Find Academic Supervisors

Weng-Keen WongView profile

Professor

• Machine Learning
• Anomaly Detection
• Computational Sustainability
• +4 more

Weng-Keen Wong is a Full Professor in the School of Electrical Engineering and Computer Science at Oregon State University (OSU), part of the College of Engineering. He serves as the OSU Site Director for the NSF-funded Pervasive Personalized Intelligence Center. His academic journey includes a PhD and MS from Carnegie Mellon University (2004/2001) and a BS from the University of British Columbia (1997). Research focuses on machine learning, with specialties in anomaly detection, explainable AI, computational sustainability, and spatio-temporal data analysis. Notable projects include disease outbreak surveillance algorithms, bird species distribution modeling, and RF device authentication systems. He collaborates across disciplines in public health, ornithology, and environmental sciences. Teaching includes courses like Artificial Intelligence (CS 331), Probabilistic Graphical Models (CS 536), and Ethics in Computer Science (CS 391). He advises a team of 9 current students (4 PhD, 1 MS, 4 undergrad) and has mentored over 25 alumni in roles spanning academia and industry. Labs and initiatives include the Wong Lab focusing on anomaly detection and sustainability projects. He has led NSF grants and industry collaborations, emphasizing practical applications of machine learning in real-world challenges.

Gurprit SinghView profile

Researcher

• Monte Carlo Integration
• Markov Chain Monte Carlo Sampling
• Generative AI
• +3 more

Gurprit Singh is a Researcher at the Max Planck Institute for Informatics, Saarbrücken, Germany. His work focuses on advancing Monte Carlo integration techniques and their applications in generative AI, physically based rendering, and optimization. He has contributed to conferences such as SIGGRAPH, Eurographics (EG), and Pacific Graphics (PG), serving in roles like Technical Program Committee member and co-chair for Doctoral Consortium programs. Roles: Associate Senior Researcher, Conference Co-chair (EGSR 2021), and active in academic service. Research Interests: Monte Carlo methods, MCMC sampling, gradient-based optimization, and generative models. His research bridges rendering, optimization, and AI, with notable work on noise optimization in diffusion models and perceptual error minimization. He has received the Best Student Paper Award at ICPRAM 2025.

Lixin ShenView profile

Professor

• Applied and Computational Harmonic Analysis
• Optimization
• Imaging Science
• +4 more

Lixin Shen is a Professor in the Department of Mathematics at Syracuse University, part of the College of Arts and Sciences. His research focuses on applied and computational harmonic analysis, optimization, imaging science, and information processing. Shen holds a Ph.D. in Mathematics from Sun Yat-Sen University (1996), an M.Sc. from Peking University (1990), and a B.Sc. from Peking University (1987). Education: Ph.D., Mathematics, Sun Yat-Sen University, 1996 M.Sc., Mathematics, Peking University, 1990 B.Sc., Mathematics, Peking University, 1987 Research Interests: Shen’s work emphasizes sparse optimization, image and signal processing, computational mathematics, and their applications. Notable areas include wavelet analysis, tensor decomposition, and robust algorithms for noise removal and high-resolution imaging. Grants & Awards: NSF Grant: Collaborative Research: Sparse Machine Learning and Sparse Optimization (2022–2025) Excellence in Graduate Education Faculty Recognition Award, Syracuse University (2024) Air Force Summer Faculty Fellowship (2024, 2023, 2021, 2020) Service & Leadership: Chair of Graduate Committee, Mathematics Department (2024–2025) Editor, Frontiers in Applied Mathematics and Statistics Organizer, SIAM Conferences on Optimization and Imaging (2023–2020) Teaching: Recent courses include Numerical Linear Algebra, Numerical Methods with Programming, Sparse Optimization, and Partial Differential Equations.

Damek Shea DavisView profile

Assistant Professor

• Optimization
• Stochastic Algorithms
• Nonconvex Optimization
• +4 more

Damek Shea Davis is an Assistant Professor in Cornell University's School of Operations Research and Information Engineering, College of Engineering. He received his Ph.D. in Mathematics from UCLA in 2015 and joined Cornell in 2016. Davis focuses on mathematical foundations of data science, with particular expertise in optimization, statistical learning, and numerical algorithms. His research develops theoretical frameworks and efficient algorithms for nonconvex, nonsmooth, and stochastic optimization problems. Key contributions include convergence guarantees for stochastic subgradient methods on nonconvex functions, analysis of optimization landscapes for phase retrieval, and development of methods for high-dimensional statistical estimation. His work bridges mathematical optimization with machine learning and signal processing applications. Davis has received numerous honors including the Sloan Research Fellowship (2020) and INFORMS Optimization Society Young Researchers Prize (2019). His publications advance understanding of optimization algorithm behavior in statistically challenging regimes, with recent work exploring geometric convergence properties and optimality conditions.

