Find Academic Supervisors

José Santos-VictorView profile

Professor

• Computer Vision
• Robotics
• Cognitive Systems
• +4 more

Prof. José Santos-Victor is a Full Professor at the Department of Electrical & Computer Engineering, Instituto Superior Técnico, Universidade de Lisboa. He leads the RBCog-Lab research infrastructure and coordinates the international PhD Program RBCog - Robotics, Brain and Cognition. His research focuses on biologically-inspired cognitive vision and humanoid robotics, addressing topics like vision-based navigation, human-robot interaction, and assistive robotics for autism therapy. Education: Ph.D. in Electrical & Computer Engineering, Instituto Superior Técnico (1995) M.Sc. in Electrical & Computer Engineering, Instituto Superior Técnico (1991) Licenciatura in Electrical & Computer Engineering, Instituto Superior Técnico (1988) Research Interests: Computer Vision, Robotics, Humanoid Systems, Biologically-Inspired Algorithms, and Assistive Technologies. Awards: IEEE Fellow (2015) for contributions to biologically-inspired cognitive vision ELLIS Fellow (2021) Correspondent Member of the Academy of Sciences of Lisbon Leadership Roles: President of ISR-Lisboa (2015-2022) Chair, Department of Electrical & Computer Engineering, IST (2021-24) Coordinator of LARSyS and Associated Laboratory Grants & Labs: Leads the RBCog-Lab, part of Portugal's Research Infrastructure Roadmap, and oversees projects like the Copyrobot therapy platform for ASD children.

Dan CharbonneauView profile

Assistant Professor

• Social Insect Behavior
• Biostatistics
• Applied Data Mining
• +3 more

Dan Charbonneau is an Assistant Professor of Practice in the College of Information Science at the University of Arizona, specializing in behavioral ecology and data science. His research focuses on social insect behavior, biostatistics, and applying data engineering methods to biological questions. He holds a PhD in Entomology from the University of Arizona and has prior postdoctoral experience at Arizona State University and the University of Pennsylvania. Education: PhD in Entomology, University of Arizona His research explores how decentralized systems like ant colonies allocate tasks, particularly examining 'inactive' workers and their roles in colony efficiency. He develops tools like ABCTracker to analyze collective behavior patterns, integrating biostatistics and computer vision. Research Trends: His articles consistently investigate task allocation mechanisms in social insects, emphasizing spatial interactions, aging effects, and bio-inspired technology applications. Key themes include colony resilience, sensor networks, and modeling collective behavior. Grants & Advising: His work bridges ecological studies with data science, though specific grants or advised students are not detailed here. He contributes to the Environmental, Health and Biological Sciences program within the College of Information Science. Labs/Teams: Engages in collaborative projects applying data engineering to biological systems, though specific lab affiliations are unspecified.

Angeles SallesView profile

Assistant Professor

• Neuroethology
• Auditory Processing
• Animal Communication
• +1 more

Angeles Salles is an Assistant Professor in Biological Sciences at UIC's College of Liberal Arts and Sciences, directing research on auditory neuroethology using bat models. Her lab investigates neural mechanisms of complex sound processing through electrophysiological and behavioral approaches. Research explores: Auditory processing hierarchies in mammalian brains Neurobiological basis of social behaviors Vocal learning mechanisms in echolocating species Publications demonstrate consistent focus on acoustic signal processing across behavioral contexts, with recent emphasis on computational neuroethology and biomimetic applications. Fieldwork complements laboratory studies examining bat communication in natural habitats.

Iman BorazjaniView profile

Associate Professor

• Computational Fluid Dynamics
• Fluid-Structure Interaction
• High-Performance Computing
• +2 more

Iman Borazjani is Associate Professor of Mechanical Engineering and Sallie and Don Davis '61 Faculty Fellow at Texas A&M University. He holds a Ph.D. in Mechanical Engineering from the University of Minnesota (2008), M.S. from Georgia Tech (2005), and B.S. from Sharif University of Technology (2002). His research focuses on computational fluid dynamics, fluid-structure interaction, high-performance computing, cardiovascular flows, and bio-inspired propulsion. Professor Borazjani has received prestigious recognitions including AIAA Associate Fellow (2021), ASME Fellow (2020), NSF CAREER Award (2015), and Fulbright Scholarship (2017-2018). He directs the Scientific Computing and Biofluids Lab, developing advanced simulation tools for complex fluid-structure interaction problems. His recent work demonstrates three primary thrusts: 1) Cardiovascular engineering with focus on heart valve dynamics and blood coagulation; 2) Bio-inspired propulsion systems using high-fidelity simulations of fish swimming; and 3) Active flow control techniques including traveling wave morphing surfaces for aerodynamic applications. His group develops specialized computational methods including curvilinear immersed boundary techniques, parallel large eddy simulations, and machine learning approaches for inverse problems in biomechanics.

