جست‌وجوی استاد و پژوهشگر

Sarah Ploskerمشاهده پروفایل

استاد

• Quantum Information Theory
• Operator Theory
• Linear Algebra
• +۳ مورد دیگر

Dr. Sarah Plosker is a Professor and Tier 2 Canada Research Chair in Quantum Information Theory at Brandon University's Department of Mathematics & Computer Science. She holds adjunct roles at the University of Manitoba, University of Regina, and University of Guelph. Her research focuses on quantum information theory, operator theory, linear algebra, and matrix analysis, with emphasis on quantum coherence, positive operator valued measures (POVMs), and quantum state transfer. She is affiliated with the Manitoba Quantum Institute and Winnipeg Institute for Theoretical Physics. Education: Ph.D. Applied Mathematics (University of Guelph, 2013), M.Sc. Mathematics (University of Regina, 2010), B.Sc. Combined Mathematics and Statistics (University of Regina, 2008). Her work is supported by CRC, CFI-JELF, and NSERC grants. She actively mentors students and co-supervises at multiple institutions. Research trends in her articles emphasize quantum resource theories, matrix structures (e.g., centrosymmetric, tridiagonal), and graph-based quantum state transfer. Recent work explores coherence measures, universality of quantum unitaries, and applications of operator algebras. Awards include the prestigious Canada Research Chair designation and major grants. Awards: Tier 2 CRC, CFI-JELF Grant, NSERC Discovery Grant. Advising & Grants: Mentors postdocs, PhD/MSc students across institutions. Supervised over 20 students since 2013, specializing in quantum information, linear algebra, and applied mathematics. Labs/Teams: Active in Manitoba Quantum Institute, Winnipeg Institute for Theoretical Physics, and collaborates with University of Regina/Manitoba groups.

Eric Ruppertمشاهده پروفایل

دانشیار

• Distributed Computing
• Shared-Memory Algorithms
• Data Structures
• +۳ مورد دیگر

Eric Ruppert is an Associate Professor in the Department of Electrical Engineering & Computer Science at York University, where he has taught computer science since 2000. He is a member of the Theory of Computing Group and teaches foundational computer science courses including algorithms and theory of computation. He has served on program committees for major conferences including PODC 2025 and WADS 2025. Ruppert received his education at the University of Toronto. He has held postdoctoral positions at Brown University and served as a visiting professor at the École Polytechnique Fédérale de Lausanne. His research focuses on the theory of distributed computing, with particular emphasis on algorithm design and lower bound proofs. His recent work has centered on shared-memory data structures for multi-core architectures. His research interests span distributed algorithms, computational complexity, and the theoretical foundations of concurrent systems. He has made significant contributions to understanding the computational power of population protocols, consensus problems, and non-blocking data structures. His publication record shows a consistent focus on distributed computing theory, with recent work emphasizing non-blocking and wait-free data structures. Over the past decade, his research has increasingly addressed challenges in concurrent programming for multi-core systems, developing efficient algorithms for queues, deques, and binary search trees that provide strong progress guarantees. Ruppert has been actively involved in the distributed computing research community, serving on program committees for major conferences including PODC and WADS. His work has been published in top-tier venues such as Distributed Computing, Journal of the ACM, and proceedings of ACM SIGACT/SIGOPS conferences. At York University, Ruppert teaches courses on algorithms, theory of computation, and computational complexity. He has supervised numerous graduate students through project courses and thesis work. He is known for his dedication to teaching, including coaching students for the annual ACM programming contest. Ruppert is a member of the Theory of Computing Group at York University, which focuses on foundational aspects of computer science including algorithms, complexity theory, and distributed systems. His work bridges theoretical computer science with practical applications in concurrent and parallel computing.