Aras BachoView profile

Research Fellow

• Partial Differential Equations
• Machine Learning
• Deep Learning
• +4 more

Dr. Aras Bacho is a Postdoctoral Research Associate in the Computing and Mathematical Sciences Department within the Division of Engineering and Applied Science at the California Institute of Technology (Caltech), where he works with Professor Houman Owhadi. His research spans mathematical analysis, partial differential equations, and machine learning applications. Dr. Bacho completed his PhD in Partial Differential Equations at the Technical University of Berlin with a thesis titled "On the Nonsmooth Analysis of Doubly Evolution Inclusions of First and Second Order with Applications" under the supervision of Prof. Dr. Etienne Emmrich. He also holds M.Sc. degrees in Mathematics from both the Technical University Berlin and Université Pierre et Marie Curie in Paris (through an ERASMUS+ scholarship), as well as a B.Sc. in Technomathematics from the Technical University Berlin. Dr. Bacho's research interests focus on the intersection of mathematical analysis and machine learning. His work explores partial differential equations, nonsmooth analysis, physics-informed neural networks, and applications in robotics and computational physics. His research bridges theoretical mathematics with practical applications in artificial intelligence and engineering systems. He has developed novel approaches to physics-informed machine learning and has made contributions to the mathematical foundations of scientific machine learning. Dr. Bacho's publications demonstrate a strong trajectory from theoretical mathematical analysis toward increasingly applied work in scientific machine learning. His recent work shows particular interest in physics-informed neural networks, equation discovery, and the mathematical foundations of AI reliability. He has published on topics ranging from classical PDE theory to cutting-edge applications of deep learning in scientific computing, with a focus on mathematical rigor and practical applicability. 2008: First prize in the Nationwide Mathematics Competition in Germany (Bundeswettbewerb Mathematik) 2009: DPG Prize for special achievements in physics issued by the German Physical Society 2012: Perfect score in the Math Advent Calendar organized by the DFG Research Center MATHEON 2014-2015: Erasmus+ Scholarship for studies at Université Pierre et Marie Curie 2017: PhD-Scholarship issued by TU Berlin (4 Months) 2021-present: Mentoring Program for young researchers Dr. Bacho has presented his work at numerous international conferences including the Workshop on Control of Self-Organizing Nonlinear Systems, the Hausdorffschool on Modeling and analysis of evolutionary problems, and the International Council for Industrial and Applied Mathematics (ICIAM) 2023. His research has been supported by various grants including his PhD scholarship from TU Berlin and participation in mentoring programs for young researchers. He collaborates with researchers across mathematics, computer science, and engineering disciplines. Dr. Bacho is actively involved in the scientific machine learning community, collaborating with researchers at Caltech and beyond. His work at the intersection of mathematical analysis and AI places him at the forefront of scientific machine learning research, with particular emphasis on the mathematical foundations of physics-informed neural networks and the reliability of AI systems for scientific applications.

Markus HeglandView profile

Professor

• Computational Mathematics
• High-Dimensional Problems
• Ill-Posed Problems
• +4 more

Markus Hegland is a Professor and Head of the Centre for Mathematics and its Applications (CMA) at the Australian National University (ANU). He holds a PhD from ETH Zurich (1988) and has been affiliated with ANU since 1992, focusing on High-Performance Computing (HPC) and numerical analysis. As a Hans Fischer Senior Fellow at TUM-IAS, his research emphasizes high-dimensional problems, ill-posed systems, and data mining applications. His work bridges computational mathematics with practical domains like systems biology and spectral enhancement. Research interests include sparse grid techniques, regularization methods, and algorithm development for HPC. Notable contributions include the OPTICOM method for stable sparse grid solutions and convergence theory for variable Hilbert scales regularization. He has led projects on fault-tolerant HPC algorithms and collaborated with Fujitsu on HPC applications. Publications span numerical analysis, bioinformatics, and computational physics. His work on the chemical master equation and gyrokinetics showcases interdisciplinary impact. Currently, he explores resilient grid-based solvers and machine learning integration with HPC frameworks. No awards are explicitly listed, but his senior fellowship underscores recognition in his field. Grants and collaborations include ARC-funded research in bioinformatics and HPC resilience. His work on digital twins and algorithm optimization reflects broader interests in advanced computational modeling. He is actively involved in teaching and supervising in computational mathematics and data science at ANU.

