جست‌وجوی استاد و پژوهشگر

Mingbin (Ben) Fengمشاهده پروفایل

دانشیار

• Quantitative Risk Management
• Financial Engineering
• Monte Carlo Simulation
• +۳ مورد دیگر

Mingbin (Ben) Feng is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo, where he also directs the Master of Actuarial Science Program. He holds professional certifications as an Associate of the Society of Actuaries (ASA) and Certified Analytics Professional (CAP). His research focuses on advanced simulation methodologies, risk management, and the integration of machine learning with financial and actuarial applications. Education: PhD in Industrial Engineering and Management Sciences, Northwestern University (2016) MS in Industrial Engineering and Management Sciences, Northwestern University (2012) MMATH in Actuarial Science, University of Waterloo (2011) BMATH in Actuarial Science and Operations Research, University of Waterloo (2010) Research Interests: Ben’s work bridges simulation analytics, actuarial science, and financial engineering. Key areas include: Green simulation techniques for output reuse and efficiency Nested simulation for tail risk estimation in complex portfolios Machine learning applications in pricing and risk management Stochastic optimization and portfolio design under risk constraints Systemic risk analysis in financial networks Publications: His recent research emphasizes scalable simulation methods for large portfolios and tail risk quantification. Notable contributions include advancements in likelihood ratio-based techniques, importance sampling, and the integration of clustering/Gaussian processes for efficient valuation. Awards: Associate of the Society of Actuaries (ASA) Certified Analytics Professional (CAP) Advising & Labs: Ben advises a dynamic research group involving PhD, MSc, and undergraduate students. His team developed the vamc R package for variable annuity modeling. Research focuses on simulation analytics, green simulation, and financial risk applications.

Christiane Lemieuxمشاهده پروفایل

استاد

• Quasi-Monte Carlo Methods
• Low-discrepancy Sequences
• Computational Finance
• +۳ مورد دیگر

Christiane Lemieux is a Professor in the Department of Statistics and Actuarial Science at the University of Waterloo and Associate Dean of Operations and Academic for the Faculty of Mathematics. Her research focuses on quasi-Monte Carlo methods, low-discrepancy sequences, and their applications in computational finance, machine learning, and risk management. Education includes a Ph.D. in Computer Science from Université de Montréal (2000), M.Sc. in Mathematics from Université de Montréal (1996), and B.Sc. in Actuarial Science from Université Laval (1994). Her research explores quasi-Monte Carlo methods for multidimensional integration, low-discrepancy sequence constructions, dependence concepts for point set characterization, machine learning-based construction paradigms, and applications in finance and risk management. Recent publications demonstrate strong emphasis on combining quasi-Monte Carlo methods with machine learning techniques, particularly graph neural networks for low-discrepancy point set generation and randomized methods for various computational applications. Awards include the Outstanding Performance Award from University of Waterloo (2012, 2018, 2022) and Journal of Complexity IBC Young Researcher Award (2004). She actively supervises graduate students and postdoctoral researchers, and has led several NSERC and MITACS grants supporting her research program.

Nathaniel Stevensمشاهده پروفایل

دانشیار

• Data Science
• Industrial Statistics
• Reliability Analysis
• +۶ مورد دیگر

Nathaniel Stevens is an Associate Professor of Statistics at the University of Waterloo, affiliated with the Department of Statistics and Actuarial Science and the Faculty of Mathematics. He holds roles as Director of the BMATH and BCS in Data Science programs and Director of the Business and Industrial Statistics Research Group (BISRG). His academic journey includes degrees from the University of Waterloo: a BMATH (2010), MMATH (2011), and PhD (2015) in Statistics. Research interests focus on the intersection of data science and industrial statistics, including experimental design, A/B testing, process monitoring, and reliability analysis. He develops methodologies for practical statistical challenges, such as measurement system comparisons and network surveillance. His work emphasizes methodological innovations with real-world applications in quality improvement and decision-making. Recipient of numerous awards, including the 2023 ENBIS Young Statistician Award, 2023 ASQ Feigenbaum Medal, and teaching distinctions like the 2022 Faculty of Mathematics Teaching Award. His contributions span over 40 peer-reviewed publications, with a focus on statistical engineering, network monitoring, and reliability analysis. Education: PhD (2015), MMATH (2011), BMATH (2010) in Statistics from University of Waterloo. Grants: Over $323K in funding, including NSERC Discovery Grants and university startup funds. Leadership: Past President of the Statistical Society of Canada’s Data Science Section, and active in editorial roles for journals like The American Statistician and Applied Stochastic Models in Business and Industry .

