جست‌وجوی استاد و پژوهشگر

Alec Mortonمشاهده پروفایل

دانشیار

• Decision Analysis
• Operations Research in Healthcare
• Health Economics
• +۵ مورد دیگر

Alec Morton is an Associate Professor (Practice) at the National University of Singapore (NUS) Saw Swee Hock School of Public Health and concurrently holds a Professor of Management Science position at the University of Strathclyde in Glasgow, UK. His career spans multidisciplinary roles across engineering, social science, business, and public health, focusing on evidence-based decision-making in healthcare. Education: PhD in Management Science (University of Strathclyde, 2000), MSc in Operational Research (University of Strathclyde, 1997), BSc in Mathematics and Philosophy (University of Manchester, 1995) Morton’s research integrates decision analysis , health economics , and operations research to address critical questions in health technology assessment , priority-setting in healthcare , and global health governance . His work on antimicrobial resistance, antibiotic reimbursement models, and health system strengthening has influenced international policy frameworks. His recent publications explore economic evaluations for global health challenges, including antimicrobial resistance , assisted dying policies , and health system equity . He is a Fellow of the Royal Society of Public Health and serves as Departmental Editor of Healthcare Management Science . Morton has held advisory roles for organizations such as the UK National Audit Office , NICE , GAVI , and the WHO Global Malaria Programme , with research funded by entities like the Health Foundation and UK Medical Research Council .

• Machine Learning
• Computational Biology
• Bioinformatics
• +۴ مورد دیگر

Catherine Mooney is a Professor in the School of Computer Science at University College Dublin (UCD), leading the Life Science Data Analytics Group (LiSDA). Her research focuses on applying machine learning to healthcare challenges, including biomarker development, clinical decision support systems, and addressing ethical and technical barriers in healthcare AI. Education: BSc in [field unspecified] from Trinity College Dublin PhD in Computer Science from UCD Prof Dip University Teaching & Learning from UCD Research Interests: Machine Learning applications in healthcare Biomarker discovery for neurological and metabolic conditions Explainable AI for clinical decision support Promoting diversity and inclusion in STEM education Her work bridges computational methods with medical challenges, emphasizing ethical AI and translational research. Notable Awards: Best Paper Award in Applied Biosciences (2023) UCD Long-Service Award (2022) Best Poster Award at ITiCSE ’20 (2020) Her research has led to impactful tools like LiSDA’s clinical decision support systems for epilepsy and pregnancy care. She also advocates for gender diversity in computing, serving in leadership roles like Vice Principal for EDI in UCD’s College of Science (2022–2024).

Vincent Y. F. Tanمشاهده پروفایل

استاد

• Online Decision Making
• Multi-Armed Bandits
• Reinforcement Learning
• +۳ مورد دیگر

Professor Vincent Y. F. Tan holds dual appointments in the Department of Mathematics and the Department of Electrical and Computer Engineering (ECE) at the National University of Singapore (NUS). He is also affiliated with the Institute of Operations Research and Analytics (IORA) and the Institute of Data Science (IDS). His research focuses on Online Decision Making, Multi-Armed Bandits, Reinforcement Learning, Information Theory, and Statistical Signal Processing. Notably, he has been actively publishing in top-tier conferences like NeurIPS, ICML, and IEEE journals, with recent works exploring topics such as low-rank adaptation, off-policy evaluation, and queueing control. Professor Tan has advised numerous PhD students, including Fengzhuo Zhang, Yujun Shi, and Junwen Yang. He has received recognition for his teaching, including a 4.7/5.0 rating for EE5137 Stochastic Processes. His work has led to impactful publications, such as the best paper award at the ICML 2025 workshop on World Models and an oral presentation at ICLR 2025. He currently serves as a Senior Area Chair for NeurIPS 2025 and an Area Editor for the IEEE Transactions on Information Theory. His research group focuses on advancing theoretical and applied aspects of machine learning, with projects funded by grants in areas like distributed optimization and adversarial robustness. He collaborates widely, including with institutions like IIT Delhi and HKUST Guangzhou. Open positions are available for motivated postdocs and students in his research areas.

