Find Academic Supervisors

• Stochastic Processes
• Financial Mathematics
• Statistics
• +3 more

Alexandra Chronopoulou is a Clinical Associate Professor in the Department of Statistics at the University of Illinois. She is affiliated with the Coordinated Science Lab and the Center for Social & Behavioral Science, contributing to interdisciplinary research in both mathematical theory and healthcare applications. Her roles emphasize bridging statistical methodologies with practical challenges in diverse fields. Her research interests span stochastic processes, including fractional Brownian motion and volatility modeling, alongside healthcare systems analysis and medical diagnostics. She applies statistical techniques to study burnout among healthcare professionals, particularly in pandemic testing environments, and explores biomechanical factors in gastrointestinal disorders. Her work also intersects with finance, focusing on risk management and option pricing through advanced stochastic frameworks. In recent years, her articles highlight trends in volatility estimation, fractional processes simulation, and healthcare ergonomics. Earlier contributions include methodological advances in sequential Monte Carlo, community detection algorithms, and multiscale diffusions. No scientific awards were explicitly mentioned in the provided texts. Though no advising or grant information was listed, her research collaborations extend to international partnerships. She contributes to labs and centers that foster interdisciplinary innovation, including work at the Coordinated Science Lab and the Center for Social & Behavioral Science.

Antal van den BoschView profile

Professor

• Computational Linguistics
• Machine Learning
• Artificial Intelligence
• +6 more

Antal van den Bosch is a Professor of Language, Communication, and Computation at Utrecht University’s Faculty of Humanities. He also serves as Board Member and Domain Chair for Social Sciences and Humanities at the Dutch Research Council (NWO). His career includes roles as Director of the Meertens Institute (KNAW) and professorships at Radboud University and Tilburg University. His research focuses on machine learning and computational linguistics, particularly Generative AI and Large Language Models. He emphasizes interdisciplinary collaboration, exploring intersections between AI and societal challenges like governance and cultural heritage. Education: Ph.D. in Advanced Computing Sciences at Maastricht University. Key affiliations include guest professorships at the University of Antwerp’s CLiPS and fellowships with EurAI and the Royal Netherlands Academy of Arts and Sciences. Research Interests: Generative AI and LLMs Language Technology Cultural AI Social Implications of AI Historical Language Analysis Articles Trends: Recent work addresses AI governance, societal impacts of generative models, and computational methods in humanities research. Projects like Better-Mods and Cultural AI Lab highlight applied AI for societal benefit. Awards: Vici Grant (NWO), KULAK Francqui Chair, and membership in prestigious academic societies. Advising & Grants: Supervises over 20 Ph.D. students. Leads projects on AI moderation tools, cultural heritage digitization, and digital humanities infrastructure. Notable grants include NWO-funded Better-Mods and Horizon 2020 initiatives like HiTiME and TwiNL. Labs/Teams: Active in CLARIAH, Nederlab, and the Digital Humanities Lab (KNAW). Software contributions include Frog (Dutch NLP suite), T-Scan, and Colibri Core.

Mayank ChadhaView profile

Adjunct Assistant Professor

• Structural Health Monitoring
• Applied Mechanics
• Machine Learning
• +7 more

Mayank Chadha is an Assistant Adjunct Professor in the Department of Structural Engineering at the University of California, San Diego. He holds a Ph.D. and M.S. in Structural Engineering from UC San Diego and a B.E. in Civil Engineering from PSG College of Technology, Anna University. His research focuses on integrating applied mechanics, machine learning, and Bayesian methodologies to address challenges in structural health monitoring (SHM), risk analysis, and optimal sensor design. He also explores the application of physics-constrained machine learning in hydrology and battery degradation modeling. Education: 2019: Ph.D. in Structural Engineering, UC San Diego 2023: Postdoctoral Fellowship, UC San Diego 2014: B.E. in Civil Engineering, PSG College of Technology Dr. Chadha’s research interests include structural health monitoring, applied mechanics, uncertainty quantification, and the development of decision-making frameworks for engineering systems. He has pioneered work on Bayesian model optimization, sensor placement strategies, and value-of-information analysis. His interdisciplinary approach bridges theoretical mechanics with practical data-driven solutions for modern engineering problems. His recent work emphasizes hybrid physics-based machine learning models for streamflow forecasting and battery RUL prediction, demonstrating the fusion of computational efficiency and physical principles. He has authored numerous articles on optimal sensor design, VoI metrics, and risk-informed decision-making in SHM systems. Scientific Awards: Gold medal for CBSE 12th exams (2010) Central Government Fellowship (2014) Gold medal for Civil Engineering proficiency (2014) In addition to academia, Dr. Chadha co-founded LogSpi LP, a quantitative investment hedge fund. He teaches courses on mechanics and signal processing, emphasizing foundational engineering principles and their real-world applications. His research also extends to the development of higher-order geometrically exact beam theories, with applications in computer graphics and shape sensing. He actively collaborates with the U.S. Army Corps of Engineers on projects involving inland waterway infrastructure and risk-based sensor design.

