جست‌وجوی استاد و پژوهشگر

Daniel Panarioمشاهده پروفایل

استاد

• Finite Fields
• Combinatorics
• Cryptography
• +۳ مورد دیگر

Professor Daniel Panario is a faculty member at Carleton University's School of Mathematics and Statistics. He holds the rank of Professor and specializes in areas such as Finite Fields, Combinatorics, and Cryptography. His research focuses on the analysis of algorithms, computational number theory, and applications in cryptography and coding theory. Education: PhD in Computer Science from the University of Toronto, postdoctoral research at the University of Waterloo, and further academic roles at institutions in Brazil and Uruguay. His expertise spans theoretical and applied mathematics, with contributions to discrete mathematics and algorithmic research. Research Interests: Finite Fields and their applications, combinatorics, cryptography, coding theory, and computational algebra. He has supervised numerous students and postdoctoral researchers, contributing to over 200 publications and holding editorial roles in top journals. Grants and Editorial Work: Editor for journals like Applicable Algebra in Engineering and Cryptography and Communications. He has organized international conferences and workshops, advancing the field of finite fields and discrete mathematics. Labs/Teams: Active member of the Ottawa-Carleton Discrete Mathematics Group, collaborating on combinatorial and algebraic research projects.

Simone Brugiapagliaمشاهده پروفایل

دانشیار

• Mathematics of Data Science
• Computational Mathematics
• Numerical Analysis
• +۵ مورد دیگر

Simone Brugiapaglia is an Associate Professor in the Department of Mathematics and Statistics at Concordia University in Montréal, Canada. His academic journey includes a PhD in Mathematical Models and Methods from Polytechnic University of Milan (2016), an MSc in Mathematics from University of Pisa (2012), and a BSc in Mathematics from University of Pisa (2010), all earned cum laude . Prior to his current role, he held postdoctoral positions at École polytechnique fédérale de Lausanne (2016) and Simon Fraser University (2016-2019). Dr. Brugiapaglia's research bridges mathematics, data science, and computational methods. Key interests include: Foundations of deep learning and neural networks Compressed sensing and sparse recovery algorithms High-dimensional approximation theory Numerical methods for PDEs and diffusion equations Physics-informed machine learning Optimization techniques for large-scale problems His work develops rigorous mathematical frameworks for data-driven algorithms. His publications (30+ including two books) consistently focus on high-dimensional computation , featuring recent advances in neural network theory (e.g., generalization bounds, rank collapse), compressed sensing techniques (e.g., greedy algorithms, unrolled networks), and physics-informed learning. A strong trend involves combining traditional numerical methods with deep learning for PDE solutions. Awards & Fellowships: Concordia Research Fellow (2023) Leslie Fox Prize for Numerical Analysis (2nd place, 2019) PIMS Postdoctoral Fellowship (2016-2018) Multiple INdAM scholarships during graduate/undergraduate studies He has supervised over 20 trainees across postdoctoral, graduate, and undergraduate levels. While specific grants aren't detailed, his fellowship history indicates sustained research funding. No explicit research labs or teams are mentioned, but his supervision record and collaborative publications suggest active leadership in research groups focused on computational mathematics.

• Statistical Inference
• Machine Learning
• Bioinformatics
• +۳ مورد دیگر

Lucy Gao is an Assistant Professor in the Department of Statistics at the University of British Columbia (UBC). Her research focuses on statistical inference, machine learning, and bioinformatics, with particular emphasis on clustering methods, high-dimensional data analysis, and optimal design theory. She holds a faculty position within UBC’s Faculty of Science and is affiliated with the Vancouver Campus. Her work spans theoretical developments in statistical methodologies and applications to biological data, such as single-cell RNA sequencing analysis. She has contributed to areas like selective inference for hierarchical clustering and algorithmic optimization for complex systems. Her email is lucy.gao@stat.ubc.ca, and she maintains a website at https://www.lucylgao.com/ . Recent publications highlight her expertise in data thinning techniques, latent variable modeling, and multiview network analysis. She has explored topics ranging from negative binomial count splitting in genomics to bilevel optimization algorithms. Her research bridges statistical theory and practical computational challenges in modern data science. No scientific awards or advising records are explicitly listed in the provided materials. Her academic contributions are primarily through peer-reviewed articles and methodological innovations.

