جست‌وجوی استاد و پژوهشگر

Wing Wah Thamمشاهده پروفایل

دانشیار

• Econometrics
• Market Microstructure
• Asset Pricing
• +۳ مورد دیگر

Associate Professor Wing Wah Tham holds a position at the School of Banking and Finance within the University of New South Wales (UNSW) Business School . He also maintains affiliations as a Marie Skłodowska-Curie Fellow and a Tinbergen Institute Fellow . Previously, he served as an Associate Professor of Financial Econometrics at the Econometric Institute, Erasmus School of Economics (2014–2016) and as an Assistant Professor there (2009–2014). Education : PhD, University of Warwick MSc, Imperial College, UK BEng, Nanyang Technological University of Singapore Research Interests : Professor Tham specializes in econometrics , market microstructure , asset pricing , and financial innovation . His work bridges quantitative finance with applications in artificial intelligence and sustainable development goals (SDG) , focusing on reproducibility, liquidity dynamics, and policy impacts. Recent Publications span high-impact journals like Journal of Finance , Management Science , and Review of Financial Studies , addressing topics such as nonstandard errors , AI for SDG mapping , and toxic arbitrage . These works are frequently presented at American Finance Association and Econometric Society meetings. Scientific Honors : PanAngora Crowell Second Prize Midwest Finance Association Best Paper Award Literati Network Awards for Excellence ARC Discovery Projects Grant Additional Activities : He has served as a visiting scholar at the Haas Business School, UC Berkeley and National University of Singapore , with interests in triathlons and ocean swimming.

Zoe Liمشاهده پروفایل

دانشیار

• Uncertainty Analysis
• Risk Management
• Stochastic Modelling
• +۷ مورد دیگر

Dr. Zoe Li is an Associate Professor in the Department of Civil Engineering at McMaster University, with an additional role as an Associate Member in the Department of Computing and Software. She specializes in developing modeling and decision-support tools to address challenges in water resources management, environmental systems analysis, and climate change impacts. Her research focuses on hydrological modeling, probabilistic forecasting, risk analysis, and environmental systems optimization. She holds a B.Eng. from China and M.A.Sc./Ph.D. degrees from the University of Regina. Her work spans interdisciplinary collaborations, including studies on dam safety under climate change, wastewater treatment plant optimization, and AI-driven environmental monitoring. She has been actively involved in projects such as CityDNA, a digital tool to model urban environments for decision-making during crises like the pandemic. Dr. Li teaches courses in environmental systems engineering and principles of environmental engineering, emphasizing uncertainty quantification and sustainable practices. Her research publications (15+ recent articles) address topics like machine learning applications in environmental systems, climate change impacts on water resources, and infrastructure resilience. She advises graduate students in environmental engineering and systems optimization. Notable grants include funding from Roche Canada for pandemic-related urban modeling. Her work is frequently published in high-impact journals like Journal of Environmental Management and Water Resources Research .

• Adaptive Experimentation
• Reinforcement Learning
• Statistical Inference
• +۳ مورد دیگر

Kelly W. Zhang is an Assistant Professor at Imperial College London's Mathematics Department (statistics section) and a faculty member in the I-X interdisciplinary AI initiative. Her research focuses on adaptive experimentation, reinforcement learning, and statistical inference with applications in healthcare and clinical trials. She holds a PhD from Harvard University and was a Postdoctoral Fellow at Columbia Business School. Notable awards include the Siebel Scholar (2023) and NSF Graduate Fellowship. Education: PhD in Computer Science from Harvard University (2023), advised by Susan Murphy and Lucas Janson; internships at Apple, Facebook AI, and eBay. Her work bridges statistical theory and practical applications in digital health interventions, with deployments in oral health and cannabis use trials. Research Interests: Reinforcement Learning Algorithms for Digital Interventions Statistical Methods in Adaptive Experimentation Clinical Trial Design and Monitoring Machine Learning Theory and Applications Awards: Siebel Scholar (2023) NSF Graduate Fellowship (PhD support) Presentations at NeurIPS 2021/2020 and Econometric Society Conference 2024 Grants and Funding: Her work has been supported through interdisciplinary initiatives at Imperial College and prior fellowships. She co-leads sessions on statistical reinforcement learning at conferences like INFORMS and JSM. Professional Activities: Co-organizer of sessions at INFORMS 2025, IMS-Bernoulli 2024, and workshops on Deployable RL. Active in academic outreach, including speaking at Amazon Berlin's StatML workshop.