• Evolutionary Biology
• Statistical Genetics
• Computational Biology
• +3 more

Paul Marjoram is a Research Professor at the University of Southern California in the Department of Population and Public Health Sciences. His research focuses on probabilistic and statistical methods for modeling evolutionary processes, including population dynamics and cancer progression. He is a pioneer in Approximate Bayesian Computation (ABC) and computational genetics. Research Interests Development of Bayesian statistical methods for evolutionary and cancer genomics Computational modeling of gene regulatory networks Epigenetic conservation and mutational signatures analysis Publications His work spans hierarchical Bayesian models, MCMC algorithms, and ABC frameworks applied to diverse biological systems, from Drosophila development to human cancer genetics. Software Contributions Methcon5 for DNA methylation analysis fmcmc (Friendly MCMC Framework) slurmR for HPC workflows

• Statistical Methods in Health Research
• Missing Data Analysis
• Longitudinal Models
• +9 more

Ashok Chaurasia is an Associate Professor at the University of Waterloo's School of Public Health Sciences. His expertise lies in applying statistical methods to address health research challenges, particularly in missing data analysis, longitudinal modeling, and computational statistics. He has mentored over 21 students in developing analytical tools for health-related disciplines like epidemiology, pediatrics, and aging studies. Education: BSc Statistics & Mathematics (Honours), University of Texas at San Antonio MSc Statistics, University of Texas at San Antonio PhD Statistics, University of Connecticut Postdoctoral Fellowship in Biostatistics at National Institute of Child Health and Human Development, NIH His research focuses on innovative statistical solutions for real-world health problems, including signal saturation rates and model selection. Notable contributions include advancing methodologies for handling missing data in youth obesity studies and exploring religious participation's impact on cognitive function in aging populations. Recent work highlights include analyzing vaccination hesitancy among Indigenous communities and developing model-modified BIC criteria for regression analysis. His publications span epidemiology, biostatistics, and health policy, with a strong emphasis on translating statistical rigor into actionable public health strategies.

Yudi PawitanView profile

Professor

• Bioinformatics
• Statistical Genetics
• Genomics
• +2 more

Yudi Pawitan is Professor at the Department of Medical Epidemiology and Biostatistics at Karolinska Institutet, where he leads the research group on Statistical and Bioinformatics Analyses of High-Throughput Molecular Data. His work focuses on developing statistical methods for genomic studies including SNP/RNA arrays and next-generation sequencing. Education includes BSc in Statistics (Bogor Agriculture Institute, 1982), MSc in Statistics (UC Davis, 1984), and PhD in Statistics (UC Davis, 1987). Research interests span bioinformatics, cancer genomics, and statistical genetics with emphasis on high-dimensional data analysis, genetic correlations, and neural cell biology. His group addresses fundamental questions in genomic data interpretation and neurodegenerative processes. Publications demonstrate strong focus on genetic epidemiology, single-cell analytics, and statistical methodologies. Recent works explore machine learning applications in longitudinal data visualization, neural cell characterization, and cancer biomarker discovery. Awards: Not documented in provided texts. Supervises doctoral candidates including Linda Lindström and Ralf Kuja-Halkola. Manages the Live Imaging Facility at St. Vincent's Centre for Applied Medical Research. Research funded by Swedish Research Council and Swedish Cancer Society grants. Leads interdisciplinary collaborations through the Statistical and Bioinformatics research group, integrating computational biology with experimental neuroscience.

