Find Academic Supervisors

Thomas E. BittnerView profile

Associate Professor

• Formal Ontology
• Applied Ontology
• Ontology of Space and Time
• +3 more

Thomas E. Bittner serves as an Associate Professor in the Department of Philosophy at the University at Buffalo, specializing in formal and applied ontology with particular focus on spatial and temporal reasoning. His academic work bridges philosophy, computer science, and geographic information systems. His research interests encompass Formal and Applied Ontology , Ontology of Space, Time and Spatial Entities , Spatio-Temporal Reasoning , and Bio-Medical Ontology . He investigates fundamental questions about spatial relations, vagueness in geographic boundaries, and quantum-inspired models for geographic information systems, developing rigorous formal frameworks applicable to both philosophical and computational domains. His publication record demonstrates consistent engagement with cutting-edge topics in ontology, particularly exploring quantum geography concepts and computational implementations of formal ontologies. Key themes across his work include the representation of spatial vagueness, quantum-inspired geographic modeling, and verifiable computational ontology systems. Bittner holds a PhD from the Technical University of Vienna and teaches courses including Logic and Spatial Ontology. His contact information lists office location at 109 Park Hall on UB's North Campus with phone (716) 645-5149.

Tim ColoniusView profile

Professor

• Fluid Dynamics
• Cavitation and Bubble Dynamics
• Aerodynamic Sound
• +5 more

Tim Colonius is the Frank and Ora Lee Marble Professor of Mechanical Engineering and Medical Engineering and holds the Cecil and Sally Drinkward Leadership Chair at the California Institute of Technology. He has been affiliated with Caltech since 1994 and currently serves as Executive Officer for Mechanical and Civil Engineering . Colonius earned his B.S. from the University of Michigan (Ann Arbor), and both his M.S. and Ph.D. from Stanford University. Research Interests: His work focuses on fluid dynamics (global instabilities, cavitation, aerodynamic sound), flow control (closed-loop control, reduced-order modeling), and biomedical applications (shock waves, lithotripsy, ultrasound). He also develops advanced numerical methods for interface capturing, immersed-boundary techniques, and high-order accuracy. Scientific Contributions: Recent publications highlight his research in multiphase flows, vortex ring collisions, turbulent jet analysis, GPU-accelerated simulations, and biomedical applications. His group uses computational and data-driven approaches to study turbulence, instabilities, and flow optimization. Scientific Awards: AIAA Aeroacoustics Award Fellow of the Acoustical Society of America Fellow of the American Physical Society (APS) NSF and DoD research grants

Isuru GodageView profile

Assistant Professor

• Soft Robotics
• Continuum Robotics
• Modular Robots
• +4 more

Isuru Godage is an Assistant Professor in the Department of Engineering Technology & Industrial Distribution at Texas A&M University's College of Engineering. He holds affiliated faculty positions in Mechanical Engineering and Multidisciplinary Engineering. His work focuses on advanced robotics systems, particularly soft robots, continuum arms, and their applications in surgery and blockchain-based collaboration. He earned a B.Sc. (Hons) in Electronic and Telecommunication Engineering from the University of Moratuwa, Sri Lanka (2007), and a Ph.D. in Robotics, Cognition, and Interaction Technologies from the University of Genova – Italian Institute of Technology, Italy (2013). Research Interests: Soft robots and continuum robots Modular robotic systems MRI-compatible surgical robotics for intracerebral hemorrhage evacuation Motion planning and control of underactuated systems Blockchain-enabled trustless collaboration between humans and robots His publications emphasize dynamic control of soft robotic arms, kinematic modeling of continuum systems, and bio-inspired designs for medical and industrial applications. Recent work explores locomotion strategies for soft quadrupeds and snake-like robots, alongside innovations in decentralized robotic data frameworks. Dr. Godage has secured grants such as the NSF CAREER Award (2021) focused on transformable soft robots and collaborative projects with the National Robotics Initiative (NRI). His research bridges robotics mechanics, control theory, and emerging technologies like blockchain for swarm robotics.

