جست‌وجوی استاد و پژوهشگر

Maja J Matarićمشاهده پروفایل

استاد

• Socially Assistive Robotics
• Human-Robot Interaction
• Assistive Technology
• +۷ مورد دیگر

Maja J Matarić is the Chan Soon-Shiong Chaired and Distinguished Professor of Computer Science at the University of Southern California's Viterbi School of Engineering, with courtesy appointments in Neuroscience and Pediatrics. She serves as founding director of the USC Robotics and Autonomous Systems Center, co-director of the USC Robotics Research Lab, and Principal Scientist at Google DeepMind. Previously, she held leadership roles as USC's interim Vice President of Research (2020-2021) and Vice Dean for Research (2006-2019). Her educational background includes: PhD in Computer Science and Artificial Intelligence from MIT (1994) MS in Computer Science from MIT (1990) BS in Computer Science from University of Kansas (1987) Matarić pioneers Socially Assistive Robotics (SAR) , a field her lab named, focusing on human-robot interaction that provides assistance through social rather than physical support. Her research targets critical health and wellness challenges including post-stroke rehabilitation, autism spectrum disorder therapy, cognitive exercises for Alzheimer's patients, ADHD academic support, and mental health interventions. She develops systems modeling user engagement, personality, and motivation, with extensive real-world deployments in schools, rehabilitation centers, and homes. Analysis of her recent publications reveals dominant themes in cognitive health robotics (2025 CHI paper on LLM-powered elder care), pediatric assistive technology (2025 IDC speech therapy review), and adaptive preference modeling (2025 HRI contrastive learning work). Her research consistently bridges machine learning with human-centered design for vulnerable populations. Major scientific recognition includes: ACM Athena Lecturer Award (2024) ACM Eugene L. Lawler Humanitarian Award (2024) ACM Fellow (2020) Presidential Mentoring Award (2011) Multiple society fellowships (AAAS, IEEE, AAAI) As a dedicated mentor, Matarić has championed underrepresented groups through CRA-W, placing numerous women in faculty positions. She leads USC Viterbi's K-12 STEM Outreach Program serving low-income Los Angeles schools and authored The Robotics Prime for student education. Her research has secured significant funding enabling real-world technology transfer, with documented impact in rehabilitation centers and homes through deployable SAR systems. Her Robotics Research Lab at USC drives innovation in embodied AI, with current projects spanning LLM-integrated elder care robots, ADHD academic companions, and autism therapy systems. The lab emphasizes co-design with end-users and rigorous real-world validation across diverse populations.

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

Chris J. Maddison is an Assistant Professor at the University of Toronto, holding joint appointments in the Department of Computer Science and the Department of Statistical Sciences. He is also a CIFAR AI Chair at the Vector Institute and a member of the ELLIS Society. Maddison earned his DPhil from the University of Oxford and previously worked as a Senior Research Scientist at Google DeepMind and a member at the Institute for Advanced Study. His research focuses on advancing machine learning methodologies, particularly in leveraging data’s natural structure for efficient learning, with applications in drug discovery, causal inference, and AI safety. Education: DPhil in Computer Science, University of Oxford His research interests span machine learning, AI safety, reinforcement learning, and the integration of logical reasoning into large language models. Maddison has contributed to foundational work on gradient estimation techniques and was a key member of the AlphaGo project. He actively explores how statistical structures in real-world data influence AI capabilities. Recent publications emphasize evaluating conversational agents, mitigating AI safety risks, and enhancing logical reasoning in LLMs. His work bridges theoretical advancements with practical applications, such as code generation and multi-agent systems. Awards: NeurIPS Best Paper Award (2014), Open Philanthropy AI Fellowship Maddison advises multiple PhD students and postdoctoral researchers, fostering collaborations across academia and industry. His former advisees now hold roles at institutions like OpenAI, Stanford, and Magic AI. He teaches advanced courses in machine learning and statistical methods, including CSC 2541 (Large Models) and STA 314 (Machine Learning). Maddison is affiliated with the Schwartz Reisman Institute for Technology and Society, extending his impact to societal implications of AI. His lab’s interdisciplinary approach combines algorithmic innovation with real-world problem-solving.