Nina MiolaneView profile

Assistant Professor

• Geometric Artificial Intelligence
• Machine Learning
• Computational Neuroscience
• +3 more

Nina Miolane is an Assistant Professor in the Department of Electrical & Computer Engineering at the University of California, Santa Barbara (UCSB). She is affiliated with the Geometric Intelligence Lab and the Center for Aging and Longevity Studies (CALS). Her research focuses on Geometric Artificial Intelligence (Geometric AI), combining machine learning with geometric and topological principles to study brain health, neural networks, and biological systems. Miolane’s work spans AI-driven medical imaging analysis, Riemannian geometry applications, and the development of interpretable neural network models. Her research interests include topological deep learning, geometric deep learning, computational neuroscience, and shape analysis in biosciences. She leads initiatives in developing frameworks like TopoX and Geomstats , which enable machine learning on non-Euclidean domains. Miolane’s projects often intersect with aging research, leveraging AI to understand brain health through MRI data and neural recordings. Publications highlight contributions in manifold optimization, topological neural networks, and applications in cancer cell morphology analysis. She has received grants such as the NSF CAREER Award for advancing shape learning in biosciences. Miolane collaborates across disciplines, integrating mathematics, computer science, and biomedical engineering to advance interpretable AI systems.

Sunil KhatriView profile

Professor

• VLSI Design
• Secure Computing Systems
• Machine Learning Hardware
• +7 more

Sunil Khatri is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, affiliated with the College of Engineering and the Computer Engineering and Systems Group. He holds a Ph.D. from the University of California, Berkeley (1999), an M.S. from the University of Texas at Austin (1989), and a B.S. from the Indian Institute of Technology Kanpur (1987). His academic progression includes roles as Assistant Professor (2000–2004) at the University of Colorado Boulder and Associate Professor (2010–2015) at Texas A&M. Educational Background: Ph.D., University of California, Berkeley, 1999 M.S., University of Texas at Austin, 1989 B.S., Indian Institute of Technology Kanpur, 1987 Research Interests: Intelligent and Secure Computing Systems: Focused on hardware architectures for machine learning (e.g., neuromorphic circuits, NoCs, sub-threshold circuits) and secure hardware design (blockchain, physically unclonable functions). Logic Synthesis and Applications: Includes VLSI CAD, gene regulatory network modeling, noise-based logic, and Boolean satisfiability (SAT) solvers. Interdisciplinary Extensions: Explores routing table compression, radar signal processors, optical networking (DWDM), and LDPC decoders. Research Trends in Publications: Recent work emphasizes neuromorphic hardware acceleration (e.g., CapsPDNet for insulator discharge prediction, FFIR filters for IoT), secure protocols (multi-factor authentication frameworks), and energy-efficient circuits (flash-based designs, low-voltage regulators). His contributions span VLSI, machine learning, and cybersecurity, often leveraging FPGA/GPU acceleration. Grants & Labs: Leads projects in the Computer Engineering and Systems Group, focusing on secure computing, machine learning hardware, and interdisciplinary VLSI applications. Active in grant-driven research with a focus on practical implementations in industry.

