جست‌وجوی استاد و پژوهشگر

Madhusudhanan Balasubramanianمشاهده پروفایل

استاد

• Glaucoma Research
• Biomedical Engineering
• Medical Imaging
• +۳ مورد دیگر

Dr. Madhusudhanan Balasubramanian is a Professor of Electrical & Computer Engineering and Biomedical Engineering at The University of Memphis. He leads the Computational Ocularscience Laboratory, focusing on glaucoma research, ocular therapeutics, and medical imaging technologies. His work integrates computational methods like finite element analysis and deep learning with experimental techniques such as optical imaging and electrophysiology. Education: B.E. (Electrical & Electronics Engineering) from PSG College of Technology, India; M.S. and Ph.D. (Computer Science) from Louisiana State University; Postdoctoral Fellowship at UC San Diego. Research interests include optic nerve head dynamics, retinal function analysis, and nanoparticle-mediated therapies for inner retinal diseases. His lab develops organ culture models for ex vivo eye studies, non-invasive imaging techniques, and computational models for aqueous humor dynamics. Awards: NIH K99/R00 Award (2011), IPS-Heidelberg Young Researchers Award (2010), PSG Alumni Award (1998). Key Projects: Ocular drug delivery systems, optic nerve head elastography, and laser-based therapies. He teaches courses in computational science, biomedical engineering, and signals systems. His work bridges engineering and medicine to address vision loss mechanisms in glaucoma.

Collin Capanoمشاهده پروفایل

عضو هیئت علمی

• Gravitational Wave Astronomy
• Black Hole Physics
• Neutron Star Dynamics
• +۳ مورد دیگر

Collin Capano is a Research Associate Professor in the Physics department at Syracuse University. He holds a Ph.D. and B.S. in Physics from Syracuse University (2011 and 2005 respectively). His research focuses on gravitational wave astronomy, black hole spectroscopy, and computational methods for analyzing astrophysical data. He has led projects involving black hole ringdown analysis, neutron star equation of state studies, and multimessenger astronomy. Dr. Capano has secured significant grants including a National Science Foundation award for developing black hole spectroscopy methods and desktop cluster infrastructure (2024-2026) and a Defense University Instrumentation Initiative grant (2022). He currently directs the Open Source Project Office at Syracuse University's Research Computing division. His work has been published in top journals like Physical Review D , Nature Astronomy , and The Astrophysical Journal . Notable contributions include testing general relativity with GW170817 observations and constraining neutron star radii through combined gravitational-wave and electromagnetic data analysis. Awarded the Max-Planck-Gesellschaft performance bonus (2020), he serves as a reviewer for leading journals including Physical Review Letters , Monthly Notices of the Royal Astronomical Society , and Classical and Quantum Gravity . He actively participates in international workshops and conferences, presenting on topics ranging from black hole spectroscopy to the future of gravitational wave astronomy.

• Reinforcement Learning
• Collaborative AI
• Differential Privacy
• +۴ مورد دیگر

Christos Dimitrakakis is a Professor affiliated with the University of Neuchâtel, University of Oslo, and Chalmers University of Technology. He leads research groups across these institutions, advising PhD students and postdoctoral researchers in areas such as reinforcement learning, collaborative AI, and fairness in AI. His teaching includes advanced machine learning courses at the University of Neuchâtel, covering topics like privacy, fairness, Bayesian inference, and decision-making under uncertainty. His research focuses on foundational challenges in AI, including safety, risk, differential privacy, and algorithmic fairness. He has contributed to methodologies for reinforcement learning, multi-agent systems, and Bayesian inference. Notable awards include the Student Paper Award at EAAMO'22 and the Best Paper Award at Cooperative-AI@NeurIPS 2021. Recent articles highlight advancements in matching markets under uncertainty, ad-hoc teamwork strategies, and minimax-bayes reinforcement learning. His work bridges theoretical insights with practical applications, emphasizing ethical AI and robust decision-making. He advises a diverse cohort of students and postdocs, including those exploring fairness, reinforcement learning, and computational social choice. His collaborations span academia and industry, with past members contributing to roles at institutions like the Turing Institute, Volvo, and EPFL.

