Probabilistic Inference Professors and Supervisors in Canada

Yiping Guoمشاهده پروفایل

استاد

McMaster UniversityCanada

Dr. Yiping Guo is a Professor in the Department of Civil Engineering at McMaster University, specializing in urban drainage systems, water distribution networks, and resilience engineering. His research integrates probabilistic models to address climate change impacts on infrastructure, including flood control and stormwater management. He holds a P.Eng. license, emphasizing practical engineering applications. Education: B.Sc., Zhejiang University M.A.Sc. and Ph.D., University of Toronto Research Interests: Dr. Guo’s work focuses on hydrologic modeling, climate resilience, and uncertainty quantification. He explores innovative solutions for sustainable urban infrastructure, including low-impact development practices and green roofs. His expertise spans frequency and time series analysis of environmental data. Publications: His recent work emphasizes advanced sensors (e.g., piezoelectric and triboelectric technologies), climate change trends, and stochastic models for water resource systems. Key themes include material innovation for energy harvesting and environmental monitoring. Achievements: Notable contributions include analytical models for stormwater control measures and interdisciplinary teaching in civil and biomedical engineering capstone projects. Labs/Teams: Engaged in research clusters like Water Security Under Climate Change and Energy-Environment nexus. Collaborates on projects involving smart materials, sensor networks, and sustainable infrastructure design.

پروفایل رسمی دانشگاه

Martin Lysyمشاهده پروفایل

دانشیار

University of WaterlooCanada

Martin Lysy is an Associate Professor and Director of the Statistical Consulting and Survey Research Unit at the University of Waterloo. His primary affiliation is with the Department of Statistics, and he is actively involved in the fields of Statistics and Quantitative Finance. His research focuses on developing advanced statistical methodologies with applications in biostatistics, environmental science, neuroscience, and machine learning. Lysy’s work often involves creating robust computational tools and software packages to address complex problems in these domains. Key research interests include Bayesian inference, spatial statistics, stochastic processes, and the statistical analysis of biological systems such as microrheology and neural spike trains. He has contributed to the development of R packages like SpatialGEV for spatial extremes and nichROVER for ecological niche modeling. His recent work emphasizes methodological innovations in capture-recapture methods for population estimation, environmental data analysis, and computational efficiency in Bayesian frameworks. Notable contributions include studies on the lockdown effects on air quality in Ontario, Canada, and interdisciplinary projects integrating machine learning with systems pharmacology. Lysy’s research bridges theoretical statistics with real-world applications, ensuring practical relevance across diverse scientific disciplines.

پروفایل رسمی دانشگاه

Ihab F. Ilyasمشاهده پروفایل

استاد

University of WaterlooCanada

Ihab F. Ilyas is a Professor and holds the Thomson Reuters–NSERC Industrial Research Chair in Data Quality at the University of Waterloo's Department of Computer Science. His research focuses on data quality, machine learning for data enrichment, probabilistic data management, and knowledge graph systems. He leads projects like Holoclean (a probabilistic data repair system) and Data Civilizer (a data unification platform). His work bridges database systems and machine learning to address challenges in data integration, error detection, and large-scale knowledge representation. Education: PhD in Computer Science from Purdue University (2004), MSc and BSc from Alexandria University, Egypt (1999 and 1995). His publications span vector search, knowledge graph construction, and data curation techniques, with recent emphasis on adaptive indexing and scalable systems. He has contributed to open-source tools and frameworks for data cleaning and machine learning integration. Research interests include probabilistic data management, machine learning applications for data quality, and scalable systems for big data. His work often addresses real-world challenges in data integration and semantic representation, with a focus on practical, deployable solutions.

پروفایل رسمی دانشگاه

Qinglong Tianمشاهده پروفایل

استادیار

University of WaterlooCanada

Qinglong Tian is an Assistant Professor in the Department of Statistics at the University of Waterloo. His research focuses on statistical methodology development with applications in reliability engineering, machine learning, and predictive modeling. He specializes in areas such as nonparametric estimation, domain adaptation, privacy-preserving data analysis, and Bayesian reliability analysis. His work bridges theoretical statistics with practical challenges in engineering and healthcare sectors. Key research interests include semi-supervised learning, ROC curve analysis, label shift adaptation, and prediction interval methods. He has contributed novel techniques for handling imperfect reference standards, continuous target shift, and heterogeneous reliability data. His recent publications address challenges in post-randomization data privacy and model-agnostic parameter estimation. Qinglong's methodological innovations are applied to real-world problems like pipeline life assessment, diagnostic testing accuracy, and failure prediction in complex systems. His interdisciplinary approach combines rigorous statistical theory with computational methods to solve modern data science challenges.

