جستجوی استاد راهنمای دانشگاهی

• Metal Forming Processes
• Advanced High-Strength Steels
• Finite Element Modeling
• +۵ مورد دیگر

Alexander Bardelcik is an Associate Professor in the Department of Mechanical Engineering at the University of Guelph. His research focuses on advanced materials processing, particularly in metal forming, microstructural characterization of multi-phase steels, and energy absorption in composite structures. He specializes in hot stamping, coating growth dynamics, and finite element modeling of materials under extreme conditions. Education: MASc, PhD (Engineering) Licenses: Professional Engineer (PEng) His research interests include: Development of novel metal forming processes (e.g., tailored hot stamping) Constitutive modeling of dual-phase and precipitation-strengthened steels 3D-printed polymer composites for energy absorption applications Coating growth kinetics in Al-Si systems Recent work emphasizes the use of artificial neural networks to model coating growth and the application of realistic microstructural models (RVE) for predicting fracture behavior in multiphase steels. His studies span from low-temperature mechanical behavior of HSLA steels to high-strain-rate characterization of advanced alloys. Teaching responsibilities include graduate courses on mechanical metallurgy, micro-mechanics of multi-phase materials, and undergraduate courses in finite element analysis and dynamics. Bardelcik collaborates with the Stronach Center for Innovation (University of Toronto) on hot stamping education and industrial applications.

Jianliang Wuمشاهده پروفایل

استادیار

• Systems Security and Privacy
• Formal Analysis
• Machine Learning Security and Privacy
• +۴ مورد دیگر

Jianliang Wu is an Assistant Professor in the School of Computing Science at Simon Fraser University. His research focuses on systems security and privacy, formal analysis, and machine learning security. He holds a PhD from Purdue University (2022), and MSc and BSc from Shandong University (2015 and 2012). His research interests include Bluetooth security, IoT privacy, and formal methods for protocol analysis. He teaches courses such as Network Security, Software Security, and Networks, including CMPT 403 (System Security and Privacy) and CMPT 479 (Special Topics in Computing Systems). His work spans both theoretical and applied cybersecurity, with a strong emphasis on real-world system vulnerabilities and mitigation strategies. Recent research highlights include systematic reviews of Bluetooth protection strategies, analyses of IoT data exposure via companion apps, and formal model-driven discovery of protocol design flaws. His contributions address critical challenges in wireless communication security and IoT ecosystem vulnerabilities. No scientific awards are explicitly mentioned in the provided text. Advising and grants details are not available here. He is affiliated with the School of Computing Science, which hosts labs like the Tangent Lab, though direct lab affiliations for Wu are not specified.

Ziad Kobtiمشاهده پروفایل

استاد

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

Ziad Kobti is a Professor and Director of the School of Computer Science at the University of Windsor. His work focuses on advancing artificial intelligence, machine learning, and data science with applications in healthcare, cybersecurity, and social network analysis. He leads initiatives such as the Graph Neural Network Lab and collaborates with industry partners like Sterling Information Technologies to foster cybersecurity talent. Kobti has organized programming competitions, co-op education programs, and research workshops, emphasizing experiential learning for students. His research spans recommendation systems, graph neural networks, pandemic modeling, and medical diagnostics, with a focus on impactful real-world solutions. He is involved in academic leadership, program development, and community outreach through events like Computer Science Demo Day. Research interests include: Graph Neural Networks and Dynamic Link Prediction Recommender Systems and Anomaly Detection Social Network Analysis and Team Formation Healthcare Informatics and Medical Imaging Cybersecurity and Privacy Risk Analysis Machine Learning for Public Health and Pandemic Preparedness Recent projects include collaborations on AI-driven crop disease detection, electric vehicle research, and palliative care simulation frameworks. He has developed frameworks like UDIS and BeComE, and contributes to open-source tools for social network analysis and anomaly detection. His work integrates multi-disciplinary approaches, combining computational models with domain-specific expertise in healthcare, agriculture, and social systems.