Emmanuel Stefanakisمشاهده پروفایل

استاد

• Geospatial Data Science
• Digital Earth
• Discrete Global Grid Systems (DGGS)
• +۱۷ مورد دیگر

Emmanuel Stefanakis is a Professor and Department Head of Geomatics Engineering at the Schulich School of Engineering, University of Calgary. He holds a PhD in Electrical and Computer Engineering (National Technical University of Athens, 1997), an MScE in Geodesy and Geomatics Engineering (University of New Brunswick, 1994), and a Dipl.Eng in Rural and Surveying Engineering (National Technical University of Athens, 1992). His research focuses on Geospatial Data Science, Discrete Global Grid Systems (DGGS), GeoAI, and applications of Geomatics in natural hazards, transportation, and climate change . He has led over 140 student projects and authored/co-authored five textbooks and 150+ articles. Notable awards include the 2023 Canadian Cartographic Association’s Award of Distinction and the 2023 UCalgary Teaching Excellence Award. Recent articles emphasize high-performance trajectory analysis, DGGS integration, and geospatial quantum computing . His work bridges theoretical advancements with practical tools for flood modeling, epidemiology, and urban planning. Grants include NSERC Discovery Grants and collaborations with industry partners like McElhanney Ltd. Professional memberships span the Canadian Institute of Geomatics, Canadian Cartographic Association, and APEGA. He served as Editor-in-Chief of Cartographica (2014–2022) and actively contributes to international conferences on geoinformatics. His educational initiatives include innovative course designs in online and distance learning. Current roles include leadership in the HALOS and Geospatial Quantum Computing projects, advancing geomatics engineering education in the digital era.

Neal Madrasمشاهده پروفایل

استاد

• Probability Theory
• Discrete Mathematics
• Mathematical Modeling in the Sciences
• +۵ مورد دیگر

Neal Madras is a Full Professor in the Department of Mathematics and Statistics at York University's Faculty of Science. His research focuses on probability theory, discrete mathematics, and mathematical modeling in the sciences. He explores applications in statistical mechanics, theoretical computer science, communications networks, and biological systems. Notable interests include self-avoiding walks, polymer behavior modeling, and disease progression mechanisms. Madras has contributed to interdisciplinary studies, such as HIV dynamics and mosquito population modeling under climate influences. He is also actively involved in mathematics competitions like the Canadian Mathematical Olympiad. His work bridges theoretical rigor and practical applications, with a focus on stochastic processes, combinatorial problems, and computational methods. Publications span topics from graph theory and knot invariants to epidemic control strategies and polymer adsorption. Madras maintains an active research program, engaging with both foundational mathematical questions and real-world problem-solving.

Dimitris Koukoulopoulosمشاهده پروفایل

استاد

• Analytic Number Theory
• Multiplicative Number Theory
• Probabilistic Number Theory
• +۴ مورد دیگر

Dimitris Koukoulopoulos is a Professor of Mathematics at the Department of Mathematics and Statistics, Université de Montréal, where he holds the Chaire Courtois II in fundamental research. He is a member of the Centre de recherches mathématiques and the Montreal Number Theory Group. His research focuses on analytic number theory, with an emphasis on multiplicative and probabilistic aspects, the anatomy of integers and permutations, sieve methods, diophantine approximation, and additive combinatorics. His research explores core questions in number theory, including the distribution of prime numbers, probabilistic properties of integers, and the application of sieve methods to Diophantine problems. Recent work highlights include studies on multiplicative functions, random polynomials, and the Erdős–Hooley Delta function, demonstrating a blend of analytical and combinatorial techniques. Professor Koukoulopoulos actively mentors M.Sc. and Ph.D. students, emphasizing alignment with his research interests. His work contributes to the Montreal Number Theory Group’s collaborative research environment, fostering interdisciplinary advancements in number theory.

Hao Zhouمشاهده پروفایل

دانشیار

• Generative AI
• Molecular Modeling
• Protein Engineering
• +۳ مورد دیگر

Hao Zhou is a Research Associate Professor at the Institute for AI Industry Research (AIR), Tsinghua University. His research focuses on developing Generative AI models for discrete symbols such as text, small molecules, and proteins. Previously, he worked as a Research Scientist/Manager at Bytedance AI Lab. Currently, he leads the Generative Symbolic Intelligence group at Tsinghua AIR and co-leads the SIA Lab, a joint laboratory with ByteDance for LLM research. His research interests span molecular representation learning, generative models for scientific discovery, and applications in drug design and protein engineering. Recent work includes advancements in 3D molecular modeling, protein language models, and non-autoregressive sequence generation. These efforts aim to bridge AI techniques with scientific domains like chemistry and biology. Recent publications highlight contributions to areas such as auto-encoder-based molecular representation (Mol-AE), multi-scale protein modeling (ESM All-Atom), and structure-based drug design (MolCRAFT). These papers reflect a focus on improving generative models' accuracy and applicability to real-world scientific challenges. Best Short Paper of INLG (2022) Ranked #1 on WMT EN-DE translation (2021) CCF NLPCC Distinguished Young Scientist (2021) Best Paper Award of ACL (2021) CAAI Doctoral Dissertation Award (2019) He advises several students, including Qiying Yu, Yuxuan Song, and Danqing Wang, and oversees grants related to AI in drug discovery and molecular modeling. His labs are actively hiring postdocs and research assistants, and he encourages student interns to join collaborative projects. Key initiatives include the SIA Lab’s work on LLM research and the Generative Symbolic Intelligence group’s exploration of AI-driven scientific breakthroughs. These efforts emphasize interdisciplinary approaches to tackle complex challenges in AI and life sciences.

