Find Academic Supervisors

• Computational Imaging
• Inverse Problems
• Numerical Analysis
• +2 more

Greg Buzzard is Professor of Mathematics and Director of the Center for Computational & Applied Mathematics (CCAM) at Purdue University's College of Science. His research develops mathematical foundations for computational imaging systems through collaborative work with electrical engineering and materials science researchers. Dr. Buzzard leads a substantial research group focusing on inverse problems in imaging science. His work combines theoretical advances in optimization with practical applications in electron microscopy, neutron tomography, and medical imaging. Key research areas include: Consensus Equilibrium frameworks for model integration Dynamic sampling algorithms for computational imaging Plug-and-play methods for image reconstruction Multi-agent systems for large-scale inverse problems Recent publications demonstrate leadership in computational tomography innovations, particularly neutron imaging techniques for materials science and spectral CT reconstruction. His team's methods enable faster, higher-resolution imaging across scientific domains from biomedical applications to aerospace materials characterization. The 2020 SIAM Imaging Sciences Best Paper Prize recognized fundamental contributions to Plug-and-Play reconstruction methods. Professor Buzzard maintains an active mentoring program with 9 current graduate students and postdocs. His interdisciplinary collaborations include sustained partnerships with the Purdue School of Electrical and Computer Engineering and materials science research groups.

Joydeep GhoshView profile

Professor

• Machine Learning
• Data Mining
• Web Mining
• +6 more

Joydeep Ghosh is the Schlumberger Centennial Chair Professor of Electrical and Computer Engineering at The University of Texas at Austin, leading the IDEAL Lab and UT-MINDS. He holds courtesy faculty positions in Biomedical Engineering, Population Health, and McCombs School of Business. Dr. Ghosh earned his B.Tech from IIT Kanpur (1983) and PhD from the University of Southern California (1988). His research focuses on machine learning, data/web mining, scalable analytics, and ethical AI, with applications in healthcare and industry. He has published over 500 papers, co-edited 20 books, and secured funding from NSF, Google, IBM, and others. Notable awards include the 2020 ICDM Research Contribution Award and 2015 IEEE Technical Achievement Award. He advises startups like CognitiveScale and teaches advanced courses in machine learning and data science, consistently recognized as 'Best Professor' by students. His lab collaborates with industry via sponsored research, addressing real-world challenges in AI and data analytics. Education: B.Tech, Electrical Engineering, IIT Kanpur (1983) Ph.D., Electrical Engineering, University of Southern California (1988) Research Interests: Predictive and Prescriptive Analytics Trustworthy AI/Explainable Machine Learning Federated Learning & Privacy-Preserving Techniques Health Informatics & Computational Phenotyping Scalable Algorithms for Big Data Grants & Industry Collaboration: Sponsored by NSF, Google, IBM, ONR, and others Co-founder/Advisor to Accordion Health, CognitiveScale, and Neonyoyo IDEAL Lab partnerships with industry for problem-driven research Labs & Teams: IDEAL Lab: Focuses on intelligent data analysis across domains UT-MINDS: Engineering Data Science and Machine Learning initiatives

Joel A. TroppView profile

Professor

• Mathematics of Data Science
• Machine Learning
• Numerical Linear Algebra
• +3 more

