جست‌وجوی استاد و پژوهشگر

Will Fithianمشاهده پروفایل

دانشیار

• Theoretical Statistics
• Applied Statistics
• Post-Selection Inference
• +۳ مورد دیگر

Will Fithian is an Associate Professor in the Department of Statistics at the University of California, Berkeley. He holds a position in the College of Letters & Science, specializing in theoretical and applied statistics. His research focuses on post-selection inference, scalable algorithms for big data, high-dimensional data analysis, and ecological statistics. Fithian has taught courses such as Theoretical Statistics (Stat 210A), Forecasting, and industry-relevant statistical methods. His work bridges statistical theory with applications in fields like genomics, ecology, and machine learning. Education and Career: While specific educational details are not explicitly provided, his academic rank and research focus suggest advanced training in statistics. He previously taught at Stanford University and has held roles such as Assistant Professor before his current position at Berkeley. Research Interests: His interests include developing robust statistical methods for handling modern data challenges, including false discovery rate control, selective inference, and computational efficiency in high-dimensional settings. He collaborates across disciplines, applying statistical tools to ecological and biomedical problems. Awards: Fithian received the Teaching Award from the Berkeley Statistics Department in 2012 and the Centennial Teaching Award (University-wide) in 2015, reflecting his dedication to pedagogy. His research contributions have been recognized through publications in top journals and conferences. Teaching and Service: He leads advanced courses like Stat 210A, a core PhD-level theoretical statistics course. His teaching emphasizes foundational concepts while addressing contemporary challenges. He also contributes to Berkeley’s Industry Alliance Program, fostering academic-industry partnerships.

Duygu Uygun Tuncمشاهده پروفایل

استادیار

• Philosophy of Science
• Epistemology
• Philosophy of Mind
• +۳ مورد دیگر

Duygu Uygun Tunc is a Collegiate Assistant Professor in the Department of Philosophy at the University of Chicago, where she has been teaching since September 2023. She is also affiliated with the Society of Fellows and holds the position of Harper-Schmidt Fellow. Her academic journey includes a dual PhD from Heidelberg University in Germany and the University of Helsinki in Finland, which she completed in October 2020 with her dissertation titled 'Communication and the Origins of Personhood'. Her research spans multiple dimensions of epistemic value in scientific research, investigating what makes individuals good researchers, groups good scientific collectives, epistemic systems good scientific communities, and research procedures good empirical studies. She works extensively on intellectual virtues in science, scientific communities, experimental methodology (including testability, theory choice, and underdetermination), and applied philosophy of science (addressing the replicability crisis, science policy, and scientific norms). A central question guiding her work is how the social nature of scientific inquiry can best be utilized to advance scientific knowledge. Her publications from 2019-2024 demonstrate a consistent focus on the intersections of philosophy of science, epistemology, and values in science. Her work often examines distributed cognition in scientific communities, the nature of scientific expertise in contemporary research environments, and the relationship between scientific methodology and social values. A notable trend across her publications is the exploration of how scientific knowledge production has become increasingly social and collaborative, requiring new conceptual frameworks to understand epistemic responsibility in team science. Marie Curie Actions Fellowship for 'Extended Scientific Virtue' project Dr. Uygun Tunc currently teaches 'Science and Values' (PHIL 23404/33404) at the University of Chicago, examining debates about the value-free ideal of science and the relationship between scientific objectivity and social values. She is actively working on a series of articles on values in science and her first book manuscript on scientific expertise, which develops a novel conception addressing distributed epistemic labor and technological extension of epistemic processes while maintaining notions of epistemic responsibility. She maintains a blog on the epistemology of science where she explores topics like trust and criticism in scientific communities.

Chun Wangمشاهده پروفایل

استاد

• Quantitative Research Methods
• Item Response Theory (IRT)
• Computerized Adaptive Testing (CAT)
• +۳ مورد دیگر

