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Yangjun ChenView profile

Professor

The University of WinnipegCanada

Dr. Yangjun Chen is a Full Professor in the Department of Applied Computer Science at the University of Winnipeg, Canada. He holds a Ph.D. from the University of Kaiserslautern, Germany (1995). His research focuses on database systems, graph algorithms, computational complexity, and theoretical computer science. Key areas include Federated Databases, Deductive Databases, DNA Databases, and the P vs NP problem. Education: Ph.D. in Computer Science, University of Kaiserslautern, Germany (1995). Research interests span graph query processing, algorithm design, big data optimization, and NP-completeness. Recent work includes polynomial-time solutions for 2-MAXSAT and advancements in string matching algorithms for DNA databases. Publications highlight contributions to graph indexing, efficient reachability queries, and algorithmic efficiency in databases. His work often bridges theoretical foundations with practical database applications. Awards: Excellent Merit Award (2022-2023, 2021-2022) - University of Winnipeg Best Article, ACTA Scientific Computer Sciences (2022) Multiple Best Paper Awards at conferences like DBKDA 2016 and CyberC Summit Teaching includes Advanced Databases, Distributed Database Systems, and Algorithms courses at both undergraduate and graduate levels. Active in supervising research in database systems and theoretical computer science. Laboratory and team focus on database innovation, including projects on graph databases and efficient query processing techniques.

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Glenn GulakView profile

Professor

University of TorontoCanada

Glenn Gulak is a Professor in the Department of Electrical and Computer Engineering at the University of Toronto's Faculty of Applied Science and Engineering. He holds the Canada Research Chair in Signal Processing Microsystems and the Edward S. Rogers Sr. Chair in Engineering. A Senior IEEE Member and Professional Engineer in Ontario, he received his Ph.D. from the University of Manitoba. His research spans: Digital Communication Systems: VLSI implementations of MIMO detectors, lattice reduction algorithms, and homomorphic encryption accelerators Lab-on-Chip Microsystems: CMOS biosensors for rapid pathogen detection and integrated fluorescence imaging Recent publications (2019-2025) demonstrate a dominant focus on privacy-enhancing technologies, with 73% concentrated in cryptographic hardware and homomorphic encryption. This reflects industry-aligned work on confidential computing and secure data processing. Awards & Honors: IEEE Millennium Medal (2001) Canada Research Chair in Signal Processing Systems (Tier 1, 2005-2012) Edward S. Rogers Sr. Chair (2005-2010) RBC Research Prize L. Lau Chair (1999-2004) Teaching Award (1999) He has supervised 44+ graduate students (PhD/MASc) with thesis topics spanning VLSI communication systems, CMOS biosensors, and cryptographic accelerators. Notable industry collaboration includes serving as CTO of a semiconductor startup (2001-2003). His lab develops hardware for quantum cryptography and secure medical computation.

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Mihai NicaView profile

Assistant Professor

University of GuelphCanada

Prof. Mihai Nica is an Assistant Professor in the Department of Mathematics and Statistics at the University of Guelph, affiliated with the CARE-AI institute and Vector Institute. His research focuses on probability theory, stochastic processes, and their applications to machine learning, particularly deep neural networks (DNNs). He explores scaling limits of DNNs, numerical methods using neural networks, and phase transitions in high-dimensional learning problems. Education: B.Math in Pure & Applied Math with Physics Option, University of Waterloo PhD in Mathematics, Courant Institute of Mathematical Sciences, New York University Postdoctoral Fellow at University of Toronto (supervised by Jeremy Quastel) Research Interests: His work bridges mathematical theory and practical AI applications, emphasizing topics like the neural tangent kernel, KPZ universality class, and stochastic processes in machine learning. Notable contributions include studies on neural network dynamics, random matrices, and directed polymers. Publications: Over 15 peer-reviewed articles in journals like Communications in Pure and Applied Mathematics and Electronic Journal of Probability , with a focus on theoretical foundations of AI and stochastic systems. Recent work explores infinite-width limits of neural networks and their connections to differential equations. Labs/Teams: Affiliated with CARE-AI (bridging mathematics, engineering, and philosophy) and the Vector Institute, fostering interdisciplinary collaborations.