Jisun Anمشاهده پروفایل

استادیار

• Computational Social Science
• Data Science
• Social Media Analysis
• +۳ مورد دیگر

Jisun An is an Assistant Professor at the Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington (IUB), leading the Social Data and AI (SODA) Lab. Previously, she held positions at Singapore Management University (SMU) and the Qatar Computing Research Institute (QCRI). She earned a Ph.D. in Computer Science from the University of Cambridge (2015), supported by EPSRC, and received the Google European Scholarship. Her research focuses on computational social science, leveraging NLP and machine learning to analyze social media, political communication, health informatics, and journalism. Education: Ph.D. in Computer Science (University of Cambridge, 2015). Notable roles include Associate Editor of EPJ Data Science and PC member for conferences like ICWSM, ACL, and AAAI. She co-organized the News and Public Opinion (NECO) workshop (2016-2020). Teaching includes courses on Performance Analytics and Computational Social Science. Research highlights include studies on media attention patterns, user engagement, hate speech detection, and public health campaigns. Her work bridges interdisciplinary gaps, combining theoretical foundations with practical computational methods. Recent projects explore discursive power in media systems and predictive modeling of collective behavior. Awards: Google European Scholarship Key Projects: Discursive Power in Media, Precision Public Health Campaigns, and Algorithmic Bias Analysis Labs/Teams: SODA Lab at IU, previously contributed to QCRI's research initiatives

Yulia Gelمشاهده پروفایل

استاد

• Uncertainty Quantification
• Artificial Intelligence
• Topological Data Analysis
• +۶ مورد دیگر

Yulia Gel is a Professor in the Department of Statistics at Virginia Tech and serves as a Part-Time Program Director-Expert at the National Science Foundation (NSF). She holds a MSc (summa cum laude) and PhD in Mathematics from Saint Petersburg State University (Russia) and completed a postdoc in Statistics at the University of Washington. Her research focuses on uncertainty quantification in AI, statistical foundations of data science, spatio-temporal processes, and applications in climate science, healthcare, and blockchain analytics. She has received prestigious awards including the NSF Director’s Award (2023), ASA Distinguished Achievement Medal (2018), and TIES Abdel El-Shaarawi Award (2014). Gel has led grants on wildfire prediction, climate informatics, and blockchain data science. She serves on editorial boards of Statistica Sinica, Electronic Journal of Statistics, and Technometrics, and organizes workshops on AI for climate sustainability and fragile Earth systems. Her research group develops topological and geometric methods for graph neural networks, with applications to digital twins, environmental justice, and public health. Education: MSc (1997), PhD (2000) in Mathematics from Saint Petersburg State University; Postdoc in Statistics at University of Washington (2001–2003). Past roles include Professor at University of Texas at Dallas (2015–2024) and Associate Professor at University of Waterloo (2004–2014). Selected visiting positions include NASA Jet Propulsion Lab (2016–2017) and Isaac Newton Institute (2016–2017). She has pioneered statistical software packages like snowboot and funtimes for network inference and time-series analysis. Awards highlight her contributions to environmetrics and statistical methodologies. Current projects include NSF-funded research on AI-driven wildfire prediction and blockchain analytics for climate resilience. Her lab’s recent work emphasizes topological methods (e.g., zigzag persistence) for graph-based forecasting and adversarial robustness.

