جستجوی استاد راهنمای دانشگاهی

Michael Gallaugherمشاهده پروفایل

استادیار

• Clustering and Classification
• Machine Learning
• Matrix and Tensor Variate Data Analysis
• +۵ مورد دیگر

Dr. Michael Gallaugher is an Assistant Professor of Statistical Science at Baylor University. He holds a Ph.D., M.S., and B.S. in Statistics from McMaster University. His research focuses on clustering and classification methodologies, particularly in matrix/tensor variate data, mixed-type data, clickstream analysis, and outlier detection. He has been recognized with prestigious awards including the Vanier Canada Graduate Scholarship and the Banting Postdoctoral Fellowship. Education: Ph.D., Statistics, McMaster University (2020) M.S., Statistics, McMaster University (2017) B.S., Statistics, McMaster University (2015) Research Interests: Dr. Gallaugher's work emphasizes advanced clustering techniques for complex data structures, including high-dimensional datasets, clickstream behavior analysis, and skewed distribution modeling. His contributions span statistical methodology development and applications in fields like bioinformatics and sports science. His recent work explores hidden Markov models for time series and robust co-clustering algorithms. Publications Trends: His publications reflect a strong focus on matrix-variate distributions, skewed data modeling, and algorithmic innovation in clustering. Recent work (2022-2025) highlights advancements in contaminated normal mixtures, co-clustering for high-dimensional data, and spatial regression models. Awards: Vanier Canada Graduate Scholarship Banting Postdoctoral Fellowship Advising & Grants: While no advisees are listed, his research has been supported by grants from the Natural Sciences and Engineering Research Council of Canada. He contributes to statistical consulting services at Baylor and collaborates internationally on methodological projects. Labs & Teams: He is affiliated with Baylor's Department of Statistical Science and collaborates with research groups focused on machine learning and statistical computing.

Boyu Zhangمشاهده پروفایل

استادیار

• Medical Image Analysis
• Machine Learning
• Deep Learning
• +۴ مورد دیگر

Boyu Zhang is an Assistant Professor in the Department of Computer Science at the University of Idaho, part of the College of Engineering. He holds a Ph.D. in Computer Science & Technology from Harbin Institute of Technology (2016), an M.S. from the same institution (2009), and a B.S. from Jilin University (2005). His research focuses on medical image analysis, deep learning, and AI applications in healthcare. Key areas include breast cancer detection via ultrasound imaging, explainable AI, graph neural networks for multi-omics data integration, and materials science predictions using machine learning. His work emphasizes interpretability in AI systems, such as the Bi-RADS-Net series for breast cancer diagnosis and the development of sharpness-aware optimizers for medical imaging tasks. He also explores multi-task learning frameworks and novel neural network architectures like SepNet for directional data analysis. His contributions span medical imaging benchmarks (e.g., BUSIS dataset) and computational methods for materials property prediction.

Weiwen Jiangمشاهده پروفایل

استادیار

• Quantum Computing
• AI Accelerators
• Hardware-Software Co-Design
• +۳ مورد دیگر

Weiwen Jiang is a tenure-track Assistant Professor in the Department of Electrical and Computer Engineering at George Mason University (GMU), affiliated with the College of Engineering and Computing (CEC). He leads the JQub lab, focusing on hardware/software co-design for computing systems, spanning classical (FPGAs, ASICs) and quantum computing applications in AI-driven fields like medical imaging and geophysics. Prior to GMU, he held a postdoctoral position at the University of Notre Dame and earned his PhD in Computer Science from Chongqing University with a joint PhD in Electrical and Computer Engineering from the University of Pittsburgh. His research emphasizes quantum computing, AI accelerators, and domain-specific computing. Notable achievements include the 2025 NSF CAREER Award, ACM Sigda Meritorious Service Award (2024), and IEEE QuantumWeek Best Paper Award (2023). His work is funded by NSF, DoE, ARO, Meta, and Leidos. He co-chaired IEEE QuantumWeek (2023–2025) and created workshops like StableQ at ESWEEK 2023. Key contributions include developing frameworks like QuPAD for quantum learning and JQub's AI-driven geophysical and medical imaging tools. His lab graduated Dr. Yi Sheng (now at University of South Florida) and Dr. Zhepeng Wang (Amazon Applied Scientist). Current research explores quantum machine learning, noise mitigation, and fairness in AI for edge devices.

