Find Academic Supervisors

Mojgan A. JadidiView profile

Associate Professor

• Geospatial Visual Analytics
• Building Information Modeling (BIM)
• 3D GIS Integration
• +5 more

Dr. Mojgan A. Jadidi serves as Associate Professor in the Teaching Stream and Director of Common Engineering & BSc Science within the Department of Civil Engineering at York University's Lassonde School of Engineering. A Professional Engineer (P.Eng) and founder of the GeoVA Lab, she leads research at the intersection of geospatial analytics, digital infrastructure, and innovative engineering education aligned with UN Sustainable Development Goals. Education: PhD in Geomatics, Université Laval (2014) MSc in Earthquake and Seismology Engineering, ROSE Center (Italy) & Université Joseph Fourier (France) BSc in Civil-Survey Engineering, Iranian University of Science and Technology Research Focus: Her pioneering work in Geospatial Visual Analytics spans 2D/3D environments, Building Information Modeling (BIM) and 3D GIS integration, and Spatial Quantum Computing applications for smart cities. She develops Infrastructure Digital Twins using sensor data fusion while revolutionizing engineering education through gamification and augmented/virtual reality pedagogies that transform complex spatial concepts into immersive learning experiences. Research Trends: Recent publications (2021-2023) demonstrate convergent innovation across three domains: (1) Building energy optimization through BIM-graph analytics, (2) Transportation safety via AI-driven situational awareness, and (3) Educational technology using VR sandboxes and visual-verbal comics. These works consistently integrate quantum computing principles and UN SDG frameworks to solve urban sustainability challenges. Scientific Recognition: ASEE Zone III Best Paper Award (2023) ASEE Saint Lawrence Best Research Paper & Poster (2022) 3D GeoInfo Conference Best Paper (2018) NSERC Postdoctoral Fellowship (2016) ESRI Student Award (2011) Erasmus Mundus Scholarship (2006) Research Leadership: As Associate Director of York's ESRI Center of Excellence, she manages multi-source funding from NSERC, Mitacs, and York University internal grants. Her professional service spans global organizations including ISPRS Commission IV (Secretary), IEEE Women in Engineering (Member), PEO Etobicoke (Chair), and buildingSMART Canada (Committee Member), driving standards for BIM and digital twin implementation in urban infrastructure. Lab Innovation: The GeoVA Lab develops cutting-edge tools including the TopoSurvey Game for immersive surveying education, PAN-Lassonde XR Sandbox for virtual lab experiences, and quantum computing frameworks for bike-sharing optimization, establishing new paradigms in spatial data interaction and engineering pedagogy.

Olivier FARGESView profile

Senior Lecturer

• Thermal Engineering
• Energy Systems
• Fluid Dynamics
• +3 more

Olivier FARGES is a Senior Lecturer and HDR (Habilitation à Diriger des Recherches) holder at the University of Lorraine, affiliated with ENSGSI (École Nationale Supérieure de Géologie et Sciences Industrielles) within the Groupe INP. He serves as Director of Industrial Partnerships at ENSGSI and is part of the LEMTA Laboratory (CNRS-University of Lorraine), focusing on multiphysics and multiscale modeling of heat transfer in complex environments. His academic roles include teaching courses such as Heat and Mass Transfer, Fluid Mechanics, Scientific Computing Modeling, and Renewable Energy. Dr. FARGES holds a Ph.D. in Energy and New R&D (2014) and an Engineering degree in Energy Engineering (2010), both from the École de Mines Albi. His research emphasizes coupled conductive-radiative heat transfer in porous media, thermal property characterization of heterogeneous materials, and Monte Carlo-based computational methods for energy systems. He has contributed to advancements in photovoltaic system modeling, solar thermal power optimization, and urban climate studies. His work bridges theoretical and applied thermal engineering, with applications in sustainable energy systems, material science, and industrial partnerships. Key research themes include radiative transfer modeling, multiphysics simulation frameworks, and the development of innovative tools for thermal property measurement and energy performance assessment.

