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Sharon WeldonView profile

Professor

• Healthcare Simulation
• Workforce Development
• Patient Safety
• +3 more

Professor Sharon Weldon serves as Professor of Healthcare Simulation and Workforce Development at the University of Greenwich's School of Health Sciences. She holds additional honorary positions at Imperial College London and Barts Health NHS Trust. Her academic background includes a BSc from University of Chester, MSc from London School of Hygiene & Tropical Medicine, and PhD from Imperial College London. Professor Weldon's research focuses on transformative simulation methodologies, healthcare communication, and workforce development. Her innovative approaches challenge conventional fidelity paradigms and generate new care systems. Research interests include sequential simulation design, team communication modeling, and policy evaluation through simulation. Her publications demonstrate strong focus on healthcare simulation methodologies, workforce education, and clinical communication systems. Recent work emphasizes cultural competence, ethics in healthcare, and innovative educational frameworks. Scientific Awards: Zenith Global Health Clinical Education and Research Award Outstanding Team of the Year Award (2022) University of Greenwich Outstanding Team of the Year Award (2023) Finalist for University Alliance Innovation Award (2023) Grants & Leadership: Secured funding from ESRC, Wellcome Trust, and AHRC. Co-leads Greenwich Learning and Simulation Centre (GLASC) and serves on ASPiH executive committee. Developed innovative Master's program in Interprofessional Healthcare Simulation. Professional Service: Associate Editor for International Journal of Healthcare Simulation; consultant to regulatory bodies influencing policy changes in simulation-based education.

• Statistics
• Biostatistics
• Mathematical Statistics
• +5 more

Ben Boukai is the Chancellor's Professor of Mathematical Sciences and Co-Director of the Biostatistics Ph.D. Program at Indiana University-Purdue University Indianapolis (IUPUI). He is affiliated with the School of Science and holds an office at LD 270L. His research focuses on mathematical statistics, particularly sequential analysis methodologies and nonlinear models with random effects, with applications in pharmacokinetics and econometrics. He also explores hierarchical Bayesian modeling and resampling techniques for parameter estimation. Education details are not explicitly provided, but his academic roles indicate advanced training in statistics. His work bridges theoretical developments with real-world applications, such as financial asset auctions and biomedical studies. No specific grants or awards are listed, though his leadership in the Biostatistics program highlights his academic influence. Contact information includes the email bboukai@iu.edu and phone (317) 274-6926.

Mónica F. BugalloView profile

Associate Professor

• Statistical Signal Processing
• Monte Carlo Methods
• Biomedicine
• +3 more

Mónica F. Bugallo is an Associate Professor in the Department of Electrical and Computer Engineering at Stony Brook University. She holds a B.S., M.S., and Ph.D. in Computer Science and Engineering from the University of A Coruña, Spain. Her research focuses on statistical signal processing, Monte Carlo methods, and their applications in biomedicine, sensor networks, and finance. She is also dedicated to STEM education and has initiated programs to engage underrepresented groups in engineering. Dr. Bugallo teaches digital communications, stochastic processes, and information theory, and has authored over 150 journal papers and two book chapters. She serves as a senior member of the IEEE, chairs the IEEE Signal Processing Society Education Committee, and has organized conferences and workshops. Her awards include the SUNY Chancellor's Award (2017), NSF CAREER Award (2010), and the IEEE Athanasios Papoulis Award (2011).

Guy LeducView profile

Professor

• Computer Networks
• Traffic Engineering
• Internet Coordinate Systems
• +7 more

