جست‌وجوی استاد و پژوهشگر

Houari Sahraouiمشاهده پروفایل

استاد

• Software Engineering
• Automated Software Engineering
• Model-Driven Engineering
• +۵ مورد دیگر

Houari Sahraoui is a Full Professor and Vice Dean of Planning and Infrastructure at the Faculty of Arts and Sciences, University of Montreal, where he also previously served as Department Director from 2013 to 2017. He leads the GEODES research group (Groupe de recherche sur les systèmes ouverts et distribués et l'expérimentation dans les logiciels) and has been actively supervising graduate students and conducting research in software engineering since at least 2000. Dr. Sahraoui's research focuses on automated software engineering , with particular emphasis on model transformations learning from examples using evolutionary approaches. His work spans reverse engineering (comprehension) and reengineering (refactoring, migration to component-based software), utilizing static and dynamic analysis techniques. He also investigates visualization of large sets of multidimensional data for software comprehension and maintenance. His research integrates artificial intelligence approaches to enhance various phases of the software development lifecycle. His recent publications demonstrate a strong trend toward integrating large language models and artificial intelligence with traditional software engineering practices. The research spans code review automation, model-driven engineering, microservices identification, and digital twins for applications like vertical farming. Many publications focus on improving automation through techniques like parameter-efficient fine-tuning, knowledge distillation, and social diversity metrics. Dr. Sahraoui was named a Fellow of Automated Software Engineering in 2023, an honor recognizing his significant and sustained contributions to the ASE community, both scientifically and professionally. This prestigious title is awarded by the IEEE/ACM International Conference Steering Committee. Dr. Sahraoui has supervised over 50 Master's and PhD students throughout his career, with recent supervision focusing on AI-assisted software engineering, code review automation, and model-driven approaches. His research has been supported by multiple grants from the Natural Sciences and Engineering Research Council of Canada (NSERC), MITACS, and industry partners, with projects spanning from 2000 to projected completion in 2031. He leads the GEODES research group (Groupe de recherche sur les systèmes ouverts et distribués et l'expérimentation dans les logiciels) which focuses on open and distributed systems and software experimentation. His current projects include 'Improving automation and assistance for software engineering tasks with generative AI' (2025-2031) and work on digital twins for vertical farming.

• Neuroscience
• Cognitive Psychology
• Autism Spectrum Disorders
• +۷ مورد دیگر

Boutheina Jemel is an Associate Professor at the School of Speech-Language Pathology and Audiology within the University of Montreal . Her research focuses on neurocognitive processes in visual and auditory information processing, particularly in autism spectrum disorders and developmental language disorders .

• Computer Vision
• Deep Learning
• Continual Learning
• +۴ مورد دیگر

Eugene Belilovsky is an Associate Professor at the University of Montreal's Department of Computer Science and Operational Research and an Assistant Professor at Concordia University's Department of Computer Science and Software Engineering. He is also an Associate Member of Mila – Quebec Institute for Artificial Intelligence. His research focuses on computer vision, deep learning, and their applications in areas like continual learning and few-shot learning at the intersection of vision and natural language processing. Belilovsky's expertise includes distributed systems, federated learning, and optimization. His work addresses challenges in model generalization, spurious correlations, and efficient training strategies. He has advised numerous graduate students, including Charles-Étienne Joseph, Medric B. Djeafea Sonwa, Gwendolyne Legate, and Irene Tenison. His recent publications highlight contributions to federated learning, continual pre-training, and fairness in AI systems. Notable work includes optimizing distributed learning protocols and mitigating forgetting in evolving data streams. Belilovsky's research also explores clinical applications, such as diagnosing hepatic steatosis using ultrasound imaging through deep learning techniques. He collaborates with institutions like Mila and the DIRO department, advancing interdisciplinary projects in AI-driven healthcare, robotics, and language modeling.

Wei Wangمشاهده پروفایل

استادیار

• Leadership
• Business Ethics
• Organizational Behavior
• +۳ مورد دیگر

Wei Wang is an Assistant Professor and The Associates Fellow in Leadership and Ethics at the Business Administration Department, Asper School of Business, University of Manitoba. His research focuses on leadership dynamics and ethics in the workplace, employing mixed-method approaches. He has been recognized with the 2019 Society of Business Ethics Emerging Scholar Award and the 2022 Anne Tsui Dare to Care Research Award for impactful work on employee well-being. His teaching expertise spans organizational behavior, negotiation, and business ethics. He leads the GLOW Lab, advancing research in leadership and ethical workplace practices. Key research interests include ethical leadership, workplace mistreatment, and the behavioral consequences of deviant coworker behaviors. His work has been published in leading journals such as Journal of Applied Psychology and Journal of Management . Beyond academia, he enjoys outdoor activities, running, and travel. Awards include the 2019 Emerging Scholar Award for contributions to business ethics and the 2022 Dare to Care Award for research prioritizing employee welfare. His lab, the GLOW Lab, drives innovative studies in leadership and ethical organizational practices.

