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Yu [Kevin] CaoView profile

Professor

• Microelectronics
• Computer Engineering
• Semiconductor Technology
• +5 more

Yu [Kevin] Cao is the Louis John Schnell Professor in the Department of Electrical and Computer Engineering at the University of Minnesota. His research focuses on microelectronics co-design for energy-efficient computing, spanning integrated circuit design, semiconductor physics, and machine learning methodologies. He leads the Microelectronics Co-design Research Group and actively collaborates with institutions like Georgia Institute of Technology, Sandia National Laboratories, and Notre Dame. His research interests include AI hardware acceleration , in-memory computing , cryogenic CMOS design , and 3D integration of heterogeneous chiplets . Current initiatives explore reconfigurable on-package systems for AI, spiking neural networks on neuromorphic hardware, and low-temperature logic technologies. Recent publications and projects highlight advancements in AI accelerators , RRAM-based compute-in-memory , graph convolutional networks , and 3D integration . His group develops tools like MN-SIM 2.0 for memristor modeling and investigates novel materials for neuromorphic systems. Grants include collaborative NSF funding for chiplet-based AI systems, CoCoSys center funding from SRC, and DOE/Sandia projects on neuromorphic hardware. Future work emphasizes scalable co-design frameworks for intelligent systems and heterogeneous integration challenges.

• Biomedical Engineering
• Nanotechnology
• Microelectronics
• +5 more

Professor Omid Kavehei is a Professor of Intelligent Microsystems in the Faculty of Engineering at the University of Sydney, serving as Deputy Head of the School of Biomedical Engineering. Previously, he held roles as a Research Fellow at the University of Melbourne and Lecturer at RMIT University. His research focuses on biomedical microsystems, nanotechnology, and brain-inspired hardware for healthcare applications, particularly epilepsy monitoring and seizure prediction. Education: PhD (specific institution not explicitly stated) His work bridges nanoelectronics and healthcare, aiming to develop low-power, brain-inspired devices for sensory perception and medical diagnostics. Key interests include neuromorphic engineering, wearable sensors, and AI-driven medical systems. Awards include the Ramaciotti Biomedical Research Award (2021), Microsoft AI for Accessibility Grant (2019), and multiple teaching/research excellence awards from the University of Sydney. His recent work explores neuromorphic cytometry for cell analysis, edge AI for ECG/EEG diagnostics, and closed-loop neurostimulation systems. Collaborations include the Sydney Nano Institute, Brain and Mind Centre, and industry partners like Microsoft. Current research students are advancing topics like bio-inspired algorithms for seizure detection, hardware-friendly machine learning models, and flexible sensor systems for aquatic environments. Grants include funding for neurophysiology platforms, quantum sensors, and semiconductor design. Labs/teams: Involved in the Centre for Drug Discovery Innovation and collaborations across engineering, neuroscience, and clinical domains.

Zhiyi HuangView profile

Associate Professor

• Theoretical Computer Science
• Algorithmic Game Theory
• Online Algorithms
• +3 more

Zhiyi Huang is an Associate Professor of Computer Science at the University of Hong Kong, leading the Computer Science Division within the School of Computing and Data Science. He holds a PhD from the University of Pennsylvania (2013) and completed a postdoctoral fellowship at Stanford University (2013–2014). His research focuses on Theoretical Computer Science, Algorithmic Game Theory, Online Algorithms, and Differential Privacy, with notable contributions to Machine Learning and Computer Networks. Education: PhD in Computer and Information Science, University of Pennsylvania (2013) Postdoctoral Researcher, Stanford University (2013–2014) Bachelor's Degree from the Yao Class at Tsinghua University (2008) Research interests span foundational areas including algorithmic game theory, online optimization, and privacy-preserving mechanisms. He has pioneered work on revenue maximization in single-parameter settings and developed novel frameworks for analyzing price of anarchy in game theory. Key Awards: Early Career Award (Research Grant Council of Hong Kong, 2014) Best Paper Award at ACM Symposium on Parallelism in Algorithms and Architectures (SPAA 2015) Morris and Dorothy Rubinoff Dissertation Award (2013) Simons Graduate Fellowship in Theoretical Computer Science (2012–2013) Recent grants include studies on algorithmic foundations of Bayesian mechanism design (HK$675,647), online primal dual techniques (HK$496,028), and privacy-preserving mechanisms (HK$931,737). His work bridges theoretical advancements with practical applications in healthcare, autonomous systems, and cybersecurity. Notable Projects: Medical predictive systems for acute cardiopulmonary events AI-driven maritime navigation using AIS data Secure federated learning frameworks with blockchain

