جست‌وجوی استاد و پژوهشگر

Akihiko Nishimuraمشاهده پروفایل

استادیار

• Bayesian Statistics
• Statistical Computing
• Public Health Data Science
• +۹ مورد دیگر

Akihiko Nishimura is an Assistant Professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. He holds a PhD from Duke University (2017) and MS and BS degrees from Stanford University (2011 and 2010). His research focuses on Bayesian methods, statistical computing, and public health data science, with applications in precision medicine and observational health data analytics. PhD, Duke University, 2017 MS, Stanford University, 2011 BS, Stanford University, 2010 Nishimura's research centers on developing advanced statistical and computational methodologies for real-world health data. His work emphasizes Bayesian inference, large-scale computing, and software development for reproducible research. He is particularly interested in using observational health data to improve clinical decision-making and advance precision medicine. He co-leads the Bayesian Learning and Spatio-Temporal modeling group (BLAST Group) and the inHealth/OHDSI Lab , collaborating with clinicians and data scientists across institutions. His recent publications reflect a strong trend in methodological innovation in Monte Carlo methods (e.g., Hamiltonian and Zigzag samplers), scalable Bayesian inference, and applications in pharmacovigilance, diabetes management, and infectious disease modeling. The articles span disciplines including biostatistics, computational statistics, public health, and bioinformatics, demonstrating a consistent focus on high-impact, computationally intensive problems in health data science. Nishimura actively contributes to the scientific community through methodological development and open science. He develops statistical software and shares teaching materials on GitHub, emphasizing reproducibility and performant computing. His involvement in the OHDSI community enables large-scale, multi-institutional studies that would not be feasible with single-source data. His work has been recognized through publications in top-tier journals such as the Journal of the American Statistical Association , Biometrika , and JAMA Ophthalmology , and has been picked up by numerous news outlets and social media platforms, indicating broad scientific and public impact. Nishimura teaches courses on performant statistical computing and advanced Monte Carlo methods, training the next generation of data scientists in efficient algorithm and software design. He mentors students and collaborators in statistical methodology and software development, fostering a culture of rigorous, reproducible, and impactful research.

Benjamin C.M. Kaoمشاهده پروفایل

استاد

• Database Management Systems
• Data Mining
• Real-time Systems
• +۱ مورد دیگر

Professor Benjamin C.M. Kao is a faculty member in the Department of Computer Science at The University of Hong Kong (HKU), affiliated with the School of Computing and Data Science. He holds a BSc from HKU (1989) and a PhD from Princeton University (1995). His career includes roles as a teaching/research assistant at Princeton (1989-1991) and a research fellow at Stanford University (1992-1995). His research focuses on Database Management Systems, Data Mining, Real-time Systems, and Information Retrieval Systems. Notable contributions include S-OLAP for sequence data analysis, collaborative resource discovery in social tagging systems, and algorithms for mining periodic patterns in sequences. He has led research grants such as the GRF-funded 'Online Analytical Processing on Sequence Data' (2008) and computational studies in uncertain data mining (2006). Professor Kao has served on program committees for major computer science conferences and reviewed for leading journals. His work bridges theoretical foundations with practical applications in data systems and information retrieval.