• Maritime safety management and leadership
• Maritime accident and casualty analysis
• Maritime Autonomous Surface Ships (MASS)
• +3 more

Tae Eun Kim is an Associate Professor in Maritime Safety Management at UiT The Arctic University of Norway, working within the Department of Technology and Security. Her research, teaching, and industrial collaboration focus on maritime safety and human factors, with particular expertise in maritime safety management, accident analysis, Maritime Autonomous Surface Ships (MASS), and human factors in maritime operations. Dr. Kim's research spans four interconnected domains: maritime safety management and leadership, maritime accident and casualty analysis, Maritime Autonomous Surface Ships (MASS), and human factors in maritime operations. She has developed assessment instruments like the Safety Leadership Self-Efficacy Scale (SLSES) and conducted STAMP-based causal analyses of maritime accidents. Her work on MASS addresses safety challenges in mixed navigational environments and examines leadership competencies for autonomous shipping operations. Her human factors research explores how technological advancements impact navigators' performance, crew dynamics, and safety outcomes, including gender parity issues in the maritime industry. Dr. Kim's publication record reveals a strong focus on the intersection of maritime safety, technology, and human performance. Her recent work increasingly addresses autonomous shipping technologies, with numerous publications on AI decision transparency, learning analytics in maritime simulator training, and multi-modal data analysis for nautical skill development. She has conducted systematic reviews on simulator training approaches and scenario design, contributing significantly to methodology development in maritime education and training. Her research demonstrates a clear trajectory toward integrating emerging technologies with traditional maritime safety practices as the industry transitions toward greater automation. Dr. Kim is actively involved in several significant research projects, including the i-MASTER EU Horizon Europe Research and Innovation Project, the REFRAME project, and the SPRICE project (Multidisciplinary approach for spray icing modelling). She is a member of both the Advanced Maritime Ship Operations research group and the Maritime Safety Science (MARSCI) Research Group, demonstrating her commitment to collaborative research in maritime safety science. Dr. Kim teaches several specialized courses at UiT, including SVF-3206 Safety Management and Accident Investigation, TEK-3014 Navigation Technology, MFA-2100 Maritime Digitalization, MFA-8010 Maritime HTO (Human-Technology-Organisation) and Innovation, and MFA-2018 Maritime Administration and Leadership. Her teaching portfolio reflects the interdisciplinary nature of her expertise, bridging engineering, safety science, and organizational behavior in maritime contexts.

• Artificial Intelligence in Education
• Digital Transformation in Education
• Educational Technologies
• +3 more

Sabine Seufert is a Full Professor of Management of Educational Innovations at the University of St. Gallen (HSG), where she has held leadership roles including Director of the Institute of Educational Management and Educational Technologies (since 2021) and Director of the Institute of Business Education and Educational Management (2009–2020). She holds a PhD in Information Management (1996) and a Habilitation in Business Education (2006) from the University of St. Gallen, specializing in educational innovation and technology integration. Her research focuses on digital transformation in education, AI applications in learning environments, and competence development in vocational training. She has pioneered initiatives like the Swiss Centre for Innovations in Learning (scil) and contributed to frameworks for hybrid intelligence in education. Her work bridges academic research and practical implementation, emphasizing ethical AI use and teacher preparedness for technological change. Key affiliations include the European Foundation for Management Development (EFMD), the editorial board of the International Journal of Learning Technology , and advisory roles for journals like Empirical Research in Vocational Education and Training . Her recent publications explore AI literacy, social robots in classrooms, and the impact of generative AI on teaching and learning. Her career spans over 25 years, including guest professorships at the University of Southern Queensland and Stanford University, leadership in educational institutions, and policy advisory roles. She is actively involved in shaping future-ready education systems through digital innovation and human-AI collaboration strategies.

Jakub KostalView profile

Associate Professor

• Computational Chemistry
• Green Chemistry
• Predictive Toxicology
• +3 more

Jakub Kostal is an Associate Professor of Chemistry and Director of the MS Environmental and Green Chemistry Program at George Washington University. He leads the Kostal Research Group, focusing on computational chemistry, green chemistry, and predictive toxicology. His work aims to develop computer models to predict chemical toxicity and design safer, sustainable chemicals. Education: PhD from Yale University (2012), B.A. from Middlebury College (2006). He collaborates with the Lapkin Group at the University of Cambridge on sustainable process engineering using AI. His research team includes graduate students Jillian Brejnik, Diana Garnica Acevedo, and Geetesh Devineni. Research interests center on reducing chemical hazards through computational methods, including predicting environmental persistence, optimizing chemical reactions for sustainability, and advancing machine learning tools for toxicity prediction. His team’s contributions include refining models for pesticide safety and designing bio-based alternatives. Publications highlight advancements in quantum mechanics modeling, in silico toxicity prediction, and sustainable chemical design. He actively engages with policymakers, including speaking at the White House on sustainable chemistry strategies.

