جست‌وجوی استاد و پژوهشگر

Julian Marvin Joersمشاهده پروفایل

پژوهشگر

• Human-Computer Interaction
• Eudaimonic Well-Being
• Human-Centred Design
• +۳ مورد دیگر

Julian Marvin Joers is a Researcher at Otto von Guericke University Magdeburg's School of Human-Centred Artificial Intelligence and Design, where he serves as Data Steward and supervisor for 13 doctoral theses. His academic journey includes an M.Sc. in Information Systems from University Hagen (2018-2021) and B.Sc. from TU Darmstadt (2013-2018), with his Dr.-Eng. dissertation on Conceptualization and Implementation of Eudaimonic Well-Being in HCI expected in 2025. His research pioneers the integration of Aristotelian virtue ethics into human-computer interaction, focusing on eudaimonic well-being through: Development of measurement frameworks like the Eudaimonic Interaction Inventory (EII) Design of AI systems supporting meaningful engagement (e.g., LaLaMoSuLe learning platform) Theoretical foundations for eudaimonic user experience beyond hedonic pleasure Well-being-centered recommender systems balancing personal growth with user needs His publication portfolio demonstrates consistent output in top venues including DIS, AIED, and HICSS, with 11 publications between 2023-2025 showing increasing citation impact (703+ downloads for his 2024 DIS paper). Current projects examine LLM-enhanced education interfaces and ethical implications of technology-induced well-being interventions. As a supervisor, he mentors students across three institutions on topics spanning AI bias detection, virtual influencer marketing, and differential privacy. His lab activities include coordination of the Human-Centred AI and Design Research Group and participation in the Erasmus+ HOMO DIGITALIS project. Professional service includes reviewing for HICSS, Mensch und Computer, and CIKM conferences. Fluent in German (native), English (professional), French (C1), and Italian (B1), Joers maintains active industry connections through Skyscraper Marketing Limited while advancing theoretical HCI frameworks that bridge philosophy and technology design.

Mohamed Nabil Lokbaniمشاهده پروفایل

مدرس

• Speech Processing
• C++ Programming
• Android Mobile Development
• +۵ مورد دیگر

Mohamed Nabil Lokbani is a Lecturer and Chief of Laboratories (IT Director) at the Department of Computer Science and Operational Research within the Faculty of Arts and Sciences at the University of Montreal . Holding a Ph.D. in Automatic and Signal Processing (1993) from Paris-XI University, he has been teaching since 1999 and managing the department's technical support team since 2007. Education : Doctorate in Automatic and Signal Processing (1993), Paris-XI University Master's (DEA) in Automatic and Signal Processing (1990), Paris-XI University Electrical Engineering Degree (1989), Houari Boumedienne University Research Interests span speech recognition algorithms, bi-text alignment tools, cross-platform software portability, and programming education. He developed the Aladin translation memory tool and focuses on practical software solutions for linguistic and engineering challenges. Teaching includes courses like: Advanced C++ Programming , Big Data Processing , Android Mobile Development , and foundational Java/Python courses. His lecture notes for these courses are frequently updated. Publications reflect expertise in speech recognition, software portability, and translation technology, with technical reports dating from 1990 to 2023. Professional Roles : Chief of Laboratories (IT Director) at DIRO since 2007 Technical Support Team Member (2000-2007) Graduate Studies Committee member since 2004 Researcher at L2S Supelec and CNET France Telecom

• Biomedical Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۴ مورد دیگر

Hoifung Poon is General Manager at Microsoft Health Futures and affiliated faculty at University of Washington Medical School. He leads Real-World Evidence (RWE) research focusing on AI applications for precision health. Poon earned a B.S. with Distinction in Computer Science from Sun Yat-Sen University and a Ph.D. in Computer Science and Engineering from University of Washington. Specializes in biomedical AI research Focuses on structuring unstructured medical data Co-PI for DARPA Big Mechanisms projects Research strength lies in biomedical multimodal learning (text, radiology, pathology, genomics) and causal learning for real-world evidence generation. His team develops methods for LLM self-verification , multi-modal fusion , and biases correction in observational data. Publications show expertise in Nature , Nature Methods , and NEJM AI , covering topics from digital pathology to clinical text analysis. Scientific recognition includes: Best Paper Awards at NAACL, EMNLP, and UAI Winner of ACM Health Best Paper Award Named Technology Champion 2022 by Puget Sound Business Journal

• Data Mining
• Artificial Intelligence & Machine Learning
• Bioinformatics & Computational Biology
• +۶ مورد دیگر

