Find Academic Supervisors

Jonas ZmuidzinasView profile

Professor

• Submillimeter Astronomy
• Astrophysics
• Superconducting Detectors
• +5 more

Jonas Zmuidzinas is the Merle Kingsley Professor of Physics at the California Institute of Technology, affiliated with the Division of Physics, Mathematics, and Astronomy. He holds joint roles as a Senior Research Scientist (2005-11) and former Chief Technologist (2011-16) at the Jet Propulsion Laboratory (JPL). His research focuses on submillimeter astronomy, instrumentation, and superconducting detector technologies. Key projects include the Caltech Submillimeter Observatory (CSO) and the CCAT (Cornell-Caltech Atacama Telescope) initiative in Chile. Education: B.S. from Caltech (1981), Ph.D. from UC Berkeley (1987). He has held academic positions at Caltech since 1989, progressing from Assistant to Full Professor. Awards include the NSF Presidential Young Investigator (1990-95) and ASCIT Teaching Award (1995). Research areas: Submillimeter astronomy, superconducting detectors (e.g., MKIDs), instrumentation development, galaxy evolution, and star formation. Leadership roles: Directed the Micro-Devices Laboratory at JPL (2007-11) and Caltech Optical Observatories (2018-23). Service: Active in NASA committees, SOFIA Science Steering Committee, and SPIE conference organization. His work bridges physics, engineering, and astronomy, emphasizing innovative technologies like MKIDs for sensitive astronomical surveys. Current efforts include developing detectors and instruments for next-generation telescopes.

Jürg Alexander SchiffmannView profile

Associate Professor

• Gas lubricated bearings
• Small-scale turbomachinery
• Mechanical design optimization
• +5 more

Jürg Alexander Schiffmann is an Associate Professor at École Polytechnique Fédérale de Lausanne (EPFL), leading the Laboratory for Applied Mechanical Design (LAMD). His work focuses on gas-lubricated bearings, small-scale turbomachinery, and automated design methodologies for energy systems. Academic Affiliation: EPFL School of Engineering Key Roles: Teaching, PhD program committee member (Energy & Robotics), Lab Director Research Interests His research bridges mechanical design optimization and small-scale energy systems , with a focus on gas bearings for turbocompressors, Organic Rankine Cycles for waste heat recovery, and herringbone grooved journal bearings . He pioneers AI-driven tools like DARTS-NETGAB for real-time turbomachinery simulation and surrogate modeling for robust design. Recent Publications span 2025–2023, emphasizing neural networks in optimization, experimental validation of gas bearings, and thermal management in high-speed turbomachinery. Trends highlight cross-disciplinary integration of AI and energy systems. Scientific Awards SwissElectric Research Award (PhD work) Advising includes supervising 25+ PhD students (e.g., Abramishvili Anna, Massoudi Soheyl) on topics like scroll expanders , rotordynamics , and haptics in automated driving . His grants involve collaborations with MIT, CERN, and industry partners like Fischer Engineering Solutions.

• DNA Replication and Repair
• Single-Molecule Imaging
• Molecular Biophysics
• +5 more

Professor Antoine van Oijen is the Interim Pro-Vice-Chancellor (Research Enterprise) at the University of Sydney's Faculty of Medicine and Health, where he oversees strategic and operational objectives. He holds an NHMRC Leadership Fellowship (2021-2025) and previously served as an ARC Laureate Fellow (2015-2020). Prior to Sydney, he led research groups at Harvard Medical School (USA), Groningen University (Netherlands), and the University of Wollongong (Australia). In Wollongong, he founded Molecular Horizons, a molecular life sciences institute, and established major core facilities. His work bridges physics and biology to advance understanding of cellular functions. Research Interests: Single-molecule imaging techniques for studying DNA replication and repair mechanisms Development of molecular biophysical tools and biosensors Antimicrobial resistance in microbial systems Protein dynamics and chaperone function His studies emphasize real-time visualization of molecular processes, such as replisome behavior in live cells and enzymatic interactions in genome maintenance pathways. Grants: 2024: Single-molecule Manipulation and Interaction Facility (SMIF) – ARC/LIEF grant 2023: USYD-Glasgow Biomedical Engineering Alliance – Ignition Grants He has contributed to interdisciplinary projects, including Molecular Horizons and cutting-edge sensor systems like the zMovi platform. His research highlights the role of molecular heterogeneity in biological processes and explores solutions to combat antibiotic resistance through One Health approaches.

