Find Academic Supervisors

Jeroen TrompView profile

Professor

• Seismology
• Computational Geophysics
• Earth Imaging
• +5 more

Jeroen Tromp serves as the Blair Professor of Geology and Professor of Geosciences and Applied and Computational Mathematics at Princeton University, where he also directs the Princeton Institute for Computational Science and Engineering (PICSciE). His work centers on theoretical and computational seismology with applications across Earth and planetary sciences. His research interests focus on imaging Earth's interior through advanced computational techniques. Key areas include surface waves, free oscillations, body waves, seismic tomography, numerical simulations of 3-D wave propagation, and seismic hazard assessment. His group develops open-source software for acoustic, elastic and poroelastic wave propagation, addressing problems in exploration geophysics, regional and global seismology, and helioseismology. Current research trends show strong emphasis on Mars seismology (InSight mission), iron spin crossover in the lower mantle, tilted transverse isotropy in Earth's inner core, and crosstalk-free waveform inversion techniques across multiple scales. Tromp actively mentors graduate students and leads collaborative projects involving seismic wavefield imaging across planetary bodies. His group maintains strong connections with NASA's InSight mission and develops computational frameworks for global centroid moment tensor inversions. The research team operates within the Department of Geosciences, leveraging high-performance computing resources through PICSciE to tackle large-scale inverse problems in seismology.

• Machine Learning
• Statistical Learning Theory
• Optimization
• +3 more

Professor Yiming Ying is a faculty member in the Faculty of Science at the University of Sydney, where he joined in December 2023. Previously, he held tenured positions at SUNY Albany (Departments of Mathematics & Statistics and Computer Science) and was a Lecturer at the University of Exeter. He completed his PhD in Mathematics at Zhejiang University (2002) and postdoctoral training at CityU Hong Kong, UCL, and University of Bristol. Research Focus His research spans statistical learning theory, optimization algorithms, trustworthy AI, and data science mathematics. Key applications include cancer informatics for early detection. His work aligns with Faculty research strengths in Data and Decisions and Decision-Making for a Sustainable Future. Recent Research Trends Analysis of recent publications shows strong focus on theoretical foundations of machine learning: differential privacy, fairness algorithms, optimization methods for AUC maximization, generalization guarantees, and robust learning techniques for adversarial settings and biological data. Awards and Honors SUNY Chancellor’s Award for Excellence (2023) University at Albany Presidential Research Award (2022) University of Exeter Merit Award (2012) Grants and Advising Significant funding includes current ARC DP250101359 (2025-2028) and multiple past NSF grants. He founded the UALBANY Machine Learning Group and currently advises PhD student Peilin LIU on operator learning.

• Reconfigurable Computing
• Field-Programmable Gate Arrays (FPGA)
• High-Level Synthesis
• +3 more

Paolo Ienne is a Professor at the Swiss Federal Institute of Technology in Lausanne (EPFL), where he leads the Processor Architecture Laboratory (LAP) within the School of Computer and Communication Sciences. His research focuses on advancing reconfigurable computing systems through innovative FPGA architectures and high-level synthesis methodologies. His primary research domains include reconfigurable computing, FPGA architecture design, dynamically scheduled dataflow circuits, and hardware acceleration techniques. Recent work emphasizes memory system optimization for FPGAs, formal verification of circuit transformations, and rapid C-to-hardware compilation flows. He has pioneered approaches for handling thousands of outstanding memory misses in FPGA accelerators and developed novel techniques for switch-block exploration without explicit pattern enumeration. Analysis of his 2023-2025 publications reveals a strong trend toward practical FPGA deployment challenges, with increasing focus on HBM integration, virtual memory systems for PCIe-attached devices, and formally verified circuit transformations. His work consistently targets real-world bottlenecks in high-level synthesis toolchains while maintaining theoretical rigor in dataflow architecture design. Professor Ienne's laboratory receives support from the Swiss National Science Foundation and industry partners including Huawei, enabling cutting-edge research in FPGA-based acceleration. His collaborative network spans major semiconductor companies and academic institutions worldwide, with frequent co-authorship on conference proceedings and journal publications in IEEE and ACM venues.

