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Evangelos E. Milios is a Professor in the Faculty of Computer Science at Dalhousie University , Halifax, Nova Scotia. He has been a faculty member since 1998 and leads the MALNIS (Machine Learning and Networked Information Spaces) research group. He is affiliated with the Institute of Big Data Analytics and served as Scientific Director of DeepSense , an innovation hub for ocean data analytics. Education: PhD in Electrical Engineering and Computer Science, MIT (1986) SM & EE, MIT (1983) Dipl. Eng. in Electrical Engineering, NTUA, Greece (1980) His research focuses on visual text analytics, text mining, graph mining, social network analysis, and machine learning . He has made significant contributions to modeling and mining of networked information spaces, with applications in data science and AI. The recent publications reflect a strong trend in data mining, robotics, pattern recognition, and semantic analysis , particularly in log analysis, pose estimation, and information retrieval. His work bridges theoretical algorithms with practical applications in robotics and web technologies. Scientific Awards and Honors: Distinguished Research Professor (2017–2022) Killam Chair in Computer Science (2006–2011) Senior Member, IEEE Professional Engineer, Ontario (1998–2024) He has served in key administrative roles including Associate Dean, Research (2008–2017) and Director of the Graduate Program (1999–2002) . He has supervised numerous graduate students and taught a wide range of courses in AI, machine learning, data science, and networking. His research is supported by major grants and collaborations, including NSERC and industry partnerships. Research Labs and Teams: MALNIS – Focuses on machine learning and networked information spaces. DeepSense – Ocean data analytics and AI innovation. Institute of Big Data Analytics – Cross-disciplinary big data research.

• Visual Text Analytics
• Modelling and Mining of Networked Information Spaces
• Text Mining
• +8 more

Rob Glew is an Assistant Professor (non-tenure track) at Desautels Faculty of Management , McGill University, with concurrent visiting researcher affiliation at the Institute for Manufacturing , University of Cambridge. His academic journey includes a PhD in Industrial Engineering & Operations Management (2023), MEng (2019), and BA (2018) from the University of Cambridge. Current roles: Assistant Professor (Teaching), Program Director for MMA (Online), Associate Director for Managing Disruption initiative Teaching portfolio spans Operations Management, Data Analytics, and Statistics at McGill's Desautels Faculty His research investigates operational challenges through dual lenses of organizational culture and technological disruption , with methodological expertise in machine learning, game theory, and mechanism design. Key research areas include: Cultural drivers of cooperation in hierarchical systems Food waste reduction through supply chain traceability Diversity impacts on prosocial behavior AI's societal implications in operations contexts Scientific Recognition : Desautels Faculty Teaching Award (2024) Cambridge Chancellor's Commendation (2021) EPSRC-Siemens Doctoral Scholar (2019) Rob has secured significant research funding from SSHRC, UKRI, and industry partners like Siemens UK. His industry collaborations span NHS, Tesco, Proctor & Gamble, and others. He currently does not accept graduate students but previously supervised at University of Cambridge.

• Operations Management
• Organizational Culture
• Sustainability
• +6 more

Shahrear Iqbal is an Adjunct Associate Professor at Queen's University and a Cyber Security Researcher at the National Research Council (NRC) Canada . He holds a PhD in Cybersecurity (2017) and MSc in Combinatorial Optimization (2011) from Queen's University, along with a BSc in Computer Science and Engineering (2008) from Bangladesh University of Engineering and Technology. Research Focus: Security and Privacy of Smart Systems, including in-vehicle security, self-aware operating systems, IoT-cloud security, and AI-driven cybersecurity. Teaching: Previously taught CISC490: Cybersecurity at Queen's University. Key Projects: Droid Mood Swing (DMS) for context-aware Android security policies Securing ECU Communications in connected vehicles FCFraud for user-side click-fraud detection

• Smart Systems Security and Privacy
• Critical Infrastructure Security
• Secure Software Engineering
• +3 more