Chun Wang is a Professor in the Department of Measurement & Statistics at the University of Washington's College of Education. His research focuses on advancing quantitative methods in educational and psychological measurement, with expertise in item response theory (IRT), computerized adaptive testing (CAT), and cognitive diagnostic modeling. He holds affiliate faculty status at the Center for Statistics and the Social Sciences. Education: B.S. in Psychology, Peking University (China) M.S. and Ph.D. in Quantitative Psychology, University of Illinois at Urbana-Champaign Research Interests: Development and validation of multidimensional/mixture IRT models Computerized adaptive testing optimization Cognitive diagnostic modeling for classroom applications Health measurement and bias detection in assessments Recent Trends in Articles: His work bridges statistical innovation with practical applications, emphasizing fairness and efficiency in assessments. Notable areas include: - Healthcare : Predictive models for discharge disposition and functional outcomes - Education Technology : Adaptive learning systems and diagnostic feedback mechanisms - Methodology : Bias detection (DIF), Bayesian estimation techniques, and computational efficiency Scientific Awards : Includes the Anne Anastasi Award (2020), McKnight Presidential Fellowship (2017), and multiple best reviewer recognitions from leading psychometrics journals. Advising & Grants: Supervised students including Xiao J., Zhu R., and Lu J.* in high-impact projects. Co-led a $10M NIH grant (AmplifyGAIN Center) to advance Gen AI in STEM education. Published extensively in Psychometrika , Journal of Educational and Behavioral Statistics , and other top outlets. Labs/Teams: Directs the Pmetrics Lab ( https://sites.uw.edu/pmetrics/ ), collaborating on cutting-edge measurement tools for education and healthcare.

Naratip Santitissadeekornمشاهده پروفایل

مدرس ارشد

• Data Assimilation
• Inverse Problems
• Geophysical Fluid Dynamics
• +۵ مورد دیگر

Naratip Santitissadeekorn is a Senior Lecturer in Data Assimilation at the School of Mathematics and Physics, University of Surrey, where he is affiliated with the Mathematics at the Interface Group. His work bridges mathematics, data science, and real-world applications in urban planning, crime analysis, and geophysical fluid dynamics. Dr. Santitissadeekorn received his PhD from Clarkson University in 2008, with a dissertation titled "Transport Analysis and Motion Estimation of Dynamical Systems of Time-Series data." His doctoral research was supervised by Professor Erik Bollt. Following his PhD, he completed two significant postdoctoral positions: from 2008-2011 at the University of New South Wales, Sydney, Australia, working with Professor Gary Froyland on numerical techniques for finite-time Lagrangian coherent set identification, with applications to delimiting the polar vortex and Agulhas rings; and from 2011-2014 at the University of North Carolina-Chapel Hill, working with Professor Chris Jones on data assimilation projects. Dr. Santitissadeekorn's research focuses on inverse problems and data assimilation in geophysical fluid dynamics, the applications of Lagrangian Coherent Structures (LCS), and computational ergodic theory. His work combines theoretical mathematics with practical applications, particularly in urban growth modeling and crime analysis. He has developed innovative methods for identifying coherent structures in fluid flows, estimating transition probabilities from spatiotemporal data, and creating data-driven frameworks for urban expansion scenarios. His research demonstrates how mathematical techniques can be applied to solve real-world problems in environmental science, urban planning, and public safety. An analysis of Dr. Santitissadeekorn's recent publications (2020-2023) reveals a strong focus on urban expansion modeling and network analysis. His work on urban growth has evolved from basic cellular automata models to sophisticated frameworks that manage uncertainty through parameter clustering and growth mode identification. His research on Hawkes processes has advanced ensemble-based filtering techniques for analyzing count data in large networks. These publications demonstrate a consistent pattern of applying mathematical rigor to complex spatiotemporal phenomena, with increasing emphasis on data-driven approaches and practical applications. Dr. Santitissadeekorn has made significant contributions to data assimilation methods, particularly through the development of the extended Poisson-Kalman filter (ExPKF) for urban crime modeling. His teaching includes courses in Algebra and Bayesian Statistics, reflecting his expertise in both theoretical and applied mathematics. While specific awards are not mentioned in the available information, his extensive publication record in high-impact journals demonstrates recognition within his field. Dr. Santitissadeekorn's research has practical implications for urban planning and law enforcement. His work on urban expansion models helps planners understand different growth trajectories, while his crime modeling research contributes to improved police patrolling strategies. His interdisciplinary approach, combining mathematics, computer science, and domain-specific knowledge, positions him at the forefront of applying data science to societal challenges.

