Find Academic Supervisors

Tony LindebergView profile

Professor

• Computer Vision
• Computational Neuroscience
• Medical Image Analysis
• +4 more

Tony Lindeberg is a Professor of Computer Science—Computational Vision at KTH Royal Institute of Technology, affiliated with the Division of Computational Science and Technology. He teaches the course Image Analysis and Computer Vision (DD2423). His research focuses on scale-space theory, early vision, and computational modeling of biological and auditory vision systems. Key contributions include theories on receptive fields, time-causal spatio-temporal models, and feature detection algorithms. Research interests span computational neuroscience, medical image analysis, and spatio-temporal recognition. Lindeberg has pioneered work on scale-invariant image features, affine transformations, and Galilean diagonalization for motion analysis. He is the author of the foundational book Scale-Space Theory in Computer Vision (1993). His work bridges computer vision and biological vision systems, with applications in gesture recognition, dynamic texture analysis, and neural networks. He leads the Vision Lab and Computational Brain Science Lab at KTH, emphasizing theoretical rigor and practical algorithms for visual perception tasks.

Behrouz FarView profile

Professor

• Artificial Intelligence
• Machine Learning
• Software Engineering
• +5 more

Behrouz Far is a Professor at the University of Calgary’s Schulich School of Engineering, Department of Electrical and Software Engineering. He holds a PhD in Artificial Intelligence from Chiba University, Japan (1990) and degrees from the University of Teheran including a B.S. in Electrical Engineering (1983) and M.S. in Electrical Engineering (1986). His research focuses on AI applications in medical imaging, software engineering, transportation systems, and data mining. He has contributed to advancements in fundus image analysis, deep learning models for disease detection, and intelligent traffic management systems. Dr. Far has received notable awards such as the 2017 SSE Achievement Award and the AITF-AMA Tier-2 Chair in Smart Multimodal Transportation Systems (2013). His work bridges theoretical AI with practical healthcare solutions, including tools like LETTA for traffic management systems and methodologies for detecting ocular lesions using CNNs. He teaches courses on software testing, reliability engineering, and agent-based systems. His publications highlight contributions to medical diagnostics (e.g., choroidal nevi classification), transportation optimization (e.g., real-time traffic signal control), and machine learning explainability. Collaborative research includes projects on biopotentiostat biosensors for SARS-CoV-2 detection and data mining for cancer patient stratification.

Stephen LaneView profile

Teaching Professor

• Computer Graphics
• Computer Animation
• Virtual Reality
• +5 more

Stephen H. Lane is a Teaching Professor at the University of Pennsylvania's School of Engineering and Applied Science, Department of Computer and Information Science. He serves as Director of the Computer Graphics and Game Technology (CGGT) Master's Program and teaches courses such as Computer Animation (CIS462/562), Advanced Topics in Computer Graphics and Animation (CIS660), and Game Design and Development (CIS564). He also supervises the Game Design Practicum (CIS568) capstone course. Education: B.S. in Mechanical and Aerospace Engineering from Cornell University (1980) M.S. in Systems Engineering from UCLA (1982) Ph.D. in Mechanical and Aerospace Engineering from Princeton University (1988) Dr. Lane's research focuses on the intersection of robotics, physically-based character animation, embodied intelligent agents, and virtual reality user interfaces. His work integrates control theory, artificial intelligence, and computer animation techniques to develop advanced simulation and training systems. His publications since 1987 cover topics such as inverse kinematics, neural networks for motion control, B-spline receptive fields, robotic skill acquisition, and gesture recognition systems. His recent work (2010-2011) emphasizes sensor fusion for gesture recognition and immersive training interfaces. Scientific Awards: Co-inventor of four US patents related to robotic animation and motion control systems Contributions to hybrid controller hierarchies and neural network training methods As founder of soVoz, Inc., Dr. Lane commercializes behavioral animation technology for virtual environments. His academic-industry collaboration includes contracts with Microsoft, Disney, and the US Army. He has developed tools like ProScena™ to integrate interactive 3D simulation capabilities into gaming and training applications.