Sepehr Assadiمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Algorithm Design
• Complexity Theory
• +۷ مورد دیگر

Sepehr Assadi is an Associate Professor and Faculty of Mathematics Research Chair at the Cheriton School of Computer Science , University of Waterloo, since 2023. His research focuses on theoretical computer science , particularly algorithm design and complexity theory for modern computation models like streaming , distributed , and sublinear time algorithms. Recognized with the 2025 Presburger Award for groundbreaking work in multi-pass streaming lower bounds Developed hierarchical embedding technique for optimal lower bounds in graph streaming Key contributions to maximal independent set , maximum matching , and minimum spanning tree problems His work has been cited over 2,500 times (h-index: 30). He has served on program committees for major conferences (FOCS, STOC, COLT) and mentors students in graph algorithms and complexity theory . Awards include Sloan Research Fellowship , NSERC Discovery Grant , and Google Research Scholar Award .

• Medical Image Processing
• Multiple Sclerosis Research
• MRI Analysis
• +۲ مورد دیگر

Daniel García Lorenzo is a Research Fellow at the McConnell Brain Imaging Centre (BIC) within the Montreal Neurological Institute, McGill University, working under Dr. D. Louis Collins in the Image Processing Lab. His research develops automatic MRI-based pipelines for Multiple Sclerosis (MS) analysis with focus on atrophy quantification. His academic background includes: Ph.D. in robust MS lesion segmentation, Université de Rennes I (INRIA, Rennes), 2010 M.Sc. in image processing & AI, Telecom-Bretagne (Brest, France) M.Eng. in telecommunications, Universidad de Valladolid (Spain) His research centers on computational neuroimaging for MS, developing segmentation algorithms that integrate expectation maximization, mean shift, and graph cuts to handle multi-sequence MRI data. He investigates workflow dependencies in lesion segmentation and longitudinal atrophy analysis to predict disease progression. Publications from 2008-2010 reveal consistent focus on MS neuroimaging, with methodological innovations in adaptive fusion, workflow optimization, and bias correction. His work bridges computer vision (Markov models, pixon representation) and clinical neurology, extending to Parkinson's disease through SPECT/PET analysis of deep brain stimulation. Daniel contributes to the Image Processing Lab's open-source ecosystem, with segmentation algorithms available for online use through the BIC platform.

Luke Schaefferمشاهده پروفایل

استادیار

• Quantum Computing
• Theoretical Computer Science
• Combinatorial Game Theory
• +۴ مورد دیگر

Luke Schaeffer is an Assistant Professor at the University of Waterloo, affiliated with the Cheriton School of Computer Science and the Institute for Quantum Computing (IQC). His academic journey includes a BMath and MMath from Waterloo, a PhD from MIT under Scott Aaronson, and postdoctoral positions at Waterloo and UMD. His research bridges quantum computing and theoretical computer science, with focuses on quantum circuit complexity, classical simulation of quantum systems, and combinatorics on words. Notable projects include studying the Clifford group's structure, low-depth quantum vs. classical circuits, query complexity of regular languages, and fermion-to-qubit encodings. He also explores non-quantum areas like combinatorial game theory and cellular automata. His work on interactive quantum advantage protocols demonstrated separations between quantum and classical circuit models, including results against AC⁰[p] and NC¹ classes. He co-developed sample-optimal classical shadow algorithms for pure states and classified Clifford gates over qubits into 57 distinct classes. Education: BMath and MMath, University of Waterloo PhD in Computer Science, MIT (advisor: Scott Aaronson) Key Research Themes: Quantum advantage and circuit complexity Automata and combinatorics on words Classical simulation techniques for quantum systems Algorithmic foundations of quantum computing He advises students in the Cheriton School of Computer Science, emphasizing quantum computing and theoretical computer science. His contributions include foundational results in quantum-classical separations and decidability of word properties via first-order logic.