Shrimai Prabhumoyeمشاهده پروفایل

استادیار مدعو

• Large Language Models (LLMs)
• Machine Learning
• Natural Language Processing (NLP)
• +۴ مورد دیگر

Shrimai Prabhumoye is a Senior Research Scientist at NVIDIA's Applied Deep Learning Research group and an Adjunct Assistant Professor at Boston University. Her research focuses on advancing large language models (LLMs), particularly in enhancing their reasoning capabilities and ensuring safety through toxicity and bias reduction. She is a core contributor to the Nemotron family of LLMs, including the state-of-the-art Nemotron4-15B. Previously, she earned her PhD from Carnegie Mellon University (2021), advised by Prof. Ruslan Salakhutdinov and Prof. Alan W. Black, with a thesis on controllable text generation and computational ethics in NLP. Education: PhD in Machine Learning (CMU, 2021), MS in Language Technologies (CMU, 2017), Undergraduate at National Institute of Technology Karnataka, India. Research Interests: LLMs, controllable generation, ethical AI, bias mitigation, data curation, and model safety. Her work emphasizes rigorous evaluation frameworks and practical applications of NLP systems. Publications span key venues like EACL, ACL, NAACL, and EMNLP, with notable contributions to bias testing, toxicity control, and model scalability. She has been recognized as a Rising Star finalist in the VentureBeat Women in AI Awards 2022 and has led teams in competitions like the Alexa Prize. Labs/Teams: Part of NVIDIA's Applied Deep Learning Research Group and contributed to CMU's Computational Ethics for NLP initiatives. Active in mentoring students on projects like politeness transfer and dataset creation.

Nikolas P Lemosمشاهده پروفایل

استاد

• Forensic Toxicology
• Cannabis Toxicology
• Post-mortem Toxicology
• +۵ مورد دیگر

Professor Nikolas P Lemos is a chartered forensic scientist and director of the Cameron Forensic Medical Sciences unit at Queen Mary University of London's William Harvey Research Institute. He holds academic affiliations with institutions in San Francisco, St Louis, and Athens, focusing on forensic toxicology, postmortem analysis, and drug-facilitated crimes. His career includes roles as Chief Forensic Toxicologist in San Francisco and Head of Forensic Toxicology at St George's University of London. His research emphasizes cannabinoids in forensic contexts, drug analysis in keratinous matrices (nails/fingernails), and forensic laboratory accreditation standards. He has co-authored over 85 peer-reviewed publications, including landmark studies on cannabinoid detection in postmortem toxicology and fingernail analysis. Professor Lemos is actively engaged in professional organizations, serving as Chair of the Toxicology Section of the American Academy of Forensic Sciences. He has provided expert testimony in over 325 criminal and civil cases globally, addressing forensic toxicology matters related to drug-facilitated crimes, driving under influence cases, and postmortem redistribution phenomena. Key achievements include designing forensic laboratory facilities in San Francisco, coordinating NAME/ABFT accreditations, and receiving a Congressional Proclamation of Achievement (2005) for contributions to community safety. His work bridges forensic science with legal systems, particularly in drug policy and toxicological evidence interpretation.

Joshua T. Vogelsteinمشاهده پروفایل

دانشیار

• Machine Learning
• Neuroscience
• Network Science
• +۴ مورد دیگر

Dr. Joshua T. Vogelstein is an Associate Professor in the Department of Biomedical Engineering at Johns Hopkins University, holding joint appointments in Biostatistics, Applied Mathematics & Statistics, Neuroscience, and Computer Science. He leads the NeuroData lab, focusing on big data science, machine learning, and connectomics. Education: PhD and MSE in Neuroscience and Applied Mathematics from Johns Hopkins (2009), BS in Biomedical Engineering from Washington University (2002). Notable achievements include co-founding the Open Connectome Project (acquired by APL) and Gigantum (acquired by NVIDIA). Recognized with the NSF CAREER Award (2020), F1000 Prime (2014), and multiple Johns Hopkins Discovery Awards. Research emphasizes statistical connectomics, network science, and applying AI to biomedical challenges. Key contributions include mapping the first insect brain connectome (Science 2023) and developing open-source tools like CloudReg and BrainLine. Collaborates with Microsoft Research and industry partners, co-founding ventures like Global Domain Partners and Mind-X. Advised over 60 trainees, teaches machine learning and data science. Promotes open science through NeuroData's ecosystem of tools and data. Current work explores organoid intelligence, prospective learning, and neural network dynamics.

