جست‌وجوی استاد و پژوهشگر

Eric D. Kolaczykمشاهده پروفایل

استاد

• Statistical Analysis of Network Data
• Machine Learning
• Data Science
• +۴ مورد دیگر

Eric D. Kolaczyk is a Professor of Statistics at McGill University , serving as the inaugural director of the Computational and Data Systems Initiative (CDSI). Previously, he held faculty positions at Boston University (1998–2022) and the University of Chicago, with visiting roles at Harvard and Parisian institutions. He earned his BS in Mathematics from the University of Chicago and MS/PhD in Statistics from Stanford University. His research focuses on statistical and machine learning methods for network data, with applications in computing, bioinformatics, chemistry, and neuroscience. Key areas include network modeling under noisy/dynamic conditions, AI-driven materials science, and statistical foundations for network analysis. He has authored three books on network analysis and contributed to flagship journals. Professional roles include serving as President of the New England Statistical Society and editorial leadership for journals such as Journal of the American Statistical Association and IEEE Transactions on Network Science and Engineering . Awards include fellowships from ASA, IMS, AAAS, and IEEE membership. Current projects span coevolving network models for social dynamics, AI-guided semiconductor design, and statistical methods for high-throughput chemistry. He has led numerous grants, including NSF and NIH-funded initiatives, and collaborates with interdisciplinary teams across academia and industry.

• Robust Statistics
• Machine Learning
• Biostatistics
• +۳ مورد دیگر

Matias Salibian-Barrera is a full Professor in the Department of Statistics at the University of British Columbia's Faculty of Science, where he leads research in robust statistical methodologies with applications across machine learning, biostatistics, and environmental sciences. His research focuses on developing computationally efficient robust techniques resistant to outliers and model misspecification. Key areas include: Robust regression and inference for high-dimensional data Robust causal inference with complex observational data Robust functional and spatial data analysis Integration of robust methods with deep learning architectures Applications in genomics, climate science, and finance His recent publications demonstrate a strong trend toward unifying robust statistics with modern machine learning, particularly in adversarial settings and high-dimensional inference, while maintaining rigorous theoretical foundations. Notable honors include: CRM–SSC Prize in Statistics Fellow of the American Statistical Association Killam Research Prize Coxeter-James Prize He actively supervises doctoral students and secures major grants from NSERC and CIHR, focusing on robust methods for biomedical and environmental data. His work involves collaborations with UBC's Machine Learning Group and the Pacific Institute for the Mathematical Sciences, where he develops open-source software for robust statistical computing.

Kashlakمشاهده پروفایل

دانشیار

• Nonasymptotic Statistics
• Concentration inequalities
• Permutation and Randomization tests
• +۷ مورد دیگر

Dr. Kashlak is an Associate Professor in the Department of Mathematical & Statistical Sciences at the University of Alberta. He holds a PhD from the University of Cambridge (2017), MSc from Johns Hopkins University (2011), and BSc from McGill University (2008). His research focuses on nonasymptotic statistics, concentration inequalities, functional data analysis, and probabilistic methods in Banach spaces. He is particularly known for developing permutation and randomization tests, integrating group theory into statistical frameworks, and applying statistical methods to diverse datasets ranging from sleep apnea diagnosis to electoral analysis. **Research Contributions**: His work bridges theoretical statistics and applied problems, with key contributions in covariance operator inference, topological hidden Markov models, and sparse precision matrix estimation. Notable publications include advancements in bootstrap methods for generalized linear models and analytic permutation testing for functional ANOVA. He has collaborated with institutions globally, including the University of Cambridge and Baylor University. **Teaching & Outreach**: He teaches advanced courses like Applied Regression Analysis (Stat 378) and Probability and Measure (Stat 571). His YouTube channels, 'Cache Lack Stats' and 'Cache Lack Math & Stats Lectures', provide accessible explanations of statistical concepts and dataset analyses. He has developed R packages like sparseMatEst for sparse covariance estimation and fdcov for functional data analysis. **Awards & Service**: Kashlak has received an NSERC Discovery Grant ($161,000), SSC New Investigator Award, and multiple grants from the University of Alberta. He organized workshops like the Alberta Math Dialogue (2019) and served as Session Chair at EcoSta 2019 and WNAR 2018. His work has been featured in Significance Magazine and presented at global conferences such as the IMS-Bernoulli Symposium and the RSS Conference. **Lab & Collaborations**: His research group focuses on statistical methodology with applications in healthcare, spatial analysis, and high-dimensional data. He actively mentors students, including Xinyu Zhang, and collaborates with researchers in machine learning and biomedical fields.

