Find Academic Supervisors

Xiaojia Shelly ZhangView profile

Associate Professor

• Topology Optimization
• Additive Manufacturing
• Structural Engineering
• +7 more

Dr. Xiaojia Shelly Zhang is an Associate Professor and David C. Crawford Faculty Scholar in the Department of Civil and Environmental Engineering at the University of Illinois at Urbana-Champaign's Grainger College of Engineering. She also holds affiliate appointments in Mechanical Science and Engineering and is a Faculty Fellow at the National Center for Supercomputing Applications (NCSA). Dr. Zhang directs the MISSION (Multi-functional Structures and Systems design Optimization) Laboratory, where she leads research at the intersection of computational mechanics, materials science, and structural engineering. Her educational background includes a B.S. (Honors) and M.S. in Civil Engineering from UIUC, followed by a Ph.D. in Structural Engineering from Georgia Tech. Dr. Zhang's research focuses on topology optimization, additive manufacturing, and multi-scale materials design, with applications spanning from civil infrastructure to soft robotics and metamaterials. Dr. Zhang's publication record demonstrates consistent excellence with numerous high-impact papers in Science Advances, Nature Communications, and top mechanics journals. Her work shows a clear progression toward increasingly complex multi-physics problems, integrating machine learning, uncertainty quantification, and sustainability considerations. Recent publications (2024-2025) emphasize practical applications in healthcare, sustainable infrastructure, and advanced manufacturing. National Science Foundation CAREER Award recipient DARPA Young Faculty Award and Director's Fellowship Multiple ASME/ASCE early career awards including Hughes Young Investigator and Leonardo da Vinci Award UIUC Campus Distinguished Promotion Award (2025) Consistent teaching excellence recognition since 2020 Dr. Zhang actively mentors students and postdocs in her MISSION Laboratory, with several advisees appearing as first authors on high-impact publications. Her research is supported by major grants from NSF, DARPA, AFOSR, and ARO. She serves on the Executive Committee of the International Society for Structural and Multidisciplinary Optimization and holds editorial positions for the Journal of Applied Mechanics and Structural and Multidisciplinary Optimization.

Frank NeumannView profile

Professor

• Artificial Intelligence
• Bio-inspired Computing
• Cybersecurity
• +3 more

Frank Neumann is a Professor in the School of Computer Science at The University of Adelaide. He leads the Optimisation and Logistics group and holds affiliations with the University of Melbourne as an Honorary Professorial Fellow. His expertise spans artificial intelligence, bio-inspired computing, and optimization. He has held leadership roles, including interim Head of School and Associate Dean (Research) in the Faculty of Engineering, Computer and Mathematical Sciences. Education: PhD in Computer Science (2006), Diplom (2002) from Christian-Albrechts-University of Kiel, Germany. Postdoctoral research at Max Planck Institute for Informatics (2006-2010). Research interests focus on evolutionary computation, cybersecurity, renewable energy optimization, and supply chain management. He has contributed to foundational theory in randomized search heuristics and practical applications in logistics and healthcare. Recent projects include NHMRC grants on personalized time-use interventions and ARC-funded initiatives in optimization and complex resource management. He has authored influential books on bio-inspired computation and theoretical evolutionary algorithms. Scientific Awards: ARC Future Fellowship, Humboldt Fellowship, NHMRC Grants. Grants: ARC Industrial Transformation Training Centre, Research Consortia Program.

Federico CorradiView profile

Assistant Professor

• Neuromorphic Computing
• Spiking Neural Networks
• Energy-Efficient Computing
• +7 more

