Find Academic Supervisors

• Machine Learning Theory
• Statistical Physics
• Neuroscience
• +5 more

Stefano Sarao Mannelli is a tenure-track Assistant Professor in the Department of Computer Science and Engineering at Chalmers University of Technology and University of Gothenburg. He also holds a Visiting Lecturer position at the University of the Witwatersrand. His research group focuses on fundamental aspects of learning in biological and artificial systems, with emphasis on bias generation, optimization dynamics, and comparative neuroscience. Education: Ph.D. in Theoretical Physics, Université Paris-Saclay (2020) M.Sc. in Electronic Engineering, Politecnico di Torino (2017) M.Sc. in Physics of Complex Systems, Politecnico di Torino/SISSA (2016) M2 in Physique Théorique, Paris Diderot/UPMC/ENS Cachan (2016) B.Sc. in Mathematics for Engineering, Politecnico di Torino (2014) Research: Dr. Mannelli develops model-based approaches to reduce complex machine learning problems into analytically tractable frameworks. His core interests include: 1) Bias amplification mechanisms in AI systems, 2) Learning differences between biological and artificial neural networks (continual/transfer/curriculum learning), and 3) Optimization in high-dimensional landscapes. His work bridges statistical physics, neuroscience, and deep learning theory. Publication Trends: Recent articles (2024-2025) predominantly analyze curriculum learning dynamics, bias propagation in optimization, and theoretical comparisons between biological and artificial learning systems. Methodologically, they combine statistical physics frameworks with control theory and high-dimensional analysis. Awards: Academic Grant (CM Lerici Foundation, 2025) Travel Grants (Guarantor of Brains, G-Research 2024) UK–IT Trustworthy AI Exchange Programme (Alan Turing Institute, 2023) SCGB Conference Award (Simons Foundation, 2023) Ph.D. Scholarship (CEA, 2017-2020) Team & Funding: Leads a research group with 2 PhD students and 1 postdoc. Secured significant funding for international workshops including Analytical Connectionism (£42K, 2023; $152K, 2024) and High-Dimensional Methods (135,500 SEK, 2025).

Kurt BuschView profile

Professor

• Quantum Photonics
• Nano-Photonics
• Computational Optics
• +6 more

Prof. Kurt Busch is a Professor of Theoretical Optics & Photonics at Humboldt University of Berlin and Group Leader at the Max-Born-Institute, with prior appointments at Karlsruhe Institute of Technology (2005-2011) and University of Central Florida (2004-2005). His research focuses on light-matter interactions in complex photonic systems, spanning quantum technologies to nanoscale optical phenomena. His educational background includes: Diplom in Physics, Universität Karlsruhe (TH), 1993 PhD in Physics, Universität Karlsruhe (TH) and Iowa State University, 1996 Postdoctoral Research, University of Toronto (Prof. Sajeev John), 1997-2000 Busch's research encompasses quantum photonics, nano-photonics, computational optics, photonic crystals, plasmonics, random media, fluctuation-induced phenomena, and Group-IV photonics. His work combines theoretical modeling with computational approaches to investigate light propagation in disordered and nanostructured materials, with applications in quantum information processing and advanced optical devices. Key methodologies include time-domain simulations and quantum electrodynamics frameworks for non-equilibrium systems. Analysis of his 15 most recent publications (2021-2025) reveals dominant trends in quantum optics (40% of articles), non-Hermitian photonics (25%), and computational nanophotonics (35%). Significant subfield intersections include topological protection in quantum states, Casimir-Polder force engineering, and nonclassical light manipulation via waveguide architectures, reflecting his group's focus on bridging fundamental quantum phenomena with photonic device applications. His scientific awards include: Editor-in-Chief, Journal of the Optical Society of America B (2019) Fellow of the Optical Society of America (2012) Carl-Zeiss Research Award (2006) Teaching Award, KIT Department of Physics (2009) Emmy-Noether Fellow, DFG (2000) While current student advising details are not specified in available materials, his Emmy-Noether fellowship (2000-2003) established his independent research group. Current grant activities likely support his Max-Born-Institute collaborations on photonic nanostructures and quantum friction phenomena, though specific projects aren't detailed in the source text. Busch leads the 'Photonic Nanostructures' research group within Humboldt University's Theoretical Optics & Photonics Department, maintaining strong ties to the Max-Born-Institute for collaborative experimental-theoretical work. His team specializes in computational modeling of quantum optical effects in plasmonic systems and topological photonic structures, utilizing high-performance computing resources for electromagnetic simulations and quantum dynamics calculations.

