جست‌وجوی استاد و پژوهشگر

• Statistical Inference
• Machine Learning
• Computational Biology
• +۳ مورد دیگر

Jon McAuliffe is an Assistant Professor at the Department of Statistics, University of California, Berkeley. He earned his Ph.D. in Statistics at Berkeley in 2005 under Michael Jordan, with a dissertation titled "Statistical Methods for Comparing Genomes." His research spans statistical inference, machine learning, and their applications in astronomy, computational biology, and theoretical statistics. Department: Statistics University: University of California, Berkeley Academic Rank: Assistant Professor Ph.D. Year: 2005 Advisor: Michael Jordan McAuliffe's research interests include Bayesian inference , variational methods , extreme event modeling , and scalable statistical algorithms . His work addresses challenges in astronomical image analysis, cancer genomics, and probabilistic modeling of complex datasets. Recent publications focus on time-uniform confidence sequences , deblending starfields , and applications in climate science . While no specific awards are listed, his contributions to methodological statistics and interdisciplinary applications are notable. He has not been explicitly linked to advising students in the provided texts but has collaborated on projects like the Celeste astronomical catalog , emphasizing computational efficiency and probabilistic modeling at petascale levels.

Longbing Caoمشاهده پروفایل

استاد

• Data mining
• Machine learning
• Anomaly detection
• +۷ مورد دیگر

Longbing Cao is a prominent academic researcher in data science and artificial intelligence, currently holding dual affiliations with Macquarie University's School of Computing in Sydney, Australia, and the University of Technology Sydney (UTS). He leads the Data Science Lab and has established himself as a leading authority in machine learning, data mining, and anomaly detection research with a prolific publication record spanning over two decades. Dr. Cao's research encompasses multiple critical domains within data science, with particular expertise in deep learning for anomaly detection, time series analysis, recommender systems, and behavior informatics. His scholarly work bridges theoretical foundations with practical applications across diverse industries including finance, cybersecurity, and e-commerce. He has pioneered innovative approaches in coupled behavior analysis, representation learning, and cross-domain collaborative filtering that have significantly advanced these fields. His research demonstrates a consistent trajectory from foundational data mining techniques to sophisticated deep learning methodologies. His publication record shows remarkable impact with 240 publications as of 2025 and 4,725 total citations. His highly influential survey papers have shaped research directions across multiple domains, with "Deep Learning for Anomaly Detection: A Review" (2021) accumulating over 1,600 citations and 25,586 downloads, establishing it as a seminal reference in the field. His recent work (2025) focuses on cutting-edge topics including diffusion models (SepDiff, ProgDiffusion), dynamic spectral graph analysis, and non-stationary time series modeling, reflecting his position at the forefront of AI research evolution. Data Science: A Comprehensive Overview (2017) - 298 citations, 41,677 downloads Data science: challenges and directions (2017) - 87 citations, 31,525 downloads AI in Finance: Challenges, Techniques, and Opportunities (2022) - 201 citations, 21,892 downloads A Survey on Session-based Recommender Systems (2021) - 346 citations, 4,563 downloads As a research leader, Dr. Cao has mentored numerous students and collaborators, contributing significantly to the development of next-generation data science researchers. His Data Science Lab serves as an interdisciplinary hub where computer scientists, statisticians, and domain experts collaborate to tackle complex data challenges. The lab's research spans theoretical advancements in machine learning algorithms to practical implementations with tangible real-world impact across multiple sectors.

