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Habib HamamView profile

Professor

• Optics
• Biomedical Engineering
• Wireless Communication
• +7 more

Habib Hamam is a Professor in the Department of Electrical Engineering at the University of Moncton's Faculty of Engineering. His research spans optics, biomedical engineering, wireless communication, and AI applications. Specializations: Diffractive elements, optical interconnections, biomedical engineering, hybrid fiber/wireless systems, and AI-driven optimization. Contact: Email habib.hamam@umoncton.ca | Phone (506) 858-4762 Recent publications highlight his focus on AI integration across healthcare, energy systems, and education, combined with blockchain for security and transparent data management. Key subdomains include medical imaging, renewable energy optimization, and IoT scalability. Notable research trends involve deep learning for diagnostics (e.g., brain tumors, diabetic retinopathy), blockchain applications in smart grids and fisheries, and quantum-inspired algorithms for energy forecasting.

• Software Engineering
• Productivity Tools
• Human-Computer Interaction
• +6 more

Gail C Murphy is a Professor in the Department of Computer Science at the University of British Columbia and currently serves as Vice-President Research & Innovation. She is also a co-founder of Tasktop Technologies Incorporated. Her research focuses on improving the productivity of knowledge workers, particularly software developers, through empirical studies and tool development for large software system evolution. Professor, Department of Computer Science, University of British Columbia Vice-President Research & Innovation, University of British Columbia Co-founder, Tasktop Technologies Incorporated Her research spans software engineering, human-computer interaction, and productivity tools. Key areas include task context modeling, code refactoring, developer collaboration in hybrid teams, and the application of generative AI in software development practices. Recent publications highlight trends in leveraging semantic analysis for task-relevant information identification, empirical studies on developer productivity, and the integration of human-centered AI tools in software engineering workflows. Awards include the IEEE Harlan Mills Award (2018) and an Impact Award (2022). Contact details include the University of British Columbia (VPRIO) address at Suite 580, 1958 Main Mall, and the Computer Science department at 201-2366 Main Mall, Vancouver, BC, Canada. Phone numbers and emails are provided for liaison purposes.

Atefeh FarzindarView profile

Part-Time Lecturer

• Natural Language Processing
• Health Informatics
• Artificial Intelligence
• +4 more

Atefeh Farzindar is a Part-Time Lecturer in the Department of Computer Science at the University of Southern California (USC), affiliated with the Viterbi School of Engineering. She holds a PhD in Computer Science from the University of Montreal and a Doctorate in automatic summarization of legal documents from Paris-Sorbonne University. Her academic roles include Adjunct Professorships at the University of Montreal and Polytechnique Montréal, alongside leadership positions in organizations like the Canadian Artificial Intelligence Association and the Natural Sciences and Engineering Research Council of Canada (NSERC). Her research focuses on Natural Language Processing (NLP), Health Informatics, Machine Learning, and their applications in healthcare and social media analysis. She has authored/co-authored influential books including Natural Language Processing for Social Media and Transforming Healthcare with Big Data and AI . Her work bridges technology and art, reflected in her painting exhibitions and a published collection of science fiction short stories, Gazelle in New York . Farzindar actively engages in industry-academia collaboration, serving as CEO and co-founder of NLP Technologies Inc., and leading non-profit initiatives like "Dreams Without Borders" to promote ethical AI development. She has received awards such as the Femmessor-Montréal contest for balancing innovation in technology and art.

• Computational Psychiatry
• Social Neuroscience
• Systems Biology
• +5 more

Guillaume Dumas is a Professor at the Faculté de médecine of Université de Montréal, specializing in computational psychiatry and social neuroscience. He leads the Laboratoire de Psychiatrie de Précision et de Physiologie Sociale at the CHU Sainte-Justine Research Center. His work bridges neuroscience, AI, and clinical practice, focusing on social cognition, brain synchronization, and neurodevelopmental disorders. Education includes a PhD in Cognitive Neuroscience from Sorbonne Université and an HDR in Clinical Neurosciences from Université de Paris. He has held roles at Institut Pasteur and Florida Atlantic University. Research interests span computational psychiatry, interbrain connectivity, and AI-driven diagnostic tools. Notable projects include the SCALE initiative studying autism across levels and InterBrain Synchronization mechanisms. His lab develops serious games for clinical assessment and biomarker discovery. Recipient of FRQS J1/J2 awards, IVADO affiliation, and grants from CIHR and FRQNT. Supervised 7 Master’s students recently, focusing on EEG, genomic variants, and AI models.