Philippa Pattisonمشاهده پروفایل

استاد

• Social Network Analysis
• Quantitative Psychology
• Mathematical Modeling of Social Processes
• +۳ مورد دیگر

Professor Philippa Pattison is a quantitative psychologist and Professor at the University of Melbourne, Australia. She is affiliated with the Melbourne School of Psychological Sciences and has held leadership roles, including Head of the School of Behavioural Sciences (2002-2003), President of the University’s Academic Board (2007-2008), Pro Vice-Chancellor (Learning & Teaching) (2009), and Deputy Vice-Chancellor (Academic) since May 2011. In her current role, she leads the University’s learning and teaching strategy, emphasizing high standards in the Melbourne Model programs. Education: Bachelor of Science (Hons) and PhD from the University of Melbourne. Her research focuses on developing mathematical models for social and behavioral phenomena, particularly social networks and network-based processes. She has pioneered work using exponential random graph models to analyze network data and enhance model fit for empirical studies. Awards: Elected Fellow of the Academy of the Social Sciences in Australia (1995). Academic Leadership: Her contributions extend beyond research to strategic academic governance, including curriculum design and academic policy. She has collaborated across the University to align academic programs with excellence in teaching and learning outcomes.

• Theoretical Computer Science
• Machine Learning
• Algorithms
• +۴ مورد دیگر

Nicholas J. A. Harvey is a Professor of Computer Science at the University of British Columbia. His research spans multiple areas of theoretical computer science with significant contributions to algorithms, machine learning theory, and network coding. Harvey's research focuses on the theoretical foundations of computer science with particular emphasis on: Theory and analysis of randomized algorithms Machine learning theory, including online learning and neural networks Network coding and information theory Combinatorial optimization and graph algorithms High-dimensional probability and statistics His work combines deep mathematical insights with practical applications in computer systems and networking. Analysis of Harvey's recent publications (2022-2025) reveals a strong focus on theoretical aspects of machine learning and optimization. His work spans continuous prediction with experts' advice, tight analyses of optimization algorithms like subgradient descent, and fundamental questions in randomized algorithms. A significant portion of his recent research addresses challenges in high-dimensional statistics, privacy-preserving estimation, and graph neural networks. His publications demonstrate a consistent pattern of addressing foundational theoretical questions with implications for practical machine learning systems. Harvey has maintained a strong publication record with an h-index of 35 and over 6,000 citations according to Google Scholar metrics. His influential papers include 'SkipNet: A Scalable Overlay Network with Practical Locality Properties' (2003), 'On the capacity of information networks' (2006), and 'Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks' (2019). Harvey has supervised numerous graduate students and postdoctoral researchers, contributing to the development of the next generation of theoretical computer scientists. His research has been supported by various grants from funding agencies including the Natural Sciences and Engineering Research Council of Canada (NSERC) and collaborations with industry partners. While specific details about laboratory facilities are not available in the provided information, Professor Harvey appears to be part of the theoretical computer science research group at the University of British Columbia, collaborating with researchers across multiple institutions on fundamental problems in algorithms and machine learning theory.

• Algorithm Design
• Dynamic Algorithms
• Graph Theory
• +۷ مورد دیگر

Gramoz Goranci is an Assistant Professor (tenure-track) of Algorithms in the Department of Computer Science at the University of Vienna. He leads research in algorithm design with strong interdisciplinary connections to optimization, graph theory, and machine learning. He is a member of the Research Group Theory and Applications of Algorithms and a board member of the Research Network Data Science. His research focuses on the design of fast dynamic algorithms for large-scale optimization problems, combining techniques from combinatorial data structures, algorithmic graph theory, numerical linear algebra, and metric embeddings. His work emphasizes both theoretical guarantees and practical efficiency. The recent publications highlight a consistent trend in dynamic and incremental graph algorithms, particularly in shortest paths, connectivity, flow, and facility location problems. His work increasingly bridges theoretical computer science with applications in machine learning and high-dimensional data. Keywords across publications include dynamic algorithms, graph sparsification, electrical flows, and optimization in metric spaces. FWF ESPRIT grant (supporting PostDoc Peter Kiss) He advises a growing team of researchers, including PostDoc Peter Kiss and PhD students Eva Szilagyi and Ali Momeni Mohammadabadi. His research is supported by independent funding and collaborations with leading institutions like ETH Zürich, University of Toronto, and UC Berkeley's Simons Institute. He has served on program committees of top conferences including FOCS, STOC, SODA, ICML, ESA, and ALENEX. He is actively involved in the academic community through seminars and talks at institutions such as ETH Zürich, University of Warwick, and Google Research. His team contributes to foundational work in dynamic graph algorithms and their applications in data science.