Chenang Liuمشاهده پروفایل

دانشیار

• Smart Manufacturing
• Data Analytics
• Machine Learning
• +۲ مورد دیگر

Dr. Chenang Liu is an Associate Professor in the Department of Industrial Engineering & Management at Oklahoma State University's College of Engineering, Architecture and Technology (CEAT). Their research focuses on smart manufacturing systems, real-time quality monitoring, and machine learning applications in manufacturing and healthcare. Ph.D., Industrial and Systems Engineering, Virginia Tech, 2019 M.S., Statistics, Virginia Tech, 2017 B.S., Mathematics (Statistics track), Zhejiang University, China, 2014 B.S., Environmental and Resource Sciences, Zhejiang University, China, 2014 Research Interests: Dr. Liu develops advanced sensing and data analytics methodologies for smart manufacturing, statistical frameworks for real-time quality control, and mathematical models integrating machine learning with healthcare applications. Their work bridges industrial engineering principles with cutting-edge data science techniques. Publication Trends: Recent articles demonstrate expertise in diabetic retinopathy prediction via interpretable AI, supply chain coordination mechanisms, EHR analytics for disease progression modeling, and combinatorial optimization algorithms. Key themes include healthcare data science, resilient manufacturing systems, and stochastic resource allocation. Scientific Recognition: Featured Article in ISE Magazine, IISE, 2019 Gilbreth Memorial Fellowship, IISE, 2018-2019 Best Poster Award, INFORMS Annual Meeting, 2018 Best Student Paper Finalist, IISE Annual Conference, 2018 Best Paper Awards at INFORMS (2017) and IISE (2017)

• Computer-aided drug discovery
• Docking
• Virtual screening
• +۸ مورد دیگر

Garrett M. Morris is an Associate Professor in Systems Approaches to Biomedicine at the University of Oxford, affiliated with the Department of Statistics and Green Templeton College. He holds roles as Deputy Director of Graduate Studies, Co-Director of the SABS R³ Centre for Doctoral Training, and Research Fellow at Green Templeton College. His research focuses on computational chemistry, drug discovery, and AI integration in biomedicine. He earned his DPhil from Oxford under Prof. W. Graham Richards, with subsequent work at The Scripps Research Institute and Oxford spinouts like InhibOx and Crysalin. Research interests include protein-ligand docking, virtual screening, and machine learning applications in cheminformatics. Notable contributions include the AutoDock software and the FightAIDS@Home project. He co-organizes conferences like the Royal Society of Chemistry’s 'AI in Chemistry' and founded Comp Chem Kitchen. His lab, Oxford Protein Informatics Group (OPIG), develops novel methods for drug discovery and evaluates AI-based docking methods' validity (e.g., PoseBusters). Recent work critiques AI docking methods' physical plausibility and generalizability. He advises numerous graduate students in statistics and drug discovery, with alumni in academia, pharma, and venture capital. Publications span molecular generation, scoring functions, and computational tools for drug design. Collaborations emphasize reproducibility, responsible research, and cloud computing in biomedicine.

Marc Plantevitمشاهده پروفایل

استاد

• Data Mining
• Machine Learning
• Artificial Intelligence
• +۷ مورد دیگر

Marc Plantevit is a Full Professor at EPITA, member of the Laboratoire LRDE (LRDE). Previously, he served as an Associate Professor at University Claude Bernard Lyon 1 (2010–2021), leading the Data Mining & Machine Learning group at LIRIS lab. He holds a PhD in Computer Science from the University of Montpellier (2008), supervised by Maguelonne Teisseire and Anne Laurent at LIRMM Lab. His research focuses on foundational data mining, graph mining, subgroup discovery, and explainable AI. He is an editorial board member of Data Mining and Knowledge Discovery Journal and has held roles such as CAPES NSI jury member and former head of the Data Mining & Machine Learning group at LIRIS. Research Interests : Data Mining, Machine Learning, Explainable AI, Graph Mining, Subgroup Discovery, Exceptional Model Mining, Constraint-based Pattern Mining, and applications in neuroscience and energy systems. His work explores interpretable AI, GNN explainability, and interdisciplinary applications like odor perception modeling and electricity price forecasting. Key Contributions : Best Paper Award at EGC'22 for work on GNN representations. Active in program committees for ECMLPKDD, IJCAI, and IEEE ICDM . Supervised PhD students working on GNN explainability, electricity forecasting, and machine learning in exposome studies. Labs & Teams : LRDE (EPITA), previously involved with LIRIS (UMR CNRS 5205) and collaborative projects with institutions like INSA Lyon and ISGlobal (Barcelona).

