جستجوی استاد راهنمای دانشگاهی

Dr. Jasmin Jelovica is an Associate Professor in the Department of Civil Engineering and Mechanical Engineering at the University of British Columbia (UBC), with a joint appointment since 2017. He holds a D.Sc. (Tech.) from Aalto University and an M.Sc. in Naval Architecture from the University of Rijeka. His research focuses on computational methods for structural analysis and optimization, emphasizing lightweight structures and mechanical response analysis. He leads the Structural Efficiency Laboratory, exploring advanced materials, welding techniques, and machine learning applications in structural design. Research Interests: Structural optimization, computational methods, advanced materials, marine vessels design, machine learning, and welding technologies. His work spans structural mechanics, vibration analysis, and multi-objective optimization frameworks. Awards: NSERC/Seaspan Industrial Research Chair in Intelligent and Green Marine Vessels (2020). Lab & Collaborations: The Structural Efficiency Laboratory at UBC focuses on innovative structural components, topology optimization, and advanced welding processes. Research is supported by collaborations with Civil and Mechanical Engineering departments. Advising & Grants: Advises graduate students in structural analysis and optimization projects. Active in conferences such as MARSTRUCT and OMAE, publishing on topics like neural network-assisted constraint handling and underwater acoustics.

• Structural Analysis
• Structural Optimization
• Computational Methods
• +۶ مورد دیگر

Osmar Zaiane is a Professor in the Department of Computing Science at the University of Alberta's Faculty of Science. With over 20 years of service at the university, he has established himself as a leading researcher in data mining and machine learning with applications across multiple domains. His educational background includes a Ph.D. in Computer Science from Simon Fraser University (1999), a Master's in Computer Science from Laval University (1992), a Master's in Electronics from Institut National des Sciences et Techniques Nucléaires and Paris XI University (1989), and a Bachelor's in Computing Science from Institut Supérieur de Gestion, Université de Tunis (1988). Zaiane's research focuses on discovering patterns in large complex datasets with practical applications. His primary research interests include data mining, machine learning, social network analysis (particularly community mining and link prediction), and precision health applications. He has made significant contributions to associative classifiers, class imbalance learning, explainable AI, and educational data mining. His work spans multiple application domains including healthcare (particularly for Alzheimer's disease prediction, diabetic retinopathy grading, and lung cancer detection), social media analysis, and natural language processing. His publication record shows a strong focus on medical AI applications, with numerous recent papers on medical image segmentation, brain network analysis, and diagnostic systems. His work increasingly integrates large language models and transformer architectures with traditional machine learning approaches. Best Paper Award at IEEE/ACM Int. Conf. on Advances in Social Network Analysis and Mining (2023) Best Paper Award at International Symposium on Foundations and Applications of Big Data Analytics (2022) Best Paper Award at International AAAI Conference on Web and Social Media (2019) Best Paper Award at 29th International Conference on Database and Expert Systems Applications (DEXA) (2018) Zaiane has supervised over 80 graduate students during his career at the University of Alberta. His research has been supported by numerous grants, particularly in the areas of precision health and educational data mining. He maintains an active research lab focusing on applied machine learning with strong industry and healthcare partnerships. His current work explores the intersection of traditional machine learning techniques with emerging large language models and vision-language models for healthcare applications.