Possu HuangView profile

Assistant Professor

• Protein Design
• Computational Biology
• Structural Biology
• +4 more

Possu Huang serves as Assistant Professor of Bioengineering in the Department of Bioengineering at Stanford University's School of Engineering. His research group operates from the Shriram Center for Bioengineering, sharing laboratory space with the Bintu Lab while maintaining an active interdisciplinary program bridging computational and experimental protein engineering. His educational journey includes: B.A. in Molecular and Cell Biology (Biochemistry) from UC Berkeley Ph.D. in Biochemistry and Molecular Biophysics from Caltech Postdoctoral training as Senior Fellow at University of Washington Dr. Huang's research focuses on atomic-precision protein design through integrated computational-experimental approaches. His lab has pioneered breakthroughs including the first computationally designed protein-protein interface, TIM barrel fold design principles, and the eOD HIV immunogen. Current work leverages machine learning , structural biology , and library optimization to develop therapeutic proteins and nanotechnology platforms, with particular emphasis on achieving natural-level complexity in engineered systems. Analysis of his 2023-2025 publications reveals three dominant trends: (1) generative AI for all-atom protein design (e.g., Protpardelle, SHAPES), (2) immunology applications targeting MHC complexes for HIV vaccine development, and (3) optogenetic tools through engineered fluorescent proteins. These works consistently integrate deep learning with wet-lab validation to solve biomedical challenges. Dr. Huang has mentored nine graduate students to completion, including PhD candidates Carla (defended June 2025), Christian (defended May 2025), and master's students Shankara and Kirsten. His lab maintains active social media presence (@possuhuanglab) and regularly publishes high-impact research while training the next generation of protein engineers. The Huang Lab operates as a dynamic interdisciplinary hub where computational biologists collaborate with experimentalists to push the boundaries of protein design. Current projects include developing tetravalent nanobody platforms, photoswitchable binders for temporal protein control, and AI-driven solutions for HIV immunogen design, all conducted within Stanford's state-of-the-art bioengineering facilities.

Doug L. JamesView profile

Professor

• Computer Graphics
• Computer Sound Synthesis
• Physically Based Modeling and Animation
• +3 more

Doug L. James is a Full Professor of Computer Science at Stanford University since 2015, following roles as Associate Professor at Cornell University (2006-2015) and Assistant Professor at Carnegie Mellon University (2002-2006). He holds a PhD in Applied Mathematics from the University of British Columbia (2001), alongside earlier degrees from the same institution and the University of Western Ontario. His research focuses on computer graphics, sound synthesis, and physically-based modeling, with notable contributions to fluid simulation, cloth animation, and medical modeling. Key achievements include the 2012 Technical Achievement Award from the Academy of Motion Picture Arts and Sciences for 'Wavelet Turbulence,' and the 2013 Katayanagi Prize. He serves as a consulting Senior Research Scientist at Pixar Animation Studios and has led roles like Technical Papers Chair at SIGGRAPH 2015. His work integrates physics-based principles with interactive systems, emphasizing real-time applications and data-driven methods. Research interests span sound synthesis for animations (e.g., cloth, water, impact sounds), deformable models for medical simulation, and tools like 'svMorph' for virtual surgery planning. His publications reflect a blend of algorithmic innovation and practical applications in film, gaming, and healthcare.

Joseph RamseyView profile

Researcher

• Causal Inference
• Machine Learning
• Computational Epistemology
• +4 more

Joseph Ramsey is a Researcher in the Department of Philosophy at Carnegie Mellon University , affiliated with the Dietrich College of Humanities and Social Sciences . He serves as Director of Research Computing and has been instrumental in developing computational infrastructure and algorithms for causal inference. Core projects: Tetrad (causal search algorithms), AProS (proof generator for logic), Causality Lab , and Laboratory for Symbolic and Educational Computing . His research spans causal modeling, algorithm design, and applications in neuroscience, bioinformatics, and education. He has contributed to software tools like Causal-learn and Py-Tetrad , enabling scalable causal discovery in high-dimensional datasets. He has received funding from NASA, NSF, and the University of Pittsburgh for projects ranging from Martian rover software to glaucoma detection models. His work integrates philosophy, computer science, and applied statistics.