Federico Corradi is an Assistant Professor in the Electrical Engineering Department at Eindhoven University of Technology (TU/e), where he leads the Neuromorphic Edge Computing Systems Lab. He also holds the position of EAISI Foundational Assistant Professor at the Eindhoven Artificial Intelligence Systems Institute. His research focuses on neuromorphic computing and engineering, spanning from efficient computational models to novel microelectronic architectures for deep learning and brain-inspired algorithms. Dr. Corradi's academic background includes a Ph.D. in Neuroinformatics from the University of Zurich and an international Ph.D. from the ETH Neuroscience Centre Zurich (2015). Prior to joining TU/e in 2022, he worked at IMEC in the Netherlands (2018-2022) where he started a group focused on neuromorphic IC design. Before that, he was with Inilabs, a spin-off from the Institute of Neuroinformatics, developing event-based cameras and neuromorphic processors (2015-2018). His research interests center around understanding natural neural computation principles to develop energy-efficient sensing and computing technologies. Key areas include: Neuromorphic computing and engineering Spiking neural networks Event-based vision systems Energy-efficient microelectronic architectures Applications in robotics, machine vision, and biomedical signal analysis Dr. Corradi's recent publications demonstrate a strong focus on practical implementations of neuromorphic systems for edge applications. His work shows consistent innovation in translating theoretical neural models into efficient hardware implementations, with particular emphasis on handling temporal data and developing brain-realistic computational models. The research spans multiple domains including radar signal processing, optical computing, and error correction systems. He serves as an active review editor for Frontiers in Neuromorphic Engineering, IEEE, and IOP Science journals, and participates in technical program committees for several machine learning and neuromorphic conferences including ICTOPEN, AICAS, AIAI, ICONS, NEWCAS, DSD, and EUROMICRO. His work has received significant media attention with coverage from multiple news outlets and social media mentions. Dr. Corradi leads the Neuromorphic Edge Computing Systems Lab, which bridges theoretical neuroscience with practical engineering challenges, creating novel solutions for real-world problems. The lab's research has practical applications in robotics, autonomous systems, and biomedical technologies, reflecting his commitment to developing energy-efficient computing solutions that can operate effectively at the edge.

Bart DhoedtView profile

Professor

• Distributed Machine Learning
• Edge Computing
• Neuromorphic Computing
• +2 more

Bart Dhoedt is a Professor at Ghent University's Department of Information Technology, affiliated with the Internet Technology and Data Science Lab (IDLab). He teaches courses on algorithms, advanced programming, software development, and distributed systems. His research bridges distributed machine learning, edge computing, and hardware-efficient AI. Research interests focus on Distributed Machine Learning (parallel processing across systems), Edge Computing (decentralized data processing), Neuromorphic Computing (brain-inspired hardware), Sensor Fusion (multimodal data integration), and Representation Learning (efficient feature extraction). Recent publications emphasize active inference, robotic navigation, and object-centric AI. Publications (2017-present) show strong trends in robotic autonomy (navigation, manipulation), active inference (Bayesian modeling), and multimodal world models , with applications in industrial automation and cognitive systems. No awards or grants are documented. He leads research at IDLab, collaborating on embedded AI and distributed systems. No advised students or external affiliations are mentioned.

Winston TimpView profile

Associate Professor

• Sequencing Technologies
• Epigenomics
• Genomics
• +3 more

Winston Timp is an Associate Professor of Biomedical Engineering at the Johns Hopkins University Whiting School of Engineering, with joint appointments in the Department of Molecular Biology and Genetics, the Division of Infectious Diseases at the School of Medicine, and the Department of Genomic Medicine. His research focuses on developing sequencing technologies to explore biological systems and clinical diagnostics, integrating biophysics, molecular biology, and computational methods. Education: Timp earned bachelor’s degrees in Biochemistry, Chemistry, Physics, and Electrical Engineering from the University of Illinois at Urbana-Champaign (2001–2002), followed by a PhD in Electrical Engineering from MIT (2007). He completed postdoctoral training at Johns Hopkins, specializing in cancer epigenetics, before joining the faculty in 2013. Research Interests: Timp’s lab develops tools for epigenome and genome analysis, including nanopore sequencing applications for diagnosing infectious diseases, characterizing cancer epigenomes, and sequencing complex genomes like those of hummingbirds and giant sequoias. His work bridges engineering and biology, addressing challenges in clinical diagnostics and fundamental biology. Notable Contributions: He holds two patents, led grants totaling $5 million from the NIH, and contributed to the first complete human genome sequence. His team’s projects include the redwood genome, hummingbird transcriptome, and studies on CHO cell genomics. Awards: Timp received the Johns Hopkins Catalyst Award (2016) and Discovery Awards (2015–2017) for collaborative research. He sits on editorial boards for Epigenetics Insights and is an affiliate of bioRxiv. Lab Activities: The Timp Lab collaborates on projects like the Human Frontier Science Program grant on hummingbird metabolism and the NSF IUCRC on CHO cell epigenomics. Positions are available for postdocs, graduate students, and technicians.

