جست‌وجوی استاد و پژوهشگر

Stéphane Bouchardمشاهده پروفایل

استاد

• Clinical Cyberpsychology
• Virtual Reality Applications in Therapy
• Telepsychotherapy
• +۶ مورد دیگر

Stéphane Bouchard is a full professor at the Université du Québec en Outaouais (UQO) and co-director of the Cyberpsychology Laboratory. He specializes in clinical cyberpsychology, focusing on virtual reality (VR) applications in mental health, telepsychotherapy, and cybersecurity's impact on cyberspace. His research addresses anxiety disorders, cognitive behavioral therapy (CBT), body image perception, and culturally adapted treatments for Indigenous communities. He previously held the Canada Research Chair in Clinical Cyberpsychology (Tier 2). Research Interests: His work spans VR-based exposure therapy, telehealth, emotion regulation, and technology-enabled mental health solutions. Notable projects include developing AI-driven VR companions for dementia patients and co-designing CBT tools with Inuit communities in Quebec. His studies integrate physiological data (e.g., EDA, ECG) to predict emotional states and enhance therapeutic outcomes. Key Achievements: Canada Research Chair in Clinical Cyberpsychology (2020-2025) Over 100 peer-reviewed publications on VR therapy applications Leading randomized controlled trials (RCTs) on VR exposure for social anxiety and generalized anxiety disorder Labs & Collaborations: The Cyberpsychology Laboratory at UQO serves as a hub for interdisciplinary projects combining psychology, computer science, and healthcare innovation. Collaborations include partnerships with Quebec’s Inuit communities and international institutions focused on digital mental health solutions.

• Cybersecurity
• Machine Learning
• Privacy-Preserving Techniques
• +۷ مورد دیگر

Xichen Zhang is an Assistant Professor at the Department of Finance, Information Systems and Management Science within the Sobey School of Business at Saint Mary's University. His research focuses on cybersecurity, privacy-preserving techniques, federated learning, fake news detection, and machine learning applications in edge-cloud collaboration and mobile crowdsensing. He holds a Ph.D. in a relevant field and has contributed to interdisciplinary projects involving blockchain-based voting systems and IoT security solutions. His work emphasizes practical solutions to modern challenges in data privacy, misinformation detection, and distributed computing. Notable contributions include the development of the TruthSeeker dataset for real/fake content analysis and frameworks like SCORE for scalable contact tracing. His articles explore topics from adversarial machine learning defenses to temporal user credibility models in social networks. Dr. Zhang's research integrates technical innovations with societal impact areas like healthcare informatics and election security. He collaborates across disciplines to address privacy challenges in both academic and industrial contexts. Current projects investigate federated reinforcement learning for IoT systems and multimodal fake news detection using image-text consistency analysis.

Shine Hongمشاهده پروفایل

مدرس

• e-learning
• First-year Academic Writing
• Language Assessment
• +۴ مورد دیگر

Shine Hong is an Academic Director and Lecturer at Trinity Western University (TWU), serving in the TWU GLOBAL department. She specializes in teaching Writing 101, English 101, and leading a writing team as a Course Lead. Her roles include guiding first-year undergraduate students in the BA in Leadership program and overseeing U1 and OG programs. Education: MA in Teaching English as a Second Language (TESOL), Trinity Western University (2011-2012) M.E. in Environmental Engineering, Inha University, Incheon, South Korea (2003-2005) Research & Expertise: Shine’s research focuses on e-learning methodologies for academic writing in English for Academic Purposes (EAP) contexts. She is also skilled in curriculum design (online/hybrid formats), teacher coaching, and haptic pronunciation techniques. Affiliations: TESL Canada, BC TEAL. Courses taught include WRTG 101 (Writing and Culture), ENGL 101 (Introduction to Writing), and LING 315 (Computer-Assisted Language Learning). Labs/Teams: Leads a writing team as Course Lead and contributes to academic support initiatives for first-year students.

Kathryn Woodcockمشاهده پروفایل

استاد

• Amusement Rides and Attractions Safety
• Human Factors Engineering
• Safety Inspection and Accident Analysis
• +۳ مورد دیگر

Dr. Kathryn Woodcock is a Professor in the School of Occupational and Public Health at Toronto Metropolitan University. She directs the THRILL Lab, focusing on human factors in amusement ride safety and inclusive design. Her expertise spans accident analysis, safety inspection, and accessibility, with over 75 peer-reviewed publications. She teaches accident theory and safety evaluation in occupational health programs and coordinates the Invitational Thrill Design Competition for student teams. Education: PhD in Mechanical and Industrial Engineering (University of Toronto), MEng and BEng in Systems Design Engineering (University of Waterloo). Research Interests: Human factors in public spaces, amusement ride safety engineering, and accessibility for deaf and disabled populations. She emphasizes knowledge transfer through industry partnerships and standards development. Professional Contributions: Serves on ASTM Committee F24, TSSA Amusement Devices Advisory Council, and chairs the Journal of Themed Experience and Attractions Studies. Recognized with awards including the Ontario Medal for Good Citizenship and Safety Impact Award (2015). Advocacy: Promotes accessibility in themed attractions and supports deaf academics. Longest-serving Deaf professor in Canada and first Deaf woman to earn an engineering PhD in Canada.