Kenan Liمشاهده پروفایل

دانشیار

• Spatial Computation
• Environmental Health
• Community Resilience
• +۱۲ مورد دیگر

Kenan Li, Ph.D., is an Associate Professor in the Department of Epidemiology and Biostatistics at Saint Louis University’s College for Public Health and Social Justice. He joined SLU in August 2022 and teaches courses such as Statistical Learning, R for Spatial Analysis, and Environmental Determinants of Health. His research bridges data science, GIS, and public health, focusing on spatial computation, environmental exposures, and community resilience. Ph.D. in Environmental Sciences, Louisiana State University M.S. in Environmental Sciences, Louisiana State University B.S. in Environmental Sciences and Applied Mathematics, Nankai University, China Dr. Li’s research interests lie at the intersection of spatial computation, environmental health, and community resilience . He develops geo-AI frameworks , integrated geo-cyber-infrastructures , and biostatistics algorithms using big data, deep learning, and sensor data. His work emphasizes understanding human-environment interactions, urban sustainability, and health disparities. His recent publications from 2023 to 2015 reveal a strong trend in spatial modeling of population dynamics , machine learning for environmental exposure analysis , and resilience assessment in vulnerable coastal regions. He has pioneered methods like Dynamic Time Warping Self-Organizing Maps and Wavelet-based Shapelet Discovery to extract meaningful patterns from high-frequency sensor data. His scientific awards include the Taylor Geospatial Institute Seed Grant (2023) , the Saint Louis University 2023 Health Research Grant , and selection for the Scholarly Undergraduate Research Grants and Experiences . He has secured funding from NSF, NIH, USC Keck School of Medicine, and the US Army Corps of Engineers. Dr. Li has advised and collaborated on numerous research projects, particularly in interdisciplinary teams studying the Mississippi River Delta and urban health interventions. He has been involved in NIH/NIBIB-funded projects and led research on emergency management of trail systems in Los Angeles County. He is actively involved in building research labs and teams focused on spatial data science and public health analytics , having previously worked at USC’s Spatial Sciences Institute and Population and Public Health Sciences Department.

Robin Crockettمشاهده پروفایل

پژوهشگر

• Document Forensics
• Academic Integrity
• Contract Cheating
• +۹ مورد دیگر

Dr Robin Crockett is the University Academic Integrity Lead at the University of Northampton, based in the Academic Registry. He is a mathematician-ethicist actively engaged in research and professional development in academic integrity, document forensics, and the detection of contract cheating and AI-generated text. He is a member of the European Network for Academic Integrity (ENAI), co-founder of the Midlands Integrity Group (UK), and has advised UK policymakers on legislation to ban essay mills. He holds Chartered Scientist and Chartered Mathematician status. MPhil, The Management of Electricity Supplies via Storage as Hydrogen, Cranfield University Master, Energy Conservation and the Environment, Cranfield University PhD, Electrostatic Damage to Semiconductor Devices, University of Southampton Master, Natural & Electrical Sciences, University of Cambridge Bachelor, Natural & Electrical Sciences, University of Cambridge Dr Crockett's research centers on document forensics and academic integrity, with core interests in Fourier theory, time-series analysis, and stylometry for identifying contract cheating. His work increasingly addresses the challenges posed by generative artificial intelligence in education. He applies mathematical and statistical methods to analyze linguistic cues, writing styles, and embedded information in student submissions. His recent publications highlight a strong trend toward understanding and mitigating academic misconduct in the AI era. Topics include AI-text detection uncertainties, forensic stylometry, and policy development for generative AI misuse. Earlier work includes environmental research on radon remediation and signal processing applications in telecommunications. Chartered Scientist Chartered Mathematician Dr Crockett has supervised PhD students, including Believe Nwamae in Computing. He has secured internal research funding, such as the Small Grants Scheme for Early Career Researchers at the University of Northampton for a project on AI-synthesized text detection. He has been an Academic Visitor at Loughborough University and served on the Turnitin Advisory Board, indicating active collaboration and external engagement. He frequently presents at academic events and contributes to policy discussions. He is affiliated with research networks including the European Network for Academic Integrity (ENAI) and the European Geosciences Union (as a former Scientific Officer). His work is supported by institutional and collaborative projects focused on advancing machine discernment of academic misconduct.

