Find Academic Supervisors

• Internet of Things
• Edge Computing
• Artificial Intelligence
• +6 more

Professor Xiaodong Liu is a faculty member at Edinburgh Napier University, affiliated with the School of Computing, Engineering and the Built Environment . His research spans Internet of Things , Edge Computing , Artificial Intelligence , and Cybersecurity , with a focus on decentralized systems and data-driven decision-making. Research Themes : IoT orchestration, federated learning, smart city infrastructure, building maintenance optimization, and automotive cybersecurity. Current Projects : Leading Swarmchestrate (EU-funded), Long-range Perceptive Autonomous Vehicles (Royal Society), and Met-Bot for Disaster Surveillance (Royal Society). His recent publications emphasize privacy-preserving edge learning , semantic IoT data validation , and deep learning for weather prediction . As a supervisor, he has guided PhD students in areas like federated learning, smart building systems, and IoT security. Collaborations include partnerships with institutions in Scotland, China, and Italy, alongside funding from European Commission , Royal Society , and Scottish Funding Council . He contributes to international conferences and journals, with notable work in IEEE Transactions , ACM TAAS , and MDPI publications.

Alexandra FedorovaView profile

Professor

• Systems Software
• Memory and Storage Management
• Accelerator-Centric Computing
• +4 more

Dr. Alexandra Fedorova is a Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC), with an Associate Member role in the Computer Science department. She leads the Systopia systems research group, focusing on system software design, memory/storage management, and accelerator-centric computing. Her work emphasizes performance optimization, energy efficiency, and hardware-software co-design. She holds a PhD from Harvard University (2006), where she researched operating system scheduling under Margo Seltzer. Prior to UBC, she was an Associate Professor at Simon Fraser University (2006–2015). Fedorova is a recipient of the Alfred P. Sloan Research Fellowship and the Anita Borg Early Career Award. She consults for MongoDB's storage engine team and collaborates with industry on storage and performance challenges. Her research spans tools like Non-sequitur for program trace visualization, studies on storage-class memory (e.g., Optane), and frameworks for GPU acceleration. Recent efforts include Sunstone (spatial accelerator scheduling) and ExtMem (application-aware memory management). Her work bridges low-level systems with high-performance computing needs. Key contributions include optimizing NUMA systems, improving storage engine performance, and exploring processing-in-memory architectures. Fedorova’s projects often involve open-source collaboration, reflected in her GitHub repositories such as vividperf and perf-logging , which support performance analysis tools.

• Soft Robotics
• Microfluidics
• Nanofabrication
• +3 more

Ramses Martinez is an Assistant Professor in the Department of Industrial Engineering and Biomedical Engineering at Purdue University . He holds a B.A. in Applied Physics from Universidad Autonoma de Madrid (2004) and a Ph.D. in Physics and Materials Science from the Spanish National Research Council (CSIC) in 2009. Prior to joining Purdue, he conducted postdoctoral research in the lab of Prof. George M. Whitesides at Harvard University, focusing on nanofabrication, microfluidics, and soft robotics. Education B.A. in Applied Physics, Universidad Autonoma de Madrid (2004) Ph.D. in Physics and Materials Science, Spanish National Research Council (CSIC) (2009) His research bridges soft robotics , flexible electronics , and nanofabrication , with a focus on creating self-powered e-textiles , omniphobic paper-based devices , and programmable mechanical metamaterials . His work has led to over 25 publications and 9 patents, emphasizing practical applications in health monitoring and industrial automation . Notable projects include waterproof electronic decals for biofluid monitoring, smart bandages for chronic wound detection, and laser nanoforming methods for scalable metallic structures. His research has been recognized through the Fulbright Fellowship and the Marie Curie IOF Grant .