• Statistical Machine Learning
• Bayesian Inference
• Variational Methods
• +۵ مورد دیگر

Michael C. Hughes ("Mike") is an Assistant Professor in the Department of Computer Science at Tufts University's School of Engineering, where he develops statistical machine learning methods for healthcare applications. His work focuses on building predictive models that extract actionable insights from complex clinical data, including electronic health records and medical imaging. PhD, Computer Science, Brown University (2016) MS, Computer Science, Brown University (2012) BS, Computer Science, Franklin W. Olin College of Engineering (2010) Research interests center on: Bayesian hierarchical models for documents, sequences, and medical images Optimization algorithms for approximate inference Model fairness and interpretability in clinical contexts Semi-supervised learning for medical diagnostics Recent publications demonstrate these capabilities through applications in cardiovascular disease diagnosis, opioid overdose forecasting, and ICU risk prediction. His lab emphasizes reproducibility through open datasets like TMED-2 and open-source tools like BNPy. Grants include NIH R01 funding for heart valve disease detection, NSF CAREER support for model interpretability, and NSF GCR funding for educational uncertainty research. Scientific awards include: NIH R01 Award (PI) for heart valve disease detection (2025) NSF CAREER Award (2024) NSF GCR Grant (2024) Best Poster Award at Time Series Workshop (ICML 2021) Top 10% Reviewer Awards at AISTATS (2023, 2022) Teaching activities include courses on Bayesian Deep Learning, Introduction to Machine Learning, and Statistical Pattern Recognition. He previously served as postdoctoral fellow at Harvard SEAS.

Banu Lokmanمشاهده پروفایل

استاد

• Operational Research
• Multi-criteria Decision Making
• Optimization
• +۳ مورد دیگر

Banu Lokman is a Professor of Operational Research (OR) at the University of Portsmouth, serving as Associate Head (Research and Innovation) in the School of Organisations, Systems and People within the Faculty of Business & Law. She leads the Centre for Innovative and Sustainable Finance and contributes to the Centre for Operational Research & Logistics. Her expertise spans multi-criteria decision-making, optimization, and their applications in healthcare and sustainability. She holds editorial roles at OMEGA and the IMA Journal of Management Mathematics and organizes the NATCOR MCDM courses. Previously, she served as Deputy Director of CORL (2011–2024), Secretary of the International MCDM Society, and Board Member of INFORMS MCDM Section. She currently chairs the INFORMS MCDM Section as President-elect/Vice-President. Education: BSc, MSc, and PhD in Industrial Engineering from Middle East Technical University (METU, Turkey), followed by postdoctoral research at Aalto University (Finland). She taught at METU (2014–2019) and held visiting roles at Aalto University. Research Interests: Focuses on developing optimization methods for multi-criteria decision problems, particularly in healthcare (e.g., optimizing prostate biopsy decisions with Portsmouth NHS Trust) and sustainability. Her work emphasizes algorithms for nondominated set representation, robust efficiency analysis, and cluster ensemble methods. Key Awards: Bernard Roy Award (2022) for outstanding contributions to Multiple Criteria Decision Aiding, and Young Researcher Award (2015). Advising & Grants: Leads a healthcare-related PhD project and contributes to projects like the Social Investment Fund collaboration with Waltham Forest Council. She actively supervises students and participates in research initiatives on supply networks and data control systems. Labs & Teams: Engaged with interdisciplinary teams in operational research and logistics, particularly in applying OR to real-world challenges such as energy market optimization and MRO supply networks.

Karim Chalakمشاهده پروفایل

استاد

• Econometric Theory
• Causal Inference
• Measurement Error
• +۲ مورد دیگر

Karim Chalak is Professor of Econometrics at the University of Manchester, specializing in econometric theory and causal inference methods. His research addresses identification challenges in models with agent interactions and measurement error. Education: PhD in Social Science from University of California, San Diego (2007) Research Interests: Develops methods for causal inference in environments with strategic interactions, omitted variables, and measurement error. Applications include returns to education, corporate finance decisions, and firearm policy impacts. Maintains international collaborations and visiting positions at leading institutions. Publications Analysis: Recent work demonstrates consistent focus on partial identification strategies, measurement error correction techniques, and applications to policy-relevant economic questions. Methodological innovations include nonparametric bounds and proxy variable frameworks.