Calvin TsayView profile

Lecturer

• Optimization Algorithms
• Process Control
• Machine Learning Optimization
• +2 more

Calvin Tsay is a Lecturer (Assistant Professor) in the Computational Optimisation Group at Imperial College London, holding the BASF/RAEng Senior Research Fellowship in Scale-Bridging Modelling. Education: PhD Chemical Engineering (UT Austin 2020), BS/BA (Rice University 2015). Research develops optimization methods bridging machine learning and process systems engineering. Specializes in mixed-integer programming for neural networks and Bayesian optimization for energy applications. Awards: President's Medal for Early Career Researcher RAEng Senior Research Fellowship CACE Best Paper (2023) COIN-OR Cup (2022) Leads research on AI-driven chemical process optimization. Supervises PhD students in ML optimization and process control. Collaborates with BASF on industrial applications.

• Computational Statistics
• Machine Learning
• Monte Carlo Methods
• +5 more

Alain Durmus is a Professor at École Polytechnique, affiliated with the CMAP (Applied Mathematics Department). His research focuses on computational statistics, machine learning, and stochastic methods for Bayesian inference and generative models. Key areas include Monte Carlo methods, stochastic optimization, and Riemannian geometry in optimization. He has contributed to theoretical advancements in Markov processes and their applications in sampling algorithms. Affiliations: École Polytechnique (CMAP), ENS Paris-Saclay (teaching roles), and other academic collaborations. Teaching: Courses on mathematical statistics, stochastic methods, probability theory, and optimization at ENS Paris-Saclay and École Polytechnique. Research Interests: Development of Monte Carlo algorithms, stochastic approximation schemes, and theoretical analysis of Markov chain convergence. Specializes in Bayesian statistics, generative models, and high-dimensional inference. His work bridges theory and application, with notable contributions to Langevin dynamics, nonreversible MCMC methods, and federated learning. He has received a Best student paper award at ICASSP 2020 for collaborative research on Bayesian computation using Sliced-Wasserstein distances. Alain’s publications span prestigious journals like the Annals of Applied Probability and SIAM, with a focus on algorithmic innovation and rigorous mathematical foundations.

George DrettakisView profile

Researcher

• Computer Graphics
• Rendering
• Image-Based Rendering
• +12 more

George Drettakis is a Senior Researcher at INRIA Sophia-Antipolis and leads the GRAPHDECO research group. He has held professorial roles at institutions including MIT, University of Reims, University of Toronto, and École Normale Supérieure. His research focuses on rendering for computer graphics and sound, with emphasis on image-based rendering, perceptual rendering, and audio-visual cross-modal effects. He has also explored interactive illumination, shadows, relighting, and generative models. Current Students: G. Kopanas (Neural Rendering), N. Violante (Generative Models), A. Petitjean (co-supervised), Y. Poirier-Ginter (co-supervised), P. Panantonakis (starting fall 2023). Postdoctoral Researchers: A. Gauthier at INRIA. His recent work includes 3D Gaussian Splatting , Diffusion-based Relighting , and Neural Radiance Fields . He has received the Eurographics Outstanding Technical Contributions Award (2007) and was named an Eurographics Fellow . He manages projects like ERC Advanced Grant FUNGRAPH and has participated in H2020 EMOTIVE , ANR SEMAPOLIS , and CROSSMOD . His group collaborates internationally and has hosted researchers from institutions such as UC Berkeley, Imperial College London, and TU Wien.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +5 more