• Computational Neuroscience
• Machine Learning
• Neural Networks
• +5 more

Prof. Sander Bohte is a part-time full professor of Computational Neuroscience at the University of Amsterdam (Swammerdam Institute of Life Sciences) and an honorary full professor of Bio-inspired Neural Networks at the University of Groningen. He serves as a Scientific Staff Member and Group Leader in the Machine Learning department at CWI, Amsterdam. His work bridges computational neuroscience and machine learning with a focus on continuous-time information processing. Key research interests include: Spiking Neural Networks with predictive coding and multi-compartment models Biologically Plausible Learning in recurrent and deep architectures Working Memory modeling via reinforcement learning Neuromorphic Computing for real-time systems and GPU acceleration His recent publications highlight trends in neural adaptation , predictive coding , and SNN hardware-software co-design . Awards include the Veni Innovational Research Grant (2004) and ERCIM grant (2013) . He actively supervises MSc theses and leads grants like the NWO KIC project 'Selfhealing Neuromorphic Systems' (2024).

Sheng YangView profile

Assistant Professor

• Additive Manufacturing
• Digital Twin Technology
• Smart Manufacturing
• +7 more

Dr. Sheng Yang is an Assistant Professor in the School of Engineering at the University of Guelph. He leads the Design Innovation and Intelligent Manufacturing (DIIM) lab, focusing on advancing additive manufacturing, generative design, and smart manufacturing technologies. His research integrates IoT, big data analytics, and bio-inspired design to address challenges in aerospace, green energy, and healthcare. Key areas include computational design for additive manufacturing, data-driven mass customization, and digital twin-based optimization. Education: Ph.D. in Mechanical Engineering from McGill University (2019), followed by a Postdoctoral Fellowship at McGill (2019–2020). Joined University of Guelph in 2020. Research interests span energy efficiency, complex system optimization, and personalized healthcare products. Recent work emphasizes digital twin synchronization in robotics, machine learning for quality prediction, and sustainable additive manufacturing processes. Notable awards include the 2019 Association of Commonwealth Universities Blue Charter Fellowship and 2018 ASME Best Paper Award. His lab actively seeks partnerships in personalized healthcare, product design, and smart manufacturing. Grants and collaborations focus on advancing manufacturing technologies and sustainability. No formal advisees listed, but active in graduate training through lab projects. The DIIM lab explores cutting-edge solutions for industrial and societal challenges through interdisciplinary approaches.

• Applied Mathematics
• Mathematical Biology
• Fluid Dynamics
• +2 more

Silas Alben is a Professor in the Department of Mathematics at the University of Michigan, affiliated with the College of Literature, Science, and the Arts. His research focuses on applied mathematics and mathematical biology, particularly fluid-structure interactions in biological systems. He employs computational simulations and laboratory experiments to study fundamental physics of flexible bodies in fluids. Research interests include biomechanics of swimming organisms, vortex dynamics in fluid-structure interactions, and thermal transport optimization. His work bridges mathematical modeling with experimental validation to understand complex physical phenomena. Publications demonstrate strong focus on fluid dynamics applications, including vortex-enhanced heat transfer, membrane flutter dynamics, and bio-inspired locomotion. Recurring themes include optimization of fluid-structure systems, vortex wake interactions, and computational methods for aeroelastic problems.

Ting HuView profile

Associate Professor

• Artificial Intelligence
• Bioinformatics
• Data Mining
• +7 more

Dr. Ting Hu is an Associate Professor in the School of Computing at Queen's University, affiliated with the Faculty of Arts and Science. She leads the Machine Intelligence & Biocomputing (MIB) Laboratory, focusing on bio-inspired AI and bioinformatics. Her research bridges evolutionary computing, machine learning, and biomedical data analysis. Dr. Hu holds a PhD in Computer Science from Memorial University and completed postdoctoral training at Dartmouth College. She teaches courses with strong student evaluations, winning the Howard Staveley Teaching Award (2019-2020) and recognition as a Mental Health Champion (2023). Education: B.Sc. in Computational Mathematics, Wuhan University M.Sc. in Computer Science, Wuhan University PhD in Computer Science, Memorial University Postdoctoral Fellowship, Geisel School of Medicine, Dartmouth College Research Interests: Evolutionary algorithms and genetic programming Interpretable and explainable AI Biomedical data mining (metabolomics, genomics) Complex network analysis Applications in precision medicine and disease prediction Awards & Recognition: Queen's AMS Undergraduate Mentorship Award (2025) IEEE CIBCB Best Student Award (2022) Howard Staveley Teaching Award (2019-2020) NSERC Discovery Grant Reviewer (2019) Memorial University's Best Professor Award (2016) Lab & Collaborations: MIB Lab develops tools like geneDRAGNN (graph neural networks for gene-disease prioritization) Active roles in IEEE Computational Intelligence Society and EuroGP Advances include vaccination strategies via graph-RL and interpretable clustering methods