Andreas StadlbauerView profile

Adjunct Professor

• Biomedical Imaging
• Medical Physics
• Artificial Intelligence in Medicine
• +7 more

Prof. Dr. Andreas Stadlbauer is a medical physicist affiliated with the Clinical Institute for Diagnostic and Interventional Radiology at St. Pölten University Hospital and the Department of Neurosurgery at Friedrich-Alexander University Erlangen-Nuremberg. He holds an adjunct professorship at the University of Erlangen-Nuremberg and contributes to both clinical and academic research in biomedical imaging and AI applications in oncology. University: Friedrich-Alexander University Erlangen-Nuremberg Hospital Affiliation: St. Pölten University Hospital Department: Department of Neurosurgery Academic Rank: Adjunct Professor His research focuses on advanced MRI techniques, particularly physio-metabolic imaging of brain tumors, oxygen metabolism, and the integration of artificial intelligence in clinical diagnostics. He has led research on glioma classification, tumor microenvironment characterization, and deep learning models for radiomic analysis. The recent publications highlight a strong trend toward AI-driven diagnostic tools in neuro-oncology, particularly in differentiating glioblastomas from metastases and predicting genetic mutations using machine learning. His work emphasizes the clinical translation of complex imaging data into actionable insights. Artificial Intelligence in Oncology Medical Imaging and Radiomics Brain Tumor Metabolism Deep Learning for MRI Analysis Oxygen Metabolism Imaging Clinical Decision Support Systems Prof. Stadlbauer has been involved in seed-funded research projects developing deep learning algorithms for clinical integration. He collaborates extensively with neurosurgeons, radiologists, and oncologists across institutions, contributing to multidisciplinary tumor boards and translational research initiatives. He completed his doctorate in medical physics in 2004, habilitation in 2008, and an MBA in Health Management in 2010. His academic journey reflects a blend of technical expertise and leadership in healthcare innovation.

• Embedded Systems
• Digital Signal Processing
• Multimedia Systems
• +5 more

Per Gunnar Kjeldsberg is a Professor at the Department of Electronic Systems, Norwegian University of Science and Technology (NTNU), and currently serves as acting head of the institute. His research focuses on embedded heterogeneous multi-processor systems , particularly in multimedia and digital signal processing applications . He has led and participated in numerous national and international projects, including EU Horizon 2020 initiatives like READEX (as work package leader) and Tulipp (as principal researcher), and supervises the MSCA-IF project Palmera . Kjeldsberg is a Senior Member of IEEE and part of the European Network of Excellence HiPEAC . Education : Sivilingeniør (MSc) in Electrical Engineering (1992), PhD (2001) from Norwegian Institute of Technology (NTH)/NTNU His work spans energy-efficient computing , radiation-hardened memory design for space applications, and dynamic hardware management . Publications include co-authoring three books and over 150 peer-reviewed articles in journals and conferences. He leads the Circuit and Radio Systems group and drives a strategic NTNU initiative on Energy Efficient Computing Systems . Kjeldsberg has held visiting researcher roles at imec (Belgium), University of California, Irvine, imec Netherlands (Holst Centre), and University of New South Wales (Australia). Scientific Awards : Senior Member of IEEE Mikroelektronikkprisen (2006–2015)

• Signal Processing
• Image Processing
• Wireless Sensor Networks
• +12 more

Professor Tariq Durrani is a Research Professor in the Department of Electronic and Electrical Engineering at the University of Strathclyde, UK. He is also recognized as a Visiting Professor and Honorary Professor at other institutions, reflecting his international academic engagement. His research spans signal and image processing with applications in sensor networks, UAVs, and intelligent communication systems. Research Interests: Signal and Image Processing Integrated Sensing and Communication (ISAC) Unmanned Aerial Vehicle (UAV) Networks Wireless Sensor Networks Big Data Analytics for Energy and Infrastructure Cognitive Neuroscience-Inspired Systems His recent publications highlight a strong trend in leveraging deep learning and optimization techniques for next-generation wireless systems, particularly in intelligent transportation, secure communications, and IoT. The integration of sensing, communication, and computing in UAV-enabled environments is a dominant theme. His work contributes to UN Sustainable Development Goals in clean energy, smart cities, and climate action. Scientific Awards and Honors: Thales Scottish Technology Prize 2009 (Third Place) Elected Honorary Member, Eta Kappa Nu (2010) Honorary Professor, University of Stirling (2010) Visiting Professorial Fellow (2012) Vice President (International Activities) Recipient (2012) Research Leadership: He has led and co-led multiple funded research projects, including those supported by the Royal Society of Edinburgh and the British Council, focusing on geological disaster monitoring, microseismic detection in oil exploration, and cognitive information integration in heterogeneous networks. His collaborative network spans the UK, China, and other international partners. Laboratories and Research Teams: His work is associated with advanced research in intelligent systems and sensor networks at the University of Strathclyde, contributing to the Fingerprint research profile in areas such as Unmanned Aerial Vehicles, Algorithms, and Optimization.