Guy Leduc is a Full Professor in the Electrical Engineering and Computer Science Department of the University of Liège, where he has served as head of the Research Unit in Networking (RUN) since 1997. He also holds a part-time professorship at the University of Brussels (ULB) since 1994. His academic career spans over three decades with significant contributions to computer networking research and leadership in international academic organizations. University of Liège faculty member since 1983 Head of Research Unit in Networking (RUN) since 1997 Part-time professor at ULB since 1994 Former chairman of IFIP TC6 (2007-2012) Associate editor of IEEE Transactions on Network and Service Management Leduc received his electrical engineering degree summa cum laude in 1983 and his doctorate in computer science in 1991, both from the University of Liège. From 1983 to 1997, he was a researcher at FNRS (Belgian Fund for Scientific Research) and is now an Honorary FNRS Senior Research Associate. His career demonstrates a consistent progression from researcher to leadership positions in academic networking research. His research spans multiple domains within computer networking, with particular expertise in traffic engineering, Internet Coordinate Systems, overlays and peer-to-peer networks, mobile ad hoc networks, multimedia networking, congestion control, and autonomic/active/programmable networks. Earlier in his career, he made significant contributions to formal methods for network protocol design and verification, particularly with the ISO E-LOTOS language. His work bridges theoretical foundations with practical implementations, as evidenced by his involvement in numerous European research projects including mPlane, ANA, ECODE, and ResumeNet. Leduc's publication record shows consistent output through 2016, with recent work focusing on network measurement, machine learning applications for network security, and decentralized network algorithms. His research demonstrates an evolution from formal methods to contemporary networking challenges while maintaining a strong theoretical foundation. Bell Telephone Prize (1985) SWIFT Prize (1989) IBM-Belgium Prize (1991) Siemens Prize (1995) Best Paper Award at IFIP Networking 2006 Best Paper Award at IFIP Networking 2010 As head of RUN, Leduc has secured funding for numerous European projects including TOTEM, MOTION, and E-NEXT. His leadership extends to editorial positions with major journals and conference organization roles. The research unit he leads maintains strong industry connections while pursuing fundamental research questions in networking. His work on network measurement and security continues to be influential in both academic and practical networking contexts. The Research Unit in Networking (RUN) under Leduc's leadership has developed significant expertise in network measurement, traffic engineering, and autonomic networking. The group maintains strong connections with European research initiatives and has contributed to several open-source networking tools, most notably the TOTEM traffic engineering toolbox. RUN's research approach combines theoretical rigor with practical implementation, addressing both fundamental networking challenges and emerging application requirements.

Haileab HilafuView profile

Associate Professor

• Causal Inference
• Statistical Learning
• Machine Learning
• +3 more

Haileab Hilafu is an Associate Professor and James & Connie Vavalides Faculty Fellow in Business Analytics and Statistics at the University of Tennessee's Haslam College of Business. With a PhD from the University of Georgia, his research integrates causal inference, statistical learning, and dimension reduction methods, applied to healthcare, retail, and environmental science. Research areas include: causal inference in hospital operations, machine learning for environmental hot-spot detection, high-dimensional regression, and cross-cultural retail analytics. Applications span healthcare quality improvement (readmission risk, surgical outcomes), precision agriculture (nitrous oxide prediction), and supply chain management. Publications bridge statistical methodology (sufficient dimension reduction, sparse regression) with domain applications in healthcare operations, environmental science, and retail analytics. Recent work emphasizes machine learning integration in empirical studies. Awards: ASCC Award (2022) MSBA Teaching Award (2018) Best Senior Student, UGA Statistics (2014) Teaching Assistant Award (2012)

• Natural Language Processing
• Computer Vision
• Deep Learning
• +3 more

Dr. Vimal Kumar Kumar serves as a Research Fellow at the University of Limerick's Department of Computer Science and Information Systems, affiliated with Lero – the Irish Software Research Centre. His academic career includes prior faculty positions as Assistant Professor at Jaypee University of Information Technology (2009-2022) and Lecturer at SRM Institute of Science and Technology (2007-2009), followed by a Postdoctoral Researcher role at University College Dublin (2022-2023). His educational background includes: PhD in A Novel sense based Hindi to Tamil machine translation system from Jaypee Institute of Information Technology (2019) Masters from Anna University Chennai (2005-2007) Bachelor from PSG College of Technology (2000-2004) Dr. Kumar's research centers on Natural Language Processing with specialized focus on machine translation for Indian languages (Hindi, Tamil), text summarization, and sentiment analysis. He extends this work into computer vision applications for medical imaging and activity recognition, plus deep learning implementations for IoT-based agricultural monitoring. His methodology frequently addresses challenges in low-resource language processing and develops optimized neural network architectures for mobile deployment. Analysis of his publication trends reveals consistent interdisciplinary innovation, particularly in healthcare applications (retinal disease detection), agricultural technology (crop yield prediction), and social media analytics. His work demonstrates strong emphasis on practical AI solutions for resource-constrained environments and underrepresented linguistic communities, often leveraging generative models and contextual embeddings. Dr. Kumar actively contributes to Lero's research initiatives, aligning his technical work with UN Sustainable Development Goals through software solutions addressing global challenges in health, food security, and digital inclusion.

