جست‌وجوی استاد و پژوهشگر

Michal Kolesárمشاهده پروفایل

استاد

• Econometrics
• Causal Inference
• Instrumental Variables
• +۴ مورد دیگر

Michal Kolesár is a Professor in the Department of Economics at Princeton University , holding this position since July 2020. Previously, he served as Assistant Professor (2014-2020) with dual appointments in Economics and the Woodrow Wilson School (2018-2020), and as Visiting Assistant Professor at MIT (2016-2017). His research focuses on econometrics , particularly causal inference , instrumental variables , nonparametric regression , and robust statistical methods . His work addresses fundamental challenges in high-dimensional data analysis, treatment effect heterogeneity, and finite-sample inference validity. Current projects include developing bias-aware methods for regularized regression and analyzing dynamic causal effects in nonlinear systems. Kolesár's recent publications reveal a strong emphasis on methodological rigor with practical applications. His work spans instrumental variable techniques (addressing contamination bias, weak identification), regression discontinuity designs (discrete running variables, measurement error), and high-dimensional inference (sparsity fragility, honest confidence intervals). Key recurring themes include finite-sample optimality, coverage probability guarantees, and robustness to model misspecification. Fellow of the International Association for Applied Econometrics (2023) Journal of Econometrics best associate editor award (2023) Sloan Research Fellowship (2019) NSF grants for high-dimensional data inference (2021-2025) and nonparametric regression (2016-2019) Graduate Economics Club teaching awards (2017, 2018) As an educator, Kolesár teaches advanced econometrics courses at both undergraduate (ECO 312, ECO 313) and graduate levels (ECO 517, ECO 519, ECO 539b). He serves as Co-editor of the Journal of Business & Economic Statistics (2024-2027) and sits on editorial boards of Econometrica , American Economic Journal: Applied Economics , and others. His professional activities include extensive peer review for top journals and organization of major econometrics conferences including the 2026 Econometric Society Winter Meeting.

Gary Froylandمشاهده پروفایل

استاد

• Dynamical Systems
• Machine Learning
• Optimisation
• +۱۲ مورد دیگر

Scientia Professor Gary Froyland is a Professor at the University of New South Wales (UNSW), affiliated with the School of Mathematics & Statistics. He leads the ARC Laureate Centre for Dynamical Systems and Data and holds an Einstein Visiting Fellowship from the Einstein Foundation Berlin. His academic credentials include a BSc (Hons 1, Medal) in Pure and Applied Mathematics from the University of Queensland and a PhD in Mathematics from the University of Western Australia. Professor Froyland's research spans two primary domains: dynamical systems and optimization. In dynamical systems, he investigates the interplay of probability and geometry in nonlinear and chaotic systems, employing tools from ergodic theory, functional analysis, and differential geometry. His work extends to applications in oceanography, atmospheric science, and granular flows. In optimization, he focuses on decision-making in complex systems with uncertain information, developing novel approaches in mathematical programming that have been applied to mining, logistics, and medical treatment planning. His recent publications demonstrate a strong focus on coherent structures in dynamical systems, linear response theory, and applications to geophysical phenomena. The research shows increasing interdisciplinary collaboration, particularly with climate scientists and data analysts, reflecting a trend toward applying advanced mathematical techniques to real-world problems in environmental science and engineering. J.D. Crawford Prize (2025) Elected Member of the Academy of Europe / Academia Europaea (2024) ARC Laureate Fellow (2024-2029) Fellow of the Society for Industrial and Applied Mathematics (SIAM) (2021) Fellow of the Australian Academy of Science (2020) Vice-Chancellor's Award for Teaching Excellence - Postgraduate Research Supervision (2015) Professor Froyland actively supervises PhD and honors students, with current advisees including Kevin Felipe Kühl Oliveira, Nicholas Peters, and Kathrin Völkner. His research is supported by multiple grants, including an ARC Laureate Fellowship (2024-2029) for "Breakthrough mathematics for dynamical systems and data," an Einstein Visiting Fellowship (2022-2026), and several ARC Discovery Projects. His work has practical applications in climate science, mining optimization, and medical treatment planning, particularly in radiotherapy. He leads the ARC Laureate Centre for Dynamical Systems and Data, which brings together researchers to develop new mathematical approaches for analyzing complex dynamical systems. The center focuses on creating methods to identify coherent structures in spatiotemporal data, with applications spanning environmental science, social science, health science, and engineering.