Joel A. Tropp is the Steele Family Professor of Applied & Computational Mathematics at the California Institute of Technology (Caltech), within the Division of Engineering and Applied Science. His academic career includes roles as Assistant Professor (2007–2012), Professor (2012–2017), and Steele Family Professor (2017–present). He holds a Ph.D. in Computational Applied Mathematics from the University of Texas at Austin (2004). Tropp's research focuses on applied mathematics, machine learning, data science, numerical algorithms, and random matrix theory, with notable contributions to matching pursuit algorithms, randomized SVD methods, and matrix concentration inequalities. Education: Ph.D. in Computational & Applied Mathematics, University of Texas at Austin (2004) M.S. in Computational & Applied Mathematics, University of Texas at Austin (2001) B.S. in Mathematics and B.A. in Plan II Liberal Arts, University of Texas at Austin (1999) Research Interests: Tropp’s work bridges applied mathematics, computer science, and engineering, emphasizing rigorous, practical algorithms for linear algebra, numerical analysis, and optimization. He develops user-friendly tools for high-dimensional probability and matrix analysis, with applications in machine learning, signal processing, and data science. His recent focus includes randomized algorithms for large-scale matrix computations, kernel methods, and quantum computing. Articles Trends: His recent publications address scalable randomized algorithms for kernel matrices, eigenvalue problems, and matrix approximation. Themes include computational efficiency, theoretical guarantees, and applications in machine learning, quantum computing, and dynamical systems. Awards: 2025 Richard P. Feynman Prize for Excellence in Teaching 2024 IMS Fellow 2020 IEEE Fellow 2019 SIAM Fellow 2008 PECASE Award Advising & Grants: Tropp has advised numerous Ph.D. students and postdoctoral researchers in areas like randomized algorithms, optimization, and quantum computing. He leads grants from ONR, NSF, and Caltech’s Carver Mead Fund, focusing on large-scale kernel computations and matrix solvers. His mentorship extends to interdisciplinary collaborations in turbulence modeling and signal processing. Labs & Teams: He contributes to Caltech’s Center for Mathematics of Information (CMI) and Computational Mathematics + X (CMX) initiatives, fostering research in data science, optimization, and computational methods.

Javier Escudero RodriguezView profile

Senior Lecturer

• Biomedical signal processing
• Brain connectivity
• Network theory
• +5 more

Dr Javier Escudero Rodriguez is a Senior Lecturer at the School of Engineering, University of Edinburgh, where he leads a research group focused on biomedical signal processing. His work addresses brain connectivity changes in diseases like Alzheimer's and epilepsy, utilizing machine learning and nonlinear analysis. Education: PhD in Telecommunications Engineering, University of Valladolid (2010) MEng in Telecommunications Engineering, University of Valladolid (2005) His research explores brain functional connectivity, integrating graph theory and tensor decomposition to understand neurodevelopmental and neurodegenerative diseases. He applies pattern recognition to clinical datasets for decision-making support, with recent work on AI tools for seizure detection and aging biomarkers. Scientific contributions include IEEE Senior Membership , Nightingale Award 2018 , and leadership in projects like CHAI (EPSRC AI Hub) and TRIG 2024 . He supervises PhD and MSc students and shares tools via datasets on Edinburgh DataShare.

Xiaodong CaoView profile

Professor

• Analysis
• Geometry
• Differential Geometry
• +3 more

Xiaodong Cao is a Professor in the Department of Mathematics at Cornell University, affiliated with the College of Arts and Sciences. He holds a Ph.D. from the Massachusetts Institute of Technology (2002). His research focuses on geometric analysis, differential geometry, and partial differential equations, particularly in the context of Ricci flow, curvature estimates, and geometric evolution equations. Education: Ph.D. in Mathematics, MIT (2002). Teaching: Courses include Introduction to Differential Geometry (MATH 4540), Linear Algebra (MATH 2210), and supervised research (MATH 4900/4901) in Spring and Fall 2025. His research interests emphasize geometric structures, curvature analysis, and the application of differential equations to geometric problems. Notable contributions include studies on Ricci solitons, curvature pinching, and Harnack inequalities in evolving manifolds. He actively engages in academic service through course coordination and departmental initiatives. No scientific awards are explicitly listed, though his extensive publication record reflects sustained academic impact. His advising and grant activities remain unspecified in the provided data. Dr. Cao’s work is accessible via his personal web page and through his teaching and research activities at Cornell.