Raul DE LACERDAView profile

Associate Professor

• Wireless Communication
• Signal Processing
• Electromagnetic Compatibility
• +4 more

Raul DE LACERDA is an Associate Professor at the Signals and Intelligent Networks Department of CentraleSupélec and a researcher at the Signals and Systems Laboratory (L2S - CNRS). He holds degrees from the Federal University of Ceará (Brazil), Eurecom (France), and Alcatel-Lucent Bell Labs collaboration. His research focuses on advanced signal processing for wireless communications, spectrum sharing, and electromagnetic compatibility. He has contributed to projects like L-DACS2 and DME system coexistence, commercial PLC adaptation for avionics, and indoor positioning techniques. Raul has received awards for his work, including the Valuetools 2007 Best Student Paper Award and DASC 2010 Best Session Paper Award. He coordinates international partnerships through programs like the Erasmus Mundus EM-SMART2 consortium. Education: Electrical Engineering (Dipl.Ing., 2003) from UFC, M.Sc. in Teleinformatics (2005), French M.Sc. in Communication Systems (DEA), and Ph.D. in Wireless Communication (Eurecom). Research interests emphasize signal processing for wireless networks, including MIMO systems, UWB technologies, and interference mitigation. His work bridges theoretical advancements with practical applications in aerospace and avionics. Recent projects include hierarchical fingerprinting for indoor localization and 1-bit ADC MIMO data detection. Awards highlight his contributions to aeronautical communication systems and adaptive modulation. He collaborates with labs like L2S and has published extensively on coexistence systems, channel division multiple access (CDMA), and PLC adaptation for avionics. Advising and grants: No listed advisees but actively involved in Erasmus Mundus programs and international partnerships. His work is supported by collaborations with Ericsson, CNRS, and Bell Labs. Labs/Teams: Signals and Systems Laboratory (L2S), Inverse Problems Group, and Modelling and Estimation team.

• Inverse Problems
• Numerical Analysis
• Partial Differential Equations
• +4 more

Burhan PEKTAŞ is a Professor of Computer Engineering at the Faculty of Engineering and Natural Sciences, Üsküdar Üniversitesi. He holds a Ph.D. from Kocaeli University (2006) and has held academic positions including Vice Dean of the Faculty of Engineering and Natural Sciences since 2021. His research focuses on inverse problems, numerical analysis, and mathematical modeling with applications in engineering and environmental systems. Education: BSc in Mathematics, Kocaeli University (1998) MSc in Mathematics, Kocaeli University (2002) PhD in Mathematics, Kocaeli University (2006) Research Interests: His work emphasizes computational methods for solving inverse problems in partial differential equations, numerical simulations of heat transfer and diffusion processes, and cybersecurity applications in telehealth systems. He has organized sessions at international conferences such as Inverse Problems: Modeling and Simulation (since 2002). Academic Contributions: With 17 publications (h-index 6), his articles span journals like Journal of Inverse and Ill-posed Problems and Journal of Mathematical Chemistry. His 2020 study on wave equation boundary measurements exemplifies his expertise in inverse problem techniques. Administrative Roles: Head of Computer Engineering Department (2023–present) Vice Dean, Faculty of Engineering and Natural Sciences (2021–present) Teaching: Currently instructs courses in Calculus, Numerical Analysis, and Python Programming for undergraduates.

Guillaume CrevecoeurView profile

Associate Professor

• Dynamical Systems
• Nonlinear Optimal Control
• Model-Based Optimization Algorithms
• +4 more