Linglong Kongمشاهده پروفایل

استاد

• Functional and Neuroimaging Data Analysis
• Statistical Machine Learning
• Robust Statistics
• +۴ مورد دیگر

Linglong Kong is a Professor and Canada Research Chair in Statistical Learning at the University of Alberta's Department of Mathematical and Statistical Sciences. He is also a Fellow at the Alberta Machine Intelligence Institute (Amii) and holds a Canada CIFAR AI Chair. His expertise spans statistical machine learning, neuroimaging data analysis, and AI-driven healthcare solutions. Education: PhD in Statistics, University of Alberta (2009) MS in Statistics, Peking University (2002) BS in Probability and Statistics, Beijing Normal University (1999) Research Interests: Kong focuses on functional neuroimaging data analysis, robust statistical methods, and applications of machine learning in healthcare. His work emphasizes fairness, privacy, and interpretability in AI systems. Key areas include differential privacy, quantile regression, and predictive modeling for medical imaging and precision medicine. Recent Trends in Articles: His publications address cutting-edge challenges in privacy-aware machine learning, federated learning frameworks, and neuroimaging biomarkers. Recent work includes advancements in fairness for precision medicine and scalable methods for high-dimensional data analysis. Awards: Fellow of Amii (2021) Great Supervisor Award (2018) Josephine Mitchell Mentoring Award (2017) Grants & Advising: Kong leads major grants on AI ethics, neuroimaging biomarkers, and labor market equality. He has advised numerous students, including PhD candidates and postdocs, in statistical machine learning and healthcare analytics. Labs & Teams: Active in the Alberta Machine Intelligence Institute (Amii) and collaborates with interdisciplinary teams in neuroimaging, precision medicine, and AI ethics initiatives.

• Functional Data Analysis
• Stochastic Optimization
• Reinforcement Learning
• +۵ مورد دیگر

Yafei Wang is an Assistant Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta, Canada, since July 2023. Previously, they served as a Lecturer at the University of Essex (2022–2023) and held a postdoctoral research fellowship at the University of Alberta (2019–2022) under Drs. Linglong Kong and Bei Jiang. Their academic journey includes a PhD from the Beijing University of Technology (2019) and a visiting PhD program at the University of Alberta (2017–2018). Education: PhD in Statistics from Beijing University of Technology (2019), supervised by Dr. Zhongzhan Zhang. Visiting PhD Student at the University of Alberta’s Department of Mathematical and Statistical Sciences (2017–2018). Research focuses on complex data analysis, with expertise in robust statistical methods for functional data (e.g., brain imaging applications), stochastic optimization for data-driven decisions, and interpretability in reinforcement learning. Current projects explore large language models in medical imaging while prioritizing fairness and privacy. Key Funding: NSERC-RGPIN Grant (2024–2029): $130,000 (PI) for Robust Statistical Modeling under Uncertainty. NSERC-DGECR Grant (2024–2029): $12,500 (PI). Networking Grant (2024–2025): £18,620 (Co-I with Peng Liu) on AI in Medical Imaging. Teaching Experience: Fall 2024: STAT 537 (Statistical Methods for Applied Research). Winter 2024: STAT 562 (Discrete Data Analysis) and STAT 432/532 (Survival Analysis). Spring 2023: MA331 (Programming and Text Analytics with R).