Vivek BoominathanView profile

Research Professor

• Computational imaging
• Computer vision
• Machine learning
• +7 more

Vivek Boominathan is an Assistant Research Professor in the Department of Electrical and Computer Engineering at Rice University. He is affiliated with the GLEE lab (Geometry, Light, & Imaging lab). His research focuses on computational imaging, combining computer vision, machine learning, applied optics, and nanofabrication to develop innovative imaging systems for applications such as robotics, medical sensing, and virtual/augmented reality. He has contributed to projects like PhlatCam (a lensless camera) and NeuWS (neural wavefront shaping). His work bridges optics, algorithms, and materials science to overcome traditional limitations in imaging systems. Boominathan's research interests include lensless imaging, optical meta-devices, turbulence mitigation, and bio-inspired imaging systems. He has developed systems like Foveated thermal imaging prototypes and real-time lensless microscopes. His lab emphasizes interdisciplinary approaches, integrating hardware design with machine learning. Key projects include: NeuWS: Neural wavefront shaping for imaging through scattering media CoIR: Compressive implicit radar for sensing applications FlatCam and PhlatCam: Ultra-thin lensless imaging devices Bioluminescence imaging in marine species His work has been published in top venues like Science Advances, Optica, and IEEE TPAMI. He collaborates with institutions like NASA JPL and industry partners on applied imaging solutions. Current research trends emphasize sensor-algorithm co-design and high-speed imaging systems for AR/VR applications. Boominathan holds a PhD in Electrical Engineering and has extensive postdoctoral experience in computational imaging. He advises projects in the GLEE lab and mentors students in hardware-software co-design for imaging systems. His lab focuses on translating theoretical innovations into practical devices with commercial potential.

• Distributed Algorithms
• Programmable Matter
• Bio-inspired Algorithms
• +5 more

Andrea W. Richa is a President's Professor at Arizona State University (ASU), holding positions in the School of Computing and Augmented Intelligence (SCAI), Barrett Honors College, and multiple research centers including the Biodesign Institute's Center for Biocomputing, Security, and Society. She specializes in distributed algorithms, programmable matter, and bio-inspired computing. Richa has led major research initiatives, including a DoD MURI award and an NSF CAREER Award, and has delivered keynote speeches at top conferences like DISC and LATIN. Her work focuses on self-organizing particle systems, wireless networks, and algorithmic foundations of active matter. Educations: PhD (Computer Science, Carnegie Mellon University, 1998), M.S. (Computer Science, Carnegie Mellon University, 1995), B.S. (Computer Science, Federal University of Rio de Janeiro, Brazil, 1989). Research Interests: Distributed algorithms, programmable matter, bio-inspired systems, wireless communication models, graph algorithms, combinatorial optimization, and resource allocation. She leads the Self-Organizing Particle Systems Lab and is part of SCAI's Theory and Algorithms group. Awards: 2024 ASU Mentorship Award, 2021 Mentor of the Year, 2017 SCAI Research Excellence Award, NSF CAREER Award (1999), and multiple grants including DoD MURI. Her research spans theoretical and applied domains, with over 150 publications in top venues. Grants: Current DoD MURI funding (2019-25), NSF awards on Markov chain algorithms and active matter (2021-25), and prior funding on programmable matter (2014-2017). Labs/Teams: SOPS Lab (sops.engineering.asu.edu), contributing to interdisciplinary research in algorithmic matter and bio-inspired systems.