• Biomedical Imaging
• Applied Functional Analysis
• Sampling Theory
• +۴ مورد دیگر

Michael Unser is a Full Professor at EPFL’s School of Engineering and Academic Director of EPFL’s Center for Imaging in Lausanne, Switzerland. His research focuses on biomedical imaging, applied functional analysis, sampling theory, wavelets, splines, and computational bioimaging. Current Position: Full Professor, EPFL School of Engineering Academic Role: Director of EPFL Center for Imaging Key Research Areas: Inverse problems, sparse stochastic processes, machine learning for imaging Dr. Unser’s research bridges mathematical theory with practical biomedical imaging applications. He has pioneered the use of splines for image processing, developed advanced regularization frameworks for inverse problems, and contributed to computational bioimaging techniques like SMLM and optoacoustic tomography. His recent work explores connections between deep learning and classical kernel methods through variational formulations. He has published over 400 journal papers and authored the seminal book on sparse stochastic processes. His scientific contributions have been recognized with multiple awards including IEEE and EURASIP Technical Achievement Awards, five IEEE-SPS Best Paper Awards, and three consecutive ERC Advanced Grants (FUN-SP, GlobalBioIm, FunLearn). He has served on editorial boards for leading journals and founded the IEEE SPS Bio Imaging and Signal Processing Technical Committee.

Stephen Frostمشاهده پروفایل

استاد

• Paleontology
• Primate Evolution
• Evolutionary Theory
• +۳ مورد دیگر

Stephen Frost is a Professor and Co-Director of Graduate Studies in the Schnitzer School of Global Studies and Languages at the University of Oregon, specializing in paleontology and primate evolution since joining as an assistant professor in 2004. His research centers on East African cercopithecids (Old World monkeys) from the late Miocene to Pleistocene, with fieldwork in Ethiopia's Afar Region through the Gona Research Project. Education: B.A., California State University-Long Beach (1994) Ph.D., City University of New York (2001) His expertise spans evolutionary theory, geometric morphometrics, and climate-driven primate evolution. Dr. Frost employs quantitative shape analysis to study fossil specimens, examining relationships between cercopithecid evolution and global climatic shifts. His fieldwork extends to Tanzania, France, and Greece, focusing on fossil recovery and paleoenvironmental reconstruction. Analysis of his recent publications reveals consistent themes: morphometric analysis of cercopithecid fossils, dietary reconstruction using stable isotopes and dental morphology, taxonomic reassessment of papionins, and biochronological frameworks for African fossil sites. His work bridges paleontology, evolutionary biology, and climate science to understand extinction patterns and adaptive responses. Based in Condon Hall (Laboratory Room 369), Dr. Frost mentors graduate students as Co-Director of Graduate Studies. His collaborative projects, particularly with the Gona Research Project, advance understanding of hominin environments and primate evolutionary trajectories in dynamic paleoclimatic contexts.

Xiaoqian Wangمشاهده پروفایل

استادیار

• Trustworthy Machine Learning
• Explainable AI
• Algorithmic Fairness
• +۳ مورد دیگر

Xiaoqian Wang is an Assistant Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University, with a courtesy appointment in Biomedical Engineering. She leads research in trustworthy machine learning, focusing on explainability, fairness, robustness, and healthcare applications. Her work has earned her the 2022 NSF CAREER Award and IEEE Senior Membership. Education: PhD in Electrical Engineering from the University of Pittsburgh (2019), advised by Prof. Heng Huang; Bachelor's from Zhejiang University (2013). Research interests include developing interpretable AI systems, mitigating algorithmic bias, and applying machine learning to healthcare challenges such as Alzheimer's disease progression modeling. Recent efforts explore spurious correlation mitigation in multi-task and vision-language models. Awards and recognitions: Recipient of the NSF CAREER Award (2022), IEEE Senior Member status (2023+). Teaching: Courses include ECE 264 (Advanced C Programming), ECE 570 (Artificial Intelligence), and ECE 695 (Machine Learning in Bioinformatics/Healthcare). Lab affiliations: Regenstrief Center for Healthcare Engineering at Purdue University.