Yoonsuck ChoeView profile

Professor

• Computational Neuroscience
• Neural Networks
• Neuroevolution
• +9 more

Yoonsuck Choe is a Professor in the Department of Computer Science and Engineering at Texas A&M University and Director of the Brain Networks Laboratory. He has held roles including Corporate Vice President at Samsung Research (2017–2019) and served as a Fellow in the College of Engineering. His expertise spans computational neuroscience, brain imaging, neuroevolution, and deep learning, with a focus on temporal brain dynamics, sensorimotor learning, and AI-driven neuroimaging analysis. Education : Ph.D. (2001), M.A. (1995) from University of Texas at Austin; B.S. (1993) from Yonsei University. His research interests include predictive neural dynamics, consciousness, thalamocortical function, and biologically inspired AI systems. Recent publications emphasize energy-efficient neural networks, tool-use evolution, and connectomics. He has received awards for teaching excellence and research innovation, including the 30th Anniversary Distinguished Alumni Award from Yonsei University (2013). Advising and grants: Prof. Choe has guided over 20 students in their theses and dissertations, focusing on topics like motor learning, neural integration, and medical imaging. His Samsung tenure involved AI core team leadership and high-throughput brain dynamics automation. Grants include work on KESM brain atlas development and neurovascular modeling. Lab and teams: Directs the Brain Networks Lab (Choe Lab) at Texas A&M, exploring brain networks, predictive systems, and AI applications. Collaborates with interdisciplinary teams in neuroscience and biomedical engineering.

Dylan ShellView profile

Professor

• Distributed AI
• Multi-Robot Systems
• Robotics
• +6 more

Dylan Shell is a Professor in the Department of Computer Science & Engineering at Texas A&M University, affiliated with the College of Engineering. His research focuses on distributed AI, multi-robot systems, and autonomous planning. He holds a B.Sc. from the University of the Witwatersrand and advanced degrees from the University of Southern California. Education: PostDoc in Robotics, University of Southern California (2009) Ph.D. Computer Science, University of Southern California (2008) MSc Computer Science, University of Southern California (2006) B.Sc. (Hons) Computer Science, University of the Witwatersrand (2001) B.Sc. Computational & Applied Mathematics and Computer Science, University of the Witwatersrand (2000) Research Interests: Distributed AI and multi-robot coordination Biologically-inspired systems for robotics Autonomous planning and decision-making Crowd modeling and multi-agent systems Recent Research Trends: His work emphasizes sensor-based planning, resilient systems, and privacy-preserving robotics. Recent papers explore optimal planning under uncertainty, combinatorial filter reduction, and multi-robot task allocation. His research often bridges theory and practice, with applications in autonomous systems, disaster response, and human-robot interaction. Awards: Montague-CTE Scholar for teaching excellence (2013–2014) Faculty Service Excellence Award (2010) Advising and Grants: Shell advises a diverse cohort of graduate and undergraduate students in the Distributed AI Robotics Lab . He has led NSF-funded projects on adaptive sampling, contact models, and privacy-aware multi-robot systems. His lab collaborates with industry partners like Intelligent Automation, Inc. Labs & Teams: He directs the Distributed AI Robotics Lab, which develops algorithms for autonomous systems and experiments in multi-robot coordination. The lab emphasizes interdisciplinary work, blending robotics, AI, and control theory.

Ankit PatelView profile

Assistant Professor

• Machine Learning
• Computational Neuroscience
• Deep Learning
• +7 more

Ankit Patel is an Assistant Professor at Baylor College of Medicine (Department of Neuroscience) and Rice University (Department of Electrical and Computer Engineering). He holds a joint appointment and a courtesy appointment in Computer Science at Rice. His research bridges machine learning, computational neuroscience, and particle physics. Patel earned his Ph.D., M.S., and B.S. in Applied Mathematics and Computer Science from Harvard University, supported by an NSF Graduate Fellowship. His work focuses on probabilistic theories of deep learning, neurally-inspired computation, and applications in medical imaging, particle physics (e.g., Large Hadron Collider collaborations), and neuroengineering. He developed the Deep Rendering Model (DRM), linking deep convolutional networks and decision forests to first principles. Key contributions include event-driven representations for recurrent neural networks, generative convolutional nets for unlabeled data, and collaborations with neuroscientists to align machine learning with biological neural networks. His recent industry experience includes algorithmic trading and missile defense systems, now applied to academic research. Education : Ph.D./M.S./B.S. in Applied Mathematics & Computer Science, Harvard University (2008, 2002) Awards : NSF Graduate Fellowship Research Themes : Deep Learning Theory, Neuroscience-ML Integration, Quantum Computing Applications

Thomas SerreView profile

Professor

• artificial intelligence
• computational neuroscience
• computer vision
• +2 more