Darshika G. PereraView profile

Assistant Professor

• Computer Engineering
• FPGA-Based Hardware Acceleration
• Neuromorphic Computing
• +5 more

Darshika G. Perera is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Colorado Colorado Springs (UCCS). Her research focuses on FPGA-based hardware acceleration, neuromorphic computing, and embedded systems optimization with applications in machine learning, cryptography, and battery management systems. She holds a PhD and has extensive experience in designing reconfigurable architectures for compute-intensive tasks. Her work bridges theoretical algorithms with practical hardware implementations, emphasizing efficiency and real-time performance. Key research areas include FPGA design methodologies, neuromorphic hardware for AI applications, and embedded systems security. She has published widely on topics such as hardware-software co-design for edge computing, optimization algorithms for genomics, and blockchain applications in healthcare IoT. Her contributions also span data mining hardware accelerators and dynamic reconfiguration techniques for cryptographic systems. Dr. Perera’s work is characterized by interdisciplinary collaborations, combining principles from electrical engineering, computer science, and applied mathematics. She is committed to advancing next-generation edge-computing platforms through innovative architectures and methodologies.

• Computer Vision
• Pattern Recognition
• Facial Analysis
• +5 more

Jorge Manuel M. C. Pereira Batista is an Associate Professor at the Department of Electrical and Computer Engineering , University of Coimbra, Portugal. He serves as a senior researcher at the Institute for Systems and Robotics (ISR-UC) and leads the Computer Vision Group. His career spans academic roles, research coordination, and industry collaboration. Academic Affiliation: University of Coimbra (Electrical & Computer Engineering Department) Research Institute: Institute for Systems and Robotics (ISR-UC) His research focuses on Computer Vision , Pattern Recognition , and applications of Differential Geometry in these fields. Key subdomains include facial analysis, visual surveillance, real-time vision systems, and machine learning integration. From 2010–2023, his recent publications highlight advancements in probabilistic models , transformer architectures , multi-branch learning , and biomimetic robotics . He has coordinated multiple funded projects such as: STORK : Avian protection systems NeuroCity : Intelligent street lighting 4D Facial Dynamics : Identity recognition iTRAFFIC : BRISA highway traffic monitoring His work bridges theoretical innovation (e.g., Riemannian manifold applications) with practical deployments in transportation, energy, and healthcare sectors.

• Machine Learning
• Neural Networks
• Uncertainty Quantification
• +4 more

Dr. Matias Valdenegro Toro is an Assistant Professor of Machine Learning at the University of Groningen within the Faculty of Science and Engineering and the Artificial Intelligence department of the Bernoulli Institute. He holds a PhD from Heriot-Watt University (2019) and a Master's in Autonomous Systems from Bonn-Rhein-Sieg University of Applied Sciences (2014). His research focuses on trustworthy machine learning models , particularly in uncertainty quantification , medical AI , and robotics , with applications in computer vision and explainable AI. He teaches courses like Introduction to Machine Learning and Deep Learning at the Bachelor and Master levels. His work emphasizes robustness in AI systems, including uncertainty estimation for medical applications, super-resolution techniques, and neuromorphic robotics. He has published widely on topics like Bayesian neural networks, prompt tuning, and sanity checks for explanations. Notable awards include Best Reviewer at ICML (2024) and Highlighted Reviewer at ICLR (2022). He collaborates with institutions like the German Research Center for Artificial Intelligence and actively contributes to open-source datasets (e.g., the Japanese Uncertain Scenes Dataset ). Key grants and activities include organizing the ENLIGHT BIP Course on Deep Learning for Forestry and teaching at the European Summer School on AI . His research also addresses regulatory challenges like the EU AI Act's implications for uncertainty quantification in general-purpose AI.