• Uncertainty Propagation
• Inverse Problems
• Optimal Learning
• +۳ مورد دیگر

Dr. Ilias Bilionis is a Professor of Mechanical Engineering at Purdue University's West Lafayette campus, leading the Predictive Science Lab. His research focuses on uncertainty quantification, inverse problems, optimal learning, and materials design, with applications in computational engineering, human-machine interaction, and high-performance buildings. He holds a strong record of awards, including the 2008-2009 Olin Fellowship and multiple academic excellence awards from his early career. His work integrates machine learning, Bayesian methods, and physics-informed models to address complex engineering challenges. Notable areas include 4D flow MRI analysis, extraterrestrial habitat systems, and energy-efficient building technologies. He has contributed to advancing methodologies in stochastic processes, optimal transport theory, and computational fluid dynamics. Awards: Extensive recognition for academic excellence and research innovation, including awards from Cornell University and the National Technical University of Athens. Labs/Teams: Heads the Predictive Science Lab, focusing on data-driven modeling and uncertainty quantification. Grants/Projects: Leads initiatives in smart energy systems, space habitat resilience, and advanced manufacturing.

Wojciech Szpankowskiمشاهده پروفایل

استاد

• Bioinformatics and Computational Biology
• Theory of Computing
• Algorithms
• +۵ مورد دیگر

Wojciech Szpankowski is the Saul Rosen Distinguished Professor of Computer Science at Purdue University, with a courtesy appointment in Electrical and Computer Engineering. He holds a Ph.D. and M.S. in Electrical and Computer Engineering from Gdansk University of Technology (1980 and 1970, respectively). His career includes visiting roles at institutions such as McGill University, Stanford, and INRIA. He directs the NSF-funded Center for Science of Information, focusing on extending information theory to modern data challenges. Szpankowski is an IEEE Fellow, Erskine Fellow, and recipient of the Humboldt Research Award (2010). His research spans algorithms, information theory, bioinformatics, and analytic combinatorics. Key contributions include work on data compression, network analysis, and quantum computing. He authored *Average Case Analysis of Algorithms on Sequences* (2001) and co-launched the interdisciplinary Institute for Science of Information (2008). His recent work addresses fundamental limits in machine learning, quantum algorithms, and privacy-preserving systems. Research Interests: Bioinformatics, algorithm analysis, quantum computing, graph theory, and information-theoretic limits of learning. His work often bridges theory and applications, such as analyzing biological networks and developing efficient compression methods for dynamic data. Awards: IEEE Fellow Erskine Fellowship Humboldt Research Award (2010) Grants & Labs: Director of the NSF Science & Technology Center for Science of Information. Active in interdisciplinary projects, including temporal network analysis and quantum information theory.

Gu-Yeon Weiمشاهده پروفایل

استاد

• Computer Science
• Computer Engineering and Architecture
• Electrical Engineering
• +۵ مورد دیگر

Gu-Yeon Wei is the Robert and Suzanne Case Professor of Electrical Engineering and Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences. He also serves as Area Chair for Electrical Engineering and Director of Undergraduate Studies for the department. His research focuses on sustainable computing, VLSI systems, hardware-software co-design, and quantum computing. Wei leads initiatives like the NSF-funded $12M sustainable computing project to reduce computing's carbon footprint by 45% within a decade. His work emphasizes energy-efficient architectures, fault-tolerant systems, and emerging memory technologies. Education details are not explicitly listed, but his academic roles suggest advanced qualifications in electrical engineering. Research interests include computer architecture, AI accelerators, and environmental impact analysis of computing systems. Recent projects explore carbon-efficient design frameworks, PFAS material modeling, and quantum computing performance modeling. Notable grants include multi-institution NSF funding for sustainability in computing. He advises on heterogeneous SoC design, edge AI inference, and noise-resilient systems. His lab (vlsiarch.eecs.harvard.edu) develops agile design methodologies for custom hardware, including open-source tools like SODA for accelerating chip development. Future work targets scalable machine learning inference, cryogenic memory systems, and end-to-end system resilience in autonomous machines.

