Find Academic Supervisors

Tse-Hsun ChenView profile

Associate Professor

• Software Engineering
• AIOps
• Software Testing
• +3 more

Tse-Hsun (Peter) Chen is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University, Montreal. He leads the Software PErformance, Analysis, and Reliability (SPEAR) lab, focusing on improving software quality through log analysis, AIOps, and mining software repositories. His research collaborates with companies like Microsoft, BlackBerry, and Ericsson. Education: PhD, MSc, and BSc in Computer Science from Queen's University and the University of British Columbia. Awards include the Gina Cody Research Award (2021) and recognition as one of the world's most active software engineering researchers (JSS study). Research interests include software testing, DevOps, and leveraging LLMs for SE tasks. Recent work emphasizes log parsing with LLMs (e.g., LibreLog) and fault localization. Graduates from his lab hold academic positions at institutions like York University and DePaul University. Teaching includes courses on software verification, testing, and process management. Active in program committees for ICSE, FSE, and MSR. Over 50 publications in top venues like TSE, ICSE, and FSE.

Yann-Gaël GuéhéneucView profile

Professor

• Software Engineering
• IoT Systems
• Static and Dynamic Analyses
• +4 more

Yann-Gaël Guéhéneuc is a Professor at Concordia University's Department of Computer Science and Software Engineering. He leads the Ptidej Team, focusing on software engineering methodologies, IoT systems, and game engine architecture analysis. His research emphasizes static/dynamic analyses, service-oriented architectures, and machine learning design patterns. Current Affiliations: Concordia University (Full-time Professor) Ptidiej Team Lead Research Interests: Specializes in IoT system testing, microservices architecture, game engine design patterns, and anti-pattern detection in multi-language systems. His work bridges theoretical software engineering principles with practical industrial applications, particularly in legacy system modernization and machine learning system design. Recent Trends in Publications: Focuses on IoT testing methodologies, machine learning architecture patterns, and service-oriented system transformations. His 2025 works advance IoT system taxonomy and game engine analysis techniques. Advising: Supervises MASc and PhD programs in Software Engineering and Computer Science Labs/Teams: Ptidej Team develops software tools for system analysis (e.g., Magnet, SyDRA)

Michael P. BarryView profile

Research Fellow

• Neuroprosthetics
• Artificial Vision
• Low-Vision Rehabilitation
• +3 more

Dr. Michael P. Barry is the Associate Director for Translational Research and a Senior Research Fellow at the Pritzker Institute of Biomedical Science and Engineering, Illinois Institute of Technology. His work focuses on neuroprosthetic design, artificial vision systems, and low-vision rehabilitation. He earned a PhD in Biomedical Engineering from Johns Hopkins University (2018) and a B.A./M.S. in Neuroscience from the same institution (2010). His research includes pioneering contributions to the Argus II retinal prosthesis and the Intracortical Visual Prosthesis (ICVP), emphasizing psychophysical evaluations and device optimization. Dr. Barry has published over 40 peer-reviewed articles and holds a patent for spatial fitting by percept location tracking (2018). He received the Envision-Atwell Award for Low Vision Research in 2017. Key projects include managing the RES-MATCH program for IIT undergraduates and advancing thermal imaging and distance-filtering systems to enhance prosthetic vision. His work integrates neurophysiological studies, clinical trials, and software development to improve visual perception for blind individuals. Collaborations span academic institutions and industry partners like Second Sight Medical Products. Professional memberships include the Association for Research in Vision and Ophthalmology (2010–2020, 2023–2024) and the Society for Neuroscience (2019, 2023). Current research emphasizes optimizing ICVP performance through EEG recordings and electrode stability analysis, while exploring applications in mobility assistance and environmental interaction.