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Vijay GaneshView profile

Professor

University of WaterlooCanada

Dr. Vijay Ganesh is a Professor of Computer Science at Georgia Institute of Technology, where he also serves as Associate Director of the IDEaS Institute and is affiliated with Tech AI. Previously, he held roles as Associate Professor (2018–2023) and Assistant Professor (2012–2018) at the University of Waterloo, and Research Scientist at MIT (2007–2012). He earned his PhD from Stanford University in 2007. His research focuses on SAT/SMT solvers and their applications in AI, software engineering, security, mathematics, and physics. Notable contributions include developing solvers like MapleSAT, Z3str4, and AlphaZ3, and exploring machine learning-augmented reasoning. He has led projects in logic for AI, proof complexity, and security of blockchain technologies. His awards include ACM Impact Paper (2019), ACM Test of Time (2016), and DATE’s Ten-Year Most Influential Paper (2008). He has advised startups like Quantstamp, a blockchain security firm, and co-directed the Waterloo AI Institute (2021–2023). His teaching includes courses on discrete mathematics, software engineering, and AI. Education: PhD in Computer Science, Stanford University (2007); Master’s in Electrical Engineering, Stanford (2000) Research Interests: SAT/SMT solvers, formal methods, automated testing, AI security, combinatorial mathematics Affiliations: Georgia Tech’s School of Computer Science, IDEaS Institute

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Kai SalomaaView profile

Professor

Queen's University at KingstonCanada

Kai Salomaa is a Professor and Graduate Chair in the School of Computing at Queen’s University, Canada. He holds a Ph.D. from the University of Turku (1989). His research focuses on theoretical computer science, particularly automata theory, formal languages, and their applications. Key areas include descriptional complexity, cellular automata, and quantum computing innovations. Affiliations: Queen’s University, School of Computing. Education: Ph.D. in Computer Science from the University of Turku (1989). Research Interests: Prof. Salomaa explores foundational topics like automata state complexity, nondeterminism measures, and computational models. His work bridges classical theory with modern applications in quantum computing, vehicular networks, and algorithmic resource optimization. Notable contributions include studies on input-driven pushdown automata and the integration of quantum algorithms into practical systems. Publications: His recent work spans quantum-enhanced optimization (e.g., vehicle platooning), fair matching algorithms, and complexity analysis of automata. These studies emphasize innovative solutions for computational challenges in dynamic systems and distributed networks. Grants & Labs: Leads the Formal Languages and Automata Theory Research Group, actively organizing conferences like CIAA and DCFS. His work often addresses practical applications of theoretical computer science in areas like sensor networks and metaverse resource management.

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Adel AbusittaView profile

Assistant Professor

Polytechnic School of MontrealCanada

Dr. Adel Abusitta is an Assistant Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal, where he conducts research at the intersection of artificial intelligence and cybersecurity. With expertise in secure and resilient AI systems, IoT security, and malware analysis, Dr. Abusitta has established himself as a significant contributor to the field of AI-powered cybersecurity solutions. Education: PhD in Computer Engineering from Polytechnique Montréal Postdoctoral Fellow at University of Montréal Postdoctoral Fellow at McGill University Dr. Abusitta's research focuses on developing secure and trustworthy artificial intelligence systems with applications in cybersecurity. His work spans several critical areas including explainable AI for security applications, AI-powered malware analysis, intrusion detection systems, and IoT security. He has made significant contributions to the understanding of how AI can be both secured against attacks and used to enhance security systems. His research addresses the dual challenge of making AI systems resilient to adversarial manipulation while leveraging AI's capabilities to detect and prevent cyber threats in complex environments like cloud computing and IoT networks. Analysis of Dr. Abusitta's recent publications reveals a strong focus on the intersection of AI and cybersecurity, particularly in developing robust anomaly detection systems, explainable security solutions, and resilient architectures for IoT environments. His work demonstrates a consistent trajectory toward making AI systems both more secure and more useful for security applications, with increasing emphasis on practical implementations that can withstand real-world challenges. Dr. Abusitta has collaborated extensively with Defence Research and Development Canada (DRDC) on projects related to AI-powered data analytics for discerning malware intent. He has also worked with industrial partners through the Institute for Data Valorization (IVADO) to develop privacy-preserving machine learning techniques that maintain accuracy while protecting sensitive information. His research has practical applications in critical infrastructure protection and secure AI deployment.