Michael Wallaceمشاهده پروفایل

دانشیار

• Biostatistics
• Public Health
• Medical Research
• +۳ مورد دیگر

Michael Wallace is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo, part of the Faculty of Mathematics. He is affiliated with research groups including Professors, Faculty, Statistics, and Biostatistics. His work focuses on statistical methodologies applied to healthcare and public health, with emphasis on dynamic treatment regimes, measurement error correction, and clinical trial design. He leads projects on personalized medicine, such as optimizing occlusion therapy for amblyopia and developing methods to address dietary intake inaccuracies in epidemiological studies. Recent research includes advancing photon absorption remote sensing for histopathology and investigating food security trends during global crises. His research interests span biostatistical methods, medical diagnostics, and public health policy. Notable contributions include developing the R package DTRreg for dynamic treatment regime estimation and advancing techniques like doubly-robust weighted ordinary least squares. He collaborates on clinical trials such as the Zoster Eye Disease Study and the ULISSE trial for uveitis diagnosis. His articles highlight methodological innovations in handling interference effects, nonadherence in treatment regimens, and interdisciplinary applications in ophthalmology and nutrition. Despite no listed awards, his work is widely cited in biostatistics and public health literature. He advises on statistical methodologies for medical imaging and dietary pattern analyses, contributing to both theoretical advancements and practical healthcare solutions.

Chengguo Wengمشاهده پروفایل

استاد

• Actuarial Science
• Finance
• Probability
• +۷ مورد دیگر

Chengguo Weng is a Professor in the Department of Statistics and Actuarial Science at the University of Waterloo, within the Faculty of Mathematics. His research spans actuarial science, finance, probability, and stochastic optimization, with a focus on risk assessment methods for insurance and financial management. He holds a PhD in Actuarial Science (2006–2009) from the University of Waterloo, and prior degrees from Zhejiang University, including a BSc (2001) and MSc (2004) in Statistics. Key research areas include stochastic optimization in insurance/finance, predictive modeling for risks, high-dimensional portfolio optimization, and statistical inference. Recent work emphasizes reinsurance design, mortality forecasting, and portfolio strategies. He advises multiple PhD and master’s students, including Yechao Meng, Zhiyi Shen, and Danqiao Guo, and collaborates with industry partners on applying academic research to real-world problems. Notably, his papers on optimal reinsurance under risk measures are among the most cited in Insurance: Mathematics and Economics . Dr. Weng is actively involved in academic events, organizing sessions at conferences like the Canadian Mathematical Society Winter Meeting and presenting on topics such as longevity risk modeling and portfolio selection. His work bridges theoretical research and practical applications, with a focus on industrial partnerships and technological innovation in finance and actuarial science.

Tony Wirjantoمشاهده پروفایل

استاد

• AI & Deep Learning in Cybersecurity
• Climate Change, Climate Finance & Climate Risk
• Computational Finance & Quantitative Finance
• +۲ مورد دیگر

Professor Tony Wirjanto holds dual appointments as a full professor at the School of Accounting & Finance (Faculty of Arts) and cross-appointed to the Cheriton School of Computer Science and Department of Statistics and Actuarial Science (Faculty of Mathematics) at the University of Waterloo. He specializes in econometrics and quantitative finance, with a focus on AI-driven cybersecurity, climate finance, and time series analysis. His research bridges finance, computer science, and environmental policy. Education: PhD in Econometrics from Queen’s University. He teaches courses on quantitative finance, financial econometrics, and sustainable finance. Notable grants include funding from the SOA Research Institute for climate change risk studies and SSHRC grants for systemic financial crises research. His work appears in top journals like Energy Economics and the Journal of Mathematical Finance. He leads professional initiatives at the World Economic Forum and contributes to institutes addressing climate, energy, and cybersecurity challenges.

