Find Academic Supervisors

• Quantitative PET/CT and SPECT/CT Imaging
• Deep Learning in Medical Imaging
• Motion Correction Algorithms
• +2 more

Chi Liu is a Professor of Radiology & Biomedical Imaging at Yale School of Medicine . He serves as Associate Director of Biomedical Imaging Technology at the Yale Biomedical Imaging Institute and Director for Research Faculty Affairs in the Radiology & Biomedical Imaging department. Education : PhD from Johns Hopkins University (2008) Postdoctoral Training : University of Washington (2010) Certification : American Board of Science in Nuclear Medicine (Nuclear Medicine Physics and Instrumentation) His research focuses on quantitative cardiac and oncological PET/CT and SPECT/CT imaging , emphasizing deep learning algorithms , reconstruction algorithms , data correction , and dynamic imaging . Key clinical applications include early detection of chemotherapy-induced cardiotoxicity , multimodality imaging of heart failure , and motion variability elimination in therapy response assessment . The 15 most recent publications reveal a strong emphasis on deep learning techniques for low-dose imaging , motion correction , and cross-tracer generalizability in PET/SPECT systems. These works span applications in cardiac imaging , neuroscience , oncology , and theranostics . Scientific Award : Bruce Hasegawa Young Investigator Medical Imaging Science Award (2012) Contact: chi.liu@yale.edu | ORCID 0000-0002-7007-1037

Simon GoodwillView profile

Research Fellow

• Sports Engineering
• Performance Analysis
• Software Development
• +3 more

Dr. Simon Goodwill is the Head of Research in Sport and Physical Activity and Head of the Sports Engineering Research Group (SERG) at Sheffield Hallam University's Academy of Sport and Physical Activity. He holds a PhD focused on tennis ball-racket impact modeling and has collaborated with the International Tennis Federation. His expertise includes novel photogrammetry techniques, software development for athlete performance analysis, and camera calibration systems like check2d/check3d. He led projects supporting Team GB athletes in multiple Olympics, contributing to 42 medals at Rio 2016. Currently, he oversees the Advanced Wellbeing Research Centre (AWRC), applying sports performance methodologies to health research. His work integrates machine vision, data acquisition hardware, and elite athlete monitoring systems, with notable contributions to the EIS Innovation Partnership and sports tech exhibits like Le Tour Yorkshire's bike simulator. Education & Research: Simon’s PhD (modeling tennis impacts) and extensive software engineering background underpin his research. His projects span biomechanics, sports engineering, and health tech, with collaborations involving UK Sport, FIFA, and Adidas. He has developed systems used by athletes in London 2012, Rio 2016, and Glasgow 2014 Commonwealth Games. Research Themes: His work emphasizes performance analysis through photogrammetry, real-time athlete tracking, and health-related applications of sports tech. Key projects include court pace rating for the ITF, iBoxer2 boxing data tools, and the AWRC’s health innovations. Grants & Impact: The AWRC, funded by UK DoH and ESIF (£15.7M), reflects his role in translating elite athlete tech to public health. His work is cited in UK government reports on Olympic legacy and has received media attention, including BBC features on GB Boxing’s iBoxer software. Advising & Leadership: Supervised 17 PhD students on topics like machine learning in taekwondo, swimming analysis, and tennis racket dynamics. Served as External/External advisor and led 10+ Olympic cycle projects. Active in the International Sports Engineering Association as Director.

Eugene DemlerView profile

Professor

• Theoretical Condensed Matter Physics
• Atomic and Molecular Physics
• Quantum Optics
• +1 more