• Machine Learning
• Biomedical Engineering
• Medical Imaging
• +5 more

Professor Anil Bharath holds the role of Professor of Biologically-Inspired Computation & Inference in the Department of Bioengineering at Imperial College London. He serves as Academic Director of Imperial Global: Singapore and co-leads the IN-CYPHER research program on AI-driven healthcare security. His research focuses on machine learning, deep networks, and biomedical applications. He earned his BEng from UCL and PhD from Imperial College, followed by roles including President of the City and Guilds College Association (2022-2024). Research Interests: Machine learning, neural networks, medical imaging, and biologically-inspired computation. Notable contributions include 2D steerable filters for shape detection (1998), Bayesian marginalisation in early computer vision, and deep learning for cardiac MRI analysis. He co-founded Cortexica Vision Systems (acquired by Zebra Technologies in 2019), applying biological neuron models to visual search technology. Labs & Affiliations: Director of the BICI Lab, affiliated with the Data Science Institute, Centre for Neurotechnology, and multiple healthcare networks. His work spans interdisciplinary projects in AI for healthcare, cardiovascular engineering, and medical device innovation. Publications: Recent work emphasizes AI in cardiology (e.g., MRI analysis for mitral regurgitation, aortic stenosis detection) and synthetic data privacy. His research bridges computational models with clinical interpretability, addressing challenges in medical imaging efficiency and diagnostic accuracy.

Kevin DorstView profile

Assistant Professor

• Epistemology
• Cognitive Science
• Decision Theory
• +3 more

Kevin Dorst is an Assistant Professor at the Massachusetts Institute of Technology (MIT), where he joined the faculty in 2022. He holds a PhD from MIT (2019) and previously taught at the University of Pittsburgh. His research focuses on epistemology and cognitive science, particularly questions about the nature of rationality, its importance, and whether human cognition aligns with rational standards. His work addresses topics such as higher-order evidence, cognitive biases (e.g., conjunction fallacy, hindsight bias), decision theory, and social epistemology. Education: PhD in Philosophy from MIT (2019). Earlier academic background includes teaching roles at the University of Pittsburgh. Research interests emphasize understanding rationality through lenses of epistemology and cognitive science, exploring how people process evidence, handle uncertainty, and navigate conflicts between beliefs and external information. His recent work critiques common cognitive biases and proposes frameworks for evaluating rational judgment in contexts like political polarization and Bayesian reasoning. Publications span theoretical and applied topics in epistemology, with a focus on formal models of belief revision, decision-making under uncertainty, and the implications of higher-order evidence for doxastic norms. Notable themes include the gambler’s fallacy, modesty in belief adjustment, and the role of epistemic consequentialism in shaping rational practices. No academic awards are listed in the provided materials. No grants or advising roles are explicitly mentioned, though his work suggests involvement in interdisciplinary research teams.

• Variational Calculus
• Partial Differential Equations
• Large Deviations Theory
• +3 more

Dr. Michiel Renger is a researcher at the Department of Mathematics, Technische Universität München (TUM), within the School of Computation, Information and Technology. His research focuses on variational calculus, partial differential equations, large deviations theory, non-equilibrium thermodynamics, and chemical reaction networks. He has contributed to advancing the understanding of macroscopic fluctuation theory, gradient flows, and their applications in stochastic systems. Teaching responsibilities include courses on higher mathematics for engineering students at TUM and specialized lectures on large deviations and convex analysis at TU Berlin. His work bridges theoretical mathematics with applications in physics and engineering, emphasizing interdisciplinary approaches. Renger’s publications span peer-reviewed journals in mathematics and physics, with a focus on rigorous probabilistic and analytical methods. He holds a PhD in Mathematics from Technische Universiteit Eindhoven (2013) and has collaborated on projects in collaboration engineering, addressing challenges in collaborative modeling and organizational design. His research also extends to applied problems like node counting in wireless networks and statistical consulting for industry.

Zhang PinView profile

Assistant Professor

• Explainable AI for Science
• Intelligent Computational Mechanics
• Uncertainty Quantification
• +5 more

