Find Academic Supervisors

Mahdi TaiebatView profile

Professor

• Theoretical Geomechanics
• Computational Geomechanics
• Constitutive Modeling
• +3 more

Dr. Mahdi Taiebat is a Professor in the Department of Civil Engineering at the University of British Columbia (UBC), affiliated with the Faculty of Applied Science. He specializes in theoretical and computational geomechanics, constitutive modeling, granular materials mechanics, and geotechnical earthquake engineering. His research addresses challenges at the intersection of engineering, physics, and applied mathematics. He leads the Theoretical & Applied Geomechanics (TAG) research group and has co-authored over 150 scientific papers. Educations: B.Sc. and M.Sc. in Civil Engineering, Sharif University of Technology, Iran (2001, 2003) Ph.D. in Civil Engineering, University of California, Davis, USA (2008) Research Interests: Theoretical and computational geomechanics Constitutive modeling of engineering materials Physics and mechanics of granular materials Geotechnical earthquake engineering Static and dynamic soil-structure interaction Awards & Honors: Norman Medal (ASCE, 2012) Discovery Accelerator Supplement (NSERC, 2015) Professor Appreciation Award (UBC Civil Engineering Undergraduate Student Club, 2011) Postdoctoral Research Fellowship, Norwegian Geotechnical Institute (2008-2009) His work includes advising graduate students on geomechanics and geotechnical earthquake engineering, with emphasis on programming skills (C++, Fortran, Python) and competency in solid/fluid mechanics. The TAG group focuses on advanced numerical methods and experimental validation, collaborating internationally on projects like the LEAP initiative. His research spans constitutive model development (e.g., SANISAND/SANICLAY), seismic site response analysis, and liquefaction mitigation strategies.

Marc ParizeauView profile

Professor

• Pattern Recognition
• Evolutionary Computation
• Neural Networks
• +6 more

Marc Parizeau is a Professor at Université Laval, affiliated with the Faculty of Science and Engineering and the Department of Electrical and Computer Engineering. His office is located at PLT-1138-B, and he can be reached at (418) 656-2131 ext. 407912 or via email at marc.parizeau@gel.ulaval.ca. Academic Background: Ph.D., École Polytechnique de Montréal, 1992 M.Sc.A., École Polytechnique de Montréal, 1987 B.Eng., École Polytechnique de Montréal, 1984 His research focuses on Pattern Recognition, Evolutionary Computation, Neural Networks, 2D and 3D Computer Vision, and parallel and distributed systems . He integrates these areas to develop scalable computational models and tools for intelligent systems. His work bridges theoretical AI with practical software engineering for high-performance environments. His teaching includes courses such as Programmation parallèle et distribuée (GIF-4104) , Réseaux de neurones (GIF-21410) , and various algorithm and programming courses in Python and engineering. His recent publications reflect a strong trend in distributed evolutionary algorithms and concurrent programming frameworks, particularly using Python-based tools like DEAP and SCOOP, emphasizing scalability and real-world deployment. Scientific Leadership and Software Contributions: Director, Calcul Québec Creator, Distributed Evolutionary Algorithms in Python (DEAP) Creator, Scalable Concurrent Operations in Python (SCOOP) Contributor, Portable Agile Classes in C++ (PACC) Contributor, Open Beagle Marc Parizeau has supervised multiple collaborative projects and students, though specific names are not listed. He has secured research support through leadership roles and software development. His work is supported by institutional and provincial computing infrastructure initiatives. Conference Involvement: Organizing Committee, High Performance Computing Symposium (HPCS'08) International Workshop on Frontiers in Handwriting Recognition (IWFHR'02) International Conference on Pattern Recognition (ICPR'02) Vision Interface (VI'99) Conférence Internationale sur l'Écrit et le Document (CIFED'98) He leads the Computer Vision and Systems Laboratory at Université Laval, a research group focused on intelligent systems, machine learning, and high-performance computing applications in vision and optimization.