Shad RoundyView profile

Associate Professor

• Energy Harvesting
• Wireless Power Transfer
• MEMS
• +5 more

Shad Roundy is an Associate Professor at the University of Utah , holding dual appointments in the Department of Mechanical Engineering and Department of Electrical and Computer Engineering within the College of Engineering . He directs the Integrated Self-Powered Sensing Lab , focusing on energy harvesting, wireless power transfer, and ubiquitous sensing systems. PhD in Mechanical Engineering, University of California, Berkeley (2003) Senior Lecturer, Australian National University (postdoctoral) His research bridges mechanical design , smart materials , and sensor networks , with significant contributions to: Self-powered biomedical implants Wearable health-monitoring devices Underground agricultural sensor networks MEMS sensor development His 15 most recent publications demonstrate interdisciplinary work spanning wireless power transfer for soil sensors, flexoelectric materials, and disease severity assessment via wearables. Articles emphasize energy efficiency , novel transducer designs , and environmental sensor applications . Scientific recognition includes: NSF CAREER Award for magnetoelectric implant powering Associate Editor, Smart Materials and Structures and International Journal of Precision Engineering and Manufacturing-Green Technology Research funded by National Science Foundation , NASA , and industry partners through the ASSIST Engineering Research Center . Publications highlight collaborations across biomedical, agricultural, and wearable technology domains.

Carl Ho (Ngai Man)View profile

Professor

• Power Electronics
• Heavy-Duty Electric Vehicles
• Renewable Energy Technologies
• +5 more

Dr. Carl Ho (Ngai Man) is a Full Professor and Canada Research Chair in Efficient Utilization of Electric Power at the University of Manitoba's Price Faculty of Engineering, Department of Electrical and Computer Engineering. Appointed Associate Head (Electrical Engineering) in July 2021, he leads the Renewable-energy Interface and Grid Automation (RIGA) Lab established with CFI funding in 2014. His educational background includes: PhD in Electronic Engineering (2007), City University of Hong Kong MEng in Electronic Engineering (2002), City University of Hong Kong BEng in Electronic Engineering (2002), City University of Hong Kong Dr. Ho's research focuses on power electronics applications for sustainable energy systems, with particular expertise in power conversion technologies for electric vehicles, renewable integration, and smart grid infrastructure. His work bridges industrial application and academic innovation, evidenced by over 40 IEEE journal publications, 80 conference papers, and 20+ patents. Current research emphasizes wide-bandgap semiconductor applications, power hardware-in-loop validation, and DC microgrid architectures for remote communities. Analysis of his recent publications reveals a strong trend toward practical implementation of power electronics solutions, with increasing focus on GaN/SiC devices, grid-forming converters, and modular architectures for microgrids. His work consistently addresses real-world challenges in efficiency, reliability, and cost-effectiveness across renewable integration, electric transportation, and power quality domains. Notable awards include: Second Place Winner for 2018 IEEE Transactions on Power Electronics Prize Paper Multiple IEEE JESTPE Star Associate Editor Awards (2022-2023) IEEE TPEL AE Excellence Award (2023) Best Student Team Regional Award in IEEE Empower a Billion Lives 2019 As an active mentor, Dr. Ho supervises numerous graduate students across multiple cohorts and leads significant research initiatives including NSERC Discovery Grants, MITACS collaborations with Power Integrations Inc., Research Manitoba Innovation Proof-of-Concept Grants, and Natural Resources Canada projects on zero-emission heavy vehicles. His RIGA Lab serves as a hub for industry-academic collaboration with Manitoba Hydro and transportation sector partners. The RIGA Lab, completed in 2016 and renovated in 2019, houses specialized equipment for power electronics prototyping, real-time simulation, and hardware-in-loop testing. Current projects include advanced wireless EV charging, GaN-based controller development, and DC microgrid solutions for remote communities, with recent recognition including a visit from Prime Minister Justin Trudeau in April 2023.

Lennart HeimView profile

Professor

• Artificial Intelligence
• Emerging Technologies
• Cybersecurity
• +3 more

Lennart Heim is a Professor of Policy Analysis at the RAND School of Public Policy and Compute Lead at the Technology and Security Policy Center within The RAND Corporation. His research focuses on AI governance, compute infrastructure, and their strategic implications for national security and global competitiveness. He advises organizations like Epoch and contributes to initiatives such as the OECD.AI Expert Group on AI Compute and Climate. Previously, he held roles at ETH Zürich and the Centre for the Governance of AI. Education: Studied computer engineering at ETH Zürich and RWTH Aachen University. Current affiliations include adjunct fellowships at GovAI and pro forecasting at INFER Pub. Research Interests: He examines how compute capacity shapes AI development, policy frameworks for AI governance, and the interplay between technology and geopolitical strategy. His work emphasizes leveraging compute for ethical AI deployment while mitigating security risks. Advising & Grants: Advises Epoch on AI trajectory analysis and has collaborated with the National Science Foundation and NIST on policy frameworks. His grants focus on AI energy requirements and global engagement strategies. Labs/Teams: Leads compute research initiatives in the Technology and Security Policy Center, engaging interdisciplinary teams to address AI’s societal and security challenges.