• Visual Perception
• Cognitive Neuroscience
• Neuroscience
• +3 more

Zijiang J He, PhD, is a Professor and Distinguished University Scholar in the Department of Psychological and Brain Sciences at the University of Louisville. His research focuses on visual perception, particularly the mechanisms underlying 3D depth perception and middle-level vision. He holds a B.S. in Biophysics from the University of Science and Technology of China (1983), M.S. in Neurobiology from Shanghai Institute of Physiology (1986), and Ph.D. in Physiological Optics & Neuroscience from the University of Alabama at Birmingham (1990). Postdoctoral training was completed at Harvard University (1994). Research interests include space vision mechanisms and middle-level vision processes. His lab explores how the brain integrates 2D retinal images into 3D spatial perception, leveraging virtual reality and psychophysical methods. Key contributions include studies on sensory eye dominance, binocular rivalry, and perceptual learning protocols for amblyopia. He leads the Visual Perception and Cognition Lab, emphasizing interdisciplinary approaches combining neuroscience, computational modeling, and behavioral experiments. Notable achievements include the Distinguished University Scholar title and impactful publications in *Nature*, *Science Advances*, and *Proceedings of the National Academy of Sciences*. His work bridges ecological regularities and neural processing, advancing understanding of visual system adaptations to spatial environments.

Rui DaiView profile

Associate Professor

• Wireless Sensor Networks
• Multimedia Communications
• Video Analytics
• +2 more

Dr. Rui Dai is an Associate Professor in the Department of Computer Science at the University of Cincinnati's College of Engineering and Applied Science. Her research focuses on wireless sensor networks, multimedia communications, and video analytics for healthcare and surveillance applications. She directs multiple NSF and NIST-funded projects on perceptual-quality-aware video systems. Research interests include quality-of-experience optimization for video analytics, compressed domain feature extraction, and edge computing frameworks for intelligent surveillance. Recent work develops deep feature compression techniques, multi-camera fall detection systems, and quality-aware video distribution strategies for 5G networks. Publications demonstrate consistent innovation in video processing for resource-constrained environments, with applications spanning healthcare monitoring, public safety networks, and embedded vision systems. Current projects investigate metaverse communication challenges for 6G networks and PHP vulnerability detection through hybrid static-fuzzing analysis.

Barbara Shinn-CunninghamView profile

Professor

• Auditory Neuroscience
• Cognitive Neuroscience
• Perception
• +5 more

Barbara Shinn-Cunningham is the Glen de Vries Dean of the Mellon College of Science at Carnegie Mellon University (CMU) and holds professorships in Psychology, Biomedical Engineering, and Electrical and Computer Engineering. She is also the founding director of CMU's Neuroscience Institute. Her research focuses on auditory neuroscience, particularly auditory attention, binaural hearing, and multisensory integration, with applications to hearing disorders and assistive technologies. Shinn-Cunningham earned her B.S. from Brown University and her M.S. and Ph.D. from MIT in Electrical and Computer Engineering. Education: B.S., Electrical Engineering, Brown University (1986) M.S., Electrical & Computer Engineering, MIT (1988) Ph.D., Electrical & Computer Engineering, MIT (1994) Research Interests: She investigates how the brain processes sound in complex environments, including spatial hearing, auditory attention deficits in aging and clinical populations, and the neural mechanisms underlying cochlear synaptopathy. Her work integrates behavioral studies, neuroimaging (EEG, fMRI), and computational modeling to bridge basic science and translational research. Awards & Recognition: Fellow, Acoustical Society of America (2009) Alfred P. Sloan Research Fellow (2000) National Security Science and Engineering Faculty Fellow (2008) Helmholtz-Rayleigh Interdisciplinary Silver Medal (2019) Advising & Grants: She mentors a diverse team of graduate students and postdocs, focusing on training the next generation of auditory neuroscientists. Her grants include funding from NSF, NIH, and the Department of Defense. She leads the LiMN Lab, which explores neural mechanisms of sensory processing and attention. Labs & Teams: Director of the Lab in Multisensory Neuroscience (LiMN) at CMU, part of the Carnegie Mellon Neuroscience Institute. Collaborates with engineers, clinicians, and marine biologists to advance auditory technology and neuroimaging techniques.

