جست‌وجوی استاد و پژوهشگر

Lele Wangمشاهده پروفایل

استادیار

• Information Theory
• Machine Learning
• Data Science
• +۷ مورد دیگر

Lele Wang is an Assistant Professor in the Department of Electrical and Computer Engineering (ECE) at the University of British Columbia (UBC), Vancouver. He is affiliated with the Mathematics of Information, Learning and Data (MILD) research cluster and the Institute of Applied Mathematics. His research focuses on information-theoretic approaches to data science and machine learning, with contributions to universal compression, coding theory, statistical inference on graphs, and noisy computing. Before UBC, he held postdoctoral positions at Stanford University, Tel Aviv University, and as an NSF Center for Science of Information fellow. Education: PhD in Electrical Engineering from the University of California San Diego (2015), B.E. in Academic Talent Program from Tsinghua University (2009). Research Interests: Developing tools in information theory, coding theory, high-dimensional statistics, combinatorics, random graphs, and deep generative models for data-driven challenges. Notable works include universal graph compression for stochastic block models and coding-theoretic approaches to distributed machine learning. Awards: Recipient of the 2017 IEEE Information Theory Society Thomas M. Cover Dissertation Award and NSF CSoI Postdoctoral Fellowship. His students have won multiple awards including GSI Awards and NSERC grants. Mentoring: Prioritizes student development in critical and creative thinking, offering courses in real analysis, probability theory, and theoretical frameworks. Hosts round-robin group meetings inspired by Thomas Cover to foster concise communication and problem-solving skills.

• Cryptography
• Cryptanalysis
• Block Ciphers
• +۵ مورد دیگر

Liam Keliher is an Associate Professor in the Department of Mathematics and Computer Science at Mount Allison University. His research focuses on cryptography, particularly cryptanalysis of block ciphers, linear and differential cryptanalysis techniques, and provable security in symmetric-key systems. He has contributed to the analysis of ciphers like Camellia, AES (Rijndael), and SPN structures, emphasizing vulnerability assessment and cryptographic protocol validation. His work spans theoretical and applied cryptography, including slide attacks on iterated ciphers, weak key identification, and the design of secure substitution-permutation networks. He has published extensively in venues such as the Selected Areas in Cryptography (SAC) workshops and peer-reviewed journals, addressing both classical ciphers (e.g., Hill cipher variants) and modern encryption standards. Notable contributions include refining bounds for differential and linear cryptanalysis on AES and evaluating security metrics for SPNs with randomized S-boxes. His research bridges foundational cryptographic principles with practical security evaluations, ensuring robustness against emerging threats. While no scientific awards are explicitly listed, his prolific publication record and conference contributions highlight his expertise in cryptanalysis and cryptographic systems design. He teaches advanced courses in computer science, such as COMP4951-A, and has supervised student projects in related areas.

Pujee Tuvaandorjمشاهده پروفایل

استادیار

• Econometrics
• Statistical Inference
• Microeconometrics
• +۳ مورد دیگر

Pujee Tuvaandorj is an Assistant Professor in the Department of Economics at York University, part of the Faculty of Liberal Arts & Professional Studies. His research focuses on econometric methods, particularly robust inference under weak identification and randomization techniques. He holds a Ph.D. from McGill University (2015), an M.A. from Hitotsubashi University (2009), and a B.A. from Kyoto University (2007). His work bridges theoretical econometrics and applied microeconometric models. Research interests span econometric theory, including permutation tests for linear models, instrumental variables analysis, and robust statistical methods. He also explores structural breaks, time series econometrics, and asymptotic theory. His recent work emphasizes methodological advancements in handling weak identification and serial dependence in econometric models. Teaching responsibilities include courses such as Introductory Statistics for Economists II , Financial Econometrics , and Econometric Theory . His publications in top journals like Quantitative Economics and Journal of Econometrics reflect his expertise in econometric inference and model development. No scientific awards are explicitly mentioned in the provided texts. His research portfolio includes contributions to regression discontinuity designs, generalized method of moments (GMM), and invariant tests. Current projects address digital adoption, cyber security, and labor market impacts on homelessness, leveraging Canadian administrative data.

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.

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

Adam Ścibior is an Adjunct Professor in the Department of Computer Science at the University of British Columbia (UBC), situated in the Faculty of Science. His research focuses on artificial intelligence, machine learning, and probabilistic programming, with a strong emphasis on autonomous systems, generative models, and reinforcement learning applications in autonomous driving. He contributes to developing simulation benchmarks, diffusion models, and algorithms for multi-agent behavior prediction. His work bridges theoretical advancements in probabilistic inference with practical implementations in robotics and computer vision. Key research areas include autonomous vehicle navigation, scene simulation, and improving synthetic training data realism. He has published extensively on topics like diffusion models for traffic scene generation, reinforcement learning benchmarks for autonomous systems, and conditional permutation-invariant flows. His research often involves collaboration across disciplines, integrating probabilistic programming with real-world applications. Notable contributions include the TorchDriveEnv benchmark for autonomous driving and advancements in diffusion-based generative models for video inpainting and interactive navigation. While no specific awards or grants are explicitly listed, his work reflects a focus on impactful contributions to AI and robotics. No advising relationships or lab affiliations are detailed in the provided materials.

Karen Gundersonمشاهده پروفایل

دانشیار

• Graph theory
• Combinatorics
• Random graphs
• +۱۰ مورد دیگر

Karen Gunderson is an Associate Professor in the Department of Mathematics at the University of Manitoba's Faculty of Science. Her research spans graph theory, combinatorics, random graphs, percolation, hypergraphs, and extremal combinatorics. Research Focus : Graph theory, combinatorics, random graphs, percolation, hypergraphs, extremal combinatorics Academic Role : Associate Professor, Acting Associate Head Graduate Contact : Karen.Gunderson@umanitoba.ca , karen.gunderson@umanitoba.ca Her work includes bootstrap percolation , random geometric graphs , and extremal hypergraph problems , with applications in network modeling and probabilistic combinatorics. Recent publications focus on adversarial burning densities, Erdos-Ko-Rado robustness, and Turán numbers in switching contexts. Academic Leadership : Co-organizer of the University of Manitoba Combinatorics Seminar and key organizer for the 2023 CanaDAM conference and Movement & Symmetry in Graphs retreat.