Eugene Demler is a Full Professor at the Department of Physics, ETH Zurich. Previously, he held academic positions at Harvard University from 1998 to 2021, including Assistant Professor (2001-2004), Associate Professor (unspecified dates), and Full Professor (2005-2021). His work bridges theoretical condensed matter physics, atomic and molecular physics, quantum optics, and quantum simulations. Education: MSc in Physics, Moscow Institute of Physics and Technology (1993) Diploma work, Lebedev Physics Institute (1992-1993) PhD in Theoretical Physics, Stanford University (1998), supervised by S.C. Zhang Demler's research focuses on strongly correlated quantum systems, spintronics, quantum sensing, and photo-induced phase transitions. His recent publications explore topics such as quantum polarons, Josephson plasmons, magnon dynamics, and terahertz spectroscopy in superconductors. He has pioneered hybrid quantum-classical methods for electron-phonon systems and cavity-mediated quantum materials. His Google Scholar articles (2023-2025) span theoretical and experimental domains, with keywords including Quantum Physics , Condensed Matter Physics , and Quantum Optics . Subfields include Quantum Control , Superconductivity , Spin Waves , Quantum Sensing , Non-Equilibrium Dynamics , and Quantum Simulation . Scientific Awards: Hamburg Prize for Theoretical Physics (2021) Simons Investigator (2021) Moore Distinguished Scholar (2020) Hanna Visiting Scholar (2019) Highly Cited Researcher (2017-2020) Senior Fellow at ETH Zurich's Institute for Theoretical Studies (2015) Simons Fellowship (2015) Distinguished Scholar at Max Planck Institute of Quantum Optics (2015) Thomson Reuters Highly Cited Researcher (2014) Siemens Research Award (2006) Johannes Gutenberg Lecture Award (2006) NSF Career Award (2002) Sloan Fellowship (2002) Demler teaches courses such as Statistical Physics and Strongly Correlated Systems in Atomic and Condensed Matter Physics . His work integrates theoretical modeling with experimental collaborations, particularly in quantum optics and condensed matter systems.

Sai RavelaView profile

Researcher

• Nonlinear Stochastic Dynamics
• Coherent Fluids
• Uncertainty Quantification
• +6 more

Sai Ravela is a Principal Research Scientist in the Department of Earth, Atmospheric and Planetary Sciences (EAPS) at the Massachusetts Institute of Technology (MIT). His research focuses on nonlinear stochastic dynamics, coherent fluid systems, uncertainty quantification, and autonomous observing technologies. He specializes in developing data-driven methodologies for natural hazard detection, climate change impacts, and environmental risk assessment. Ravela’s work integrates computational science with geophysical applications, including storm surge modeling, extreme rainfall analysis, and geothermal exploration. He pioneers techniques like neural dynamical systems and adversarial learning to improve predictive accuracy in nonstationary climate regimes. His contributions span environmental monitoring systems, autonomous aircraft resilience frameworks, and policy-informed climate vulnerability assessments. Key research areas include: Coastal flood risk in Bangladesh and Vietnam Dynamic data-driven applications systems (DDDAS) Machine learning for geosciences and environmental systems Uncertainty quantification in complex fluid dynamics He leads interdisciplinary projects at MIT’s Computational Science and Engineering (CSE) program, advancing methods for data assimilation, surrogate modeling, and real-time environmental observatories. His innovations bridge theoretical frameworks with practical solutions for climate adaptation and disaster resilience.

Erkut ErdemView profile

Professor

• Computer Vision
• Machine Learning
• Visual Saliency Prediction
• +5 more

Erkut Erdem is a Professor in the Department of Computer Engineering at Hacettepe University, where he leads the Computer Vision Laboratory (HUCVL). His research focuses on computer vision and machine learning, particularly on incorporating different kinds of context (spatial, temporal and cross-modal) into visual processing across all levels from low to high-level vision. He received his Ph.D. (2008), M.Sc. (2003), and B.Sc. (2001) from Middle East Technical University. Prior to joining Hacettepe University in 2010, he completed a post-doctoral fellowship at Ecole Nationale Supérieure des Télécommunications (2009-2010) and held visiting researcher positions at UCLA (2007) and Virginia Tech (2004). His current research interests include Visual Saliency Prediction, Automatic Image Description, Video/Photoset Summarization, Image Filtering, and Image Editing. Recent work has focused on multimodal learning with video-language models, diffusion-based image editing, and event-based vision for low-light conditions. His research has been published in top venues including NeurIPS, ICLR, ICCV, SIGGRAPH, and ACL. He has received significant recognition including The Young Researcher Award from Turkish Academy of Sciences and being named a 2022 Outstanding Associate Editor of IEEE Transactions on Multimedia. He has secured multiple research projects funded by TUBITAK and received gift funds from Adobe Research for text-guided image synthesis work. Current Teaching: BBM202: Algorithms, AIN434/BBM444: Fundamentals of Computational Photography Graduate Supervision: 6 current Ph.D. students, numerous recent graduates including Burak Ercan (2024) and Aysun Kocak (2023) Professional Affiliations: Co-affiliated with Koç University and İş Bank AI Center (KUIS AI)