Caroline Lemieuxمشاهده پروفایل

استادیار

• Programming Languages
• Software Engineering
• Fuzz Testing
• +۶ مورد دیگر

Caroline Lemieux is an Assistant Professor at the Department of Computer Science, University of British Columbia (UBC), with research focused on advancing software correctness, security, and performance through innovative testing and synthesis techniques. Her work bridges Programming Languages and Software Engineering , particularly in fuzz testing, specification mining, and program synthesis. PhD from University of California, Berkeley (2021), advised by Koushik Sen Postdoctoral researcher at Microsoft Research, NYC (2021-2022) Key contributions: FuzzFactory , CodaMOSA , Arvada , and Gauss Her research integrates machine learning with traditional testing methods, exemplified by projects like RLCheck (reinforcement learning for test generation) and AutoPandas (neural synthesis for dataframes). Recent publications analyze generator-based fuzzing challenges and propose hybrid strategies combining coverage feedback with AI-driven insights. Scientific Awards : ACM/SIGSOFT Best Paper Award (ESEC/FSE 2019) ACM/SIGSOFT Tool Demonstration Award (ISSTA 2019) ACM/SIGSOFT Distinguished Artifact Award (ISSTA 2019) NSERC Postgraduate Scholarship-Doctoral (PGS D) UBC Governor General's Silver Medal (2016) Teaching roles include: 2025W2: CPSC 539L - Topics in Programming Languages 2024W2: CPSC 410 - Advanced Software Engineering 2023W2: CPSC 410 (with Alex Summers) She supervises graduate and undergraduate researchers working on projects like ExploTest (automated unit test generation) and GRIMOIRE (grammar extraction from pseudo-rules). Her research team collaborates with institutions including Microsoft Research, Google, and academic partners in systems security and AI-driven testing.

Dirk Bernhardt-Waltherمشاهده پروفایل

استاد

• Visual Perception
• Cognitive Neuroscience
• Scene Categorization
• +۵ مورد دیگر

Professor Dirk Bernhardt-Walther is an academic at the University of Toronto, serving as Program Director of the Cognitive Science Program and Department of Psychology . He investigates neural and computational mechanisms underlying high-level sensory perception, focusing on real-world scenes, mid-level vision, and visual aesthetics. Education: PhD in Computation and Neural Systems (Caltech, 2006), M.Phil (University of Cambridge) Research Focus: His lab employs fMRI, MEG, EEG , and GAN-generated stimuli to study scene categorization, perceptual organization, and aesthetic processing. Recent work explores curvature perception, emotion representation in scenes, and neural dissociations between computational and subjective visual metrics. Laboratory Members: The Bernhardt-Walther Lab includes PhD students like Gaeun Son (scene perception), Charlotte Leferink (scene representation), and Dela Farzanfar (aesthetic processing), alongside postdocs and collaborators. Advising: Supervises graduate students in projects combining computational modeling, psychophysics, and neuroimaging, particularly those with backgrounds in computer science or cognitive neuroscience.

Marco Pedersoliمشاهده پروفایل

استادیار

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

Marco Pedersoli serves as an Assistant Professor at École de technologie supérieure (ETS) in Montreal since February 2017, where he leads research in computer vision and machine learning. His work focuses on reducing computational costs and annotation requirements for deploying vision algorithms on embedded devices, positioning ETS at the forefront of Montreal's AI ecosystem. His academic journey includes: Ph.D. from Autonomous University of Barcelona (UAB) under Jordi Gonzàlez and Juan José Villanueva Post-doctoral research at INRIA Grenoble with Cordelia Schmid and Jakob Verbeek (2015-2016) Research at KU Leuven with Tinne Tuytelaars (2012-2015) Dr. Pedersoli's research tackles deep learning bottlenecks through weakly-supervised methodologies and computational efficiency innovations . His three core projects address: Reduced Supervision : Developing weakly/semi-supervised learning for images, video, audio and text Exploration Learning : Optimizing data selection in unstructured environments Efficient Computation : Accelerating deep learning training and inference These efforts enable vision algorithms to run on resource-constrained portable devices. Publication trends (2014-2022) reveal consistent focus on weak supervision (60% of works) and computational efficiency (30%), with recent expansion into medical imaging and multimodal emotion recognition. Key venues include CVPR, ICCV, NeurIPS and ECCV. His accolades include: Best Paper Award at ICIAR 2019 NVIDIA Titan X Pascal hardware donation Dr. Pedersoli actively mentors 18 graduate students across PhD and MSc programs, with notable placements at Huawei and Radio Canada. His lab secures competitive tax-free funding for projects with international collaborations, including Element AI and European institutions. Current openings emphasize Python/C++ proficiency and deep learning expertise. He leads a dynamic research group at ETS developing open-source tools for Roi-Pooling, weakly-supervised detection, and 3D object recognition, maintaining active GitHub repositories with community contributions. Recent WACV 2023 acceptances demonstrate ongoing productivity following medical leave.

