Find Academic Supervisors

• Neuromorphic Engineering
• Neural Networks
• Edge Computing
• +3 more

Shih-Chii Liu holds the rank of Privatdozent (Associate Professor) in the Department of Information Technology and Electrical Engineering at ETH Zürich. He is affiliated with the Institute of Neuroinformatics , a joint institute between the University of Zurich and ETH Zurich. His research focuses on neuromorphic engineering, bio-inspired neural hardware, and edge computing systems, emphasizing energy-efficient algorithms and sensor technologies. Key research areas include neuromorphic sensors for real-time data processing, sparsity-aware neural networks, and adaptive computing architectures for edge devices. His work spans applications such as speech enhancement, wearable health monitoring, and bio-inspired keyword spotting systems. He leads the Sensors Research Group, which develops neuromorphic systems integrating novel sensors, spiking neural networks, and low-power hardware accelerators. Recent projects include the DeltaKWS low-power keyword spotting IC, EFLOP computational cost metrics for spiking networks, and NeuroBench benchmarking frameworks for neuromorphic systems. His contributions emphasize bridging biological neural principles with practical engineering solutions for IoT and embedded systems. Liu teaches courses such as Neuromorphic Engineering I and collaborates on cross-disciplinary projects involving neuroprosthetics, smart wearables, and multimodal sensor fusion. His work is characterized by hardware-software co-design approaches to tackle challenges in real-time, low-latency, and energy-constrained computing environments.

Philipp AllgeuerView profile

Researcher

• Robotics
• Humanoid Robotics
• Bipedal Locomotion
• +5 more

Dr. Philipp Allgeuer is a Postdoctoral Research Associate at the Knowledge Technology Research Group within the Department of Informatics at the University of Hamburg. His work focuses on humanoid robotics, bipedal locomotion, and sensor fusion. He holds a PhD from the Autonomous Intelligent Systems Group at the University of Bonn, alongside dual bachelor's degrees in Mechatronic Engineering and Mathematical/Computer Sciences (both with First-Class Honors). His research contributions include the development of the igus Humanoid Open Platform and the NimbRo-OP series of humanoid robots, recognized with awards like the RoboCup HARTING Open Source Award (2016) and the Best Humanoid Award (2018). He has authored influential papers on fused angles for robot balance, tilt phase space representations, and neuro-inspired control architectures. Allgeuer's teams have dominated RoboCup competitions, winning titles in AdultSize and TeenSize leagues multiple times. His open-source software frameworks (e.g., rot_conv_lib , attitude_estimator ) and hardware designs are widely used in robotics research. Recent work explores multimodal human-robot interaction and AI-driven robotic task coordination. He is affiliated with the Knowledge Technology Research Group and contributes to projects like the NICOL humanoid robot, bridging social interaction and reliable manipulation. His research spans from low-level control algorithms to high-level behavior planning systems.

Nabil ImamView profile

Assistant Professor

• Computational Neuroscience
• Parallel Computing
• Probabilistic Methods
• +3 more

Nabil Imam is an Assistant Professor at the School of Computational Science and Engineering within the College of Computing at Georgia Institute of Technology. He holds a Ph.D. in electrical engineering and neuroscience from Cornell University, advised by Rajit Manohar and Barbara Finlay. Prior to academia, he conducted research at IBM and Intel Labs, focusing on neuromorphic engineering and AI. His current research integrates computational neuroscience, probability theory, and control systems to model biological computation, with an emphasis on process algebras for asynchronous circuits and systems. Education: Ph.D. in Electrical Engineering and Neuroscience, Cornell University (Advisors: Rajit Manohar, Barbara Finlay) Research interests include computational neuroscience, parallel computing, probabilistic methods, and neuromorphic systems. His work bridges biological neural mechanisms with technological applications, such as neuromorphic olfactory circuits and cortical development models. Notable contributions include neuromorphic chips featured in Science and Nature . His publications highlight interdisciplinary trends in neural coding, neuromorphic hardware, and evolutionary neuroscience. Recent work explores dual computational systems in mammalian brain evolution and self-organizing cortical structures. Earlier projects include scalable spiking-neuron integrated circuits (Science, 2014) and neurosynaptic cores with event-driven architectures (Best Paper Award, 2012). Awards: Best Paper Award at IEEE International Symposium on Asynchronous Circuits and Systems (2012) Teaching includes CSE 8803: Computational Methods for Complex Systems. His lab investigates process algebra frameworks for asynchronous systems and biological computation principles. Collaborations span industry (IBM, Intel) and academic institutions. Future directions emphasize theoretical neuroscience and neuromorphic technology applications.