• Information and Communication Technology
• Electrical and Electronic Engineering
• Artificial Intelligence
• +3 more

Paweł Tarnowski is a researcher at the Institute of the Theory of Electrical Engineering, Measurement and Information Systems , Faculty of Electrical Engineering, Warsaw University of Technology. His work focuses on biomedical signal processing, emotion recognition, and machine learning applications in affective computing and human-computer interaction. Research areas include Information and Communication Technology (ICT) Electrical and Electronic Engineering Artificial Intelligence Neuroscience Human Factors His recent publications highlight trends in emotion recognition using multimodal physiological signals (EEG, EOG, GSR), driver fatigue detection, and deep learning techniques like 1D-CNN for medical diagnostics. Articles also address urban traffic monitoring and neuromarketing research via EEG analysis. At Warsaw University of Technology, he supervises thesis projects and contributes to interdisciplinary research in biomedical engineering and signal processing. Labs/Teams: Institute of the Theory of Electrical Engineering, Measurement and Information Systems .

Gang-Ding PengView profile

Professor

• Photonics
• Optical Fiber Communications
• Nonlinear Optics
• +5 more

Professor Gang-Ding Peng is a leading academic in photonics and optical communications at the School of Electrical Engineering and Telecommunications , University of New South Wales (UNSW). With over three decades of experience, his research focuses on silica and polymer optical fibers, fiber lasers, sensors, and photonic signal processing. Education: B.Sc. in Physics (1982, Fudan University), M.Sc. in Applied Physics (1984), Ph.D. in Electronic Engineering (1987, both Shanghai Jiao Tong University) Career: Lecturer at Shanghai Jiao Tong (1987-1988), Postdoctoral Fellow at Australian National University (1988-1991), UNSW Faculty since 1991, Queen Elizabeth II Fellow (1992-1996) Research Interests: Specialized in specialty optical fibers, nonlinear optics, and photonic devices. His work spans: Silica and polymer fiber amplifiers/lasers Electro-optic and liquid-crystal-doped fibers Multi-core fiber Bragg grating systems Photonic crystal fiber sensing 3D-printed optical components Quantum and neuromorphic photonic applications Scientific Awards: Queen Elizabeth II Fellowship (1992-1996) Fellow and Life Member of OSA & SPIE Supervision: Active mentor in Bi/Er co-doped fibers, 3D-printed optical fibers, and fiber sensing technologies.

Christopher CrickView profile

Associate Professor

• Artificial Intelligence
• Cognitive Science
• Human-Robot Interaction
• +7 more

Christopher Crick is an Associate Professor in the Department of Computer Science at Oklahoma State University. He leads the Robotic Cognition Laboratory, focusing on grounding developmental psychology and cognitive science in embodied AI systems, while improving robotics through human cognition-inspired models. University: Oklahoma State University Department: Computer Science Academic Rank: Associate Professor Email: chris.crick@okstate.edu, chriscrick@cs.okstate.edu Research Interests: Artificial Intelligence Cognitive Science Human-Robot Interaction Atmospheric Sciences (via UAV applications) Machine Learning Medical Informatics Scientific Activities: NSF-funded research in robotics, UAVs, and AI Professional memberships: Cognitive Science Society, ACM, AAAS Editorial roles and conference reviewing in robotics and AI Lab: Robotic Cognition Laboratory

Thivya KANDAPPUView profile

Assistant Professor

• Human-Machine Collaborative Systems
• Pervasive Sensing & Systems
• Human-Computer Interaction
• +6 more

Thivya KANDAPPU is an Assistant Professor at the School of Computing and Information Systems, Singapore Management University (SMU). Her research focuses on mobile and wearable computing systems for human cognition monitoring, memory modeling, and technology-enhanced learning. Education: PhD in Computer Science, University of New South Wales (2014) Research Interests span: Human-Centric Wearable Systems Cognitive State Analysis Event-Based Vision & Sensing Privacy in Pervasive Systems Health & Wellbeing Applications Smart City Mobility Analytics Selected Publications demonstrate expertise in eye tracking, cognitive monitoring, and privacy-preserving wearables, with recent work appearing in NeurIPS and ACM IMWUT. Research Grants include projects funded by MOE and A*STAR on cognitive dynamics, privacy-aware systems, and multimodal travel analytics. Professional Service involves organizing AutoMLPerSys 2025, and serving on TPCs for ACM MobiSys 25, IEEE PerCom 25, and ICDCN 25.