Deborah TalmiView profile

Associate Professor

• Emotional Cognition
• Memory Mechanisms
• Neurobiology of Emotion
• +7 more

Dr. Deborah Talmi is a University Associate Professor in the Department of Psychology and a Fellow of Lucy Cavendish College at the University of Cambridge. Her research focuses on the cognitive, computational, and neurobiological mechanisms underlying emotional processes, particularly their intersection with memory, perception, and attention. She leads the Emotional Cognition Lab, which employs behavioral experiments, EEG, fMRI, and computational modeling to explore how emotions influence memory formation and retrieval. Roles: Associate Professor (Department of Psychology), Fellow (Lucy Cavendish College), Principal Investigator (Emotional Cognition Lab) Affiliations: School of Biological Sciences, Cambridge Neuroscience Her research investigates questions such as why emotional experiences feel a certain way, how emotion shapes memory, and the neural basis of similarity perception. A recent study demonstrated that emotionally charged stimuli evoke more similar neural representations than neutral ones, even when controlling for thematic content. This work was published in the Journal of Neuroscience and is part of broader efforts to model emotional memory using frameworks like the Bayesian Brain hypothesis. Dr. Talmi collaborates with institutions like the Swiss Centre for Affective Sciences and employs interdisciplinary methods, including animal models of emotional memory with Prof. Amy Milton. She actively mentors PhD students and welcomes volunteers through the SONA platform. Her lab’s findings have implications for understanding chronic pain, decision-making, and mental health. Her work has been featured in The Conversation and TEDx, emphasizing public engagement. She is also committed to advancing education, as seen in her reflections on remote teaching during the pandemic.

Auguste GenovesioView profile

Researcher

• Computational Bioimaging
• Bioinformatics
• Machine Learning
• +7 more

Auguste Genovesio is a Research Director (DR INSERM) leading the Computational Bioimaging and Bioinformatics team at the Centre for Computational Biology within the École Normale Supérieure (ENS) in Paris. His work focuses on large-scale cellular morphology analysis, integrating machine learning, microscopy, and computational modeling to study cellular responses to perturbations. His team develops algorithms for analyzing high-dimensional biological data, with applications in drug discovery, functional genomics, and neuroscience. Education and Affiliations: Genovesio’s research is anchored at ENS and collaborates with institutions like Institut Curie, Collège de France, and ESPCI. His lab develops open-source tools such as PySpacell and ALFA , advancing spatial analysis and genomic data processing. Research Interests: His group combines deep learning, bioinformatics, and experimental biology to tackle challenges in cellular dynamics, morphological heterogeneity, and predictive modeling. Recent work includes applying diffusion models to reveal subtle phenotypes and optimizing microscopy image analysis pipelines. Key Projects: Cross-modal knowledge distillation for transcriptomics, latent diffusion models for small datasets, and super-resolution microscopy via StyleGAN regularization. Applications: Collaborations in drug screening, neurobiology (e.g., Drosophila memory studies), and cancer cell analysis. Publications: Over 50 peer-reviewed articles since 2007, including work in Nature Communications , Developmental Cell , and NeurIPS . Recent focus on generative AI for biological image analysis and self-supervised learning biases. Grants & Awards: While specific grants aren’t listed, his lab’s cutting-edge research suggests significant institutional and collaborative support. No explicit awards mentioned in texts. Labs/Teams: Director of the Computational Bioimaging group, part of the Functional Genomics section at ENS. Supervises PhD students and postdocs in AI-driven biology and computational microscopy.

Bruno A. OlshausenView profile

Professor

• Computational Neuroscience
• Visual Perception
• Sensory Coding
• +4 more

Professor Bruno A. Olshausen is affiliated with the Helen Wills Neuroscience Institute and the School of Optometry at the University of California, Berkeley. He also serves as the Director of the Redwood Center for Theoretical Neuroscience , focusing on computational models of sensory coding and visual perception. Ph.D. in Computation and Neural Systems (Caltech, 1994) M.S. and B.S. in Electrical Engineering (Stanford, 1987 and 1986) His research investigates how the brain processes sensory information by developing probabilistic models of natural images and neural circuits. Key contributions include sparse coding models that replicate receptive field properties of the primary visual cortex (V1), and work on extending these models to learn invariances and hierarchical structures. He has also collaborated with electrical engineers to design low-power analog memory systems inspired by brain computation, and developed software tools like SPARSENET and SPARSEPYR for neural signal processing. His work spans computational neuroscience, theoretical modeling, and interdisciplinary applications in vision science. As an educator, he has co-instructed courses such as Vision Science 206D (Neuroanatomy of the visual system) and Vision Science 212B (Visual neurophysiology), and independently taught Vision Science 265: Neural Computation at Berkeley and Psychology 290 at UC Davis. He co-edited the book Probabilistic Models of the Brain: Perception and Neural Function (MIT Press, 2002) and organized workshops at institutions like the Gordon Research Conference and Nature Neuroscience .