Patrick Baylisمشاهده پروفایل

استادیار

• Environmental Economics
• Climate Change Economics
• Energy Economics
• +۴ مورد دیگر

Patrick Baylis is an Assistant Professor of Environmental Economics at the Vancouver School of Economics, University of British Columbia. His research focuses on how people respond to environmental threats such as wildfires, air pollution, and extreme temperatures. He employs large datasets, natural language processing, and spatial information in his work, primarily using R/RStudio and Python for data analysis. Dr. Baylis earned his PhD from the University of California Berkeley in 2016 in Agricultural and Resource Economics. Prior to joining UBC, he was a postdoctoral fellow at the Stanford Center on Food Security and the Environment. He also worked as a research assistant at the Energy Institute at Haas and is a proud alumnus of Carleton College. His research interests span environmental economics with a particular focus on climate change impacts, energy economics, and behavioral responses to environmental threats. His work often examines the intersection of climate, health, and economic behavior, using innovative methods like social media data analysis to measure sentiment responses to temperature changes and other environmental factors. Analysis of his publication record reveals a strong focus on using big data approaches to understand human responses to environmental challenges. His work frequently appears in top journals including Nature Climate Change, PNAS, and the Journal of Public Economics. His research spans multiple subfields including wildfire economics, air quality valuation, climate migration, and the behavioral impacts of temperature extremes. Dr. Baylis has received significant media attention for his work, with coverage in major outlets including The New York Times, The Washington Post, The Guardian, and The Atlantic. His research on temperature and suicide rates, as well as climate perception, has been particularly influential in connecting environmental conditions with human behavior and mental health outcomes. He maintains an active research program examining critical environmental challenges, including wildfire risk, air pollution impacts, climate adaptation strategies, and the economic dimensions of climate change. His work combines rigorous economic analysis with innovative data collection and processing methods to provide new insights into how humans respond to environmental threats.

Brian Ingallsمشاهده پروفایل

استاد

• Systems Biology
• Synthetic Biology
• Mathematical Modeling
• +۳ مورد دیگر

Brian Ingalls is a Professor in the Department of Applied Mathematics and cross-appointed to Biology at the University of Waterloo. His research applies mathematical and control-theoretic approaches to biological systems, including genetic regulatory networks, microbial communities, and cellular metabolism. Institutional Affiliation: Faculty of Mathematics, University of Waterloo Contact: bingalls@uwaterloo.ca His work focuses on systems biology and synthetic biology , particularly sensitivity analysis of biochemical networks, optimal experimental design, and mathematical modeling of cellular processes. Research funding comes from NSERC and CIHR . Notable contributions include the textbook Mathematical Modeling in Systems Biology (MIT Press, 2013) and the Ingalls Quantitative Cell Biology Lab , which investigates intracellular and intercellular network dynamics through computational and experimental methods. Key Collaborations: iGEM Waterloo, Chemical Engineering, and international synthetic biology networks Advising: Mentored 15+ graduate students and postdocs across applied math, biology, and engineering fields

Muhammad Asaduzzamanمشاهده پروفایل

استادیار

• Software Engineering
• Mining Software Repositories
• Empirical Software Engineering
• +۲ مورد دیگر

Muhammad Asaduzzaman is an Assistant Professor in the School of Computer Science within the Faculty of Science at the University of Windsor. His research focuses on software engineering, particularly software maintenance, mining software repositories, and recommendation systems for developers. Research interests span empirical studies of software artifacts, API usage analysis, and improving developer productivity through tools like COSTER for API element identification. Recent work examines dependency management in Maven ecosystems and AI-assisted code completion. Publications show consistent focus on analyzing developer activities through platforms like Stack Overflow and GitHub. Current investigations include LLM applications for code synthesis and technical debt impact analysis.