Daniel A. SpielmanView profile

Professor

• Spectral Graph Theory
• Algebraic Graph Theory
• Laplacian Matrices
• +7 more

Daniel A. Spielman is a Sterling Professor of Computer Science, Statistics & Data Science, and Mathematics at Yale University. He serves as the inaugural James A. Attwood Director of the Institute for Foundations of Data Science (FDS) and was previously co-Director of the Yale Institute for Network Science (YINS). He is also a member of TILOS, the NSF Institute for Learning-Enabled Optimization at Scale. His research interests span Spectral Graph Theory, Algebraic Graph Theory, Laplacian Matrices, Expander Graphs, and Random Walks on Graphs. Spielman has made fundamental contributions to understanding the mathematical foundations of data science, including work on smoothed analysis of algorithms, spectral sparsification, and solutions to the Kadison-Singer problem. Spielman's publications demonstrate a consistent focus on developing efficient algorithms with strong theoretical foundations. His work bridges pure mathematics, theoretical computer science, and practical applications in network analysis and machine learning. His research has evolved from foundational work on smoothed analysis to recent contributions in experimental design using discrepancy theory. His scientific honors include: Nevanlinna Prize for contributions to mathematical aspects of computer science Two Gödel Prizes (2008 and 2015) for outstanding papers in theoretical computer science MacArthur Fellowship ('Genius Grant') Simons Investigator award Membership in the National Academy of Sciences and American Academy of Arts and Sciences Spielman has advised numerous PhD students who have gone on to prominent positions in academia and industry. His teaching includes advanced courses in Spectral Graph Theory and Computation and Optimization. He has developed important software packages like Laplacians.jl for solving Laplacian linear equations and related problems.

Michael C.L. CHAUView profile

Professor

• Business Analytics
• Artificial Intelligence
• Web Mining and Social Media
• +8 more

Professor Michael C.L. CHAU serves as Professor and Deputy Area Head of Innovation and Information Management at The University of Hong Kong's Faculty of Business and Economics. He also holds the position of Associate Director at the HKU HKJC Centre for Suicide Research and Prevention, and serves on the HKU Senate and Court. His academic journey includes a Ph.D. in Management Information Systems from the University of Arizona and a B.Sc. in Computer Science (Information Systems) from the University of Hong Kong. Dr. Chau's research spans business analytics, artificial intelligence, web mining, fintech, smart health, and security informatics. His work focuses on applying data/text/web mining techniques to business, education, and social domains. He leads the Artificial Intelligence Research Group at HKU Business School, which has secured significant funding from the Hong Kong Research Grants Council, Food and Health Bureau, and other agencies for projects including 'The Invisible Hand in Crowdfunding' and 'Factors Moderating the Predictive Power of Social Media Sentiment on Stock Returns'. His publication portfolio includes over 150 articles in premier journals like MIS Quarterly, JMIS, and IEEE Transactions, with recent work exploring large language models, hate speech detection, and disaster-related social media analysis. His research demonstrates strong interdisciplinary connections across computer science, information systems, and business applications. INFORMS ISS Design Science Award (2020) IEEE ITSS Leadership Award in Intelligence and Security Informatics (2020) AIS Sandra Slaughter Service Award (2016) HKU Outstanding Young Researcher Award (2014) Faculty Research Postgraduate Supervision Award (2020 & 2025) Cited over 9,400 times (h-index = 47) Dr. Chau actively mentors doctoral students and has supervised numerous PhD graduates who now hold academic positions at institutions including Fudan University, Xi'an Jiaotong-Liverpool University, and the University of Maryland. His research grants portfolio demonstrates sustained funding success across diverse areas including blockchain, mental health applications, and social media analytics. The Artificial Intelligence Research Group he leads maintains strong industry connections through projects with Hong Kong Red Cross and the Hospital Authority.

Wenyao XuView profile

Professor

• Embedded Systems
• Cyber-Physical Systems
• Body Sensor Networks
• +3 more

Wenyao Xu is a Professor and Associate Department Chair in the Department of Computer Science and Engineering at the University at Buffalo, School of Engineering and Applied Sciences. His research focuses on embedded systems, body sensor networks, biomedical signal analytics, telemedicine, and human-computer interaction. He leads the Embedded Sensing and Computing (ESC) Lab, pioneering innovations in wearable health monitoring and non-invasive medical technologies. Dr. Xu holds a PhD in Electrical and Electronics Engineering from UCLA (2013), an MEng in Embedded Systems from Zhejiang University (2008), and a BEng in Electrical Engineering from Zhejiang University (2006). He has received multiple accolades, including the UB Exceptional Scholar—Sustained Achievement Award (2021) and SEAS Senior Career Researcher of the Year (2020). His research spans cutting-edge areas like mmWave-based sensing for health monitoring, mHealth applications for behavioral interventions (e.g., Sense2Quit app for smoking cessation), and wearable devices for gait analysis and vital sign detection. Recent work includes advancements in through-clothing wound monitoring, voice- and gesture-based authentication systems, and AI-enhanced medical imaging. Publications reflect his interdisciplinary impact, with over 150 peer-reviewed papers in top venues. His lab collaborates closely with clinical partners to translate technologies into practical healthcare solutions. Dr. Xu also emphasizes mentorship, having been honored with the President Emeritus and Mrs. Meyerson Award for teaching excellence (2020).