Mathias Drtonمشاهده پروفایل

استاد

• Graphical models
• Algebraic statistics
• Causal inference
• +۳ مورد دیگر

Prof. Mathias Drton holds the Chair of Mathematical Statistics at the Technical University of Munich (TUM), within the Department of Mathematics and School of Computation, Information and Technology. His research focuses on graphical models, algebraic statistics, causal inference, and multivariate data analysis. He has authored numerous publications in top-tier journals and conferences, including work on conditional independence, sparse factor analysis, and causal discovery in linear models. Drton has supervised a large number of theses, mentoring students in areas like high-dimensional statistics, graphical models, and causal inference. He is actively involved in teaching advanced courses such as 'Graphical Models in Statistics' and 'Fundamentals of Mathematical Statistics.' His academic contributions span theoretical developments in statistical methodology and computational tools, including R packages like SEMID and symRC . Drton collaborates internationally, contributing to projects like the TUM-ICL Mathematical Sciences Hub and Exzellenzcluster MCQST. His work bridges algebraic methods with statistical challenges, addressing identifiability in latent variable models and robust graphical modeling under non-Gaussian assumptions. Recent research emphasizes causal structure learning under partial homoscedasticity, distribution-free independence tests, and multi-domain causal representation learning. Drton’s lab actively explores applications in genomics, epidemiology, and machine learning, leveraging both theoretical rigor and practical computational methods.

• Applied Mathematics
• Mathematical Biology
• Computational Biology
• +۴ مورد دیگر

Ruth Baker is a Professor of Applied Mathematics at the University of Oxford and a key member of the Mathematical Institute . Her work bridges mathematics, computational modeling, and biology to address complex developmental systems. Research Interests : Developing mathematical frameworks for cell and tissue-level biological processes Integrating computational and statistical methodologies with experimental data Exploring data-driven modeling for multidisciplinary collaboration Scientific Awards : Simons Investigator (2024-2029) Royal Society Wolfson Research Merit Award (2017-2022) FIMA (2021) FRSB (2020) Leverhulme Research Fellowship (2017-2019) London Mathematical Society Whitehead Prize (2014)

• Applied Mathematics
• Financial Mathematics
• Numerical and Computational Mathematics
• +۳ مورد دیگر

Xiaoping Lu is an Associate Professor at the School of Mathematics and Applied Statistics, University of Wollongong, Australia. She has served as Academic Program Director for the Bachelor of Mathematics (Advanced) program since 2008 and holds an ORCID identifier (0000-0003-1090-8437). Her research focuses on applied mathematics and financial mathematics, particularly in option pricing, stochastic volatility models, and computational finance. Research Themes: Transaction cost modeling, regime-switching financial markets, numerical methods for PDEs, utility-indifference valuation, and stochastic optimization algorithms. Awards: 2024 AustMS-WIMSIG Anne Penfold Street Award 2024 Cheryl E. Praeger Travel Award Leadership: President of the Asia Pacific Consortium of Mathematics for Industry (APCMfI) since 2024; leadership roles in ANZIAM and WIMSIG committees. Teaching: Coordinated courses like MATH142, MATH141, and MATH283; currently available for PhD supervision in topics including financial derivatives and stochastic liquidity risk. Funding: Contributed to grants like 'The AI Tutor' (2024) and industry partnerships for advanced mathematics education.