Darren Scottمشاهده پروفایل

استاد

• Transportation Geography
• Geographic Information Science (GIScience)
• Sustainable Transportation
• +۱۳ مورد دیگر

Dr. Darren Scott is a Professor in the School of Earth, Environment & Society at McMaster University, specializing in Geographic Information Science (GIScience) and teaching courses such as GIS Programming and Data Processing Using Python. He previously served as an Assistant Professor at the University of Louisville’s Department of Geography and Geosciences (1999–2002) and held a Visiting Research Professor position at the Swiss Federal Institute of Technology (Zurich) in 2008. His academic background includes a BA (Honors and Co-op) in Geography from Saint Mary’s University (1991), an MA from the University of Western Ontario (1994), and a PhD from McMaster University (2000). Dr. Scott’s research focuses on transportation systems, aging populations, GIScience innovations, and sustainable infrastructure. He has pioneered work in route choice modeling, transportation demand analysis, and the impact of demographic changes on mobility. In 2008, he established TransLAB, a research lab within his school, which explores advanced transportation topics using GPS and geospatial tools. His projects have been funded by major agencies like NSF, SSHRC, and NSERC, addressing issues ranging from electric vehicle adoption to flood resilience in emergency services. His teaching emphasizes Python programming and advanced GIS applications, reflecting his expertise in geospatial technologies. He has contributed to policy debates on urban sustainability, particularly regarding Canada’s Greenbelt legislation and telework trends during the pandemic. Despite no explicitly mentioned scientific awards, his extensive grant history and scholarly output highlight his impactful contributions to the field. Dr. Scott’s advising and grant activities center on interdisciplinary studies, including the McMaster Monitoring My Mobility Study (MacM3), which tracks mobility patterns of older adults. His work bridges transportation engineering, urban planning, and public health, often leveraging big data and GIS tools to analyze real-world scenarios in cities like Hamilton and Calgary.

Jiannan Wangمشاهده پروفایل

دانشیار

• Database Systems
• Data Management
• Data Cleaning
• +۵ مورد دیگر

Jiannan Wang is an Associate Professor in the School of Computing Science at Simon Fraser University (SFU). He holds a Ph.D. from Tsinghua University (2013) and a B.Sc. from Harbin Institute of Technology (2008). His research focuses on database systems, data management, and data science, with particular emphasis on data cleaning, crowdsourcing, and big data technologies. He leads the SFU Data Science Research Group, aiming to accelerate data science workflows through innovative tools like DataPrep and ConnectorX. Education: Ph.D. in Computer Science and Technology, Tsinghua University, China (2013) B.Sc. in Computer Science and Technology, Harbin Institute of Technology, China (2008) Research Interests: Dr. Wang's work spans database systems, data cleaning, crowdsourcing, and big data education. He develops open-source tools for data scientists to streamline data preparation and analysis. His lab's mission is to make data science more efficient through technologies like DataPrep and ConnectorX . Awards: IEEE TCDE Rising Star Award (2018) CS-Can|Info-Can Outstanding Early Career Researcher Award (2020) VLDB Best Experiments, Analysis & Benchmark Paper Award (2021) PVLDB Distinguished Review Board Member Award (2020) Advising & Leadership: Director of SFU's Professional Master's Program in Big Data and Visual Computing. Supervised over 20 graduate and undergraduate students, many of whom have gone on to roles at top companies like Google, Amazon, and Huawei. Lab & Teams: Part of the SFU Data Science Research Group and the SFU Big Data Academic Advisory Committee. His lab collaborates with industry partners and contributes to open-source projects in data management and machine learning.