François LareauView profile

Professor

• Computational Lexicology
• Multilingual Text Generation
• Collocations
• +4 more

Professeur agrégé at the Department of Linguistics and Translation, Faculty of Arts and Sciences, Université de Montréal. Specializes in computational lexicology, multilingual text generation, and semantic-syntax interfaces. Serves as Director of the Observatoire de linguistique Sens-Texte (OLST). Holds a Ph.D. from Université de Montréal and Université Paris, with postdoctoral research at Macquarie University (Sydney), Universitat Pompeu Fabra (Barcelona), and Universität Stuttgart (Germany). Education: Ph.D. in Linguistics, Université de Montréal and Université Paris, 2008 Research Interests: Lexical semantics and morphology Automatic text generation Collocation processing Meaning-Text Theory applications Polarized Unification Grammar Key Grants/Projects (selected): Le lexique entre humains et machines , FRQSC grant (2025-2030), leading a multidisciplinary team Automatic generation of lexicographic definitions , SSHRC grant (2018-2023) Twitter-MPhon , SSHRC grant (2021-2025) analyzing morphophonological variation via social media Advising: Supervised over 20 graduate students since 2013, focusing on multilingual generation, collocation modeling, and computational lexicography. Labs/Teams: Directs OLST and collaborates with international networks like MARQUIS for environmental text generation systems.

• Computational Terminology
• Natural Language Processing
• Corpus Linguistics
• +4 more

Patrick Drouin is a Professor and Department Director at the Université de Montréal , based in the Faculté des arts et des sciences and the Departement de linguistique et de traduction . His research focuses on computational terminology, corpus linguistics, and the automatic acquisition of specialized lexicons. He leads major projects like the NÉONUM initiative for monitoring Quebec French neology and the Lexique entre humains et machines team exploring human-machine lexical interactions. Teaching: He teaches advanced terminology (TRA-3455), localization workshops (TRA-6301), and directs programs in Linguistics and Translation at both undergraduate and graduate levels. Over 10 PhD and Master's students have been supervised by Drouin since 2007, focusing on topics like semantic shifts, terminological evolution, and corpus-based lexicography. Research Projects: Co-leads interdisciplinary teams funded by FRQSC and SSHRC, including studies on morphophonological variation via Twitter data, transdisciplinary vocabulary for primary education, and the historical analysis of terminology in translation studies. Key contributions include the SyllabO+ corpus and the QC-FR-Diac-V1 semantic shifts dataset. Grants: Secured over CAD 2M in funding for projects like Twitter-MPhon (2021–2025) and Lexiques nucléaires (2019–2025), emphasizing computational methods in linguistic research. Labs/Teams: Active in the Equipe SE (Lexical Studies) and collaborates with institutions like IRIC for biology-informatics interfaces. His work bridges theoretical linguistics with applied technologies for terminology management and language education.

JasleenView profile

Lecturer

• Machine Learning
• Healthcare Technology
• Natural Language Processing
• +4 more

Jasleen is a Lecturer at the Edwards School of Business, part of the University of Saskatchewan. Her work spans interdisciplinary research in machine learning, thermal imaging technologies, and Punjabi language studies. She has contributed to advancements in healthcare technology through studies on lung cancer treatment protocols and facial emotion recognition systems. Her research interests prominently feature applications of artificial intelligence in healthcare diagnostics, development of novel algorithms for material science analysis, and exploration of postcolonial narratives in South Asian literature. Notable work includes innovations in infrared thermography for non-destructive testing and quantum Fourier transform spectroscopy techniques. Jasleen's publications demonstrate a focus on both technical engineering solutions and humanities-based scholarship, reflecting her unique academic positioning at the intersection of business education and cutting-edge scientific inquiry. She has collaborated on projects ranging from spectral domain quantum interferometry to sociological studies of Punjabi immigrant mental health.