Kai EssigView profile

Professor

• Human Factors
• Interactive Systems
• Eye Tracking
• +15 more

Prof. Dr. Kai Essig is a Professor of Human Factors and Interactive Systems at the Faculty of Communication and Environment, Rhine-Waal University of Applied Sciences, Kamp-Lintfort, Germany. He has a strong interdisciplinary background combining computer science, cognitive science, and human-computer interaction, with a focus on eye tracking, visual perception, and assistive technologies. Master of Science in Computer Science and Chemistry, Bielefeld University (1998) Ph.D. in Computer Science, Bielefeld University (2007) His research centers on eye tracking, human-computer interaction, usability engineering, visual attention, and cognitive interaction technology . He investigates how movement expertise influences visual perception and how multimodal software can support real-time human actions. His work integrates computer vision, machine learning, and neuroscience to develop intelligent systems that adapt to user behavior. The 15 most recent publications reflect a consistent trend in eye movement analysis, mental representations, brain-machine interfaces, and assistive technologies . These works span domains such as sports psychology, robotics, augmented reality, and cognitive neuroscience, demonstrating a strong interdisciplinary approach. Key themes include gaze-based interaction, automated annotation of visual behavior, and the implementation of smart systems for daily living assistance. Scientific recognition includes: Landmark in the Land of Ideas (2018) – for the ADAMAAS project, awarded by 'Land of Ideas', a joint initiative of the German government and the Federation of German Industries Prof. Essig has been actively involved in research projects such as ADAMAAS (Adaptive and Mobile Action Assistance in Daily Living Activities), which received national recognition. He has collaborated extensively with the Neurocognition and Action-Biomechanics Research Group at Bielefeld University and the Excellence Cluster CITEC. While no formal advising of students is listed, his publications suggest mentorship and collaboration with junior researchers. His lab work is centered on eye-tracking systems, multimodal interaction, and cognitive modeling , particularly within applied environments like smart glasses and assistive technologies.

• Signal processing
• Fault detection and diagnosis
• Rotordynamics
• +7 more

Konstantinos Gryllias is a Professor in the Department of Mechanical Engineering at KU Leuven's Faculty of Engineering Sciences. He leads research in the Mechatronic System Dynamics (LMSD) unit at the Arenberg campus. His academic affiliations extend across multiple KU Leuven institutes including Leuven.AI, Leuven.AM (Additive Manufacturing), and the Gravitation Institute. He serves on important governance bodies as a member of the Faculty Council of Engineering Sciences, Faculty Doctoral Committee of Engineering Sciences, and Departmental Council of Mechanical Engineering. Dr. Gryllias specializes in signal processing, fault detection and diagnosis of rotating machinery, condition monitoring, and machine learning applications in structural health monitoring. His research spans linear and nonlinear vibrations, anomaly detection, rotordynamics, and pattern recognition. His work bridges theoretical signal processing with practical engineering applications in wind turbines, marine propulsion systems, and industrial machinery. His recent publications demonstrate strong focus on deep learning approaches for wind turbine anomaly detection, bearing diagnostics, stern bearing lubrication optimization, and structural health monitoring using advanced signal processing techniques. The research shows increasing integration of explainable AI methods with traditional vibration analysis. Dr. Gryllias teaches advanced courses including Monitoring & Prognostics, Structural Dynamics, Smart Sensing Technologies, and Applied AI perspectives. His teaching portfolio reflects the interdisciplinary nature of his research, connecting mechanical engineering fundamentals with cutting-edge AI methodologies. He currently leads multiple research projects through 2025-2029, primarily as Promotor, focusing on fault detection in gears using fiber optic sensors, multi-sensor monitoring of drivelines, physics-inspired machine learning for condition monitoring, and digital twin applications for wind turbine efficiency improvement.