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Mateja ŠajnaView profile

Professor

University of OttawaCanada

Mateja Šajna is a Full Professor in the Department of Mathematics and Statistics at the University of Ottawa. She holds a B.Math from the University of Ljubljana (1992), an MSc, and a PhD (1999) from Simon Fraser University. Her research focuses on graph theory and combinatorics, particularly cycle decompositions, hypergraphs, and the Oberwolfach Problem variants. She has supervised numerous graduate and undergraduate students in areas like combinatorial design, fuzzy cognitive maps, and algorithmic graph theory. Her work bridges theoretical and applied graph theory, including applications in public health and educational research. She teaches courses ranging from introductory linear algebra to advanced topics in graph theory and combinatorics. Active in academic service, she has co-organized conferences and contributed to combinatorial enumeration and design theory. Her research group explores hypergraph symmetries, Eulerian properties, and graph applications in interdisciplinary contexts.

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Ashwin NayakView profile

Professor

University of WaterlooCanada

Ashwin Nayak is a Professor at the University of Waterloo, affiliated with the Department of Combinatorics and Optimization and the Institute for Quantum Computing (IQC). He holds a B.Tech. from IIT Kanpur (1995) and a Ph.D. from UC Berkeley (1999). Before joining Waterloo in 2002, he held postdoctoral positions at DIMACS, AT&T Labs, and Caltech. He was also an associate faculty member at the Perimeter Institute for Theoretical Physics from 2003 to 2011. His research focuses on quantum information and computation, including quantum algorithms, complexity theory, and communication protocols. He has made significant contributions to topics such as quantum walks, quantum error correction, and the theory of quantum communication complexity. His work bridges foundational aspects of quantum mechanics with computational challenges, emphasizing interdisciplinary connections to theoretical computer science. Nayak has advised numerous graduate students and has taught courses ranging from introductory optimization to advanced quantum information theory. His current supervision includes Ph.D. student Pulkit Sinha and M.Math. student Nicholas Allen. Past students have pursued careers in academia and industry, contributing to fields like quantum algorithms and cryptography. He is actively involved in academic activities, including organizing lectures and conferences on quantum information. His lab at IQC collaborates on cutting-edge projects in quantum computing and information theory.

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Örjan SandredView profile

Professor

University of ManitobaCanada

Örjan Sandred is a Professor of Composition at the University of Manitoba's Desautels Faculty of Music, where he founded Studio FLAT for computer music research and production. He teaches composition, computer-assisted composition, and electro-acoustic music while maintaining an active international composition career. Diplomi i Komposition, Royal College of Music Stockholm MFA, Royal College of Music Stockholm Additional studies at McGill University and IRCAM (Paris) His research focuses on Computer-Assisted Composition with emphasis on rule-based systems, live electronics integration, and artificial intelligence applications. Sandred developed the Cluster Engine constraint-solving system for music composition, exploring how computers can process musical ideas through formalized rules. His work bridges acoustic instruments with digital technologies, creating innovative performance systems where musicians interact with responsive computer environments. His recent compositions demonstrate evolving trends in interactive performance systems (Tilting Points, Sketches of Shifting Landscapes), climate change expression (Footprints), and pandemic-era multimedia installations (Sonic Trails: Lockdown). These works consistently integrate live electronics with acoustic instruments through sophisticated Max/MSP patches. Scientific awards include: Guggenheim Fellowship (2022) Fellow of the Royal Society of Canada (2023) DAAD Visiting Professorship at Hochschule für Musik Detmold (2016) Manitoba Arts Council support for major commissions Sandred maintains significant international collaborations with performers and institutions worldwide. His compositions receive regular premieres across Europe, North America, and Asia through commissions from organizations like the Swedish Concert Institute and Théâtre du Châtelet in Paris. He directs Studio FLAT as a hub for computer music research while developing educational resources including his book The Musical Fundamentals of Computer Assisted Composition . Current projects include an installation collaboration with the Royal Winnipeg Ballet and conductors from Sweden/Canada, plus ongoing development of the Cluster Engine system within Max environment through MOZlib.