Javier Orlandiمشاهده پروفایل

استادیار

• Neuroscience
• Complexity Science
• Statistical Physics
• +۵ مورد دیگر

Javier Orlandi is an Assistant Professor in the Department of Physics and Astronomy at the University of Calgary, where he leads the Complex Neural Systems Lab as a Canada Research Chair. His research bridges systems neuroscience, complexity science, and statistical physics, focusing on understanding distributed coding principles in the brain and their impact on perception and learning. He employs computational and experimental approaches, including large-scale calcium imaging and optogenetic manipulation, in collaboration with institutions such as the Hotchkiss Brain Institute and the Computational Neuroscience Platform. Orlandi earned his Ph.D. in Physics from the University of Barcelona (2015), studying spontaneous activity in neuronal cultures. Subsequent postdoctoral research included work at the University of Barcelona (2015–2016) on Huntington’s disease, followed by the University of Calgary (2016–2019) and RIKEN Center for Brain Science in Japan (2019–2022), where he explored cortical processing during decision-making. His research has addressed criticality in neural systems, network inference, and the impact of genetic mutations on brain activity. Key areas of interest include self-organized criticality, neuro-glia communication, and the development of tools like NETCAL for analyzing calcium imaging data. Orlandi has organized challenges to advance connectomics and holds the Eyes High Postdoctoral Fellowship and Canada Research Chair awards. His work integrates theoretical models with experimental data to uncover fundamental principles of neural computation. Affiliations: Complexity Science Group, Hotchkiss Brain Institute, Computational Neuroscience Platform Education: Ph.D. in Physics (University of Barcelona, 2015) Grants: Canada Research Chair, Eyes High Postdoctoral Fellowship Labs: Complex Neural Systems Lab at University of Calgary

Lap Chi Lauمشاهده پروفایل

استاد

• Algorithms
• Optimization
• Spectral Graph Theory

Lap Chi Lau is a Professor at the University of Waterloo's Cheriton School of Computer Science. His primary research explores algorithms, optimization, and spectral graph theory. His recent publications focus on graph algorithms, spectral methods, and combinatorial optimization, with consistent applications to network design and experimental methodologies. Publications demonstrate advanced techniques in graph sparsification, eigenvalue methods, and efficient algorithm design for complex computational problems. His work frequently intersects theoretical computer science and applied mathematics, contributing to foundational improvements in graph partitioning, spectral clustering, and randomized algorithms.

• Power-law distributions
• Normal-Laplace distributions
• Circular distributions
• +۶ مورد دیگر

William J. Reed is a Professor in the Department of Mathematics and Statistics at the University of Victoria, with a distinguished career spanning theoretical and applied statistics. His work bridges mathematical theory with real-world applications across ecology, finance, and biology, focusing on distributional phenomena in complex systems. Dr. Reed earned his Ph.D. from the University of British Columbia and has maintained an active research program for over three decades. His scholarly contributions are characterized by rigorous statistical modeling of natural and socioeconomic patterns. Research centers on probability distributions—particularly power-law, Normal-Laplace, and circular distributions—with applications in forest fire dynamics, income inequality, gene family evolution, and financial markets. His work explains why power-laws emerge universally across disciplines through mechanistic stochastic models. Recent publications emphasize survival analysis with bathtub-shaped hazard rates and directional data modeling. His publication trend (2000-2010) reveals interdisciplinary innovation: developing the double Pareto-lognormal distribution for size phenomena, modeling sexually transmitted disease networks, and creating Brownian-Laplace motion for financial applications. These works consistently connect theoretical distribution theory to empirical patterns in nature and society. Scientific Awards: No specific awards documented in source material Dr. Reed has supervised six graduate students on distribution-focused theses, including Peter Ott (1995) on animal abundance estimation, Tony Ho (1997) on wildfire modeling, and Fan Wu (2008) on Normal-Laplace applications. While grant details aren't specified, his collaborative work with B.D. Hughes demonstrates sustained research productivity across mathematical biology, economics, and environmental science.