• Pediatric Critical Care Medicine
• Anesthesiology
• Neurophysiological Monitoring
• +۳ مورد دیگر

Alireza Akhondi-Asl is an Assistant Professor of Anesthesia at Harvard Medical School and Boston Children's Hospital, specializing in pediatric critical care medicine. His work focuses on integrating advanced analytical techniques with clinical practice to improve outcomes in critically ill children. He leads research in neurophysiological monitoring, machine learning applications for clinical decision support, and optimizing protocols for pediatric ICU care. His affiliations include Harvard Catalyst, where he contributes to translational research initiatives. Research interests include EEG-based sedation monitoring, causal inference methodologies in healthcare, and predictive modeling for organ dysfunction. He has developed automated clinical tools like the Pediatric Sequential Organ Failure Assessment (PSOFA) calculator and explored in silico models for cerebral hemodynamics in sepsis. His studies often address practical challenges in nutrition support, fluid management, and surgical outcomes for congenital disorders. Key contributions include advancing understanding of brain connectivity through dynamic Bayesian models and evaluating the impact of emerging evidence on clinical practices. His work bridges computational methods with real-world pediatric critical care challenges, aiming to enhance both diagnostic accuracy and patient management strategies.

Elizabeth Tiptonمشاهده پروفایل

استاد

• Statistical Methodology
• Meta-Analysis
• Causal Inference
• +۳ مورد دیگر

Elizabeth Tipton is a Professor of Statistics and Data Science at Northwestern University, affiliated with the Department of Education and Social Policy (by courtesy). She serves as Co-Director of the Statistics for Evidence-Based Policy and Practice (STEPP) Center and is an Institute for Policy Research (IPR) Fellow. Her work focuses on developing statistical methods for improving causal generalization in randomized trials and meta-analysis, with applications in education, psychology, and health sciences. Education: 2011: Ph.D. in Statistics, Northwestern University (Certificate in Education Science) 2005: M.A. in Sociology, University of Chicago 2001: B.A. in Mathematics, Transylvania University Research Interests: Elizabeth’s research emphasizes enhancing the generalizability of randomized trials and synthesizing evidence from meta-analyses. She develops methodologies for handling dependent effect sizes, propensity score techniques, and visualization tools (e.g., Meta-Analytic Rain Cloud plots) to aid decision-making. Her work bridges statistical rigor with practical applications in education policy, behavioral science, and health. Grants & Funding: Her research has been supported by the National Science Foundation, Institute of Education Sciences, Spencer Foundation, Raikes Foundation, and others. Labs & Collaborations: Co-Director of the STEPP Center, which focuses on methodological advancements for evidence-based policy. Active collaborations include projects on intervention design, heterogeneity analysis, and translational research.

Yun Yangمشاهده پروفایل

دانشیار

• Machine Learning
• Bayesian Inference
• High-Dimensional Statistics
• +۴ مورد دیگر

Yun Yang is an Associate Professor in the Department of Mathematics at the University of Maryland, College Park . Previously, he held positions as Associate Professor (2022–2022) and Assistant Professor (2018–2022) at the University of Illinois at Urbana-Champaign, and Assistant Professor at Florida State University (2016–2018). He earned a B.S. in Mathematics from Tsinghua University (2011) and a Ph.D. in Statistics from Duke University (2014). His research focuses on Bayesian inference , high-dimensional statistics , machine learning , and optimal transport . Recent work emphasizes applying optimal transport and Wasserstein gradient flows to statistical problems, including regression, clustering, and generative modeling. He also investigates algorithmic scalability and theoretical guarantees for modern statistical methods. Key contributions include advancements in diffusion models for manifold structures, minimax-optimal distribution estimation, and Bayesian model selection via variational approximations. His work bridges theoretical foundations with practical applications in data science and optimization. Service roles include Associate Editorships at Bayesian Analysis (2025–present) and Journal of Computational and Graphical Statistics (2023–present), and Area Chair for AISTATS (2022–present).