• Power Electronics
• Control Systems
• Digital Control
• +7 more

Ali Khajehoddin is a Professor in the Department of Electrical & Computer Engineering at the University of Alberta, and holds the Senior UofA Engineering Research Chair in Energy Systems. He earned his Ph.D. in Electrical Engineering from Queen’s University, Canada, in 2010. His research focuses on high-power-density power converters, microinverter design for photovoltaic systems, grid integration, energy storage, and smart grid technologies. He co-founded a startup for power analyzers and smart metering systems and led R&D at SPARQ Systems Inc., commercializing microinverters from 2010 to 2013. Education: Ph.D., Electrical Engineering, Queen’s University (2010) His research interests include power electronics control systems, digital control, high-frequency power supplies, and renewable energy systems. He has received the Second Place Paper Award from IEEE Transactions on Power Electronics (2022) and serves as an Associate Editor for multiple IEEE journals. Key research contributions span soft-switching techniques, high-efficiency inverters, and battery energy storage systems. He leads the uAPEL (University of Alberta Power Electronics Lab) research group, advancing innovations in power electronics for sustainable energy systems.

Eun-Seok KimView profile

Professor

• Operations Management
• Supply Chain Management
• Combinatorial Optimization
• +3 more

Dr. Eun-Seok Kim is a Reader in Operations Management and Programme Director for MSc International Business at Queen Mary University of London. He holds a PhD in Operational Research from KAIST (South Korea). Previously, he was a Senior Lecturer at Middlesex University. His research focuses on combinatorial optimization and scheduling theory within operations and supply chain management. Methodologies include mathematical programming and stochastic analysis, with applications in telecommunication and actuarial science. He is a member of the Centre for Globalisation Research (CGR). Teaching responsibilities span undergraduate and postgraduate courses such as Operations Management, Quantitative Analytics, and Cryptocurrency & Blockchain Programming. His supervision expertise welcomes doctoral students with backgrounds in mathematics, computer science, or industrial engineering. Publications emphasize optimization challenges in manufacturing, energy systems, and insurance risk allocation. Recent work includes multi-objective models for steel industry production planning and algorithmic advancements in scheduling theory.

Shanxin YuanView profile

Lecturer

• 3D Computer Vision
• Reconstruction and Rendering
• Multimodal Learning
• +5 more

Dr. Shanxin Yuan is a Lecturer in Digital Environment at the School of Electronic Engineering and Computer Science, Queen Mary University of London. Previously, he worked as a Senior Research Scientist at Huawei Noah’s Ark Lab, London. His research focuses on computer vision and machine learning, particularly 3D digital humans, computational photography, and multimodal learning. Key research interests include hand pose estimation, neural rendering, and image restoration techniques such as super-resolution and HDR imaging. Research Interests: 3D Computer Vision, Reconstruction, and Rendering Neural Rendering for Deformable Objects Low-level Vision and Image Restoration Multimodal Learning and Vision-Language Integration Applications in Immersive Gaming, Music Understanding, and Fashion AI Recent Research Trends: Recent publications emphasize advancements in 3D hand modeling, neural radiance fields, and adaptive image restoration techniques. Work on motion retargeting and spatial-temporal modeling reflects growing focus on dynamic human digitization and realistic rendering. Labs & Affiliations: Active in the Digital Environment Research Institute (DERI) and the Centre for Multimodal AI , collaborating on projects that bridge computer vision with real-world applications.