YooJung Choiمشاهده پروفایل

استادیار

• Probabilistic Machine Learning
• Tractable Probabilistic Modeling
• Algorithmic Fairness
• +۶ مورد دیگر

YooJung Choi is an Assistant Professor at the School of Computing and Augmented Intelligence, Arizona State University. Her research focuses on probabilistic machine learning, trustworthy AI, and tractable probabilistic modeling. She holds a PhD in Computer Science from UCLA and has been recognized with awards like the Cisco Research Award and Simons-Berkeley Research Fellowship. Education: PhD in Computer Science (University of California, Los Angeles). Research interests include probabilistic reasoning, fairness, robustness, and interpretability. Her work bridges theoretical foundations and practical applications, with contributions to probabilistic circuits and optimal transport. Notable achievements include co-organizing TPM 2024 and presenting at venues like NeurIPS and AAAI. She actively promotes fairness in AI through frameworks that address label bias and discrimination patterns. Awards: Cisco Research Award, Simons-Berkeley Fellowship, AAAI 2023 New Faculty Highlights. Teaching: Courses on artificial intelligence, machine learning, and research supervision. Service: Tutorial leadership on probabilistic circuits, workshop organization, and academic talks globally.

Salvatore Ruggieriمشاهده پروفایل

استاد

• Data Mining
• Knowledge Discovery
• Discrimination Discovery and Prevention
• +۹ مورد دیگر

Salvatore Ruggieri is a Full Professor in the Department of Computer Science at the University of Pisa, where he teaches in the Master Programme in Data Science and Business Informatics. He is affiliated with the KDD LAB, a joint research group of ISTI-CNR and the University of Pisa, and actively contributes to national and European AI initiatives such as XAI, NoBIAS, TAILOR, and SoBigData.eu. His research focuses on data mining and knowledge discovery, with a strong emphasis on ethical AI. Key areas include discrimination discovery and prevention, fairness, privacy, explainable AI (XAI), causal inference, and classification algorithms. He has led significant projects such as ENFORCE, a national FIRB project on legal and computational enforcement of non-discrimination and privacy rights in ICT systems (2010–2014), and has served as program chair for the XIII Italian Symposium on Artificial Intelligence (2014). The recent publications (2018–2023) highlight a consistent trend in interpretable and fair machine learning, including selective classification, stability of interpretable models, and causal reasoning for fairness. His work often involves collaboration with leading researchers like Dino Pedreschi and Riccardo Guidotti, and appears in top venues such as AAAI, IEEE TKDE, and WIREs. His scientific honors include the award for the best Ph.D. thesis in Theoretical Computer Science from the Italian Chapter of EATCS. Best Ph.D. Thesis in Theoretical Computer Science, Italian Chapter of EATCS He advises and collaborates with numerous researchers in the KDD LAB and has contributed to major grants and research initiatives in AI and data science. He is involved in educational programs, including the National Ph.D. in Artificial Intelligence - Society, and promotes interdisciplinary research at the intersection of computer science, law, and ethics. Ruggieri is a member of the KDD LAB, where he leads research in ethical and transparent AI. He is also part of large collaborative networks such as SoBigData.eu and HumanE-AI-Net, which aim to build socially responsible and human-centered AI systems.

Tara M. Sinclairمشاهده پروفایل

استاد

• Macroeconomics
• Labor Economics
• Econometrics
• +۷ مورد دیگر

Tara M. Sinclair is Professor of Economics and International Affairs and Chair of the Department of Economics at the George Washington University. She is also affiliated with the Elliott School of International Affairs through the Institute for International Economic Policy and the Institute for European, Russian, and Eurasian Studies. Additional affiliations include the GW Institute of Public Policy Research, the Regulatory Studies Center, and GW-CIBER. Sinclair has held influential public sector roles, including Deputy Assistant Secretary for Macroeconomics at the U.S. Department of the Treasury (2022–2024) and founding Chief Economist at Indeed Hiring Lab. PhD in Economics, Washington University in St. Louis (2005) AM in Economics, Washington University in St. Louis (2001) BA in Foreign Languages, Wheaton College (1996) Her research centers on macroeconomic fluctuations, labor market dynamics, forecasting, and the application of big data to economic analysis. She employs econometric and time series methods to model business cycles, evaluate forecast accuracy, and assess policy impacts. Her work connects academic research with real-world decision-making, particularly in labor markets and monetary policy. Sinclair frequently uses online job posting data to generate timely insights into employment trends. Her recent publications focus on post-pandemic labor market transformations, remote work persistence, forecast evaluation, and regulatory economics. She has published in top journals such as Labour Economics , Journal of Applied Econometrics , Oxford Bulletin of Economics and Statistics , and International Journal of Forecasting . Her research leverages real-time data to analyze topics like unemployment insurance claims, job mismatch, and global hiring trends. Scientific recognition includes membership in the Conference on Research in Income and Wealth (CRIW), the Federal Forecasters Consortium Governing Board, and authorship at VoxEU. She has served as a Special Government Employee on the Bureau of Labor Statistics Technical Advisory Committee. She advises and collaborates with numerous researchers and institutions. Her leadership roles include directing the GW Center for Economic Research (former) and leading research initiatives at Indeed Hiring Lab. She teaches graduate and undergraduate courses in macroeconomics and econometrics, and is frequently cited in major media outlets including the New York Times , Wall Street Journal , and Financial Times , and appears on networks such as CNN, NPR, and Bloomberg. Her research team includes collaborators from Federal Reserve Banks, international organizations, and academic institutions. She leads projects funded by GW-CIBER and contributes to policy reports for the U.S. Treasury and Indeed Hiring Lab.

