جست‌وجوی استاد و پژوهشگر

Zin Linمشاهده پروفایل

استادیار

• Quantum Photonics
• Electromagnetics
• Nanophotonics
• +۴ مورد دیگر

Zin Lin is an Assistant Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech, based at the Virginia Tech Research Center in Arlington. His research focuses on inverse design principles in nanophotonics, computational modeling, and scientific machine learning, with applications in quantum photonics, electromagnetics, and optical imaging. He leads the Inverse Design and Discovery Group, emphasizing large-scale optimization for physical systems and novel device discovery through physics-based AI. Education: Postdoc in Applied Mathematics at MIT (2018–2022), Ph.D. in Applied Physics from Harvard University (2018), and a B.A. in Physics and Mathematics from Wesleyan University (2012). He is a recipient of the National Science Foundation Graduate Fellowship (2014–2018). Research interests include inverse design of nanophotonic devices, topology optimization, quantum optics, and computational imaging. His group explores cutting-edge topics like metasurface engineering, terahertz wave generation, and bio-chemical sensing through physics-driven optimization frameworks. Recent work emphasizes scalable optical systems, such as end-to-end optimized metalenses and meta-optics for imaging, as well as quantum control in graphene-based metasurfaces. Key contributions span nonlinear frequency conversion, high-energy particle detection via nanophotonic scintillators, and topology-optimized multi-layered optical systems. Notable awards include the NSF Graduate Fellowship. His team actively pursues interdisciplinary projects at the intersection of wave physics, machine learning, and high-performance computing, with open positions for PhD students and postdocs.

Fabrizia Mealliمشاهده پروفایل

استاد

• Causal Inference
• Econometrics
• Statistical Methods
• +۳ مورد دیگر

Fabrizia Mealli is a Full-time Professor in the Department of Economics at the European University Institute (EUI), serving as Director of Graduate Studies. She holds a concurrent leave position as Professor of Statistics at the University of Florence's Department of Statistics, Informatics, Applications (DiSIA). Her research focuses on causal inference, econometric methods, Bayesian analysis, and missing data, with applications in social and biomedical sciences. Education & Academic Roles: Professor of Statistics, University of Florence (on leave) Full-time Professor, EUI Department of Economics (since 2023) Visiting positions at Harvard University (2001, 2015, 2017) Research Interests: Her work centers on causal inference methodologies, including principal stratification, interference, and bipartite settings. She develops statistical techniques for observational and experimental studies, Bayesian approaches, and applications in policy evaluation and network effects. Awards & Editorial Roles: Elected Fellow of the American Statistical Association (ASA) President-elect of the Society for Causal Inference (SCI) Associate Editor for Biometrika , Journal of the American Statistical Association , and Annals of Applied Statistics Grants & Collaborations: Her research has been applied to policy evaluation (e.g., hospital readmission programs, immigration policies) and public health interventions (e.g., air pollution effects). Labs & Teams: Leads initiatives on causal inference under interference and network analysis, collaborating with global institutions and researchers in statistics and econometrics.

Mohsen Badieyمشاهده پروفایل

استاد

• Ocean Acoustics
• Signal Processing
• Machine Learning
• +۴ مورد دیگر

Mohsen Badiey , Professor in the Department of Electrical and Computer Engineering at the University of Delaware's College of Engineering, leads the Ocean Acoustics & Engineering Laboratory (OAELab) with facilities at Evans Hall and STAR campus. His interdisciplinary work spans applied physics, mechanical systems, ocean sensing, and computational signal analysis. Research Focus: Geoacoustic inversion, waveguide physics, machine learning for seabed classification, and underwater communication challenges. Key Projects: Shallow Water 2006 (SW06) and Shallow Water Acoustic in Random Media (SWARM95) experiments analyzing nonlinear internal wave dynamics. Scientific Contributions include developing dictionary learning techniques for sound speed profile analysis, advancing graph neural networks for underwater signal processing, and studying acoustic propagation through intense internal waves. His work emphasizes both fundamental and applied research with field data collection and computational modeling. Recent Publications demonstrate expertise in physics-based machine learning for source localization, seabed classification, and time-varying signal reconstruction. His team's 2024 studies on transiting ocean observers and Sobolev graph networks highlight cutting-edge methodologies. Laboratory: OAELab employs specialized instrumentation for broadband acoustic signal analysis, combining experimental data with computational approaches to solve real-world oceanographic problems.

• Stochastic Processes
• Financial Mathematics
• Statistics
• +۳ مورد دیگر

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 Boschمشاهده پروفایل

استاد

• Computational Linguistics
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

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 Chadhaمشاهده پروفایل

استادیار مدعو

• Structural Health Monitoring
• Applied Mechanics
• Machine Learning
• +۷ مورد دیگر

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
• +۳ مورد دیگر

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
• +۹ مورد دیگر

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 Pawitanمشاهده پروفایل

استاد

• Bioinformatics
• Statistical Genetics
• Genomics
• +۲ مورد دیگر

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
• +۵ مورد دیگر

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 Dorstمشاهده پروفایل

استادیار

• Epistemology
• Cognitive Science
• Decision Theory
• +۳ مورد دیگر

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
• +۳ مورد دیگر

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 Pinمشاهده پروفایل

استادیار

• Explainable AI for Science
• Intelligent Computational Mechanics
• Uncertainty Quantification
• +۵ مورد دیگر

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 Richمشاهده پروفایل

استاد

• Game and Decision Theory
• Epistemology
• Philosophy of Science
• +۶ مورد دیگر

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. Georgievمشاهده پروفایل

استاد

• Complex Systems
• Condensed Matter Physics
• Non-Equilibrium Thermodynamics
• +۴ مورد دیگر

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.