Ryan TungView profile

Associate Professor

• Vibrations and Dynamics
• Microsystems Engineering
• Fluid-Structure Interactions
• +3 more

Ryan Tung is an Associate Professor in the Department of Mechanical Engineering at the University of Nevada, Reno. His research focuses on advanced microscopy techniques and microsystems engineering, particularly in atomic force microscopy (AFM) sensor design and dynamics. He leads studies in vibrations, fluid-structure interactions, and viscoelastic material characterization at micro- and nanoscales. His work emphasizes developing novel sensor geometries for enhanced resolution and accuracy in AFM applications, including contact resonance spectroscopy and biomimetic sensing systems. Recent contributions address dynamic phenomena in high-speed AFM, hydrodynamic effects in microscale environments, and optimization of cantilever dynamics for precise material property measurements. Key innovations include plate-like sensors for nanometrology, curvature-based stiffening mechanisms, and methodologies for mitigating unexplained artifacts in AFM imaging. His interdisciplinary approach bridges mechanical engineering principles with MEMS fabrication and computational modeling techniques. Research collaborations span fluid dynamics, aerospace applications of bio-inspired sensors, and advanced material characterization under varying environmental conditions. Current efforts aim to improve AFM sensor versatility for biomedical and industrial nanoscale applications.

Floris Van BreugelView profile

Assistant Professor

• Robotics
• Biomechanics
• Machine Learning
• +5 more

Floris van Breugel is an Assistant Professor in the Department of Mechanical Engineering at the University of Nevada, Reno (UNR), within the College of Engineering. His interdisciplinary research focuses on studying insects to develop bio-inspired robotics and control systems. The lab employs advanced techniques like real-time tracking, high-speed video, and virtual reality to analyze animal behavior, then applies machine learning and control theory to translate these insights into robotic systems. Key areas include multi-sensory integration in insects, odor plume tracking, and vision-based navigation. Education: PhD in Control and Dynamical Systems from Caltech (2014), supported by NSF and Hertz fellowships. Postdoctoral research at University of Washington with Jeff Riffell and J Nathan Kutz under Sackler and Moore-Sloan-WRF fellowships. Joined UNR in 2019 and received a Sloan Fellowship in Neuroscience (2020). Research Interests: Robotics, biomechanics, machine learning, neuroscience, ecology, and data science. The lab integrates engineering principles with biological insights to create robust robotic control systems, emphasizing sensory integration and environmental adaptation. Awards: Sloan Fellowship in Neuroscience (2020) Sackler Fellowship in Biophysics Moore-Sloan-WRF Fellowship in Data Science NSF and Hertz Graduate Fellowships Advising & Grants: Actively hiring PhD students and undergraduates in data-driven robotics, multi-sensory integration, and bio-inspired systems. Lab activities include collaborative projects with ecologists and neuroscientists to study insect behavior in natural environments. Labs & Teams: Leads the Van Breugel Lab at UNR, emphasizing interdisciplinary collaboration across engineering, biology, and data science. The lab's work spans from fundamental neuroscience to applied robotics, with a focus on Reno's growing tech and outdoor innovation ecosystem.

David Feil-SeiferView profile

Professor

• Socially Assistive Robotics
• Human-Robot Interaction
• Machine Learning
• +2 more

David Feil-Seifer is a Professor of Computer Science & Engineering at the University of Nevada, Reno. He serves as the Founding Director of the Autonomous Robotics Center of Nevada (ARCoN) and Graduate Director for the Robotics M.S. program. He holds a B.S. from the University of Rochester, and M.S./Ph.D. from the University of Southern California, with postdoctoral training at Yale University. His research focuses on Socially Assistive Robotics (SAR), socially-aware navigation, and long-term human-robot interaction. Key projects include AI solutions for speech-language pathology shortages, socially-aware navigation in public spaces, and robotics for post-stroke rehabilitation and autism support. His publications primarily explore human-robot interaction frameworks, educational robotics, and machine learning applications in healthcare and environmental monitoring. Recent work shows strong emphasis on accessible robotics education and healthcare applications. USC VSoE Best Dissertation Award NSF/CRA Computing Innovation Fellowship Mellon Award for Mentoring Order of Arete He has mentored over 100 undergraduate students and leads the Socially Assistive Robotics Group (SARG). Current grants include NSF-funded projects on robot motion legibility and AI for exceptional education. Dr. Feil-Seifer directs robotics laboratories focused on human-centered AI and multi-robot systems for disaster response and healthcare applications.