Christos Thrampoulidisمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning Theory
• Statistical Signal Processing
• +۳ مورد دیگر

Christos Thrampoulidis is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC), part of the Faculty of Applied Science. Previously, he held the same position at the University of California, Santa Barbara (2018–2020) and was a Postdoctoral Researcher at MIT (2016–2018). He earned his Ph.D. and M.Sc. in Electrical Engineering from Caltech (2016 and 2012) and a Diploma in ECE from the University of Patras, Greece (2011). His research focuses on machine learning theory , optimization , high-dimensional statistics , and statistical signal processing . Specific interests include the theoretical foundations of deep learning, neural collapse phenomena, and the analysis of overparameterized models. He has contributed to understanding imbalanced data challenges, model compression, and safe optimization techniques. Key academic achievements include the Qualcomm Innovation Fellowship (2014) and the A. Mentzeolopoulos Fellowship . He teaches graduate courses on Optimization and Signals & Systems at UBC. His research group (MILD Group) actively explores topics like language model geometry, federated learning, and algorithmic reasoning in transformers. Notable publications include work on neural-collapse geometry, imbalance-aware learning, and the theoretical underpinnings of transformers. He advises multiple Ph.D., M.Sc., and undergraduate students, fostering a collaborative environment centered on equity, diversity, and inclusion.

Arpan Gujaratiمشاهده پروفایل

استادیار

• Real-Time Systems
• Distributed Systems
• Fault Tolerance
• +۵ مورد دیگر

Arpan Gujarati is an Assistant Professor in the Department of Computer Science at the University of British Columbia (UBC), affiliated with the Systopia Lab. His research focuses on real-time systems, distributed systems, fault tolerance, and reliability analysis in cloud and cyber-physical systems domains. He holds a PhD from the Max Planck Institute for Software Systems (MPI-SWS) and has postdoctoral and research experience at MPI-SWS and UBC. Education: PhD in Real-Time Systems (MPI-SWS/TU Kaiserslautern, 2020), Postdoctoral Researcher at MPI-SWS (2020), Research Associate at UBC (2022–2023), B.Sc. from Birla Institute of Technology and Science (BITS Pilani, India). Research interests include distributed real-time systems, reliability analysis of cloud applications, fault-tolerant machine learning, and scheduling algorithms. His work bridges theory and practice, addressing challenges in ultra-reliable CPS and resilient distributed systems. Awards: Best Dissertation Award (SIGBED, 2020), Best Paper Awards (RTSS 2022, ECRTS 2018), Distinguished Artifact Award (OSDI 2020). Advising: Supervises PhD and undergraduate students in distributed systems and resilience engineering. Active in UBC’s Computer Science Graduate Program, teaching courses like CPSC 416 (Distributed Systems) and CPSC 538G (Topics in Computer Systems). Labs/Teams: Leads the Systopia Lab, collaborating with industry partners on projects like robotic arm datasets and self-driving lab tools (RABIT). Involved in research groups focused on machine learning resilience and real-time systems.

Nguyen Phong Hoangمشاهده پروفایل

استادیار

• Network Security
• Online Privacy
• Internet Measurement
• +۳ مورد دیگر

Nguyen Phong Hoang is an Assistant Professor in the Department of Computer Science at the University of British Columbia (UBC), affiliated with the Systopia Lab and Security and Privacy Group. His research focuses on network security, online privacy, and Internet measurement, with particular emphasis on improving networked system security and reinforcing privacy through large-scale data collection. He has pioneered projects like GFWeb and GFWatch to measure China’s Great Firewall and other censorship mechanisms. Prior to UBC, he was a Postdoctoral Scholar at the University of Chicago under Prof. Nick Feamster, and completed his PhD at Stony Brook University under Prof. Michalis Polychronakis. His work has been supported by the OTF’s Information Controls Fellowship, enabling collaborations with groups like the Citizen Lab (U of Toronto) and UMass Amherst. He currently teaches courses on computer networking and network security at UBC. Research interests include DNS censorship detection, domain name encryption protocols (e.g., DNS-over-HTTPS), and methods to evade Internet censorship. His publications analyze global network interference, connection tampering, and the impact of encryption protocols on censorship resistance. He has developed tools like GFWeb’s interactive dashboard for longitudinal censorship monitoring and contributed to improving I2P (Invisible Internet Project) resilience against surveillance. Phong has also served on program committees for major conferences including IEEE S&P, USENIX Security, and IMC. Phong’s academic journey includes time at Kyoto University and JAIST in Japan, where he explored location privacy in dating apps and network anonymity. His interdisciplinary approach bridges technical cybersecurity solutions with policy implications, contributing to both academic discourse and public understanding of Internet freedom challenges.