• Statistical Models for High-Dimensional Data
• Bioinformatics
• Infectious Disease Modeling
• +5 more

Dr. Sujay Datta is a Professor and Graduate Academic Advisor in the Department of Statistics at the University of Akron. His research focuses on developing statistical models for high-dimensional data, with applications in bioinformatics, infectious diseases, and cancer research. Additional interests include sequential/multistage designs, Bayesian inference, and machine learning techniques. He holds a Ph.D. in Statistics from the University of Connecticut, Storrs (1995).

Moinak BhaduriView profile

Assistant Professor

• Change-Point Detection
• Stochastic Processes
• Repairable Systems
• +2 more

Dr. Moinak Bhaduri is an Assistant Professor in Mathematical Sciences at Bentley University. His research applies stochastic modeling to change-point detection, spatio-temporal processes, and repairable systems. He holds a Ph.D. from the University of Nevada, Las Vegas. His interdisciplinary work spans finance (market spillovers), environmental science (hurricane interactions), legal analytics (defamation trials), and social dynamics (immigration networks). Publications leverage methods like hidden Markov chains and recurrence rate ratios. No awards or student mentorship details are provided.

Persi DiaconisView profile

Professor

• probability theory
• combinatorics
• group theory
• +7 more

Persi Diaconis is the Mary V. Sunseri Professor of Statistics and Professor of Mathematics at Stanford University, with a joint appointment in the Symbolic Systems Program. He has held these positions since 1998 and previously served as a professor at Harvard University and Cornell University. His work bridges mathematics and statistics with applications across scientific computing and data analysis. Diaconis is renowned for his research in probability theory, combinatorics, and group theory, with a specialty in rates of convergence of Markov chains . His current research focuses on adapting mathematical developments to practical applications in large real-world simulations. He has opened up new areas in Markov chain theory including rates of convergence to quasi-stationarity and the study of "features" in chains. His work extends to statistical analysis of graph and network data, generalizations of de Finetti's notion of exchangeability, and connections between statistics and graph limit theory. An analysis of his recent publications reveals a strong focus on Markov chain theory, combinatorial probability, and statistical applications. His work demonstrates interdisciplinary connections between pure mathematics, theoretical statistics, and practical computing problems. Diaconis frequently collaborates with researchers like Sourav Chatterjee, Susan Holmes, and Jason Fulman on problems ranging from card shuffling to network analysis. Honorary doctorate from University of St Andrews Mary V. Sunseri Professorship at Stanford University Fellow of the Center for Advanced Study in the Behavioral Sciences (1999-2000) Diaconis has advised numerous doctoral students including Michael Howes, Zhiqi Li, Andrew Lin, and Nathan Tung. His research has been supported by various grants that enable his work on Markov chains, combinatorial probability, and statistical theory. He has developed important connections between theoretical mathematics and practical statistical applications, influencing both academic research and real-world problem solving. While not explicitly mentioned as leading a specific lab, Diaconis collaborates extensively with researchers across Stanford and globally. His work with the Symbolic Systems Program connects mathematics with cognitive science and computer science. His research group focuses on probabilistic and combinatorial problems with applications to data science and scientific computing.