Urban FaselView profile

Lecturer

• Machine Learning
• Autonomous Systems
• Aeroelasticity
• +2 more

Urban Fasel is a Lecturer in Data-Driven Aerospace Engineering at Imperial College London's Department of Aeronautics. His research integrates machine learning with aerospace systems for applications in autonomous flight, aeroelastic control, and composite structures. Research spans co-design optimization, adaptive structures, and data-driven control methods, with particular focus on morphing wings for airborne energy systems and flapping-wing micro aerial vehicles. Recent work explores sparse modeling techniques for nonlinear dynamics discovery and robust control under uncertainty. Publications emphasize computational efficiency in aeroelastic modeling, reinforcement learning for flow control, and Bayesian approaches for system identification. Emerging themes include interpretable machine learning for distributed systems and morphing wing optimization for eVTOL applications.

Chandrajit BajajView profile

Professor

• Computational Biology
• Imaging Sciences
• Computational Geometry
• +5 more

Chandrajit Bajaj is a Professor of Computer Science and Computational Applied Mathematics Chair in Visualization at the University of Texas. He leads the Center for Computational Visualization (CVC) within the Oden Institute of Computational Engineering and Sciences. His research focuses on actionable intelligence, machine learning, computational biology, imaging sciences, and visualization. He holds a B.Tech. from IIT Delhi and a Ph.D. from Cornell University. Professional Experience: Professor at University of Texas since 1997; Director of CVC and Image Analysis & Visualization Center. Previously held roles at Purdue University and visiting positions at Cornell. Awards: AAAS, ACM, IEEE, and SIAM Fellowships; Distinguished Alumnus of IIT Delhi; ICES-Sid Moncreif Awards; multiple best paper awards. Research: Develops algorithms for geometric modeling, bioinformatics, and predictive data sciences. Current projects include microscopy optimization, generative design, and nature-inspired models. Sponsors include NSF and NIH. Education: B.Tech. in Electrical Engineering (IIT Delhi), M.S./Ph.D. in Computer Science (Cornell).

Massimo VergassolaView profile

Professor

• Biophysics
• Embryonic Development
• Animal Navigation
• +2 more

Massimo Vergassola is Professor of Physics at École Normale Supérieure (ENS-PSL) and CNRS Research Director, leading the Quantitative Biology Center. His research bridges physics and biology, focusing on navigation strategies, embryonic development, and collective behavior. Research combines theoretical modeling and experimental approaches to study biological information processing, from olfactory navigation in insects to glider flight optimization. Recent work examines developmental waves in Drosophila embryogenesis and sensory-motor integration in navigation. Awards include the Grand Prix EADS from the French Academy of Sciences and CNRS Bronze Medal. Research is supported by ANR, NIH, Simons Foundation, and Fondation de la Recherche Médicale.

Michael AmirView profile

Research Fellow

• Multi-Agent Systems
• Swarm Robotics
• Machine Learning
• +3 more

Dr. Michael Amir is a Research Fellow in the Prorok Lab at the University of Cambridge's Department of Computer Science and Technology, affiliated with Trinity College. His research focuses on multi-agent systems and swarm robotics, emphasizing scalable algorithms with provable safety guarantees at the intersection of machine learning and formal mathematics. He explores how interactions among autonomous systems can enhance decision-making while ensuring safety and efficiency in unknown environments. His work includes algorithm design for swarm dispersion, energy optimization, and fault-tolerant systems. Key areas of research include cooperative multi-agent learning, bio-inspired robotics (e.g., ant and locust models), and stigmergy-based coordination. He has published in top venues such as IEEE Transactions on Robotics and explores applications like environmental monitoring and grid patrolling. His articles highlight trends in optimizing robotic swarms for energy efficiency, minimizing travel, and achieving uniform dispersion. He collaborates on projects involving decentralized task allocation and probabilistic pursuit strategies. Amir actively engages with the academic community, contributing to both theoretical advancements and practical implementations in autonomous systems. His current affiliation and location at the William Gates Building, University of Cambridge, underscore his role in fostering innovation in robotics and AI safety. He is open to collaborations and discussions on multi-agent systems and AI ethics.