Ian Yi HanView profile

Assistant Professor

• Programme Evaluation
• Population Health
• Health Services Research
• +4 more

Dr. Ian Yi Han is an Assistant Professor at the Saw Swee Hock School of Public Health, National University of Singapore (NUS), and Co-Director of the Center for Health Intervention and Policy Evaluation Research (HIPER). His research focuses on evaluating community-based health interventions, telehealth programs, and the impact of built environments on health behaviors. He holds a Ph.D. in Behavioral Nutrition from Columbia University, an M.A. in Psychology in Education, and a B.Sc. in Neural Science & Psychology from New York University. Key research areas include programme evaluation, population health, health services research, and dietary behaviors. He explores how interventions can improve patient experiences and population health outcomes, particularly through telemedicine and lifestyle modifications. His work has been published in journals like npj Digital Medicine, Metabolism, and the Journal of Nutrition Education and Behavior. Notable projects include studies on blood pressure telemonitoring, diabetes management during the pandemic, and supermarket intervention strategies for obesity prevention. Dr. Han has contributed to policy initiatives through roles such as Senior Research Fellow at the National University Health System and Lecturer at Columbia University. He advises on primary care research and collaborates on global health projects, emphasizing interdisciplinary solutions to public health challenges.

Angelo PeccerilloView profile

Professor

• Igneous petrology
• Geochemistry
• Volcanology
• +2 more

Angelo Peccerillo is a distinguished Full Professor of Petrology at the University of Perugia, Italy, with an extensive academic career spanning multiple Italian universities. He previously served as Full Professor of Petrology at the Universities of Calabria (1993-1997) and Messina (1985-1993), and as Associate Professor of Volcanology (1982-1985) and Assistant Professor (1971-1982) at the University of Florence. He earned his Laurea Degree in Geology from the University of Florence in 1970 and has been a member of Academia Europaea since his election. Peccerillo's research focuses on the petrology and geochemistry of magmatic processes, with particular expertise in igneous petrology, geochemistry, volcanology, orogenic volcanism, and continental rift magmatism. His scholarly work has established him as a leading expert in understanding the complex relationships between mantle processes, crustal interactions, and volcanic activity in the Mediterranean region and beyond. His research methodology integrates petrological, geochemical, and geophysical approaches to develop comprehensive models of magmatic systems. Analysis of Peccerillo's recent publications reveals a consistent focus on magmatic processes across diverse geological settings. His work spans from detailed studies of specific volcanic systems like the Aeolian Islands and Ethiopian Rift to broader investigations of mantle structure and geodynamic processes in the Mediterranean region. A recurring theme is the integration of multiple analytical approaches to model magma plumbing systems, understand mantle composition, and elucidate the geodynamic evolution of volcanic regions, particularly examining the relationships between deep and shallow mantle processes. Antonio Feltrinelli Price 2006 for Earth Sciences, awarded by the National Academy of Lincei, Italy Professor Peccerillo has supervised numerous MSc and PhD students both at his home institution and abroad. His editorial leadership includes serving as Chief Editor of the "European Journal of Mineralogy" from 2006 to 2008 and membership on the editorial boards of several prestigious journals including "Lithos," "Journal of Volcanology and Geothermal Research," "Geological Society of Italy Bulletin," and "Open Mineralogy Journal." He has been actively involved in research evaluation, serving on national and international panels for organizations such as NSF, ESF, CNR, MIUR, and NATO. Peccerillo maintains strong international scientific connections through visiting positions at institutions like the Australian National University, University of Gottingen, and University of Paris XI at Orsay. His leadership extends to professional organizations, having served as President of the Italian Petrology Group and currently as a Member of Academia Europaea. His administrative experience includes serving as Department Head and Coordinator of PhD programs at the Universities of Messina, Cosenza, and Perugia.