Dr. Zhang Pin is a Presidential Young Professorship (PYP) Assistant Professor in the Department of Civil and Environmental Engineering at the National University of Singapore (NUS). He previously held roles at the University of Cambridge as a Royal Society Newton International Fellow, Department Teacher, and Wolfson College Research Associate. His research group focuses on explainable AI for science, intelligent computational mechanics, uncertainty quantification, granular mechanics, coastal infrastructure resilience, and digital twins. Dr. Zhang earned his PhD from The Hong Kong Polytechnic University (2022), with a visiting scholar year at the University of Oxford. He holds a Master of Engineering (2019) and a Bachelor of Engineering (2016) from Hunan University and Hefei University of Technology, respectively. His research interests include: Explainable AI for scientific modeling Physics-informed machine learning Uncertainty quantification in geotechnical systems Micro/macro-scale granular mechanics Resilient coastal infrastructure design Digital twin technologies Notable achievements include recognition as a World’s Top 2% Scientist (2023-2024), PolyU PhD Thesis Merit Award, and the prestigious Royal Society Newton International Fellowship. His work bridges computational methods with geotechnical engineering challenges, emphasizing practical AI applications. Dr. Zhang has advised students like He GF and Wu HN, and his research has been published in top-tier journals such as Computer Methods in Applied Mechanics and Engineering and Géotechnique . He actively contributes to professional bodies including the British Geotechnical Association and International Society for Soil Mechanics (ISSMGE).

Patricia RichView profile

Professor

• Game and Decision Theory
• Epistemology
• Philosophy of Science
• +6 more

Prof. Dr. Patricia Rich is a Junior Professor of Philosophy of Economics at the University of Bayreuth since the winter 2019-20 semester. She holds positions in the Faculty for Cultural Studies and the Department of Philosophy. Her research focuses on game and decision theory, epistemology, and the philosophy of science, particularly in economics, cognitive science, and biology. Current projects include strategic behavior in social-epistemic contexts, decision-making under ambiguity, and computational complexity in cognitive evolution. Prior to Bayreuth, she was a postdoc at the University of Hamburg (2017-19) and the University of Bristol (2015-16), focusing on decision-making frameworks and epistemic utility theory. She earned her Ph.D. from Carnegie Mellon University in 2015, with a dissertation reconciling rationality concepts in economics and psychology. Her interdisciplinary work bridges philosophy with cognitive science and economics. She teaches courses at Bayreuth (details on the P&E intranet) and maintains an active research agenda reflected in recent publications. Contact: Patricia.Rich@uni-bayreuth.de or plrich.wordpress.com .

Georgi Y. GeorgievView profile

Professor

• Complex Systems
• Condensed Matter Physics
• Non-Equilibrium Thermodynamics
• +4 more

Georgi Yordanov Georgiev is a Professor of Physics at Assumption College and an Affiliate Associate Professor at Worcester Polytechnic Institute. He holds a Ph.D. in Physics from Tufts University (2002) and postdoctoral experience at Northeastern University. His research focuses on complex systems, non-equilibrium thermodynamics, self-organization, and materials science, particularly in polymers and nanocomposites. He has published extensively on topics such as entropy-driven systems, energy efficiency in evolving networks, and the interplay between carbon nanotubes and liquid crystals. Education: B.A., Sofia University, Physics and Chemistry M.S., Sofia University, Physics and Chemistry M.S., Tufts University, Physics Ph.D., Tufts University, Physics (2002) Postdoc, Northeastern University, Physics (2002–2004) Research Interests: Dr. Georgiev’s work bridges physics and complexity science, emphasizing quantitative measures of self-organization and energy dynamics. His studies on nanocomposites explore how carbon nanotubes influence polymer crystallization and thermal/electrical properties. He also investigates entropy production in systems like Bénard cells and applies variational principles to model complex systems. Recent Articles: His recent work includes Bayesian approaches to biological morphogenesis, entropy-driven mechanisms in Bénard convection, and energy efficiency in evolving technological systems. These studies highlight interdisciplinary connections between physics, biology, and materials science. Advising and Collaborations: Dr. Georgiev has advised numerous students, including Kaitlin Henry, Erin Gombos, and Michael Daly. He collaborates with institutions like the University of Massachusetts Lowell and actively participates in conferences on complex systems. Labs and Teams: Leverages experimental facilities for polymer characterization (X-ray scattering, ellipsometry) and computational modeling to study self-organization phenomena in both synthetic and biological systems.