Richard MA TianbaiView profile

Associate Professor

• Systems & Networking
• Computer Networks
• Distributed Computing
• +3 more

Richard MA Tianbai is an Associate Professor at the School of Computing, National University of Singapore (NUS), and serves as IT Coordinator for Computing Facilities. He holds a B.Sc. (First-Class Honors) and M.Phil. from the Chinese University of Hong Kong, followed by an M.Sc. and Ph.D. from Columbia University, USA. His research focuses on systems & networking, distributed computing, and internet economics, with projects addressing cloud-based big data systems and internet peering agreements. Richard has received multiple awards, including the Best Paper Award Runners-up at ACM Mobihoc 2020 and the Teaching Excellence Award from NUS School of Computing in 2019. His work bridges theoretical models with practical applications in network economics and distributed systems. Education: B.Sc. (First-Class Honors), Computer Science & Engineering, Chinese University of Hong Kong (2002) M.Phil., Computer Science & Engineering, Chinese University of Hong Kong (2004) M.Sc., Columbia University (2008) Ph.D., Columbia University (2010) Research Interests: Cloud-Based Big Data Systems: Innovating serverless paradigms for real-time analytics (e.g., Stream as a Service). Internet Economics: Modeling peering agreements, premium peering dynamics, and regulatory alternatives to net neutrality. Distributed Computing: Auto-scaling frameworks (e.g., Elasticutor, DRS) for real-time stream processing. His research emphasizes performance guarantees and resource optimization in dynamic networked systems. Awards: Best Paper Award Runners-up (ACM Mobihoc 2020) Teaching Excellence Award (School of Computing 2019) Bell Labs Best Paper Award (IEEE SDP 2015) Best Paper Awards at ICNP 2014 and IC2E 2013 Grants & Advising: Recipient of research grants supporting projects like “Testbed for Innovative Inter-Networking Research.” Advises on doctoral projects related to stream processing and network economics. His work is funded by industry collaborations and institutional grants. Labs & Teams: Part of the Distributed Network Analysis (DNA) Research Group and Advanced Networking & Systems Research Group, fostering interdisciplinary collaborations in networking and distributed systems.

Yu JihongView profile

Professor

• Inorganic synthesis
• Porous material chemistry
• Ecological environment materials
• +3 more

Prof. Yu Jihong is a full Professor at the Department of Chemistry, Jilin University, and serves as Director of the International Center of Future Science and Vice President of the Chinese Chemical Society. She leads the State Key Laboratory of Inorganic Synthesis and Preparative Chemistry. Her research focuses on designing nanoporous materials for energy and environmental applications, with over 320 publications in top journals like Science and Nature Communications. Education: BS (1989), MS (1992), PhD (1995) from Jilin University Postdoctoral: Hong Kong University of Science and Technology (1996–1997), Tohoku University (1997–1998) Research interests include inorganic synthesis, porous materials, catalytic materials, and environmental applications. Her work bridges computational methods with experimental synthesis, yielding innovations in zeolite structures and catalytic systems. Awards: IUPAC Distinguished Women in Chemistry Award (2017), TWAS Fellow (2016), Chinese Academy of Sciences Academician (2015), and multiple national science prizes. She is an Associate Editor of Chemical Science and leads editorial boards for Materials Horizons and others. Her articles emphasize novel synthetic methods, catalytic systems, and sustainable materials. Key themes include accelerating zeolite crystallization, designing nanocatalysts, and developing porous membranes for environmental applications.