Anthony FilippiView profile

Associate Professor

• Imaging Spectroscopy
• Hyperspectral Remote Sensing
• GIS-Based Modeling
• +5 more

Dr. Anthony Filippi is an Associate Professor and Director of Graduate Programs at Texas A&M University. His research focuses on remote sensing, geographic information systems (GIS), and machine learning applied to aquatic and terrestrial environments. He leads the Fluvial-GEOS Lab, studying riverine/floodplain systems using remote sensing and GIS technologies. Educational Background: Ph.D. in Geography, University of South Carolina (2003) M.S. in Geography, University of South Carolina B.A. in Geography, Kansas State University Research Interests: Imaging spectroscopy, hyperspectral remote sensing of rivers and coastal oceans, GIS-based modeling, data fusion, aquatic optics, and machine learning. His work addresses coastal ocean bathymetry estimation, floodplain dynamics, and applications in environmental monitoring, including hazardous waste site tracking and agricultural studies. Recent Research Trends: Recent publications highlight advancements in UAS-based image analysis, LSTM networks for floodplain classification, and environmental policy impacts on forest resources. His work integrates machine learning with remote sensing to improve ecological and geomorphological understanding. Labs/Teams: Director of the Fluvial-GEOS Lab, focusing on remote sensing and GIS applications in riverine environments.

Andre WibisonoView profile

Assistant Professor

• Machine Learning
• Optimization
• Sampling
• +4 more

Andre Wibisono serves as Assistant Professor in Yale University's Department of Computer Science with a secondary appointment in Statistics & Data Science, joining the faculty in 2021 after postdoctoral research at University of Wisconsin-Madison and Georgia Institute of Technology. His educational background includes: Ph.D. in Computer Science, UC Berkeley M.A. in Statistics, UC Berkeley M.Eng. in Computer Science, MIT S.B. in Mathematics and Computer Science, MIT Wibisono's research focuses on algorithm design for machine learning through optimization, sampling, and game theory , leveraging dynamical systems and information theory to develop accelerated discrete-time algorithms from continuous dynamics. His work provides theoretical foundations for efficient machine learning systems with applications in generative modeling and constrained optimization. Recent publications (2023-2025) demonstrate consistent innovation in Hamiltonian-based optimization , constrained-space sampling , and min-max game convergence , characterized by rigorous mathematical analysis connecting continuous dynamics to discrete algorithms. Key trends include randomized integration for acceleration, phi-divergence convergence guarantees, and symplectic geometry applications to mirror descent. Scientific recognition includes: NSF CAREER Award for developing algorithmic frameworks bridging continuous and discrete dynamics He actively mentors current students (Siddharth Mitra, Kaylee Yang, Jane Lee, Qiang Fu, Peter Wang) and has guided two postdocs to faculty positions. Research is funded through the NSF CAREER award and collaborative CIF grants focused on Hamiltonian dynamics for sampling and optimization. His Yale research group develops theoretical foundations for next-generation machine learning algorithms, emphasizing mathematical rigor in optimization and sampling with applications to generative modeling and constrained inference problems.

Linyi LiView profile

Assistant Professor

• Deep learning
• Trustworthy machine learning
• Computer security
• +4 more

Linyi Li is an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU), leading the Trustworthy Artificial Intelligence (TAI) Lab. His research focuses on certifiably trustworthy deep learning systems, combining machine learning and computer security. He holds a PhD from the University of Illinois Urbana-Champaign (UIUC) and a B.Eng. from Tsinghua University. Affiliations: Simon Fraser University, TAI Lab Education: PhD in Computer Science, UIUC, 2023 B.Eng (Cum Laude), Tsinghua University, 2018 His research interests include deep learning , trustworthy machine learning , large language models , and software engineering . He emphasizes rigorous certification of robustness, fairness, and numerical reliability in AI systems. Recent work includes the InfiBench benchmark for evaluating code LLMs and advancements in neural network verification. Recent Research Trends: His publications span certified robustness, fairness guarantees, and scalable verification techniques for deep learning models. He also explores scientific evaluation of foundation models and adversarial defense mechanisms. Awards: Rising Stars in Data Science AdvML Rising Star Award Wing Kai Cheng Fellowship Finalist: Qualcomm Innovation Fellowship (2022) Winner: VNN-COMP'23 Competition (Team α, β-CROWN) Advising & Grants: As a PI, he oversees the TAI Lab's research. Though no specific grants are listed, his work is funded through competitive awards and university resources. Labs/Teams: Leads the TAI Lab at SFU, focusing on foundational and applied research in trustworthy AI.