Silvia ToluView profile

Associate Professor

• Neurorobotics
• Bio-inspired Control Systems
• Neuromorphic Engineering
• +3 more

Silvia Tolu is an Associate Professor at the Technical University of Denmark's Department of Electrical and Photonics Engineering, specializing in Neurorobotics. She leads the NeuroRobotics Technology Lab (NRT-LAB), focusing on bio-mimetic control architectures for compliant robotic systems. Her research integrates neuroscience, computer science, and biology to develop solutions for assistive robotics and neurodegenerative disease diagnosis. Her research interests span: Neuro-robotics and neuromorphic engineering Bio-inspired control systems and adaptive motor control Machine learning for robotic applications Human-robot compliant interaction Cerebellar control models Publications primarily focus on neurorobotics, bio-inspired control, and human-robot interaction, with recent advances in learning-based control systems for soft robots and aerial manipulation. Awards include the AEG Elektrofonden Research Grant and funding for human-robot interaction safety research. Current projects include LOCOPD (Lundbeck Foundation), AEROTRAIN (EU Marie Curie ITN), and compliant human-robot interaction systems. She supervises multiple PhD students in neurorobotics and maintains international collaborations across Europe and Asia. Laboratory resources include advanced robotic platforms for musculoskeletal and soft robot control.

Ramin HasaniView profile

Researcher

• Machine Learning
• Robotics
• Neural Networks
• +3 more

Ramin Hasani is a researcher at TU Wien's Cyber-Physical Systems department. He holds a Dr.techn. (Doctor of Engineering) and specializes in machine learning applications for robotics, control systems, and biologically-inspired neural networks. His work focuses on developing interpretable neural architectures like Liquid Time-Constant Networks and Neural Circuit Policies, emphasizing safety and stability in autonomous systems. Hasani's research bridges neural network theory with practical robotics challenges, including autonomous racing, medical data analysis, and adversarial robustness. Key research areas include continuous-time neural networks, formal verification of neural ODEs, and bio-inspired control mechanisms derived from biological neural circuits (e.g., Caenorhabditis elegans). He collaborates extensively with institutions like MIT and ETH Zurich, contributing to projects in health-monitoring systems and end-to-end robot learning frameworks. His publications consistently address real-world challenges such as sepsis prediction via reinforcement learning and robust CNN architectures for image classification. Recent work highlights include developing stable recurrent networks through Gershgorin loss functions and advancing zero-shot transfer learning for autonomous systems. Hasani's interdisciplinary approach integrates principles from neuroscience, control theory, and machine learning to create auditable, high-performance AI solutions for cyber-physical environments.

Ralph Etienne-CummingsView profile

Professor

• Neuromorphic Engineering
• Neural Prosthetics
• Biomedical Robotics
• +5 more

Ralph Etienne-Cummings is the Julian S. Smith Professor of Electrical and Computer Engineering at Johns Hopkins University (JHU), where he also serves as Vice Provost for Faculty Affairs. He holds secondary appointments in Computer Science and is affiliated with JHU's Applied Physics Lab. His work spans three decades, pioneering advancements in neuromorphic engineering, neural prosthetics, and biomorphic robotics. Etienne-Cummings leads the Computational Sensory Motor Systems Laboratory and has developed systems for closed-loop neural interfaces, prosthetics, and biomedical sensors. Education: BSc in Physics (1988), Lincoln University MSEE (1990) and PhD (1994) in Electrical Engineering, University of Pennsylvania Research Interests: His work focuses on neuromorphic systems, bio-inspired algorithms, and neural prosthetics. Key areas include spinal cord stimulation for mobility restoration, wearable health monitoring, and ultrasonic imaging for infertility treatment. He has contributed to silicon Central Pattern Generators (CPGs) for bipedal robotics and developed the first large-scale neural computer using VLSI chips. His lab explores organoid intelligence and biohybrid systems, blending neuroscience with engineering. Impact & Recognition: Named Fellow of AIMBE (2021) and IEEE (2012) Recipient of JHU Discovery Awards (2018–2019) and NSF CAREER Award (1996) Developed the 'Microbead'—a 0.009mm³ wireless neural stimulator Industry & Outreach: Served as founding director of JHU's Institute of Neuromorphic Engineering and advised firms like Panasonic and Avago. Testified in federal court on intellectual property disputes. Recognized as a 'ScienceMaker' in the HistoryMakers Archive for contributions to African American STEM leadership. Labs & Collaborations: Directs the Computational Sensory Motor Systems Lab. Collaborates with DARPA on prosthetics and the NIH on bioelectronic medicine. His work bridges academia and industry, emphasizing practical applications of neural engineering.