پروفایل رسمی دانشگاه

Hong Zhangمشاهده پروفایل

استادیار

University of WaterlooCanada

Large-scale data analytics
Distributed ML training and serving systems
Application and network scheduling
+۵ مورد دیگر

Hong Zhang is an Assistant Professor at the University of Waterloo, specializing in large-scale data analytics, distributed machine learning systems, and cloud computing. His research focuses on advancing robust and efficient methods for modern AI systems, including topics like adversarial robustness, zero-knowledge proofs for deep learning, and scalable inference techniques for large language models. He holds a Ph.D. in Computer Science from the Hong Kong University of Science and Technology and dual bachelor's and master's degrees in Electronics and Communications Engineering from Huazhong University of Science and Technology. Education: Ph.D., Computer Science and Engineering, Hong Kong University of Science and Technology (2019) B.Sc. and M.Sc., Electronics and Communications Engineering, Huazhong University of Science and Technology, China (2013) Research Interests: Hong's work bridges theoretical foundations and practical applications in distributed ML systems, data center networking, and secure AI. Key areas include optimizing large-scale training/serve pipelines, mitigating adversarial vulnerabilities, and developing privacy-preserving techniques like zero-knowledge proofs for deep learning. His contributions span both algorithmic innovations and real-world system implementations. Publications Trends: Recent work emphasizes scalable inference acceleration (e.g., Eagle series), trustworthy AI (e.g., generative model guidelines), and cryptographic safeguards for ML (e.g., zkdl/zkllm frameworks). He frequently explores intersections between security, efficiency, and scalability in modern AI systems. Awards/Grants: No specific awards or grants mentioned in available texts. Advising & Labs: While student names aren't listed, his research group likely focuses on distributed systems and secure ML. Active collaborations likely involve cloud computing and adversarial machine learning domains.

پروفایل رسمی دانشگاه

Matthias Schonlauمشاهده پروفایل

استاد

University of WaterlooCanada

Matthias Schonlau is a Professor in the Department of Statistics at the University of Waterloo. He previously worked as a statistician at the RAND Corporation (1999-2011), where he led the RAND Statistical Consulting Service. He holds a PhD from the University of Waterloo (1997) and a Master's from Queen's University (1993). His research focuses on survey methodology, natural language processing for open-ended questions, data visualization, and statistical software development. Key contributions include the Hammock Plot for mixed data visualization and automated classification algorithms for open-ended survey responses. His work spans algorithmic innovation (e.g., occupation coding, multi-label classification) and statistical software tools (e.g., HAMMOCK and RFOREST modules for Stata). Recent projects address semi-automated classification, one-shot learning, and text dataset distillation. He has held sabbaticals at the University of Auckland (2015-2016) and DIW Berlin (2009-2010), collaborating with the Max Planck Institute. Major awards include the Humboldt Research Prize (2022) and ASA Fellowship. His publications emphasize bridging statistical methods with practical applications, including books like Applied Statistical Learning (2023) and peer-reviewed articles in computational statistics and machine learning.

پروفایل رسمی دانشگاه

Chen Sunمشاهده پروفایل

استادیار مدعو

University of WaterlooCanada

Chen Sun is an Adjunct Assistant Professor at the Department of Data and Systems Engineering, The University of Hong Kong. His research focuses on compiler testing, program analysis, static binary taint analysis, and software engineering methodologies. Notable areas include leveraging large language models (LLMs) for compiler testing baselines, probabilistic delta debugging, and enhancing program reduction techniques across multiple languages. Recent work addresses ransomware defense mechanisms, fuzzing countermeasures, and database management system (DBMS) bug discovery through configuration transformations. Research interests span compiler optimization, fault localization, cybersecurity, and algorithmic efficiency. His publications from 2023-2025 highlight trends in combining AI with traditional software engineering challenges, such as improving type inference for Java code and developing syntax-guided program reduction frameworks. Articles frequently address practical security concerns like protecting embedded devices from protocol fuzzing and mitigating memory leaks through sanitizer-based localization. No scientific awards are explicitly mentioned. Advising and grants sections remain underdeveloped in available data. Current affiliations emphasize academic-industrial collaboration through adjunct roles focused on cutting-edge software reliability and security research.

پروفایل رسمی دانشگاه

Robert Gensمشاهده پروفایل

پژوهشگر

McGill UniversityCanada

Robert Gens is a researcher at the University of Washington , affiliated with the College of Engineering and the Department of Computer Science and Engineering . His work focuses on advancing machine learning architectures, particularly Sum-Product Networks (SPNs) , with applications in computer vision and deep learning. Education: S.B. in Electrical Engineering and Computer Science from MIT (2009) , Ph.D. in Computer Science and Engineering from the University of Washington ( 2016 ). His research integrates insights from neuroscience, graphics, and mathematics to develop algorithms capable of modeling infinite visual data as stable concepts. Key contributions include structural learning, discriminative training, and computational efficiency in SPNs. Notable publications span NIPS , ICML , and ICLR venues, with a focus on SPN optimization and compositional modeling. Trends in his work emphasize tractable probabilistic models , neural network efficiency , and cross-disciplinary algorithm design . Awards include the Google PhD Fellowship in Deep Learning and an NIPS 2012 Outstanding Student Paper Award . Current research involves Deep Symmetry Networks at the Seattle Laboratory of Robotics.

پروفایل رسمی دانشگاه