Fred Liuمشاهده پروفایل

استادیار

• Asset pricing
• Big data in finance
• Financial econometrics
• +۲ مورد دیگر

Fred Liu is an Assistant Professor at the Department of Economics & Finance within the Gordon S. Lang School of Business and Economics at the University of Guelph. His research focuses on the intersection of financial econometrics, machine learning, and quantitative finance. Dr. Liu's work emphasizes applications in asset pricing, risk management, and big data analytics in financial markets. Key research themes include cryptocurrency market dynamics, high-frequency trading strategies, algorithmic methods for market predictability, and regulatory capital frameworks. He employs advanced machine learning techniques such as deep learning and quantile regression to solve complex financial problems. His publications address critical topics like intraday market behavior, executive communication analysis via AI, and Basel III regulatory impacts on risk models. While no specific awards are listed, his active publication record reflects engagement with cutting-edge financial research. Advising and grant details are not explicitly provided in the available text. Dr. Liu's work contributes to both academic discourse and practical applications in financial risk management and quantitative strategies.

Wuyang Chenمشاهده پروفایل

استادیار

• Deep Learning
• Scientific Machine Learning
• Computer Vision
• +۷ مورد دیگر

Wuyang Chen is an Assistant Professor at the School of Computing Science, Simon Fraser University. He holds a Ph.D. in Electrical and Computer Engineering from The University of Texas at Austin (2023). His research focuses on integrating AI with physics, developing principled deep learning methods, and advancing areas like scientific machine learning (SciML), computer vision, and foundation models. Notable contributions include PDE-Controller for LLM-based scientific reasoning and neural operators for fluid reconstruction. He has received prestigious awards, including the INNS Doctoral Dissertation Award (2024) and the NSERC Discovery Grant. Dr. Chen serves as an Area Chair for NeurIPS 2025 and ICML 2025. His lab, DeLTA, emphasizes AI applications in science and hosts active collaborations. He teaches CMPT 419/983 (Special Topics in AI) and mentors students in research areas like model efficiency and self-supervised learning. Education: Ph.D., Electrical and Computer Engineering, The University of Texas at Austin (2023) Recent Awards: INNS 2024 Doctoral Dissertation Award, iSchools Doctoral Dissertation Award 2024, Google GCP Credit Award Key Activities: Organized NeurIPS 2024 workshop on Foundation Models for Science; presented at AAAI 2025, SIAM CSE25, and Berkeley BLISS seminar

• Computer Science
• Data Structures
• Algorithms
• +۳ مورد دیگر

Joshua Jung is an Assistant Professor in the Department of Mathematical and Computational Sciences at the Faculty of Science, University of Toronto. His position focuses on teaching in the Computer Science program (CSC). Contact: josh.jung@utoronto.ca . Research interests inferred from departmental affiliation include Data Structures Algorithms Software Engineering Machine Learning Programming Languages Computer Science

Björn Herrmannمشاهده پروفایل

استادیار

• Auditory perception and cognition
• Aging and hearing loss
• Speech and communication
• +۲ مورد دیگر

Björn Herrmann is an Assistant Professor (Status Only) at the University of Toronto and a Scientist at the Rotman Research Institute, Baycrest Academy for Research and Education. His research program examines auditory aging through neurophysiological, cognitive, and real-world lenses, with a focus on improving communication in older adults with hearing challenges. He holds a Tier II Canada Research Chair and leads the Auditory Aging Lab. Education: Apprenticeship in Industrial Mechanics (Germany) Diploma (MSc equivalent) in Communication Psychology (Germany) PhD in Psychology (Germany) Postdoctoral training: Max Planck Institute for Human Cognitive and Brain Sciences (Germany), Purdue University (USA), Western University (Canada) Research Focus: Dr. Herrmann investigates how aging and hearing loss impact listening engagement, neural sound processing, and speech comprehension. His work integrates magnetoencephalography (MEG), electroencephalography (EEG), eye-tracking, and qualitative methods to study sensory-cognitive interactions during challenging listening conditions. Key themes include neural hyperresponsivity in aging auditory cortex, eye-movement biomarkers of listening effort, and real-world adaptations like audiobook usage in sensory impairment. Awards & Recognition: Tier II Canada Research Chair in Auditory Aging Lab & Team: The Auditory Aging Lab develops neurobehavioral tools (e.g., automated speech-recall assessments using AI) and explores interventions to reduce social disengagement. Current projects include cross-language speech perception modeling and neurodynamics of story comprehension in noise. The lab actively recruits graduate students and postdoctoral researchers, supported by government, foundation, and private grants.

