جست‌وجوی استاد و پژوهشگر

• Environmental Toxicology
• Ecotoxicology
• Metabolomics
• +۳ مورد دیگر

Sean M. Richards is a Professor at the University of Tennessee at Chattanooga (UTC) specializing in environmental toxicology, ecotoxicology, and metabolomics. His research focuses on the effects of toxicants such as polycyclic aromatic hydrocarbons (PAHs), metals, and pharmaceuticals on human and ecological health, including studies on low birth weight in Hamilton County infants and the ecological impacts of pharmaceuticals in surface waters. Current Projects: Investigating placental exposure to pollutants, metabolomic profiling for cancer and fetal anomaly detection, and pharmaceutical ecotoxicology. Collaborations: Partnerships with institutions like Columbia University, University of Salerno, and Erlanger Hospital. His work includes probabilistic risk assessments, biomarker quantitation (e.g., HPLC, atomic absorption spectroscopy), and interdisciplinary applications of metabolomics in toxicology and environmental health. Recent publications highlight metabolomic trends in endometrial cancer screening, pharmaceutical environmental persistence, and fetal growth restriction linked to placental toxicants. Dr. Richards teaches courses such as Environmental Toxicology (BIOL 5320), Risk Assessment (ESC 5650), and Hazardous Waste Management (CHEM 5160). He serves on the US EPA FQPA Science Review Board and as Associate Editor for Ecotoxicology.

• Statistics
• Machine Learning
• Bioinformatics
• +۴ مورد دیگر

Ping Ma is a Professor of Statistics with a courtesy appointment in Computer Science at the University of Georgia. His research focuses on developing innovative statistical and machine learning methodologies for complex high-dimensional data, with applications spanning bioinformatics, computational biology, social network analysis, and anomaly detection in power systems. Research interests include: Statistical Methodology : Nonparametric modeling, optimal transport theory, subsampling techniques, and functional regression for large-scale data Computational Biology : Spatial transcriptomics analysis, single-cell data integration, virology classification, and gene regulatory networks Machine Learning Innovations : Knowledge distillation for LLMs, tensor analysis, quantum-inspired algorithms, and ensemble learning for model robustness His recent publications demonstrate a strong trend toward interdisciplinary applications, particularly in developing AI/statistical tools for biomedical research (47% of recent papers), advancing foundational machine learning techniques (33%), and solving engineering challenges like power grid security (20%). Methodologically, 67% focus on novel algorithm development while 33% refine existing techniques for scalability.

Raaz Dwivediمشاهده پروفایل

استادیار

• Causal Inference
• Reinforcement Learning
• Personalized Decision-Making
• +۲ مورد دیگر

Raaz Dwivedi is Assistant Professor in Operations Research and Information Engineering at Cornell University and Cornell Tech. His research develops statistical and computational methods for personalized decision-making, focusing on causal inference, reinforcement learning, and distribution compression. Recent publications advance kernel thinning techniques, counterfactual inference methods, and adaptive nearest-neighbor algorithms with applications in healthcare and recommendation systems. Research appears in top venues with 15+ publications since 2022. Awards and honors: Blackwell-Rosenbluth Award (2024) ASA Best Student Paper Award (2022) MIT LIDS Best Presentation Award Harvard Teaching Excellence Award FODSI Postdoctoral Fellowship Holds PhD in EECS from UC Berkeley and BTech from IIT Bombay.