Catherine StinsonView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning Ethics
• Computational Psychiatry
• +4 more

Catherine Stinson is an Assistant Professor and Queen’s National Scholar in Philosophical Implications of Artificial Intelligence at Queen's University. She holds joint appointments in the School of Computing and the Philosophy Department, blending technical and philosophical expertise to address ethical challenges in AI. PhD in History & Philosophy of Science, University of Pittsburgh (2013) MSc in Computer Science, University of Toronto (2013) Dr. Stinson's research focuses on methodological and ethical dimensions of artificial intelligence, including: Bias and explanation in machine learning Computational psychiatry Embodied intelligence Community-led design AI ethics education Her recent publications analyze corporate influence in NLP, privacy implications of large language models, and documentation practices in AI datasets. They explore intersections between technical performance, ethical responsibility, and societal impact. Scientific awards include: Queen’s National Scholar Finalist for Outstanding Certification at Transactions in Machine Learning Research As director of the Ethics and Technology Lab at Queen's, she leads interdisciplinary projects challenging algorithmic bias, investigating AI creativity, and developing tools to resist targeted violence. Current initiatives include: Critical evaluation of chatbots for mental health support Tracking (In)justice project on police-involved deaths in Canada

• Hybrid Systems
• Networked Control Systems
• Event-Triggered Control
• +7 more

Maurice Heemels is a Full Professor at Eindhoven University of Technology (TU/e), leading the Control Systems Technology group. He holds additional professorships in EAISI Mobility, EAISI Foundational, EAISI Health, and EAISI High Tech Systems. His research focuses on hybrid and networked systems, emphasizing resource-aware control, event-triggered strategies, and cyber-physical systems integration. He is an IEEE Fellow and chairs the IFAC Technical Committee on Networked Systems. Academic Background: MSc and PhD in Mathematics (TU/e, 1995 and 1999, both summa cum laude ) Visiting Professorships: ETH Zurich (2001), UC Santa Barbara (2008) Industry Experience: Research & Development at Océ NV Research Interests: Hybrid Systems, Networked Control, Event-Triggered Control Model Predictive Control (MPC) in healthcare and high-tech systems Cyber-Physical Systems for applications like lithography and precision agriculture Key Contributions: Developed Hybrid Integrator-Gain (HIGS) systems and Projection-Based Control methodologies Recipient of a VICI Grant for wireless control systems research Oversaw over €7M in research funding from NWO, EU, and industry Awards & Recognition: Automatica Outstanding Service Award (2014) Best Paper Awards (EBCCSP 2017, etc.) Invited Keynote Speaker at ECC, CDC, and others Grants & Projects: Current Projects: COMEDI (Cost-effective Mechatronics), PROACTHIS (Projection-based Control) Past Projects: Fault Detection in Wafer Scanners, Drone-based Farming Labs & Teams: Active in TU/e’s Cyber-Physical Systems and Systems Engineering research groups, collaborating globally on nonsmooth dynamics and hybrid systems.

Mohammad RostamiView profile

Academic

• Machine Learning
• Transfer Learning
• Domain Adaptation
• +5 more

Mohammad Rostami is a Research Assistant Professor at the University of Southern California (USC) in the Department of Computer Science and Electrical and Computer Engineering, with a joint appointment at the USC Information Sciences Institute (ISI). He holds a PhD in Electrical and Systems Engineering from the University of Pennsylvania and additional degrees in Robotics, Philosophy, Electrical Engineering, and Pure Mathematics from prestigious institutions including the University of Waterloo and Sharif University of Technology. His research focuses on machine learning in data-scarce environments, particularly transfer learning, domain adaptation, low-shot learning, and improving learning efficiency through continual and collective learning. He incorporates symbolic logic and neuro-symbolic approaches to address challenges in catastrophic forgetting and knowledge retention. Applications span medical imaging, computer vision, and explainable AI. Rostami has received several accolades including the UPenn Best PhD Dissertation Award, IJCAI Distinguished Student Paper Award, and University of Waterloo Outstanding Achievement Award. His work bridges theoretical advancements with practical implementations, emphasizing real-world applications in healthcare and autonomous systems. He teaches graduate courses in applied natural language processing and knowledge graph construction. Rostami advises students at all academic levels and collaborates with remote researchers, emphasizing motivated, long-term project commitments.