Xianyang Zhangمشاهده پروفایل

استاد

• High-dimensional statistics
• Functional data analysis
• Time series analysis
• +۸ مورد دیگر

Xianyang Zhang is a Professor in the Department of Statistics at Texas A&M University, affiliated with the College of Arts & Sciences. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (2013) and a B.S. from the University of Science & Technology of China (2008). His research focuses on high-dimensional statistics, functional data analysis, kernel methods, and genomics, supported by grants from NIH, NSF, and Texas A&M. Education: Ph.D., Statistics, University of Illinois at Urbana-Champaign, 2013 B.S., Statistics, University of Science & Technology of China, 2008 Research Interests: Xianyang Zhang develops statistical theories and methodologies for complex data structures, including high-dimensional inference, kernel-based testing, change-point detection, and microbiome analysis. His work bridges computational and theoretical statistics, addressing challenges in genomics, omics-wide studies, and spatial statistics. Key Contributions: Developed KDist , a package for kernel and distance-based statistical inference Authored fastcpd for efficient change-point detection Advanced covariate-adaptive FDR control methods for omics studies Contributed to microbiome analysis tools like MicrobiomeStat and LinDA Advising & Grants: Advises multiple Ph.D. students in statistics and interdisciplinary projects Recipient of NIH and NSF grants for high-dimensional statistical research Collaborates with institutions like Mayo Clinic and Chinese University of Hong Kong Labs/Teams: Leads research groups focused on statistical methodology development, software implementation, and applications in computational biology and genomics.

Bin Chenمشاهده پروفایل

دانشیار

• Econometrics
• Financial Econometrics
• Time Series Analysis
• +۴ مورد دیگر

Bin Chen is an Associate Professor of Economics at the University of Rochester, affiliated with the School of Arts & Sciences. He holds a Ph.D. in Economics from Cornell University (2007), an M.A. from Xiamen University (2001), and a B.A. from Xiamen University (1998). His research focuses on econometric theory and financial econometrics, particularly in testing structural changes, diffusion models, and nonparametric methods. He has published in top journals like Econometrica , Journal of Econometrics , and Econometric Theory . Key awards include the PEPR Grant (2011–2013) and the Chinese Government Award for Outstanding Students Abroad (2005). He has advised graduate students such as Liquan Huang and served on committees for Tak Wai Chau (Economics) and Zhen Chen (Biostatistics). His computational expertise includes MATLAB, GAUSS, and STATA, with access to financial databases like CRSP and NYSE TAQ. Chen has presented at conferences worldwide, including the Econometric Society World Congress and symposia in Seoul and Xiamen. He is a member of the Econometric Society and has reviewed for journals such as Journal of Econometrics and Quantitative Economics .

Javier Hidalgoمشاهده پروفایل

استاد

• Econometrics
• Semiparametric Estimation
• Time Series Analysis
• +۴ مورد دیگر

Professor Javier Hidalgo is a Professor of Econometrics at the Department of Economics, London School of Economics and Political Science (LSE). He holds roles as Co-Director of the STICERD Econometrics Programme and has extensive editorial experience with journals such as Journal of Econometrics and Econometric Theory . His expertise spans econometric theory, with a focus on semiparametric estimation, long-memory processes, and structural change models. Education: PhD in Economics from LSE (1990), M.Sc. in Econometrics and Mathematical Economics (1985), and a Licenciatura in Mathematics from Universidad Complutense de Madrid (1982). Research Interests: Includes semiparametric estimation, dependence in economic analysis, diagnostic testing, and long-memory processes. His work emphasizes methodological advancements in econometric analysis of nonstationary and dependent data. Grants & Awards: Secured ESRC grants totaling over £400k for research on nonstationary economic data and long-memory processes. His 2015 article received the prestigious Tjalling C. Koopmans Econometric Theory Prize. Teaching & Supervision: Teaches advanced econometric courses including EC309 and EC518. Supervised 3 PhD students and served as Program Director for the M.Sc. in Econometrics and Mathematical Economics (2004–2019). Labs/Teams: Active in STICERD’s Econometrics Programme, fostering collaborative research in time series and econometric theory.

• Cybersecurity
• Network Security
• IoT Security
• +۵ مورد دیگر

Prof. Joaquin GARCIA ALFARO is a Professor at Telecom SudParis, affiliated with the SCN department. His research focuses on cybersecurity, network security, quantum computing applications, and resilience engineering in cyber-physical systems. He has contributed to advancements in intrusion detection systems, blockchain integration in cellular networks, and privacy-preserving frameworks for IoT and healthcare. University: Telecom SudParis Key Research Areas: Cybersecurity, Quantum Computing, IoT Security, Resilience Engineering Labs: SAMOVAR laboratory His work emphasizes practical solutions for real-world challenges, including secure data provenance, digital twin implementations, and energy-efficient edge computing. Recent research explores quantum-resistant protocols and collaborative drone systems.