• Political Behaviour
• Electoral Systems
• Canadian Politics
• +۷ مورد دیگر

Laura Stephenson is a Professor and Department Chair in the Department of Political Science at Western University's Faculty of Social Science. With a PhD from Duke University, she has established herself as a leading scholar in political behavior research, particularly focusing on Canadian and comparative electoral politics. Her work examines how institutions and context influence political attitudes, electoral preferences, and citizen engagement with the political process. Dr. Stephenson's educational background includes: PhD in Political Science from Duke University Professor Stephenson specializes in political behavior, with research focused on understanding how institutions and context influence attitudes, electoral preferences, and engagement with politics. Her work spans multiple areas including strategic voting, municipal elections, gender and politics, and electoral systems. She employs both quantitative and qualitative methodologies to examine voting behavior across different political contexts, with particular expertise in Canadian federal and municipal politics. Her current research projects include the Canadian Election Study, Canadian Municipal Election Study, and Consortium on Electoral Democracy (C-Dem). Her recent publications demonstrate a strong focus on contemporary political challenges including democratic norms, electoral reform, gender and nonbinary identities in politics, and the impact of the pandemic on political behavior. These works reflect her commitment to understanding evolving political landscapes and citizen attitudes in both Canadian and comparative contexts. Dr. Stephenson leads several major research initiatives: Canadian Election Study Canadian Municipal Election Study Consortium on Electoral Democracy (C-Dem) Her previous research projects include the Toronto Election Study and Making Electoral Democracy Work. Through these projects, she has secured significant research funding and built extensive collaborative networks across North America and Europe. Professor Stephenson is actively involved in mentoring graduate students and has contributed to methodology training in Canadian universities. Her work on the Canadian Election Study provides vital infrastructure for political scientists studying Canadian electoral behavior.

Naomi Zimmermanمشاهده پروفایل

دانشیار

• Atmospheric Science
• Air Quality Monitoring
• Climate Change Mitigation
• +۷ مورد دیگر

Naomi Zimmerman is an Associate Professor in the Department of Mechanical Engineering at the University of British Columbia (UBC) and holds the Canada Research Chair in Real-World Air Quality Sensing. She leads the iREACH (Integrated Research in Energy, Air, Climate & Health) laboratory, focusing on technology and policy interventions for air quality and climate outcomes. Her work bridges atmospheric science, environmental engineering, and public health. B.A.Sc. in Chemical Engineering from University of Waterloo Ph.D. in Chemical Engineering and Applied Chemistry from University of Toronto Postdoctoral Fellowship at Carnegie Mellon University’s Center for Atmospheric Particle Studies Her research addresses real-world air pollutant behavior through low-cost sensor networks, machine learning models, and interdisciplinary collaboration with institutions like UBC’s Liu Institute and IRES. Key areas include transportation emissions (e.g., LNG vehicles, autonomous vehicles), wildfire smoke impacts, and environmental justice. The 15 most recent publications highlight her expertise in PM2.5 calibration, spatial modeling, and emissions from gasoline direct injection engines. Keywords span environmental science, atmospheric chemistry, and machine learning, with sub-fields covering wildfire smoke, urban monitoring, and climate policy. Canada Research Chair (2024–present) Zimmerman collaborates with regulatory agencies and non-profits, emphasizing stakeholder engagement. Her lab develops tools for rapid environmental policy evaluation. No active student recruitment is currently ongoing, though she welcomes exceptional fellowship applicants.