Halina Frydmanمشاهده پروفایل

استاد

• Survival Analysis
• Markov Processes
• Stochastic Modeling
• +۷ مورد دیگر

Halina Frydman is a Professor in the Department of Statistics and Operations Research at the Leonard N. Stern School of Business, New York University, where she has been a faculty member since 1978. Her academic work bridges statistical theory and real-world applications in finance and labor economics. Institution: New York University School: Leonard N. Stern School of Business Department: Department of Statistics and Operations Research Academic Rank: Professor Email: hf2@stern.nyu.edu Education: Ph.D. in Mathematical Statistics, Columbia University, 1978 M.A. in Mathematical Statistics, Columbia University, 1974 B.S. in Physics and Mathematics, Cooper Union, 1972 Research Interests: Professor Frydman specializes in survival analysis and Markov processes , with a strong focus on their applications in financial modeling and labor market dynamics . Her work explores mixture models of Markov chains to capture heterogeneity in longitudinal data, particularly in the context of corporate credit rating migrations and employment/unemployment transitions. She also contributes to methodological advances in stochastic modeling and statistical inference for time-to-event data. Publication Trends: Her recent research, reflected in reconstructed articles, demonstrates a consistent focus on developing and applying advanced statistical models—particularly survival models, Markov chains, and mixture models—to problems in finance and economics. There is a clear progression toward more complex, data-driven models incorporating Bayesian methods, high-dimensional estimation, and time-varying effects. Scientific Awards: No awards explicitly mentioned in the source text. Advising and Grants: While specific advisees and grant funding are not listed in the available text, Professor Frydman's long-standing research program and publications in premier journals such as the Journal of the American Statistical Association and The Journal of Finance suggest a significant scholarly impact and likely history of research sponsorship. She teaches core courses including Regression & Forecasting Models , Stochastic Processes I , and Stochastic Models in Finance , indicating active engagement in graduate education. Labs and Research Teams: No specific laboratories or research groups are mentioned in the provided content. However, her research aligns with interdisciplinary efforts in financial statistics and econometric modeling, potentially involving collaboration within NYU’s broader quantitative research community.

• Artificial Intelligence
• Machine Learning
• Probabilistic Graphical Models
• +۷ مورد دیگر

Helge Langseth is a Professor at the Department of Computer Technology and Informatics , within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU). His research focuses on Artificial Intelligence , Machine Learning , and Probabilistic Graphical Models , particularly Bayesian Networks and their applications in Decision Support Systems . Langseth's work addresses Explainable AI (XAI) , Reinforcement Learning , and Recommender Systems . He has contributed to Bayesian Optimization , Probabilistic Modeling , and Robotic Control in oceanic environments. His recent publications emphasize transparency , fairness , and scalability in AI systems, with applications spanning maritime trade, migraine diagnosis, and power grid management. He is affiliated with the Intelligent Systems Research Group at NTNU and actively mentors doctoral and master's students. Co-authored works with Yanzhe Bekkemoen , Sverre Herland , and Jørgen Hanssen reflect his role in advising the next generation of AI researchers.

Michael Pyrczمشاهده پروفایل

استاد

• Subsurface Data Analytics
• Geostatistics
• Machine Learning
• +۳ مورد دیگر

Michael Pyrcz is a Professor in the Hildebrand Department of Petroleum and Geosystems Engineering and holds the rank of Associate Professor in the Jackson School of Geosciences at the University of Texas at Austin. He is the recipient of the B. J. Lancaster Professorship in Petroleum Engineering and the George H. Fancher Centennial Teaching Fellowship in Petroleum Engineering. His research focuses on subsurface data analytics, geostatistics, and machine learning applications in energy systems and CO2 sequestration. Pyrcz teaches widely, including through online lectures and GitHub workflows, and has authored over 50 peer-reviewed publications and a textbook on spatial data analytics. His work integrates machine learning with geoscience challenges, such as uncertainty quantification in reservoir modeling and CO2 storage site evaluation. He leads initiatives in energy data analytics through the Freshman Research Initiative and collaborates with industry on workflow development. Key research areas include generative AI for subsurface models, stochastic methods for fracture networks, and anomaly detection in geologic monitoring. Education: Background in petroleum engineering and geosciences (details not explicitly provided). Grants/Advising: Extensive industry collaboration and mentorship roles at Chevron prior to UT Austin. Labs/Teams: Maintains active GitHub repositories (GeostatsGuy), YouTube lecture series (GeostatsGuyLectures), and social media outreach (X/GeostatsGuy).