Jing Zhao is a Professor at Beijing Institute of Technology's School of Automation, Department of Computer Science, with an extensive publication record spanning artificial intelligence, computer vision, medical imaging, and robotics. Their interdisciplinary research bridges theoretical computer science with practical applications in healthcare, agriculture, and engineering. Research interests focus on cutting-edge AI methodologies including deep learning architectures, neural rendering techniques, and multimodal systems. Notable work includes advancements in 3D reconstruction (Gaussian Splatting), medical image analysis for cancer diagnosis, and novel approaches to EEG-based emotion recognition. The research demonstrates strong interdisciplinary connections between computer science and domain-specific applications. Recent publications reveal a consistent trend toward practical AI implementations across diverse fields. Jing Zhao's work shows expertise in adapting theoretical machine learning concepts to solve real-world problems in medical diagnostics, agricultural monitoring, and robotics. The research portfolio demonstrates particular strength in developing novel neural network architectures for specialized applications. As a corresponding author on numerous high-impact publications, Jing Zhao appears to lead a productive research group with collaborations spanning multiple institutions and disciplines. The publication record indicates significant contributions to both theoretical computer science and applied AI solutions across various domains.

Xiangfeng YangView profile

Associate Professor

• Large Deviations Theory
• Probability Theory
• Stochastic Processes
• +11 more

Xiangfeng Yang is an Associate Professor and Docent in the Department of Mathematics at Linköping University, Sweden, affiliated with the Division of Applied Mathematics (TIMA). His work bridges theoretical probability and applied mathematical modeling, with a strong emphasis on large deviations theory and stochastic processes. His research interests include: Large Deviations Theory Random Matrices and Sample Covariance Matrices Stochastic Bridges (Bernstein and Markov) Longest Runs, Gaps, and Extremal Eigenvalues Hidden Markov and Reciprocal Processes Modern Multivariate Statistical Analysis Xiangfeng Yang's recent publications (2015–2024) reflect a consistent and deep engagement with probabilistic asymptotics and extreme behavior in stochastic systems. His work spans theoretical foundations in probability, applications in random matrix theory, and modeling in financial and environmental contexts. A notable trend is his focus on large deviation principles across diverse settings—ranging from eigenvalues of random matrices to gaps in Poisson processes and runs in Markov chains. He frequently collaborates with PhD students, indicating an active supervisory role. His scientific contributions are published in reputable journals such as Journal of Applied Probability , Stochastic Processes and their Applications , and Theory of Probability and Mathematical Statistics . While no explicit awards are listed, the quality and volume of his output suggest recognition within the mathematical community. Xiangfeng Yang has supervised several PhD students to completion, including Joseph Okello Omwonylee, Denise Uwamariya, and Stefane Saize, with research topics closely aligned with his expertise in large deviations and stochastic modeling. His research likely involves collaboration within the Applied Mathematics division, though no specific lab or team name is mentioned. He continues to contribute actively to modern theoretical and applied probability.

Neill CampbellView profile

Professor

• Visual Computing
• Machine Learning
• Computer Vision
• +13 more

Neill Campbell is a Professor of Visual Computing and Machine Learning in the Department of Computer Science at the University of Bath . He is the Director of the Centre for the Analysis of Motion, Entertainment Research and Applications (CAMERA) and co-director of the Centre for Mathematics and Algorithms for Data (MAD) . He holds an external position as Honorary Associate Professor at University College London and is a Royal Society Industry Fellow. His research spans visual computing, machine learning, and their applications in graphics, vision, and healthcare. His educational background includes a Master of Engineering and a Doctor of Philosophy in Engineering from the University of Cambridge, with his PhD focusing on Automatic 3D Model Acquisition from Uncalibrated Images. Neill Campbell’s research interests lie at the intersection of shape modeling , machine learning , and computer vision . He develops probabilistic and deep learning models to understand and generate visual content, with applications in digital humans, virtual production, biomechanics, and medical imaging. His work emphasizes uncertainty quantification, generative modeling, and alignment learning using Gaussian processes and Bayesian nonparametrics. He leads interdisciplinary projects that bridge computer science and mathematical sciences. The recent publications reflect a strong trend in probabilistic modeling , geometric deep learning , and medical applications . Topics include Gaussian process-based shape modeling, anomaly detection in safety-critical systems, and sparse approximations for geometric representations. His work increasingly integrates theoretical machine learning with real-world applications in health, engineering, and creative industries. Royal Society Industry Fellow Neill Campbell actively supervises PhD students across multiple Centres for Doctoral Training (ART-AI, SAMBa, CDE) and leads significant research grants such as MyWorld (UKRI Strength in Places Fund) and REMODEL (EPSRC). His group collaborates with industry leaders like Rolls-Royce, NVIDIA, Adobe, and DNEG, and he supports student internships and industrial placements. He is also involved in spin-out activities, including contributions to Forceteck Ltd. He leads the Visual Computing Group and is deeply embedded in research centers including CAMERA , MAD , and MyWorld . His team includes postdoctoral researchers and PhD students working on 3D reconstruction, biomechanics, inverse problems, and generative models, fostering a collaborative and interdisciplinary research environment.