Alireza SaberkariView profile

Associate Professor

• Analog and Mixed-Signal Integrated Circuits
• Low-Power Electronics
• RF and Wireless Power Transfer
• +7 more

Alireza Saberkari is an Associate Professor and Docent at Linköping University, affiliated with the Department of Electrical Engineering and the Division of Electronics and Computer Engineering (ELDA). His work bridges analog, digital, and mixed-signal electronics with applications in biomedical systems, wireless power, and energy-efficient integrated circuits. Research Interests: His research spans low-power analog and mixed-signal IC design, energy harvesting, RF circuits, and the miniaturization of Nuclear Magnetic Resonance (NMR) systems for portable spectroscopy. He is actively involved in developing integrated solutions for wireless cell fluorescence detection and intelligent surfaces for mid-range wireless power transfer. Recent Publication Trends: His recent publications (2024–2025) emphasize circuit-level innovations in switched-capacitor amplifiers, RF-DC rectifiers, and NMR front-ends, reflecting a strong focus on energy efficiency, integration, and system co-design for portable and biomedical applications. Scientific Projects: Nuclear Magnetic Resonance (NMR) Miniaturization for Spectroscopy (VR, 2023–2026) Efficient Mid-Range Wireless Power Transfer with Intelligent Surfaces (EMPTIS, ELLIIT, 2023–2027) ENGINED - Efficient Organic Energy Module (Vinnova, Phase I, 2023–2024) Micrometer-Scale Wireless Cell Fluorescence Detection Device (SSF Med-X, 2020–2024) Advising and Grants: While no formal students are listed, his leadership in multiple externally funded projects indicates active supervision and mentorship. He has secured funding from major Swedish research agencies including VR, ELLIIT, Vinnova, and SSF, demonstrating strong grant acquisition and collaborative research capabilities. Labs and Research Teams: He is a core member of the Electronics and Computer Engineering (ELDA) division at Linköping University, contributing to a multidisciplinary team focused on advancing electronics for real-world applications in healthcare, energy, and communication systems.

Zhengyuan ZhouView profile

Assistant Professor

• Machine Learning
• Reinforcement Learning
• Stochastic Optimization
• +12 more

Zhengyuan Zhou is an Assistant Professor in the Department of Technology, Operations, and Statistics at the Leonard N. Stern School of Business, New York University. He is also associated faculty at the Department of Computer Science and Engineering, Tandon School of Engineering, and affiliated with the NYU Center for Data Science. He joined NYU Stern in 2020 after serving as an IBM Goldstine Research Fellow and a Visiting Scholar at NYU Stern during 2019–2020. Education: Ph.D., Electrical Engineering, Stanford University, 2019 Master’s in Computer Science, Stanford University Master’s in Statistics, Stanford University Master’s in Economics, Stanford University B.A., Mathematics, UC Berkeley B.S., Electrical Engineering and Computer Sciences, UC Berkeley His research centers on the intersection of machine learning, stochastic optimization, control theory, and game theory, with a focus on data-driven decision-making. He develops algorithms for reinforcement learning, contextual bandits, and policy learning under uncertainty, with applications in inventory control, revenue management, and auction bidding. His work emphasizes sample efficiency, computational tractability, and robustness. The recent publications reflect a strong trend in distributionally robust learning , offline reinforcement learning , and multi-agent systems , particularly in settings with delayed feedback, adversarial environments, and adaptive data collection. His articles span top journals in operations research, machine learning, and control theory. Scientific Awards and Honors: IBM Goldstine Fellowship (2019–2020) INFORMS Nicholson Award Finalist (2017, 2018) NSF and ONR grants (multiple, 2021–2027) NYU Research Catalyst Prize (2023) Horizon Robotics, Bain, and JP Morgan faculty awards (2021) CRA Outstanding Undergraduate Researcher (2013) Zhou advises PhD students in operations management and has served on dissertation committees at Georgia Tech and Tsinghua University. He has received over $1.8 million in research funding from NSF, ONR, and industry partners. He is actively involved in editorial roles as Associate Editor for Management Science , Operations Research , and Mathematics of Operations Research , and as Area Chair for NeurIPS, ICML, and ICLR. He also mentors high school students through logic and cryptography programs at Stanford’s Pre-Collegiate Summer Institute.