Simon HadfieldView profile

Associate Professor

• Computer Vision
• Robotics
• Machine Learning
• +3 more

Dr Simon Hadfield is an Associate Professor (Reader) in Robot Vision and Autonomous Systems at the University of Surrey, affiliated with the Centre for Vision, Speech and Signal Processing (CVSSP) within the School of Computer Science and Electronic Engineering. His research focuses on advancing computer vision and machine learning techniques for real-world applications, particularly in robotics. He holds a PhD and certifications such as FHEA (Fellow of the Higher Education Academy) and AUS (Advanced University Skills). Research Interests: - Effective exploitation of novel visual sensors - Practical application of computer vision to robotics - Efficient dynamic reconstruction techniques - Real-time robotics applications - Sensor fusion and calibration systems Recent projects include the NIMROD initiative exploring analogue visual sensors and contributions to multimillion-pound research efforts in sustainable packaging and agricultural robotics. His work emphasizes bridging the gap between lab-based techniques and real-world deployment, with publications in top-tier conferences and journals. Notable achievements include developing paradigms for low-complexity 3D reconstruction and proposing methods for depth estimation and localization. His research has been funded by grants such as a £1m award for robotic applications in farming. Collaborations and lab affiliations include CVSSP, where he leads projects at the intersection of vision, robotics, and autonomous systems. Future work continues to focus on scalable vision techniques and ethical AI practices in representation learning.

Jonathan Weaver-RosenView profile

Assistant Professor

• Design Theory and Methodology
• Computational Methods for Design Optimization
• Engineering Education

Jonathan Weaver-Rosen is an Instructional Assistant Professor in the Department of Mechanical Engineering at Texas A&M University, holding a Ph.D. and B.S. in Mechanical Engineering from the same institution. His research specializes in design automation methodologies, particularly parametric optimization and adaptive systems design. Research interests focus on: Design theory and methodology Computational methods for design optimization Engineering education pedagogy and curriculum development His work emphasizes preparing engineers to effectively utilize analysis tools and collaborate in teams. Awards include: 2024 Peggy L. & Charles Brittan Outstanding Undergraduate Teaching Award 2023 ASME Best Teacher Award 2020 J. Mike Walker '66 Impact Award

Oskar KvimanView profile

Researcher

• Bayesian phylogenetics
• Probabilistic machine learning
• Variational inference
• +15 more

Oskar Kviman is a doctoral student at KTH Royal Institute of Technology working in the Lagergren Lab within the Division of Computational Science and Technology. His research bridges machine learning, statistics, and computational biology with a focus on developing and applying advanced probabilistic methods. His primary research interests include: Bayesian phylogenetics and probabilistic machine learning Variational inference, variational auto-encoders, and sequential Monte Carlo methods Generative AI techniques including flow matching, Schrödinger bridges, and diffusion models Computational cancer research focusing on differential expression testing and spatial transcriptomics Kviman's publication record demonstrates significant contributions to variational inference methodology, particularly in phylogenetics and generative modeling. His work spans top machine learning conferences including ICML, NeurIPS, and AISTATS, showing consistent development of techniques that improve efficiency and accuracy in probabilistic modeling. Recent publications focus on multi-marginal flow matching, variational resampling, and mixture learning in black-box variational inference. He has been recognized for his peer review contributions as a Top reviewer (10%) for AISTATS 2023. Kviman has supervised master's theses for Xindi Liu and Ricky Molén at KTH and serves as a lecturer for 'Statistical Methods in Applied Computer Science' since 2021, while previously working as a teaching assistant for 'Machine Learning, Advanced Course' and 'Deep Learning, Advanced Course'.

• Operations Research
• Scheduling
• Production Planning
• +7 more

Zehra Düzgit is an Assistant Professor in the Department of Industrial Engineering at Istanbul Bilgi University, where she has been serving since 2020. She earned her PhD in Industrial Engineering from Boğaziçi University in 2015 and has remained affiliated with Istanbul Bilgi University as an Instructor (2015–2020) and Research Assistant (2011–2015). Her academic work is centered on optimization and logistics within industrial systems. Education: PhD, Industrial Engineering, Boğaziçi University, 2015 MS, Engineering and Technology Management, Boğaziçi University, 2007 BS, Systems (Industrial) Engineering, Yeditepe University, 2003 Her research interests include Operations Research, Scheduling, Production Planning, Warehouse Optimization, and Meta-heuristic Algorithms. She applies these to real-world problems in logistics, manufacturing, and service systems. Her work often involves algorithmic solutions for warehouse order picking, hybrid flowshop scheduling, and due date assignment under dynamic and stochastic environments. The recent articles demonstrate a strong focus on warehouse logistics, particularly in automotive and food packaging industries, with increasing integration of robotics and automation. Her publications span journal articles, book chapters, and conference proceedings, showing consistent scholarly output. She frequently collaborates with students and colleagues on applied industrial engineering problems. Scientific Contributions: Extensive publications in peer-reviewed journals and conference proceedings Contributions to Springer Lecture Notes and edited volumes Active participation in national and international conferences (YA/EM, ISPR, LMSCM, EURO) Zehra Düzgit has advised numerous students on research projects related to production planning, scheduling, and logistics. Her teaching portfolio includes core industrial engineering courses such as Optimization Methods, Production Planning and Control, and Engineering Statistics. She has not received any explicitly mentioned scientific awards. She is involved in research teams focusing on warehouse systems, scheduling algorithms, and logistics optimization, often in collaboration with industry partners.