Bikramjit DasView profile

Associate Professor

• Data Science
• Machine Learning
• Operations Research
• +7 more

Bikramjit Das is an Associate Professor and Associate Head of Pillar (Graduate Programme) at Singapore University of Technology and Design (SUTD). He holds a PhD in Operations Research from Cornell University and prior to SUTD, was a postdoctoral researcher at ETH Zurich’s RiskLab. His research focuses on extreme events analysis using applied probability, optimization, and statistical learning, with applications in finance, telecommunications, federated learning, and climate modeling. He teaches courses in Probability, Stochastic Modeling, and Analytics, and directs the Master of Science in Technology and Design (Data Science) program. Education: PhD in Operations Research (Cornell University), B.Stat & M.Stat (Indian Statistical Institute). Research emphasizes heavy-tailed distributions, risk contagion, and network modeling. Key areas include risk analysis in financial networks, robust optimization under uncertainty, and extreme value theory. His work bridges theoretical probability and real-world applications in data science and public policy. Notable contributions include studies on asymptotic independence in high dimensions, robust newsvendor models, and inference techniques for heavy-tailed data. His articles explore topics ranging from federated learning under noise to climate modeling and congestion phenomena in sparse networks. Collaborations include visiting positions at MIT and the Karlsruhe Institute of Technology. Active in academic leadership, he has contributed to technical reports on healthcare provider choice analysis and probabilistic flood risk assessments for nuclear power plants.

Michael PittView profile

Professor

• Statistical Methodology
• Bayesian Inference
• Sequential Monte Carlo Methods
• +5 more

Professor Michael Pitt is a faculty member in the Department of Mathematics at King's College London, holding the rank of Professor of Statistics within the Faculty of Natural, Mathematical & Engineering Sciences. He completed his doctorate in Statistics at the University of Oxford (Nuffield College) in 1997, followed by postdoctoral research at Imperial College London. His career includes roles at the University of Warwick (1999-2016) as Assistant and Associate Professor before joining King's College. Education: PhD in Statistics, University of Oxford (1997) Postdoctoral Research, Imperial College London (1997-1999) Research Interests: Focuses on advanced statistical methodologies including particle filtering, sequential Monte Carlo (SMC), Markov chain Monte Carlo (MCMC), and their applications in financial econometrics and biostatistics. Key areas include: Development of computationally efficient algorithms for Bayesian inference Analysis of financial time series and stochastic volatility models Multivariate copula models for dependence structures Clinical outcome analysis in cardiology through statistical revascularization studies Publications: Recent work emphasizes methodological advancements in pseudo-marginal methods, correlated particle filtering, and adaptive sampling techniques. Notable contributions include applications in cardiac clinical outcomes and high-dimensional density modeling. Labs/Teams: Active member of the Research Centre for Non-Equilibrium Science (CNES) , focusing on interdisciplinary non-equilibrium systems, and part of the Statistics Group within the Department of Mathematics.