• Data Mining
• Machine Learning
• Social Network Analysis
• +۱۴ مورد دیگر

Prof. Alex Hernandez-Garcia is an Assistant Professor at the Université de Montréal's Department of Computer Science and Operational Research (DIRO) and a member of Mila, the Quebec AI Institute. He holds a PhD from the University of Osnabrück and completed postdoctoral research with Yoshua Bengio and David Rolnick at Mila. His research focuses on machine learning for scientific discovery, particularly in climate science, materials design, and health applications. He is a proponent of open science and equity initiatives, serving on Mila's Equity, Diversity, and Inclusion Committee and co-organizing the 'Against Military AI' reading group. Educations: B.Sc. and M.Sc. in Audiovisual Systems Engineering and Communication/Multimedia from Universidad Carlos III de Madrid (2014). PhD in Cognitive Science and Deep Learning from the University of Osnabrück (2020). Postdoctoral Fellow at Mila (2020–2022). Research interests include generative models (e.g., GFlowNets), active learning, and their applications to climate modeling, drug discovery, and sustainable materials. Notable projects include ThisClimateDoesNotExist.com and collaborations on electrocatalyst design. He teaches courses on machine learning and advises initiatives like the AI4Good Lab. Key Contributions: Development of GFlowNets for scientific exploration. Advocacy for open science and anti-military AI. Co-lead of Mila's Sustainability Committee. Awards: Marie Sklodowska-Curie ITN NexGenVis Fellowship (PhD). Current projects emphasize ethical AI, climate action, and interdisciplinary collaborations with domain scientists.

• Machine Learning
• Climate Science
• Health Sciences
• +۴ مورد دیگر

Sébastien Le Digabel is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He holds affiliations with GERAD (Groupe d'études et de recherche en analyse des décisions) and IVADO (Institut de valorisation des données), where he contributes to research in optimization and artificial intelligence. Professor Le Digabel's research focuses on black-box optimization and derivative-free methods, particularly the Mesh Adaptive Direct Search (MADS) algorithm. His work spans three main areas: development of optimization methods, application to engineering problems, and development of optimization software. He has made significant contributions to extending MADS for constrained optimization, mixed-variable problems, multiobjective optimization, and stochastic optimization. His recent publications (2023-2025) demonstrate continued innovation in handling complex optimization challenges including categorical variables, hierarchical constraints, and multi-fidelity approaches. These works appear in top journals like Computational Optimization and Applications, SIAM Journal on Optimization, and Optimization and Engineering. Professor Le Digabel actively supervises graduate students, having completed 8 PhD theses and 10 Master's theses with topics spanning multiobjective optimization, hyperparameter tuning, smart grid applications, and power systems optimization. His research is supported by diverse funding sources including NSERC, FRQNT, InnovÉÉ, IVADO, Hydro-Québec, Rio Tinto, and Huawei-Canada, reflecting the broad applicability of his work across academic and industrial domains. Professor Le Digabel leads the NOMAD Research Group and is the primary developer of the NOMAD optimization software, which implements the MADS algorithm and serves as a standard tool in the derivative-free optimization community. The software is available at www.gerad.ca/nomad and continues to evolve with new capabilities for challenging optimization problems.

• Operations Research
• Mathematical Optimization
• Black-box Optimization
• +۵ مورد دیگر

Guillaume-Alexandre Bilodeau is a Full Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. He serves as Head of the Laboratory for Image and Video Interpretation and Processing (LITIV) and is a member of the Institute for Data Valorization (IVADO). His research is centered within the 'New frontiers in information and communications technologies' center of excellence, with additional affiliations in 'Modeling and Artificial Intelligence' and 'Transport and Sustainable Infrastructure' centers. Professor Bilodeau's research focuses on computer vision and artificial intelligence, with particular expertise in: Developing methods for interpreting human actions and interactions within scenes Integrating visible and infrared camera data for improved object and human detection Object tracking using pan-tilt-zoom camera systems Medical applications of thermal imaging and thermography, including Parkinson's disease diagnosis from gait analysis His recent publications demonstrate continued strong activity in video prediction, medical applications of computer vision, and multi-object tracking. Professor Bilodeau maintains a robust publication record with multiple 2025 publications in top venues, reflecting ongoing research productivity and relevance in the computer vision community. Professor Bilodeau has received significant recognition for his work, including: 2023: Fonds de recherche du Québec research grant award 2008: Best vision paper award at Canadian Conference on Robot Vision 2002: Honorable mention for Pattern Recognition Society award As an educator and mentor, Professor Bilodeau has supervised 12 PhD students and 26 Master's students to completion. He teaches courses in multimedia technologies and computer vision. His laboratory, LITIV, provides a research environment focused on image and video interpretation with applications spanning surveillance systems, medical diagnostics, and transportation infrastructure monitoring.