Lihua LeiView profile

Assistant Professor

• Econometrics
• Causal Inference
• Conformal Inference
• +4 more

Lihua Lei is an Assistant Professor of Economics at the Stanford Graduate School of Business (GSB) with a courtesy appointment as Assistant Professor of Statistics. They are also a Faculty Fellow at the Institute for Economic Policy Research (SIEPR). Dr. Lei completed their Ph.D. in Statistics at UC Berkeley under the supervision of Professors Peter Bickel and Michael Jordan, followed by postdoctoral research in the Statistics Department at Stanford University with Professor Emmanuel Candès. Dr. Lei's research focuses on the intersection of statistics and economics, with particular expertise in: Econometrics Causal Inference Conformal Inference Multiple Hypothesis Testing Network Analysis High Dimensional Statistical Inference Optimization Their publication record shows a strong trajectory in developing statistically rigorous methods with practical applications. Recent work demonstrates increasing focus on conformal inference techniques, causal effect estimation, and high-dimensional statistical problems. Dr. Lei's research bridges theoretical statistics with real-world economic applications, particularly in developing methods with formal statistical guarantees for policy evaluation and decision making. Dr. Lei has received the 2015-2016 Outstanding Graduate Student Instructor Award and has taught theoretical statistics courses at UC Berkeley, including STAT 210A and STAT 210B with Professors Michael Jordan and Martin Wainwright. As an active methodological researcher, Dr. Lei has developed several widely used R packages including adaptMT, cfcausal, dbh, and transferUQ, which implement their methodological contributions for broader application by the research community. Their ongoing work continues to influence both statistical theory and economic applications through innovative methodological developments.

Prashant Nalini VasudevanView profile

Assistant Professor

• Cryptography
• Complexity Theory
• Theoretical Computer Science
• +2 more

Prashant Nalini Vasudevan serves as an Assistant Professor and NUS Presidential Young Professor in the Department of Computer Science at the National University of Singapore's Faculty of Computing. His research focuses on the theoretical foundations of cryptography and complexity theory, with particular emphasis on identifying computational hardness assumptions that can support cryptographic constructions. Dr. Vasudevan's research interests span Cryptography , Complexity Theory , and Theoretical Computer Science , with specific focus on statistical zero-knowledge proofs, fine-grained cryptographic primitives, and the intersection of cryptography with data privacy regulations. His work explores how computational hardness can be leveraged to build secure systems while addressing modern privacy concerns such as the right to be forgotten. Analysis of his recent publications reveals a strong trend toward statistical zero-knowledge systems, batch verification techniques, and formal approaches to data deletion. His research bridges theoretical computer science with practical privacy concerns, particularly in the context of machine learning systems and regulatory compliance. The work demonstrates increasing sophistication in cryptographic protocols while maintaining rigorous security proofs. NUS Presidential Young Professorship (2021-2026): Computational Hardness Assumptions and the Foundations of Cryptography NRF Fellowship (2022-2027): Fine-Grained Cryptography Dr. Vasudevan actively mentors multiple PhD students including Haoxing Lin, Yunqi Li, and Kel Zin Tan, along with postdoctoral researcher Rohit Chatterjee. His research group focuses on theoretical cryptography with applications to privacy-preserving systems. Current projects include developing new cryptographic primitives based on fine-grained hardness assumptions and exploring the theoretical foundations of data deletion in machine learning contexts.

Nick HarveyView profile

Professor

• Theory of Machine Learning
• Randomized Algorithms
• Convex Optimization
• +3 more

Dr. Nick Harvey is a Full Professor in the Department of Computer Science at the University of British Columbia (UBC), with an affiliation to the Department of Mathematics. He leads the Mathematics of Information, Learning and Data (MILD) research cluster. His research focuses on randomized algorithms, machine learning theory, and convex optimization, with notable contributions to algorithm design and analysis. Harvey has taught courses such as Randomized Algorithms (CPSC 436R/536N), Theory of Computing (CPSC 421), and Advanced Algorithms (CPSC 420). He has received prestigious awards including the NeurIPS 2018 Best Paper Award and the CS-Can/Info-Can Outstanding Young Research Prize (2014). His work emphasizes probabilistic techniques in algorithms, including applications in graph theory, streaming algorithms, and privacy-preserving methods. Harvey supervises graduate and undergraduate students, contributing to their academic and professional development. His research group explores cutting-edge topics in algorithmic theory and machine learning, with ongoing projects in algorithmic fairness, optimization, and data science. Key achievements include advancing the Lovász Local Lemma, developing efficient graph sparsification techniques, and pioneering work on the complexity of matrix completion. Harvey's teaching philosophy prioritizes clarity and rigor, reflected in his award-winning pedagogy. He actively engages in academic service, including curriculum development and conference organization.