Mikael Pichotمشاهده پروفایل

دانشیار

• Geometric Group Theory
• Analysis
• Group Theory
• +۳ مورد دیگر

Mikael Pichot is an Associate Professor in the Department of Mathematics and Statistics at McGill University, located in Montreal, Quebec. His research focuses on Geometric Group Theory, Analysis, and related areas such as Operator Algebras and Metric Geometry. He is actively involved in organizing academic events and seminars, including the Group Theory Seminar and CRM-ISM Colloquium, and has contributed to numerous collaborations, often with co-authors like Sylvain Barre and Robert Graham. His teaching includes courses such as Math 223 (Linear Algebra) and Math 570/571 (Higher Algebra sequences). Pichot's research interests span topics like geometric group actions, sofic dimension, L2 invariants, and random groups. He has published extensively in peer-reviewed journals and presented at international conferences. His work often bridges algebraic, geometric, and analytic approaches to group theory. Despite his active academic contributions, no specific awards or grants are explicitly listed in the provided texts. He maintains a personal webpage and collaborates on projects such as 'Surgery on discrete groups' and 'Random groups and nonarchimedean lattices.' His research also touches on foundational questions in operator algebras and geometric topology, reflecting a deep engagement with the structural properties of groups and their applications.

Daniel Wiseمشاهده پروفایل

استاد

• Geometric Group Theory
• Geometry
• Topology
• +۳ مورد دیگر

Professor Daniel Wise is a James McGill Professor in the Department of Mathematics and Statistics at McGill University. His research focuses on Geometric Group Theory, with contributions to the study of cubical complexes, hyperbolic groups, and subgroup structures. He is renowned for his work on the virtually special cube complexes, which has had profound implications in low-dimensional topology and geometric group theory. Dr. Wise has received prestigious awards, including the 2016 Guggenheim Fellowship and the 2016 Jeffrey-Williams Prize. His research interests span geometric group theory, topology, and combinatorial group theory, with a particular emphasis on the interplay between algebraic properties of groups and their geometric realizations. He has authored influential papers on topics such as cubulation of groups, growth rates of subgroups, and the structure of hyperbolic groups. His work has been recognized through collaborations with leading institutions and his contributions to resolving key conjectures in the field. Dr. Wise’s research also touches on cube complexes, nonpositive curvature, and the geometry of 3-manifolds.

Evangelos E. Miliosمشاهده پروفایل

استاد

• Visual Text Analytics
• Modelling and Mining of Networked Information Spaces
• Text Mining
• +۸ مورد دیگر

Evangelos E. Milios is a Professor in the Faculty of Computer Science at Dalhousie University , Halifax, Nova Scotia. He has been a faculty member since 1998 and leads the MALNIS (Machine Learning and Networked Information Spaces) research group. He is affiliated with the Institute of Big Data Analytics and served as Scientific Director of DeepSense , an innovation hub for ocean data analytics. Education: PhD in Electrical Engineering and Computer Science, MIT (1986) SM & EE, MIT (1983) Dipl. Eng. in Electrical Engineering, NTUA, Greece (1980) His research focuses on visual text analytics, text mining, graph mining, social network analysis, and machine learning . He has made significant contributions to modeling and mining of networked information spaces, with applications in data science and AI. The recent publications reflect a strong trend in data mining, robotics, pattern recognition, and semantic analysis , particularly in log analysis, pose estimation, and information retrieval. His work bridges theoretical algorithms with practical applications in robotics and web technologies. Scientific Awards and Honors: Distinguished Research Professor (2017–2022) Killam Chair in Computer Science (2006–2011) Senior Member, IEEE Professional Engineer, Ontario (1998–2024) He has served in key administrative roles including Associate Dean, Research (2008–2017) and Director of the Graduate Program (1999–2002) . He has supervised numerous graduate students and taught a wide range of courses in AI, machine learning, data science, and networking. His research is supported by major grants and collaborations, including NSERC and industry partnerships. Research Labs and Teams: MALNIS – Focuses on machine learning and networked information spaces. DeepSense – Ocean data analytics and AI innovation. Institute of Big Data Analytics – Cross-disciplinary big data research.