• Evolutionary Ecology
• Theoretical and Quantitative Methods in Natural Resource Management
• Conservation Biology
• +۴ مورد دیگر

Evan G. Cooch is an Associate Professor in the Department of Natural Resources and the Environment at Cornell University, affiliated with the College of Agriculture and Life Sciences (CALS). He joined Cornell in September 1999 and holds membership in the graduate fields of Ecology and Evolutionary Biology, and Risk Analysis, Communication & Policy. He is also a Faculty Fellow at the Cornell Center for a Sustainable Future and the Institute for Computational Sustainability. Education: BS in Chemistry and Biology (St. Lawrence University, 1981); PhD in Population + Evolutionary Ecology (Queen’s University, 1990); Post-doctoral fellowships at University of Pennsylvania (1990–1991) and USGS-Patuxent Wildlife Research Center (1991–1992). His research focuses on applying theoretical and quantitative methods to natural resource management and conservation. While primarily focused on teaching, his courses include Applied Population Ecology, Advanced Conservation Biology, and Decision Making in Natural Resource Management. He emphasizes making complex concepts accessible for students. No recent articles or scientific awards are explicitly listed, though his work aligns with broader themes in ecological sustainability and policy. He actively engages with teaching and institutional sustainability initiatives.

Joséphine Gantoisمشاهده پروفایل

استادیار

• Biodiversity Conservation
• Ecological Footprint Analysis
• Agricultural Landscapes
• +۵ مورد دیگر

Joséphine Gantois is an Assistant Professor in Human Dimensions of Biodiversity Conservation at the University of British Columbia, jointly appointed in the Institute for Resources, Environment and Sustainability (IRES) and the Food and Resource Economics Program within the Faculty of Land and Food Systems. Her work bridges economics, ecology, and data science to address ecological footprints in agricultural and natural landscapes. She holds a Ph.D. in Sustainable Development from Columbia University, an M.P.A. in International Development from the London School of Economics, and advanced degrees in economics and the sciences from École Polytechnique. Research Focus: Dr. Gantois investigates practical solutions for reconciling land use incentives with conservation goals, particularly in agricultural areas. Her research emphasizes causal inference methods, integrating remote sensing, machine learning, and qualitative tools like interviews. Key areas include biodiversity monitoring, policy impact assessment, and ecosystem function analysis. She has explored habitat restoration in Ontario grain farms during her postdoctoral work under Dr. Claire Kremen at UBC. Teaching & Engagement: She teaches in the Master of Food and Resource Economics (MFRE) program, focusing on interdisciplinary approaches to sustainability challenges. Her work highlights the intersection of human behavior, policy design, and ecological outcomes, aiming to inform actionable conservation strategies.

• Machine Learning
• Medical Imaging
• Biomedical Engineering
• +۳ مورد دیگر

Dr. Cheng Ouyang is a Departmental Lecturer at the University of Oxford's Institute of Biomedical Engineering, part of the Department of Engineering Science. Affiliated with St. Peter's College, his research focuses on developing data-efficient, robust machine learning approaches for medical imaging and signal analysis. Key interests include domain generalization, few-/zero-shot learning, uncertainty modeling, and multimodal learning applied to medical data such as ultrasound and MRI. Prior to Oxford, he conducted postdoctoral research in cardiac imaging at Imperial College London, where he also earned his PhD in Computing. His work emphasizes practical medical applications, such as accelerating MRI reconstruction and enhancing ECG classification through multimodal techniques. Recent contributions include the CMRxRecon2024 dataset for cardiac MRI and federated learning approaches for low-dose CT denoising. His methods address challenges in generalizability, stability, and user interaction in clinical AI systems. Awards and recognitions are pending explicit mentions in the text. Dr. Ouyang's research spans foundational machine learning theory and applied biomedical engineering, with a focus on bridging gaps between algorithmic innovation and clinical utility. His lab collaborates across disciplines to advance medical imaging analysis and decision support systems.