• Human-Computer Interaction
• Automatic Speech Recognition
• Natural User Interfaces
• +۶ مورد دیگر

Cosmin Munteanu is an Associate Professor at the Institute of Communication, Culture, Information and Technology (University of Toronto Mississauga) and cross-appointed to the Faculty of Information and Department of Computer Science at the University of Toronto. He directs the Technologies for Ageing Gracefully Lab (TAG Lab) , focusing on sociotechnical research at the intersection of user experience, digital inclusion, aging, NLP, and ethics. Research Interests span human factors in voice/language interfaces, empowering marginalized groups (older adults, low-literacy users), ethical design, and multimodal assistive technologies. Key Contributions address voice user interface design, aging populations, and balancing usability with ethical considerations in field studies. Scientific Awards include Best Paper at PACLING (2003), Diversity Award (2020), and multiple Best of CHI/MobileHCI Honorable Mentions. Committee Service includes ACM SIGCHI MobileHCI Steering Committee (Deputy Chair), co-founder of Conversational User Interfaces Steering Committee, and AGE-WELL Network leadership. Teaching covers courses like User Experience Design, Interaction Design, and Natural Language Computing at graduate and undergraduate levels.

• Authentication
• Brain-Computer/Machine Interface
• Cybersecurity
• +۱ مورد دیگر

Dr. Garima Bajwa is an Assistant Professor in the Department of Computer Science at Lakehead University, where she joined in January 2021. Her research focuses on authentication systems, brain-computer/machine interfaces, cybersecurity, and machine learning applications. She holds a Ph.D. in Computer Science and Engineering from the University of North Texas (2016), an MEng in Electrical and Computer Engineering from the University of Waterloo (2011), and a B.Tech in Electronics and Communication Engineering from Mody Institute of Technology & Science, India (2009). Education: Ph.D., Computer Science & Engineering, University of North Texas (2016) MEng, Electrical & Computer Engineering, University of Waterloo (2011) B.Tech, Electronics & Communication Engineering, Mody Institute (2009) Her research interests span cybersecurity frameworks for neurotechnology, explainable AI in healthcare diagnostics, and multimodal fusion of EEG and image data. Recent work includes EEG signal anonymization, federated learning for medical diagnosis, and reproducibility in BCI research. Her publications explore cutting-edge topics like BCI standardization, driver distraction detection, and quantum computing integration. Grants and lab affiliations are not explicitly mentioned in the provided text.

Angel Ariasمشاهده پروفایل

استادیار

• Language Testing and Assessment
• Validity Theory
• Measurement Theory
• +۳ مورد دیگر

Angel Arias is an Assistant Professor in the School of Linguistics and Language Studies at Carleton University. He holds a Ph.D. in Educational Measurement from Université de Montréal, an M.A. in Applied Linguistics from Carleton University, and a B.A. in Education and Modern Languages from Universidad Dominicana in the Dominican Republic. His research focuses on psychometric models and mixed-methods approaches to language assessment, emphasizing validation frameworks that consider sociocultural contexts and linguistic diversity. Dr. Arias has served as an external consultant for Québec’s Ministry of Education and chaired the Test Validity Research and Evaluation Special Interest Group of the American Educational Research Association (AERA). He is trilingual in Spanish, English, and French, with prior teaching experience in multilingual settings. His work bridges theoretical validation principles with practical applications in classroom assessment and multilingual testing environments. His recent publications explore topics such as teacher preparation for multilingual learners, validation methodologies in educational testing, and the application of psychometric tools to assess language proficiency. He has presented globally at conferences like the American Association for Applied Linguistics (AAAL) and the International Test Commission (ITC). Dr. Arias teaches courses on language assessment, applied statistics, and curriculum design at both undergraduate and graduate levels. He actively contributes to professional development initiatives, including developing culturally responsive assessment practices and advocating for equity in language testing systems.

Renjie Liaoمشاهده پروفایل

استادیار

• Deep Learning
• Machine Learning
• Computer Vision
• +۴ مورد دیگر

Renjie Liao is an Assistant Professor (tenure-track) in the Department of Electrical and Computer Engineering (ECE) at the University of British Columbia (UBC), with an associated appointment in the Department of Computer Science. He is also a Faculty Member at the Vector Institute and a Canada CIFAR AI Chair. Prior to UBC, Dr. Liao was a Visiting Faculty Researcher at Google Brain and held a Senior Research Scientist position at Uber Advanced Technologies Group during his PhD. He earned his B.Eng. (Automation) from Beihang University, M.Phil. (CS) from the Chinese University of Hong Kong, and PhD (CS) from the University of Toronto. His research focuses on probabilistic and geometric deep learning , with key contributions in deep generative models, geometric deep learning, neural algorithmic reasoning, and generalization bounds. Notable areas include 3D point cloud analysis, self-driving systems, and healthcare applications using graph neural networks. His work bridges theoretical foundations (e.g., PAC-Bayes bounds) with practical applications like motion forecasting and medical imaging. Education: B.Eng. in Automation, Beihang University (2011) M.Phil. in Computer Science, CUHK (2015) PhD in Computer Science, UofT (2021) Dr. Liao has received awards such as the RBC Graduate Fellowship and Connaught International Scholarship. His lab (Deep Structured Learning Lab) emphasizes principled mathematical approaches to solving complex problems. He advises students in machine learning, computer vision, and robotics, encouraging applications from those with strong coding/mathematical backgrounds. Labs/Teams: Deep Structured Learning Lab (UBC) Vector Institute Collaboration