Andrew ErwinView profile

Assistant Professor

• Physical Human-Robot Interaction
• Rehabilitation Robotics
• Bio-inspired Robotics
• +3 more

Dr. Andrew Erwin is an Assistant Professor in Mechanical Engineering at the University of Cincinnati, focusing on robotics, human-robot interaction, and rehabilitation engineering. He holds a PhD and MS from Rice University (2018, 2014) and a BS from the University of Massachusetts Amherst (2012). Prior to UC, he was a postdoc at the University of Southern California and the Jet Propulsion Laboratory. His research explores how forces and movements are executed in healthy individuals, and how robotic devices can assist or restore function post-injury. Key areas include rehabilitation robotics, bio-inspired systems, haptic interfaces, and motor learning. He has received prestigious awards such as the NASA Postdoctoral Program Fellowship (2018) and the IEEE/ASME Transactions on Mechatronics Best Paper Award (2017). Dr. Erwin’s work integrates biomechanics, control systems, and neurophysiology. His lab develops devices like the SE-AssessWrist for wrist assessment and explores planetary seismometers for space missions. He maintains an active Google Scholar profile with over 25 publications. Education: PhD, Mechanical Engineering, Rice University, 2018 MS, Mechanical Engineering, Rice University, 2014 BS, Mechanical Engineering, University of Massachusetts Amherst, 2012 His current research emphasizes curriculum design for robotics learning, human-robot collaboration, and adaptive control systems. He offers a PhD position for Fall 2025 focusing on these areas.

Youngchan KimView profile

Lecturer

• Quantum Biology
• Biophotonics
• Bioimaging
• +3 more

Dr. Youngchan Kim is a Lecturer in Quantum Biology at the University of Surrey , serving as Director of the Quantum Biology Doctoral Training Centre (QB-DTC). He is affiliated with multiple departments including the School of Biosciences, Advanced Technology Institute, and Quantum Sciences Group. PhD in Physics (2011), Korea Advanced Institute of Science and Technology MSc in Physics (2008), KAIST BSc in Physics (2006), Chung-Ang University Graduate Certificate in Learning and Teaching (2022), Advance HE His research focuses on quantum phenomena in biological systems at physiological temperatures, particularly using femtosecond optical spectroscopy and genetically engineered fluorescent proteins to explore evolutionary adaptations and develop quantum-bio-inspired technologies like room-temperature single-photon sources. The 15 most recent publications span quantum biology, biophotonics, and optical spectroscopy, with particular emphasis on quantum coherence in biological systems , terahertz birefringence , fluorescent protein dynamics , and biomedical imaging innovations . These works demonstrate his interdisciplinary approach bridging physics, biology, and medical applications. As QB-DTC Director, he leads transdisciplinary initiatives fostering collaboration between quantum physics and biosciences. His technical expertise includes time-correlated single-photon counting , common-path interferometry , and ultrafast fluorescence depolarization techniques.

Cynthia SungView profile

Associate Professor

• Computational Robotics
• Origami Robotics
• Soft Robotics
• +2 more

Cynthia Sung is an Associate Professor in the Mechanical Engineering and Applied Mechanics department at the University of Pennsylvania's School of Engineering and Applied Science, with secondary appointments in Computer and Information Science and Electrical and Systems Engineering. She directs the Sung Robotics Lab, focusing on computational methods for robot design, origami robotics, planning for distributed systems, and fabrication of reconfigurable systems. Her lab is funded by NSF, ONR, ARO, NASA, and Penn Health-Tech. Her research spans four key areas: Computational co-design integrating mechanical, electronic, and software components Soft/origami robotics leveraging compliance for adaptable systems Distributed planning for multi-robot coordination Novel fabrication techniques for deployable structures Publications show consistent focus on robotic mechanisms with recent trends in magnetic origami reconfiguration (2025), underwater jet coordination (2025), educational robotics kits (2025), and tunable-stiffness actuators (2024). Article keywords predominantly fall in Robotics, Material Science, and Control Systems. Awards & Recognition ONR Young Investigator Award (2023) NSF CAREER Award (2019) Johnson & Johnson Women in STEM2D Scholars Award (2020) Popular Mechanics Breakthrough Award (2017) Research Teams & Advising Leads the GRASP Lab-affiliated Sung Robotics group. Current doctoral students include Zhiyuan Yang (jet propulsion), Daniel Feshbach (kinematic design), and Gabriel Unger (reconfigurable structures). Recent graduates include Yipeng Zhang (MSc, SALP robotics) and Christopher Kim (PhD, self-sensing actuators).