Nandini Dendukuriمشاهده پروفایل

دانشیار

• Biostatistics
• Diagnostic Test Accuracy
• Bayesian Inference
• +۵ مورد دیگر

Nandini Dendukuri, PhD is a Scientist at the Research Institute of the McGill University Health Centre (RI-MUHC) at the 5252 de Maisonneuve site. She holds dual affiliations with the Infectious Diseases and Immunity in Global Health Program and the Centre for Outcomes Research and Evaluation (CORE). Academically, she serves as an Associate Professor in the Department of Medicine, Faculty of Medicine and Health Sciences at McGill University, specifically within the Division of Clinical Epidemiology at the MUHC. Dr. Dendukuri's research program centers on the development of advanced statistical methodologies for diagnostic test accuracy evaluation in the absence of perfect reference standards. Her work spans Bayesian inference, latent class modeling, and meta-analysis techniques specifically designed for diagnostic accuracy studies. She has made seminal contributions to addressing methodological challenges such as conditional dependence between diagnostic tests, verification bias, and limitations of composite reference standards. Her research bridges theoretical statistical methodology with practical applications in infectious disease diagnostics, particularly tuberculosis where perfect reference tests are often unavailable. An analysis of Dr. Dendukuri's extensive publication record reveals consistent innovation in diagnostic test methodology with increasing sophistication in statistical modeling approaches. Her recent work (2024-2025) demonstrates expansion into diverse application areas including cardiovascular disease, emerging infectious diseases like COVID-19, and novel diagnostic technologies, while maintaining her core methodological focus. Her publications show particular strength in applying latent class models to complex diagnostic scenarios across multiple disease domains. Dr. Dendukuri actively collaborates with researchers across multiple disciplines, particularly in epidemiology, infectious diseases, and technology assessment. Her work with hospital-based technology assessment units has directly contributed to translating scientific evidence into healthcare policy. She maintains strong connections between statistical theory and practical healthcare applications, frequently developing software implementations (particularly rjags programs) of her methodological innovations to facilitate adoption by the research community.

Apurva Narayanمشاهده پروفایل

استاد مدعو

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۶ مورد دیگر

Dr. Apurva Narayan is an Affiliate Professor in Computer Science and Data Science, and an Associate Member in Mechanical Engineering at the University of British Columbia's Irving K. Barber Faculty of Science. His research focuses on artificial intelligence, machine learning (particularly explainable AI and quantum ML), data mining, cybersecurity of cyber-physical systems, and decision-making under uncertainty. He holds a PhD from the University of Waterloo (Systems Design Engineering) and a Bachelor's in Electrical Engineering from Dayalbagh Educational Institute. Education: PhD in Systems Design Engineering, University of Waterloo (2015) Bachelor of Engineering in Electrical Engineering, Dayalbagh Educational Institute (2008) Research Interests: Explainable AI/ML and quantum machine learning Data analytics and mining Safety/security of cyber-physical systems Graph-theoretic analysis of complex systems Decision-making under uncertainty Reverse engineering complex software systems Awards: Systems Society of India Young Scientist Award (under 40) MITACS Accelerate Award (2013) Dr. T.E. Unny Memorial Award (2011) Multiple teaching nominations at University of Waterloo Advising & Grants: Supervises graduate students in AI/ML and cybersecurity. Secured grants including MITACS Accelerate and Waterloo scholarships. Active in patent filings related to timed regular expressions. Labs/Teams: Engaged in collaborations within the UBC Department of Computer Science and cross-disciplinary projects with Mechanical Engineering on system reliability and quantum computing applications.