Dongheui Leeمشاهده پروفایل

استادیار

• Human motion understanding
• Physical human-robot interaction
• Machine learning in robotics
• +۵ مورد دیگر

Dongheui Lee is an Assistant Professor at the Institute of Automatic Control Engineering (LSR) within the Faculty of Electrical Engineering and Information Technology at Technische Universität München (TUM). She leads the Dynamic Human Robot Interaction for Automation System Lab. Her research focuses on human motion understanding, physical human-robot interaction, and machine learning in robotics. Education: B.S. and M.S. in Mechanical Engineering from Kyunghee University (2001-2003), PhD in Mechano-Informatics from the University of Tokyo (2007). Prior roles include research scientist at KIST Korea (2001-2004) and project assistant professor at the University of Tokyo (2007-2009). Research Interests: Human-robot collaboration, probabilistic robotics, motion recognition, and incremental lifelong learning mechanisms. She has contributed to advancements in motion primitives, compliant physical interaction, and real-time object tracking. Selected Awards: Finalist for KUKA Service Robotics Best Paper Award (2009), Hirose Scholarship (2006-2007), and multiple grants from KRF, KOSEF, and international robotics competitions. Key Publications: Focus on prioritized inverse kinematics, motion imitation, and adaptive control systems. Her work bridges robotics theory and practical applications in humanoid robots and human-robot interaction.

Theodore Papamarkouمشاهده پروفایل

استاد

• Bayesian Deep Learning
• Topological Deep Learning
• Healthcare AI
• +۳ مورد دیگر

Professor Theodore Papamarkou is a leading researcher in Bayesian and topological approaches to deep learning, with a focus on healthcare applications. His work addresses scalability challenges in machine learning by integrating Bayesian inference and topological data analysis into deep learning frameworks. He contributes to the UN Sustainable Development Goals through his research in Digital Futures and the Centre for Digital Trust and Society. Key projects include collaborations on financial crime prevention and digital trust. He received the 2023 ECML PKDD best paper award and serves as Editor-in-Chief of ACM Transactions on Probabilistic Machine Learning. His research spans topics like uncertainty quantification, material microstructure analysis, and predictive model interpretability. Professor Papamarkou has contributed to 19 peer-reviewed publications and actively participates in academic activities such as editorial work and conference organization. His interdisciplinary approach bridges computer science, statistics, and healthcare, emphasizing ethical and practical AI applications.

Fabio Nobileمشاهده پروفایل

استاد

• Scientific Computing
• Uncertainty Quantification
• Numerical Analysis
• +۵ مورد دیگر

Fabio Nobile is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences (SB), Department of Mathematics (MATH), holding the CADMOS Chair in Scientific Computing and Uncertainty Quantification. He leads the CSQI (Chair of Scientific Computing and Uncertainty Quantification) group. His work focuses on numerical methods for partial differential equations (PDEs), uncertainty quantification, stochastic modeling, and computational fluid dynamics. He is involved in collaborative projects involving fluid-structure interaction, cardiac electro-mechanics, and energy systems. Professor Nobile has extensive teaching experience, including courses on advanced analysis, stochastic simulation, and numerical integration of stochastic differential equations. He supervises numerous PhD students and has contributed to over 200 peer-reviewed publications, covering topics such as low-rank approximation methods, multilevel Monte Carlo techniques, and optimal control under uncertainty. His research emphasizes interdisciplinary applications, including biomedical engineering (e.g., personalized cardiac simulations) and renewable energy (e.g., probabilistic load forecasting). He collaborates with industries and academic institutions globally, advancing computational methodologies for engineering and scientific challenges.

Michelle Zhuمشاهده پروفایل

استاد

• Parallel and Distributed Computing
• Big Data Analytics
• Cloud Computing
• +۴ مورد دیگر