Andrew D. Eatonمشاهده پروفایل

استادیار

• Social Work
• Health Disparities
• HIV/AIDS Research
• +۸ مورد دیگر

Dr. Andrew D. Eaton is an Assistant Professor at the University of Regina’s Faculty of Social Work (Saskatoon Campus) and holds a Status-Only Assistant Professorship at the Factor-Inwentash Faculty of Social Work, University of Toronto. He earned his PhD and MSW from the University of Toronto and his BSW from Toronto Metropolitan University. His research focuses on HIV/AIDS, mental health crises, aging populations, sexual health, and harm reduction, with a commitment to community-based participatory research. Eaton’s work addresses complex comorbidities affecting marginalized groups, particularly sexual and gender minorities. He leads projects such as the '3 Infections, 1 Fight Study' using geospatial tools to reduce harm from injection drug use, and the 'ReportNeedles.ca' initiative to map needle disposal hotspots. His studies also explore cognitive remediation therapies for aging HIV-positive populations and crisis mental health response models. He holds grants from CIHR, SSHRC, CTN, and OHTN. His publications emphasize innovative interventions, including cognitive training programs, civilian-led crisis response systems, and global comparative studies on sexual identities. Eaton’s lab (eaton-lab.com) fosters collaboration between academics, practitioners, and communities to translate research into actionable health policies and practices.

Ivan Canayمشاهده پروفایل

استاد

• Econometric Theory
• Partially Identified Models
• Moment Inequalities
• +۵ مورد دیگر

Ivan Canay is a Professor of Economics and Director of the Mathematical Methods in the Social Sciences Program at Northwestern University’s Weinberg College of Arts & Sciences. He holds a PhD from the University of Wisconsin, Madison (2008). His research focuses on econometric theory, particularly developing statistical methods for assessing partially identified models, including tests for moment inequalities and randomization-based inference techniques. Recent work addresses challenges in clustered data analysis, covariate-adaptive randomization, and regression discontinuity designs. Canay’s academic contributions include advancing methodologies for handling non-ignorable cluster sizes and improving the robustness of inference in settings with limited data. He serves as an associate editor for the Journal of Econometrics , Journal of Business and Economic Statistics , and Econometrics Journal . His work bridges theoretical econometrics with practical applications in policy evaluation and causal inference. Key research themes include: Partially identified models and moment inequality frameworks Bootstrap methods for clustered data Covariate-adaptive randomization in clinical trials Statistical software development (e.g., Stata modules) His publications emphasize methodological rigor while addressing real-world complexities in economic data. Current projects likely expand his work on inference under structural constraints and improving accessibility of econometric tools for applied researchers.

Sara Magliacaneمشاهده پروفایل

استادیار

• Causality
• Causal Representation Learning
• Causality-inspired Machine Learning
• +۴ مورد دیگر

Sara Magliacane is an Assistant Professor at the University of Amsterdam , affiliated with the Amsterdam Machine Learning Lab (AMLab) and the Informatics Institute . She also holds a Research Scientist position at the MIT-IBM Watson AI Lab and has been an ELLIS Scholar since 2022. Education PhD in Artificial Intelligence (2017), VU Amsterdam MSc in Computer Engineering (2011), Politecnico di Milano BSc in Computer Engineering (2008), Università degli Studi di Trieste Research Focus : At the intersection of Causality and Machine Learning , her work addresses Causal Representation Learning from high-dimensional data (images, sequences) Causal Discovery in latent confounder scenarios Causality-inspired Reinforcement Learning for robustness and adaptability Neurosymbolic AI for theoretical guarantees Publication Trends : Her recent work explores Factored adaptation in non-stationary environments (NeurIPS 2022) Temporal causal identifiability (ICML 2022) Binary interaction-based causal discovery (UAI 2023) Safe exploration in visual RL (HSCC 2021) Structure learning lower bounds (NeurIPS 2020) Scientific Recognition : ELLIS Scholar (2022–present) Spotlight presentations at ICML 2022 and ICLR 2022 Advising & Collaborations : Currently supervising 6 PhD students at the University of Amsterdam and AUMC, with 12 alumni advisees. Collaborates with researchers at MIT-IBM Watson AI Lab, Simons Institute, and TUM.