Francesco FabianoView profile

Research Fellow

• Multi-agent systems
• Epistemology
• Autonomous planning
• +7 more

Francesco Fabiano is a Research Fellow at the Department of Computer Science, University of Oxford, and an Affiliated Faculty Member at New Mexico State University. He previously served as Assistant Professor at New Mexico State University (2023–2024) and held Adjunct Professor roles at Saint Joseph’s University and University of Parma. His research spans neuro-symbolic AI, multi-agent systems, epistemology, and autonomous planning. Postdoctoral Research Associate at University of Oxford (2025–present) Ph.D. in Computer Science from University of Udine (2018–2021) Master’s Degree in Computer Science from New Mexico State University (2017–2018) Bachelor’s Degree in Computer Science from University of Parma (2013–2016) His work focuses on neuro-symbolic architectures that integrate formal logic with machine learning, particularly for multi-agent epistemic planning and ethical decision-making frameworks . He explores hybrid systems combining System-1/System-2 cognitive paradigms to enhance AI trustworthiness. Recent publications analyze knowledge representation in neuro-symbolic systems, the role of large language models in planning, and heuristic optimization in multi-agent epistemic solvers like EFP. His work also addresses ethical AI and explainable data-to-text frameworks . Best Ph.D. Thesis Award by GULP (2022) He collaborates with IBM Watson Research Lab on cognitive theory-inspired AI paradigms and contributes to open-source planning tools like EFP. His teaching experience includes courses in Applied Machine Learning , Automated Planning , and LaTeX programming .

• Instrumentation Electronics
• Memristor Modelling
• Signal Integrity
• +6 more

Dr. Christos Papavassiliou is an Associate Professor in the Department of Electrical and Electronic Engineering at Imperial College London, part of the Faculty of Engineering. His research focuses on instrumentation electronics, memristor modeling, signal integrity, and novel device technologies such as SiGe devices, RF MEMS, and ReRAM. He leads the Space Lab and collaborates with the National Centre for Scientific Research in Athens. He holds senior membership in IEEE and is a member of the IET. Education: Ph.D. in Applied Physics, Yale University (1983–1989) MPhil in Applied Physics, Yale University (1983–1988) MS in Applied Physics, Yale University (1983–1985) B.S. in Physics, MIT (1979–1983) Research Interests: Memristor-based neuromorphic computing and stochastic systems High-performance instrumentation hardware and data acquisition Multi-state memristive memory and selectorless arrays Integration of memristors with CMOS for hybrid circuits Applications in biomedical wearables and edge AI deployment Key Contributions: Developed novel memristor models for circuit simulation Pioneered work on memristor-based true random number generators Advanced understanding of resistive drift and energy-constrained storage Designed FPGA-based systems for analog circuit emulation Labs & Teams: Active in the Space Lab at Imperial College, focusing on interdisciplinary research in electronics and space applications.

• Machine Learning
• Database Systems
• Artificial Intelligence
• +3 more

Christopher Re is a Professor in the Department of Computer Science at Stanford University, affiliated with the Stanford AI Lab, Machine Learning Group, and Center for Research on Foundation Models. His research focuses on the intersection of machine learning, database systems, and scientific computing, with applications in humanitarian efforts, scientific discovery (e.g., extrasolar neutrinos, DNA foundation model Evo), and industry partnerships with companies like Apple and Google. He has been recognized with prestigious awards, including the MacArthur Foundation Fellowship and multiple test-of-time awards. His work emphasizes advancing thermal materials, phase-change memory, and ultrafast electron microscopy technologies. Re's research contributions span database theory, systems, and machine learning, with best papers at PODS 2012, SIGMOD 2014, and ICML 2016. His lab’s innovations have been incorporated into products globally, and he actively invests in technology startups. Key projects include developing thermal interface materials for 3D integrated circuits and exploring energy-efficient neuro-inspired memory systems. His awards reflect sustained excellence: NeurIPS 2020 and PODS 2022 test-of-time awards, along with recent accolades for student-led initiatives at MIDL 2022 and ICLR22. Re’s interdisciplinary approach bridges academia and industry, driving both scientific and humanitarian impact.