Asuman Ozdaglarمشاهده پروفایل

استاد

• Optimization
• Machine Learning
• Game Theory
• +۷ مورد دیگر

Asuman Ozdaglar is the MathWorks Professor of Electrical Engineering and Computer Science and Department Head of EECS at MIT. She also serves as Deputy Dean of Academics for the MIT Stephen A. Schwarzman College of Computing. Her research focuses on large-scale networked systems, including optimization, game theory, social networks, and distributed algorithms. Education: BS in Electrical and Electronics Engineering from Middle East Technical University (1996), SM (1998) and PhD (2003) in Electrical Engineering and Computer Science from MIT. Research emphasizes nonlinear optimization, machine learning, and network economics. She leads work on robust algorithms, misinformation dynamics, and networked systems. Affiliated with the Laboratory for Information and Decision Systems (LIDS) and the Operations Research Center (ORC). Her contributions span theoretical and applied domains, including distributed optimization methods, social network analysis, and privacy-preserving data mechanisms. Active in shaping academic policy through her roles in the College of Computing.

Ali Ghodsiمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Natural Language Processing
• +۳ مورد دیگر

Ali Ghodsi is a Professor at the University of Waterloo and Director of the Data Science Lab, with affiliations at the Vector Institute. His research spans machine learning, deep learning, and artificial intelligence, with applications in natural language processing, bioinformatics, and computer vision. His group develops theoretical frameworks and algorithms for analyzing large-scale datasets, focusing on neural network architectures, knowledge distillation, and model efficiency. Current projects include deep learning for identity control, computational antibody design, and generative AI/large language models. Ghodsi has authored influential tutorials on diffusion models, graph neural networks, and large language models. Notable research contributions include computational methods for de novo peptide sequencing from mass spectrometry data, green simulation-assisted reinforcement learning, and efficient natural language processing models. His lab maintains collaborations with industry partners including Google, Amazon, and Roche.

Samuel W.K. Wongمشاهده پروفایل

دانشیار

• Data Science Methodology
• Protein Structure Analysis
• Reliability Engineering
• +۴ مورد دیگر

Samuel W.K. Wong is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. He holds a Ph.D. in Statistics from Harvard University (2013) under Prof. Samuel Kou. His research focuses on statistical methodology for complex data science challenges in protein structure modeling, dynamic systems inference, and reliability engineering of wood-based products. He has held academic positions at the University of Florida (2013–2018) and has been at Waterloo since 2018. His research interests include Bayesian computation, statistical inference for dynamic systems, and spatial-temporal data analysis. Notable contributions include the development of manifold-constrained Gaussian processes (MAGI package) and sequential Monte Carlo methods for protein folding studies. He has advised over 15 graduate students and researchers, many of whom are now in academic or industry roles worldwide. Wong has received teaching distinctions at Harvard and holds awards including the Nash Medal (2008) for academic excellence. His work bridges computational statistics with applications in bioinformatics, structural engineering, and environmental science. He has published extensively in top-tier journals like Journal of Computational and Graphical Statistics and Biometrics , and collaborates with wood scientists to improve real-time lumber quality assessment using laser imaging data. His teaching portfolio includes courses on probability theory, statistical inference, and spatial data analysis at both undergraduate and graduate levels. Beyond academia, he maintains an active passion for classical piano performance, having performed recitals combining music with his statistical research interests.

Melissa C. Smithمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +۵ مورد دیگر

Melissa C. Smith is a Professor of Electrical and Computer Engineering and Associate Dean for Graduate Studies at Clemson University. She holds a Ph.D. from the University of Tennessee and degrees from Florida State University. Her research focuses on machine learning, reconfigurable computing, and high-performance systems, with applications in embedded systems and interdisciplinary scientific advancements. Before joining Clemson in 2006, she was a research associate at Oak Ridge National Laboratory (ORNL), contributing to projects like the Spallation Neutron Source and PHENIX experiments. Education: Ph.D., Electrical and Computer Engineering, University of Tennessee M.S., Electrical Engineering, Florida State University B.S., Electrical Engineering, Florida State University Research Interests: Machine Learning and AI High-Performance and Reconfigurable Computing System Performance Modeling Embedded Systems Articles Summary: Her recent work spans machine learning applications, GPU/FPGA architectures, speech enhancement, and medical systems. Key themes include optimizing heterogeneous computing for real-time and scientific workloads, and advancing interdisciplinary solutions through architecture-application co-design. Lab & Collaborations: Leads the Future Computing Technologies Lab and collaborates with ORNL and national labs on projects like GEMmaker and HPC-enabled medical systems.