• Journalism and Communication
• Media Phenomenology
• Philosophy of Technology
• +2 more

Wang Yingji is a tenured professor and doctoral supervisor at the School of Art and Media, Tongji University. He holds a PhD in Literary Studies and has served as director of the China Culture and Art Communication Research Center, member of the China Artists Association, and editorial board member of "Media Criticism" journal. BA: Chinese Language and Literature, Renmin University of China (1995) MA: Journalism, Beijing Normal University (2004) PhD: Literary Studies, Beijing Normal University (2007) His research focuses on the intersections between Journalism and Communication, Media Phenomenology, Philosophy of Technology, and Traditional Chinese Culture. His work explores embodied cognition in media interaction, technological mediation of human perception, and historical epistemology of media systems. The articles demonstrate his expertise in media phenomenology (Hubert Dreyfus analysis), digital reading behavior studies, VR technology ideologies, and historical reconstruction of Chinese communication theories. His publications span from 2016-2022 with consistent CSSCI indexing. National Social Science Fund General Project: "Chinese Modern Public Opinion Thought History" (2021) Major Project Subtopic: "Chinese Media Archaeology in Civilizational Diversity" (2020) Ministry of Education Project: "Husserl's Media and Communication Thought" (2020) Completed NSFC Project: "Rumor Propagation and Governance in Public Emergencies" (2011) As educator, he teaches courses like "Chinese Classical Media Art", "Media Research Methods", and "Communication Theory". His administrative roles include leadership positions in the Chinese Society of Journalism History and multiple journal editorial boards.

Stuart KurtzView profile

Professor

• Theoretical Computer Science
• Computational Complexity Theory
• Type Theory
• +4 more

Stuart Kurtz is a Professor in Computer Science and the College at the University of Chicago, and serves as Master of the Physical Sciences Collegiate Division. He holds the endowed position of George and Elizabeth Yovovich Professor. His research focuses on theoretical computer science, including computational complexity theory, randomness in computation, type theory, and formal logic. He has contributed to foundational areas such as the Berman-Hartmanis Isomorphism Conjecture and the computational properties of random sets. Kurtz is affiliated with the Theoretical Computer Science and Programming Languages Groups at the University of Chicago. He has been recognized with the 2009 Quantrell Award for teaching excellence. His service roles include Director of Undergraduate Studies and Department Chair in Computer Science. He actively mentors Ph.D. students and has advised multiple graduates in complexity theory and related fields. His academic background includes a Mathematics Ph.D. from the University of Illinois, supervised by Carl Jockusch. He approaches type theory as an intersection of formal logic and functional programming. Research interests span measure-theoretic randomness, computational logic, and complexity class separations. His recent work explores connections between theoretical computer science and interdisciplinary fields like physics and statistics. In teaching, Kurtz has instructed courses such as Formal Language Theory, Discrete Mathematics, and Honors Intro Programming. His service contributions include roles in the Computation Institute and Toyota Technological Institute at Chicago. His lab affiliations and collaborative work reflect a strong commitment to advancing theoretical foundations in computer science.

Roderich GrossView profile

Senior Lecturer

• Swarm Robotics
• Modular Robotics
• Computational Intelligence
• +5 more

Roderich Gross is a Senior Lecturer in the Department of Automatic Control and Systems Engineering at the University of Sheffield. He is also a Visiting Scientist at CSAIL, MIT, and leads the Enabling Technologies theme at Sheffield Robotics. His academic journey includes a Ph.D. in engineering science from Université libre de Bruxelles (2007), followed by postdoctoral fellowships as a JSPS Fellow (Tokyo Institute of Technology), Research Associate (University of Bristol), and Marie Curie Fellow (EPFL & Unilever). Research Interests : Swarm robotics, self-reconfigurable robots, multi-robot coordination, robotics software/tools (human-robot interaction interfaces, formal design tools), machine learning for behavior inference (Turing Learning, GANs), autonomous systems, natural computing (swarm intelligence, evolutionary algorithms). Scientific Contributions : Inventor of Turing Learning, a machine learning method for behavior inference. Key work includes swarm coordination, self-assembly, fault-tolerant quadcopters, energy-efficient drone delivery, and infrared-based swarm communication. Scientific Recognition : Held prestigious fellowships including JSPS and Marie Curie, and served as Associate Editor for leading robotics journals (IEEE Robotics and Automation Letters, Swarm Intelligence) and conference roles (General Chair DARS 2016, Program Co-Chair GECCO 2018). Grants : Principal Investigator on Horizon Europe OpenSwarm (£463,699), EPSRC Core Capital (£104,196), DSTL Multi Robot Systems (£98,781), and industry-funded modular robotics projects. Labs : Affiliated with the Natural Robotics Lab at the University of Sheffield.