Matthew JohnsonView profile

Professor

• Algorithmic Graph Theory
• Combinatorial Optimization
• Swarm Robotics
• +2 more

Matthew Johnson is Professor and Head of Department in Computer Science at Durham University. He leads research in algorithmic graph theory and combinatorial optimization through the Algorithms and Complexity research group. His work addresses fundamental problems in graph theory with applications in network design and optimization. Professor Johnson coordinates large-scale research projects including RoboRoyale (€3.27M) and BioDiMoBot (€8M), focusing on swarm robotics and bio-inspired systems. He teaches courses on Algorithms and Data Structures and Networks and their Structure. His research group develops novel approaches to graph partitioning, swarm coordination, and computational complexity. Recent work includes breakthroughs in understanding the complexity of graph transversals and forbidden subgraph problems, with publications in Algorithmica and Theoretical Computer Science.

Dayou LiView profile

Professor

• Robotics
• Artificial Intelligence
• Nanotechnology
• +5 more

Professor Dayou Li is a Professor of Robotics and Director of the Institute for Research in Applicable Computing (IRAC) at the University of Bedfordshire. He holds a BEng and MSc from Northern Jiaotong University (China) and a PhD from Cardiff University. His research focuses on robotic systems, AI, and nanotechnology, with expertise in multiagent systems, fuzzy logic, neural networks, and automatic control. He has led EU-funded projects under FP7/H2020 and collaborates internationally with institutions in Australia, Canada, China, Europe, Japan, and South Korea. Professor Li's teaching expertise includes Robotics, Artificial Intelligence, Software Engineering, and Programming. His research interests span e-Learning, machine learning, and robotics applications in healthcare and smart environments. He coordinates PhD and part-time student supervision, fostering interdisciplinary innovation through projects like the 5G-ERA platform for connected robotics and laser interference nanomanufacturing techniques for advanced materials. His work integrates robotics with emerging technologies such as swarm intelligence, SLAM algorithms for weak-environment navigation, and oxy-fuel combustion optimization for sustainable energy systems. He actively contributes to advancing robotic applications in smart cities, healthcare assistive devices, and nanoscale manufacturing processes.

Elhadj BenkhelifaView profile

Professor

• Cloud/Edge Computing
• Bio-Inspired Computing
• Cybersecurity
• +7 more

Elhadj Benkhelifa is a full Professor of Computer Science and Digital Innovations at Staffordshire University, leading the Head of Professoriate role to advance university strategies. He founded the Smart Systems, AI and Cybersecurity Research Centre (SSAICS), directing 15 staff and 23 PhD students. Previously, he held roles at Cranfield University and the University of the West of England. His interdisciplinary research focuses on cybersecurity, cloud/edge computing, bio-inspired systems, and AI applications in healthcare and digital transformation. He is a Visiting Professor at Universities of Suffolk, Lyon3, and Paris 8, and chairs the IEEE UK&I Section's Education & STEM office. Education includes a PhD in Artificial-Life (Bio Inspired Evolvable and Self-Healing Systems) from UWE Bristol Robotics Lab, alongside postgraduate certifications in research supervision and higher education. His work bridges academia and industry, addressing challenges in cybersecurity, IoT, and healthcare through applied research. Research interests span cloud computing paradigms, semantic technologies, and AI ethics, with over 150 publications. His recent articles emphasize AI-driven cybersecurity solutions, quantum computing applications, and medical diagnostics using deep learning. He actively contributes to open-access publishing and advises on national cybersecurity strategies. Awarded Fellow of the UK Higher Education Academy, he leads impactful projects including malware detection systems, semantic knowledge graphs for public health, and blockchain-based authentication frameworks. His teaching spans undergraduate to PhD levels, with over 100 supervised projects. He also serves on advisory boards for cybersecurity initiatives and Staffordshire Police's evidence-based practices.