Thomas Serre is a distinguished academic serving as the Thomas J. Watson, Sr. Professor of Science at Brown University, holding joint appointments in Cognitive, Linguistic, and Psychological Sciences and Computer Science. He is affiliated with the Carney Institute for Brain Science and the Data Science Institute. His research focuses on understanding neural computations underlying visual perception, bridging biological and artificial vision systems. He leads the Center for Computation & Visualization and co-directs the Center for Computational Brain Science. Dr. Serre earned a Ph.D. in Neuroscience from MIT (2006) and an MSc in EECS from Télécom Bretagne (2000). His work has been featured in prominent journals and media outlets like the BBC and Scientific American. He has received prestigious awards, including the PAMI Helmholtz Prize (2021) and DARPA Young Faculty Award (2014). His research explores visual reasoning, neural oscillations, and deep learning applications in healthcare and paleobotany. He collaborates with experts in neuroscience, engineering, and computer science, securing funding from NSF, ONR, and DARPA. Teaching includes courses on computational methods, deep learning, and programming. Affiliations: Carney Institute for Brain Science, Data Science Institute Key Projects: Brain-inspired visual reasoning models, AI for paleobotanical analysis, spinal neuromodulation interfaces Awards: 2022 PAMI Mark Everingham Prize, NSF Early Career Award (2013), International Chair in AI (2019) Grants: ONR, NSF, DARPA (e.g., $2.5M for visual reasoning models) His lab actively recruits students and postdocs, emphasizing machine learning and neuroscience intersections. Collaborators include David Borton and Justin Fallon, advancing interdisciplinary research in AI and neurotechnology.

William H WarrenView profile

Professor

• Perception and Action
• Spatial Navigation
• Visual Control of Locomotion
• +3 more

William H. Warren is Chancellor's Professor of Cognitive and Psychological Sciences at Brown University. He leads the Virtual Environment Navigation Lab (VENLab) and has held this position since 1982. His research focuses on the visual control of human locomotion and navigation, applying dynamical systems theory and virtual reality techniques. He has served as Chair of Brown's Department of Cognitive and Linguistic Sciences (2002-2010) and remains active in academic leadership roles. Education: BA (Hampshire College, 1976), PhD (Experimental Psychology, University of Connecticut, 1982) Postdoctoral Training: University of Edinburgh (1983) Research interests include human crowd behavior, obstacle avoidance, and the interplay between vision and action. Key contributions involve modeling collective motion using agent-based simulations and analyzing spatial navigation through path integration and cognitive graphs. His work bridges basic science and applied domains like robotics and urban design. Notable awards include the Ken Nakayama Medal (2023), NIH Career Development Award, and Brown's Elizabeth Leduc Teaching Award. Active grants focus on crowd dynamics and locomotion control, totaling over $3.5M in funding since 2019. Teaching includes courses such as Perception and Action and Core Concepts in Cognitive Sciences . Academic service includes leadership roles in the International Society for Ecological Psychology and editorial boards in top journals.

Jessica ShangView profile

Associate Professor

• Fluid Dynamics
• Biological Fluid Systems
• High-Energy Density Hydrodynamics
• +4 more

Jessica Shang is an Associate Professor in the Department of Mechanical Engineering at the University of Rochester, affiliated with the Hajim School of Engineering & Applied Sciences. She also serves as a Staff Scientist at the Laboratory for Laser Energetics (LLE). She holds a PhD from Princeton University (2015). Her research focuses on fluid dynamics across diverse systems, including brain fluid mechanics, high-energy density (HED) hydrodynamics, and biologically-inspired hydrodynamic sensing. She collaborates extensively with institutions like the University of Rochester Medical Center, LLE, and SLAC National Accelerator Laboratory. Her work integrates experimental and computational methods to explore turbulence, fluid-structure interactions, vortex dynamics, and HED transport phenomena. Recent studies include biomimetic sensor development, X-ray photon correlation spectroscopy for multiscale flows, and laser-driven shockwave dynamics. Her research bridges fundamental physics with biomedical applications, particularly in understanding cerebral fluid dynamics and developing novel diagnostic techniques for extreme environments. Key collaborations involve multi-institutional efforts to study HED flows, perivascular brain fluid mechanics, and turbulence characterization using advanced imaging. Her experimental setups often utilize ultrafast X-ray microscopy and laser-driven platforms to probe material behavior under extreme conditions. She maintains an active research program with a strong emphasis on translational applications in biomedical engineering and energy sciences.