Amoon Jamzad is an Adjunct Assistant Professor at the School of Computing, Queen's University, and a postdoctoral fellow at Med-I Lab. He holds a BSc in Electrical Engineering (Electronics) from University of Tehran (2007), an MSc in Biomedical Engineering (2010), and a PhD in Biomedical Engineering (2015). His doctoral research focused on noninvasive ultrasound analysis of kidney stones. He later served as a guest lecturer and lab instructor at University of Tehran for 3 years before joining Queen's University in 2019. BSc: Electrical Engineering (Electronics), University of Tehran (2007) MSc: Biomedical Engineering, University of Tehran (2010) PhD: Biomedical Engineering, University of Tehran (2015) Dr. Jamzad's research centers on cancer cell detection, particularly in developing ultrasonic and optical spectroscopic devices for surgical margin detection. His work integrates advanced machine learning techniques with clinical applications, focusing on breast and prostate cancer surgery. He has pioneered the use of mass spectrometry imaging for cancer margin assessment and developed open-source platforms like ViPRE and MassVision for AI-driven medical data analysis. His recent publications demonstrate expertise in self-supervised learning, domain adaptation, and uncertainty quantification within medical imaging contexts. While no specific awards are mentioned, his contributions to surgical oncology tools and multi-center prostate cancer studies indicate significant clinical impact. He has also explored temporal enhanced ultrasound for composite defect detection and electrosurgical cautery state recognition through deep learning.

• Biomedical Engineering
• Medical Imaging
• Cancer Research
• +7 more

Laurent Charlin is an Associate Professor at HEC Montréal and holds an adjunct appointment in Computer Science at Université de Montréal. His research focuses on machine learning for decision-making with applications in recommender systems, reinforcement learning, and optimization.

• Machine Learning
• Artificial Intelligence
• Recommender Systems
• +6 more

Christopher Pal is a Full Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. With a Ph.D. from the University of Waterloo, he has held academic positions at the University of Rochester and the University of Toronto, and industry roles at Interval Research and Microsoft Research's Interactive Visual Media Group. Fields of Expertise: Artificial Intelligence, Computer Vision, Pattern Recognition, Machine Learning, and Natural Language Processing Affiliations: CIFAR Chair in Artificial Intelligence, Institute for Data Valorization (IVADO) Member His research focuses on deep learning applications in visual question answering , medical image segmentation , and generative models . Recent work involves multimodal data analysis for climate modeling and vision-language systems for code generation. Key projects include CarbonSense for climate flux modeling and GeoCoder for geometry problem-solving AI. His 15 most recent publications (2023-2025) span topics from diffusion models to multi-agent systems , with emphasis on video generation , 3D animation , and environmental applications . Scientific recognition includes: CIFAR Chair in Artificial Intelligence IVADO Institute Membership Top-2% cited researcher (2021) He has supervised 22 Ph.D. and Master's students, with recent graduates working on generative AI , reinforcement learning , and medical imaging . Current research grants include MITACS-funded projects in software engineering agents and drone imagery analysis for tropical forest conservation.

• Artificial Intelligence
• Computer Vision
• Pattern Recognition
• +6 more

Charles Blattberg is a Professor of Political Philosophy at the Université de Montréal within the Faculty of Arts and Science, Department of Political Science. He has been affiliated with the Centre de recherche en éthique (CRÉ) and Centre canadien d'études allemandes et européennes (CCÉAE) . Education: University of Toronto (BA), McGill University (MA), Sorbonne (DEA), Oxford University (PhD) Research focuses on justice, political community, moral pluralism, hermeneutics, and the metaphysics of political wholes and parts. Articles and books explore patriotic politics, multiculturalism, AI ethics, Zionism, Foucault, Lasch, and intercultural relations. Blattberg has supervised numerous PhD and Master's students, covering topics like political conspiracy theories, inclusive solidarity, AI ethics, Habermas' discourse ethics, intercultural urban policy, and historical racism. He has received funding from FRQSC , FRQNT , and SSHRC for collaborative ethics and political theory projects. Scientific awards include: Lady Davis Visiting Professor at Hebrew University (2005–2006) Lady Davis Visiting Professor at Hebrew University (2012–2013)

• Political Philosophy
• Justice and Community
• Multiculturalism
• +7 more

Ioannis Lambadaris is a Full Professor and Chancellor’s Professor at Carleton University's Department of Systems and Computer Engineering, Faculty of Engineering and Design. Holding a Ph.D. from the University of Maryland, he has contributed extensively to network performance analysis over 25+ years. Specializes in stochastic processes, cloud computing, and wireless edge systems Led Ericsson 5G Chair initiatives Supervised over 70 graduate students His research spans QoS control , VNF placement optimization , and IoT indoor localization , with over 170 publications. Recent work focuses on reinforcement learning and deep learning in network resource allocation. Scientific Recognition: Chancellor’s Professor Ericsson 5G Chair Contact: ioannis@sce.carleton.ca | Office: Mackenzie 4448, Ottawa, ON

• Network Traffic Modeling
• Quality of Service (QoS) Control
• Cloud Data Center Resource Allocation
• +8 more