• Neuromorphic Computing
• Organic Electronics
• Bioelectronics
• +3 more

Paschalis Gkoupidenis is an Associate Professor in the Department of Electrical and Computer Engineering at North Carolina State University (since August 2024) and a Group Leader at the Department of Molecular Electronics at the Max Planck Institute for Polymer Research. His research focuses on developing organic neuromorphic devices for neuro-inspired information processing, learning, sensing, and bio-interfacing. Research Interests Dr. Gkoupidenis specializes in hardware-based implementation of neuromorphic architectures, which offer efficient ways of data manipulation and processing, especially in data-intensive applications. His work explores how organic materials and devices can be used for neuro-inspired devices and bioelectronics, leveraging their attractive characteristics such as ability to operate in electrolytes, spatiotemporal response, analogue memory phenomena, tunability via chemical synthesis, low-cost fabrication processes, and biocompatibility. His research group investigates various concepts for inducing neuroplasticity, learning forms, and spatiotemporal information processing functions at a single-device level, as well as new paradigms of neuromorphic architectures at circuit level. These neuro-inspired functions are essential for trainable/adaptable circuits in energy-restricted environments and for local signal processing in bioelectronics. Scientific Contributions Development of organic neuromorphic devices for neuro-inspired information processing Research on synaptic plasticity functions in organic electrochemical transistors Exploration of neuromorphic device architectures with global connectivity through electrolyte gating Investigation of functional connectivity of organic neuromorphic devices by global voltage oscillations Advancement of organic neuromorphic devices for adaptive sensing and novel computing paradigms in bioelectronics Affiliations Associate Professor, Department of Electrical and Computer Engineering, North Carolina State University (since August 2024) Group Leader, Organic Neuromorphic Electronics, Max Planck Institute for Polymer Research (since 2017) Postdoctoral Researcher, Department of Bioelectronics, EMSE, France (2015-2017)

Christopher John RozellView profile

Professor

• Computational Neuroengineering
• Artificial Intelligence
• Neuroscience
• +7 more

Christopher John Rozell is the Julian T. Hightower Chaired Professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology's College of Engineering. He serves as Executive Director of the Institute for Neuroscience, Neurotechnology & Society (INNS) and directs the Sensory Information Processing Lab (SIPLab). His research bridges computational neuroscience, machine learning, and neurotechnology, with clinical applications in treatment-resistant depression and neuromodulation therapies. Education: B.S.E. in Computer Engineering & B.F.A. in Music, University of Michigan (2000) M.S. and Ph.D. in Electrical Engineering, Rice University (2002, 2007) Postdoctoral Scholar, Redwood Center for Theoretical Neuroscience, UC Berkeley Research Focus: Dr. Rozell's interdisciplinary work spans computational neuroengineering, theoretical neuroscience, and artificial intelligence. He develops data analysis tools inspired by neural processing and creates therapeutic neurotechnologies. Key areas include: computational psychiatry (developing DBS therapies for depression), neural dynamics modeling, brain-computer interfaces, and societal impacts of neurotechnology. His lab focuses on high-dimensional data analysis, neural coding principles, and scalable neuromodulation approaches. Publication Trends (2023-2025): Recent works concentrate on deep brain stimulation mechanisms for depression, computational modeling of neural/autonomic dynamics, and machine learning applications in neuroscience. Dominant themes include biomarker discovery for treatment response, neural interoception modulation, probabilistic modeling of latent states, and brain-computer interface taxonomy. Clinical translation of neurotechnology is a consistent focus across publications. Awards & Honors: Elected AIMBE Fellow (2025) NIH BRAIN Initiative Photo/Video Award (2024) Congressional Panelist for BRAIN Initiative 10th Anniversary (2024) Sigma Xi Best Faculty Paper (2024) Neuro Open Science International Prize (2022) W. Howard Ector Outstanding Teacher Award (2019) McDonnell Foundation 21st Century Science Award (2014) NSF CAREER Award (2014) Leadership & Training: Dr. Rozell co-founded Neuromatch, Inc. to build global computational neuroscience communities. He advises Motif Neurotech and the Institute of Neuroethics. His mentees have received prestigious fellowships (Schmidt, Fulbright, NIH K99/R00) and hold leadership positions across academia and industry. Research is supported by NIH BRAIN Initiative, NSF, and private foundations. Labs & Initiatives: Directs the Sensory Information Processing Lab (theoretical neuroscience/neuroengineering) and the Institute for Neuroscience, Neurotechnology & Society (addressing ethical/societal implications). Neuromatch promotes open, accessible computational neuroscience training globally.

• Robotics
• Neurosciences
• Biomechanics
• +4 more

Philippe Souères is a Researcher at LAAS-CNRS (Laboratory for Analysis and Architecture of Systems) within the University of Toulouse. He leads the Gepetto team, focusing on advanced robotics, humanoid motion generation, and the integration of human biomechanics into robotic systems. His work bridges robotics, neuroscience, and biomechanics, emphasizing optimal control, sensor-based systems, and human-robot interaction. Research interests include robot control (optimal control, nonlinear systems), neurosciences (human motor control, sensorimotor integration), and biomechanics (organization of human movement). Notable contributions include models for humanoid locomotion, human-like motion generation using inverse dynamics, and studies on task-dependent balance in humans and robots. Souères has authored/co-authored over 50 publications, including award-winning work on human movement modeling for robotics. He has supervised numerous PhD students and contributed to projects like the EAR initiative (Robot Audition) and the development of the Pyrène humanoid robot. His research also extends to aerial robotics, ducted fan vehicles, and interdisciplinary collaborations with neuroscientists and biomechanics experts. Awards include the Best Conference Paper Award (2010) for integrating human movement invariants into humanoid robotics. Souères is actively involved in academic outreach, appearing in media discussions on robotics ethics and technological innovation.