Liping Liuمشاهده پروفایل

دانشیار

• Machine Learning
• Data Science
• Deep Learning
• +۳ مورد دیگر

Liping Liu is an Associate Professor in the Department of Computer Science at Tufts University's School of Engineering. He holds a Ph.D. from Oregon State University and has held postdoctoral positions at Columbia University and Tufts. His research focuses on machine learning, generative models, graph learning, and their applications in biochemical data analysis and fluid dynamics simulation. His work on graph generative methods earned the NSF CAREER Award. Education: Ph.D. (Oregon State University, 2016), M.Sc. (Nanjing University, 2009), B.S. (Hebei University of Technology, 2006). Research Interests: Machine Learning, Deep Learning, Generative Models, Time Series, Graph Learning. Dr. Liu's research emphasizes probabilistic modeling and neural networks, addressing challenges in graph generation, data-driven physics simulation, and biochemical analysis. His recent work includes advancements in graph-based recommendation systems, turbulence modeling, and enzymatic reaction prediction. His publications span top AI conferences like NeurIPS, ICML, and ICLR. He has secured grants totaling over $9 million, including the NSF CAREER Award and NIH funding for metabolomics and enzymatic promiscuity studies. His teaching includes courses on generative models, deep learning, and machine learning for graph analytics. Awards: NSF CAREER Award (2023), NIH grants, DARPA ACT-NOW project (2019). Service: NSF panelist, program committee member for AAAI, NeurIPS, and IJCAI.

Kyu Young Hanمشاهده پروفایل

دانشیار

• Optical Nanoscopy
• Biophotonics
• Super-Resolution Imaging
• +۳ مورد دیگر

Kyu Young Han is an Associate Professor in Optics & Photonics at CREOL, The College of Optics and Photonics, University of Central Florida. His research focuses on developing advanced optical tools for biological and neuroscience applications, including super-resolution imaging (STED/GSD microscopy), label-free single-molecule imaging, and novel microscopy techniques. He holds a patent commercialized by Leica Microsystems and has received the 2020 NIH Maximizing Investigators’ Research Award (MIRA). Education: BS and PhD in Chemistry from Seoul National University (2004 and 2010). Postdoctoral work at the University of Illinois (2011–2016) and Max Planck Institute for Biophysical Chemistry (Germany), specializing in optical microscopy innovation. Research interests emphasize nanoscopy, biophotonics, and interdisciplinary applications in cell biology. His group explores nuclear structure in mammalian cells, DNA-protein interactions, and RNA imaging in live cells. Recent work includes optimizing imaging techniques like TIRF microscopy and integrating deep learning for faster, less damaging STED imaging. Publications span cutting-edge advancements in microscopy resolution, photobleaching reduction, and single-molecule analysis. He advises multiple PhD students and collaborates across disciplines, contributing to Parkinson’s disease research through imaging-driven molecular studies.

Oscar Carl Olof Dahlstenمشاهده پروفایل

دانشیار

• Quantum Information Science
• Information Thermodynamics
• Foundations of Quantum Theory
• +۳ مورد دیگر