• Neural Network Dynamics
• Computational Neuroscience
• Theoretical Physics
• +3 more

Prof. Raoul-Martin Memmesheimer is a Professor at the University of Bonn's Institute of Genetics , contributing to the Transdisciplinary Research Area (TRA) - Life and Health . His research focuses on understanding neural network dynamics across microscopic, mesoscopic, and large-scale phenomena, integrating mathematical approaches with neurophysiological insights. Key interests include the dynamics of precise spiking activity, collective network behavior, and computational principles underlying neural systems. Education & Background : While specific educational details are not listed, his position as a Professor indicates advanced academic qualifications in theoretical neuroscience or related fields. Research Interests : Memmesheimer's work bridges theoretical physics and computational neuroscience, addressing topics like spiking neuron models, learning in neural networks, and the emergence of complex behaviors. His group employs methods from computer science and applied mathematics to study how neural systems perform computations through their dynamic properties. Publications : Recent work explores topics such as gradient descent learning in spiking networks, STDP-based assembly dynamics, and oscillatory phenomena in hippocampal regions. These publications highlight his focus on both foundational theory and applications to biological systems. Awards & Collaborations : While no specific awards are listed, his participation in TRA Life and Health underscores collaborative efforts in transdisciplinary health-related research. His work is supported by the University of Bonn's strong focus on transdisciplinary innovation. Labs & Teams : His research group operates within the Institute of Genetics, leveraging interdisciplinary resources at the University of Bonn to advance theoretical neuroscience and computational biology.

Chantal SternView profile

Professor

• Cognitive Neuroscience
• fMRI
• Memory Encoding
• +2 more

Chantal Stern is Professor and Department Chair at Boston University's College of Arts and Sciences, where she directs the Cognitive Neuroimaging Laboratory and Cognitive Neuroimaging Center. Her research uses fMRI to study memory formation, spatial navigation, and neurodegenerative conditions. Research examines neural mechanisms underlying visual/spatial information processing, memory encoding/retrieval dynamics, and cognitive changes in normal aging and dementia pathologies. Current projects investigate navigation systems, attentional networks, and computational models of reasoning. Publications demonstrate consistent methodological innovation in neuroimaging, particularly in mapping temporal aspects of navigation and developing predictive models integrating perceptual and abstract reasoning systems.

Ahmed M. A. SayedView profile

Senior Lecturer

• Distributed Systems
• Machine Learning Systems
• Federated Learning
• +7 more

Dr. Ahmed M. A. Sayed is a Senior Lecturer (equivalent to Associate Professor) and Director of the MSc Big Data Science Programme at Queen Mary University of London's School of Electronic Engineering and Computer Science. He leads the SAYED Systems Group and focuses on distributed systems, federated learning, edge computing, and network optimization. His research bridges system design and machine learning, emphasizing scalability and efficiency. Education: PhD in Computer Science (HKUST, 2017), M.Sc. and B.Sc. (Assiut University, 2012 and 2007). Prior roles include Research Scientist at KAUST and Senior Researcher at Huawei's Future Network Lab. Research Interests: Systems for ML, federated learning, edge/Cloud computing, network congestion control, and IoT. He has secured £730K+ in grants, including a UKRI-EPSRC grant for the KUber project (2024–2027). Awards: 2024 Best Student Paper (IJCAI FL Workshop), Hong Kong PhD Fellowship (2013–2017), and numerous travel grants. Actively supervises PhD/MSc students and postdocs. Grants & Leadership: PI of UKRI-EPSRC KUber project, Co-I in HKRGC and KAUST grants. Organizes workshops at venues like MobiSys and serves on TPC for ICML, EuroSys, and NeurIPS. Labs: Leads SAYED Systems Group, affiliated with Networks Group and DT4SGD Lab at Queen Mary.