Göran KauermannView profile

Professor

• Nonparametric Models
• Semi- and nonparametric analysis
• Generalized Linear Models
• +2 more

Prof. Dr. Göran Kauermann is a Full Professor of Statistics at the Ludwig-Maximilians-University Munich , holding the Chair of Applied Statistics in Social Sciences, Economics and Business . His research spans nonparametric models, generalized linear models, and network data analysis, with applications in economics, epidemiology, and data science. Education: Diplom in Economic Mathematics (1991, TU Berlin), PhD in Statistics (1994), Habilitation (Venia Legendi) in Statistics (2000) Kauermann’s research interests focus on penalized regression , network analysis , and statistical modeling in economics, social sciences, and public health. Recent work explores label uncertainty in machine learning , spatio-temporal conflict diffusion , and dynamic network models for economic and social data. Scientific trends in his publications include penalized splines for nonlinear modeling, network flow estimation in social and economic contexts, and label variation analysis in machine learning. His collaborations span climate zone classification , Covid-19 mortality modeling , and smart city parking analytics . Scientific Awards: Bruce Russett Award (2020) for political network research Leadership Roles: He served as Dean of the Faculty of Mathematics, Informatics and Statistics (2019–2021), Speaker of the Elite Master Program in Data Science (2016–2026), and Chair of the German Statistical Society (2005–2013). He also held editorial roles in journals like AStA Advances in Statistical Analysis and Statistical Modelling .

Amirreza KhodadadianView profile

Lecturer

• Uncertainty Quantification
• Numerical Analysis
• Computational Mechanics
• +7 more

Dr. Amirreza Khodadadian is a Lecturer in Mathematics at the School of Computer Science and Mathematics, Keele University, since August 2023. He holds a Ph.D. from the University of Vienna (2017), followed by postdoctoral positions at the Technical University of Vienna and Leibniz University Hannover. His research focuses on uncertainty quantification, numerical methods for stochastic PDEs, finite element methods, computational mechanics, and machine learning applications in nanoelectronics and biological systems. Key research interests include Bayesian inversion, multiscale modeling, reduced-order methods, and the design of nanoscale sensors. He has collaborated with institutions like the University of Oxford and secured an Austrian Science Fund (FWF) grant (476k€) for nanozyme sensor research. Dr. Khodadadian mentors postdoctoral researchers, including Dr. Samaneh Mirsian, and actively publishes in top-tier journals such as Journal of Computational Physics and Computer Methods in Applied Mechanics and Engineering . His work bridges applied mathematics with engineering challenges, emphasizing efficient numerical algorithms for real-world problems like battery degradation, groundwater contamination, and biomedical sensor optimization. Recent projects involve machine learning integration for enhanced predictive modeling. Education: Ph.D. in Mathematics, University of Vienna, Austria (2017) Postdoctoral Fellowships: TU Vienna (2018), Leibniz University Hannover (2018–2022) Grants/Awards: Austrian Science Fund (FWF) Grant: Single Atom Catalysts as Nanozymes in FET Sensors (2023) Advising: Postdoctoral Mentor: Dr. Samaneh Mirsian (Keele University) Dr. Khodadadian’s publications span computational mechanics, stochastic modeling, and interdisciplinary applications, reflecting his expertise in translating mathematical theory into practical engineering solutions.

Kristofer ReyesView profile

Associate Professor

• Applied Mathematics
• Computational Chemistry
• Materials Science
• +6 more

Kristofer Reyes is an Associate Professor in the Department of Materials Design and Innovation at the University at Buffalo (School of Engineering and Applied Sciences). His research focuses on computational and statistical methods applied to materials science, particularly in developing machine learning frameworks for small-data regimes. He leads the Computational and Statistical Material Science (CSMS) Lab, which emphasizes Bayesian models, reinforcement learning, and fusion of physics-based knowledge. Education: PhD in Applied and Interdisciplinary Mathematics, University of Michigan, 2013 BS in Computer Science and Mathematics, Purdue University, 2004 Research Interests: Reyes' work bridges computation, mathematics, and materials science. Key areas include: - Autonomous experimentation systems (e.g., self-driving fluidic labs) - Bayesian optimization and decision-making under uncertainty - Machine learning for nanomaterials synthesis (e.g., perovskite nanocrystals) - Integration of physics-informed models in AI workflows - High-cost experimental design optimization Recent Achievements: SMARTDOPE paper awarded 'Best in Advanced 2023' Developed AlphaFlow for autonomous chemical synthesis Pioneered 'self-driving labs' for materials discovery Labs & Teams: CSMS Lab (134 Bell Hall) focuses on problem-fluent models for materials research. Current projects include quantum circuit optimization, bio-chemical pathway modeling, and sustainable nanomanufacturing. Collaborates with industry and national labs on AI-driven materials development.