Dr. Michelle Zhu is a Professor and Associate Director for Faculty and Academic Affairs at the School of Computing, Montclair State University. She previously held roles as Associate Professor and Director of Undergraduate Programs at Southern Illinois University Carbondale. Dr. Zhu holds a Ph.D. in Computer Science from Louisiana State University and a B.S. in Biomedical Engineering from Zhejiang University. Her research focuses on parallel/distributed computing, big data analytics, and high-performance networking, supported by grants from NSF, DOE, and NVIDIA. She has authored over 150 peer-reviewed publications. Education: Ph.D., Computer Science, Louisiana State University (2005) M.Sc., Computer Science, Louisiana State University (2002) B.S., Biomedical Engineering, Zhejiang University (1996) Her research interests span parallel computing architectures, cloud workflow scheduling, and cybersecurity. She has led initiatives integrating computational thinking into STEM education and developed robotics-based learning tools. Her work has been funded through NSF grants such as the $1.1M "Assimilating Computational and Mathematical Thinking into Earth and Environmental Science" project (2017–2022). Dr. Zhu’s articles explore topics like blockchain-based cloud security, GPU-accelerated Gibbs sampling, and edge computing deployment strategies. She actively contributes to academic governance, serving on Montclair State’s Middle States accreditation committee and the University Academic Assessment Council. Key Grants: NSF MRI: Multimodal Collaborative Robot System (MCROS), $321,737 (2021–2024) DOE: Scalable Application Support Platform for E-Sciences, $389,398 (2009–2013) Service Roles: Curriculum Committee Chair, Computer Science Department Blue Ribbon Task Force for Gen Ed Redesign (2019–2020) She collaborates on robotics projects like MCROS and leads outreach efforts to engage pre-university communities in AI and robotics education.

Dave Clarkeمشاهده پروفایل

استاد مدعو

• Quantitative Exercise Biology
• Exercise Physiology
• Sports Analytics
• +۳ مورد دیگر

Dr. Dave Clarke is an Adjunct Professor in the Department of Biomedical Physiology and Kinesiology at Simon Fraser University, leading the Clarke Laboratory for Quantitative Exercise Biology . His research focuses on Exercise-responsive cell signaling , Sports analytics using wearable sensors , Evidence-based exercise programming , and Clinical exercise physiology . The lab integrates mathematical modeling, clinical insights, and biomechanical approaches to address questions in athletic performance, metabolic disorders, and wearable technology applications. Dr. Clarke collaborates with institutions such as TRIUMF and the BC Cancer Research Centre. Publications highlight his work in critical power modeling , AMP-activated protein kinase dynamics , and mitochondrial disease treatments . His lab emphasizes interdisciplinary training, offering mentorship in experimental design, data analysis, and translational research. The Clarke Lab is affiliated with SFU’s Faculty of Health Sciences and maintains active outreach programs for public health education. Notable projects include developing hydraulic models for recovery kinetics prediction and validating chloride test strips for sodium excretion analysis. The lab’s work has been featured in journals like Frontiers in Sports and Active Living and Orphanet Journal of Rare Diseases . For real-time updates, follow the lab on Facebook .

James V. Burkeمشاهده پروفایل

استاد

• Convex Optimization
• Non-smooth Optimization
• Variational Analysis
• +۷ مورد دیگر

James V. Burke is a Professor of Mathematics at the University of Washington with extensive contributions to optimization theory and its applications. His academic career spans several decades, during which he has developed fundamental theories in nonsmooth and convex optimization, variational analysis, and computational methods for complex optimization problems. Research Focus Burke's primary research centers on convex-composite optimization , where he has established critical theoretical foundations and practical algorithms. His work on weak sharp minima has become foundational in optimization theory, providing essential insights into solution stability and error bounds. He has made significant advances in gradient sampling algorithms for nonsmooth, nonconvex optimization problems, which have broad applications in engineering and data science. More recently, Burke has applied optimization techniques to state estimation problems , particularly developing robust Kalman smoothing methods using Student's t-distributions and other non-Gaussian models. His research bridges pure mathematical theory with practical computational methods, demonstrating consistent innovation across multiple subfields of optimization. Academic Contributions Burke has taught numerous graduate-level courses including Math 509 (Optimal Control), Math 554 (Linear Analysis), and specialized courses on convex analysis and optimization. His research collaborations span multiple institutions, with frequent co-authorship with leading optimization researchers such as Tim Hoheisel, Adrian Lewis, and Michael Overton. He regularly presents his work at major conferences including SIAM Optimization and ICCOPT, with his most recent presentation at the SIAM Conference on Optimization in Seattle (June 2023).