Daniel GermanView profile

Professor

• Software Engineering
• Software Evolution
• Open Source Software Development
• +4 more

Dr. Daniel German is a Professor in the Department of Computer Science at the University of Victoria, part of the Faculty of Engineering and Computer Science. He holds a PhD from the University of Waterloo, specializing in software engineering and open source ecosystems. His research focuses on software evolution, open source development practices, intellectual property issues in software systems, and licensing compliance challenges in modern AI/ML environments. German has contributed extensively to understanding dependency management, developer workflows, and legal aspects of software development. His work includes seminal studies on code provenance tracking (e.g., Cregit), library dependency management, and the sociotechnical dynamics of open source communities like the Linux kernel and GitHub ecosystems. German has explored critical topics such as licensing inconsistencies in software projects, ethical implications of AI-generated code, and the integration of open source components into proprietary systems. He is actively involved in software engineering education, examining how students engage with open source projects and the challenges of maintaining code quality in large-scale distributed systems. German’s research has practical implications for both academic and industrial software development practices, addressing real-world issues like security vulnerabilities in dependency chains and the legal risks of AI training data usage.

Nicolas GodboutView profile

Professor

• Photonics
• Optical Fiber Components
• Quantum Cryptography
• +6 more

Nicolas Godbout is a Full Professor and Department Director of the Department of Engineering Physics at Polytechnique Montréal. He holds a Ph.D. from Polytechnique Montréal and serves as Head of the Fiber Optics Laboratory and as a Researcher at the Center for Optics, Photonics and Lasers (COPL). His academic leadership extends to teaching courses including Fundamentals of Photonics, Waveguide Optics, Lasers, Quantum Optics, and Current Subjects in Photonics. Dr. Godbout's research spans multiple areas of photonics with particular emphasis on optical fiber components, quantum cryptography, quantum information, optical telecommunications, and nonlinear optics. His work bridges fundamental theoretical investigations with practical applications in telecommunications, biomedical imaging, and quantum technologies. He has made significant contributions to photonic lantern development, quantum key distribution systems, and optical fiber component design. Analysis of his recent publications reveals a strong trend toward interdisciplinary research combining photonics with biomedical applications, quantum information processing, and advanced computational modeling. His work demonstrates consistent innovation in optical fiber technology, with increasing focus on quantum applications and biomedical instrumentation over the past decade. The development of open-source tools like SuPyMode and PyMieSim highlights his commitment to advancing research methodology in the field. $377,000 awarded for Quantum Photonics Quebec projects (2022) Appointment as director of INTRIQ (2019) Dr. Godbout has supervised an extensive number of graduate students, with 9 completed Ph.D. theses and 21 Master's theses under his guidance, plus one currently in progress. His research has been supported by significant funding, including semiconductor research initiatives that received $120 million from the Canadian government as recently as July 2024. He actively collaborates with industry through Castor Optics, a company he co-founded with Professor Caroline Boudoux that specializes in optical fiber components. As Head of the Fiber Optics Laboratory and researcher at COPL, Dr. Godbout leads a dynamic research team focused on advancing photonics technology. His laboratory work spans from fundamental quantum optics research to practical applications in telecommunications and biomedical imaging. Recent activities include significant contributions to semiconductor research initiatives at Polytechnique Montréal, reflecting his leadership in positioning the institution at the forefront of advanced technology development.

Bryan SarloView profile

Lecturer

• Artificial Intelligence
• Video Game Development
• Computer Science Education
• +3 more

Bryan Sarlo is a Lecturer in the Department of Computer Science at Western University since Fall 2017. He specializes in teaching foundational courses such as CS1033, CS1027, CS2033, and Python programming for non-CS students in the Western Integrated Science (WISc) program. He authored a digital multimedia textbook tailored for CS1033. Education: BSc in Computer Science, Nipissing University MSc in Computer Science (Thesis: AI in Video Game Development), Western University, supervised by Dr. Mike Katchabaw Research Interests: Focus on AI applications in video games, procedural content generation, and educational technology. His MSc work explored AI-driven society simulation in modern video games, with a focus on agent-based modeling and procedural generation techniques. Publications: Notable works include studies on artificial society generation, particle swarm optimization, and visualization frameworks applied to fuel consumption simulation through serious games. These contributions bridge game development, optimization algorithms, and educational tools. Awards: The 'Teaching Awards' section is listed but no specific awards are mentioned in available texts. Advising & Grants: No formal advisees or grants are listed in the provided materials. His role emphasizes teaching and curriculum development rather than direct graduate supervision. Labs/Teams: No specific lab or team affiliations are detailed in the text.