Maryam GhasemaghaeiView profile

Associate Professor

• Artificial Intelligence
• Data Analytics
• Algorithmic Bias
• +5 more

Dr. Maryam Ghasemaghaei is an Associate Professor and Area Chair of Information Systems at McMaster University's DeGroote School of Business. She holds the Canada Research Chair in EDI Issues in Artificial Intelligence (Tier II) and was named a 2022 University Scholar for exceptional contributions to research, teaching, and service. Her 2023 Research Excellence Award further underscores her impact in advancing data analytics and AI ethics. Research Focus: Dr. Ghasemaghaei investigates the organizational and societal implications of data-driven technologies. Her work explores algorithmic bias, equitable AI deployment, psychological responses to IT overload, and behavioral patterns in technology adoption. She emphasizes interdisciplinary approaches to address ethical challenges in machine learning and big data analytics. Publication Trends: Recent research (2024–2025) centers on ethical AI frameworks, emotional dynamics in digital communities, and cognitive strategies in technology use. Her articles in journals like Information Systems Journal and Internet Research demonstrate a consistent focus on human-centered technology design and bias mitigation. Awards & Honors: Canada Research Chair in EDI Issues in AI (Tier II) McMaster University Scholar (2022) DeGroote Research Excellence Award (2023) Leadership: She serves on editorial boards for Decision Support Systems and Internet Research , and coordinates mini-tracks at ICIS/AMCIS conferences. Her CRC grant supports pioneering work in inclusive AI systems.

Mohamed Nabil LokbaniView profile

Lecturer

• Speech Processing
• C++ Programming
• Android Mobile Development
• +5 more

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

• Respiratory assistance in intensive care
• Nutrition in intensive care
• Computerization of diagnostic and therapeutic assistance
• +3 more

Philippe Jouvet is a Clinical Professor in the Department of Pediatrics at Université de Montréal's Faculty of Medicine. He is affiliated with CHU Sainte-Justine, a leading pediatric healthcare institution in Montreal, Canada. His clinical and research activities focus on advanced technologies for critical care environments, particularly in respiratory assistance and diagnostic automation. Dr. Jouvet's research spans pediatric intensive care innovations, including machine learning applications for clinical decision-making, respiratory distress detection, and safety protocols for aerosol transmission. His work addresses key challenges in mechanical ventilation, hypoxemia triage, and artifact detection in physiological signals, with a strong emphasis on cross-national collaborative studies. His recent publications demonstrate expertise in integrating artificial intelligence with clinical workflows, evidenced by studies on LoRA adapters for LLMs, vision transformers for rPPG estimation, and hybrid neural networks for signal processing. His work also includes critical contributions to international guidelines for pediatric ventilator liberation and acute respiratory distress syndrome management.

• Biomedical Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +4 more

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

Alán Aspuru-GuzikView profile

Professor

• Quantum Computing
• Theoretical Chemistry
• Machine Learning
• +3 more

Alán Aspuru-Guzik is Professor of Chemistry and Computer Science at the University of Toronto, holding the Canada 150 Research Chair in Theoretical Chemistry, Canada CIFAR AI Chair at the Vector Institute, and Google Industrial Research Chair in Quantum Computing. He directs the Acceleration Consortium, a strategic initiative uniting industry, government, and academia for pre-competitive research on the 'lab of the future'. His educational background includes a PhD in Physical Chemistry from UC Berkeley and BSc in Chemistry from the National Autonomous University of Mexico. Research spans quantum information, chemistry, and machine learning—pioneering quantum algorithms for chemical simulations, investigating quantum coherence in photosynthesis, and accelerating discovery of organic semiconductors, photovoltaics, and batteries. Current focus centers on autonomous chemical laboratories integrating AI and robotics. Recent publications (2024-2025) reveal dominant trends: self-driving laboratories for materials synthesis, quantum computing applications in molecular simulation, and generative AI for metal-organic frameworks and drug discovery. Key methodologies include reinforcement learning for experimental design and vision-language models for reaction mining. Major awards include: Chemical Engineering Medal (2023) John C. Polanyi Award (2023) MIT TR35 (2010) Sloan Research Fellowship (2009) Canada 150 Research Chair (2018) As Director of the Acceleration Consortium, he leads multi-institutional efforts in autonomous research systems, supported by Canadian government chairs and Google funding. His editorial role at Digital Discovery and co-founding of Zapata AI (quantum software) and Kebotix (robotic labs) demonstrate commitment to transforming scientific discovery through AI-driven automation.