• Neuromorphic Computing
• Spiking Neural Networks
• Bio-inspired Artificial Intelligence
• +8 more

Dr James Knight is a Senior Research Fellow in Informatics at the School of Engineering and Informatics, University of Sussex. He is currently an EPSRC Research Software Engineering Fellow (2022–2026), leading research in neuromorphic computing and spiking neural networks. His work bridges computer science, neuroscience, and robotics, with a focus on bio-inspired artificial intelligence and computational modeling of insect cognition. Education: PhD in Computer Science, University of Manchester (2013–2016) MPhil in Advanced Computer Science, University of Cambridge (2012–2013) BEng in Electronic Engineering, University of Warwick (2003–2006) His research interests center on neuromorphic computing , spiking neural networks , and bio-inspired AI , particularly drawing insights from insect neuroscience to develop efficient and robust machine learning systems. He explores event-based vision, neural hardware acceleration, and computational models of insect navigation and perception. The 15 most recent publications (2024–2025) reflect a strong trend in developing and benchmarking neuromorphic algorithms, including training methods like Eventprop, hardware-software co-design (e.g., FeNN processor), and modeling biological systems such as insect central complex and mushroom bodies. These works span journals like Nature Communications , PLoS Computational Biology , and Neuromorphic Computing and Engineering , as well as IEEE and Springer conferences. Scientific Grants: Unlocking spiking neural networks for machine learning research (EPSRC, 2022–2026) – Principal Investigator ActiveAI – active learning and selective attention for robust, transparent and efficient AI (EPSRC, 2019–2022) – Research Team Member Dr Knight actively collaborates with leading researchers including Thomas Nowotny, Andrew Philippides, and Paul Graham. His work contributes to both theoretical advances in neural computation and practical applications in robotics and AI. He is involved in developing frameworks such as NeuroBench for standardized evaluation of neuromorphic systems. Laboratories and Research Teams: He is part of the neuromorphic computing and computational neuroscience research groups at the University of Sussex, contributing to interdisciplinary projects that integrate software engineering, AI, and biological modeling.

Catherine SchumanView profile

Assistant Professor

• Neuromorphic Computing
• Artificial Intelligence
• Hardware-Software Co-Design
• +7 more

Catherine Schuman is an Assistant Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville, part of the Tickle College of Engineering. Her work focuses on neuromorphic computing, spiking neural networks, and AI-driven hardware-software co-design. She holds a Ph.D. and B.S. in Computer Science and Mathematics from the University of Tennessee (2015 and 2010, respectively). Research interests include neuromorphic systems, energy-efficient computing architectures, stochastic devices, and applications of neuromorphic computing in scientific domains like materials science and quantum materials. She emphasizes benchmarking frameworks (NeuroBench) and real-world applications such as radiation detection and control systems. Her recent publications (2023-2025) highlight advancements in neuromorphic hardware-software co-design, spiking reinforcement learning frameworks, and neuromorphic implementations for scientific computing. Notable contributions include frameworks like SpikeRL, NeuroPong, and the RISP neuroprocessor, emphasizing open-source tools for embedded neuromorphic computing. No scientific awards are explicitly listed in the provided information. She actively explores cross-disciplinary applications, including combustion control and materials discovery through AI-enhanced device design.