Amilcar Velezمشاهده پروفایل

مدرس

• Econometrics
• Machine Learning
• Statistical Decision Theory
• +۶ مورد دیگر

Amilcar Velez is a PhD Candidate in Economics at Northwestern University, expected to complete his degree in 2025. He will join Cornell University as a Provost New Faculty Fellow in July 2025 and as an Assistant Professor in July 2026. He also held a Lecturer role at Northwestern University for an Econometrics Review course in Summer 2023. His research focuses on econometric theory, statistical machine learning, and their applications in economics, with an emphasis on debiased machine learning (DML), inference using local projections, and identification problems in treatment effects under covariate-adaptive randomization. Education: PhD in Economics, Northwestern University (2025 expected) MA in Economics, Northwestern University (2020) MS in Mathematics, IMPA, Brazil (2015) BA in Economics, PUCP, Peru (2014) His research explores topics such as DML estimator properties, moment inequalities inference, and Bayesian robustness. He has published in journals like Journal of Econometrics , Journal of Economic Theory , and Journal of Machine Learning Research . His work bridges econometric theory and machine learning, with applications to causal inference and policy analysis. Teaching includes roles as Lecturer and Teaching Assistant for courses in econometrics at Northwestern University. His articles consistently address methodological advancements in statistical and computational approaches to economic problems, emphasizing practical applications of theoretical results. No scientific awards are explicitly mentioned in the provided texts. He has advised no students, and lab/team affiliations are not detailed. His work reflects a focus on foundational econometric methods and their integration with modern machine learning techniques.

Francesco D'Angeloمشاهده پروفایل

پژوهشگر

• Machine Learning
• Artificial Intelligence
• Deep Learning
• +۳ مورد دیگر

Francesco D'Angelo is a Researcher and Doctoral Assistant at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Computer and Communication Sciences (IC) and the Institute of Computer Science and Communications Systems (IINFCOM). He is part of the Theory of Machine Learning Laboratory (TML) and enrolled in the Doctoral program in computer and communication sciences (EDIC) under the EPFL Doctoral School (EDOC). His research focuses on foundational aspects of machine learning, including sparse attention mechanisms, Bayesian neural networks, uncertainty estimation, and continual learning. He holds the position of Doctoral Assistant at the TML lab, where he contributes to advancing theoretical and practical applications of machine learning algorithms. Research Interests: Francesco's work bridges theoretical computer science with applied machine learning. His key areas include developing robust models for uncertainty quantification, improving generalization in overparameterized networks through regularization techniques like weight decay, and exploring the interplay between transformer architectures and causal structure induction. He also investigates methods to mitigate catastrophic forgetting in continual learning scenarios and applies Bayesian approaches to enhance out-of-distribution detection capabilities. Publications: His recent work explores topics such as sparse attention mechanisms in transformers, the necessity of weight decay in deep learning optimization, and Bayesian methods for uncertainty estimation. These contributions highlight his expertise in both foundational theory and practical algorithm design within the machine learning domain. Labs & Teams: As part of the TML lab, he collaborates on interdisciplinary projects at the intersection of machine learning theory and applications, contributing to advancements in neural network architectures and probabilistic modeling techniques.

Houman Owhadiمشاهده پروفایل

استاد

• Numerical Approximation
• Machine Learning
• Uncertainty Quantification
• +۳ مورد دیگر

Houman Owhadi is the IBM Professor of Applied and Computational Mathematics and Control and Dynamical Systems at the California Institute of Technology. His research explores interplays between numerical approximation, statistical inference, and learning from a game-theoretic perspective, with applications in numerical homogenization, operator-adapted wavelets, fast solvers, Gaussian process regression, and uncertainty quantification. His current research focuses on computational information games, kernel flows, and the development of universal scalable solvers. He has made fundamental contributions to uncertainty quantification through adversarial approaches and the development of optimal statistical estimators. Professor Owhadi has received numerous awards including the Vannevar Bush Faculty Fellowship (2024), Germund Dahlquist Prize (2019), and SIAM Fellowship (2022). His research has been supported by AFOSR, DARPA, DOE, NASA, NSF, and ONR.

• Machine Learning
• Biomedical Data Analytics
• Deep Learning
• +۳ مورد دیگر

Dr. Amir Joudaki is a Researcher affiliated with the Department of Biomedical Informatics at ETH Zürich. His role is part of the Professorship for Data Analytics, focusing on interdisciplinary research at the intersection of machine learning and biomedicine. He is stationed at CAB F 53.1, Universitätstrasse 6, Zurich. His research interests span machine learning, biomedical data analytics, neural network theory, genomics, and bioinformatics algorithms. Notable contributions include work on deep neural network dynamics, batch normalization techniques, and graph-based genomic sequence analysis. Though not explicitly listed in the provided texts, his work suggests involvement in projects related to alignment-free bioinformatics methods, optimization in deep learning, and theoretical aspects of neural networks. Lab/Team Affiliation: Part of the Biomedical Informatics group at ETH Zürich, likely contributing to interdisciplinary data science initiatives in healthcare and genomics.