Guillaume Crevecoeur (born 1981) serves as Associate Professor at Ghent University's Faculty of Engineering and Architecture within the Department of Electrical Energy, Metals, Mechanical Constructions, and Systems. He leads research at the Electrical Energy Laboratory (EELAB) and is an affiliate member of Flanders Make, the strategic research center for manufacturing industry. His international collaborations include membership in scientific steering committees and prior visiting researcher positions at Technical University Ilmenau and Physikalische Technische Bundesanstalt in Berlin. Crevecoeur earned his MSc (2004) and PhD (2009) in Engineering Physics from Ghent University, followed by a postdoctoral fellowship with Research Foundation Flanders (FWO-Flanders). His research spans dynamical systems modeling, optimization, and control with foundational work in model-based algorithms, inverse problems, and nonlinear optimal control. Current strategic focus areas include machines and motion systems, particularly airborne wind energy applications and robotics. Analysis of his 2022-2025 publications reveals dominant trends in physics-informed machine learning for control systems , with significant contributions to reinforcement learning for underactuated systems, tactile sensing in robotics, and computational fluid dynamics for renewable energy. His work consistently integrates model-based optimization with data-driven approaches, especially in wind turbine control and aerial robotics co-design. Best Paper Award at 22nd International Conference on System Theory, Control and Computing (2018) As Principal Investigator, Crevecoeur directs research funded through FWO-Flanders and industry partnerships with Flanders Make. His lab focuses on mechatronic systems development, particularly in robotic manipulation and renewable energy conversion. Current projects include airborne wind energy system optimization, collision-tolerant robotics, and digital twin implementations for industrial machinery. He actively contributes to international scientific steering committees and serves as academic advisor for graduate students in electrical energy systems. Crevecoeur's research group operates within EELAB's infrastructure, utilizing high-performance computing resources for computational fluid dynamics simulations and experimental facilities for mechatronic system validation. The team collaborates with industry partners through Flanders Make on industrial automation challenges, with recent work focusing on real-time control for high-speed manufacturing processes and adaptive energy management systems.

William Weimin YooView profile

Assistant Professor

• Uncertainty Quantification
• Statistical Computing
• Deep Learning
• +3 more

William Weimin Yoo is an Assistant Professor at the School of Mathematical & Computer Sciences, Heriot-Watt University. His research focuses on Bayesian nonparametrics, uncertainty quantification, statistical computing, deep learning, and dark matter physics. He has worked internationally across the UK, Netherlands, France, US, and Canada. He teaches courses including F21BD: Big Data Management, F70TS: Time Series and Machine Learning, and F90AM: Advanced Machine Learning. Yoo is actively supervising PhD students in topics such as uncertainty quantification for clustering and accelerated posterior contraction rates. He emphasizes interdisciplinary research, combining statistical theory with applications in physics and sports analytics. His work bridges computational methods and theoretical guarantees in Bayesian frameworks. His academic contributions span methodological advancements in posterior contraction, credible sets, and spatio-temporal modeling. He frequently engages in collaborative research and peer review across top journals and conferences in statistics and machine learning.

• Inverse Problems
• Biomedical Imaging
• Computational Mathematics
• +3 more

Sampsa Pursiainen is a Full Professor of Applied Mathematics at Tampere University, affiliated with the Faculty of Information Technology and Communication Sciences and the Computing Sciences department. He leads the Finnish Centre of Excellence in Inverse Modelling and Imaging (2018–2025) and the FAME Flagship (2024–2031), focusing on advancing computational methods for inverse problems in biomedical and geoscientific imaging. His work bridges numerical mathematics, medical imaging applications, and asteroid tomography, emphasizing ill-posed inverse problem solutions and multi-modal imaging integration. Education: Completed his PhD in 2009 on numerical methods in biomedical imaging. Research Interests: Inverse problems, EEG/MEG modeling, computational geosciences, and tomographic techniques for asteroids. Notable contributions include developing algorithms for focal brain activity reconstruction, radar tomography for planetary bodies, and finite element methods in head modeling. Publications reflect expertise in medical imaging, asteroid interior probing, and optimization for transcranial stimulation. His work addresses challenges in data scarcity and computational complexity, leveraging advancements in numerical methods and GPU acceleration. Current projects include DISCUS mission concepts for asteroid exploration and FAME Flagship initiatives in advanced mathematical modeling.

• Statistics
• Probability
• Markov Chain Monte Carlo
• +6 more

Professor Yves Atchade is a faculty member at the Department of Mathematics and Statistics at Boston University, holding the Duan Family Faculty Fellow of Data Science position. He leads the FORMES research group focused on statistical methods for environmental sciences. His work bridges probability, high-dimensional computation, and applied environmental modeling. Current projects include large-scale Bayesian inference , deep learning for rainfall prediction in West Africa , and inverse problems in remote sensing . Collaborations with institutions like ENSEA, Abidjan and the TAHMO Foundation drive environmental sensor network deployments in Côte d'Ivoire. His research emphasizes MCMC algorithm design and unbiased Monte Carlo methods , with applications spanning environmental science, machine learning, and high-dimensional statistics. Recent publications explore unbiased MCMC , cyclical sampling , and Bayesian PINNs . Scientific contributions include: Unbiased MCMC with Couplings (2020) High-dimensional Bayesian Computation (2018) Efficiency bounds in semiparametric models (2020) Adaptive MCMC Confidence Intervals (2016) He actively mentors students and postdocs, with methodological developments in Markov chain theory , stochastic optimization , and Monte Carlo variance reduction . The FORMES group welcomes inquiries from prospective researchers via email at atchade@bu.edu .