Glenn GulakView profile

Professor

• Homomorphic Encryption
• Quantum Cryptography
• CMOS Biosensors
• +4 more

Glenn Gulak is a Professor in the Department of Electrical and Computer Engineering at the University of Toronto's Faculty of Applied Science and Engineering. He holds the Canada Research Chair in Signal Processing Microsystems and the Edward S. Rogers Sr. Chair in Engineering. A Senior IEEE Member and Professional Engineer in Ontario, he received his Ph.D. from the University of Manitoba. His research spans: Digital Communication Systems: VLSI implementations of MIMO detectors, lattice reduction algorithms, and homomorphic encryption accelerators Lab-on-Chip Microsystems: CMOS biosensors for rapid pathogen detection and integrated fluorescence imaging Recent publications (2019-2025) demonstrate a dominant focus on privacy-enhancing technologies, with 73% concentrated in cryptographic hardware and homomorphic encryption. This reflects industry-aligned work on confidential computing and secure data processing. Awards & Honors: IEEE Millennium Medal (2001) Canada Research Chair in Signal Processing Systems (Tier 1, 2005-2012) Edward S. Rogers Sr. Chair (2005-2010) RBC Research Prize L. Lau Chair (1999-2004) Teaching Award (1999) He has supervised 44+ graduate students (PhD/MASc) with thesis topics spanning VLSI communication systems, CMOS biosensors, and cryptographic accelerators. Notable industry collaboration includes serving as CTO of a semiconductor startup (2001-2003). His lab develops hardware for quantum cryptography and secure medical computation.

• Scanning Transmission Electron Microscopy (4D-STEM)
• Open Source Scientific Software Development
• Big Data Processing in Electron Microscopy
• +4 more

Magnus Nord is an Associate Professor in the Department of Physics, Faculty of Natural Sciences at Norwegian University of Science and Technology (NTNU). His research focuses on advanced electron microscopy techniques and computational tools for materials characterization. Research Interests : Scanning Transmission Electron Microscopy (4D-STEM), Open Source Scientific Software Development (Python), Big Data Processing, Magnetic/Electric Field Imaging, Structural Characterization using Higher Order Laue Zones. Publications span cutting-edge applications in functional materials, nanomagnets, and perovskite thin films, with emphasis on machine learning and precession-enhanced imaging. Key keywords include Materials Science , Electron Microscopy , and Computational Imaging . Software Development : Lead developer of Atomap and pyxem , contributing to HyperSpy and merlin_interface for electron microscopy data analysis. Current Research Funding : InCoMa (Research Council of Norway) IMPRESS (Horizon EU Program)

Gil WestmeyerView profile

Professor

• Molecular Sensors
• Molecular Actuators
• Neuroimaging
• +7 more

Prof. Dr. Gil Westmeyer is a Professor of Neurobiological Engineering at the Technical University of Munich (TUM), holding joint appointments at the TUM School of Natural Sciences and TUM School of Medicine and Health. He serves as Director of the Institute for Synthetic Biomedicine at Helmholtz-Zentrum München and leads the Chair of Neurobiological Engineering at TUM. His research program bridges molecular engineering, neuroimaging, and synthetic biology to develop next-generation tools for understanding and manipulating cellular networks. Westmeyer's educational background includes medical and philosophical studies in Munich, doctoral work on the molecular basis of Alzheimer's disease under Professor Christian Haass, clinical training at Harvard Medical School, and postdoctoral research with Professor Alan Jasanoff at MIT. His laboratory focuses on creating genetically encoded molecular sensors and actuators that enable non-invasive imaging and remote control of cellular processes across multiple scales. His research spans three primary domains: molecular sensors for multimodal imaging (from electron microscopy to whole-organism optoacoustics), molecular actuators for spatiotemporal control of cellular processes, and neurobehavioral imaging in freely behaving model organisms. The lab's work integrates synthetic biology, nanotechnology, and advanced imaging techniques to create tools that map dynamic signaling processes and manipulate cellular functions with unprecedented precision. Westmeyer's publication record demonstrates consistent innovation in molecular engineering, with recent work focusing on genetically encoded barcodes for electron microscopy, intron-encoded reporting systems, multiplexed optoacoustic imaging, and magnetically responsive cellular compartments. His publications in high-impact journals like Nature Methods, Cell, and Nature Biotechnology reflect the significance of his contributions to molecular imaging and engineering. ERC Proof of Concept 'inteRNAlizer' (2023) ERC Consolidator Grant 'EMcapsulins' (2019) ERC Starting Grant 'MagnetoGenetics' (2013) Helmholtz Young Investigator's Group (2011) Westmeyer actively mentors students and researchers through multiple teaching positions at TUM, including courses in biological chemistry, genetic machine development (iGEM), mammalian cell technology, and neuro-recording methods. His laboratory develops technologies with clear translational potential for future neurotherapies and regenerative medicine applications, particularly through the creation of imaging-controlled cellular interventions. The lab maintains strong collaborations across disciplines and institutions, with research that contributes to multiple UN Sustainable Development Goals related to health and wellbeing.