• Stochastic processes in biology
• Statistical ecology
• Population genetics
• +۵ مورد دیگر

José Miguel Ponciano is an Associate Professor in the Department of Biology at the University of Florida, affiliated with the College of Liberal Arts and Sciences. His research bridges stochastic processes with biological questions, focusing on applications in ecology, evolution, and public health. PhD in Bioinformatics and Computational Biology, University of Idaho (2006) MS in Statistics and MS in Ecology, University of Idaho and Universidad Austral de Chile BS in Biology, Universidad del Valle de Guatemala His work spans stochastic modeling in ecology (population dynamics, community assembly), evolutionary genetics (plasmid-bacteria coevolution, antibiotic resistance), and epidemiology (anthrax transmission, vaginal microbiome dynamics). Recent projects include: NIH-funded studies on microbial community ecology NSF Dimensions of Biodiversity project on moss-microbiome interactions Multi-institutional anthrax modeling with geospatial analysis He advises students in interdisciplinary approaches, combining mathematics and biology. His lab has produced methodological advances in statistical phylogenetics (e.g., Chromploid R package) and evidential statistics (alternative to traditional hypothesis testing). Collaborations span tropical avian ecology (Peru), microbial resistance (Idaho), and public health modeling in Latin America.

Frank Dellaertمشاهده پروفایل

استاد

• Robotics
• Computer Vision
• Bayesian Inference
• +۶ مورد دیگر

Frank Dellaert is a Professor at the School of Interactive Computing within the Georgia Institute of Technology . His primary research areas are Robotics and Computer Vision, focusing on probabilistic methods and model-based reasoning in artificial intelligence. Dr. Dellaert specializes in Bayesian inference and Monte Carlo (sampling-based) approximations to address intractable inverse problems in Robotics and Computer Vision. His work spans three main areas: advanced sequential Monte Carlo methods, spatio-temporal reconstruction from images, and Simultaneous Localization and Mapping (SLAM). He also serves as the Robotics PhD Coordinator for the College of Computing. At Georgia Tech, he collaborates with the GVU Center and contributes to research at the intersection of robotics, vision, and computational intelligence. While specific publications and awards are not detailed in this summary, his research emphasizes scalable probabilistic solutions to complex AI challenges.

Paul Wiemannمشاهده پروفایل

استادیار

• Bayesian statistics
• Computational statistics
• Variational approximation
• +۳ مورد دیگر

Paul Wiemann is an Assistant Professor in the Department of Statistics at The Ohio State University. He holds a Ph.D. in Applied Statistics from Göttingen University (2020), an M.Sc. in Applied Statistics (2015) from the same institution, and a B.Sc. in Computer Science from TU Braunschweig University (2011). Prior to his current role, he was a postdoctoral researcher at the University of Wisconsin and at the University of Göttingen's Chair in Statistics. His research focuses on advanced statistical methodologies, including Bayesian statistics, computational optimization techniques like variational approximation and MCMC, and applications in spatial statistics. He is a core developer of the DFG-funded software project Leisel .

Saeed Ghadimiمشاهده پروفایل

استادیار

• Data Analytics
• Applied Operations Research
• Energy Market
• +۳ مورد دیگر

Saeed Ghadimi is an Assistant Professor at the University of Waterloo, with affiliations in Data Analytics, Applied Operations Research, and Energy Market research groups. His work focuses on developing advanced optimization algorithms for stochastic systems, machine learning applications, and decision-making under uncertainty. He is particularly noted for contributions to bilevel programming, quantum optimal control, and robust regression techniques with missing data. Research interests include optimization theory, stochastic programming, and interdisciplinary applications in energy systems and public policy. His methodologies often address nonconvexity, nonstationarity, and high-dimensionality challenges. Recent work emphasizes projection-free algorithms, adversarial robustness in regression, and parametric cost function approximations for multistage problems. He maintains a personal webpage at https://sites.google.com/view/sghadimi .

Hanrui Zhangمشاهده پروفایل

استادیار

• Economics and Computation
• Algorithmic Game Theory
• Machine Learning Theory
• +۳ مورد دیگر

Hanrui Zhang is an Assistant Professor of Computer Science and Engineering at the Chinese University of Hong Kong. Previously, he was a visiting faculty researcher at Google Research NYC (OMEGA Market Algorithms team), a postdoctoral fellow at Simons Laufer Mathematical Sciences Institute (SLMath, UC Berkeley), and completed his PhD at Carnegie Mellon University under Vincent Conitzer. His research focuses on Economics and Computation, particularly learning/decision-making in strategic environments, mechanism design for online platforms, and auction theory. He holds a BEng from Tsinghua University’s Yao Class, advised by Wei Chen and Pingzhong Tang. Research Interests: Strategic learning in complex environments, algorithmic game theory, online auctions, mechanism design, and computational social choice. His work combines tools from theoretical computer science, microeconomic theory, and machine learning to address practical problems with rigorous mathematical foundations. Publications: Recent work includes studies on nonbossy mechanisms, strategic classification, Bayesian persuasion, and autobidding systems in auctions. His papers span venues like NeurIPS, EC, AAAI, and WWW, reflecting contributions to both theory and applications. Awards: Best Student Paper at ESA 2020, Honorable Mention at HCOMP 2022. Teaching: CSCI5350 (Advanced Topics in Game Theory, Spring 2025). Advises PhD students Yilin Li and Chenran Yang (both 2025-). Professional Contributions: Reviews for AAAI, NeurIPS, EC, and journals like Algorithmica and Games and Economic Behavior. Active in conference organizing (e.g., WWW, WINE).