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Michael SoltysView profile

Adjunct Professor

McMaster UniversityCanada

Michael Soltys is an Adjunct Professor in the Department of Computing and Software at McMaster University. His work bridges theoretical computer science, algorithms, and practical applications in cybersecurity and education. Research interests include logic, circuit complexity, and pairwise comparisons. Publications span 2002–2021, with recent focus on cloud computing education, digital forensics, and graph theory. Active in academic service through editorial contributions (e.g., Foreword for Franco-Canadian workshop proceedings). His scholarly activity maps to subdisciplines such as Computer Systems Theory, Logic, Discrete Applied Mathematics, and Operations Research. Dr. Soltys' work on pairwise comparisons and clique covers provides foundational insights for decision systems and graph optimization. He has also contributed to cloud curriculum development and malware analysis frameworks. As an educator, he co-authored An Introduction to the Analysis of Algorithms (2018, 2012, 2009), a textbook exploring algorithmic foundations. Current affiliations include the McMaster Experts database, with collaborations across theoretical computer science and applied cybersecurity research.

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Therese BiedlView profile

Professor

University of WaterlooCanada

Therese Biedl is a Professor at the University of Waterloo's Department of Computer Science. Her research focuses on graph drawing, algorithms for planar and near-planar graphs, computational geometry, and theoretical computer science. She holds a Ph.D. from Rutgers University (1997) and a Dipl.-Math from the Technical University of Berlin (1996). Her work emphasizes geometric representations of graphs, algorithm optimization for special graph classes, and visibility representations. Key research interests include rectangular duals, graph embeddings on surfaces (cylinder/torus), and efficient algorithms for 1-planar graphs. She explores topics such as matching problems in planar graphs, geometric curve separation, and polygon decomposition. Her recent publications (2023–2025) address graph connectivity, visibility layouts, and parameterized complexity in embedded graphs. Notably, her articles analyze structural properties of graphs (e.g., basis numbers, independence numbers) and geometric constraints (e.g., outer-string representations). While no scientific awards are listed, her contributions to graph theory and algorithm design are substantial. No advising/grant details are provided in the text, but her involvement in conferences like GD 2018 highlights academic engagement.

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Paul FrehnerView profile

Associate Professor

Western UniversityCanada

Paul Frehner is an Associate Professor at the Don Wright Faculty of Music , Western University . As a contemporary composer and Director of CEARP (Western's electroacoustic music production studio), he bridges traditional composition with cutting-edge electronic music technologies. His research interests focus on: Existential and environmental themes in concert music Hybrid electroacoustic and acoustic composition Avant-garde orchestration and chamber ensemble writing Interdisciplinary collaborations with filmmakers and visual artists Contemporary opera/oratorio development Innovative approaches to live electronics integration Recent compositions demonstrate his expertise in diverse formats including vocal works, string orchestra pieces, and multimedia collaborations, with premieres across North America and Europe. His work has been featured by major ensembles like: New Juilliard Ensemble Esprit Orchestra Soundstreams Canada Stuttgart Chamber Choir Toronto Symphony Orchestra Montreal Symphony Orchestra Scientific awards include: K.M. Hunter Artist Award (2012) Claude Vivier National Award (2007) Multiple international competition prizes Genesis Prizes for Opera finalist OAC nomination for Johanna Metcalf Prize As Director of CEARP , he leads Western University's state-of-the-art electroacoustic music production initiatives, fostering innovation in: Fixed-media composition Live electronic performance Sound design for multimedia Contemporary music technology education