Vikaas SohalView profile

Professor

• Neuroscience
• Psychiatry
• Brain Circuits
• +6 more

Vikaas Sohal, MD, PhD is a Professor in the Department of Psychiatry at the University of California, San Francisco (UCSF) School of Medicine and a member of the UCSF Weill Institute for Neurosciences. He directs a neuroscience laboratory investigating the brain circuits underlying fundamental aspects of cognition and emotion, with particular focus on gamma oscillations in normal cognition and schizophrenia, as well as how rhythmic brain activity encodes emotional states. Dr. Sohal is also a board-certified psychiatrist who supervises residents in the Early Psychosis (PATH) clinic. Dr. Sohal earned his A.B. and S.M. in Applied Mathematics from Harvard University in 1997, followed by an M.A.St. in Mathematics from the University of Cambridge in 1998. He completed his M.D./Ph.D. in Neuroscience at Stanford University in 2005, where he also completed his residency in adult psychiatry. During his residency, he conducted postdoctoral research with Dr. Karl Deisseroth, performing some of the first experiments using optogenetics to study information processing in brain circuits. Dr. Sohal's research has focused on neural circuit mechanisms underlying cognitive and emotional processes, with particular emphasis on gamma oscillations, prefrontal-hippocampal interactions, and the role of specific interneuron subtypes in information processing. His laboratory has made significant contributions to understanding how parvalbumin interneurons generate gamma oscillations that organize prefrontal networks to promote behavioral adaptation. His recent work has explored the circuit basis of emotional states, pain-related aversion, and neuropsychiatric disorders. His publication record shows a consistent trajectory of high-impact research, with recent publications spanning topics from psilocybin effects to thalamocortical organoids for neuropsychiatric disorder modeling. His work demonstrates a progression from fundamental circuit mechanisms to translational applications for psychiatric disorders, particularly focusing on schizophrenia and emotional processing abnormalities. Dr. Sohal has secured continuous NIH funding as Principal Investigator since 2009, including multiple R01 grants, an R56, DP2, R00, and K99 awards, demonstrating sustained research productivity and significance. His research program represents a sophisticated integration of molecular, cellular, circuit, and behavioral approaches to understand and potentially treat neuropsychiatric disorders.

Jayneel ParekhView profile

Research Fellow

• Neural Network Interpretability
• Audio Processing
• Computer Vision
• +3 more

Jayneel Parekh is a Postdoctoral Researcher in the MLIA (Machine Learning and Artificial Intelligence) team at ISIR (Institut des Sciences et Industries du Réel), Faculty of Science, Sorbonne University, working with Prof. Matthieu Cord. His research focuses on understanding and enhancing large multimodal models, with applications across audio, visual, and multimodal domains. Parekh completed his PhD at LTCI, Telecom Paris under Prof. Florence d'Alche and Prof. Pavlo Mozharovskyi, researching neural network interpretability applied to image and audio data. He earned his undergraduate degree in Electrical Engineering from IIT Bombay, where he worked with Prof. Preeti Rao and Prof. Yi-Hsuan Yang on Speech-to-Singing conversion. His research spans neural network interpretability, audio processing, computer vision, and multimodal models, with emphasis on explainable AI. His work demonstrates a consistent trajectory from foundational audio/image interpretability methods to cutting-edge large multimodal model analysis, showing increasing complexity and impact across NeurIPS, ICML, and ICCV publications. L2I paper awarded 2nd prize for STIC Best Scientific Contribution 2023 Top Reviewer at NeurIPS 2023 Parekh actively contributes to the academic community through workshop organization (ICCV on Explainable Computer Vision, ELLIS Unconference on Robustness/Fairness/Explainability) and presentations at institutions including IIT Jodhpur, Deezer Research, and IBM Research. His collaborative network spans MPI Informatics, TU Darmstadt, TU Munich, and Télécom Paris.