Pierre FouilhouxView profile

Professor

• Combinatorial Optimization
• Mathematical Programming
• Polyhedral Approaches
• +4 more

Pierre Fouilhoux is a Full Professor in Computer Science and Discrete Mathematics at Sorbonne Paris Nord University (formerly Paris 13 University), affiliated with the LIPN Laboratory and the Algorithmics and Combinatorial Optimization (AOC) Team. He previously held a position at Sorbonne Université's LIP6 Laboratory. His research focuses on Combinatorial Optimization, Mathematical Programming, Polyhedral Approaches, and Operational Research, with contributions to scheduling, network design, and robust optimization. Education: PhD in Computer Science (2004): Université Blaise Pascal, Clermont-Ferrand. Habilitation à Diriger des Recherches (HDR, 2018): Sorbonne Université. DEA in Computer Science and Operational Research, License and Master in Computer Science, and Mathematics License. Research Interests: His work addresses polyhedral structures, exact algorithms, and combinatorial optimization problems such as scheduling, unit commitment, and network reliability. He has developed Branch-and-Cut methods and symmetry-breaking techniques for integer programming. Publications: Over 30 peer-reviewed articles in journals like European Journal of Operational Research , Mathematical Programming , and Discrete Applied Mathematics . Recent work includes anchor-robust scheduling, unit commitment polytopes, and orbitopal fixing. Awards: 2006 Grand Prize for Young Researcher from Clermont-Ferrand for his contributions to science popularization and research communication. Teaching: Courses on Operations Research, Combinatorial Optimization, and Linear Programming at Sorbonne University and SUP Galilée. Supervised over 20 graduate and doctoral students, including topics like multi-objective optimization and virtual network embedding. Labs/Teams: Active in the AOC Team at LIPN, GDR-RO research group, and ISCO conferences. Collaborates with institutions like EDF R&D and Colt-Telecom on applied optimization problems.

Constantin PapeView profile

Assistant Professor

• Deep Learning
• Computer Vision
• Biomedical Imaging
• +4 more

Constantin Pape is a Junior Professor at Georg-August University Göttingen in the Institute of Computer Science, where he leads the Computational Cell Analytics research group since March 2022. His work focuses on developing deep learning and AI methods for biology and medicine, with particular emphasis on biomedical imaging applications. His research spans two main areas: building foundation models for biology and medicine, and protein structure analysis in cryogenic electron microscopy and super-resolution microscopy. Dr. Pape's group is dedicated to open source and open science, contributing to projects like the bioimage.io modelzoo, ome.ngff image data format, and MoBIE Fiji plugin. His research is funded by the DFG through a Sachbeihilfe, the SFB1286 on Quantitative Synaptology, and the Multiscale Bioimaging Cluster of Excellence (MBExC). Dr. Pape's work on foundation models includes Segment Anything for Microscopy (Nature Methods, 2025) and extensions to histopathology and medical imaging. His group develops segmentation models for various applications, including SynapseNet for automatic synapse reconstruction. He has published extensively in top venues including Nature Methods, Cell, Science, and ECCV. His teaching includes a lecture on Deep Learning for Computer Vision and seminars on Deep Learning in Biology and Medicine at the University of Göttingen, as well as co-organizing the Deep Learning for Image Analysis course at EMBL and teaching advanced image analysis through the Hertha Sponer College.