Yi Huangمشاهده پروفایل

استاد

• Atmospheric Radiation
• Remote Sensing
• Climate Dynamics
• +۳ مورد دیگر

Yi Huang is a Professor in the Department of Atmospheric and Oceanic Sciences at McGill University, Canada. His research focuses on atmospheric radiation and its role in climate and weather variability, with emphasis on remote sensing techniques and radiative feedback analysis. He leads a research group integrating satellite observations and numerical modeling to address climate and weather challenges. His group actively collaborates with international institutions and has supervised numerous graduate students and postdocs. Key research areas include radiative kernel analysis, stratospheric water vapor dynamics, and Arctic climate feedbacks. The group maintains an active website with publication lists and research data. Education: Ph.D. in Atmospheric and Oceanic Sciences (not explicitly detailed in text), but mentions visiting at McGill (2018-2020). Research interests span atmospheric radiation, radiative remote sensing, and climate feedback mechanisms. Recent work includes studies on Arctic radiative processes, spectral radiative kernels, and stratospheric water vapor impacts on global circulation patterns. The Huang Group collaborates with institutions like Environment and Climate Change Canada and has secured grants for climate monitoring projects. Graduate students and postdocs are involved in satellite data analysis, climate model evaluation, and field campaigns. Labs/Teams: The group operates within McGill's Atmospheric and Oceanic Sciences department, leveraging advanced computational facilities and satellite datasets. Collaborations include international initiatives like the HiSRAMS instrument development project.

Yuan Dingمشاهده پروفایل

استاد

• Software Systems
• Distributed Systems
• Large-scale Software Reliability
• +۳ مورد دیگر

Professor Yuan Ding is a Canada Research Chair in Systems Software at the University of Toronto , affiliated with the Faculty of Applied Science and Engineering and serving as Chair of the Computer Engineering Group . He joined the Department of Electrical and Computer Engineering in 2013 and was promoted to Professor in 2023. Education: PhD in Computer Science (2012) from University of Illinois at Urbana-Champaign, B.E. in Computer Science and Engineering and B.S. in Mathematics (2006) from Beihang University Research Interests focus on improving the reliability and performance of large-scale software systems , particularly distributed and systems software. His work addresses critical issues like upgrade failures , log compression , performance profiling , and non-intrusive debugging . Key methodologies include empirical studies of real-world failures, static analysis, and runtime optimization. Publication Trends show expertise in operating systems , distributed systems , and software engineering . Recent work includes μSlope (log compression) and Relational Debugging (root-cause analysis), while earlier studies investigated JVM performance and distributed failure patterns . Scientific Awards : Canada Research Chair (2023), McCharles Prize (2018), NetApp Faculty Fellowship (2013–2016), ACM SIGSOFT Distinguished Paper (2011), Saburo Muroga Fellowship (2006–2007), University Gold Medal (2005) Advising & Collaborations : Supervised 15+ PhD and Master’s students, including Yongle Zhang (Purdue), Xu Zhao (Facebook), and Xiang Ren (Northeastern). Founded startup YScope to commercialize log compression tools like CLP , now deployed at Uber.

Yllias Chaliمشاهده پروفایل

استاد

• Natural Language Processing
• Text Summarization
• Text Generation
• +۴ مورد دیگر

Yllias Chali is a Professor at the Department of Mathematics and Computer Science, University of Lethbridge, Canada. His research focuses on Natural Language Processing , Artificial Intelligence , and Human Language Technologies , with a focus on text summarization, question answering, and knowledge representation. He has supervised numerous graduate students and secured funding from NSERC for projects in these areas. Teaching : Courses include Fundamentals of Programming I , Artificial Intelligence , and Statistical Methods and Machine Learning . Research Trends : Recent publications emphasize abstractive text summarization using BART, graph attention networks , multi-hop question generation , and faithfulness in NLP models. His work integrates machine learning , syntactic analysis , and semantic modeling . Advising : Collaborates with graduate students on NLP projects, including Narjes Delpisheh and Elozino Egonmwan. Funded by NSERC. Labs & Teams : Leads NLP research initiatives at the University of Lethbridge, contributing to computational linguistics and machine learning advancements.