• Data Mining
• Artificial Intelligence & Machine Learning
• Bioinformatics & Computational Biology
• +6 more

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.

Parminder Singh KangView profile

Associate Professor

• Supply Chain Management
• Machine Learning
• Artificial Intelligence
• +5 more

Dr. Parminder Singh Kang serves as Associate Professor in the Department of Decision Sciences at MacEwan University's School of Business, specializing in supply chain optimization through advanced analytics and machine learning. His cross-disciplinary research integrates people, process, and technology elements to solve complex industrial and service operations challenges. Education: PhD from De Montfort University M.Sc. from De Montfort University Dr. Kang's research centers on applying AI/ML, combinatorial optimization, and simulation modeling to enhance supply chain resilience and customer-centric service systems. His work bridges theoretical frameworks with industry applications, focusing on sustainable digitization, risk profiling, and process improvement through evolutionary algorithms and Lean/Six Sigma methodologies. Recent projects address circular economy integration and Service 4.0 transformation. His publication portfolio reveals strong trends in machine learning applications for supply chain risk analysis, text mining for competency mapping, and digital twin development for customer-centric operations. The research spans theoretical models like HI-TOP to practical case studies on pandemic disruptions and ERP implementation, demonstrating industry-relevant academic contributions. Scientific Awards: Best Paper Award, Administrative Sciences Association of Canada (2018) Horace Pops Medal Award, Wire Cable Conference (2015) Dr. Kang supervises senior student independent studies while teaching business intelligence, machine learning, and supply chain management courses at undergraduate, graduate, and MBA levels. His research is funded by NSERC, MITACS, Alberta Innovates, and industry partners including Fluor Canada, Apana Technologies, and the City of Calgary, supporting projects valued at over $2M annually. He leads collaborative research with Panjab University, CPA Canada, and TATA Steel Europe, focusing on supply chain digitization and process optimization. His work with UK-based SMEs and Canadian organizations drives practical implementation of analytics frameworks in real-world operational environments.

Daniel AloiseView profile

Professor

• Data Mining
• Optimization
• Mathematical Programming
• +5 more

Daniel Aloise is a Full Professor at the Department of Computer Engineering and Software Engineering, Polytechnique Montréal. He is a member of GERAD (Group for Research in Decision Analysis) and IVADO (Institute for Data Valorization), focusing on data science, optimization, and mathematical programming. His career spans institutions in Brazil and Canada, with significant contributions to clustering, classification, and operational research. Ph.D. in Exact algorithms for minimum sum-of-square clustering (HEC Montréal, 2009) Research Interests include data mining, optimization, mathematical programming, and algorithms. His work addresses challenges in big data, clustering algorithms, and classification models, applying these to diverse fields such as psychology, engineering, marketing, and disaster response. He explores polynomial-time algorithms for complex clustering problems and deep learning frameworks for unsupervised classification. Recent Articles emphasize optimization techniques (Benders decomposition, column generation), wireless signal prediction, bike-sharing inventory rebalancing, and serious games for disaster response data. These works integrate operations research, machine learning, and computational efficiency. Scientific Awards include the 2024 Omega Best Paper Award, 2023 CAPTRS Serious Games Award, and multiple CNPq Productivity Scholarships (2015–2018, 2012–2014). He received distinctions for his Ph.D. thesis and placement in international competitions. Supervision covers 7 Ph.D. and 12 Master's theses completed at Polytechnique Montréal, addressing topics like bug severity detection, anomaly analysis, and vehicle routing optimization. His lab collaborates with industry partners on real-time decision-making systems.