellow of the Royal Academy of Engineering Fellow of the Materials Research Society Fellow of the American Physical Society Fellow of IOM3, IOP, and Women Engineers Society Fellow of the American Academy of Arts and Sciences Recipient of ERC Advanced Grant Editor-in-Chief of APL Materials Dr. Driscoll leads a vibrant research group that has secured significant funding including her Royal Academy of Engineering Research Chair, an ERC Advanced Grant, and an ECCS-EPSRC grant in collaboration with researchers from the USA. She has founded the Cambridge Centre for Neuromorphic Computing (Neucam) in 2023. Her group operates world-leading growth equipment including pulsed laser deposition with RHEED control, high temperature oxide sputtering, and spatial ALD systems. She collaborates extensively across the University of Cambridge and with international partners to solve complex materials challenges, with her group's role often being to identify optimal materials for functional goals, predict fabrication methods, and then create and characterize these materials.
Kristofer Pister is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He co-directs the Berkeley Sensor and Actuator Center (BSAC) and the Ubiquitous Swarm Lab. His career spans groundbreaking innovations in Micro/Nano Electro Mechanical Systems (MEMS), Control Systems, and Low-Power Circuits, with a focus on Smart Dust and synthetic insects. Education: Ph.D. and M.S. in EECS from UC Berkeley (1992, 1989); B.A. in Applied Physics from UC San Diego (1986). His research areas include MEMS , Control Systems , Robotics , and Integrated Circuits , with recent work on self-powered micro-sensors, crystal-free radios, and interplanetary swarm networks. Key awards include the ISA Albert F. Sperry Founder Award (2009) , Alexander Schwarzkopf Prize (2006) , and the NSF CAREER Award (1996) . He has authored numerous influential publications in wireless sensor networks and microrobotics. His lab, Ubiquitous Swarm Lab , explores distributed robotics and swarm intelligence. Pister emphasizes open collaboration in research, ethical conduct in academia, and efficient resource utilization for graduate students.
Soumya Dutta is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur (IITK), where he leads the INSIGHT: Intelligent Scientific and Visual Computing of Big Data Research Group. He joined IIT Kanpur in October 2022 after working as a Scientist II at Los Alamos National Laboratory (LANL) from July 2019 to August 2022, and previously as a Postdoctoral Research Associate at LANL from June 2018 to July 2019. His educational background includes a Ph.D. and M.S. in Computer Science and Engineering from The Ohio State University (2011-2018), where he was part of the GRAVITY research group, and a B.Tech. in Electronics and Communication Engineering from West Bengal University of Technology, India (2005-2009). Research Interests: Dr. Dutta's research focuses on the intersection of machine learning, visual computing, big data, and high-performance computing. His primary research areas include Machine Learning for Visual Computing and Image Analysis, Big Data Visualization and Analytics, Data Science and HPC, Machine Learning for Scientific Computing, and Explainability and Interpretability of AI Models. His work addresses various big data characteristics including the 5 Vs: Volume, Velocity, Variety, Veracity, and Value. He develops techniques that make complex machine learning models more interpretable and explainable, enabling their effective adoption in real-life applications across scientific domains, social media, IoT, healthcare, and industry applications. Dr. Dutta's research group has secured multiple funded projects including: DAVi: An Intelligent Data Analytics and Visualization Framework (funded by ISRO), Intelligent Visual Computing of Extreme-scale Data for Accelerating Scientific Discovery (IIT Kanpur Initiation Grant), Enabling Interactive Big Data Analytics and Visualization at Exascale (SERB), Development of AI-Enabled National Portal for Efficient Search of Missing People (C3iHub), and Proactive and Generalized Deepfake Defense Mechanisms (C3iHub). Best Reviewer, Honorary Mention Award for IEEE Transactions on Visualization & Computer Graphics (TVCG), 2021 Best Paper Award at ISAV 2021, co-located with Supercomputing (SC) LAAP Award at Los Alamos National Laboratory, 2021 Best Paper Award at TopoInVis 2019 Best Paper Award at ISAV 2018, co-located with Supercomputing (SC) Best Poster Award in 12th Annual CSE Student Poster Exhibition, The Ohio State University, 2018 Best Poster Award in 11th Annual CSE Student Poster Exhibition, The Ohio State University, 2017 Best Paper Honorable Mention Award at IEEE Visualization Conference (IEEE VIS) 2016 Dr. Dutta actively mentors a large group of students including Ph.D., M.Tech., and B.Tech. students. His current Ph.D. students include Shanu Saklani, Sankhadeep Bhowmick, Ananya Chaturvedi, Arpita Santra, Anubhav Dixit (co-supervised), and Robin Shah. He has supervised numerous M.Tech. students with thesis topics ranging from uncertainty-aware neural networks to deepfake detection. Dr. Dutta currently teaches courses including CS360 - Introduction to Computer Graphics and CS661 - Big Data Visual Analytics. The INSIGHT research group collaborates internationally with researchers from Meta, Oak Ridge National Laboratory, and National Taiwan Normal University. The group's work focuses on building machine learning and data science-based solutions to analyze large-scale multifaceted data in a scalable way, enabling interactive and interpretable analytics of complex data from scientific simulations, social media, IoT, healthcare, and other application domains.
Olga Fink is a Tenure Track Assistant Professor at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Department of Intelligent Maintenance and Operations Systems (IMOS) within the School of Architecture, Civil and Environmental Engineering (ENAC). She also holds roles in PhD program committees for Civil and Environmental Engineering (EDCE) and Robotics, Control, and Intelligent Systems (EDRS). Her research focuses on machine learning for infrastructure monitoring, predictive maintenance, and physics-informed AI models. She teaches courses on machine learning, data science for infrastructure, and advanced deep learning topics. Fink advises multiple PhD students and is involved in interdisciplinary projects such as ThermoNeRF (multimodal 3D thermal modeling) and physics-informed neural networks for fault diagnostics. Her work bridges AI and engineering with applications in smart infrastructure, energy systems, and industrial IoT. Education: PhD in Engineering (inferred from role) Affiliations: IMOS Lab, ENAC-SGC, EPFL PhD Committees (EDCE, EDRS) Key Research Themes: Explainable AI, Digital Twins, Structural Health Monitoring, Domain Adaptation Her publications (2023–2025) emphasize robust AI for industrial systems, including fault detection in high-voltage equipment, multimodal data fusion, and physics-consistent models. She collaborates on EU and industry-funded projects, focusing on real-world applications like predictive maintenance and energy efficiency.