Sabine Süsstrunkمشاهده پروفایل

استاد

• Computational Imaging
• Computational Photography
• Color Image Processing
• +۴ مورد دیگر

Sabine Süsstrunk is a Full Professor and Director of the Images and Visual Representation Laboratory (IVRL) at EPFL's School of Computer and Communication Sciences. She holds a BS in Scientific Photography from ETH Zürich, MS from Rochester Institute of Technology, and PhD from University of East Anglia. Her career includes positions at Hewlett-Packard Labs and Corbis Corporation. Her research explores computational imaging, computational photography, color processing, computer vision, and image quality. Key interests include near-infrared applications, multispectral imaging, and computational aesthetics. Her work bridges hardware and software solutions for imaging challenges. Publications demonstrate consistent focus on advancing generative models (diffusion models, neural cellular automata), 3D reconstruction (NeRF variants), and media integrity (DeepFake detection). Recent trends show increased emphasis on 3D vision, robustness in generative AI, and video analysis. Awards & Honors: IS&T/SPIE Electronic Imaging Scientist of the Year (2013) Raymond C. Bowman Teaching Award (2018) EPFL AGEPoly IC Polysphere Award (2020) 8 Best Paper/Demo Awards Fellowships: IEEE, IS&T, ELLIS, AIIA She leads the IVRL lab and advises PhD candidates while serving as President of the Swiss Science Council. Research is supported through competitive grants and industry collaborations.

Karthika Mohanمشاهده پروفایل

استادیار

• Causal Inference
• Graphical Models
• Missing Data
• +۳ مورد دیگر

Dr. Karthika Mohan is an Assistant Professor of Computer Science in the College of Engineering at Oregon State University, affiliated with the School of Electrical Engineering and Computer Science. Her research bridges artificial intelligence and causal inference, focusing on graphical models, missing data, and non-IID data challenges. Her work has been recognized with the Google Outstanding Graduate Research Award. She serves as an associate editor for the Journal of Causal Inference and has secured NSF funding for research on incomplete data. Dr. Mohan mentors students in causal inference methods and maintains collaborations with institutions like UC Berkeley and UCLA. Her laboratory develops innovative approaches for causal reasoning in AI systems.

Jeffrey Heinzمشاهده پروفایل

استاد

• Theoretical Linguistics
• Computational Learning Theory
• Phonology
• +۳ مورد دیگر

Jeffrey Heinz is a Professor at Stony Brook University, with a joint appointment in the Department of Linguistics and the Institute for Advanced Computational Science. He holds a Ph.D. from UCLA (2007) and previously served on the faculty at the University of Delaware from 2007–2017. His research bridges theoretical linguistics, computational learning theory, and formal language models, focusing on phonology, linguistic typology, and grammatical inference. He has contributed to influential works on computational phonology and edited volumes on topics like phonological stress and learning theory. Key academic achievements include the 2017 Linguistic Society of America Early Career Award for contributions to computational inference in language. His work emphasizes the intersection of formal models and empirical linguistics, with applications to reduplication, phonological processes, and machine learning benchmarks like MLRegTest. Heinz has co-authored a book on grammatical inference and guest-edited special issues in Machine Learning and Phonology . His research also extends to interdisciplinary applications, such as modeling human-robot interaction and pediatric motor rehabilitation through grammatical inference techniques.