Sameer Borwankarمشاهده پروفایل

استادیار

• Digital Platforms
• Human-AI Interaction
• Online Marketplaces
• +۵ مورد دیگر

Sameer Borwankar is an Assistant Professor in the Department of Information Systems at the Desautels Faculty of Management, McGill University. His research focuses on digital platforms, human-AI interaction, and platform economics, employing empirical models and machine learning tools. He holds a Ph.D. in Management from Purdue University's Daniels School of Business, along with dual master's degrees in Economics and MBA from the same institution. Prior to academia, he worked at Cummins Inc.'s digital implementation team and as a software engineer in financial services. His research investigates content platform moderation, multi-sided platform ecosystems, and unstructured data analysis. Notable areas include misinformation dynamics on social media, crowdsourcing for fact-checking, and AI's societal impacts. He teaches courses on information systems project management, technology project management, and AI/deep learning fundamentals. Recent work explores geopolitical AI developments, including China's DeepSeek and Qwen models, and the implications of platform policies on misinformation. His interdisciplinary approach bridges technical analysis with policy implications, addressing real-world challenges like data privacy and platform governance. Dr. Borwankar maintains an active blog discussing AI ethics, platform governance, and technology policy. He advises students in McGill's MBA, MM in Analytics, and PhD in Management programs, emphasizing practical applications of theoretical concepts.

Bruce Doréمشاهده پروفایل

استادیار

• Consumer Neuroscience
• Social Influence
• Marketing Communications
• +۳ مورد دیگر

Bruce Doré is an Assistant Professor in Marketing at McGill University's Desautels Faculty of Management and Director of the Affective Mechanisms Lab. His research focuses on understanding how ideas and emotions spread, combining methodologies from neuroscience, behavioral science, and computational modeling. He holds a PhD from Columbia University and a BSc from the University of Guelph. Research Interests: Prof. Doré explores mechanisms behind impactful communication, consumer word-of-mouth, and emotional contagion through interdisciplinary approaches. His work includes neuroimaging studies of decision-making, natural language processing, and computational modeling of social influence dynamics. Grants & Awards: SSHRC DNDRI Supplement (2022-2024) NSERC Discovery Grant (2021-2026) NSERC Early Career Supplement (2021-2022) SSHRC Insight Grant (2021-2025) McGill HBHL Startup Supplement (2021-2023) Advising & Labs: As Director of the Affective Mechanisms Lab, he leads projects on health messaging effectiveness and social network dynamics. His grants focus on topics like mental health communication, alcohol consumption behavior, and viral emotional content spread.

Stephen Thomasمشاهده پروفایل

استادیار

• Natural Language Processing
• Machine Learning
• Big Data
• +۴ مورد دیگر

Stephen W. Thomas is an Assistant Professor & Distinguished Teaching Fellow of Management Analytics at the Smith School of Business, Queen’s University. He holds a PhD in Computer Science from Queen’s University and has expertise in Natural Language Processing (NLP), machine learning, and data analytics. His research focuses on practical and fair AI applications, including bias detection and ethical considerations in NLP models. He has held roles as Executive Director of Smith's Analytics & AI Ecosystem and directs the Master of Management Analytics and Master of Management in Artificial Intelligence programs. His work bridges academia and industry, with consulting experience in big data and AI. Education: PhD (Computer Science, Queen’s University 2012), M.S. (Computer Science, University of Arizona 2009), B.S. (Computer Science, New Mexico State University 2006). Research Interests: NLP applications in messy real-world data, fair AI, AutoML for NLP, temporal databases, and mining software repositories. He has published in IEEE Transactions journals and led initiatives like τBench for temporal benchmarking. Awards: Scotiabank Scholar, multiple Professor of the Year awards, Queen’s Graduate Award Scholarship, and Raytheon Advanced Scholarship Program recipient. He has advised over 15 students in analytics and AI projects. Teaching: Courses include Machine Learning & AI, Text Analytics, Big Data, and Mathematical Analysis across Smith’s programs. He is a frequent speaker on AI ethics and management applications.