Stefan Kremerمشاهده پروفایل

استاد

• Pattern Recognition
• Structural Patterns
• Deep Learning
• +۷ مورد دیگر

Stefan Kremer is a Professor at the University of Guelph. His research focuses on learning to recognize, categorize, and generate structural patterns in complex data, utilizing artificial neural networks, support vector machines, deep belief networks, hidden Markov models, evolutionary algorithms, and deep learning. His lab emphasizes problem-driven approaches, particularly in domains like biology. Methods include deep learning, spatio-temporal pattern recognition, and bioinformatics. Contact: skremer@uoguelph.ca

Joshua Speagleمشاهده پروفایل

استادیار

• Astrostatistics
• Data Science
• Machine Learning
• +۷ مورد دیگر

Joshua Speagle is an Assistant Professor jointly appointed in the Department of Statistical Sciences and the David A. Dunlap Department of Astronomy & Astrophysics at the University of Toronto. He is also an Associate Member of the Dunlap Institute for Astronomy & Astrophysics and a Member of the Data Sciences Institute. His research lies at the intersection of statistics, astronomy, and computer science, focusing on astrostatistics and data-intensive astrophysics. His research interests include astrostatistics, data science, machine learning, statistical inference, and Bayesian methods. He develops novel statistical learning techniques to extract insights from large, complex datasets, particularly from astronomical surveys. His work emphasizes interpretability, robust inference, and computational efficiency, with applications to galaxy formation, stellar photometry, and 3D dust mapping. The trends in his recent publications reflect a strong focus on interdisciplinary methodologies, particularly in Bayesian inference, nested sampling, and machine learning applied to astrophysical problems. His work consistently bridges theoretical statistics with practical applications in astronomy, emphasizing open-source software and reproducible research. Banting Postdoctoral Fellowship Dunlap Fellowship Joshua Speagle is deeply committed to mentorship and collaboration. He co-leads the Astrostatistics Research Team (ART) with Gwen Eadie, mentoring students and postdocs across disciplines. He is involved in graduate and undergraduate research programs, including the Astronomy & Astrophysics Summer Undergraduate Research Program (SURP). He teaches courses in statistics and astronomy and serves on committees within the University of Toronto and professional societies such as the AAS, ASA-AIG, and SSC-DSA. He co-leads the interdisciplinary Astrostatistics Research Team (ART), which fosters a collaborative, inclusive environment focused on cutting-edge research at the intersection of statistics and AI. The team emphasizes open and accessible science, releasing open-source tools like dynesty and brutus , and mentoring the next generation of data scientists.

• Electroacoustic Music
• Sound Art
• AI and Music Creativity
• +۸ مورد دیگر

Douglas Van Nort is an Associate Professor at York University's Department of Computational Arts, School of the Arts, Media, Performance and Design (AMPD). He holds a PhD in Music Technology from McGill University and has held roles including Canada Research Chair in Digital Performance (2015-2025), Banting Fellow at Concordia University (2013-2014), and Research Associate at Rensselaer Polytechnic Institute (2008-2013). His work integrates electroacoustic music, AI-driven systems, and immersive performance environments. Education: PhD in Music Technology (McGill, 2010), MFA in Electronic Arts (Rensselaer, 2003), MA/BA in Mathematics (SUNY Potsdam, 2001). Research Interests: Telematic performance, human-machine improvisation, ecoacoustics, and Deep Listening practices. Van Nort's awards include the 2010 ICMC Best Paper Award and Canada Research Chair designation. His grants total over CAD 5M, including CFI infrastructure funding for the DisPerSion Lab. He teaches courses in electro-acoustic music, interactive media, and digital performance. His DisPerSion Lab explores distributed performance and sensorial immersion.