• Computational Modelling of Neural Networks
• Biophysical Modelling of EEG/MEG
• Diffusion Tractography
• +2 more

Professor Thomas R. Knösche is a Group Leader at the Methods and Development Group Brain Networks within the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig, Germany. His research focuses on computational modeling of neural networks, biophysical modeling of EEG/MEG data, diffusion tractography, and the neurocognition of language and music. He holds a professorship at the institute and leads research initiatives in brain networks and neuroimaging methodologies. His work bridges theoretical neuroscience with practical applications in understanding brain function through advanced computational techniques. Contact information includes his office phone (+49 341 9940-2619) and lab address: Stephanstraße 1A, 04103 Leipzig, Saxony, Germany.

Wei SunView profile

Associate Professor

• Power System Restoration
• Self-healing Smart Grid
• Cyber-Physical Systems
• +5 more

Wei Sun is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Central Florida, where he also serves as the Director of the Siemens Digital Grid Lab. His research focuses on power system restoration, self-healing smart grids, cyber-physical security, and renewable energy integration. Dr. Sun received his Ph.D. from Iowa State University in 2011, and his M.S. and B.S. from Tianjin University in 2007 and 2004, respectively. Prior to joining UCF, he was an Assistant Professor at South Dakota State University (2013-2015), a power system engineer at Alstom Grid (2011-2012), a visiting scholar at the University of Hong Kong (2011), and an intern at California Independent System Operator (2010). His research interests include: Power System Restoration and Self-healing Smart Grid Resilient and Secure Critical Infrastructure Cyber-Physical Systems Renewable Energy and Microgrid Distributed Energy Resources Integration Dr. Sun's recent publications demonstrate strong focus on cyber-physical security in power systems, distributed energy resource integration, and resilient grid operations. His work shows increasing emphasis on AI and machine learning applications for grid security and resilience, particularly in the context of high renewable penetration. His notable scientific awards include: Microsoft Software Engineering Innovation Foundation Award (2014) Best Paper Award, 2019 IEEE PES ISGT Asia Mentor of the Year, UCF Graduate Student Association (2019) Dr. Sun has successfully secured multiple research grants totaling millions of dollars from agencies including the US Department of Energy, National Science Foundation, Florida Center for Cybersecurity, and Microsoft. He currently serves as PI or Co-PI on several major projects including "Secure and Resilient Operations Using Open-Source Distributed Systems Platform (OpenDSP)" funded by the Department of Energy. He leads the Siemens Digital Grid Laboratory at UCF, which is equipped with utility-grade software and hardware including Spectrum Power Microgrid Management System, Power System Simulator for Engineering, and Siemens Distribution Feeder Automation. The lab provides capabilities for both software modeling and hardware-in-the-loop testing of power systems.

• Natural Language Processing
• Natural Language Generation
• Machine Learning
• +4 more

Prof. Anya Belz is Full Professor of Computer Science at Dublin City University's School of Computing and Science Lead at ADAPT Research Centre. A leading NLP researcher with PhD-level expertise, she specializes in natural language generation, evaluation methodologies, and multimodal systems. Recipient of multiple best paper awards and NAACL Test of Time Award nomination. Research innovations include foundational work on statistical language generation (deployed in weather forecasting systems), comparative evaluation frameworks, vision-language integration, and reproducibility quantification. Current EPSRC-funded ReproHum project coordinates 20 global labs studying evaluation consistency. Achievements : Developed industry-deployed generation systems for accessibility applications Pioneered cross-modal alignment techniques for image description Authored 100+ publications spanning generation, evaluation, and reproducibility

• Theoretical Plasma Physics
• Magnetic Reconnection
• Plasma Turbulence
• +4 more

James F. Drake is a Distinguished University Professor in the Department of Physics at the University of Maryland, College Park, with affiliations at the Institute for Physical Science and Technology (IPST) and the Institute for Research in Electronics and Applied Physics (IREAP). He holds a B.S., M.S., and Ph.D. in Physics from UCLA (1975). His research focuses on theoretical plasma physics, particularly magnetic reconnection and plasma turbulence, with applications to space physics, solar flares, and magnetic fusion. Key contributions include elucidating the role of whistler waves in reconnection dynamics and advancing understanding of energy release mechanisms in plasmas. Dr. Drake’s awards include the American Physical Society Fellowship and the Humboldt Senior Scientist Research Award. He teaches advanced physics courses (e.g., Physics 604, 611, 761-762) and has pioneered computational models to study reconnection, turbulence, and particle acceleration. Recent work leverages Parker Solar Probe data to explore solar wind dynamics and reconnection in near-Sun environments. Education: UCLA (B.S., M.S., Ph.D. in Physics, 1975) Research Themes: Magnetic Reconnection, Plasma Turbulence, Space Plasma Dynamics Notable Achievements: Leader in reconnection theory; developer of kinetic simulation frameworks; contributor to NASA missions