Lan WeiView profile

Associate Professor

• Nanoelectronic devices
• Cryogenic CMOS electronics
• GaN-based devices and circuits
• +5 more

Lan Wei is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, Canada. She leads the Waterloo Emerging Integrated Systems Group, focusing on device-circuit co-optimization, cryogenic CMOS for quantum computing, and emerging technologies like GaN, RRAM, and low-dimensional materials. Her work bridges nanoelectronics and system-level applications, with notable contributions to the MIT Virtual Source GaN HEMT (MVSG) compact model, an industry-standard tool. Education: B.S. in Microelectronics and Economics, Peking University (2005) M.S. and Ph.D. in Electrical Engineering, Stanford University (2007, 2010) Research Interests: Nanoelectronic devices Cryogenic CMOS for quantum computing GaN-based circuits and systems RRAM-based neuromorphic computing Device-circuit interactive design Publications reflect her expertise in GaN modeling, quantum computing hardware, and RRAM applications. Recent work emphasizes scalable quantum control circuits and error-resilient neural networks using emerging technologies. Awards include the 2019 Ontario Early Researcher Award and the 2020 UWaterloo President's Excellence Award in Research. She has served on technical committees for IEDM, DATE, and ICCAD, and contributed to the ITRS roadmap. Teaching includes courses like ECE 240 (Electronic Circuits) and ECE 730 (Solid State Devices). Her group actively seeks graduate students with interest in integrated systems and nanoelectronics.

Matthew DunbabinView profile

Professor

• Environmental Robotics
• Vision-Based Perception
• Marine Robotics
• +4 more

Matthew Dunbabin is a Professor at Queensland University of Technology (QUT) and Chief Investigator at the Australian Centre for Robotic Vision (ACRV). His expertise spans environmental robotics, with a focus on vision-based autonomous systems for marine conservation, water quality monitoring, and greenhouse gas management. He holds a PhD from QUT and a BEng (Aerospace) from RMIT. Dunbabin has led projects at CSIRO and QUT, developing robots like COTSBot and RangerBot to combat marine pests and promote reef restoration. His work has earned national and international awards, including the 2019 Australian Water Association Award and 2016 Google Impact Challenge. Education: PhD in Engineering, Queensland University of Technology (Queensland, Australia) BEng (Hons) in Aerospace Engineering, Royal Melbourne Institute of Technology (Melbourne, Australia) Research Interests: Environmental robotics and autonomous systems Vision-based perception and classification Marine habitat restoration and pest control Greenhouse gas monitoring via autonomous vehicles Cooperative robotics and sensor networks Awards & Recognition: 2019 Australian Water Association Award (SAMMI Project) 2019 Good Design Award - Sustainability (RangerBot) 2016 Google Impact Challenge People’s Choice Award (RangerBot AUV) 2010 Australian ICT Industry Association National iAward (iSnet) 2006 Queensland Engineering Excellence Innovation Award (Starbug Project) Grants & Projects: ARC Centre of Excellence for Robotic Vision (ACRV): Leading robotic vision research (2014–present) Revolutionising Protection Against Air Pollution: Air quality monitoring networks (2015–present) Establishing Advanced Networks for Air Quality Sensing: Sensor development for urban environments (2017–present) Labs & Collaborations: ACRV at QUT Institute for Future Environments (QUT) CSIRO Autonomous Systems Laboratory (2001–2013)