• Machine Learning
• Kernel Methods
• Model Selection
• +11 more

Gavin Cawley is a Professor in the School of Computing Sciences at the University of East Anglia (UEA), with additional affiliations to the Data Science and AI group, the Centre for Ocean and Atmospheric Sciences, and the Statistics group. His research spans machine learning, bioinformatics, climate modeling, and environmental data analysis. His primary research interests include Machine Learning , Kernel Methods , Model Selection , Bayesian Regularization , Bioinformatics , and Climate Modeling . He has made significant contributions to understanding overfitting in model selection, sparse logistic regression for gene and cancer classification, and time series classification using ensemble methods. His work bridges theoretical machine learning with practical applications in biology, archaeology, and environmental science. The recent trend in his publications shows a strong interdisciplinary focus, combining machine learning with climate science (e.g., Arctic sea ice prediction) and molecular biology (e.g., protein domain movements). His work often involves developing and evaluating statistical models for complex real-world problems, emphasizing robustness, interpretability, and predictive accuracy. While no specific awards are listed in the provided text, his extensive publication record in top journals such as Journal of Machine Learning Research , Bioinformatics , and Neural Networks , along with high citation counts (e.g., over 1,800 citations for his 2010 paper on overfitting), indicates significant recognition in the academic community. He has also contributed to organizing major machine learning challenges, such as the ChaLearn AutoML and Active Learning challenges. He has supervised or collaborated with numerous researchers across disciplines, though specific student names are not listed. His work involves methodological development in model selection, kernel learning, and survival analysis, often applied to biological and environmental datasets. He has been involved in projects related to predictive uncertainty, ozone forecasting, and microbial growth modeling. While no specific lab or team name is mentioned, his affiliations with the Data Science and AI group and the Centre for Ocean and Atmospheric Sciences suggest active participation in interdisciplinary research teams focused on data-driven environmental and biological modeling.

Angelos AlexopoulosView profile

Assistant Professor

• Theoretical and Applied Econometrics
• Bayesian Analysis
• Computational Statistics
• +4 more

Angelos Alexopoulos is an Assistant Professor at the Department of Economics , Athens University of Economics and Business . He has held Research Associate positions at the University of Cambridge, University College London, and University of Exeter in the UK. PhD: Athens University of Economics and Business Research Focus: Computational Statistics, Econometrics, Bayesian Analysis, Network Modelling Publications span Bayesian inference, epidemic forecasting, machine learning for fraud detection, and econometric methodology. Key journals include Journal of the Royal Statistical Society , Journal of Computational and Graphical Statistics , and Statistics and Computing . 2024: Gaussian invariance in MCMC 2024: Epidemic nowcasting models 2023: VAT fraud detection with ML Awards include certifications in Deep Learning (Coursera), Blockchain (edX), and Object-Oriented R Programming (DataCamp).

• Probability Theory
• Branching Processes
• Products of Random Matrices
• +7 more

Prof. Dr. Sebastian Mentemeier is a permanent Professor (W2) in the Department of Mathematics 2 at the Institute for Mathematics, Mathematics Education and Computer Science Education, University of Hildesheim, Germany, a position he has held since October 2019. He also holds significant administrative roles as Vice Dean and Dean of Studies in Faculty 4 (Mathematics, Natural Sciences, Economics & Computer Science), and is a member of the Institute's Board and the Central Commission for Studies and Teaching. His research is centered on advanced probability theory, with a focus on branching processes, products of random matrices, and extreme value theory for time series. His research interests include: Non-Gaussian limit theorems Branching processes, particularly Multitype Branching Random Walks Products of random matrices Extreme value theory for time series Heavy-tailed random variables Conditional limit theorems His recent publications, spanning from 2012 to 2022, demonstrate a consistent and high-impact research trajectory in theoretical probability. The articles reveal a strong trend in analyzing the asymptotic behavior of complex stochastic systems, particularly those defined by recursive equations and random matrix products. His work frequently intersects with statistical mechanics and time series analysis, often investigating the tail behavior and limit laws of solutions to stochastic fixed-point equations, with a particular emphasis on heavy-tailed and multivariate settings. Prof. Mentemeier has been a Principal Investigator on two major DFG projects: 'Nonlinear stochastic fixed-point equations with applications in statistical mechanics' (2017-2023) and 'Products of Random Matrices, Noncommutative Branching Random Walks, and Multitype Branching Random Walks in Random Environments' (since 2021). He is an active member of the academic community, serving as a referee for journals such as Stochastic Processes and their Applications and Journal of Theoretical Probability , and has co-organized international conferences on recursive stochastic processes and branching models. He supervises PhD and Master's students, although specific names are not listed on his profile. He has led a research group within the Department of Mathematics 2 and is a key member of the Institute's academic board. His work is conducted within the broader context of the Faculty of Mathematics, Natural Sciences, Economics & Computer Science at the University of Hildesheim.