• Computer Animation
• Machine Learning
• Virtual Reality
• +6 more

Professor Xiaosong Yang serves as Professor and Deputy Head of Department at the National Centre for Computer Animation, Bournemouth University. With over 30 years of experience in research, education, and professional practice, he leads the MSc AIM program and has established himself as a prominent figure in computer animation and AI applications. His work bridges academic research with industry applications through collaborations with organizations including Disney Research and Double Negative. Professor Yang's research spans computer animation, machine learning, data mining, digital health, virtual reality, augmented reality, medical visualization, and surgery simulation. His innovative work in muscle-based animation has received international recognition from prestigious outlets such as NewScientist and MIT Technology Review. He has developed novel techniques that have been successfully integrated into animation production pipelines, demonstrating practical applications of his research. His publication record includes over 140 peer-reviewed papers in prestigious journals and conferences, with recent work focusing on AI-driven animation techniques, medical visualization, and human-computer interaction. The research trends show a consistent progression from foundational computer animation techniques toward increasingly sophisticated AI applications in both entertainment and healthcare domains. Recognized Research Supervisor from UK Council for Graduate Education Professor Yang has supervised over 30 PhD students and numerous postdocs, research assistants, and international visiting scholars. His research impact is further evidenced by securing over 35 research grants from major funding bodies including the European Commission, Arts and Humanities Research Council, British Academy, Leverhulme, British Council, Newton Fund, Innovate UK, Wessex AHSN, and Higher Education Innovation Fund. He serves on the Peer Review College of the Arts and Humanities Research Council (AHRC) UK and the Computer Science Evaluation Group of the Natural Science and Engineering Research Council of Canada (NSERC). He actively contributes to the academic community as a program committee member for international conferences (SIGGRAPH Asia, CGI, CASA), conference program chair (CGI2012, CASA2020/2022), and reviewer for top journals (TVCG, ACM ToG, C&G) and conferences (SIGGRAPH, Eurographics, ISMAR).

Corentin DumeryView profile

Researcher

• Computer Vision
• Computer Graphics
• 3D Reconstruction
• +4 more

Corentin Dumery is a Doctoral Assistant at École Polytechnique Fédérale de Lausanne (EPFL) , affiliated with the Computer Vision Laboratory (CVLAB) and Machine Learning and Optimization Laboratory (MLO) within the School of Computer and Communication Sciences . His research bridges Computer Vision and Computer Graphics , focusing on 3D scene reconstruction, garment modeling, and neural rendering techniques. Previously, he interned at Meta Redmond , worked at CEA Paris-Saclay on polycube mapping, and was a visiting researcher at ETH Zurich under Prof. Olga Sorkine-Hornung . Corentin holds dual MSc degrees in Computer Science from National University of Singapore (NUS) and Télécom Paris . His work emphasizes 3D content creation for AR/VR Diffusion models for garment reconstruction Neural radiance field optimization Polycube mapping for hexahedral meshing His recent publications (2022–2025) span top venues like SIGGRAPH , ICCV , and CVPR , addressing challenges in 3D Gaussian splatting, view-consistent NeRF training, and single-view garment recovery. He also contributes to academic service as an Outstanding Reviewer at CVPR25 and co-organizes workshops like OpenSUN3D . At EPFL, he serves as Head Teaching Assistant for courses CS433 Machine Learning (2023–2024) and CS442 Computer Vision (2023–2024). Additionally, he is the VP/Treasurer of EPIC , EPFL's computer science PhD association.

• Financial Mathematics
• Investment Analysis
• Capital Budgeting
• +4 more

Carlo Alberto MAGNI is a Full Professor in the Department of Economics "Marco Biagi" at the University of Modena and Reggio Emilia. He teaches courses including General and Financial Mathematics and Principles and Models for Managerial Decisions for the Business Economics and Management degree program. Professor MAGNI's research focuses on financial mathematics, investment analysis, and capital budgeting. His work centers around the development of the Split-Screen Approach for project appraisal, which unifies financial planning and investment analysis into a single theoretical framework. His research spans corporate finance, accounting, operations research, and mathematical methods of economics, with particular emphasis on NPV-consistency of rates of return, project valuation methodologies, and financial modeling techniques. His publication record shows a consistent output of high-quality research, with numerous articles in top journals. His work demonstrates a strong integration between theoretical finance concepts and practical applications, particularly in Excel-based financial modeling. The Split-Screen Approach represents his signature contribution to the field, providing innovative methods for project appraisal and financial planning. Professor MAGNI has developed comprehensive teaching materials, including a videobook for his courses that combines video lectures with texts related to course topics. His educational approach emphasizes the application of mathematical tools in economic and business analysis, with focus on developing students' ability to model and evaluate long-term managerial decisions.