Erik HoyView profile

Associate Professor

• Quantum Chemistry
• Electron Transport
• Quantum Sensors
• +3 more

Erik Hoy is an Associate Professor in the Department of Chemistry & Biochemistry at Rowan University, part of the College of Science & Mathematics. He holds a Ph.D. in Physical Chemistry from the University of Chicago and postdoctoral training at Johns Hopkins University and Northwestern University. His research focuses on quantum chemistry methods for studying electron transport, molecular devices, and organic materials. Key areas include developing novel Green’s function approaches combined with high-accuracy quantum chemistry techniques to design quantum sensors, electronic devices, and renewable energy materials. Education: Postdoctoral Associate at Johns Hopkins University and Northwestern University; Ph.D. in Physical Chemistry (University of Chicago); B.S. in Chemistry (Tennessee Tech University). Research Interests: Quantum Chemistry, Electron Transport, Quantum Sensors, Molecular Electronics, Computational Organic Reactions. His group emphasizes high-accuracy electronic structure methods and their application to nanoscale systems, with recent work on molecular junctions and electrocatalysis. Awards: NSF CAREER Award (2024), NSF ExpandQISE Award (2024), Eppley Foundation Grant (2023). His group has secured grants for projects like 'Strongly Correlated Molecular Qubits' and 'Quantum Sensors.' Advising & Grants: Supervises a diverse group of undergraduate and graduate students, including notable advisees such as Grace McGowan and Kale Kroenke. Collaborates with industry and academic partners, such as the Lofland group at Rowan and the Sand group at Butler University. Lab Activities: The Hoy Research Group develops computational tools like pyRUQT and contributes to software like MoleMod. Their work is highlighted in journals like Advanced Functional Materials and Physical Chemistry Chemical Physics .

Ryder C. WinckView profile

Associate Professor

• Robotics
• Control Systems
• Nonlinear Control Theory
• +3 more

Ryder C. Winck is an Associate Professor of Mechanical Engineering at Rose-Hulman Institute of Technology. He holds a PhD (2012) and MS (2009) from Georgia Institute of Technology, and dual BS/BA degrees (2007) from Rice University. His research focuses on robotics, control systems, nonlinear control theory, and human-robot interaction for supernumerary robotic limbs. He contributes to the multidisciplinary robotics minor program and has consulted for Deka R&D and Software Automation, Inc. His work bridges theoretical control systems with practical robotics applications, including multi-cylinder systems and biometric interfaces. Academic Degrees: PhD (Georgia Tech, 2012), MS (Georgia Tech, 2009), BS/BA (Rice University, 2007) Research interests emphasize system dynamics and robotics, with publications spanning mechatronics, control theory, and robotics ergonomics. His recent articles explore valve control optimization, robotic limb performance evaluation, and tensor decomposition techniques in multi-system control. Consulting Experience: Deka R&D, Software Automation, Inc. No awards listed. Advising and grant details not specified in text. Active in lab/teams related to robotics and control systems.

Xi ChenView profile

Professor

• Statistical Inference
• Stochastic Optimization
• Machine Learning
• +2 more

Xi Chen is a Full Professor and Andre Meyer Faculty Fellow at NYU Stern School of Business, with joint appointments at Courant Institute of Mathematical Sciences and Center for Data Science. He holds a PhD in Machine Learning from Carnegie Mellon University and completed postdoctoral training at UC Berkeley. Professor Chen's research spans machine learning applications in operations management, blockchain technology, and quantitative finance. His research focuses on statistical inference for streaming data, stochastic optimization, dynamic pricing, and decentralized finance. He has led industry collaborations with Google, Meta, Amazon, and financial institutions, solving technical challenges in digital advertising and quantitative trading. Professor Chen serves as Area Editor for Operations Research and Associate Editor for top journals including Management Science and Annals of Statistics. He has authored nearly 100 publications and co-edited books on joint learning-optimization frameworks and Web3 technologies.