• Machine Learning
• Computer Vision
• Natural Language Processing
• +3 more

Professor Andreas Geiger leads the Autonomous Vision Group (AVG) at the University of Tübingen , heading the Department of Computer Science and serving as core faculty at the Tübingen AI Center . He is Principal Investigator in the ML in Science cluster of excellence and CRC Robust Vision , while coordinating the ELLIS PhD program . Develops machine learning models for computer vision, NLP, and robotics Focus on 2D/3D representations, geometry/material reconstruction, and robust AI Applications in autonomous vehicles, VR/AR, and document analysis His research has produced hundreds of publications with significant impact, including multiple best paper awards at top venues. The Scholar Inbox platform he co-created revolutionizes academic paper discovery, winning business model awards at Tübingen AI Center spinoff events. Key research areas include: Neural rendering and 3D scene understanding Self-driving perception and planning systems Simulation frameworks for autonomous validation Efficient reinforcement learning architectures Recent awards include: CVPR 2024 Best Paper Sage 10-Year Impact Award 2024 IEEE PAMI Young Researcher Award 2018 Active in CyberValley and ELLIS Institute Tübingen , he maintains strong industry collaborations through initiatives like the ML ⇌ Science Colaboratory . His group's work appears in journals like TPAMI and conferences including SIGGRAPH 2025.

• Additive Manufacturing
• Computational Mechanics
• Topology Optimization
• +4 more

Albert To is a Professor at the Swanson School of Engineering, University of Pittsburgh, where he holds the William Kepler Whiteford Professorship. He serves as Director of both the MOST-AM Consortium and the ANSYS Additive Manufacturing Research Laboratory. Since joining Pitt in 2008, he has advanced from assistant to associate (2014) and full professor (2019). Education: BS, MS, and PhD from UC Berkeley; MS from MIT Postdoctoral Research: Northwestern University with Wing Kam Liu Dr. To's primary research interests center around design optimization for additive manufacturing, multiscale methods, and computational mechanics. His work focuses on fast process modeling and topology optimization for metal additive manufacturing. He directs the ANSYS Additive Manufacturing Research Laboratory, which houses advanced metal 3D printers including EOS DMLS, Optomec LENS, and ExOne binder jetting systems. In 2016, he founded the MOST-AM Consortium, which now includes over 30 member companies and research labs collaborating on additive manufacturing research. His research has been consistently supported by major funding agencies including NASA, DOD, DOE, NSF, America Makes, and industry partners like ANSYS. The recent publications demonstrate a strong focus on addressing key challenges in metal additive manufacturing processes, particularly laser powder bed fusion and wire-arc directed energy deposition technologies. His work spans from fundamental process modeling to practical applications in materials science and mechanical engineering. NSF BRIGE Award (2009) Air Force Summer Faculty Fellowship (2009) Board of Visitors Faculty Award (2016) Carnegie Science Award (2018) Best Student Paper Award, 46th Acoustic Emission Working Group Meeting (2003) Dr. To has secured substantial research funding from government agencies and industry partners to advance additive manufacturing technologies. His MOST-AM Consortium facilitates collaboration between academia and industry, accelerating the translation of research findings into practical applications. He has advised numerous graduate students who have contributed to his extensive publication record in top journals. His laboratory at the University of Pittsburgh is equipped with state-of-the-art metal 3D printing systems, enabling both fundamental research and applied development in additive manufacturing. The MOST-AM Consortium provides a framework for industry collaboration, ensuring research addresses real-world challenges in the field.