Laurence Perreault-Levasseurمشاهده پروفایل

دانشیار

• Cosmology
• Machine Learning
• Gravitational Lensing
• +۵ مورد دیگر

Laurence Perreault-Levasseur is an Associate Professor at Université de Montréal and an Associate Member of Mila. She specializes in applying machine learning methods to cosmology, with affiliations at the Flatiron Institute and Perimeter Institute. Her research focuses on gravitational lensing, dark matter, and precision cosmology. She holds a Canada Research Chair in Computational Cosmology and Artificial Intelligence. Education: PhD (University of Cambridge, 2015), M.Sc. and B.Sc. (McGill University). Research Interests: Machine learning for cosmological inference, strong gravitational lensing, galaxy cluster characterization, and dark matter studies. Affiliations: CRAQ (Québec Astrophysics Research Centre), Mila (Quebec AI Institute). Her work includes developing Bayesian methods for inverse problems and neural networks for astrophysical data analysis. Key projects involve precision cosmology via machine learning and reconstructing early-universe conditions using generative models.

• Generalization in deep learning
• Sparse estimation
• Portfolio optimization using reinforcement learning
• +۶ مورد دیگر

LIU Peng is an Assistant Professor of Quantitative Finance (Practice) at the Lee Kong Chian School of Business, Singapore Management University. He holds a Ph.D. in Statistics and Data Science (Part-time) from the National University of Singapore (2021), an M.S. in Business Analytics (2015), and a B.Eng. in Electronic Science and Technology (2012). Prior to his academic role, he worked as a Manager at Standard Chartered Bank (2019–2022) and in analytics roles at Marina Bay Sands and IBM. Education: Ph.D. (NUS), M.S. (NUS), B.Eng. (Beijing Technology and Business University) His research focuses on generalization in deep learning, sparse estimation, portfolio optimization via reinforcement learning, financial text mining, risk management, and Bayesian optimization. His work bridges theoretical advancements with practical applications in quantitative finance and data science. Notable contributions include studies on explainable neural networks, Bayesian optimization frameworks for portfolio management, and risk analytics integrating human decision-making. His recent articles emphasize model risk assessment, cost-aware optimization, and financial data analysis. Awards: Best Ph.D. Graduate Research Award (NUS, 2020), Google TensorFlow Developer Certificate (2020–2023) He teaches courses in quantitative finance, machine learning, and risk management, and has secured grants including the Research Capability Building Fund (2023–2025). His research aligns with strategic priorities in digital transformation and financial innovation.

• Bayesian Statistics
• Cluster Analysis
• Data Science
• +۵ مورد دیگر

Bettina Grün is an Associate Professor and Deputy Head of the Institute for Statistics and Mathematics at Vienna University of Economics and Business (WU). Her research focuses on Bayesian mixture models, cluster analysis, and applying statistical methods to sustainability, tourism, and environmental studies. She has led multiple research projects, including studies on environmental behavior in tourism and advanced text modeling in economics. Grün holds a PhD in Technical Mathematics from TU Wien (2006) and a Habilitation in Statistics from Johannes Kepler University Linz (2012). She has authored over 150 publications in top journals like Journal of Environmental Management and Expert Systems with Applications . Her work emphasizes practical applications, such as reducing hotel waste through behavioral interventions and developing R packages like movMF and circlus for statistical clustering. Grün has received awards including the AIEST Best Contribution Award (2019) and the MRS Silver Medal (2016). Grün teaches courses on statistical modeling and leads projects like Analysis of Central Bank Communication (2022–2026) and Environmentally Friendly Behavior in Tourism (2019–2024).