Robert Bergevin is a Full Professor in the Department of Electrical Engineering and Computer Engineering at Laval University's Faculty of Science and Engineering, where he has been employed since 1990 and achieved full professor status in 2001. He is also a member of CeRVIM (Research Center in Robotics, Vision and Machine Intelligence) and actively participates in graduate recruitment. Dr. Bergevin's educational background includes: Ph.D. in Electrical Engineering from McGill University (1985-1990), with thesis titled "Primal Access Recognition of Visual Objects" under Professor Martin D. Levine M.Sc.A. in Biomedical Engineering from École Polytechnique de Montréal (1982-1984), with thesis on "Modeling and numerical simulation of a nuclear magnetic resonance imaging system" under Professor Robert Guardo B.Sc.A. in Electrical Engineering (Communications specialty) from École Polytechnique de Montréal (1978-1982) Professor Bergevin's research spans cognitive computer vision, pattern recognition, and information systems design methodologies. His work is guided by a unique methodology that progresses "from the general to the particular" to achieve "simply communicable and universally applicable understanding." He has been a researcher in cognitive computer vision since 1985, with particular focus on ontology, methodology, and categorization. His research interests include Ontology of Cognitive Digital Vision, Cognitive Digital Vision Development Methodology, Categorization in cognitive digital vision, Analysis and understanding of images and videos, and Understandable artificial intelligence. As a generalist, he is also a proponent of the science of global anticipatory design (R. Buckminster Fuller) and general semantics (Alfred Korzybski). Analysis of Professor Bergevin's recent publications reveals a strong focus on video anomaly detection, carried object detection, and human activity recognition. His work consistently applies cognitive principles to computer vision problems, often developing novel methodologies for segmentation, tracking, and recognition. The research shows progression from foundational work on image analysis to more complex spatio-temporal understanding of video content, with increasing integration of deep learning techniques in recent years while maintaining a focus on interpretable and cognitively-inspired approaches. Among his professional recognitions: Teaching Star, Faculty of Science and Engineering (2019, 2012) Professor Bergevin has supervised numerous graduate students throughout his career, including four PhD candidates and four Master's students in recent years. His current research includes the "Cyber-physical systems and materialized machine intelligence" project funded by Université Laval, École de technologie supérieure, and Fonds de recherche du Québec - Nature and technologies, running from 2019 to 2026. He was also the director of the bachelor's program in computer engineering from 2001 to 2010 and served as Area Editor for the journal Computer Vision and Image Understanding from 2002 to 2017. As a member of CeRVIM (Research Center in Robotics, Vision and Machine Intelligence), Professor Bergevin collaborates with researchers across multiple disciplines to advance the fields of robotics, computer vision, and machine intelligence. His work bridges theoretical foundations with practical applications, particularly in the analysis of human activities and object recognition in complex visual scenes.

• Cognitive Computer Vision
• Pattern Recognition
• Artificial Vision
• +7 more

Linbo Wang is an Associate Professor at the Department of Statistical Sciences , University of Toronto, with cross-appointments to the Department of Computer and Mathematical Sciences at U of T Scarborough and the Department of Computer Science at U of T. He is also an Adjunct Associate Professor at the University of Washington and a Canada Research Chair in Causal Machine Learning. B.Stat. from Peking University (2011) PhD in Biostatistics from the University of Washington (2016) Postdoctoral Fellowship at Harvard T.H. Chan School of Public Health His research focuses on causal inference , machine learning , and biostatistics , particularly causal modeling, graphical models, and robust inference for high-stakes domains like healthcare and justice. Recent work includes causal mediation analysis, instrumental variable methods, and trustworthy AI frameworks emphasizing fairness , explainability , and stability . His publications span causal inference for longitudinal studies, missing data, high-dimensional models, and biomedical applications. Awards include the NSERC Discovery Accelerator Supplement , Ontario Early Researcher Award , and recognition as a Canada Research Chair . He is affiliated with the Vector Institute and the Data Sciences Institute , co-organizing conferences like the 23rd Meeting of New Researchers in Statistics and Probability and serving as tutorial co-chair for the 39th Conference on Uncertainty in Artificial Intelligence . Currently, he is accepting students and postdocs for research in causal inference and machine learning.

• Causal Inference
• Biostatistics
• Data Science
• +4 more

Waldemar W. Koczkodaj is a Professor at Laurentian University in Sudbury, Ontario, Canada. His research focuses on pairwise comparisons, inconsistency analysis, and mathematical modeling in decision-making systems, with applications in computer science, public health, and robotics. Research Interests: Inconsistency indicators, group theory in pairwise comparisons, geometric modeling, and AI-driven decision support systems. Collaborations: Works with researchers like Arturo Tozzi, Jacek Szybowski, and Dominik Strzałka on mathematical and computational frameworks. Email: waldemar.koczkodaj@cs.laurentian.ca Article Trends: Recent publications blend topology, Lie theory, and Grassmannian geometry with pairwise comparisons for applications in robotics, data security, and pandemic prediction. Key subfields include consistency-driven decision-making, geometric optimization, and AI-enhanced medical scales. Academic Contributions: Developed the CONCLUDER system for decision-making and frameworks for inconsistency reduction in pairwise comparisons. His work spans theoretical mathematics, AI ethics, and public health data analysis.