Brent Petersenمشاهده پروفایل

استاد

• Digital Communications
• Antenna Arrays
• CubeSats
• +۶ مورد دیگر

Brent Petersen is a Professor and Director of Graduate Studies in the Department of Electrical and Computer Engineering at the University of New Brunswick (UNB). He holds a PhD from Carleton University and has held academic roles since 1997. His research focuses on optical and wireless communications, with expertise in digital communications, antenna arrays, CubeSats, and electromagnetics. Education: B.Eng., Carleton University M.A.Sc., University of Waterloo Ph.D., Carleton University Roles: Director of Graduate Studies (2023–present; 2006–2009) Leadership in CubeSat NB initiative His research interests span communications systems, signal processing, and aerospace applications. Key contributions include work on MIMO systems, antenna optimization, and CubeSat technology. He has advised over 30 graduate students and holds patents in wireless communication and sensor systems. Awards include the CNSR 2006 Best Paper Award and IBM's Invention Achievement Award. Publications focus on topics like wireless network optimization, antenna design, and space communications. He actively engages in academic governance, supervising graduate programs and contributing to curriculum development.

Alison Coxمشاهده پروفایل

استادیار

• Behavior Analysis
• Intellectual and Developmental Disabilities
• Psychopharmacology
• +۳ مورد دیگر

Alison Cox is an Assistant Professor in the Department of Applied Disability Studies at Brock University, affiliated with the Faculty of Social Sciences. She holds a Ph.D. in Psychology from the University of Manitoba and is a Board Certified Behavior Analyst at the doctoral level. Her expertise spans behavioral interventions for individuals with dual diagnoses, psychotropic medication impacts on behavior, and skill acquisition strategies. Dr. Cox’s research focuses on variables affecting challenging behavior, including psychotropic medication effects and treatment generalization/maintenance. She has published extensively in journals like the Journal of Applied Behavior Analysis and Behavior Modification . Her work emphasizes practical applications in clinical settings, such as program evaluations and staff training. Education: Ph.D. in Psychology, University of Manitoba Research Interests: Behavioral pharmacology, challenging behavior intervention, single-case methodology Her recent articles explore automation of functional analysis interpretation, medication training for behavior analysts, and program evaluations for treatment facilities. Dr. Cox has received multiple grants and awards, including the Faculty of Social Sciences Research Assistantship (2023) and the Raymond F. Currie Graduate Fellowship (2014–2015). She actively contributes to professional organizations like the Association for Behavior Analysis International (ABAI), serves on advisory committees, and delivers workshops on behavioral assessment and medication impacts. Her lab collaborates on projects addressing restraint application trends and psychotropic medication monitoring in clinical practice.

Pouria Ramaziمشاهده پروفایل

استادیار

• Systems and Control
• Decision-making
• Causality
• +۴ مورد دیگر

Pouria Ramazi is an Assistant Professor in Mathematics & Statistics at Brock University. He holds a Ph.D. from the University of Groningen (Netherlands) and a Postdoc from the University of Alberta (Canada). His research focuses on Systems & Control, Decision-making, Causality, Machine Learning, and Bayesian Networks, with applications in epidemiology, environmental science, and social dynamics. He teaches courses such as MATH 1P66, MATH 1P97, and STAT 4P89/5P89 (Bayesian & Causal Bayesian Networks). His work bridges mathematical theory with real-world problems, including forecasting disease outbreaks, analyzing vaccination decision-making, and modeling environmental systems. Recent studies include predicting cyanobacterial blooms using incomplete data and revealing behavioral strategies in public health contexts. Ramazi’s publications span top journals like PNAS Nexus, IEEE Transactions on Automatic Control, and the Journal of Royal Society Interface. He collaborates internationally on topics ranging from climate-dependent pandemic mitigation to multi-agent reinforcement learning for environmental impact assessment.

