Paul Thagard is Distinguished Professor Emeritus of Philosophy at the University of Waterloo and Fellow of the Royal Society of Canada. His interdisciplinary research spans philosophy, cognitive science, and psychology, developing theories of mind and social behavior. Research focuses on: Cognitive architecture of emotions and decision-making Philosophical foundations of artificial intelligence Social dynamics of misinformation Recipient of the Killam Prize, Molson Prize, and elected Fellow of multiple scholarly societies. Authored influential books on consciousness, scientific discovery, and the nature of truth. Current work examines AI cognition and misinformation propagation.
Dr. Ryan Grant is an Assistant Professor in the Department of Electrical and Computer Engineering at Queen’s University, Canada. He leads the Computing at Extreme Scale Advanced Research (CAESAR) lab and is affiliated with the Ingenuity Labs Research Institute. His expertise spans cloud computing, high-performance networks, low-level hardware-software interfaces, and energy-efficient supercomputing systems. Dr. Grant holds a PhD from Queen’s University (2012) and previously worked at Sandia National Laboratories (2012–2021), where he contributed to critical supercomputer communication protocols now deployed globally. He has authored over 80 peer-reviewed articles and received prestigious awards including the R&D100 Award and Queen’s University’s 125th Engineering Alumni Award. His research emphasizes advancing Canada’s supercomputing infrastructure to support AI, climate science, and national security applications. Education: PhD in Computer Engineering, Queen’s University (2012) MSc in Computer Engineering, Queen’s University (2005) BSc in Computer Engineering, Queen’s University (2004) Research Interests: Dr. Grant’s work focuses on optimizing supercomputing architectures for extreme-scale systems, with an emphasis on: High-performance networking and MPI communication protocols Power/energy management in HPC systems AI-driven network traffic prediction and resource disaggregation GPU-accelerated computing and cloud infrastructure integration National sovereignty in supercomputing for sensitive applications (e.g., defense, healthcare) Awards & Recognition: R&D100 Award (Oscars of Research) U.S. Defense Programs Awards Public Good Innovator Award Queen’s University 125th Engineering Alumni Award Grants & Labs: Dr. Grant directs the CAESAR lab, one of the world’s leading supercomputing architecture research groups. His work is supported by grants from Canadian and international agencies, focusing on sovereign supercomputing and HPC-AI convergence. Labs/Teams: CAESAR Lab (Queen’s University) Ingenuity Labs Research Institute
Lillian Olule is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of New Brunswick, based at Head Hall D68 in Fredericton. Her research focuses on electromagnetics, wireless communication, and RF sensing technologies. Her work spans antenna design, energy harvesting systems, and biomedical sensing applications. Recent projects include metamaterial-based energy harvesters, reconfigurable wireless platforms, and non-contact vital sign monitoring using radar technology. Her publication trends show a strong emphasis on practical RF applications, with consistent work in antenna miniaturization and efficiency optimization. Notable contributions include advancements in RF sensing for healthcare diagnostics and sustainable energy solutions.
Dr. Nasrin Eshraghi Ivari is an Instructor in the Department of Geodesy and Geomatics Engineering at the University of New Brunswick. Her research focuses on spatio-temporal data analysis, clustering algorithms, and indoor localization systems. Recent publications address data stream processing, affinity propagation clustering, and FPGA CAD tool improvements. Her technical expertise spans algorithm development for real-time data streams, hardware design optimization using Verilog, and traffic management systems. Research demonstrates consistent focus on computational efficiency in spatial data processing, with applications in people counting systems, indoor navigation, and campus traffic optimization.
Sudhir Mudur is Professor in Computer Science and Software Engineering at Concordia University. His research spans computer graphics, vision, augmented/virtual reality, and deep learning with applications in geometric modeling and 3D interaction. Recent work develops neural cloth deformation techniques, personalized visual dubbing systems, and differentiable subdivision surfaces. Mudur has held positions internationally including at INRIA France and Michigan State University, and chaired Concordia's department since 2007. He contributes to graphics standards and has managed R&D projects across academic and industry settings.