Serge Provostمشاهده پروفایل

استاد

• Multivariate statistical analysis
• Probabilistic models in environmental sciences
• Serial correlation
• +۷ مورد دیگر

Serge Provost is a Professor in the Department of Statistical and Actuarial Sciences at the University of Western Ontario. His office is located in WSC 280, and he can be contacted via phone (519-661-2111 x83624) or email (provost@uwo.ca). Research Focus: Professor Provost specializes in multivariate statistical analysis, distribution theory, and probabilistic modeling with applications in environmental sciences and actuarial science. His work emphasizes: Development of approximation methods for complex distributions Innovations in density estimation techniques Statistical inference for incomplete data Serial correlation analysis Functional linear modeling frameworks Publication Trends: His recent articles (2011-2016) predominantly focus on theoretical and computational statistics, with recurring themes in distribution theory, orthogonal polynomial approximations, and specialized probability models. Research frequently intersects with actuarial mathematics, time series diagnostics, and nonparametric estimation methods. Graduate Supervision: Currently advises three PhD students: Cong Nie Zhaoqi Yang Yishan Zang

• Machine Learning
• Computer Vision
• Bioinformatics
• +۴ مورد دیگر

Anthony Bonner is an Associate Professor in the Department of Computer Science at the University of Toronto, where he has been a faculty member since 1991. He is also a member of the Collaborative Graduate Program in Genome Biology and Bioinformatics (CGPGBB). Previously, he served as a visiting professor at the University of Pennsylvania in 1999. His academic journey includes roles as an assistant professor at Indiana University, Bloomington, and a post-doctoral fellow at INRIA-Rocquencourt, France. Dr. Bonner holds a B.Sc. in Mathematics and Physics from the University of Toronto, followed by M.S. and Ph.D. degrees in Computer Science from Rutgers University (1990 and 1991, respectively). His research interests span machine learning, computer vision, bioinformatics, and genome mapping, with earlier work focusing on artificial intelligence, logical inference, database systems, and workflow management. His current research emphasizes generative AI for images and machine learning applications, as seen in courses like CSC2541 (Generative AI for Images) and CSC490 (Capstone Design in Machine Learning for Vision). Dr. Bonner’s teaching includes advanced topics such as neural networks, kernel methods, and computational photography. He has contributed to curriculum development in machine learning, emphasizing practical applications and cutting-edge methodologies.

Xin Bingمشاهده پروفایل

استادیار

• Data Science
• Theoretical Statistics
• Mathematical Statistics
• +۶ مورد دیگر

Xin Bing is an Assistant Professor at the University of Toronto, specializing in Data Science and Theoretical Statistics. His research focuses on high-dimensional statistics, latent factor models, and statistical methodology with theoretical guarantees. He holds a Ph.D. from Cornell University, an MS from the University of Washington, and a BS from Shandong University. His work addresses challenges in multivariate analysis, model-based clustering, and applications in genetics, neuroscience, and immunology. Recent contributions include advancements in kernel ridge regression, latent factor analysis, and robust federated learning. He actively publishes in top journals like The Annals of Statistics and Bernoulli . Key areas of exploration include developing efficient estimation algorithms for mixture models and understanding statistical-computational trade-offs. His GitHub repositories, such as LOVE and STRS , reflect his focus on overlapping clustering and self-tuning rank selection techniques.

• Machine Learning
• Statistical Learning Theory
• Kernel Methods
• +۴ مورد دیگر

Danica J. Sutherland is an Assistant Professor in the Department of Computer Science at the University of British Columbia (UBC), holding a Canada CIFAR AI Chair and affiliation with Amii (Alberta Machine Intelligence Institute). She leads research in machine learning theory, optimal transport, kernel methods, and fairness in AI. Previously, she was affiliated with the Toyota Technological Institute at Chicago (TTIC), the Gatsby Computational Neuroscience Unit (UCL), and completed her PhD at Carnegie Mellon University under Jeff Schneider. Education: PhD in Machine Learning (Carnegie Mellon University, 2016), B.A. in Computer Science (Swarthmore College, 2011). Her work spans theoretical foundations of deep learning, active learning, generative models, and applications of optimal transport. She co-developed the POT Python Optimal Transport library and advocates for trans rights through initiatives like Queer in AI. Research focuses on statistical learning theory, kernel-based methods for hypothesis testing, and scalable graph neural networks. Recent work includes studies on LLM finetuning dynamics, privacy-preserving generative models, and bias in language models. She teaches advanced courses on statistical learning theory and machine learning, emphasizing theoretical rigor and practical applications. Awards include an Outstanding Paper at ICLR 2025 and a Best Paper at ACM FAccT 2023. Her lab collaborates widely, with projects funded by CIFAR, NSERC, and industry partnerships. She advises a team of PhD and master’s students, prioritizing mentorship for queer and marginalized scholars. Labs/Teams: PI of the UBC Machine Learning Theory (ML-TN) group, co-lead of the Kantorovich Initiative (optimal transport research), and core member of the Canadian AI Institute (CAIDA). She actively contributes to open-source tools like POT and promotes inclusive AI through community initiatives.