Xiaojian Xuمشاهده پروفایل

استاد

• Statistics
• Mathematical Modeling
• Data Analysis
• +۳ مورد دیگر

Xiaojian Xu is a Professor of Mathematics at Brock University, holding a PhD from the University of Alberta. His research focuses on optimal regression designs, robust statistical methods, and reliability engineering, particularly in accelerated life testing. He has published extensively in statistics and interdisciplinary fields such as biomedical engineering and sociology. Education: PhD, University of Alberta Research Interests: Dr. Xu specializes in developing robust statistical methodologies for non-standard data scenarios, including censored data, model misspecification, and heteroscedasticity. His work bridges theoretical statistics and applied problems in engineering, health sciences, and social sciences. Key areas include optimal experimental design, sequential sampling strategies, and mixed-effects modeling. Teaching: Recent courses include Mathematical Statistics I (MATH 2P82), Multivariate Statistics (MATH 5P86), and Linear Models (MATH 5P83). Labs/Teams: Affiliated with Brock University's Department of Mathematics and Statistics, contributing to interdisciplinary research initiatives involving reliability analysis and data science applications.

Matthew Johnsonمشاهده پروفایل

دانشیار

• Cosmology
• Theoretical Physics
• String Theory
• +۵ مورد دیگر

Matthew Johnson is an Associate Professor in the Department of Physics and Astronomy at York University and an Associate Faculty member at the Perimeter Institute for Theoretical Physics. His research focuses on theoretical cosmology, including cosmic inflation, eternal inflation, dark energy, and confronting fundamental theories with observations of the Cosmic Microwave Background (CMB). He explores topics such as topological defects, string theory, and gravitation, with a particular interest in designing data analysis algorithms to interpret cosmological datasets. Johnson's affiliations include York University and the Perimeter Institute. His work spans computational and theoretical approaches to understanding the universe’s origins and evolution. He collaborates widely, contributing to projects like the Atacama Cosmology Telescope and the CMB-HD Collaboration. His research interests emphasize cosmological models, phase transitions in the early universe, and the implications of string theory’s extra dimensions for cosmology. He investigates the Multiverse concept and the dynamics of bubble universes in eternal inflation scenarios. Johnson actively supervises graduate students in the York University Physics and Astronomy program and collaborates with institutions globally. His funding sources include NSERC, the Foundational Questions Institute, and the New Frontiers in Astrophysics and Cosmology grant program.

Paul Scholzمشاهده پروفایل

استادیار

• Astrophysical Transients
• Fast Radio Bursts
• Pulsars
• +۴ مورد دیگر

Paul Scholz is an Assistant Professor in the Department of Physics and Astronomy at York University, affiliated with the Faculty of Science. He is eligible to supervise students in the Physics and Astronomy Graduate Program. His research focuses on observational studies of astrophysical transients, particularly fast radio bursts (FRBs), pulsars, magnetars, and neutron stars using the Canadian Hydrogen Intensity Mapping Experiment (CHIME). He investigates FRB origins, their extreme environments, and their potential as probes of cosmic structure. Scholz's work emphasizes high-energy astrophysics and utilizes advanced radio telescopes and data analysis techniques. His research interests include analyzing FRB polarization properties, morphological characteristics, and their host galaxy environments. He collaborates on projects involving CHIME/FRB outriggers, real-time event alert systems, and multiwavelength studies. Scholz's contributions span instrumental design (e.g., CHIME/FRB out riggers) and statistical methods for classifying rare astrophysical events. Key research trends in his publications involve FRB localization, repeating FRB behavior, and connections to neutron star magnetism. His work also explores FRBs' utility in probing intergalactic media and dark matter. While no specific awards are listed, his extensive CHIME/FRB collaboration highlights his active role in advancing radio astronomy. Though no advising details or grants are explicitly stated, his eligibility to supervise and prolific publication record suggest active student mentorship and research funding. His involvement with CHIME reflects participation in large-scale astrophysical projects. Labs or teams are not explicitly mentioned, but his affiliation with CHIME/FRB indicates collaboration within this major observational facility.