Dr. Alvine Boaye Belle is an Assistant Professor in the Department of Electrical Engineering & Computer Science at Lassonde School of Engineering, York University. She leads the DARE! research group and serves on multiple international committees, including ICSE and RE conferences. Her work bridges software engineering with equity, diversity, and inclusion (EDI) initiatives. PhD in Software Engineering (École de Technologie Supérieure, University of Quebec) 2-year Industrial Postdoctoral (University of Ottawa) Graduate Diploma in Public Administration & Governance (McGill University) Dr. Belle's research focuses on system assurance for autonomous systems, generative AI applications in software engineering, and EDI in computing . She applies machine learning to safety case automation and vulnerability detection, as shown in her publications with high-impact journals. Her recent work explores deep learning and SVM models for Android malware detection with 99% accuracy. She mentors a diverse group of students across Bachelor's, Master's, and PhD levels, emphasizing accessibility and social impact in technology. Keynote speaker at Black History Month events Moderator of EDI-focused panels at ICSE conferences Editorial board member for journals like IEEE Software and Information and Software Technology
Hui Jiang is a Professor in the Department of Electrical Engineering and Computer Science at the Lassonde School of Engineering, York University in Toronto, Canada. He holds the professional engineering designation (P.Eng) and maintains an active research program in machine learning and artificial intelligence with an office located in Room 3014 of the Lassonde Building at 4700 Keele Street. Dr. Jiang's research focuses on machine learning and artificial intelligence, with particular emphasis on deep learning theory and methods, as well as their applications in speech and language processing and computer vision. His work spans from fundamental machine learning concepts to cutting-edge AI technologies including transformers, diffusion models, and neural network architectures. He has developed methods such as the Fixed-size Ordinally Forgetting Encoding (FOFE) for named entity recognition and contributed significantly to convolutional neural networks for speech recognition. His publication timeline shows consistent contribution to the field, beginning with foundational work in speech recognition and progressing to comprehensive frameworks in machine learning. His recent work focuses on explaining complex AI concepts through his blog and textbook, demonstrating a commitment to both research advancement and education in the AI community. IEEE SPS Best Paper Award (2016) for "Convolutional Neural Networks for Speech Recognition" Dr. Jiang has authored the textbook "Machine Learning Fundamentals" published by Cambridge University Press in 2021, which provides a comprehensive introduction to both traditional machine learning methods and modern deep learning techniques. He maintains an active technology blog where he shares detailed technical insights on machine learning concepts, with recent posts covering diffusion models, transformers, and GPT architecture. His complete publication list is available on his Google Scholar profile, and he can be reached via email at huijiang@yorku.ca for academic and research inquiries.
Professor Peter Lian is a faculty member in the Department of Electrical Engineering & Computer Science at York University's Lassonde School of Engineering. He holds prestigious fellowships from IEEE, Canadian Academy of Engineering, and Singapore Academy of Engineering. His research focuses on biomedical circuits, low-power systems, and embedded AI for IoT sensors. He has served in numerous leadership roles within IEEE, including IEEE Board of Directors (2024-2025) and President of the IEEE Circuits and Systems Society (2018-2021). Education: B.Sc., College of Economics & Management, Shanghai JiaoTong University (1984) Ph.D., Electrical Engineering, National University of Singapore (1994) Research Interests: Specializes in wearable/implantable biomedical sensors, ultra-low-power circuits, event-driven sensor systems, and embedded AI. His work bridges biomedical engineering with low-power electronics design. Awards Summary: 2023 IEEE Mac Van Valkenburg Award for biomedical systems leadership 2023 Best Paper Award for cardiac arrhythmia classifier research 1996 IEEE Guillemin-Cauer Award for digital filter design Advising & Grants: Advised over 20 graduate students whose work received 30+ awards including IEEE grants. Active in mentoring student research teams in low-power biomedical systems and IoT sensor design. Labs & Collaborations: Leads research initiatives in biomedical circuits and energy-efficient systems, collaborating with industry on wearable health monitoring technologies.
Lanny Zrill is an Adjunct Research Professor in the Department of Economics at Carleton University, Ottawa, Ontario, Canada. He holds a M.A. from Queens University and a Ph.D. from the University of British Columbia (UBC). His primary research focus is Behavioral Economics, employing methodologies from decision theory, revealed preference analysis, and experimental economics. He is particularly interested in computationally intensive experimental designs to enhance data richness and experimental power. Dr. Zrill’s research explores preference modeling, choice prediction, and decision-making processes, with applications in public policy and individual behavior analysis. He is available for contract research assistance in experimental design, data analysis, and critical proofreading.
Dr. Hillary C. Maddin is a Professor in the Department of Earth Sciences at Carleton University, Canada. Her research focuses on vertebrate paleontology and evolutionary developmental biology, particularly the evolution of cranial morphology in tetrapods. She leads the Maddin Lab, which integrates paleontology, genetics, and functional morphology to study morphological diversity over large time scales. Dr. Maddin is also an advisor for the Vertebrate Palaeontology Concentration and actively promotes equity, diversity, and inclusion (EDI) in STEM through her role as the department’s EDI representative. Her work includes annual field programs in Nova Scotia and Siberia, exploring Carboniferous and Permo-Triassic rock formations. She supervises a diverse team of graduate and undergraduate students, focusing on projects such as the evolution of amphibian skulls, developmental genetics, and paleoecological studies. The Maddin Lab emphasizes collaborative research, with partnerships like the Carleton-New Brunswick Museum project investigating Carboniferous vertebrates. Key research themes include the functional and developmental origins of anatomical structures, such as the skull-neck boundary in tetrapods and the evolution of limblessness in amniotes. Dr. Maddin employs advanced techniques like micro-CT scanning to analyze fossil morphology and has contributed to taxonomic revisions of ancient vertebrates, including embolomerous tetrapods and temnospondyls. Her fieldwork and lab-based studies have led to significant discoveries, such as the identification of new species like Infernovenator steenae , and insights into early amniote ecosystems. The lab also prioritizes mentorship, offering opportunities for students to engage in cutting-edge research and interdisciplinary projects.