Rocco Servedioمشاهده پروفایل

استاد

• Computational Complexity Theory
• Computational Learning Theory
• Property Testing
• +۳ مورد دیگر

Rocco Servedio is a Professor in the Department of Computer Science at Columbia University, where he leads research in theoretical computer science with a focus on computational complexity theory, learning theory, and the role of randomness in computation. He previously served as Chair of the Computer Science Department from 2018 to 2021. He holds a Ph.D., MS, and AB in Mathematics from Harvard University. His research interests include property testing, computational learning theory, and algorithmic lower bounds. He has contributed to foundational work in areas like junta testing, trace reconstruction, and convexity testing. Servedio has held leadership roles in major conferences such as STOC, CCC, and COLT, and has mentored students through courses like Unconditional Lower Bounds and Derandomization . Education: Ph.D. in Computer Science, Harvard University MS in Computer Science, Harvard University AB in Mathematics, Harvard University His research bridges theoretical computer science and applied mathematics, with recent work exploring the intersection of Gaussian processes, convex geometry, and algorithmic efficiency. Servedio's contributions to the field are exemplified through his involvement in high-impact conferences and his leadership in advancing fundamental computational theories.

• Hydraulic Engineering
• Fluid Mechanics
• Water Resources Management
• +۳ مورد دیگر

Michael C. Johnson is a Professor in the Department of Civil and Environmental Engineering at Utah State University (USU), affiliated with the Utah Water Research Laboratory (UWRL). He holds a PhD in Fluid Mechanics and Hydraulics from USU (1996), with research focused on hydraulic modeling, flow meter performance, valve analysis, and dam/spillway design. His work bridges physical and numerical methods, addressing challenges in water resources engineering and infrastructure. Education: PhD, Fluid Mechanics and Hydraulics (Groundwater, Hydrology, Water Resources), Utah State University, 1996 MS, Fluid Mechanics and Hydraulics, Utah State University, 1994 BS, Civil and Environmental Engineering, Utah State University, 1992 Research Interests: Johnson specializes in hydraulic structures, physical and computational fluid dynamics, flow meter calibration, and energy dissipation systems. His work emphasizes practical applications like spillway design, low-head dam safety, and turbine testing. Notable projects include the Lower Baker Dam Spillway model and Archimedes Turbine evaluations. His research often combines laboratory experiments with CFD simulations to optimize water infrastructure performance. Recent Articles Trends: His publications (2019–2025) highlight advancements in CFD modeling for pressure recovery, flow meter accuracy under disturbed conditions, and valve performance optimization. Key themes include improving meter testing protocols, mitigating turbulence effects, and enhancing hydraulic infrastructure resilience. Awards and Recognition: Outstanding Graduate Mentor, 2020 Multiple AWWA Best Paper Awards (2007, 2010, 2012) Outstanding Teacher Awards (2002–2003) Advising & Labs: Johnson has mentored over 25 graduate students, guiding research in areas like valve performance, spillway hydraulics, and meter calibration. He leads the UWRL’s hydraulics team, overseeing projects such as the Orville Dam Spillway model and large-scale physical modeling initiatives. His lab facilities support both experimental and computational approaches to water engineering challenges.

• Software Engineering
• Cloud Computing
• Microservices Architecture
• +۳ مورد دیگر

Hua Ming is an Associate Professor of Computer Science and Associate Director of Academic Programs at the College of Innovation and Technology, The University of Michigan-Flint. His research focuses on scalable regression testing for high-performance microservices in cloud environments. Email: huaming@umich.edu Research Interests : Cloud Computing Microservices Architecture High Performance Computing Regression Testing Grants : National Science Foundation Collaborative Research Grant ($400,000.00) for "Scalable Regression Testing for High Performance Microservices in the Cloud" (2025-2029), Principal Investigator Current Courses : CSC 801

Christoph Heinzlمشاهده پروفایل

استاد

• Scientific Visualization
• Visual Analytics
• Immersive Analytics
• +۳ مورد دیگر

Christoph Heinzl is a Professor of Cognitive Sensor Systems at the University of Passau since September 2022. He leads the Knowledge-based Image Processing research group at the Fraunhofer Development Center X-ray Technology (EZRT) . His academic background includes a PhD in Informatics and a Habilitation in 2022 , both from TU Wien . Research Focus: Scientific visualization, visual analytics, immersive analytics, virtual/augmented reality, machine learning, and X-ray computed tomography (XCT). Key Trends: Development of novel visualization techniques for complex volumetric data (e.g., dynamic volume lines, visual coherence frameworks), parameter space analysis, and cross-virtuality collaboration tools. Applications: Aerospace component inspection, defect analysis in composites (CFRP, GFRP), porosity quantification, and 4DCT time-series exploration.