Neville HoganView profile

Professor

• Rehabilitation Engineering
• Robotics
• Motor Neuroscience
• +3 more

Neville Hogan is a Professor of Mechanical Engineering and Brain & Cognitive Sciences at MIT, and Director of the Newman Laboratory for Biomechanics and Human Rehabilitation. He holds a Ph.D. from MIT and has been a faculty member since 1979, leading roles in the Mechanical Engineering department's System Dynamics and Control division. His research bridges biology and engineering, focusing on motor neuroscience, rehabilitation robotics, and human-robot interaction. He co-founded Interactive Motion Technologies, Inc. and is a board member at Advanced Mechanical Technologies, Inc. Education: Dip. Eng. (Dublin College of Technology), M.S., M.E., and Ph.D. (MIT) Research interests include developing robots for neurological rehabilitation, such as gentle physiotherapy devices for stroke patients. His work emphasizes physically cooperative machines and human balance control analysis. Awards include Honorary Doctorates from Delft University and Dublin Institute of Technology, and the Silver Medal from Ireland’s Royal Academy of Medicine. Awards: Honorary Doctorates, Silver Medal, and other distinctions Led the Newman Lab, focusing on biomechanics and human rehabilitation. Advised numerous students (not listed here). His publications explore dynamic primitives, impedance control, and human-robot interaction, with applications in exoskeletons and rehabilitation therapies.

Nick HeardView profile

Professor

• Computational Bayesian Inference
• Clustering
• Changepoint Analysis
• +6 more

Nick Heard is a Professor and Chair in Statistics at the Department of Mathematics, Faculty of Natural Sciences, Imperial College London. His research focuses on computational Bayesian inference, clustering, and changepoint analysis applied to dynamic networks (e.g., computer networks, social networks) and bioinformatics. He leads the EPSRC-funded NeST project on Network Stochastic Processes and Time Series, collaborating with universities including Bristol, Oxford, and LSE. His work bridges statistical theory with applied problems in cyber-security and neuroscience. Research Interests - Modelling large dynamic networks - Changepoint analysis and anomaly detection - Statistical methods for cyber-security - Bayesian computation and inference - Spectral clustering and graph embeddings Grants & Collaborations - Co-leads the NeST project on Dynamic graph embeddings: procedures and inference - EPSRC Programme Grant (EP/T004870/1) supporting Network Stochastic Processes and Time Series research Software & Tools - Developed open-source packages for Bayesian changepoint analysis (e.g., changepoints ) - Code for p-value combination methods ( standardised_partial_product )

Philippe DixonView profile

Assistant Professor

• Biomechanics
• Neuroscience
• Machine Learning
• +3 more

Dr. Philippe Dixon is an Assistant Professor in the Department of Kinesiology & Physical Education at McGill University, with a division in Biomechanics and Neuroscience. He holds adjunct professor roles at the University of Montreal (School of Kinesiology and Physical Activity Sciences) and the University of Laval (Department of Kinesiology). His research focuses on human movement biomechanics using motion capture systems and wearable sensors, combined with machine learning for health and athletic performance optimization. He has expertise in gait analysis, muscle coactivation patterns in cerebral palsy, and predictive modeling of physiological states. Dr. Dixon earned a Post-doctoral fellowship in Public Health at Harvard University, a PhD in Engineering Science from the University of Oxford, and dual degrees in Biomechanics and Physics from McGill University. His education includes a Bachelor of Education in Mathematics and Physics (McGill), a Master of Science in Biomechanics (McGill), and a PhD in Engineering Science (Oxford). He has received grants such as the NSERC Discovery Grant (2022–2027) and the FRQSC AUDACE Grant (2022). His work emphasizes wearable sensor integration, with contributions to datasets like NACOB and tools like OpenOFM. He currently supervises Master’s and PhD students in biomechanics and machine learning applications. Key research themes include gait adaptations on uneven surfaces, machine learning for cough detection via smart garments, and musculoskeletal coordination in clinical populations. His articles span biomechanical modeling, wearable sensor validation, and neuro-musculoskeletal analysis, reflecting interdisciplinary innovation in human movement science.

• Biotechnology
• Systems Engineering in Bioprocessing
• Model-Based Tools
• +4 more