Oscar Carl Olof Dahlsten is an Associate Professor in the Department of Physics at City University of Hong Kong. He works in the field of quantum information science with research spanning information thermodynamics, foundations of quantum theory, and quantum computation and machine learning. His academic journey includes training at Imperial College and previous positions at ETH Zurich, NUS Singapore, Oxford University, and SUSTech before joining CityUHK. Dahlsten's research interests focus on the intersection of quantum mechanics and information theory. His work explores how quantum systems process information, the thermodynamic implications of quantum operations, and the application of quantum principles to computational problems. Key areas include quantum causal inference, quantum energy harvesting, black hole information theory, and quantum machine learning algorithms. His fingerprint analysis shows strong contributions to Quantum Theory (100%), Statistical Mechanics (55%), Quantum Dot physics (55%), and Free Energy concepts (40%). Recent publications demonstrate a strong trend toward experimental validation of quantum information concepts, particularly in quantum causal inference and quantum thermodynamics. His work bridges theoretical foundations with practical applications, especially in energy harvesting and quantum computing. The integration of quantum principles with thermodynamic laws appears as a consistent theme across his recent publications. Dahlsten currently serves as Principal Investigator for the GRF project 'Exploiting Quantum Systems for More Efficient Extraction of Energy From Random Sources' starting September 1, 2025. He actively supervises PhD students in quantum information science and is accepting new PhD candidates. His research group focuses on cutting-edge problems at the intersection of quantum information, thermodynamics, and computation.

Chong Liuمشاهده پروفایل

استادیار

• Machine Learning
• Bayesian Optimization
• Bandit Algorithms
• +۳ مورد دیگر

Dr. Chong Liu is an Assistant Professor of Computer Science at the State University of New York at Albany (SUNY Albany) in the College of Nanotechnology, Science, and Engineering. He received his PhD in Computer Science from UC Santa Barbara in 2023 and completed a postdoctoral fellowship at the University of Chicago's Data Science Institute (2023-2024). His research focuses on Machine Learning and AI for Science, particularly Bayesian optimization, bandit algorithms, generative models, and AI applications in drug discovery. He has received the SUNY IITG/OER Impact Grant and serves as Associate Editor for IEEE-TNNLS, Area Chair for ICML/AISTATS, and editorial board reviewer for JMLR. PhD: UC Santa Barbara (2023), advised by Yu-Xiang Wang Postdoc: University of Chicago Data Science Institute (2023) Research Interests : Broad: Machine Learning, Optimization, AI for Science Specific: Bayesian optimization, Bandit algorithms, Active learning, Experimental design, Generative models, AI for drug discovery Applications: Binding affinity prediction, Drug screening, Policy optimization Recent Article Trends : His 2024-2025 publications focus on extending Bayesian optimization theory under practical constraints, quantum-accelerated bandit methods, and multi-objective optimization for drug discovery. Earlier works include private learning frameworks and human-in-the-loop systems. Scientific Awards : 2025: SUNY IITG/OER Impact Grant Professional Activities : Organized NeurIPS workshops on AI for Drug Discovery (2023, 2025), co-organizing INFORMS sessions, and serving on program committees for ICML, NeurIPS, ICLR, and AAAI. He has given invited talks at institutions including University of Chicago, UC Santa Barbara, and Genentech. Teaching : Teaching courses like Numerical Methods (CSI 401) and Machine Learning (CSI 436/536) with syllabi spanning 2024-2025 semesters.

Paul Cohenمشاهده پروفایل

استاد

• Artificial Intelligence
• Data Analysis
• Education Informatics
• +۴ مورد دیگر

Paul Cohen is a Professor of Computer Science at the University of Pittsburgh's School of Computing and Information (SCI), where he also directs the Modeling and Managing Complicated Systems Institute (MOMACS). Previously, he served as the founding Dean of SCI from 2017 to 2020. Before joining Pitt, he was a Program Manager at DARPA (2013–2017), leading initiatives like Big Mechanism and Communicating with Computers. Earlier roles include founding director of the University of Arizona’s School of Information: Science, Technology and Arts (SISTA), and professor at the University of Southern California’s Information Sciences Institute and the University of Massachusetts. Education: PhD in Computer Science and Psychology (Stanford University), MS in Psychology (UCLA), BS in Psychology (UC San Diego). Research Interests: Focuses on artificial intelligence, machine learning, natural language processing, and modeling complex systems like cell signaling pathways and socio-environmental interactions. His work emphasizes explainable AI, human-computer communication, and interdisciplinary problem-solving. Key Contributions: Authored Empirical Methods for Artificial Intelligence and over 200 peer-reviewed articles. His research spans robotics, education technology (e.g., the AnimalWatch tutoring system), and collaborative analysis tools like COLAB. He has won a Telly Award for his video on systemic challenges and a Best Paper award for spatial language learning frameworks. Awards & Recognition: Elected Fellow of the AAAI, recipient of the Telly Award, and winner of the Best Paper Award at the IEEE Conference on Development and Learning. Leadership & Outreach: Advocates for polymathy in education to address global challenges. His work includes developing curricula for complex systems thinking and promoting diversity in STEM through initiatives like AnimalWatch.