Baoxin Huمشاهده پروفایل

استاد

• Remote Sensing
• Geomatics Engineering
• LiDAR Data Processing
• +۵ مورد دیگر

Dr. Baoxin Hu is a Professor in Geomatics Engineering at York University's ESSE department since 2003. She leads airborne LiDAR and hyperspectral missions, focusing on vegetation canopy characterization and wetland ecosystems. Her research integrates multi-source remote sensing data for environmental monitoring, including DEM generation and permafrost analysis. She directs the Earth Observation Laboratory located in Petrie Halls. Education background includes prior visiting fellowships at the Canadian Space Agency. Research emphasizes algorithm development for 3D scene reconstruction, forest succession stage analysis, and EAB infestation detection. Key areas include spatial-temporal modeling, InSAR technology applications, and machine learning integration with geospatial data. Publications span LiDAR-based methods (e.g., boundary adjustment algorithms, individual tree crown delineation) to multi-source data fusion for carbon modeling. Active in developing CNN frameworks for land cover classification and environmental impact assessments. Collaborates on projects involving RADARSAT-2 data and MODIS time-series analysis for northern Ontario ecosystems.

Jarek Gryzمشاهده پروفایل

استاد

• Database Systems
• Artificial Intelligence
• Cognitive Science
• +۴ مورد دیگر

Professor Jarek Gryz is a faculty member in the Department of Electrical Engineering and Computer Science at York University's Lassonde School of Engineering. He also holds an affiliation with the Department of Philosophy, exploring intersections between AI and cognitive science. His research focuses on database systems, artificial intelligence, data mining, and ethical implications of AI. Gryz has advised multiple PhD graduates and contributes to projects like privacy in AI, explainability challenges, and query optimization. His work spans theoretical foundations and practical implementations, including systems like DB2 and SPARQL query processing. Education: Doctorate in Computer Science (not explicitly stated in texts). Research Interests: Gryz investigates core database technologies (e.g., skyline queries, FP-tree algorithms) alongside emerging areas like AI ethics and privacy. He explores technical challenges in creating moral agents and ensuring transparency in black-box systems. His work bridges computer science with philosophy, addressing societal impacts of AI. Publications: Recent articles highlight advancements in database efficiency, ethical AI frameworks, and constrained clustering. Key themes include optimizing query performance, analyzing maximal vectors, and developing explainable algorithms. Awards: None explicitly mentioned. Advising & Grants: Gryz has supervised students such as Nima Shahbazi and Nikolay Yakovets, contributing to projects like FP-tree bounds and property path query planning. His research aligns with IBM’s DB2 optimizations and forensic data analysis (e.g., 3D crime scene databases). Labs/Teams: Engaged with York’s EECS department and interdisciplinary groups exploring AI ethics and cognitive science applications.

Aaron Bergمشاهده پروفایل

استاد

• Hydrology
• Remote sensing
• Agricultural water management
• +۳ مورد دیگر

Aaron Berg is a Professor and Graduate Program Coordinator in the Department of Geography, Environment & Geomatics at the University of Guelph's College of Social and Applied Human Sciences. He leads research on soil moisture monitoring using remote sensing and hydrological modeling, with applications in agriculture, climate interactions, and northern water systems. Research Networks: Transformative Sensor Technologies and Smart Watersheds Northern Water Futures Morwick G360 Groundwater Research Institute