Zoran J. StankovicView profile

Professor

• Antenna Design and Optimization
• Direction of Arrival (DoA) Estimation
• Artificial Neural Networks in Electromagnetics
• +7 more

Zoran J. Stankovic is a Full Professor at the Faculty of Electronics, University of Niš, Serbia, in the Department of Telecommunications. He has been an integral part of the institution since earning his PhD in 2007, progressing from Assistant to Associate Professor (2020) and Full Professor (2024). He is actively involved in research, education, and academic leadership. Education: PhD in Telecommunications, Faculty of Electronics, University of Niš (2007) Master's in Telecommunications, Faculty of Electronics, University of Niš (2002) Bachelor's in Electronics and Telecommunications, Faculty of Electronics, University of Niš (1994) His research focuses on applying artificial neural networks to solve complex problems in electromagnetics and wireless systems. Key areas include antenna design optimization , direction-of-arrival (DoA) estimation , microwave cavity modeling , and smart textile antennas . His work bridges machine learning with electromagnetic theory, enabling efficient and adaptive RF system design. The analysis of his recent publications reveals a strong trend in using deep and hybrid neural networks for electromagnetic modeling and signal processing. His research emphasizes real-world applications such as wearable antennas, mobile source tracking, and intelligent antenna systems, often published in high-impact IEEE and Wiley journals. Scientific Awards: Commemorative Plaque from the Yugoslav Society for Microwave Techniques and Technologies (2005) for outstanding scientific results in microwave engineering Advising and Grants: While specific students are not listed, he has supervised numerous academic works. He has participated in 17 research projects (13 national, 4 international including DAAD, NATO, COST) and 8 educational development projects (including ERASMUS+, TEMPUS, WUS). These reflect sustained funding and academic collaboration. Labs and Teams: He is the founder and head of the Laboratory for Antennas and Propagation at the Faculty of Electronics, Niš. He is actively involved in organizing and leading the international conferences TELSIKS and ICEST , serving on their program and organizational committees, demonstrating strong leadership in the academic community.

Mateo Valero CortésView profile

Professor

• Computer Architecture
• High-Performance Computing
• Parallel Computing
• +3 more

Mateo Valero Cortés is a renowned Professor of Computer Architecture at the Polytechnic University of Catalonia and Director of the Barcelona Supercomputing Center (BSC). He has held academic and leadership roles since 1974, advancing high-performance computing (HPC) and computer architecture research. His work includes pioneering contributions to vector architectures, multithreading, and instruction-level parallelism. Education includes a Telecommunications Engineering degree from the Polytechnic University of Madrid (1974) and a PhD in Telecommunications Engineering from the Polytechnic University of Catalonia (1980). His research spans over 700 publications, focusing on HPC systems, parallel computing, and supercomputing infrastructure. Key research interests include vector processing, super-scalar processors, and task-based programming models. Recent work emphasizes scalable architectures for exascale computing and energy-efficient hardware-software co-design. Notable achievements include the Eckert-Mauchly Prize (highest in computer architecture), Seymour Cray Award, and Charles Babbage Prize. He has led initiatives like the Spanish Supercomputing Network (RES) and PRACE (European HPC partnership). Academic affiliations include the Royal Academy of Engineering of Spain, ACM Fellow, and IEEE Fellow. He has received 13 honorary doctorates and awards such as Mexico’s Order of the Aztec Eagle. Current projects include the Mont-Blanc HPC prototype and ERC-funded research on multi-core chip design. His BSC oversees over 300 researchers and manages MareNostrum supercomputers.