Zhibin Chenمشاهده پروفایل

استادیار

• Transportation Network Modeling and Optimization
• Intelligent Transportation Systems
• Discrete Optimization
• +۴ مورد دیگر

Zhibin Chen is an Assistant Professor of Engineering at NYU Shanghai and concurrently a Global Network Assistant Professor within the broader New York University system. Since January 2019 he has led research and teaching activities at the Division of Engineering and Computer Science in Shanghai, while maintaining university-wide collaborations through his Global Network appointment. Education Ph.D. in Transportation Engineering, University of Florida (2017) Research Interests Dr. Chen’s scholarship centres on Transportation Network Modeling and Optimization , Intelligent Transportation Systems , and Discrete Optimization . He integrates operations research, data science, and engineering to address emerging challenges in electric mobility, autonomous vehicles, and large-scale urban networks. Recent thrusts include: Data-driven analytics of electric-vehicle charging behaviour under usage heterogeneity. Optimization of charging and swapping infrastructure for electric buses and trucks. Network-level deployment and control strategies for connected and automated vehicles. Day-to-day traffic dynamics and equilibrium models with elastic demand. Pricing, policy, and incentive design for sustainable transportation systems. Scientific Awards Stella Dafermos Best Paper Award – awarded at the 95th Transportation Research Board Annual Meeting. Ryuichi Kitamura Paper Award – also conferred at the 95th TRB Annual Meeting. Editorial & Professional Service Dr. Chen currently serves on the Editorial Advisory Board of Transportation Research Part C: Emerging Technologies , shaping the editorial direction of the leading journal in his field. Grants & Collaborations While specific grant identifiers are not disclosed in the provided text, Dr. Chen’s extensive publication record in top-tier journals ( Transportation Science , Transportation Research Parts B, C, D , IEEE ITS , Applied Energy ) and his editorial role indicate sustained research funding and active collaboration with international partners across North America and China. Laboratories & Teams Operating within the Division of Engineering and Computer Science at NYU Shanghai , Dr. Chen leads a research group focused on next-generation mobility analytics, leveraging the university’s interdisciplinary ecosystem and NYU’s Global Network resources to advance smart and sustainable transportation.

Dong Gongمشاهده پروفایل

مدرس ارشد

• Computer Vision
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Dr. Dong Gong is a Senior Lecturer and ARC DECRA Fellow (2023-2026) at the School of Computer Science and Engineering (CSE), UNSW. He holds an adjunct position at the Australian Institute for Machine Learning (AIML), University of Adelaide. His research focuses on machine learning challenges in dynamic environments, including continual learning, foundation models, generative models, and applications in interdisciplinary areas like mining and agriculture. Research interests include learning with non-ideal supervision, foundation model adaptation, generative models, and interdisciplinary problems combining CV/ML with domain-specific applications. His work often addresses real-world scenarios such as mineral exploration and soil trait analysis using CV/ML technologies. Outstanding Reviewer: NeurIPS 2018 Outstanding Area Chair: ACM MM 2024 ARC DECRA Fellowship (2023-2026) Advising and grants: Actively supervises PhD/MPhil students in computer vision and ML. Collaborates with industry and government on research projects. Utilizes advanced infrastructure like UNSW's Katana supercomputing cluster and Gadi (NCI). Labs/Teams: Involved in interdisciplinary research groups at UNSW CSE and AIML, focusing on dynamic learning paradigms and real-world applications of AI.

Peter Bach Andersenمشاهده پروفایل

پژوهشگر

• Electric Vehicle Integration
• Grid Services
• Battery Energy Storage
• +۴ مورد دیگر