Xihong Linمشاهده پروفایل

استاد

• Statistical Machine Learning and AI
• Statistical Genetics and Genomics
• Causal Inference and Mediation Analysis
• +۴ مورد دیگر

Xihong Lin is a Professor of Statistics at Harvard University and a Professor of Biostatistics at the Harvard T.H. Chan School of Public Health. She is a distinguished academic, holding membership in both the National Academy of Sciences and the National Academy of Medicine. Her research focuses on scalable statistical inference for big data, statistical machine learning, causal inference, and integrative data analysis, with applications in genomics, public health, and precision medicine. Lin’s work addresses challenges in analyzing large-scale genomic and multi-ancestry data, including methods for rare variant association testing, ancestry-adjusted sample analysis, and scalable computing frameworks. Her contributions span biobank studies (e.g., UK Biobank, TOPMed) and clinical applications in lung cancer, cardiovascular health, and smoking cessation. Her scientific awards reflect her leadership in statistical genetics and public health. Key research trends include leveraging single-cell sequencing for functional genomics, developing ensemble machine learning methods for health subtyping, and enhancing polygenic risk prediction across diverse populations. Lin’s methodologies prioritize interpretability and scalability, enabling impactful analyses of complex observational and genomic datasets. Awards: Member, National Academy of Sciences; Member, National Academy of Medicine Her grants and advising efforts focus on interdisciplinary collaborations, bridging statistics, AI, and domain sciences. Lin leads initiatives to improve genomic data management and ethical use of federated data (e.g., FADI framework). She is affiliated with labs advancing statistical genetics and cloud-based workflows (e.g., STAAR workflow).

Julia Morleyمشاهده پروفایل

دانشیار

• Social Impact Measurement
• Financial Reporting Regulation
• Organizational Decision-Making
• +۳ مورد دیگر

Dr. Julia Morley is an Associate Professor (Education) and MSc Programme Director in the Department of Accounting at the London School of Economics and Political Science (LSE). She specializes in social impact measurement, financial reporting regulation, and organizational decision-making. Her research explores biases in group decision-making and the emergence of norms in performance measurement, particularly in social sector organizations. She holds a PhD from LSE and previously worked in finance, media, and consulting before transitioning to academia. Education includes a degree in Economics from Pembroke College, Cambridge, an MSc in Philosophy of the Social Sciences from LSE, and a PhD in Accounting. Her work bridges sociology, social psychology, and philosophy, analyzing issues such as fair value accounting, social impact bonds, and regulatory outcomes in standard-setting bodies like the IASB. Dr. Morley has published extensively on topics including subgroup influence on boards, elite networks in social investment, and the ethical implications of social impact measurement. She has contributed to policy through roles like Commissioner on the Alternative Commission on Social Investment and governance positions with Capella House School and the Valence Mary Trust. Teaching spans undergraduate and postgraduate courses, including Accounting for Managerial Decision-Making and Corporate Responsibility and Sustainability. She leads MSc programs in Accounting and Finance, and Accounting, Organisations and Institutions.

Santiago Segarraمشاهده پروفایل

دانشیار

• Network Theory
• Machine Learning
• Graph Signal Processing
• +۵ مورد دیگر

Santiago Segarra is the W. M. Rice Trustee Associate Professor in the Department of Electrical and Computer Engineering at Rice University, with courtesy appointments in Computer Science and Statistics. He joined Rice in 2018 and collaborates with Microsoft Research since 2022. His expertise spans network theory, machine learning, graph signal processing, and optimization. Segarra earned his B.Sc. in Industrial Engineering from ITBA (2011), and M.S. and Ph.D. in Electrical and Systems Engineering from the University of Pennsylvania (2014-2016), followed by a postdoc at MIT (2016-2018). Research Focus: His work integrates algebraic topology, signal processing, and machine learning to analyze networked systems. Key areas include social/technological network clustering, graph-based data analysis, and applications in neuroscience and communication networks. Recent projects address fair graph learning, distributed GNN training, and network topology inference. Awards: Penn’s Wolf Award for Best Dissertation (2017), Argentine National Engineering Honors (2011), and ITBA’s Best Thesis Award (2011). Grants/Sponsors: Supported by NSF, ONR, and industry collaborations. Labs/Groups: Leads the Rice Wireless group and collaborates with Microsoft Research on applied network science. Advises students in interdisciplinary research combining theory and real-world applications.