Reg Kulpergerمشاهده پروفایل

استاد

• Stochastic Processes
• Bootstrapping Techniques
• Asymptotic Methods
• +۳ مورد دیگر

Dr. Reg Kulperger is a Professor in the Department of Statistical and Actuarial Sciences at the University of Western Ontario. He holds a PhD from Carleton University (1978) and teaches courses in statistical inference, probability, and stochastic processes. Research expertise includes inference for stochastic processes, bootstrapping techniques, asymptotic methods, and financial modeling. Current projects involve stochastic forest fire growth modeling, change point detection in environmental processes, and option pricing under non-normal distributions. His publications demonstrate applications across environmental science, finance, and hydrology, with methodological innovations in spatial statistics and empirical processes. Recent graduate supervision includes students working on inference for stochastic processes and computational statistics.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Qian WANG is a Lecturer at the Faculty of Law of Université de Montréal, contributing to both teaching and interdisciplinary research. Her work bridges legal education with advanced computational methods, though her recent publications primarily focus on artificial intelligence and computer science. Research interests span a broad spectrum including artificial intelligence, machine learning, natural language processing, and computer vision. Her work often intersects with systems-level challenges such as operating system control and cognitive modeling, demonstrating a unique interdisciplinary approach. Publications reflect a trajectory from foundational contributions in graph theory and saliency detection (2020-2021) to cutting-edge advancements in large language models and reasoning frameworks (2023-2025). Key themes include improving AI capabilities through dynamic reasoning mechanisms and exploring the ethical dimensions of autonomous systems. No academic awards or grants are explicitly noted in the provided materials, though her prolific publication record indicates active research engagement. She currently holds no listed advisees or doctoral supervision roles. Labs or collaborative teams associated with her research are not detailed in the available information.

Serdar Yukselمشاهده پروفایل

استاد

• Stochastic Control Theory
• Information Theory
• Probability Theory
• +۵ مورد دیگر

Serdar Yuksel is a Professor in the Department of Mathematics and Statistics at Queen's University, Canada, affiliated with the Faculty of Arts and Science. He holds a Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign (UIUC), and B.Sc. from Bilkent University. His research focuses on stochastic control theory, information theory, probability theory, and their applications in decentralized systems, networked control, and reinforcement learning. Yuksel's work bridges control, information, and probability, addressing challenges in stochastic stability, optimal control under partial/decentralized information, and ergodic properties of dynamical systems. Education: Ph.D. (2006) - Electrical and Computer Engineering - UIUC M.Sc. (2003) - Electrical and Computer Engineering - UIUC B.Sc. (2001) - Electrical and Electronics Engineering - Bilkent University Research Interests: Stochastic control theory, including optimization and stability of Markovian systems Information theory, particularly zero-delay coding and systems with feedback Probability theory, focusing on Markov processes and ergodic theory Decentralized and networked control systems under information constraints Reinforcement learning and approximation methods for stochastic systems Awards: 2013 CAIMS/PIMS Early Career Award in applied mathematics Teaching and Academic Leadership: Yuksel has taught courses such as Optimization and Control of Stochastic Systems, Mathematics of Engineering Systems, and served as Chair of Mathematics & Engineering at Queen's University. He advises numerous graduate and undergraduate students, mentoring projects in control theory, optimization, and applied mathematics. Labs/Teams: Leads the Stochastic Control (and Related Fields) research group, collaborating on topics like decentralized control, information structures, and mean-field games. Active in organizing seminars and international collaborations.