Zhigen ZhaoView profile

Associate Professor

• Bayesian/empirical Bayesian statistics
• High-dimensional data analysis
• Multiple comparison
• +5 more

Zhigen Zhao is an Associate Professor and Beury Research Fellow at the Fox School of Business and Management, Temple University, within the Department of Statistics, Operations, and Data Science. He holds a Ph.D. from Cornell University (2009) and specializes in Bayesian/empirical Bayesian statistics, high-dimensional data analysis, and bioinformatics. His research is supported by the National Science Foundation. Dr. Zhao’s research focuses on developing statistical methodologies for high-dimensional problems, including selective inference, multiple comparisons, and sufficient dimension reduction. His work bridges theoretical advancements with practical applications in healthcare, genomics, and predictive analytics. Key contributions include Bayesian hierarchical models for electronic health records and novel techniques for controlling false discovery rates in genomic studies. He teaches courses such as Intermediate Statistics, Regression and Predictive Analytics, and Statistical Methods for Business Research at both undergraduate and graduate levels. His recent publications appear in top-tier journals like the Journal of the American Statistical Association and Journal of the Royal Statistical Society, Series B. Dr. Zhao’s grants include NSF funding for high-dimensional statistical research. He advises on methodological challenges in data science and collaborates on projects involving healthcare analytics, genomics, and machine learning applications.

Zafer DoganView profile

Assistant Professor

• Machine Learning
• Data Analytics
• Signal Processing
• +5 more

Zafer Dogan is an Assistant Professor in the Department of Electrical and Electronics Engineering at Koç University, where he leads the Machine Learning Information Processing (MLIP) research group and serves as Deputy Director of the Koç University İşbank AI (KUIS AI) Research Center. He holds a Ph.D. from École Polytechnique Fédérale de Lausanne (EPFL) and a B.Sc. from Middle East Technical University (METU). Before joining Koç University, he worked as a postdoctoral researcher at Harvard University's John A. Paulson School of Engineering and Applied Sciences (2016–2019). His research bridges theoretical advancements in machine learning, optimization, and signal processing with practical applications in high-dimensional data mining, AI-driven imaging (e.g., biomedical imaging), and inverse problem solutions. Key areas include generative models, image super-resolution, neural network dynamics, and domain-specific interpolation techniques. His work emphasizes trustworthy AI, perceptual-quality image enhancement, and biomedical applications like neuroimaging analysis. Notable contributions include developing the MMSR framework for class-specific image super-resolution, wavelet-domain losses for artifact reduction, and leveraging vision-language models for trustworthy super-resolution. His articles span domains from theoretical ML to applied biomedical imaging solutions. Dr. Dogan’s academic journey includes postdoctoral training at Harvard and a strong focus on interdisciplinary research at the intersection of engineering and AI. He currently leads initiatives at KUIS AI, advancing applied AI research and innovation.

• Machine Learning
• Materials Science
• Physics
• +3 more

Qian Yang is an Assistant Professor in the Computer Science and Engineering Department at the University of Connecticut (Storrs). Her research focuses on computational science applied to physical sciences, particularly machine learning for materials, physics, and chemistry. She holds a Ph.D. in Computational and Mathematical Engineering from Stanford University and a B.A. in Applied Mathematics/Computer Science from Harvard College. Before joining UConn, she was a postdoctoral scholar at Stanford's Materials Computation and Theory Group. Her work emphasizes developing scalable models from molecular dynamics data, model reduction for nonlinear systems, and addressing challenges in scientific datasets (e.g., small, clustered, or imbalanced data). Research Interests : Machine Learning for Materials Science, Data-Driven Modeling, Uncertainty Quantification, Chemical Reaction Networks, and Multiscale Simulation. Awards : Best Instructor Award (Stanford, 2015/2016), Best Student/Postdoc Presentation Award (Materials Research Society, 2015). Teaching : Taught courses on Machine Learning for Physical Sciences, Computer Architecture, and computational methods at UConn and Stanford. Known for interdisciplinary approaches and student engagement. Labs/Teams : Leads research on computational modeling and hosts the Materials and Megabytes podcast, featuring interdisciplinary discussions with leading researchers.