Brandon KarchewskiView profile

Teaching Professor

• Computational Geoscience
• Engineering Geology
• Natural Hazards
• +2 more

Brandon Karchewski is an Associate Head (Undergraduate) and Teaching Professor in the Department of Earth, Energy and Environment at the University of Calgary. He earned his PhD in Civil Engineering from McMaster University and teaches courses including Engineering Geology, Computational Methods, and Natural Disasters. His research program focuses on: Computational methods in geophysics and geomechanics Geoscience education innovation Climate change impacts on frozen soils Inverse modeling applications Karchewski has pioneered virtual field experiences and developed open-source tools for modeling climate impacts on permafrost. His educational research examines metaphor use in geoscience communication and field pedagogy. Recent computational work includes Python-based permafrost modeling and stochastic inversion methods for biogeochemical transport. He leads the geophysics field school program emphasizing team-based learning. Award recognition includes: Geoscience Teaching Award (2019) Best Poster Award for teaching innovation research (2018) Team Teaching Excellence award (2016) Multiple teaching assistant awards

Cristiano PolitowskiView profile

Assistant Professor

• Software Engineering
• Video Game Development
• Artificial Intelligence for Software Engineering (AI4SE)
• +3 more

Cristiano Politowski is an Assistant Professor in the Department of Computer Science at Ontario Tech University's Faculty of Science. His research focuses on software engineering for video games, automated testing, AI4SE, and empirical studies. He holds a Ph.D. in Software Engineering from Concordia University (2022), an M.Sc. in Computer Science from Universidade Federal de Santa Maria (2017), and a B.Sc. in Computer Science from Universidade Regional do Estado do Rio Grande do Sul (2014). Prof. Politowski has extensive postdoctoral experience at Université de Montréal (GEODES group) and École de Technologie Supérieure (ÉTS), focusing on game engine architecture, requirements specification, and certifiable RTOS systems. He has organized workshops like ASE4Games 2021/2022 and FaSE4Games 2024, emphasizing game software engineering. His research interests span video game development challenges, software testing methodologies, and leveraging AI for software engineering. Notable projects include analyzing game engine subsystem coupling, assessing video game balance with autonomous agents, and curating datasets like PlayMyData. Prof. Politowski has secured $20K from MITACS for a 5G infrastructure project during the pandemic and received the Concordia Accelerator Award (2021). His work has been published in venues like MSR, ICSE, and IEEE Transactions.

James ForbesView profile

Associate Professor

• Dynamics and Control
• Robotics
• Nonlinear Control
• +5 more

James Forbes is an Associate Professor in the Department of Mechanical Engineering at McGill University. He holds the title of William Dawson Scholar and is affiliated with the Dynamics Estimation & Control of Aerospace & Robotics Systems research group. His primary research focus is on Dynamics and Control, with emphasis on navigation, guidance, and control (GNC) techniques for robotic systems. He teaches courses such as MECH 309 (Numerical Methods), MECH 412 (System Dynamics), and advanced topics in control systems. Forbes earned his Ph.D. in Aerospace Science and Engineering from the University of Toronto, following an M.A.Sc. from the same institution and a B.A.Sc. in Mechanical Engineering from the University of Waterloo. His research interests include nonlinear state estimation (batch methods, filtering), control synthesis via optimization (LQR, LMI approaches), and data-driven modeling using Koopman operator techniques. Applications span unmanned aerial vehicles (UAVs), autonomous underwater vehicles (AUVs), and SLAM systems. He has developed the navlie Python package for state estimation on Lie groups. Notable awards include the William Dawson Scholar distinction. His recent work focuses on multi-UAV localization, robust control algorithms, and sensor fusion techniques. He collaborates on projects involving UWB-based positioning and inertial navigation systems.