• Statistical Foundations of Data Science
• Large Language Models
• Distribution Shift & Transfer Learning
• +۴ مورد دیگر

Subha Maity is an Assistant Professor in the Department of Statistics & Actuarial Science at the University of Waterloo. Their research focuses on statistical foundations of data science, including large language models, distribution shift, performative prediction, fairness in ML, and missing data mechanisms. They hold a PhD from the University of Michigan (advised by Prof. Yuekai Sun and Prof. Moulinath Banerjee) and completed undergraduate/masters studies at the Indian Statistical Institute, Kolkata. Education: PhD in Statistics, University of Michigan Master's & Undergraduate, Indian Statistical Institute, Kolkata (Mathematics/Statistics) Recent work includes CARROT router for LLM optimization (arXiv 2024), AISTATS 2025 papers on strategic prediction and performative prediction, and NeurIPS 2024 contributions on weak supervision evaluation. Teaching includes computational statistics (Winter 2025) and undergraduate statistics courses since 2018. Labs/Teams: Active in developing benchmarks like SPROUT dataset and releasing models via HuggingFace community contributions.

Andrew Doxeyمشاهده پروفایل

استادیار

• Microbiology
• Genomics
• Virology
• +۵ مورد دیگر

Dr. Andrew Doxey is an Assistant Professor in the Department of Biology at the University of Waterloo. His research focuses on microbial genomics, toxin biology, and pathogen-host interactions, with particular expertise in bioinformatics-driven discovery of novel bacterial toxins and metagenomic analysis of infectious disease systems. His work spans diverse areas including virology, proteomics, and environmental microbiology, often integrating high-throughput sequencing technologies with computational modeling. Key research themes include the evolutionary origins of neurotoxins, functional characterization of microbial gene clusters, and development of bioinformatics tools for genomic analysis. His lab has made significant contributions to understanding Clostridium botulinum toxin evolution, metaproteomic profiling of human microbiota, and wastewater-based pathogen surveillance. He maintains active collaborations in virology, environmental microbiology, and computational biology. Recent studies highlight his work on SARS-CoV-2 pathogenesis, including gene expression dynamics in respiratory infections and the impact of cannabis smoke on immune responses. He has pioneered methods for metatranscriptomic profiling of pediatric sinusitis and developed open-source lateral flow immunoassay techniques for biomarker detection. No formal awards or grants are listed in the provided text, though his extensive publication record indicates sustained research activity. His research group's work is accessible via his personal lab website (http://doxey.uwaterloo.ca/), which details ongoing projects in microbial genomics and environmental surveillance applications.

Meiyappan Nagappanمشاهده پروفایل

دانشیار

• Empirical Software Engineering
• Mining Software Repositories
• Software Security
• +۳ مورد دیگر

Academic Profile Dr. Meiyappan (Mei) Nagappan is an Associate Professor at the University of Waterloo's Cheriton School of Computer Science. He holds a Ph.D. (2011) and M.Sc. (2008) from North Carolina State University, and a B.Sc. from Anna University, India (2006). Research Focus His research spans empirical software engineering, mining software repositories, and mobile app store analysis. Current investigations focus on software security, static analysis techniques, and diversity in software engineering. Recent work critically examines AI programming assistants like GitHub Copilot, evaluating their performance, security implications, and impact on developer workflows. Key Research Areas Diversity analysis in open-source contributions and academic publishing Vulnerability detection using machine learning Bug localization and automated debugging Cross-platform app analysis Publications Research demonstrates strong focus on empirical validation of developer tools, with recent emphasis on AI-assisted programming. Work appears in top software engineering venues covering security, testing, and developer productivity.

Sharon Fergusonمشاهده پروفایل

استادیار

• Computer-Supported Cooperative Work
• Human-Computer Interaction
• Future of Work
• +۵ مورد دیگر

Sharon Ferguson is an Assistant Professor in the Management Sciences Department at the University of Waterloo and a faculty affiliate at the Schwartz Reisman Institute for Technology and Society. Her research focuses on understanding and improving teamwork through digital trace data analysis, particularly in Human-AI collaboration and virtual environments. She holds a PhD in Mechanical and Industrial Engineering from the University of Toronto (2024), where she also earned a BASc in Industrial Engineering (2020). Her work has been recognized with awards such as the 2023 Teaching Assistant Award and the Reviewer's Favourite Award at the International Conference on Engineering Design. Ferguson has led projects on Human-AI decision-making, student retention in AI, and the impact of remote work on engineering teams. She previously interned at Slack’s Future of Work team and served as President of the Graduate Society of Women Engineers. Education: PhD in Mechanical and Industrial Engineering (2024), University of Toronto BASc in Industrial Engineering (2020), University of Toronto Research Interests: Computer-Supported Cooperative Work Human-Computer Interaction Human-AI Collaboration Virtual Collaboration Engineering Design Processes Diversity and Inclusion in STEM Publications Trends: Her recent work emphasizes leveraging digital communication data to study team dynamics, with a focus on psychological safety, gendered language patterns, and the impact of AI on decision-making. She frequently publishes in top venues like CHI and CSCW, addressing hybrid work challenges and innovation in distributed teams. Awards and Grants: NSERC Canada Graduate Scholarship Microsoft AI & The New Future of Work Grant (2024) Human-AI Interaction Fellowship Ethics of AI Fellowship Technology and Society Fellowship Advising & Labs: Currently recruiting graduate students at the University of Waterloo. Active in the Ready Lab (University of Toronto) and COoKIE Group, focusing on algorithmic solutions for shared understanding in teams. She also leads projects on student social belonging and conflict analysis in hybrid design teams.