Jason Heinمشاهده پروفایل

دانشیار

• Mechanistic Organic Chemistry
• Automated Reaction Analysis
• Self-Driving Laboratories
• +۷ مورد دیگر

Jason Hein is an Associate Professor in the Department of Chemistry at the University of British Columbia's Faculty of Science. His research focuses on the development of automated reaction analysis technology and self-driving laboratories that integrate robotics with synthetic organic chemistry. Dr. Hein leads the Hein Lab, which pioneers innovative solutions for mechanistic organic chemistry, catalytic reaction mechanisms, and chemical manufacturing processes. His research interests center on creating modular robotic tools and integrated analytical hardware for automated reaction profiling, with applications in pharmaceutical manufacturing, battery materials processing, and sustainable chemistry. The lab's work combines advanced robotics, artificial intelligence, and process analytical technology to develop self-optimizing chemical systems that accelerate discovery and improve manufacturing efficiency. Analysis of Hein's recent publications reveals a strong focus on AI-driven laboratory automation, with particular emphasis on crystallization optimization for battery materials, computer vision for process monitoring, and interoperable software systems for self-driving laboratories. His work bridges fundamental mechanistic understanding with practical industrial applications, particularly in lithium extraction from waste brines and pharmaceutical process development. NSERC Postdoctoral Fellowship Dr. Hein's research program includes significant grant funding supporting the development of self-driving laboratory technologies and their application to challenging chemical problems. His lab actively collaborates with industry partners in pharmaceuticals and clean energy sectors to translate fundamental insights into deployable technologies. Current projects focus on battery-grade lithium carbonate production, continuous manufacturing processes, and AI-optimized chemical synthesis. The Hein Lab operates as a multidisciplinary research environment combining expertise in organic chemistry, robotics engineering, computer science, and data analytics to create the next generation of autonomous chemical discovery systems.

Robert Xiaoمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Virtual Reality
• Augmented Reality
• +۴ مورد دیگر

Robert Xiao is an Assistant Professor in the Department of Computer Science at the University of British Columbia (UBC), affiliated with the Designing for People research cluster. He holds a Ph.D. from Carnegie Mellon University and a BMath from the University of Waterloo. His research focuses on interactive technologies, including VR/AR interfaces, sensing systems, and cybersecurity. Notable contributions include Lumitrack (tracking system), TouchTools (touch interaction), and CVE-2023-37271 (Python sandbox exploit). Education: Ph.D., Human-Computer Interaction Institute, Carnegie Mellon University; BMath, Computer Science & Combinatorics, University of Waterloo Affiliations: Core member of UBC's Designing for People cluster Research interests span novel input modalities, mixed-reality systems, and security challenges. He actively competes in DEF CON CTF (multiple 1st places) and publishes in top venues like CHI, UIST, and ISMAR. Recent work explores VR decision-making, low-latency tracking, and collaborative AR/VR environments. Awards: SIGCHI Outstanding Dissertation Award, CHI Honorable Mention, DIS Honourable Mention Teaching includes courses on computer systems (CPSC 213), human-computer interaction (CPSC 554X), and cybersecurity (CPSC 436S). His lab develops tools like SurfShare (surface sharing) and VirtualNexus (collaborative AR).

Tushar Sharmaمشاهده پروفایل

استادیار

• Software Engineering
• Software Quality
• Code and Design Quality
• +۱۰ مورد دیگر

Tushar Sharma is an Assistant Professor in the Faculty of Computer Science at Dalhousie University, Canada. His research focuses on software engineering, particularly software quality, refactoring, technical debt, and the application of machine learning in software engineering (ML4SE). He leads the SMART Lab and is actively involved in projects related to Green AI and sustainable software development. PhD : Software Engineering, Athens University of Economics and Business, Greece (2019) MS : Computer Science, Indian Institute of Technology-Madras, India His research interests span software design and architecture, code and design quality, refactoring, technical debt, mining software repositories, and applied machine learning for software engineering. He is particularly interested in sustainable AI, green software engineering, and the use of large language models for code. His work bridges empirical studies with practical tool development to improve software maintainability and quality. His recent publications highlight a strong trend in code smell detection, refactoring automation, energy-aware AI, and the reliability of large language models in software engineering. He has developed tools like Designite and DPy and contributed datasets such as MaRV and DACOS, emphasizing empirical validation and reproducibility in software engineering research. Dean's Research Excellence Award Best Artifact Award, SCAM 2023 IEEE Senior Member Tushar Sharma has secured significant research funding, including an NSERC Discovery Grant for DevQOps, Mitacs Accelerate grants with industry partners, and contributions to the $154M Canada First Research Excellence Fund project. He actively mentors students and collaborates with industry. He leads the SMART Lab at Dalhousie and has organized workshops such as SATToSE 2018. He is also a founding developer of Designite, a widely used software design quality assessment tool.