Marc Deisenrothمشاهده پروفایل

استاد

• Machine Learning
• Environment and Sustainability
• Robotics and Control
• +۱ مورد دیگر

Professor Marc Deisenroth holds the Google DeepMind Chair of Machine Learning and Artificial Intelligence at University College London (UCL). He is part of the UNESCO Chair on Artificial Intelligence at UCL and holds a visiting faculty position at the University of Johannesburg. He co-leads the Sustainability and Machine Learning Group at UCL, focusing on data-efficient machine learning, probabilistic modeling, and autonomous decision-making applied to climate science, nuclear fusion, and robotics. His research spans machine learning (data-efficient methods, Gaussian processes, reinforcement learning), environmental sustainability (weather forecasting, renewables), robotics (robot learning, control systems), and signal processing (Kalman filtering, dynamical systems). He has served in key roles at top conferences like ICML, NeurIPS, and ICLR, and has received awards including the President’s Award for Outstanding Early Career Researcher (2018) and paper awards at ICRA, ICCAS, and FAccT. Key contributions include scalable Gaussian process libraries (GPdoemd, GPflux), Bayesian optimization for robotics, and probabilistic weather forecasting models. His work bridges theory and application, emphasizing environmental and societal impact through collaborations with the National Oceanography Centre and the UN Global AI Network.

Radu Herbeiمشاهده پروفایل

استاد

• Statistical Inference for Stochastic Processes
• Monte Carlo and MCMC Methods
• Inverse Problems
• +۳ مورد دیگر

Radu Herbei is a Professor of Statistics at The Ohio State University, affiliated with the Department of Statistics. He joined the faculty in 2006 and has been funded by NSF and ONR. His research focuses on statistical inference for stochastic processes, including stochastic differential equations and stochastic partial differential equations, with an emphasis on 'exact' inference methods that avoid user-selected grids or approximations. He develops exact Markov chain Monte Carlo (MCMC) techniques using approximations of intractable probability density functions and explores high-performance GPU computing to address computational challenges. His education includes a PhD in Statistics from Florida State University (2006). Research areas include Bayesian statistics, Monte Carlo methods, inverse problems, and uncertainty quantification. Notable contributions include work on the Bernoulli factory algorithm for exact Bernoulli random variates and applications in phylogenetics and environmental modeling. Collaborations span computational biology, oceanography, and ecological systems. Key publications include developments in taxicab MCMC samplers for discrete spaces, Bayesian function registration, and statistical inference for stochastic differential equations. His work bridges theoretical advancements with computational tools, addressing complex modeling challenges in diverse scientific domains.

Jaehan Baeمشاهده پروفایل

استادیار

• Planet Formation
• Protoplanetary Disks
• Hydrodynamic Simulations
• +۵ مورد دیگر

Jaehan Bae is an Assistant Professor in the Department of Astronomy at the University of Florida. His research focuses on understanding planet formation through numerical simulations and observational studies of protoplanetary disks using facilities like ALMA. He holds a Ph.D. from the University of Michigan (2017) and prior degrees from Seoul National University (B.S. 2006, M.S. 2008). Education: Ph.D., University of Michigan, 2017 M.S., Seoul National University, 2008 B.S., Seoul National University, 2006 Research Interests: Bae investigates planet-disk interactions, hydrodynamic instabilities, and gas dynamics in protoplanetary disks. His work integrates observations (e.g., ALMA), simulations, and machine learning to uncover planet formation mechanisms. Key areas include disk evolution, gap opening, and embedded planet detection through substructures like spiral arms and asymmetries. Grants & Advising: No specific grant details or students are listed. His research is supported through observational projects like exoALMA and theoretical collaborations.

Perry de Valpineمشاهده پروفایل

استاد

• Ecological Modeling
• Computational Statistics
• Population Ecology
• +۲ مورد دیگر

Perry de Valpine is Professor in the Department of Environmental Science, Policy, and Management at UC Berkeley. He holds a PhD in Ecology from UC Davis (2000) and a B.S. in Applied Mathematics from Yale University (1993). His research develops mathematical models and statistical methods for complex ecological systems, with focus on computational frameworks for hierarchical modeling. Research includes ecological applications to bird communities, forest dynamics, species distributions, and population ecology. Leads development of NIMBLE statistical computing platform for Bayesian analysis. Directs the de Valpine Research Group investigating population dynamics and computational statistics. Teaching responsibilities include courses on ecological modeling and statistical computing. Maintains active research program bridging ecology, statistics, and computational methods.