• Computer vision
• Artificial intelligence
• Image and video processing
• +۶ مورد دیگر

Matt J. Kusner is an Associate Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. He holds additional affiliations as a Senior Academic Member at Mila - Quebec Artificial Intelligence Institute and as a Member at the Institute for Data Valorization (IVADO). Previously, he served as an Associate Professor at University College London and the University of Oxford. Professor Kusner's research spans machine learning, with particular focus on causal inference, fairness in algorithms, representation learning, and domain adaptation. His work bridges theoretical foundations with practical applications in natural language processing, algorithmic fairness, and scientific computing domains including plasma physics and renewable energy systems. His research interests include Pattern Analysis and Artificial Intelligence, Algorithms, and Learning and Inference Theories. His publication record shows consistent output across top machine learning venues including NeurIPS, ICML, and ICLR, with recent work trending toward causal machine learning methods and applications in scientific domains. Professor Kusner has 42 publications spanning from 2014 to 2025, demonstrating sustained research productivity. Turner Dissertation Award for best doctoral dissertation in Computer Science & Engineering Professor Kusner received his PhD in Computer Science from Washington University in St. Louis in 2016 under Kilian Weinberger. His work has been featured in major media outlets including The Guardian, Forbes, and the Harvard Business Review, and he has presented at prestigious institutions including the Federal Reserve Banks, Cambridge Centre for Mathematical Sciences, and the Royal Society.

• Machine Learning
• Artificial Intelligence
• Pattern Analysis
• +۵ مورد دیگر

Ettore Merlo is a Full Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal, where he leads research in cybersecurity, artificial intelligence, and software systems. He holds an M.Sc. from the University of Turin and a Ph.D. from McGill University. His affiliations include membership in the Institute for Data Valorization (IVADO), focusing on data science and AI innovation. His research integrates software engineering with AI, emphasizing: Software artifact analysis (static/dynamic/symbolic) AI-driven security solutions for clone detection, malware analysis, and access control Fairness and robustness in machine learning systems Evolutionary analysis of software vulnerabilities Recent publications (2022-2025) demonstrate a strong focus on ethical AI, including bias mitigation in neural networks, automated anomaly detection, and certification of safety-critical ML systems. His work frequently applies graph neural networks, unsupervised learning, and formal verification methods to industrial and cybersecurity challenges. Professor Merlo has supervised 25 graduate students (10 PhD, 15 Master's), with projects ranging from avionics software to phishing kit analysis. While no scientific awards are listed, his extensive publication record includes 136 works spanning journals, conferences, and technical reports. Collaborations include partnerships with industrial telecommunication firms and international academia. No dedicated lab is specified, but his research aligns with Polytechnique Montréal's 'New Frontiers in Information and Communications Technologies' center.

• Cybersecurity
• Artificial Intelligence
• Software Engineering
• +۵ مورد دیگر

Enas AlTarawneh is a Post Doctoral Fellow at Toronto Metropolitan University, working with the Canada Excellence Research Chair (CERC) in Health Equity and Community Wellbeing from September 2024 to September 2025 within the Digital Health Technologies and Methods department. She holds a PhD candidacy at York University's Lassonde School of Engineering, demonstrating interdisciplinary expertise bridging engineering and health sciences. Her educational trajectory emphasizes innovation in technology-driven health solutions. Dr. AlTarawneh pioneers emotion-aware artificial intelligence agents through the integration of machine learning and emotional intelligence, utilizing advanced graph neural networks and large language models. Her research employs rigorous Human-Computer Interaction (HCI) and Human-Robot Interaction (HRI) evaluation frameworks, directly contributing to digital health accessibility and community wellbeing. This work positions her at the critical intersection of AI ethics and equitable healthcare delivery. Her academic excellence has been recognized through prestigious scholarships and awards, though specific honors are not detailed in available documentation.

• Machine Learning
• Emotional Intelligence
• Artificial Intelligence
• +۴ مورد دیگر