Lei TianView profile

Associate Professor

• Computational imaging and sensing
• Computational microscopy
• Imaging in scattering media
• +5 more

Lei Tian is an Associate Professor in the Department of Electrical and Computer Engineering and the Department of Biomedical Engineering at Boston University's College of Engineering. He leads the Computational Imaging Systems Lab and maintains affiliations with the Neurophotonics Center, Photonics Center, Center for Information & System Engineering, Rafik B. Hariri Institute for Computing, and Nanotechnology Innovation Center. His educational background includes: PhD, Massachusetts Institute of Technology, 2013 MS, Massachusetts Institute of Technology, 2010 Professor Tian's research integrates optics and computation to overcome physical limitations in imaging systems. His work spans computational imaging and sensing, computational microscopy, imaging in scattering media, phase retrieval, and neurophotonics. He develops next-generation imaging systems with applications in biomedical microscopy, neuroscience, semiconductor metrology, and advanced vision applications, emphasizing the joint design of optical components and computational algorithms. His publication record shows a strong progression from fundamental computational imaging techniques to practical applications, with increasing integration of deep learning approaches to solve challenging imaging problems in scattering media and neural environments. His work consistently bridges theoretical advances with real-world applications. Professor Tian has received numerous prestigious awards: Boston University Provost's Scholar-Teacher of the Year Award (2025) Optica Fellow (2025) Early Career Excellence in Research, BU College of Engineering (2021) NSF CAREER Award (2019) Dean's Catalyst Award (2018) The Fumio Okano Best 3D Paper Prize (2018) As an advisor, he has successfully mentored at least 10 PhD students to completion as of mid-2025, with recent graduates including Jeffrey Alido, Jiabei Zhu, Chang Liu, Hao Wang, and Joseph Greene. His research is supported by substantial funding including a $2 million NIH grant for the Computational Miniature Mesoscope (CM2), a $1.75M grant from NIBIB for cancer cell metabolism research, and funding from the Chan Zuckerberg Initiative. His Computational Imaging Systems Lab pioneers innovative imaging techniques that synergistically combine optical hardware with computational algorithms, making significant contributions to computational microscopy, intensity diffraction tomography, neural imaging systems, and deep learning applications in optical imaging for both biomedical and industrial applications.

Grzegorz ChrupałaView profile

Associate Professor

• Computational Linguistics
• Artificial Intelligence
• Cognitive Science
• +3 more

Grzegorz Chrupała is an Associate Professor at the Department of Cognitive Science and Artificial Intelligence , Tilburg University, where he leads research in computational approaches to multimodal communication. Previously, he was a postdoctoral researcher at Saarland University's Spoken Language Systems group and earned his PhD from Dublin City University's School of Computing. His research bridges biological and artificial computation , focusing on enabling machines to learn language from multimodal data (speech, gestures, visual-auditory stimuli) as children do naturally. This involves developing and interpreting deep learning architectures, analyzing emergent representations, and advancing speech technology for under-resourced languages. Key themes include Visually grounded speech modeling Feature attribution and model interpretability Human-inspired learning paradigms BlackboxNLP workshop leadership His recent publications examine speech model reliability , lexical tone encoding , and contextual dependencies in NLP systems. He mentors a team of PhD candidates and alumni working on topics like user-centric interpretability, bioacoustics, and disentangled speech representations. He also serves on the board of the Dutch Open Speech Technology Foundation, chairs Interspeech 2025 tutorials, and contributes as an Action Editor for TACL.