Jane (Pu) Gaoمشاهده پروفایل

استادیار

• Random graph theory
• Asymptotic graph enumeration
• Theoretical computer science
• +۲ مورد دیگر

Jane (Pu) Gao is an Assistant Professor in the Department of Combinatorics and Optimization at the University of Waterloo. Her research focuses on random graph theory, asymptotic graph enumeration, theoretical computer science, randomized algorithms, and stochastic processes. She has contributed to understanding sparse random graph structures, matroid evolution, and algorithmic processes in semi-random graph models. Her work often intersects probability, combinatorics, and discrete mathematics. Education: PhD in Combinatorics and Optimization, University of Waterloo (2009) Master's essay in Mathematics, University of Waterloo (2006) Research Trends: Her recent publications emphasize Hamiltonian cycle construction in semi-random processes, embedding theorems for random graphs, and the analysis of random regular graphs. These studies highlight applications in algorithm design, stochastic modeling, and structural graph theory. Grants & Collaboration: Collaborates widely with researchers like A. Frieze, N. Wormald, and P. Pralat on projects involving random graphs, matroids, and combinatorial optimization. No specific grants are listed here. Labs/Teams: While no specific lab is mentioned, her work is collaborative, leveraging interdisciplinary approaches within the Combinatorics and Optimization department.

Huajie Zhangمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Graphical and Probabilistic Models
• +۵ مورد دیگر

Huajie Zhang is a Professor at the Faculty of Computer Science , University of New Brunswick, with over 14 years of service. He holds a PhD in Computer Science from the University of Western Ontario and an MSc from Harbin Institute of Technology. Research Interests: Machine Learning Data Mining Graphical and Probabilistic Models Intelligent Systems Transfer Learning Semi-supervised Learning Efficient Learning Algorithms for Large Data Publication Trends: Huajie Zhang's work focuses on probabilistic models like Bayesian networks and Naive Bayes, emphasizing accuracy, efficiency, and adaptability in semi-supervised and transfer learning contexts. His recent papers explore scalable algorithms for big data, hybrid classifiers, and optimization techniques. Scientific Awards: Best Paper Award (Second Place) at FLAIRS Conference 2004 Teaching Activities: CS6735 Machine Learning CS3383 Algorithm Design

Yanglei Songمشاهده پروفایل

استادیار

• Sequential Decision-Making
• Mathematical Statistics
• High-Dimensional Statistics
• +۷ مورد دیگر

Yanglei Song is an Assistant Professor in the Department of Mathematics and Statistics at Queen's University, affiliated with the Faculty of Arts and Science. His research focuses on sequential decision-making problems, including hypothesis testing, change detection, and multi-armed bandits, alongside mathematical statistics such as U-statistics, high-dimensional statistics, and cut-point analysis. Education: Ph.D. in Statistics, University of Illinois at Urbana-Champaign (2019) M.Sc. in Mathematics, University of Illinois at Urbana-Champaign (2016) B.Eng. in Electronic Engineering, Tsinghua University (2012) Research Interests: Sequential decision-making frameworks with applications in hypothesis testing and stochastic optimization. Statistical methodologies for high-dimensional data and nonparametric modeling. Development of robust algorithms for reinforcement learning and adaptive systems. Mathematical foundations of U-statistics and their computational guarantees. Publications Trends: Recent work emphasizes theoretical advancements in bandit algorithms, robust model approximation, and statistical inference under covariate-adaptive randomization. Contributions span machine learning, computational statistics, and applied mathematics, with a focus on practical implementations and rigorous theoretical analysis. Students: J. Liu Y. Zhou Z. Wang Z. Zhao N. Li M. Zhou Labs/Teams: Maintains an active research group focused on statistical learning and sequential decision-making, with open-source projects such as Stratified incomplete local simplex tests for nonparametric regression analysis.