Edward KimView profile

Associate Professor

• Computer Vision
• Sparse Coding
• Neuromorphic Computing
• +12 more

Edward Kim is an Associate Professor in the Department of Computer Science at Drexel University's College of Computing & Informatics. His research spans computer vision, sparse coding, neuromorphic computing, and AI, with a focus on neuro-inspired machine learning and robust, interpretable models. Research Interests: Computer Vision Sparse Coding and Dictionary Learning Neuromorphic and Spiking Neural Networks Explainable and Adversarially Robust AI Multimodal Learning Medical Image Processing His recent publications highlight a strong trend in developing biologically inspired, robust, and interpretable machine learning models, particularly using sparse coding and spiking neural networks. Themes include adversarial robustness, model confidence calibration, and cross-modal integration. His work often bridges neuroscience and AI, aiming to create more human-like and trustworthy systems. Scientific Awards: NSF CAREER Award (2019) Longsview Fellow (collaborative project, 2021) Dr. Kim advises several graduate students in the SPARSE Lab and has secured significant research funding from the NSF, DARPA, and the Bill & Melinda Gates Foundation. His grants focus on ethical AI, racial bias in ML, and digital health platforms. He also contributes to academic leadership as a Provost Fellow at the Drexel Solutions Institute and co-chair of computer vision tracks at major conferences. Labs and Teams: He leads the SPARSE (SPiking And Recurrent SOFTwarE) Coding Lab, which investigates biologically inspired learning models beyond traditional deep learning. The lab integrates neuroscience principles to improve stability, interpretability, and robustness in AI systems.

Gabriella CasalinoView profile

Assistant Professor

• Computational Intelligence
• eHealth
• Data Stream Mining
• +5 more

Gabriella Casalino is an Assistant Professor at the University of Bari Aldo Moro, Department of Computer Science, and a key researcher at CILAB - Computational Intelligence Lab. Her work focuses on Computational Intelligence methods for interpretable data analysis, particularly in eHealth, Data Stream Mining, and eXplainable Artificial Intelligence (XAI) within medical and educational domains. She has contributed to innovative approaches in smartphone-based health monitoring, fuzzy logic applications, and remote vital sign detection via photoplethysmography. Education : Ph.D. in Computer Science, with advanced training at institutions like Universitat de Girona and Université de Mons. Research Trends : Recent publications highlight applications of evolving granular computing, neuro-fuzzy systems, and explainable AI in hypertension prediction, bipolar disorder monitoring, and educational data analysis. Key subfields include remote health monitoring, medical data streams, and hybrid AI models. Grants : Research funded by AIRC (Italian Cancer Research Foundation), focusing on computational methods for healthcare challenges. Labs & Collaborations : Active in CILAB, collaborating on projects involving mHealth solutions, cardiovascular risk assessment, and intelligent educational systems.

Shantanu ChakrabarttyView profile

Professor

• Applied Physics
• Biomedical & Biological Imaging
• Devices & Circuits
• +5 more

Shantanu Chakrabartty is the Clifford W. Murphy Professor and Vice Dean for Research and Graduate Education at the McKelvey School of Engineering, Washington University in St. Louis. He holds affiliations with the Institute of Materials Science & Engineering . His research focuses on analog and neuromorphic computing, energy-efficient sensors, and biomedical instrumentation. Education: PhD, Johns Hopkins University, 2004 MS, Johns Hopkins University, 2002 BTech, Indian Institute of Technology, 1996 Research Interests: Professor Chakrabartty explores frontiers in neuromorphic integrated circuits, leveraging silicon and hybrid substrates to achieve energy efficiency and high-resolution sensing. His work includes self-powered sensors, machine learning processors, and biomedical tools. Recent projects involve neuro-inspired quantum optimization and low-power radar systems funded by a $1.9M grant. Awards: National Science Foundation CAREER Award (2010) MSU Teacher-Scholar Award (2011) MSU Innovation of the Year Award (2012) Labs & Teams: Former Director of the Adaptive Integrated Microsystems Laboratory at Michigan State University. Current projects emphasize interdisciplinary collaborations in materials science and biomedical engineering.