Melissa Fergusonمشاهده پروفایل

استاد

• Implicit Social Cognition
• Social Psychology
• Cognitive Psychology
• +۳ مورد دیگر

Melissa J. Ferguson is a Professor of Psychology at Yale University since 2020, previously affiliated with Cornell University's Psychology Department (2002-2020). Her research focuses on implicit cognitive processes underpinning evaluation, goal-pursuit, self-control, and social behavior. She investigates impression updating, prejudice expression, and behavioral control mechanisms. Ph.D., Social Psychology, New York University (2002) As director of Yale's Implicit Social Cognition Lab, Ferguson explores how implicit evaluations evolve through reinterpretation and new evidence. Her work spans human-robot interaction, moral agency, and political bias dynamics, with funding from the National Science Foundation and National Institutes of Health. Recent publications analyze: (1) Robot competence evaluation mechanisms, (2) Implicit bias correction methods, (3) Legal decision-making patterns, and (4) Prejudice shifts during political events. She contributes to high-impact outlets like Psychological Science and PNAS . Best Paper Award, ISCON 2016

Andreas Markoulakisمشاهده پروفایل

مدرس

• Behavioral Economics
• Experimental Economics
• Microeconometrics
• +۳ مورد دیگر

Andreas Markoulakis is a Lecturer in the Department of Economics at the University of Warwick. He teaches modules including EC134: Topics in Applied Economics, EC229: Economics of Strategy, and EC138: Introduction to Environmental Economics. His research spans Behavioral Economics, Experimental Economics, and Microeconometrics, with a focus on energy policy, environmental economics, and pedagogical innovations. He has conducted studies on the One-Minute Paper (OMP) teaching intervention, analyzing its impact on student engagement and comprehension across seminar sessions. This intervention, implemented in EC138, employs paper-based feedback to assess student understanding and has shown discrepancies in responses when framing questions differently (e.g., addressing hesitant students explicitly). Education: PhD in Economics from the University of Kent Key research areas: Energy security policy, creative production incentives, and the application of experimental methods in economics Teaching responsibilities include advising international students and assessing graduate teaching assistants His work on the OMP intervention highlights lower comprehension rates when questions explicitly acknowledge student hesitation, suggesting potential barriers for non-native English speakers or shy students. He also explores long-term learning retention and the correlation between OMP feedback and academic performance. Future research directions include expanding the OMP analysis to broader student demographics and integrating assessment with formal exam performance data. Contributions to educational practices include publishing findings on small-group teaching effectiveness and fostering transparent communication between instructors and students. Ongoing projects involve studying semiconductor economics' impact on labor markets and AI's role in creative industries.

Daniel Gottliebمشاهده پروفایل

استاد

• Contract Theory
• Behavioral Economics
• Insurance Markets
• +۳ مورد دیگر

Professor Daniel Gottlieb holds the position of Professor of Managerial Economics and Strategy at the Department of Management, London School of Economics and Political Science (LSE). He joined LSE in 2019, after roles at Washington University’s Olin School of Business and the Wharton School of the University of Pennsylvania. Previously, he served as a visiting researcher at Harvard and Princeton Universities. He earned his PhD in Economics from MIT in 2009 and originates from Rio de Janeiro, Brazil. His research focuses on contract theory, behavioral economics, and insurance markets, integrating theoretical frameworks with experimental methods (field, lab) and observational data. Notable contributions include work on long-term contracting with time-inconsistent agents, stochastic impatience, and moral incentives in credit markets. His research has appeared in top journals such as the American Economic Review, Econometrica, and the Journal of Political Economy. He currently serves on editorial boards for leading economics journals, including the American Economic Review and the Journal of Economic Behavior and Organization. Key research interests include behavioral contract design, insurance market dynamics, and the interplay between time preferences and economic decision-making. His work bridges traditional economic theory with behavioral insights to address real-world inefficiencies in contractual and insurance frameworks. Prof. Gottlieb’s academic trajectory reflects a deep engagement with interdisciplinary research, combining rigorous theoretical analysis with empirical validation. His editorial roles and institutional affiliations highlight his influence in shaping contemporary economic discourse.