• Computer Science
• Mathematics
• Artificial Intelligence
• +3 more

Abdulsalam Yassine is an Assistant Professor in the Department of Software Engineering at Lakehead University. He holds a Ph.D. and MSc in Electrical and Computer Engineering from the University of Ottawa and a BSc in Electrical Engineering from Beirut Arab University. Ph.D., Electrical and Computer Engineering, University of Ottawa MSc., Electrical and Computer Engineering, University of Ottawa BSc., Electrical Engineering, Beirut Arab University His research focuses on behavioral analytics , predictive analytics , big data systems , and IoT systems , with applications in smart cities , smart grids , and industrial contexts. Recent work explores energy management strategies for battery systems in peak demand scenarios. Key research areas : Smart Grids Energy Management Predictive Analytics Industrial Applications Funding is available for motivated students with relevant qualifications to collaborate on his ongoing projects.

• Behavioral Analytics
• Predictive Analytics
• Big Data Systems
• +5 more

Paco Awissi is a Lecturer at McGill University's School of Continuing Studies, specializing in professional education. With over 15 years of industry experience in data science and machine learning, he has held key roles at organizations including National Bank of Canada, TELUS, and Morgan Stanley. His expertise focuses on leveraging data-driven strategies to foster business growth and technological innovation. Awissi's academic credentials include a Bachelor of Commerce (BComm) and a Master of Business Administration (MBA) from HEC Montréal. He has further enriched his expertise through professional development programs at Harvard Business School, MIT Sloan, and Stanford University. His primary research and teaching interests encompass Data Science , Machine Learning , and applied Business Analytics , emphasizing real-world implementation in corporate environments. No awards, publications, or student advising roles are documented in the available information.

• Data Science
• Machine Learning
• Business Analytics
• +1 more

Maryse Bernard is a Clinical Assistant Professor at the Faculty of Medicine, University of Montreal, affiliated with the Department of Radiology, Radiation Oncology and Nuclear Medicine. Her work focuses on advancing medical imaging technologies and their application in diagnostic and therapeutic contexts. Faculty of Medicine, University of Montreal Department of Radiology, Radiation Oncology and Nuclear Medicine Email: maryse.bernard@umontreal.ca Her research interests include: Medical Imaging Artificial Intelligence applications in healthcare Personalized radiation therapy Nuclear medicine imaging techniques She contributes to academic programs and clinical training initiatives within the department.

• Medical Imaging
• Artificial Intelligence
• Radiation Oncology
• +3 more

Dr. Kelly Bronson is an Assistant Professor at the School of Sociological and Anthropological Studies within the Faculty of Social Sciences at the University of Ottawa. She holds the Canada Research Chair (II) in Science & Society, focusing on political and economic decision-making around controversial technologies such as GMOs, big data, and hydraulic fracturing. Her work bridges technical knowledge with community values to shape evidence-based policy-making. PhD in Cultural Studies (Science and Technology), York University (2013) MA in Sociology of Biotechnology, University of Saskatchewan (2005) BScH in Environmental Biology, Queen's University (2001) Dr. Bronson specializes in Science and Technology Studies , examining the intersection of big data, agriculture, and policy-making. Her research spans environmental justice, Indigenous knowledge integration, and responsible innovation frameworks. She advocates for grassroots leadership and critical analysis of corporate data strategies in agrifood systems. Recent publications highlight themes in digital agriculture , data assetization , and living labs for sustainability. Her work addresses socio-ecological implications of agribusiness, ethical AI, and stakeholder inclusion in policy design. Key trends include critiques of technological determinism and power dynamics in innovation. Canada Research Chair (II) in Science & Society Contributions to journals like Big Data & Society and Journal of Responsible Innovation Dr. Bronson teaches courses on Research Design in Sociology and Science & Society decision-making. She leads the Science and Technology Studies Food and Agriculture Network (STSFAN) and contributes to sustainability transitions through participatory frameworks. Her work emphasizes interdisciplinary collaboration and ethical governance in technological development.

• Science and Technology Studies
• Environmental Policy
• Applied Ethics - Robotics and Artificial Intelligence
• +13 more