Professor Raymond Tak-Yan Ng is a distinguished faculty member in the Department of Computer Science at the University of British Columbia's Faculty of Science. His research spans data mining, bioinformatics, health informatics, and natural language processing. He serves as the Director of the Data Science Institute at UBC and holds the Canadian Research Chair on Data Science and Analytics. Additionally, he works part-time as the Chief Informatics Officer at the PROOF Centre of Excellence for the Prevention of organ failures based at St. Paul's Hospital. Professor Ng received his academic training at prestigious institutions: B.Sc. (Hons) in Computer Science from the University of British Columbia (1986) M.Math. in Computer Science from the University of Waterloo (1988) Ph.D. in Computer Science from the University of Maryland, College Park (1992) Professor Ng's research focuses on developing data mining tools that place the human user front and center in the discovery process. His work emphasizes constraint-based mining, unified models for analysis and mining, performance optimization, and new data mining capabilities like outlier detection. In bioinformatics, he applies data mining techniques to link clinical and genomic data, particularly for cancer analysis. His health informatics research develops biomarker panels for conditions related to organ failures in hearts, lungs, and kidneys. His natural language processing work focuses on creating metadata from unstructured conversations to facilitate access to raw data. Professor Ng has received numerous prestigious awards recognizing his contributions: Outstanding Paper Award Genome BC Award Academic Data Leader List Killam Research Prize Fellow of the Royal Society of Canada Bio-IT Best Practices Award CASCON Best Exhibit Award Best Paper Award from ACM SIGMOD Professor Ng has supervised numerous graduate students, with doctoral dissertations completed from 2009-2021 and master's theses from 2013-2022 covering diverse topics from RNA-binding proteins to financial knowledge graphs. His research has been supported by significant grants enabling work at the intersection of computer science, healthcare, and data science. His collaborative approach has led to partnerships with medical researchers, clinicians, and industry partners to translate computational methods into practical healthcare applications. Professor Ng leads the Data Systems and Mining Laboratory at UBC and is deeply involved with multiple research centers including the CAIDA (Centre for Artificial Intelligence Decision-making and Action), the Data Science Institute, the Blockchain at UBC initiative, and the Language Sciences Institute. He collaborates extensively with the PROOF Centre of Excellence and various health research institutes including the Centre for Heart Lung Innovation and the Providence Health Care Research Institute. His interdisciplinary work bridges computer science with clinical applications through partnerships with medical researchers and healthcare institutions.

Sébastien Le Digabelمشاهده پروفایل

استاد

• Operations Research
• Mathematical Optimization
• Black-box Optimization
• +۵ 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.

Alain Hertzمشاهده پروفایل

استاد

• Operations Research
• Management Science
• Combinatorial Optimization
• +۶ مورد دیگر

Alain Hertz is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He is affiliated with two major research centers: Institut de valorisation des données (IVADO) and Groupe d'études et de recherche en analyse des décisions (GERAD). His expertise spans operations research, management science, and algorithm development with applications across multiple industries. Professor Hertz specializes in combinatorial optimization, graph theory, algorithms, heuristics and metaheuristics. His research bridges theoretical mathematics with practical applications in scheduling, vehicle routing, and decision support systems. Recent work explores graph-based approaches for recommender systems and chemical graph theory, demonstrating the versatility of his mathematical frameworks. His publication record shows consistent output across top journals in operations research and graph theory. Recent articles (2021-2025) focus on extremal graph theory, chemical graphs, graph coloring variants, and applications of graph theory to recommender systems. This demonstrates both theoretical depth and practical relevance in his research trajectory. Prix de la ministre de l'Éducation, du Loisir et du Sport - Gouvernement du Québec (2011) Professor Hertz has supervised 13 doctoral students and 18 master's students, with thesis topics ranging from optimization methods for semi-supervised clustering to wind farm network design. His supervision approach emphasizes both theoretical foundations and practical applications, preparing students for careers in both academia and industry. His research has been supported by various grants enabling collaborative work with international partners. Through his affiliations with IVADO and GERAD, Professor Hertz contributes to interdisciplinary research initiatives that connect mathematical theory with real-world problems in data science, artificial intelligence, and industrial engineering.

Guillaume-Alexandre Bilodeauمشاهده پروفایل

استاد

• Computer vision
• Artificial intelligence
• Image and video processing
• +۶ 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.

Matthew Joseph Kusnerمشاهده پروفایل

دانشیار

• Machine Learning
• Artificial Intelligence
• Pattern Analysis
• +۵ 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.

Ettore Merloمشاهده پروفایل

استاد

• Cybersecurity
• Artificial Intelligence
• Software Engineering
• +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.