David EppsteinView profile

Professor

• Graph Algorithms
• Computational Geometry
• Graph Theory
• +3 more

David Eppstein is a Distinguished Professor of Computer Science at the University of California, Irvine (UCI), affiliated with the Donald Bren School of Information & Computer Sciences. He holds academic leadership roles as director of the Center for Algorithms and Theory of Computation and associate director of the Center for Algorithms, Combinatorics, and Optimization. His research focuses on graph algorithms, computational geometry, discrete mathematics, and geometric graph theory. Eppstein earned a B.S. in Mathematics from Stanford University (1984) and a Ph.D. in Computer Science from Columbia University (1989). Research Interests: Graph drawing, information visualization, dynamic graph algorithms, mesh generation, optimal triangulation, K-shortest paths, subgraph isomorphism, data depth, exponential-time algorithms for NP-hard problems. Awards: ACM Fellow (2012), AAAS Fellow (2017), Distinguished Professor (2020), Best Paper Awards (2023, 2022), and SIAM recognition. Grants: Co-PI on a $1.2M NSF grant (2022) for geometric graph research, and previous NSF grants for algorithm studies (2016). His work bridges theoretical computer science and practical applications, including contributions to graph visualization, geometric algorithms, and combinatorial optimization. Notable recent achievements include resolving open questions in graph biplanarity and authoring the book Forbidden Configurations in Discrete Geometry (2018).

Keval VoraView profile

Associate Professor

• Parallel & Distributed Computing
• Irregular Big Data Processing
• High Performance Computing

Keval Vora is an Associate Professor at the School of Computing Science, Simon Fraser University. His research focuses on scalable solutions for modern data analytics systems, particularly in graph processing and distributed computing. He leads the Parallel Data and Computing Lab (PDCL), developing systems like Peregrine , GraphBolt , and GraphBolt . Contact: TASC1 9419, keval@sfu.ca. Education: PhD in Computer Science from the University of California, Riverside (2017). Previously worked at Morgan Stanley on low-latency trading software. Teaching: Courses include Distributed Systems (CMPT 431) and Special Topics in Networks and Systems (CMPT 982). Advises graduate and undergraduate students on projects involving distributed systems and graph analytics. Research Interests: Parallel/Distributed Computing, Irregular Big Data Processing, High-Performance Computing. His work emphasizes efficient techniques with provable guarantees for large-scale systems. Software Contributions: Peregrine (pattern-based analytics), GraphBolt (dynamic graph processing), and Lumos (disk-based graph processing). These systems address challenges in scalability, efficiency, and real-time data handling.

Nicolas PerkowskiView profile

Professor

• Stochastic Analysis
• Probability Theory
• Stochastic Partial Differential Equations
• +3 more

Prof. Nicolas Perkowski is a Professor in the Department of Mathematics at Freie Universität Berlin, specializing in Stochastic Analysis and Probability Theory. He holds roles such as Vice Spokesperson of DFG CRC/TRR 388 (since 2024) and Chair of the Master of Mathematics Examination Board. His research focuses on stochastic partial differential equations (SPDEs), rough paths, and applications in mathematical physics. Notable contributions include work on singular SPDEs, fractional processes, and the KPZ equation. Perkowski has authored/co-authored numerous publications in top journals like the Annals of Probability and Communications in Mathematical Physics, and he serves as an associate editor for several journals. His institutional responsibilities include leadership in research collaborations and academic governance. Education: PhD and diploma in stochastic population models (details not explicitly provided in text). Research Interests: Stochastic Analysis, Probability Theory, SPDEs, Mathematical Physics, Nonlinear Filtering. Recent Articles: Focus on fractional processes, SPDEs with singular terminal conditions, and stochastic sewing lemmas. His work bridges theoretical probability with applications in physics and engineering, with a strong emphasis on rigorous mathematical frameworks for complex stochastic systems.