Kyle O'KeefeView profile

Professor

• Global Navigation Satellite Systems (GNSS)
• Indoor Positioning and Navigation
• Ultra-wideband Ranging
• +6 more

Kyle O'Keefe is a Professor in the Department of Geomatics Engineering at the University of Calgary's Schulich School of Engineering. He holds dual B.Sc. degrees in Geomatics Engineering (University of Calgary, 2000) and Honours Physics (University of British Columbia, 1997), and a Ph.D. in Geomatics Engineering (University of Calgary, 2004). He is a Professional Engineer (P.Eng.) registered with the Association of Professional Engineers and Geoscientists of Alberta since 2005. His research focuses on positioning and navigation technologies, including Global Navigation Satellite Systems (GNSS) advancements Ultra-wideband (UWB) ranging for vehicle/pedestrian navigation Indoor positioning using wireless signals Wearable sensor integration for biomechanics and navigation GNSS spoofing detection and cybersecurity Notable projects include: Development of UWB-augmented GNSS for RTK surveying (2007–present) Wearable sensor systems for rowing/kayaking motion analysis (2017–present) CanX-2 nanosatellite GPS receiver operations (2008) Igliniit project with Inuit hunters for Arctic environmental monitoring (2006–2009) Multi-constellation GNSS evaluation across 20+ years He has received prestigious awards including the Michael Richey Medal (2011) and multiple Best Paper Awards at IPIN and ION conferences. His teaching includes courses like Advanced GNSS Theory and Wireless Location. Active in professional organizations, he co-edits special journal issues and advises industry on emerging navigation technologies.

Jeff HuangView profile

Associate Professor

• Software Engineering
• Programming Languages
• Concurrency
• +4 more

Jeff Huang is an Associate Professor in the Department of Computer Science & Engineering at Texas A&M University, affiliated with the College of Engineering. His research focuses on software engineering, programming languages, concurrency, and runtime verification, with notable contributions to static analysis, race detection, and vulnerability mitigation in concurrent systems. Education: Postdoc, Computer Science, University of Illinois at Urbana-Champaign (2013-2014) Ph.D., Computer Science, Hong Kong University of Science and Technology (2012) B.E., Electrical Engineering, National University of Defense Technology, China (2008) Research Interests: Huang's work bridges theoretical foundations and practical applications in concurrency debugging, static analysis tools, and cybersecurity for smart contracts. He emphasizes scalable solutions for pointer analysis, race detection, and vulnerability detection in distributed systems and blockchain technologies. Recent Trends in Publications: His recent work explores AI-driven program execution (e.g., SGLang), blockchain security (e.g., Smart Contract analysis), and dynamic/static analysis techniques for memory safety. These studies underscore advancements in automated tools for securing concurrent and distributed systems. Awards: 2023 ACM SIGSOFT Distinguished Paper Award 2019 DARPA Young Faculty Award 2016 NSF CAREER Award 2013 ACM SIGSOFT Outstanding Doctoral Dissertation Award Advising & Grants: Huang has advised PhD students including Bozhen Liu and Peiming Liu, who have contributed to OpenMP race detection and pointer analysis tools. His grants include NSF awards for pointer analysis as a service and DARPA funding for young faculty research. Labs & Teams: He leads the O2 Lab, focused on concurrency verification and cybersecurity, collaborating with industry partners like Coderrect Inc. and DOE on scalable static analysis frameworks.

Mehdi AdibiView profile

Research Fellow

• Systems Neuroscience
• Sensorimotor Processing
• Neural Coding
• +9 more

Mehdi Adibi is a Research Fellow at Monash University's Department of Physiology and the Biomedicine Discovery Institute. He leads the Neurodigit Lab, focusing on neural coding and sensorimotor processing in rodents and humans. Previously, he held NHMRC CJ Martin and ARC DECRA fellowships. He earned his PhD in Neuroscience (2014, UNSW), MSc in Telecommunications Engineering (2008, Iran University of Science and Technology), and BSc in Electrical Engineering (Isfahan University of Technology). His research integrates electrophysiology, optogenetics, and behavioral paradigms to study tactile perception and sensorimotor integration. Key areas include whisker-mediated touch in rodents, cortical dynamics, and translational applications like stroke rehabilitation. Technological innovations include tactile stimulators and modular behavioral setups. He supervises PhD/Masters students and has received awards such as the ARC DECRA Fellowship (2019) and NHMRC CJ Martin Fellowship (2015). Community contributions include CSIRO's Scientists in Schools program and neuroscience outreach.