Julia A. Palaciosمشاهده پروفایل

دانشیار

• Statistical Methods in Evolutionary Genomics
• Infectious Diseases Modeling
• Bayesian Nonparametric Methods
• +۷ مورد دیگر

Julia A. Palacios is an Associate Professor of Statistics and Biomedical Data Science at Stanford University, with a courtesy appointment in Biology. She leads the Palacios Lab, focusing on developing statistical methods for evolutionary genomics, infectious diseases, and stochastic processes impacting public health. Her work integrates Bayesian nonparametric techniques, probabilistic AI, and computational statistics to address challenges in genetics, health, and cancer research. Her educational background includes a PhD in Statistics and postdoctoral research in computational biology. Current lab members include postdocs Bingjing Tang and Isaac Goldstein, PhD students Yi-Ting Tsai, Ivan Specht, Julie Zhang, and Leda Liang, and undergraduate researcher Shinnosuke Yagi. Former postdocs like Airam Blancas and Jaehee Kim have moved to faculty positions at ITAM and Cornell, respectively. Research funding includes NIH, NSF, Sloan Foundation grants, and the Terman Fellowship. Key contributions span phylodynamic modeling, coalescent theory, and real-time pathogen surveillance. Her lab's software tools include phylodyn (R package for phylodynamic inference) and adaPop (Bayesian population dynamics inference). Awards include the Sloan Research Fellowship and Gabilan Fellowship. Teaching roles include courses like Stats 376 and Stats 305A . Her lab actively recruits students and postdocs for research in evolutionary stochastic processes and biomedical data science. Labs/Teams: Palacios Lab at Stanford's Department of Statistics, collaborating with institutions globally on pandemic tracking and genomic studies. Current projects focus on multifurcating trees in infectious diseases, Bayesian nonparametric coalescent models, and computational tools for public health.

• Energy Systems
• Applied Mathematics
• Statistical Modeling
• +۳ مورد دیگر

Prof. Chris Dent is a Professor at the University of Edinburgh's School of Mathematics, specializing in the intersection of mathematical sciences and engineering. He holds a fellowship at the Alan Turing Institute and directs the Statistics Consultancy Unit. His career spans theoretical physics, operational research, and energy systems analysis, with notable contributions to National Grid strategies, policy-driven decision support, and pandemic data communication. Education: MMath (Cambridge), PhD in Theoretical Physics, MSc in Operational Research (Edinburgh). Research focuses on energy system reliability, uncertainty quantification, and interdisciplinary policy modeling. He critiques data communication practices in crises, advocating for rigorous reporting frameworks. Key activities include projects on electricity supply security, smart grid optimization, and post-pandemic economic recovery. Collaborations involve government bodies, the National Grid, and international energy networks. The Statistics Consultancy Unit supports cross-disciplinary projects in academia and industry.

Giacomo Lanzaniمشاهده پروفایل

استادیار

• Economic Theory
• Learning
• Decision Theory
• +۳ مورد دیگر

Giacomo Lanzani is an Assistant Professor in the Department of Economics at the University of California, Berkeley, starting July 2025. He currently holds a Postdoctoral Fellowship at the Harvard Department of Economics. He earned his PhD in Economics from MIT in June 2023. His research focuses on economic theory , with a dual emphasis on learning mechanisms under nonstandard decision criteria and the impact of social networks on economic interactions . A key theme in his work is the interplay between learning dynamics and decision-making processes in complex environments. His academic journey includes significant contributions to decision theory, particularly in analyzing how agents learn when faced with misspecifications or bounded rationality. He explores how these factors influence long-run stability, disagreement in opinion dynamics, and strategic interactions within networks. His work frequently bridges economic theory with insights from computer science and probability theory. Lanzani’s research has been recognized with prestigious awards, including the Aliprantis Prize 2025 and the Best Paper Award at the European Winter Meeting of the Econometric Society 2024. His publications appear in top journals such as Econometrica , Journal of Political Economy , and Review of Economic Studies . Before joining Berkeley, he was a visiting scholar at MIT and has collaborated with leading institutions like Harvard and UC Berkeley. His current research agenda includes exploring nonlinear fixed points in economic systems and analyzing heterogeneous learning in product markets.