Giulio Ciminiمشاهده پروفایل

دانشیار

• Statistical Physics
• Complex Networks
• Economic Networks
• +۱۲ مورد دیگر

Giulio Cimini is Associate Professor of Theoretical Physics in the Department of Physics at the University of Rome Tor Vergata and a Research Associate at the 'Enrico Fermi' Research Center. He is a statistical physicist with a strong interdisciplinary focus on complex networks and their applications in socio-economic systems. His research interests include: Statistical Physics of Complex Networks Reconstruction and Validation of Economic Networks Social Network Interactions and Financial Markets Systemic Risk and Financial Contagion Scientific Success, Fitness, and Complexity Adaptive Social Recommendation Codon Usage Bias and Protein Interaction Networks His recent publications reveal a strong trend in applying statistical physics to real-world networks, particularly in finance and social systems. Key themes include the modeling of systemic risk in supply chains and financial networks, the dynamics of collective action on platforms like Reddit (e.g., the GameStop short squeeze), and the development of network reconstruction methods using maximum entropy and optimal transport frameworks. His work often combines empirical analysis with theoretical modeling. Scientific awards and recognitions include: Associate Editor, Frontiers in Physics – Interdisciplinary Physics Board Member, Network Science Society Member, Council of the Complex Systems Society Steering Committee, CCS/Italy He has advised or collaborated with numerous researchers, particularly in projects related to economic networks and complex systems. His work has been supported by Italian national grants such as PRIN and PNRR. He leads or co-leads research projects including RENet and C2T. His research is conducted within interdisciplinary teams involving physicists, economists, and computer scientists, often in collaboration with institutions like ISC-CNR, IMT Lucca, and the Network Science community.

• Reinforcement Learning
• Bandit Algorithms
• Learning Theory
• +۴ مورد دیگر

Claire Vernade is a Group Leader at the University of Tübingen within the Cluster of Excellence Machine Learning for Science. She holds an Emmy Noether award (2022) and an ERC Starting Grant (2024) for her projects FoLiReL and ConSequentIAL , focusing on theoretical reinforcement learning and non-stationary environments. Education: PhD from Telecom ParisTech (2017) Post-doctoral researcher at University of Magdeburg (2018) Her research bridges sequential decision making, bandit problems, and reinforcement learning theory. Recent work explores lifelong learning, distributional RL, and game-theoretic approaches to PCA, emphasizing mathematical rigor and algorithmic innovation. Recent publications highlight trends in non-stationary RL , continual learning , and bandit algorithms with complex feedback structures. Key subfields include meta-learning, adaptive control, and theoretical guarantees in dynamic programming. Scientific Awards: Emmy Noether Award (2022) ERC Starting Grant (2024) Outstanding Paper Award & Oral Presentation, ICLR (2021) She mentors PhD and master's students in theoretical machine learning, with current advisees including Nicolas Nguyen, Onno Eberhard, and Ziyad Sheebaelhamd. Her lab actively recruits candidates in bandit algorithms and RL theory through the IMPRS-IS and ELLIS doctoral programs. Claire co-leads diversity initiatives like Tübingen Women in Machine Learning and Women in Learning Theory, advocating for inclusivity in AI research. She has organized workshops at ICML and EWRL, and contributed to union activism in the tech industry.