Sean Kauffmanمشاهده پروفایل

استادیار

• Formal Methods
• Real-time Systems
• Software Engineering
• +۴ مورد دیگر

Sean Kauffman is an Assistant Professor in the Department of Electrical and Computer Engineering at Queen's University, Faculty of Engineering and Applied Science. He holds his office in Walter Light Hall, Room 611, and can be reached at sean.k@queensu.ca or by phone at 613-533-6000 ext. 77360. Dr. Kauffman earned his Ph.D. in Electrical and Computer Engineering from the University of Waterloo before completing a two-year postdoctoral position at Aalborg University in Denmark. Notably, he returned to academia after accumulating over a decade of industry experience as a software engineer, with his final industry role being Principal Software Engineer at Oracle. His research expertise spans several critical areas in computer science and software engineering, with a particular focus on safety-critical software systems. His work significantly contributes to the fields of Formal Methods, Runtime Verification, Anomaly Detection, and Explainable AI. Dr. Kauffman has established productive research collaborations with prestigious organizations including NASA's Jet Propulsion Laboratory, the Embedded Systems Institute, QNX, and Pratt and Whitney Canada. Dr. Kauffman's research output demonstrates a consistent focus on event stream analysis, formal verification techniques, and the development of practical tools for system monitoring. His most notable contribution is the nfer language and toolset, which has become influential in the runtime verification community for its ability to abstract event streams into meaningful temporal hierarchies. His publications reveal a progression from theoretical foundations to practical implementations, with applications spanning spacecraft telemetry, autonomous vehicles, and embedded systems. Among his scientific contributions, Dr. Kauffman has received recognition for his work on the complexity analysis of nfer evaluation, developing methods for annotating control-flow graphs for formalized test coverage criteria, and creating frameworks for anomaly detection in embedded systems. His research has been published in top-tier venues including Science of Computer Programming, International Journal on Software Tools for Technology Transfer, and proceedings of major conferences like Runtime Verification and NASA Formal Methods. As an educator, Dr. Kauffman employs active learning techniques, productive failure approaches, and peer instruction to foster student engagement. His industry background informs his teaching approach, providing students with practical insights into real-world software engineering challenges, particularly in safety-critical domains. Dr. Kauffman leads the CritLab research group at Queen's University, which focuses on critical systems research. The lab develops tools and techniques for analyzing and verifying systems where failures could have severe consequences, with applications in aerospace, automotive, and other safety-critical domains. His work on the nfer language has spawned related projects including nvis for visualizing temporal interval hierarchies.

• Mathematical Optimization
• Conic Optimization
• Smart Grids
• +۷ مورد دیگر

Miguel Anjos is Professor and Chair of Operational Research at the School of Mathematics, University of Edinburgh. He serves as Deputy Head of School and leads the Data and Decisions research theme. His professional roles extend to Chair of the Mathematical Optimization Society (2023–2025) and Vice-President (International Activities) of INFORMS. He holds Fellowships from the Humboldt Foundation, EUROPT, and the Canadian Academy of Engineering. Research Focus: Mathematical optimization for large-scale nonlinear problems in engineering applications, particularly conic optimization in facility layout and power grid optimization. Collaborations: National Grid ESO, Hydro-Québec, Schneider Electric, Rio Tinto, EDF, and Yeji Data Lab. Datasets: Maintains QAPLIB, FLPLIB, and Jones Benchmark for optimization research. His work addresses smart grid challenges: maximizing grid efficiency, integrating renewables, optimizing energy storage, and enabling customer participation. Former students have secured positions at universities (University of Tennessee, Western Ontario) and tech companies (Amazon, Shopify). Scientific Awards: Fellow of the Humboldt Foundation Fellow of EUROPT Fellow of the Canadian Academy of Engineering He has supervised PhD researchers including Elspeth Adams (2014 MOPTA runner-up), Christian Bingane (2019 CORS Student Paper Competition winner), and Mathieu Tanneau (2018–2021 FRQNT Merit Scholar). His postdocs have contributed to bilevel attacks in AI, energy storage scheduling, and electric vehicle infrastructure planning.

Samira Abbasgholizadeh-Rahimiمشاهده پروفایل

استادیار

• Artificial Intelligence in Primary Healthcare
• Digital Health Technologies
• Medical Machine Learning
• +۵ مورد دیگر

Dr. Samira Abbasgholizadeh-Rahimi serves as Assistant Professor at McGill University in the Faculty of Medicine and Director of Artificial Intelligence in Family Medicine (AIFM) . She holds the Canada Research Chair in Advanced Digital Primary Health Care and maintains affiliations with Mila – Quebec AI Institute and Lady Davis Institute for Medical Research . B.Eng., Ph.D. from McGill University President-Elect, Canadian Operational Research Society Her interdisciplinary research bridges Artificial Intelligence and Primary Healthcare , with specific focus on: AI-enabled decision support tools Explainable machine learning for clinical contexts Medical knowledge graphs and NLP applications Addressing algorithmic bias in vulnerable populations Ageism mitigation in health AI systems Digital support for neurocognitive disorders Recent publications demonstrate her expertise in: AI ethics and EDI integration (4 papers since 2024) Geriatric depression prediction models (2025 JMIR study) Shared decision-making frameworks (2022-2023 series) Conscious AI debates in clinical settings (2024 commentary) Scientific recognition includes: 2022 NAPCRG New Investigator Award Cross-institutional collaborations across Canada, Europe, and Asia Research funding sources: Fonds de recherche du Québec – Santé Natural Sciences and Engineering Research Council Brocher Foundation (Switzerland) CIHR SPOR grants Industry partnership with Roche Canada Lab activities including: Developing HOPE (Home-Based Older Adults' Depression Prediction) model Creating incorpoRATE SDM assessment framework Building evidence-based decision support tools for dementia caregivers Training next-gen AI healthcare researchers through Mila collaborations