Sarah LondonView profile

Associate Professor

• Neuroscience
• Neural Plasticity
• Learning and Memory
• +2 more

Sarah London is an Associate Professor at the University of Chicago's Department of Psychology. She received a BA in Biology and Psychology from Middlebury College and a PhD in Neuroscience from UCLA, followed by postdoctoral research at University of Illinois Urbana-Champaign. Her research examines neural mechanisms of developmental learning using zebra finch songbirds as models. Key interests include experience-dependent neural plasticity, sensitive periods in development, and molecular/genomic correlates of behavior, with implications for human speech acquisition and neurodevelopmental disorders. Publications focus on autism spectrum disorder, Down syndrome neurodevelopment, advanced MRI methodologies, and computational neuroimaging approaches. Recent work investigates white matter development, functional connectivity patterns, and early behavioral biomarkers in neurodevelopmental conditions. Leads the London Lab studying neurogenomics of learning potential and critical period mechanisms.

Tamara PolajnarView profile

Researcher

• Natural Language Processing
• Computational Linguistics
• Distributional Semantics
• +2 more

Dr. Tamara Polajnar is a Visiting Researcher at the Department of Computer Science and Technology, University of Cambridge. Her research focuses on computational linguistics and natural language processing, with particular emphasis on distributional semantics and semantic compositionality. Her research interests span multiple areas including computational semantics, distributional models, and semantic composition. She develops tensor-based methods for representing meaning and investigates techniques for semantic analysis of text at different levels from phrases to documents. Her work combines theoretical linguistics with practical machine learning approaches. Her publications primarily focus on compositional distributional semantics, exploring how to effectively combine word vectors to represent larger linguistic units. Recent work examines discourse-level representations and evaluation methods for semantic models. She has contributed datasets and frameworks that advance the understanding of how computational models can capture linguistic phenomena. She actively contributes to the research community through the development of specialized datasets and evaluation resources. Her work on RELPRON provides valuable resources for evaluating compositional distributional models, while her research on short scientific summaries addresses challenges in processing academic text.

Ankur MoitraView profile

Professor

• Theoretical Computer Science
• Machine Learning
• Algorithms
• +3 more

Ankur Moitra is the Norbert Wiener Professor at the Massachusetts Institute of Technology (MIT), affiliated with the Department of Electrical Engineering and Computer Science and the Theory of Computation research group. His work focuses on foundational aspects of algorithms, machine learning, and theoretical computer science, with applications to cryptography, quantum computing, and statistical learning. He leads research initiatives in algorithmic robustness, tensor decomposition methods, and reinforcement learning. Moitra’s research spans algorithm design, computational complexity, and probabilistic models. Notable contributions include robust learning algorithms for mixtures of distributions, tensor decomposition techniques, and theoretical analyses of reinforcement learning and adversarial machine learning. His work bridges mathematical theory with practical computational challenges, often addressing algorithmic guarantees under noise or adversarial conditions. His recent publications highlight advancements in areas such as cryptographic watermarking, quantum Hamiltonian learning, and efficient sampling from complex distributions. Despite no explicitly listed awards, his contributions have influenced major areas of theoretical computer science. His research group collaborates across MIT’s School of Science and the Computer Science and Artificial Intelligence Laboratory (CSAIL).

Laura GrigoriView profile

Professor

• Numerical Linear Algebra
• High Performance Computing
• Deterministic and Randomized Algorithms
• +4 more

Laura Grigori is a Full Professor and Chair of High Performance Numerical Algorithms and Simulations at EPFL's School of Basic Sciences (SB) Department of Mathematics (MATH). Her research focuses on numerical linear algebra, high performance computing, and tensor computations, with applications in astrophysics and molecular simulations. She leads the HPNalgs lab and teaches courses in numerical analysis and HPC. Her awards include the SIAM Supercomputing Career Prize (2024) and SIAM Fellow distinction (2020). She advises four PhD students and has authored numerous papers on communication-avoiding algorithms, randomized methods, and parallel linear algebra techniques. Her work addresses scalability challenges in scientific computing and large-scale data analysis. Labs/Teams: HPNalgs Lab (https://www.epfl.ch/labs/hpnalgs/) Grants: ERC Synergy Grant (2019) for Extreme-scale Computational Chemistry