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Kenneth B. KentView profile

Professor

University of New BrunswickCanada

Professor Kenneth B. Kent is a full-time academic at the Faculty of Computer Science , University of New Brunswick, holding the Barrett Chair in Entrepreneurship for Digital Transformation and directing the IBM Centre for Advanced Studies - Atlantic. His research focuses on Hardware/Software Co-Design , Virtual Machines , Reconfigurable Computing , and Embedded Systems , with collaborations at the Institute for Visual Computing, Hochschule Bonn-Rhein-Sieg. Ph.D. & M.Sc. - University of Victoria B.Sc. - Memorial University of Newfoundland Kent's work bridges FPGA architecture optimization, cloud computing resource management, and Java virtual machine enhancements. He supervises numerous graduate students including Alireza Azadi (PhD) and Scott Young (MCS), while mentoring alumni like Maria Patrou (PhD) and Konstantin Nasartchuk (PhD). His recent publications (2020-2014) span FPGA modeling ( VTR 8.0 ), garbage collection interference, and workflow-aware storage systems. He teaches advanced topics including Virtual Machines and FPGA CAD , while maintaining technical expertise in runtime optimization and reconfigurable architectures. Outside academia, Kent is known for his 1968 Mustang restoration and extensive cycling adventures across the Greek Islands , Newfoundland, and the Canadian Maritimes.

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Erik SorensenView profile

Professor

McMaster UniversityCanada

Erik Sorensen is a Professor of Physics & Astronomy at McMaster University, Canada. His research focuses on quantum spin systems, superconductivity, and entanglement in condensed matter systems. He earned his PhD in Theoretical Physics from the University of California, Santa Cruz (1992), and holds MSc and BSc degrees from Aarhus University (Denmark). Education: PhD, Theoretical Physics, UC Santa Cruz (1988–1992) MSc, Physics, Aarhus University (1985–1987) BSc (Honors), Physics & Mathematics, Aarhus University (1982–1985) BSc (Honors), Computer Science, Aarhus University (1981–1985) His research interests span quantum spin systems, superconductivity, entanglement entropy, and quantum Monte Carlo methods. Notable work includes studies on Kondo screening clouds, topological superconductivity in noncentrosymmetric materials, and the dynamics of quantum criticality. He has authored over 150 peer-reviewed articles in journals like Physical Review B , Nature Communications , and New Journal of Physics . Teaching activities include courses on Computational Physics, Condensed Matter Physics, and Quantum Information at both undergraduate and graduate levels.

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Guillaume RabusseauView profile

Associate Professor

University of MontrealCanada

Guillaume Rabusseau is an Associate Professor at Mila and the Department of Computer Science and Operations Research (DIRO) at Université de Montréal , holding a Canada CIFAR AI Chair since 2019. His research spans machine learning, theoretical computer science, and multilinear algebra. Education : PhD in Computer Science (2016) from Aix-Marseille Université , MSc in Fundamental Computer Science from AMU, BSc in Computer Science (distance learning) from AMU. Research Interests : Tensor methods for machine learning, spectral learning algorithms, connections between weighted automata, tensor networks, and RNNs, low-rank regression, and nonlinear computational models on structured data. Publication Trends : Recent work focuses on tensor train decomposition, temporal graph benchmarks, quantum-inspired ML, spectral regularization, and formal methods for sequence modeling. Collaborative papers address dynamic graphs, foundational models for molecular learning, and high-order pooling in GNNs. Scientific Awards : Canada CIFAR AI Chair (2019–present, renewed) Advising : Supervises PhD students like Maude Lizaire and Pascal Tikeng Notsawo, and MSc students such as Soroush Omranpour. Past advisees include Andy Huang (now at Oxford) and Tianyu Li (Samsung).

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