Ram Murtyمشاهده پروفایل

استاد

• Number Theory
• Mathematical Logic
• Indian Philosophy
• +۵ مورد دیگر

Ram Murty is the A. V. Douglas Distinguished University Professor and Queen's Research Chair at Queen's University , cross-appointed to the Departments of Mathematics and Philosophy . His research spans number theory , mathematical logic , and Indian philosophy . He earned his Ph.D. from MIT in 1980 under the supervision of Harold Stark. His research focuses on zeta and L-functions , prime number distribution , modular forms , elliptic curves , and cryptography . He has made significant contributions to Artin's conjecture , the Langlands program , and Selberg's conjectures . Prof. Murty has authored over 20 books and 200 research papers . His notable works include Introduction to the Circle Method (2023), Indian Philosophy: An Introduction (2013), and The Mathematical Legacy of Srinivasa Ramanujan (2012). His publications span a wide range of topics in pure and applied mathematics. He has received numerous accolades, including: CRM-Fields-PIMS Prize (2024) Fellow of the Royal Society of Canada (1990) Fellow of the American Mathematical Society (2012) Simons Fellowship (2013–2014) Queen's Research Chair (2002) Killam Research Fellowship (1998–2000) Jeffery-Williams Prize (2003) He has supervised over 40 Ph.D. and M.Sc. students , including notable alumni like David Clark , Francesco Pappalardi , Chantal David , and Kaneenika Sinha . He currently mentors Nicolo Fellini and Becca Carter as doctoral students. Prof. Murty is also actively involved in graduate education and mentorship , having received the Award for Excellence in Graduate Student Supervision (2018) . He holds adjunct professorships at institutions such as McGill University, TIFR Mumbai, and IIT Mumbai.

Andrei Bulatovمشاهده پروفایل

استاد

• Constraint problems
• Computational complexity
• Combinatorics
• +۳ مورد دیگر

Andrei Bulatov is a Professor of Computing Science at Simon Fraser University (SFU), affiliated with the School of Computing Science within the Faculty of Applied Sciences. His research focuses on computational complexity, constraint satisfaction problems (CSP), combinatorics, and universal algebra. Bulatov earned his Ph.D. and M.Sc. in Mathematics from Ural State University, Russia, in 1995 and 1991, respectively. His work bridges theoretical computer science and algebra, with notable contributions to the complexity classification of CSPs. He has received a Best Paper Award at FOCS 2002 for his dichotomy theorem on three-element set constraints. Bulatov’s research also explores counting CSPs, algorithms for satisfiability, and applications of algebraic methods in discrete mathematics. He is part of the Algorithms & Theory Group and the Computational Logic Laboratory at SFU. Bulatov’s publications span journals like Journal of Computer and System Sciences, Theoretical Computer Science, and SIAM Journal on Computing, addressing topics from graph theory to randomized algorithms. His academic service includes roles in professional organizations and editorial work. Bulatov’s teaching includes courses such as Discrete Mathematics (MACM 101) and Directed Reading (CMPT 894).

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.

Anthony Quasمشاهده پروفایل

استاد

• Ergodic Theory
• Dynamical Systems
• Probability
• +۳ مورد دیگر

Anthony Quas is a Professor of Mathematics and Statistics at the University of Victoria, where he holds a Canada Research Chair and serves as the Pacific Institute for the Mathematical Sciences (PIMS) Site Director. He actively contributes to PIMS through membership on the Digital Collaboration Committee and as editor of the educational publication "Pi in the Sky" magazine, fostering mathematical outreach and collaboration. His academic foundation includes a Ph.D. earned at the University of Warwick (UK), establishing his expertise in advanced mathematical analysis. His research specializes in ergodic theory and dynamical systems, with significant investigations into Lyapunov exponents, matrix product stability, mixing phenomena in stochastic processes, and applications to random graph theory. His work bridges pure mathematics with physical modeling, addressing perturbation effects in deterministic systems and probabilistic structures in metric spaces. Dr. Quas has been honored with the prestigious Canada Research Chair award, recognizing his scholarly contributions. His professional activities extend to organizing PIMS-supported symposia, contributing to industrial mathematics initiatives like the M2PI program, and presenting solutions to complex problems from platforms like MathOverflow. Through editorial leadership and committee service, he advances mathematical education and interdisciplinary collaboration across academic and industrial sectors.