• Cognitive Neuroscience
• Machine Learning
• Neuroimaging
• +7 more

Karim Jerbi is a Full Professor at the University of Montreal's Department of Psychology, within the Faculty of Arts and Sciences. He holds the Canada Research Chair (CRC) in Systems Neuroscience and Cognitive Neuroimaging (Junior 2) and leads interdisciplinary research programs. His affiliations include the CRM — Centre de recherches mathématiques, CERNEC — Centre de recherche en neuropsychologie et cognition, CRIUGM — Centre de recherche de l'Institut universitaire de gériatrie de Montréal, PhysNum — Laboratoire de physique numérique, BRAMS — Laboratoire international de recherche sur le Cerveau, la Musique et le Son, and CIRCA — Centre interdisciplinaire de recherche sur le cerveau et l'apprentissage. These affiliations emphasize his collaborative and interdisciplinary approach to neuroscience and neuroimaging. His research investigates cognitive functions and dysfunctions using advanced signal processing and machine learning applied to multimodal brain data. Key interests include neural oscillations, brain connectivity, and the interplay between cognitive processes and neuroimaging techniques like MEG and EEG. He explores topics such as sleep dynamics, decision-making, mindfulness meditation effects, and neurodegenerative disorders, bridging computational models with clinical applications. Jerbi's recent work highlights trends in AI-driven neuroimaging tools (e.g., MEEGNet, NeuroPycon), criticality in brain networks, and the impact of consciousness states on neural activity. His grants include leading the UNIQUE initiative (2019–2024) and co-directing projects like 'Partnership for Abundant Intelligences' (2024–2029), funded by organizations like FRQNT and SSHRC. These efforts aim to integrate AI, mathematics, and clinical research for transformative insights into brain function. Scientific Awards: Canada Research Chair (CRC) in Systems Neuroscience and Cognitive Neuroimaging (Junior 2). In advising, he has guided students in topics like AI-based biomarker identification, hypnosis dynamics, and motor intention decoding. His labs and teams, such as the CRM and CERNEC, focus on mathematical foundations of neural systems and neuropsychological cognition, respectively. Collaborations span diverse fields, including gériatrie, music cognition, and computational intelligence, reflecting his commitment to cross-disciplinary innovation.

• Computational Sociology
• Social Movements
• Gender and Sexuality Studies
• +5 more

Laura Nelson is an Associate Professor in the Department of Sociology at the University of British Columbia and Director of the Centre for Computational Social Science. She specializes in computational sociology, social movements, gender studies, and intersectionality, employing text analysis, machine learning, and network methods to study organizational inequality, STEM equity, and historical activism. Education: PhD (2014) - University of California, Berkeley MA (2009) - University of California, Berkeley BA (2006) - University of Wisconsin-Madison (Phi Beta Kappa) Her research spans feminist movements, environmental activism, and computational methods, with a focus on combining quantitative analysis with qualitative interpretation. Current projects include studying gender-equity idea diffusion in higher education networks (funded by an NSF grant), analyzing social movement media coverage, and developing frameworks for computational grounded theory. Key publication trends reveal interdisciplinary work across gender studies , computational social science , historical sociology , and organizational equity . Articles frequently utilize machine learning , text mining , and network analysis to examine intersectionality , activism cycles , and institutional change . Scientific Awards: Best Meta-Reviewer, 12th International Conference on Social Informatics (2020) Outstanding Faculty of the Year at Northeastern University (2020) As a methodological innovator, she has advised graduate students like Jinyang Yu and developed courses on Python/R for social scientists, computational text analysis, and sociological theory. Her work appears in journals such as American Sociological Review , Signs , and Social Science Quarterly , with replication repositories available on GitHub and Zenodo.

Paula BrancoView profile

Assistant Professor

• Machine Learning
• Data Mining
• Data Science
• +8 more

Paula Branco is an Assistant Professor at the School of Electrical Engineering and Computer Science, University of Ottawa (since 2020). Previously, she was a PostDoctoral Fellow at Dalhousie University's Faculty of Computer Science (2019). She holds a PhD in Computer Science from the University of Porto, alongside a MSc in Data Mining and a Mathematics degree. She is an external researcher at INESC TEC's LIAAD laboratory. Her research focuses on Machine Learning, particularly utility-based learning and imbalanced domains, with applications in cybersecurity, healthcare, and environmental monitoring. Key areas include anomaly detection, fraud detection, rare event prediction, and spatio-temporal data analysis. Recent work emphasizes resampling techniques for imbalanced regression (e.g., SMOGN, ImbalancedLearningRegression Python package), evaluation metrics for imbalanced domains, and cybersecurity applications like intrusion detection and malware analysis. She has supervised seven graduate students and contributed to open-source projects like UBL (R package) and ImbalanceMetrics. Her teaching includes courses on databases and AI for cybersecurity. She actively publishes in top venues and collaborates on tools like SMOGN-LIDTA17 and NeighborhoodBiasResamplingRegression.