Kelsey Allenمشاهده پروفایل

استادیار

• Cognitive Science
• Machine Learning
• Robotics
• +۴ مورد دیگر

Kelsey Allen is an Assistant Professor in the Department of Computer Science at the University of British Columbia (UBC) and a Senior Research Scientist at DeepMind. Her research bridges cognitive science, machine learning, and robotics, focusing on understanding and replicating human-like problem-solving, tool use, and physical reasoning. She holds a PhD from MIT (2016) under Josh Tenenbaum and a B.Sc. in Physics from UBC (2010). Research Interests : Allen investigates computational mechanisms underlying human complex behaviors, particularly tool use and design. Her work emphasizes endowing machines with flexible problem-solving abilities. Key themes include lifelong learning, embodied cognition, and integrating symbolic and neural approaches. Awards & Recognition Best Paper Award at Robotics: Science and Systems (RSS) 2018 Oral Presentation at Cognitive Science Society 2019 Spotlight at NeurIPS 2018 and ICLR 2019 Key Projects : Includes developing graph network simulators for rigid body dynamics, tools for physical design optimization, and studies on human tool-use learning. Her work often combines empirical experiments with machine learning models to bridge human and artificial intelligence.

Reid Holmesمشاهده پروفایل

استاد

• Software Engineering
• End-user Programming
• Software Maintenance
• +۳ مورد دیگر

Reid Holmes is a Professor in the Department of Computer Science at the University of British Columbia , part of the Faculty of Science . His research focuses on improving software engineering practices, particularly in end-user programming, developer tool design, and empirical software engineering. He leads the Software Practices Lab and has contributed extensively to understanding developer workflows, testing methodologies, and educational tools for programming. Education: PhD in Computer Science, University of Calgary (2008) MSc in Computer Science, University of British Columbia (2004) BSc in Computer Science, University of British Columbia (2002) Research Interests: End-user programming environments, software testing, developer productivity tools, human-centered AI, educational technology, and empirical studies on software development practices. His work emphasizes bridging the gap between theoretical advancements and practical usability for both professional developers and novice programmers. Recent Article Trends: Focus on hybrid programming environments (e.g., block-based and graph-based systems), human-AI collaboration in testing/assertion generation, and age-inclusive IDE design. His research often involves empirical evaluations of tool effectiveness and developer workflows. Awards: FSE Most Influential Paper Award ICSE Most Influential Paper Award UBC Computer Science Teaching Award CS-Can/Info-Can Outstanding Research Prize Advising & Grants: Supervised over 30 graduate students and postdocs. Noted for collaborative projects with industry partners (e.g., Mozilla, Microsoft). Active in curriculum development for software engineering education. Labs/Teams: Leader of the Software Practices Lab , collaborating with industry and academic partners on tools like CodeShovel , AutoAssert , and Devy (conversational developer assistant).

Peter Westمشاهده پروفایل

استادیار

• Natural Language Processing
• Artificial Intelligence
• Large Language Models
• +۳ مورد دیگر

Peter West is an incoming Assistant Professor at the University of British Columbia (UBC) Computer Science Department, specializing in Natural Language Processing (NLP) and AI. His research focuses on understanding the capabilities and limitations of large language models (LLMs) and generative AI systems, emphasizing their divergence from human intuition and alignment challenges. He holds a PhD from the University of Washington (2024), supervised by Yejin Choi, and a BSc (Honours Computer Science) from UBC (2017). His work has been recognized with awards including Best Method Paper at NAACL 2022 and Outstanding Paper awards at ACL 2023 and EMNLP 2023. He conducted internships at the Allen Institute for AI and Microsoft Research’s NLP group. Research interests include analyzing LLM behavior through a natural sciences lens, exploring model capabilities versus human expectations, and developing decoding algorithms to infuse models with algorithmic logic. His recent publications address generative AI paradoxes, constrained text generation, and symbolic knowledge distillation. He serves on panels for NeurIPS workshops and is beginning a postdoc at Stanford with Chris Potts. His research group at UBC seeks students interested in generative AI’s analytical frontiers.