Peter Bach Andersen serves as Head of Section and Senior Researcher at the Department of Wind and Energy Systems, Technical University of Denmark (DTU). Based at Elektrovej 329A in Kgs. Lyngby, he leads research in electric vehicle integration and prosumer energy systems with an ORCID identifier 0000-0002-5202-3584. His work spans grid services, battery storage, and smart charging infrastructure within DTU's EV Lab ( www.evlab.dk ). Andersen's research focuses on electric vehicle grid integration , specializing in charging infrastructure planning, battery energy storage systems, and virtual power plant concepts. His fingerprint analysis reveals dominant expertise in Electric Vehicle Engineering (100%), Battery Engineering (13%), Ancillaries (12%), and Power Engineering (11%). Current projects emphasize flexibility quantification, grid service delivery through EV clusters, and Nordic grid characteristics. His work contributes significantly to UN Sustainable Development Goals related to clean energy and sustainable cities. His publication portfolio shows strong trends toward grid-interactive EV systems with recent emphasis on conditional connection agreements, fast-charging urban impacts, and data-driven battery health prognosis. The research demonstrates increasing focus on practical implementation challenges and market integration aspects of vehicle-grid systems. Andersen actively supervises four PhD students (Menchaca, Striani, Unterluggauer, Sevdari) across projects including charging infrastructure planning, EV clustering methods, and urban charging infrastructure impacts. He leads the FLOW project (2022-2026) investigating flexible energy systems for optimal EV integration while participating in multiple EU-funded initiatives. His research group maintains strong industry connections through DTU's EV Lab, focusing on real-world validation of grid services using commercial EVs and chargers.

Scott Richard Floydمشاهده پروفایل

دانشیار

• Radiation Oncology
• Brain Tumor Research
• DNA Damage Response
• +۷ مورد دیگر

Scott Richard Floyd is the Gary Hock and Lyn Proctor Associate Professor of Radiation Oncology at Duke University School of Medicine, with secondary appointments as Associate Professor of Radiation Oncology and Assistant Research Professor in Pharmacology and Cancer Biology. He is also a Member of the Duke Cancer Institute and serves as Associate Radiation Director of the Duke Center for Brain and Spine Metastasis and Associate Program Director of the Duke Radiation Oncology Residency Program. Dr. Floyd received his M.D. and Ph.D. from Yale University in 2002. His professional training includes: Internship in Internal Medicine at Hospital of Saint Raphael (2002-2003) Residency in Radiation Oncology at Harvard Medical School, Harvard Radiation Oncology Program (2003-2007) Clinical Investigator position at MIT Koch Institute for Integrative Cancer Research (2012-) Dr. Floyd's research focuses on DNA damage signaling and repair in brain tumor cells, particularly investigating how chromatin changes mediated by epigenetic modifiers affect the DNA damage response (DDR). His lab develops small animal irradiation techniques and mouse models of glioblastoma to test the effects of epigenetic writers and readers on DDR in clinically relevant model systems. He aims to identify strategies to enhance tumor cell killing while protecting normal brain tissues from radiation damage. His work on brain slice models provides a platform for studying stroke, Alzheimer's disease, Huntington's disease, and brain tumors while reducing animal use. Analysis of Dr. Floyd's recent publications reveals a strong focus on brain metastases, radiation necrosis, and the application of artificial intelligence in radiation oncology. His work increasingly integrates radiogenomics, machine learning, and novel radiation techniques to improve outcomes for patients with brain tumors. There's a clear trend toward interdisciplinary collaboration, particularly in combining radiation therapy with immunotherapy and targeted agents, as evidenced by his leadership in the Consortium for Intracranial Metastasis Academic Research (CIMARa). Dr. Floyd has secured significant research funding, including: NEUROD1 function in SCLC fate and plasticity (NIH, 2024-2029) Synthetic lethality with BET bromodomain inhibition (American Cancer Society, 2025-2026) ASPET SURF Institutional Award (2018-2028) Multiple NIH R01 grants supporting his research on DNA damage response and brain tumor biology Dr. Floyd leads the Floyd Lab, which focuses on studying mechanisms of DNA damage signaling and repair in brain tumor and other mammalian cells. His lab utilizes advanced techniques including small animal irradiation, mouse models of glioblastoma, and organotypic brain slice culture platforms. He is also involved in Project Brainslice, which develops experimental model systems using brain slices to research new treatments for brain diseases. His clinical expertise centers on the treatment of benign, primary, and metastatic tumors of the brain and spine, making him a key figure in Duke's brain and spine metastasis program.