• Software Engineering
• Artificial Intelligence
• Chatbots
• +3 more

Dr. Ahmad Abdel Latif is an Assistant Professor in the Department of Electrical and Software Engineering at the University of Calgary. Previously, he served as a postdoctoral researcher at DASLab, Concordia University, and earned his Ph.D. in Software Engineering from Concordia under Dr. Emad Shihab. His master’s degree was obtained from King Fahd University of Petroleum and Minerals (KFUPM). His research focuses on software engineering advancements in artificial intelligence, including chatbots, software quality, and mining software repositories. Notable projects include analyzing dependency ecosystems (e.g., NPM, Maven) for security risks, developing AI-driven chatbots for code repositories, and exploring machine learning model management challenges. He also investigates ethical implications of AI-generated code and open-source collaboration dynamics. Dr. Latif teaches ENSF 381: Full Stack Web Development and actively publishes on topics such as vulnerability mitigation, LLM-based chatbots, and software bot detection. His work bridges technical innovation with practical applications in cybersecurity, open-source ecosystems, and AI integration.

Mahmoud AlfadelView profile

Assistant Professor

• Software Ecosystems
• Software Releases
• Build Systems
• +5 more

Mahmoud Alfadel is an Assistant Professor in the Department of Computer Science at the University of Calgary, Faculty of Science. His research focuses on software ecosystems, build systems, security vulnerabilities, and mining software repositories. He holds a PhD in Software Engineering from Concordia University (2021), an M.S. from KFUPM (2017), and a B.S. from Damascus University (2013). Education: Bachelor of Science in Information Technology, Damascus University (2013) Master of Science in Software Engineering, King Fahd University of Petroleum and Minerals (KFUPM, 2017) Doctor of Philosophy (Ph.D.) in Software Engineering, Concordia University (2021) His research emphasizes empirical studies in DevOps practices, continuous integration, and software security. Recent work explores vulnerability lifecycle analysis in Golang, fuzz testing adoption in open-source projects, and dependency-induced waste in CI pipelines. His publications reflect a focus on improving software quality and security through automated tools and empirical analysis. Awards and Grants: No specific awards or grants are listed in the profile. His work is supported through empirical studies and academic collaborations. Advising and Labs: No student advisees or specific lab affiliations are noted in the provided materials. His research is likely conducted through collaborative projects with students and industry partners.

Joshua SpeagleView profile

Assistant Professor

• Astrostatistics
• Data Science
• Machine Learning
• +7 more

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.

Hermann EberlView profile

Professor

• Mathematical Biology
• Engineering Mathematics
• Applied Differential Equations
• +5 more

Hermann Eberl is a Professor in the Department of Mathematics & Statistics at the University of Guelph, affiliated with the College of Engineering and Physical Sciences. His research focuses on Mathematical Biology and Engineering, emphasizing modeling, analysis, and simulation of biological systems and bio/environmental engineering processes using differential equations and numerical methods. He leads the Computational Biomathematics Laboratory, which houses a diverse research group of undergraduates, graduate students, and postdocs. Key research areas include microbial biofilms, honeybee health dynamics, agricultural systems, and environmental bioprocesses. His work is supported by funding agencies such as NSERC, CFI, and OMAFRA. Students under his supervision have excelled in interdisciplinary journals and secured careers in academia, industry, and government. Dr. Eberl holds full supervisory privileges in the Mathematics and Biophysics graduate programs and actively mentors students pursuing MSc/PhD degrees. His lab is located in MacNaughton Building, with a focus on integrating mathematical modeling with real-world applications in biotechnology and environmental engineering. Research outputs span biofilm reactor modeling, quorum sensing mechanisms, and gut microbiome interactions, with notable contributions to the ADM1jl computational tool for anaerobic digestion simulations. Collaborations emphasize interdisciplinary problem-solving, reflecting his commitment to bridging mathematical theory with practical engineering challenges.