Chris ChipotView profile

Researcher

• Theoretical Chemistry
• Molecular Dynamics
• Biophysics
• +3 more

Chris Chipot is a Researcher and former Research Director at the CNRS, affiliated with the University of Lorraine. He holds adjunct faculty positions at the University of Illinois (Department of Physics) and the University of Chicago (Department of Biochemistry and Molecular Biology). He directs an Associate International Laboratory between CNRS and the University of Illinois at Urbana-Champaign. His expertise spans theoretical chemistry, molecular dynamics simulations, and membrane protein studies. Education: PhD in Theoretical Chemistry (1994, Henri Poincaré University, France) with a fellowship from Roussel Uclaf Institute. Habilitation (2000, University of Lorraine). Research Interests: Focuses on protein-ligand binding free energies, membrane protein dynamics, computational methods for molecular simulations, and biophysical techniques. His work integrates theoretical approaches with experimental validations, particularly in drug design and membrane biology. Publications: Recent articles highlight advancements in free-energy calculations, membrane protein dynamics, and scalable molecular dynamics software (NAMD). His research bridges computational and experimental biophysics, addressing challenges in structural biology and drug discovery. Awards: Fellowship from the Roussel Uclaf Institute during PhD studies. Advising & Grants: Leads collaborative projects via the CNRS-UIUC lab, fostering international research partnerships. His work has been supported by institutional and international grants, though specific funding details are not detailed here. Labs/Teams: Directs the CNRS-UIUC Associate International Laboratory, promoting interdisciplinary research in computational biophysics and membrane protein studies.

• Wireless Communications
• Signal Processing
• 5G/6G Networks
• +3 more

Mikko Valkama is a Professor at the Department of Communications Engineering , part of the Faculty of Information Technology and Communication Sciences at Tampere University . His research focuses on advanced wireless communication systems, positioning technologies, and integrated sensing and communication (ISAC). He holds an Orcid ID ( 0000-0003-0361-0800 ) and can be reached at mikko.valkama@tuni.fi . Research interests span 5G/6G networks , RF antenna design , deep learning for signal processing , and millimeter-wave systems . He leads projects on positioning algorithms (e.g., mmWave SLAM, NLOS mitigation), ISAC architectures, and hardware-efficient transmitter linearization. Notable contributions include works on DECT-2020 NR standards, phase-based localization, and RIS-assisted systems. In 2025 alone, his group published over 30 articles on topics such as: Antenna array design for Ka-band and wideband applications Machine learning for power amplifier predistortion Bistatic radio SLAM and mmWave mapping Covert transmission and physical-layer security His work bridges theoretical advancements with practical implementations, often validated through experimental setups (e.g., TUJI1 dataset for indoor localization). No scientific awards were explicitly listed in the provided texts.

James T. EnnsView profile

Professor

• Perception
• Attention
• Vision
• +3 more

James T. Enns is a Professor and Distinguished University Scholar in the Department of Psychology within the Faculty of Arts at the University of British Columbia. His research primarily focuses on the role of attention in human vision, with secondary interests in developmental psychology and human-machine interaction. Dr. Enns' research interests span perception, attention, vision, cognition, development, and human-machine interaction. His work explores how the human mind selects information, with particular emphasis on visual attention mechanisms. His laboratory research investigates the fundamental processes of visual perception and how attention modulates these processes across different contexts and developmental stages. Analysis of Dr. Enns' publication record reveals consistent engagement with visual perception, attentional mechanisms, and cognitive processing. His research demonstrates expertise in both theoretical frameworks and experimental methodologies related to visual attention, with applications spanning basic cognitive science to potential implementations in human-computer interaction systems. His work often bridges theoretical cognitive psychology with practical applications. Canadian Society for Brain, Behaviour and Cognitive Science Donald O. Hebb Distinguished Contribution Award (2013) Distinguished University Scholar, UBC (2004) Robert E. Knox Master Teaching Award (2004) Royal Society of Canada Fellow (2002) Killam Faculty Research Prize (1994) Killam Faculty Research Fellowship (1993) Society of Experimental Psychology Fellow Dr. Enns has served as Editor for the Journal of Experimental Psychology: Human Perception and Performance, and as Associate Editor for Psychological Science, Consciousness and Cognition, and Visual Cognition. His research has been supported by grants from NSERC, the Canadian Foundation for Innovation, the Australian Research Council, BC Health, and Nissan. He has authored textbooks on perception, edited research volumes on the Development of Attention, and published numerous scientific articles on vision, attention, and cognitive science. Dr. Enns is currently accepting graduate students and continues to mentor the next generation of cognitive scientists. Dr. Enns leads the UBC Vision Lab, which focuses on how the human mind selects information. The lab conducts research on visual attention, perception, and cognitive processes using a variety of experimental methodologies.