Shixiang ZhuView profile

Assistant Professor

• Machine Learning
• Data Science
• Operations Research
• +7 more

Shixiang (Woody) Zhu is an Assistant Professor in Data Analytics at the Heinz College of Information Systems and Public Policy, Carnegie Mellon University. He holds a PhD in Machine Learning from Georgia Institute of Technology (2022) and B.S./M.S. in Computer Science from Beijing University of Posts and Telecommunications (2017). His research bridges machine learning, operations research, and statistics, focusing on sequential modeling, human-AI collaboration, and energy systems operations. He has received awards including the IEEE Power & Energy Society Best Paper Award (2025) and was a finalist for the INFORMS Wagner Prize (2021). Education : PhD in Machine Learning, Georgia Tech (2017–2022) B.S./M.S. in Computer Science, BUPT (2010–2017) His research emphasizes spatio-temporal data analysis , decision making under uncertainty , and applications to energy systems, healthcare, and public policy. Notable projects include optimizing police zone design (Wagner Prize finalist) and enhancing grid resilience through robust optimization. He actively collaborates with institutions like Argonne National Laboratory and NSF-funded projects. Awards : Best Paper Award, IEEE Power & Energy Society (2025) Gen-AI Fellows (2024) Finalist, INFORMS Wagner Prize (2021) Advising & Grants : Advises PhD students Zekai Fan, Wenbin Zhou, and others Recipient of Block Center Seed Grant (2024), NSF funding (2024) His work spans energy resilience, public policy optimization, and causal inference in social systems. He co-leads the INFORMS Data Mining Society and reviews for top journals like Operations Research and Management Science.

• Software Engineering
• Artificial Intelligence
• Machine Learning
• +7 more

Maxime CORDY is a Research Scientist at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) , University of Luxembourg, within the Security, Design and Validation group (SerVal) . He holds a PhD from the University of Namur (Belgium, 2014) and specializes in software engineering, applied artificial intelligence, and cybersecurity. His work focuses on adversarial machine learning, deep learning robustness, and model checking for critical systems. Research interests include adversarial attacks on tabular data , energy system optimization , code understanding models , and software quality assurance . Recent projects address challenges in secure AI deployment, automated test generation, and fault detection in large language models. Publications emphasize empirical studies on adversarial defenses, data augmentation for code models, and energy consumption forecasting. He contributes to tools like Daedalux (variability-aware model checking) and benchmarks like Tabularbench for adversarial robustness evaluation. Current affiliations include leadership within the SerVal group and collaborations on interdisciplinary projects such as MALETSQUE (Machine Learning Techniques for Software Quality Evaluation). His work bridges theoretical computer science with practical applications in energy systems, medical imaging, and space program design.

Emre SalmanView profile

Professor

• Integrated Circuits (ICs)
• VLSI Systems
• Energy-Efficient Computing
• +5 more

Emre Salman is a Professor in the Department of Electrical and Computer Engineering at Stony Brook University (SUNY), where he directs the Nanoscale Circuits and Systems (NanoCAS) Lab. His research focuses on nanoscale IC design, energy-efficient computing, and biomedical electronics, with notable contributions to 3D integrated circuits and wireless energy harvesting for IoT and healthcare applications. Education : PhD in Electrical Engineering, University of Rochester (2009) MSc in Electrical and Computer Engineering, University of Rochester (2006) BSc in Microelectronics Engineering, Sabanci University, Turkey (2004) Research Interests : Salman’s work spans energy-efficient integrated circuits, secure IoT systems, and implantable medical devices. He pioneers techniques like charge-recycling logic and thermal-aware design for post-Moore computing. His group develops monolithic 3D ICs to address power/thermal challenges in AI accelerators and biomedical implants. Articles Trends : Recent publications highlight advancements in triboelectric energy harvesters for knee implants, thermal covert channel mitigation in 3D processors, and energy-efficient DNN accelerators. He emphasizes sustainability and security in emerging technologies like ReRAM-based computing and AC circuits for wireless IoT. Awards : 2023-2024 IEEE Distinguished Lecturer 2018 IEEE Region 1 Technological Innovation Award 2013 NSF CAREER Award Advising & Grants : Salman has directed multiple NSF, NIH, and industry-funded projects. He advises students on topics like hardware security and biomedical electronics, with a focus on translating research into commercializable technologies. Labs & Teams : The NanoCAS Lab collaborates with Brookhaven National Lab and industry partners (e.g., AMD, Samsung) to bridge academic research with real-world applications in energy-efficient computing and secure 3D ICs.