Professor Cleo Kontoravdi is a Professor of Biological Systems Engineering at the Department of Chemical Engineering, Imperial College London, within the Faculty of Engineering. Her roles include Director of Postgraduate Studies (2021–present) and Postgraduate Admissions Tutor (2018–2021). She holds affiliations with key research centers such as the Centre for Process Systems Engineering, Centre for Synthetic Biology, and Future Vaccine Manufacturing Research Hub. Her research focuses on applying systems engineering principles to bioprocessing, integrating model-based tools like sensitivity analysis and optimization with experimental work on mammalian cell cultures and vaccine production. Key areas include metabolic flux analysis, media optimization, and multiscale modeling. Her recent publications highlight advancements in hybrid modeling frameworks, mRNA vaccine process design, and metabolic engineering of CHO cells. She actively contributes to vaccine manufacturing strategies and sustainable biopharmaceutical supply chains. Education: PhD (2007) and MEng (2002) in Chemical Engineering from Imperial College London. Professional experience spans academic roles (since 2007) and industry R&D at Lonza Biologics (2006–2007). Her work bridges computational biology, bioprocess engineering, and systems biology, addressing challenges in vaccine development and production efficiency. Awards: Not explicitly listed in the provided text. Research grants and collaborations are implied through her involvement in high-impact projects like the Future Vaccine Manufacturing Hub. She advises on bioprocess optimization, glycoengineering, and biomanufacturing sustainability.

Patrick JennyView profile

Professor

• Computational Fluid Dynamics
• Turbulent Reactive Flows
• Multiphase Flow Modeling
• +3 more

Prof. Patrick Jenny is a Full Professor at the Department of Mechanical and Process Engineering and Head of the Institute of Fluid Dynamics at ETH Zurich. His research focuses on computational fluid dynamics (CFD), numerical methods for turbulent and multiphase flows, and reservoir simulation. He has held positions at ChevronTexaco and Cornell University, and received the National Latsis Prize 2005. PhD in CFD from ETH Zurich (1997) Postdoctoral work at Cornell University (1997–1999) Senior Researcher at ChevronTexaco (1999–2003) Research interests include: turbulent reactive flows, PDF modeling, multi-scale reservoir simulation, and data assimilation in engineering systems. He teaches courses on fluid dynamics, turbulence, and computational methods. Over 100 peer-reviewed publications span topics like fracture modeling, LES/RANS coupling, and particle-laden flows. His work bridges academia and industry, addressing challenges in energy systems, environmental engineering, and numerical algorithms. Winner: National Latsis Prize 2005 Led over 20 PhD projects and collaborates with institutions globally. His lab develops open-source tools for CFD and energy systems analysis.

Benno LiebchenView profile

Professor

• Soft Matter Physics
• Active Matter
• Colloidal Systems
• +3 more

Prof. Benno Liebchen holds a faculty position at the Technische Universität Darmstadt within the Institute for Condensed Matter Physics , part of the Faculty of Physics. He leads the Liebchen Group , dedicated to advancing research in the Theory of Soft Matter , focusing on active matter, colloidal systems, and non-equilibrium phenomena. His work explores collective behavior in self-propelled particles, phase transitions in active fluids, and adaptive strategies in smart materials. Research Interests include: Active matter dynamics and pattern formation Non-equilibrium statistical mechanics Biophysical systems and biomimetic design Computational modeling of soft matter Recent publications highlight breakthroughs in intelligent active particles , self-reverting vortices , and motility-induced phase coexistence . His lab develops tools like the AMEP Python package to analyze active systems. Teaching responsibilities include advanced modules in soft matter physics. Collaborative projects involve interdisciplinary approaches to microswimmer behavior and machine learning-driven optimization of collective systems. Contact: +49 6151 16-24509 / Office: S2|04 104

Ed ChienView profile

Assistant Professor

• Computer Science
• Graphics
• Computational Engineering
• +3 more

Ed Chien is an Assistant Professor at Boston University's Department of Computer Science within the College of Arts & Sciences. He specializes in applying differential geometry and topology to graphics, computational engineering, and machine learning. Previously, he was a postdoctoral researcher at MIT's CSAIL and Bar-Ilan University. His work focuses on mathematically rigorous solutions to problems in geometric data processing and optimal transport. Education PhD in Mathematics, Rutgers University (2015) A.B. in Mathematics & Physics, Dartmouth College (2009) Research Highlights Dr. Chien's research includes fundamental studies on hexahedral mesh topology for Finite Element Modeling, optimal transport applications in machine learning, and singularity-free geometric algorithms. His work bridges theoretical mathematics with computational tools for engineering and graphics. Publications span top venues like NeurIPS, Eurographics, and SIGGRAPH, reflecting contributions to geometry processing and machine learning intersections. Program committee roles include Eurographics SGP (2019) and AAAI (2020). Awards & Recognition No specific awards listed, but notable service includes program committee memberships and peer-reviewed contributions to leading conferences. Grants & Advising Active in mentoring through academic positions but no explicit grant details provided. Research focuses on advancing geometric algorithms and optimal transport methodologies.