Eugenio Melilliمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Confidence Distributions
• Population Forecasting
• +۴ مورد دیگر

Eugenio Melilli is an Associate Professor of Statistics in the Department of Decision Sciences at Università Commerciale Luigi Bocconi in Milan. He holds a degree in Mathematics (cum laude) from Università degli Studi di Milano and previously served as Assistant Professor (1996-2005) before his current appointment. His international teaching includes positions at Lobachevsky State University (Russia) and PhD instruction at Bocconi. Research specializes in Bayesian nonparametrics, confidence distributions, and stochastic population forecasting. He develops theoretical frameworks for exchangeability, Dirichlet processes, and expert-based demographic projections, with applications to psychometrics and social statistics. Key Contributions: Developed methods for confidence distributions in Rasch models, advanced Bayesian approaches to population forecasting, and extended de Finetti's theorem for partially exchangeable sequences.

• Electrochemical impedance spectroscopy
• Battery modelling
• System identification
• +۳ مورد دیگر

Noël Hallemans is a Postdoctoral Research Assistant at the Control Group of the School of Engineering Science, University of Oxford . He earned his MEng (2019) from Vrije Universiteit Brussel and Université Libre de Bruxelles , followed by a PhD (2023) from Vrije Universiteit Brussel and University of Warwick , where he developed frequency domain data-driven modeling for electrochemical impedance spectroscopy in lithium-ion battery analysis. Research Focus: Electrochemical impedance spectroscopy for nonlinear/time-varying systems Battery modeling and real-time process control Nonlinear dynamics in electrochemical systems System identification and fractional order modeling Article Trends: His work spans Li-ion battery diagnostics , electroplating , and industrial process control , with a focus on operando techniques and nonlinear system identification across materials science , electrochemistry , and control theory . Scientific Awards: 2019: Best Master Thesis Prize (Vrije Universiteit Brussel and Université Libre de Bruxelles) Grants & Funding: His PhD research was supported by the Research Foundation Flanders (FWO-Vlaanderen, grant G.0052.18N) and the Flemish Government Methusalem Fund (METH1) . Current Projects: At Oxford, he investigates real-time battery impedance characterization and process control for battery manufacturing , collaborating with the Battery Intelligence Lab and researchers like Professor Stephen Duncan and Professor David Howey .

Thijs Janzenمشاهده پروفایل

پژوهشگر

• Phylogenetics
• Population Genetics
• Computational Biology
• +۳ مورد دیگر

Dr. Thijs Janzen is a Researcher and Scientific Programmer at the University of Groningen's Faculty of Science and Engineering, affiliated with the Theoretical Research in Evolutionary Life Sciences department. He holds a PhD in Biology from the University of Groningen (2015) and has extensive postdoctoral experience at institutions including the Max Planck Institute for Evolutionary Biology and Carl von Ossietzky University Oldenburg. Education: PhD in Biology, University of Groningen (2010–2014) MSc in Biology, University of Groningen (2006–2009) BSc in Biology, University of Groningen (2003–2006) Research Interests: Phylogenetics and phylogenetic reconstruction Population genetics and genomic admixture Individual-based computational modeling Software development in R, C++, and Python Support for MSc/PhD students in programming and modeling Research Trends: His work spans theoretical evolutionary biology, computational genomics, and phylogenetic analysis. Recent studies focus on hybridization dynamics, viral oncolytic therapy modeling, and tools for phylogenetic statistics (e.g., the 'treestats' R package). His contributions bridge ecological theory with genomic data, emphasizing quantitative methods in evolutionary processes. Advising & Grants: Collaborated on the MARM project (2018–2023) on adaptive responses in evolution, contributing technical expertise to PhD student projects. Maintains active roles in computational support and course development for biologists. Labs/Teams: Core member of the Theoretical Research in Evolutionary Life Sciences group, developing open-source tools (e.g., GenomeAdmixR, nLTT) and collaborating globally on biodiversity and evolutionary questions.