Guangdong ZhuView profile

Researcher

• Concentrating Solar Power
• Geothermal Energy
• Hybrid Renewable Energy Systems
• +3 more

Guangdong Zhu serves as Group Manager III-Systems Engineering and Senior Researcher at the National Renewable Energy Laboratory (NREL), leading the Thermal Energy Systems Group within the Energy Conversion and Storage Systems Center. He joined NREL in 2010 and specializes in concentrating solar power, geothermal technologies, and hybrid renewable energy systems development. His academic credentials include: Bachelor of Thermal Engineering from Northeast China Institute of Electric Power Engineering Master of Thermal Engineering from Xi'an Jiaotong University PhD in Mechanical Engineering from the University of New Mexico Zhu's research focuses on solar collector characterization, geothermal power plant modeling, and renewable energy hybridization, with expertise spanning heat transfer, fluid mechanics, stress analysis, and numerical modeling. His work addresses critical challenges in thermal energy storage and power generation efficiency for decarbonization. Recent publications (2025) reveal strong trends toward geological thermal energy storage for grid-scale applications, geothermal network optimization via HEATNETS, advanced materials testing for solar concentrators, and precision modeling of power tower flux distribution. These efforts collectively advance the reliability and scalability of renewable thermal systems. Key recognitions include: NREL Outstanding New Partnership Award (2016) NREL President’s Award (2016) As Associate Editor for the ASME Journal of Energy Resources Technology and Executive Committee Member of the ASME Advanced Energy Systems Division, Zhu actively shapes research standards while mentoring junior staff. His group manages NREL's concentrating solar power research portfolio with significant DOE funding. The Thermal Energy Systems Group operates specialized facilities for solar field testing, geothermal simulation, and hybrid system integration, supporting national initiatives for clean energy transition through experimental validation and computational modeling.

Ole-Christoffer GranmoView profile

Professor

• Machine Learning
• Artificial Intelligence
• Tsetlin Machines
• +7 more

Professor Ole-Christoffer Granmo is a distinguished academic at the University of Agder, Norway, where he serves as Professor in the Department of Information and Communication Technology. He is the Founding Director of the Centre for Artificial Intelligence Research (CAIR) at the University of Agder, leading cutting-edge research in artificial intelligence and machine learning. Dr. Granmo obtained his master's degree in 1999 and his PhD in 2004, both from the University of Oslo. His academic journey has been marked by significant contributions to the field of AI, most notably the creation of the Tsetlin machine in 2018, for which he received the AI research paper of the decade award from the Norwegian Artificial Intelligence Consortium (NORA) in 2022. Professor Granmo's research primarily focuses on logical and causal world modeling across multiple modalities including images, sound, and natural language. His work spans logical auto-encoding, convolution, regression, transformer architectures, and reinforcement learning, all with the overarching goal of creating ultra-low-power artificial general intelligence through transparent logical learning and reasoning. His publications reveal a strong emphasis on interpretable AI systems, hardware implementations, and applications across diverse domains including cybersecurity, healthcare, social media analysis, and bioinformatics. AI Research Paper of the Decade (2022) - Norwegian Artificial Intelligence Consortium (NORA) Eight paper awards in machine learning Professor Granmo has coordinated over seven research projects and mentored 55+ master's students and nine PhD students. His leadership extends to co-founding the Norwegian Artificial Intelligence Consortium (NORA) and establishing two companies: Anzyz Technologies AS and Tsense Intelligent Healthcare AS. As an advisor at Literal Labs, he actively bridges academic research with practical industry applications, demonstrating his commitment to translating theoretical innovations into real-world solutions that address complex challenges across multiple sectors.