Nicolas Davidenkoمشاهده پروفایل

دانشیار

• Cognitive Psychology
• Perception
• Face Recognition
• +۵ مورد دیگر

Nicolas Davidenko is an Associate Professor in the Department of Psychology at the University of California, Santa Cruz (UCSC). He leads the High Level Perception Lab, focusing on behavioral and computational studies of human perception, particularly face recognition, spatial orientation, and visual ambiguity. His work emphasizes 'top-down' processes like attention and expectations. Davidenko holds a Ph.D. in Psychology from Stanford University (2006), an M.S. in Statistics from Stanford (2004), and an A.B. in Mathematics from Harvard (1998). His research explores how humans perceive and interpret complex visual information, including studies on illusions, virtual reality, and misophonia. He has developed parametric models of faces to study memory encoding and drawing accuracy. Notable achievements include a Top-10 Finalist placement in the 2015 Best Illusion of the Year Contest for his 'Mind-controlled motion' research. He teaches courses such as PSYC 121 (Perception), PSYC 139K (Face Recognition), and advanced cognitive research seminars. Davidenko also runs the CSASS Matlab Workshops, training researchers in statistical tools. His lab includes graduate students and postdocs, with alumni like Jennifer Day (Ph.D. ’19) and Pat Samermit (Ph.D. ’18). Recent projects include investigations into vection in VR environments, cross-sensory modulation of aversive sounds, and time perception in virtual reality. His work bridges cognitive psychology, neuroscience, and computational modeling, contributing to understanding how perception shapes human interaction with the environment.

Neal Sullivanمشاهده پروفایل

استاد

• Electrochemical Engineering
• Energy Storage
• Renewable Energy Technologies
• +۳ مورد دیگر

Neal Sullivan is a Professor of Mechanical Engineering at the Colorado School of Mines (CSM), leading experimental research at the Colorado Fuel Cell Center as its director. His expertise lies in electrochemical ceramics, with a focus on fuel cells, electrolyzers, and membrane reactors for energy conversion and storage. Sullivan’s work spans from materials development to large-scale system integration, addressing applications such as hydrogen production, CO₂-to-fuels processes, and geothermic fuel cell systems for unconventional oil recovery. His research is supported by grants from the U.S. Department of Energy (DOE), NASA, and industry partners, totaling over $15M. Notable projects include the development of proton-conducting ceramic electrolyzers for water splitting, high-efficiency hybrid SOFC-IC engine systems, and Mars-based CO₂ methanation. Sullivan has led collaborative efforts with global leaders in electrochemistry, emphasizing scalability and durability in energy systems. Key contributions include innovations in protonic ceramic fabrication, catalyst integration, and multi-stack system design. His lab focuses on bridging early-stage materials research with full-scale demonstrations, achieving power outputs up to 100 kW. Sullivan’s work has been published in top journals like Nature Energy and International Journal of Hydrogen Energy , with a strong emphasis on practical applications and renewable energy solutions. Labs/Teams: Director of the Colorado Fuel Cell Center. Grants/Advising: PI/co-PI on multiple DOE and NASA grants, including $5M for hybrid SOFC systems and $1.5M for geothermic fuel cells. Advises on advanced materials and system integration for energy storage and conversion.

Sung Hoon Choiمشاهده پروفایل

استادیار

• Econometrics
• Financial Econometrics
• Machine Learning
• +۶ مورد دیگر

Sung Hoon Choi is an Assistant Professor in the Department of Economics at the University of Connecticut, part of the College of Liberal Arts and Sciences. His research focuses on developing econometric tools for analyzing big data, machine learning applications, and forecasting using high-dimensional panel datasets. He holds a Ph.D. in Economics from Rutgers University (2021), an M.A. in Applied Statistics from Yonsei University (2016), and a B.A. in Statistics from the University of California, Berkeley (2013). His research interests include econometric theory, financial econometrics, and high-frequency data analysis. Notable areas of concentration are large panel data and factor models, high-dimensional data techniques, and volatility matrix analysis. He teaches courses such as Econometrics I and III for Ph.D. students, and Python programming for economists at undergraduate and master's levels. Recent publications focus on volatility modeling using factor structures, high-frequency financial data, and panel data econometrics. His work addresses challenges in structural information analysis, standard errors for clustered panels, and feasible generalized least squares methods. He collaborates with researchers like Donggyu Kim and Jushan Bai, contributing to leading journals like the Journal of Econometrics and Econometric Theory .