Carlos Vázquezمشاهده پروفایل

استاد

• Computer vision
• Medical imaging
• 3D reconstruction
• +۴ مورد دیگر

Carlos Vázquez is a Professor at the Department of Software Engineering and IT, École de technologie supérieure (ÉTS). He holds a B.Eng. and M.Sc. from ISPJAE, Cuba, and a Ph.D. from INRS, Montreal. His research focuses on computer vision, medical imaging, 3D reconstruction, and immersive video technologies. He co-leads the Summit Tech Research Chair in Immersive and Interactive Video and is affiliated with the Multimedia Research Laboratory (LABMULTIMEDIA) and the Open Innovation Laboratory in Health Technologies (LIO-ÉTS). His expertise includes stereoscopic imaging, multi-view video coding, and GPGPU programming. Notable research axes are software systems, multimedia, cybersecurity, and health technologies. He has supervised numerous doctoral and master’s theses, including works on 3D spine reconstruction, personalized femur modeling, and immersive video compression. Key contributions include advancements in medical imaging analysis, 3D reconstruction from biplanar radiographs, and efficient video coding for virtual reality. His work bridges clinical applications and engineering, with implications in surgical planning, patient rehabilitation, and multimedia systems optimization.

• Computer Science
• formal methods
• Security
• +۶ مورد دیگر

Marc Frappier is a full professor at Université de Sherbrooke with over 25 years of academic experience. His research focuses on formal methods , security , and software synthesis . He holds a PhD from the University of Ottawa (1995), a Master's (1990) and Bachelor's (1986) from Université de Sherbrooke. Research Areas: Formal specification/verification, intrusion detection, access control, ASTD/TASTD modeling, automotive systems Grants: NSERC, MITACS, PSEPC, CSE Canada, Natural Resources Canada (total over $2.5M) Selected Publications highlight formal methods in automotive systems, security policy verification, and real-time modeling. His work spans cross-domain collaborations including healthcare, industrial systems, and cybersecurity. Scientific Awards: Distinguished Service Award 2019 (Cs-Can | Info-Can) Committee Roles : Program Committee member for 15+ conferences (2012-2021), including ABZ, ICFEM, HASE, and RAMiCS.

• Causal Inference
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Dhanya Sridhar is an Assistant Professor at the Department of Computer Science and Operations Research (DIRO) of the University of Montreal and a core academic member of Mila - Quebec Artificial Intelligence Institute. She holds a Canada CIFAR AI Chair and co-leads the IVADO R3AI working group on safe and aligned AI. PhD from University of California Santa Cruz Postdoctoral research at Columbia University Data Science Institute Her research focuses on integrating causality and machine learning to build AI systems that are robust to distribution shifts, capable of efficient task adaptation, and aligned with human knowledge. Recent work includes causal effects of social interactions on US election participation and causal modeling in geriatric-oncology patient outcomes. Current research themes: Causal representation learning Temporal causal inference Counterfactual modeling Causal abstraction in large models Responsible AI development Scientific awards: Canada CIFAR AI Chair Major grants: NSERC Discovery Grant (PVX20965-RGP) 2023-2029 NSERC DGECR Grant 2023-2025 Multiple MITACS Acceleration Quebec grants Advising: PhD students: Philippe Brouillard, Shruti Joshi, Mizu Nishikawa-Toomey, Tom Marty, Cristian Manta

Yun Zhouمشاهده پروفایل

دانشیار

• Operations Management
• Supply Chain Management
• Revenue Management
• +۷ مورد دیگر

Yun Zhou is an Associate Professor in the Department of Operations Management at DeGroote School of Business, McMaster University. His research bridges theoretical operations research with practical applications in supply chain optimization, revenue management, and healthcare logistics. PhD: Rotman School of Management, University of Toronto Doctorate & B.Sc.: Tsinghua University Dr. Zhou specializes in revenue management , supply chain coordination , and dynamic pricing , particularly in on-demand delivery systems and sharing economy platforms . His work addresses uncertainty in demand , perishable product inventory , and healthcare operations challenges like vaccine allocation and surgical scheduling . Recent publications focus on robotic warehouse systems (e.g., rack-moving robots for order picking), price and matching dynamics in sharing economies , and blood supply chain coordination . These span optimization algorithms , stochastic modeling , and empirical retail analytics . SSHRC Grant: Addressing societal challenges through operations research