Dana YangView profile

Assistant Professor

• Statistical Inference
• Machine Learning
• Computational Complexity
• +4 more

Dana Yang is an Assistant Professor in the Department of Statistics and Data Science at Cornell University. She joined Cornell in Spring 2022 after completing a Simons-Berkeley fellowship focusing on computational complexity of statistical inference at UC Berkeley. Previously, she was a postdoctoral associate at Duke University’s Fuqua School of Business. Her education includes a B.S. in Mathematics from Tsinghua University and M.A./Ph.D. in Statistics from Yale University. Yang’s research interests span statistical inference, machine learning, and computational complexity. She focuses on problems involving planted structures (e.g., matching recovery), privacy-preserving algorithms, high-dimensional statistics, and graph theory. Her work bridges theoretical foundations with practical applications in data science. Her recent articles explore phase transitions in statistical recovery, private convex optimization, and algorithmic fairness. Notable contributions include studies on planted matching problems, community detection efficiency, and secure sequential learning protocols. No scientific awards were explicitly mentioned in the provided materials. Her advising history and grant involvement remain unspecified in the current data. Dana Yang is affiliated with Cornell’s Comstock Hall facility, though specific lab or team affiliations were not detailed.

Peter FrazierView profile

Associate Professor

• Sequential Decision-Making Under Uncertainty
• Optimal Learning
• Bayesian Optimization
• +10 more

Peter Frazier is the Eleanor and Howard Morgan Professor of Engineering at Cornell University, holding a joint appointment in the School of Operations Research and Information Engineering (ORIE) and serving as a Senior Staff Applied Scientist at Uber. He specializes in sequential decision-making under uncertainty, optimal learning, and Bayesian optimization, with applications in healthcare, e-commerce, and transportation. Frazier earned his B.S. from Caltech (2000) and his M.A. and Ph.D. from Princeton University (2007–2009). His research bridges academia and industry, addressing challenges like Uber’s pricing systems and pandemic response strategies at Cornell. He has received prestigious awards, including the NSF CAREER Award and AFOSR Young Investigator Award, and teaches courses in simulation, optimization, and data science. Education: B.S. in Physics/Engineering (Caltech, 2000); M.A./Ph.D. in Operations Research and Financial Engineering (Princeton, 2007–2009). Research Focus: Optimal learning methods, Bayesian optimization, sequential decision-making, and their applications in simulation, healthcare, and e-commerce. Awards: NSF CAREER Award (2012), AFOSR Young Investigator Award (2010), Best Paper Award at ACM EC (2014). Industry Roles: Senior Staff Applied Scientist at Uber (2022–present), leading pricing and data science teams; previously involved in Uber’s surge pricing and Kinetics projects. His work emphasizes real-world impact, such as designing Cornell’s pandemic response strategy using surveillance testing and contributing to Uber’s pricing algorithms. Frazier’s research also explores applications in materials science, drug discovery, and climate modeling, leveraging Bayesian methods and machine learning.

Nan YeView profile

Senior Lecturer

• Machine Learning
• Statistics
• Optimization
• +4 more

Dr. Nan Ye is a Senior Lecturer in Statistics and Data Science at the University of Queensland's School of Mathematics and Physics. His research focuses on machine learning, statistics, and optimization, with contributions to sequential decision making, weakly supervised learning, and probabilistic graphical models. He holds a PhD in Computer Science from the National University of Singapore (NUS) and double first-class honors in Computer Science and Applied Mathematics from NUS. Previously, he held postdoctoral positions at QUT, UC Berkeley, and NUS. Dr. Ye teaches advanced courses such as STAT3007/7007 Deep Learning, covering topics from foundational machine learning to state-of-the-art deep learning architectures and applications. His work has been published in top venues like NeurIPS, ICML, and UAI, earning awards including the IJCAI-JAIR Best Paper Prize (2022) and UAI Best Student Paper Award (2014). His research interests span theoretical and applied machine learning, including reinforcement learning, optimization algorithms, and their applications in fields like healthcare and environmental science. He actively supervises students in these areas and collaborates on interdisciplinary projects. Dr. Ye's academic profile includes extensive contributions to open-source tools and educational materials, reflecting his commitment to advancing both research and pedagogy in data science.