• Machine Learning
• IoT Security
• Autonomous Systems
• +۳ مورد دیگر

Professor Rashid Rashidzadeh is a faculty member in the Faculty of Engineering at the University of Windsor. He specializes in Machine Learning, IoT Security, and Autonomous Systems, with a focus on integrating these technologies into engineering education. He has advised numerous students in first-year design courses and advanced research projects, including work on autonomous emergency vehicles, IoT security for 5G devices, and hyperloop pod development. His teaching responsibilities include the Cornerstone Design course, where students develop autonomous systems and navigate engineering challenges. He has organized workshops on Python and Machine Learning, engaging both university and high school students. His research projects span industrial automation (e.g., Hiram Walker distillery software integration) and high-stakes competitions like the SpaceX Hyperloop Pod Challenge. Professor Rashidzadeh has mentored over 30 students in projects such as: Programming model railcars to navigate obstacle courses Designing cybersecurity safeguards for 5G IoT devices Building hyperloop pods for high-speed transport competitions His work emphasizes hands-on learning and industry collaboration, with projects showcased in media and academic platforms.

Marysa Laguëمشاهده پروفایل

استادیار

• Climate Modelling
• Land-atmosphere interactions
• Climate Dynamics
• +۶ مورد دیگر

Marysa Laguë is an Assistant Professor in the Department of Geography within the Faculty of Arts at the University of British Columbia. She is a climate scientist specializing in understanding how terrestrial processes impact the atmosphere and surface climate across scales from individual plants to entire planets. Her research focuses on how changes in the land surface modify energy and water fluxes between the land and atmosphere, and how these changes subsequently affect atmospheric dynamics and climate both locally and remotely. Dr. Laguë serves as a member of the Graduate and Postdoctoral Studies program at UBC. Dr. Laguë earned her PhD in Atmospheric Sciences and MSc degrees in both Atmospheric Sciences and Applied Mathematics from the University of Washington. Her educational background provides her with strong theoretical and modeling expertise that informs her comprehensive research approach. Her research can be broadly categorized into three interconnected areas: understanding land-atmosphere interactions in the modern climate system, exploring fundamental physical connections between terrestrial and planetary processes using idealized models, and climate model development. She studies how changes in vegetation impact cloud formation, temperatures, water vapor, and atmospheric circulation in ways that can feed back on surface climate. On the idealized side, she investigates how continental configurations fundamentally alter global-scale climate patterns. Her work often involves developing and using numerical models of varying complexity to quantify land's role in the coupled Earth System. She is particularly interested in understanding where the atmosphere cares about changes in the land surface, and what particular properties of the land surface it is that the atmosphere cares about. Dr. Laguë's recent publications demonstrate a consistent focus on land-atmosphere interactions, with particular emphasis on how terrestrial evaporation, surface albedo, and vegetation properties influence climate dynamics. Her research spans from local-scale processes to planetary-scale phenomena, including studies of exoplanet climates. She frequently employs both complex Earth system models like the Community Earth System Model and idealized modeling frameworks to isolate specific mechanisms. A recurring theme across her work is the development and application of the Simple Land Interface Model (SLIM), which allows researchers to test how individual land-surface properties modify energy and water fluxes to the atmosphere. Dr. Laguë is actively involved in mentoring graduate students and is interested in supervising Master's students, Doctoral students, and Postdoctoral Fellows. She supports interdisciplinary research collaborations and is open to supervising students interested in public scholarship through the Public Scholars Initiative. She also encourages experiential learning opportunities like internships for her graduate students and emphasizes the importance of interdisciplinary research approaches. She is the lead scientific developer of the Simple Land Interface Model (SLIM), an idealized land surface model that couples with the Community Earth System Model. This tool allows researchers to isolate the effects of individual land surface properties on the Earth system. Her work has significant implications for understanding climate change impacts, land management strategies, and even the potential habitability of exoplanets. Her research methodology includes using climate models, Earth system models, numerical Earth system models, Python, Jupyter, the Coupled Model Intercomparison Project (CMIP), and Fortran programming for climate model development.