Seth Gilbertمشاهده پروفایل

دانشیار

• Distributed Systems
• Algorithmic Techniques
• Blockchain Technology
• +۳ مورد دیگر

Seth Gilbert is the Dean's Chair Associate Professor and Head of the Department of Computer Science at the National University of Singapore. His research focuses on designing robust, scalable algorithms for large-scale distributed systems, particularly in the domains of Byzantine agreement, dynamic networks, and contention resolution. He holds a PhD from MIT (advised by Nancy Lynch) and completed a postdoc at EPFL under Rachid Guerraoui. His work emphasizes three core techniques: accountability (identifying network failures), work-sharing (task distribution in asynchronous environments), and contention resolution (efficient resource allocation). Notable contributions include the DConstructor (PODC 2020) and Polygraph (ICDCS 2021) protocols for accountable Byzantine agreement. He has received multiple awards, including the Edsger W. Dijkstra Prize (2021) and Best Paper Awards at DISC, ICDCS, and IPDPS. Prof. Gilbert has served as program chair for DISC 2021, OPODIS 2019, and SPAA 2016, and currently chairs the DISC Steering Committee. His career includes industry experience at Microsoft (Visual Studio team) and early work on solar car engineering at Yale University.

Justin Silvermanمشاهده پروفایل

استادیار

• Bayesian Statistics
• Machine Learning
• Genomics
• +۷ مورد دیگر

Dr. Justin Silverman is an Assistant Professor of Information Science & Technology, Statistics, and Medicine at Pennsylvania State University, with affiliations in the Department of Statistics and the Institute for Computational and Data Science. He holds an MD from Duke University School of Medicine (2020), a PhD in Computational Biology and Bioinformatics from Duke (2019), and a BS in Physics and Biophysics from Johns Hopkins University (2011). His research integrates Bayesian statistics, machine learning, and genomics to address impactful biomedical questions. Key focuses include microbiome analysis, gene expression studies, and scale-reliant inference methodologies. His work emphasizes methodological rigor and clinical relevance, with applications in personalized medicine, agriculture, and epidemiology. The Silverman Lab develops cutting-edge tools like the fido and philr packages for compositional data analysis. Collaborations span academia and industry, addressing challenges in livestock health, viral surveillance, and precision medicine. He also leads Anarres Analytics LLC, translating statistical methods into practical solutions for clients. His advising includes PhD and MS students working on topics like scalable Bayesian models, microbial co-occurrence networks, and topological data analysis in oncology. Notable projects include studies on ancient microbiomes, dairy cow treatments, and pandemic modeling. Labs and teams: Silverman Lab (PSU), Anarres Analytics. Current positions: PI, startup founder, and collaborator in multi-institutional projects.

Paul Gustafsonمشاهده پروفایل

استاد

• Biostatistics
• Epidemiology
• Bayesian Analysis
• +۳ مورد دیگر

Paul Gustafson is a Professor and Head of the Department of Statistics at the University of British Columbia's Faculty of Science. He holds a faculty position at the Vancouver Campus and can be contacted via gustaf@stat.ubc.ca. His research focuses on advanced statistical methodologies with applications in epidemiology, public health, and clinical research. Key interests include Bayesian analysis, causal inference, measurement error correction, and meta-analysis. Dr. Gustafson's work emphasizes methodological innovations in handling complex data challenges such as exposure misclassification, missing data, and observational study biases. He collaborates on projects addressing opioid use disorder treatment efficacy, infectious disease epidemiology, and health policy. Current advisees include Nathaniel Wu Dyrkton, Daniel Daly Grafstein, Seren Lee, Yuan Xia, and Mallory J Flynn. His research outputs span statistical theory, applied public health studies, and computational methods. Recent work highlights include Bayesian approaches to sample size determination, probabilistic bias analysis in diagnostic studies, and frameworks for evaluating treatment benefit predictors using observational data. Dr. Gustafson is also involved in global initiatives such as the Zika Virus Individual Participant Data Consortium. Notable methodological contributions include developing techniques for partially identified models, correcting measurement errors in health surveys, and advancing causal inference methodologies. He has contributed to R packages like rtestim for epidemic modeling and CRTpowerdist for cluster randomized trial analysis.