Rajen Shahمشاهده پروفایل

استاد

• Statistical Inference
• High-Dimensional Data Analysis
• Machine Learning
• +۳ مورد دیگر

Professor Rajen Shah is a faculty member in the Statistical Laboratory at the University of Cambridge, part of the Department of Pure Mathematics and Mathematical Statistics (DPMMS) within the Faculty of Mathematics. His research focuses on statistical methodology, particularly in high-dimensional data analysis, machine learning, and robust statistical inference. He is known for contributions to areas such as change-point regression, inverse propensity score weighting, and efficient estimation techniques in complex models. Key research interests include developing novel methods for variable selection, robust hypothesis testing, and scalable algorithms for large-scale data. He has collaborated on interdisciplinary projects, such as functional genomics studies (e.g., screening conserved genes of unknown function). His work often emphasizes theoretical rigor alongside practical applications in fields like causal inference and computational statistics. Prof. Shah has published extensively in top-tier journals like The Annals of Statistics , Bernoulli , and Journal of the Royal Statistical Society Series B . His recent articles address challenges in cross-validation for change-point detection, rank-transformed subsampling, and sandwich boosting methods. He is actively involved in the academic community, contributing to the Cambridge Statistics Clinic and supervising research in statistical methodology. His research group is affiliated with the Statistical Laboratory at the Centre for Mathematical Sciences, Cambridge. The lab focuses on advancing statistical theory and applications, with a strong emphasis on high-dimensional and assumption-lean methods.

Jonathan Scarlettمشاهده پروفایل

دانشیار

• Machine Learning
• Information Theory
• High-Dimensional Statistics
• +۳ مورد دیگر

Jonathan Scarlett is an Associate Professor jointly appointed in the Department of Computer Science, Department of Mathematics, and Institute of Data Science at the National University of Singapore (NUS). He also serves as Assistant Dean (Graduate Studies) in the School of Computing. His research focuses on information theory, machine learning, and high-dimensional statistics, with applications to optimization, group testing, and statistical inference. Education: Ph.D. (Information Engineering), University of Cambridge (2014) B.Eng. (Electrical Engineering) and B.Sci. (Computer Science), University of Melbourne (2010) Research Interests: Algorithmic foundations of machine learning and statistical estimation Information-theoretic limits and adaptive algorithms Applications in group testing, compressed sensing, and DNA storage Robust optimization under uncertainty and adversarial settings His work bridges theoretical guarantees with practical algorithm design, emphasizing scalable solutions for high-dimensional problems. Key Achievements: Recipient of Singapore NRF Fellowship (2018) and NUS Presidential Young Professorship Listed in MIT Technology Review's 'Innovators Under 35' Asia Pacific (2021) Over 100 publications in top venues like ICML, NeurIPS, and IEEE Transactions Advising & Grants: Supervised multiple PhD students in areas like Bayesian optimization, group testing, and compressed sensing Lead researcher on projects supported by NRF and NUS grants Developed novel frameworks for safe Bayesian optimization and robust bandit algorithms Labs & Collaborations: Active in the Information Theory and Statistical Learning Group at NUS, collaborating with global institutions like EPFL and MIT on data science challenges.