Georgiy Krylov is an Assistant Professor at the Faculty of Computer Science, University of New Brunswick. He holds a PhD from UNB (2024), and master's and bachelor's degrees from Nazarbayev University (2018 and 2016). His research focuses on high-performance computing, compilers/runtime environments, FPGA CAD, and quantum/reversible computing. Education: PhD in Computer Science, University of New Brunswick (2024) MSc in Computer Science, Nazarbayev University (2018) BSc in Computer Science, Nazarbayev University (2016) Research Interests: His work spans compiler optimization, quantum circuit design, FPGA applications, and ahead-of-time (AOT) compilation techniques. He explores geometric refactoring for quantum circuits, heterogeneous logic implementations, and GPU acceleration in genetic algorithms. Publications Trends: Recent work emphasizes AOT compilation frameworks (e.g., Eclipse OMR), quantum computing optimizations, and FPGA-based CAD tools. Earlier studies focused on GPU-accelerated algorithms for quantum circuit synthesis. Grants/Advising: No specific grants or advisees listed. Active in open-source compiler toolchains and hardware-software co-design projects.
Dr. Andreas Hirt is an Assistant Professor in Computer Science at the University of Northern British Columbia, holding a PhD and MSc from the University of Calgary and a BSc from UNBC. His research focuses on cybersecurity fundamentals including network anonymity protocols, cryptographic systems, and performance evaluation of secure communication frameworks. Primary research domains include: Design and analysis of anonymity protocols like Taxis and Buses systems Scalability challenges in private communication networks Security threat modeling for distributed systems Applications of data mining in cybersecurity His most cited work examines performance tradeoffs in strong anonymity systems, addressing latency and scalability constraints. Recent publications demonstrate expertise in practical protocol implementation and adversarial analysis. Dr. Hirt is currently not accepting graduate students.
Dr. Prashanth Cheluvasai Ranga is an Assistant Professor in the School of Computer Science at the University of Windsor. He holds an M.S. from the University of Texas at Dallas (2001) and a Ph.D. from Auburn University (2006), both in Computer Science. Research interests: His work spans systems programming, operating systems architecture, database systems design, high-performance computing solutions, and algorithmic analysis. He focuses on optimizing computational efficiency in complex systems.
Periklis Andritsos is an Associate Professor at the Faculty of Information (iSchool) of the University of Toronto. He holds a PhD in Computer Science from the University of Toronto and a BSc in Electrical and Computer Engineering from the National Technical University of Athens. Prior to his return to Toronto, he held academic positions at the University of Lausanne (Switzerland), University of Trento (Italy), Free University of Bozen-Bolzano (Italy), and was a Visiting Professor at the Technical University of Berlin (Germany). His research focuses on large-scale data analysis, structure discovery, and applying information-theoretic and probabilistic techniques to identify redundancies and errors in evolving datasets. He developed the LIMBO algorithm for categorical data clustering, widely used in academia and industry. Professor Andritsos has received several awards, including the Techcrunch 50 Award (2009) as co-founder of Thoora.com and Best Paper Awards at ICPM (2021), ADBIS (2019), and DSS (2017). He teaches courses such as INF1343H (Data Modeling and Database Design) and INF2190H (Introduction to Data Analytics). His current advisee is Mirai Gendi. He is affiliated with the Data Sciences Institute at the University of Toronto and has led grants from MITACS, SSHRC, and the Data Sciences Institute. His work spans customer journey mapping, process mining, and distributed data systems. Andritsos has also contributed to industry collaborations, including co-founding Odaia Intelligence (2018–present). His media engagements include interviews on data privacy, AI ethics, and technology trends in outlets like CBC, The Toronto Star, and Voice Magazine. His research bridges theoretical foundations with practical applications in data management and analytics.
Mercedes Garcia-Holguera is an Assistant Professor in the Department of Architecture at the University of Manitoba. A LEED-accredited professional, her research advances biomimetic design principles and energy performance simulation in architectural practice. Her ecomimetic methodology applies ecological principles to optimize resource use in buildings, translating biological strategies into sustainable design frameworks. Research integrates BIM tools with early-stage energy simulation to enhance environmental decision-making in architectural design processes. Pedagogically, Garcia-Holguera develops transdisciplinary approaches connecting material science, ecology, and computational design. She seeks PhD candidates with expertise in computational tools and sustainable design to advance biomimetic applications in architectural practice.