• Aerothermodynamics
• Rarefied Gas Dynamics
• Computational Fluid Dynamics (CFD)
• +3 more

Dr. Georgii Oblapenko is a postdoctoral researcher at the Aerodynamics Center (ACOM), RWTH Aachen University, since November 2023. He has held postdoctoral positions at the German Aerospace Center (DLR) Institute of Aerodynamics and Flow Technology (2021–2023) and the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin (2018–2021). Earlier, he worked as a research engineer at Saint-Petersburg State University's Department of Hydroaeromechanics (2013–2017). His research focuses on mathematical modeling of continuum and rarefied gas flows with thermochemical non-equilibrium, particle-based simulation methods (DSMC, DVM), and uncertainty quantification in aerothermodynamics. He has contributed to the development of advanced kinetic theory approaches for vibrational relaxation, chemical reactions, and transport coefficients in high-temperature flows. Notable achievements include a personal fellowship from the Alexander von Humboldt Foundation (2021–2023) and collaborations with institutions like the University of Texas at Austin and DLR. His teaching activities include instructing Math I and Math II at RWTH Aachen. Scientific Awards: Alexander von Humboldt Foundation Fellowship. His work spans aerothermodynamics, plasma physics, and computational methods, with recent publications addressing entropy-stable simulations, vibrational non-equilibrium effects, and hybrid particle-continuum modeling.

• Structural Health Monitoring
• Machine Learning
• Data Science
• +7 more

Dr. Ali Anaissi is a Lecturer in Data Science at the School of Computer Science, University of Sydney. His research focuses on structural health monitoring (SHM) using machine learning and sensor networks to detect damage in infrastructure such as bridges and roads. He has applied these techniques to the Sydney Harbour Bridge and developed a vehicle-mounted sensor system for road condition assessment. His work emphasizes condition-based maintenance over traditional time-based approaches. He teaches courses including Principles of Data Science (COMP5310), Machine Learning and Data Mining (COMP5318), and Software Development in Java (COMP9103). His research spans structural damage detection, federated learning, tensor decomposition, and IoT applications in smart homes and healthcare. Key projects include developing a smart pothole detection system and exploring adversarial attacks in UAV services. He has authored numerous publications in journals like IEEE Transactions on Cybernetics and conferences such as IJCNN and ICDMW. His work integrates machine learning with domain knowledge to improve infrastructure safety and efficiency. Dr. Anaissi's contributions include pioneering the use of adaptive one-class SVMs in SHM and developing frameworks for privacy-preserving fitness systems. His team's sensor-driven approaches aim to transform infrastructure maintenance through real-time health scoring and automated anomaly detection.

Geoff NichollsView profile

Associate Professor

• Bayesian Inference
• Computational Statistics
• Monte Carlo Methods
• +4 more

Geoff Nicholls is an Associate Professor of Statistics at the University of Oxford, affiliated with St Peter's College as a Fellow and Tutor. He holds a B.Sc. in Physics from the University of Canterbury (New Zealand) and a PhD in High-Energy Physics from the University of Cambridge. His research focuses on Bayesian inference, computational statistics, and Monte Carlo methods applied to interdisciplinary problems in geoscience, linguistics, genetics, and archaeology. Notable contributions include semi-modular Bayesian inference frameworks and embedded diachronic sense change models for ancient languages. He has advised students like Lorenzo Pacchiardi and Jessie Jiang, and collaborates on tools like the TraitLab package for binary tree-like data analysis. Key research areas include statistical methods for partial orders, biclustering forensic data, and scalable Bayesian techniques for high-dimensional problems. His work bridges theoretical developments with practical applications, such as modeling historical power dynamics using 12th-century documents and analyzing linguistic evolution in ancient Greek texts.