Sirisha Rambhatlaمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Representation Learning
• +۴ مورد دیگر

Sirisha Rambhatla is an Assistant Professor at the University of Waterloo, holding appointments in the Management Sciences Department (Faculty of Engineering), Systems Design Engineering, and the David R. Cheriton School of Computer Science (Faculty of Mathematics). She leads the CriticalML lab, focusing on reliable AI for healthcare, aviation, and climate change, leveraging deep learning and spatiotemporal analysis. Her work bridges theory and practice, with publications at top venues like NeurIPS, ICLR, and clinical journals. Affiliations include the Waterloo AI Institute and the Waterloo Institute for Sustainable Aeronautics. Education: Ph.D. and M.S. in Electrical Engineering, University of Minnesota (2019, 2012) B.Tech in Electronics and Telecommunication Engineering, College of Engineering Roorkee (2010) Research Interests: Representation Learning, Spatiotemporal Data Analysis, AI for Healthcare, Computer Vision, and provable algorithms. Her work emphasizes interpretable AI and applications in surgery, intelligent automation, and climate modeling. Awards: 2021 Merit Award for Excellence in Postdoctoral Research E. Bruce Lee Memorial Fellowship University Bronze Medalist (2010) Teaching: Courses include Advanced Machine Learning (MSCI 546/MSE 546), Big Data Analytics (MSCI 623), and Special Topics in Management Engineering (MSCI 598/MSE 598). Labs/Teams: Leads the CriticalML lab, focusing on trustworthy AI systems for critical applications.

Hong Zhangمشاهده پروفایل

استادیار

• 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.

Victor Zhongمشاهده پروفایل

استادیار

• Machine Learning
• Natural Language Processing
• Reinforcement Learning
• +۳ مورد دیگر

Victor Zhong is an Assistant Professor in the Department of Computer Science at the University of Waterloo. He holds a PhD from the University of Washington (2023), an MS from Stanford University (2016), and a BASc in Computer Engineering from the University of Toronto (2014). His research focuses on machine learning, natural language processing, reinforcement learning, and artificial intelligence, with particular emphasis on dialogue systems, text-to-SQL translation, and multimodal agents. Zhong’s work addresses challenges in automated workflow generation, reward function design, and policy improvement using language feedback. His research interests include developing robust question answering systems (e.g., RoMQA benchmark), simplifying bioinformatics analysis through conversational interfaces, and evaluating language models in real-world enterprise contexts (e.g., Spider 2.0). He has contributed to foundational NLP tasks like dialogue state tracking, neural network-based query translation, and grounding language in dynamic environments. Recent publications explore open-ended tasks in real computer environments (OSWorld benchmark) and the limitations of parametric/non-parametric memories in language models. Zhong’s work bridges theoretical advancements with practical applications, aiming to automate data science workflows and enhance AI agents’ real-world adaptability. Though no specific grants or awards are listed, his prolific publication record reflects sustained innovation in machine learning and NLP domains.

Roger Melkoمشاهده پروفایل

استاد

• Quantum Computing
• Condensed Matter Physics
• Machine Learning in Physics
• +۳ مورد دیگر

Roger Melko is a Professor at the University of Waterloo, affiliated with research groups focused on Quantum Information & Computing and Quantum Matter. His work bridges quantum physics, machine learning, and computational methods to advance understanding of complex quantum systems. He is a prominent figure in applying neural networks to quantum state reconstruction and quantum simulation. Research interests span quantum computing architectures, condensed matter physics (e.g., quantum spin liquids, Hubbard models), and leveraging machine learning for quantum phase transitions, entanglement analysis, and device optimization. His contributions include neural network wavefunctions, quantum-assisted generative models for particle detection, and economic analysis of quantum technologies. Recent articles highlight advancements in quantum simulation using recurrent neural networks, hybrid quantum-classical algorithms for high-energy physics, and robust quantum dot autotuning. His work emphasizes interdisciplinary approaches, integrating AI with quantum hardware development.