Shigang YueView profile

Professor

• Artificial Intelligence
• Computer Vision
• Robotics
• +4 more

Professor Shigang Yue is a Professor of Computer Science at the University of Leicester's Computing and Mathematical Sciences department. His research focuses on artificial intelligence, computer vision, robotics, neuroscience, and bio-inspired systems. He has published over 200 papers and led EU-funded projects. He holds a PhD from Beijing University of Technology (1996) and was an Alexander von Humboldt Research Fellow (2000-2001). Research interests include bio-inspired visual neural networks, autonomous robotics, medical image analysis, machine learning, and swarm intelligence. He has coordinated EU FP7 and Horizon 2020 projects and examined over 20 PhD theses. His work bridges biological systems and robotics, emphasizing collision avoidance, motion detection, and swarm robotics. Notable contributions include the I2Bot platform for insect navigation simulation, bio-inspired collision detection models (e.g., LGMD), and frameworks for small target tracking. He is an EPSRC peer reviewer and editorial board member for international journals. Labs and teams: Active in developing bio-inspired robotics systems and collaborative platforms like Colcos φ for swarm robotics. His group welcomes PhD and visiting students in AI and robotics.

Jaap den ToonderView profile

Professor

• Microsystems Engineering
• Microfluidics
• Biomedical Microdevices
• +4 more

Jaap den Toonder is a Full Professor and Chair of the Microsystems research Section at Eindhoven University of Technology (TU/e). His research focuses on innovative microsystems, including microfluidic chips, biomedical microdevices, and soft microrobotics, inspired by biological principles. He leads the Microfab/lab, a state-of-the-art facility for microfluidic device fabrication and analysis. Den Toonder's work integrates engineering and biology, emphasizing applications in healthcare and technology. Education: MSc in Applied Mathematics (cum laude) and PhD (cum laude) from Delft University of Technology, followed by R&D roles at Philips Research. He was a part-time professor at TU/e from 2004–2013 before transitioning to full-time academia. Research Interests: Microfluidics, nature-inspired actuators, smart medical devices, and cancer-on-chip models. His group develops organs-on-chips to study cancer metastasis and vascular systems, leveraging biomimetic approaches and advanced fabrication techniques. Publications & Impact: Over 140 scientific papers, 45 patents, and ERC Advanced Grant (2019). He founded ARTIC Technologies and advises multiple startups. His work bridges academia and industry, with applications in diagnostics, drug development, and wearable health monitoring. Awards: 2023 Fellow of the Netherlands Academy of Engineering. Recognized for contributions to microsystem innovations and biomedical engineering. Teaching & Labs: Courses on microfabrication, heat/flow in microsystems, and hands-on lab work. Directs the Microfab/lab facility, enabling cutting-edge microfluidic research. Collaborates with hDMT (human organ and Disease Model Technologies Institute) on interdisciplinary projects.

Jim PortegiesView profile

Assistant Professor

• Mathematical Analysis
• Machine Learning
• Artificial Intelligence
• +4 more

Jim Portegies is an Assistant Professor in the Applied Analysis group at TU/e's Centre for Analysis, Scientific Computing and Applications (CASA). He holds affiliations with EAISI Foundational and EAISI Mobility within the Eindhoven Artificial Intelligence Systems Institute (EAISI). His research focuses on applying mathematical analysis, geometry, and measure theory to develop universal learning algorithms that mimic human learning processes. Education: MSc in Industrial & Applied Mathematics and Applied Physics (TU/e, 2009); PhD in Mathematics from NYU's Courant Institute (2014). Postdoc at Max Planck Institute for Mathematics in Sciences (Leipzig, 2014–2016) before returning to TU/e. Member of TU/e Young Academy of Engineering. Research Interests: Mathematical foundations of machine learning Geometric data analysis Design of biologically-inspired learning systems Nonlinear dimensionality reduction techniques Notable Achievements: Member of TU/e Young Academy of Engineering Recipient of multiple grants for AI/ML research Labs/Teams: Active in EAISI Foundational (theoretical AI) and EAISI Mobility (applied AI for mobility systems).