Ronojoy Adhikariمشاهده پروفایل

مدرس

• Statistical Physics
• Soft Matter
• Stochastic Processes
• +۲ مورد دیگر

Dr Ronojoy Adhikari is a Lecturer in the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge, affiliated with the Faculty of Mathematics. His research focuses on statistical physics, soft matter, stochastic processes, Bayesian inference, and machine learning. He has taught Mathematical Biology (2018–2021) and Electrodynamics (2021–2023). His work bridges theoretical frameworks with experimental insights, addressing phenomena such as active matter dynamics, non-equilibrium thermodynamics, and stochastic modeling of biological systems. Key contributions include studies on autophoretic particles, path probabilities in stochastic systems, and Bayesian approaches to epidemiological modeling. His research group, part of the Soft Matter program at DAMTP, explores interdisciplinary topics like colloidal crystallization and enzymatic network kinetics. Notable publications highlight investigations into fluctuating hydrodynamics, entropy production measurements, and the mechanics of rigid inclusions on curved surfaces. His interdisciplinary approach integrates computational methods (e.g., lattice Boltzmann simulations) with mathematical rigor to understand complex systems. While no awards are explicitly listed, his extensive publication record underscores sustained academic impact. Ongoing research includes projects on path probabilities, active particle dynamics, and the interplay between geometry and material behavior in Cosserat solids. Advising and grants are not explicitly detailed in the provided texts, but his role as a faculty member suggests involvement in student supervision and collaborative projects. His work frequently appears in top journals like Physical Review Letters , Journal of Fluid Mechanics , and Science Advances , reflecting high-quality contributions to theoretical and applied physics.

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.

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.

Elliot Huiمشاهده پروفایل

دانشیار

• Biomedical Engineering
• Microfluidics
• Spatial Cell Biology
• +۳ مورد دیگر

Elliot Hui, Ph.D., is an Associate Professor in the Department of Biomedical Engineering at the University of California, Irvine (UCI), within the Samueli School of Engineering. His research focuses on biological microtechnology, including spatial cell biology, microscale tissue engineering, global health diagnostics, and microfluidic computing. He leads the Hui Lab, which develops tools for automating biochemical reactions, controlling cellular organization, and understanding tissue development dynamics. Key achievements include pioneering microfluidic logic systems for autonomous laboratory automation and creating novel cell culture platforms to study intercellular communication in tissues. His work bridges engineering and biology, addressing challenges in diagnostics and regenerative medicine. Notable contributions include the development of a programmable finite state machine for microfluidic control and a SLAS Fellowship awarded to his student Erik. Research Interests: Microfluidic devices, cell-cell interaction modeling, tissue engineering, and lab-on-a-chip systems. Labs/Teams: Hui Lab at UCI, specializing in microscale biological systems and automation. Publications span topics such as microfluidic computing architectures, tissue dissociation devices, and Bayesian experimental design. His work emphasizes applications in global health diagnostics and mechanistic studies of cellular processes.

Daniela Calvettiمشاهده پروفایل

استاد

• Large Scale Scientific Computing
• Computational Inverse Problems
• Uncertainty Quantification
• +۴ مورد دیگر

Daniela Calvetti is the James Wood Williamson Professor in the Department of Mathematics, Applied Mathematics, and Statistics at Case Western Reserve University. Her research focuses on large-scale scientific computing, computational inverse problems, uncertainty quantification, and predictive modeling in neuroscience, metabolism, and cellular physiology. She holds a PhD from the University of North Carolina-Chapel Hill. Her work integrates advanced mathematical techniques with biomedical applications, including brain energy metabolism modeling, MEG/EEG source reconstruction, and computational methods for medical imaging. Notable contributions include Bayesian hierarchical algorithms for inverse problems and interdisciplinary collaborations bridging mathematics with neuroscience and physiology. Recent research highlights include developing sparsity-promoting Bayesian models for tomography, computational frameworks for neuromuscular control variability, and predictive models of disease dynamics like post-pandemic COVID-19 recurrence. Her methodologies emphasize statistically inspired preconditioning and adaptive meshing techniques to enhance computational efficiency in solving complex inverse problems. Dr. Calvetti has published extensively across computational science, inverse problems, and biomedical applications. She leads a research group advancing interdisciplinary computational methods with applications in neuroscience, virology, and metabolic systems.