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Juergen DingelView profile

Professor

• Software Engineering
• Model-Driven Engineering
• Formal Methods
• +7 more

Juergen Dingel is a Professor in the School of Computing at Queen's University, Canada. He joined the faculty in 2000 and holds a PhD in Computer Science from Carnegie Mellon University (1999). His research focuses on software modeling, model-driven engineering, formal methods, and formal verification, with applications in real-time systems and embedded systems. He leads the Modeling and Analysis in Software Engineering (MASE) research group. Education: PhD in Computer Science, Carnegie Mellon University (1999) M.Sc. in Pure and Applied Logic, Berlin University of Technology (1994) M.Sc. in Computer Science, Berlin University of Technology (1992) Research Interests: Model-driven engineering and transformation Formal specification and verification Automated testing and debugging Real-time and embedded systems Service composition and distributed systems His work emphasizes practical tools like Papyrus-RT and MDebugger , integrating formal methods into software development. Grants & Collaborations: Funded by NSERC, OCE, and industry partners (IBM, GM, Ericsson) Focus on automotive systems, IoT, and safety-critical applications Service: Editorial board member for SoSyM , STTT , and JOT Former chair of the MODELS Steering Committee (2016–2018) PC co-chair for MODELS 2014 and FMOODS/FORTE 2011 Labs & Teams: Leads the MASE group, which develops open-source tools for model-driven engineering. Collaborates with industry on automotive and IoT projects.

Manolis SavvaView profile

Associate Professor

• Computer Graphics
• Computer Vision
• 3D Scene Analysis
• +4 more

Manolis Savva is an Associate Professor in the School of Computing Science at Simon Fraser University and holds the Canada Research Chair in Computer Graphics. He specializes in 3D scene analysis, generative methods for 3D content, and computer graphics for AI. His research bridges computer graphics, vision, and robotics. Education: Ph.D. (Computer Science, Stanford University, 2016), MS (Computer Science, Stanford, 2012), B.A. (Physics & Computer Science, Cornell, 2009). Research Interests: Human-centric 3D scene analysis, generative 3D content creation, AI-driven rendering, and applications in robotics. Key projects include Habitat (Embodied AI platform), ScanNet , and ShapeNet datasets. Recent Articles: Focus on articulated object modeling, 3D scene synthesis, and AI-driven visualization. Notable work includes SceneMotifCoder (generating object arrangements) and R3DS (panoramic scene understanding). Awards: CHCCS Early Career Award (2022), ICLR 2023 Outstanding Paper Award, ICCV 2019 Best Paper Nomination, and SGP 2020 Dataset Award (ScanNet). Lab/Teams: Leads research groups in 3DLG (3D Learning and Graphics) and GrUVi (Graphics and Vision). Collaborates on projects like AI Habitat and HomeRobot .

Salem LahlouView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• GFlowNets
• +7 more

Salem Lahlou is an Assistant Professor in the Machine Learning Department at the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), having joined in September 2024. He previously served as a Senior Researcher at the Technology Innovation Institute (TII) in 2024. His academic background includes a PhD from Mila and Université de Montréal (UdeM) under Yoshua Bengio (2023), with prior studies in applied mathematics at École Polytechnique and statistical learning at École Normale Supérieure Paris-Saclay. His research focuses on developing more capable and reliable AI systems through three interconnected pillars: Novel Method Development : Core contributions to Generative Flow Networks (GFlowNets), uncertainty estimation techniques (DEUP), and curriculum learning frameworks Large Language Model Advancement : Enhancing reasoning capabilities and alignment through preference optimization and trace-based learning Community Tooling : Creation of torchgfn library for GFlowNets and benchmarks including BabyAI, FinChain, and LLM-BabyBench Core research areas span Machine Learning, GFlowNets, Uncertainty Estimation, LLM Reasoning, Reinforcement Learning, and AI for Science. Recent publications (2023-2025) demonstrate strong emphasis on GFlowNet theory/improvements (8+ papers), LLM reasoning evaluation (FinChain, LLM-BabyBench), uncertainty quantification, and societal AI impacts. Key application domains include mathematical reasoning, financial systems, privacy preservation, and cognitive science. He currently advises graduate students including Junyi (privacy risks in SNNs) and Abhijith (LLM reasoning). His group collaborates with MBZUAI faculty (Nils Lukas, Alham Fikri, Mingming Gong, Martin Takac) and industry partners on projects involving Conversational AI, Personalization, and Affective AI.

Florian HerzigView profile

Professor

• Number Theory
• Representation Theory

Florian Herzig is a Professor in the Department of Mathematics at the University of Toronto. His research focuses on Number Theory and Representation Theory, particularly mod p and p-adic representations of reductive groups. Education: Ph.D., Harvard University Certificate of Advanced Study in Mathematics (Part III), University of Cambridge B.A., University of Cambridge Research Interests: Herzig works on modular and p-adic representations, with applications to the Langlands program. His work explores Serre-type conjectures, local-global compatibility, and cohomology of Shimura varieties. Scientific Awards: He was named a Fellow of the American Mathematical Society in 2025. Publications & Collaborations: Herzig has co-authored major works with Christophe Breuil, Yongquan Hu, and Benjamin Schraen. His research appears in leading journals like Inventiones Mathematicae and Duke Mathematical Journal. Teaching & Seminars: He has taught graduate courses on linear algebraic groups and p-modular representations. He participates in the Arizona Winter School and international workshops on representation theory.

• Energy Efficiency in Software
• Multicore Memory Systems
• Hardware/Software Co-Design
• +3 more

Arrvindh Shriraman is an Associate Professor and Program Director of Software Systems at Simon Fraser University's School of Computing Science in Surrey. His research focuses on energy-efficient software, multicore memory systems, and optimizing hardware/software interfaces for parallel programming. He holds a Ph.D. (2010) and M.S. (2006) in Computer Science from the University of Rochester, and a B.Eng. (2004) from the University of Madras. He teaches courses on parallel programming and energy-conscious software design. His research interests include synchronization mechanisms for domain-specific architectures, cache optimization, and FPGA-based acceleration. He has contributed to frameworks like Mu-grind for HLS-generated RTL instrumentation and TAPAS for parallel accelerator generation. Notable projects include RANGE-BLOCKS for synchronization in domain-specific systems and TapeFlow for gradient computation in neural networks. His work emphasizes real-time verification of autonomous systems and safety-critical trajectory planning for underwater vehicles. Shriraman collaborates closely with the Tangent Lab, exploring cutting-edge solutions in hardware-software co-design and embedded systems. His teaching and research bridge theoretical computer science with applied engineering challenges, addressing scalability, efficiency, and safety in modern computing systems.

Myrto MavrakiView profile

Assistant Professor

• Arithmetic Geometry
• Dynamical Systems
• Unlikely Intersections
• +7 more

Myrto Mavraki is an Assistant Professor in the Department of Mathematics at the University of Toronto, with affiliations to both the St. George and Mississauga campuses. She specializes in arithmetic geometry and dynamical systems, particularly the theory of unlikely intersections and canonical heights in families of rational maps. Institution: University of Toronto School: Faculty of Arts and Science Department: Department of Mathematics Rank: Assistant Professor Her research focuses on deep connections between arithmetic geometry and dynamical systems. Key areas include equidistribution, variation of canonical heights, preperiodic points, and unlikely intersections in families of maps, especially on the projective line and in elliptic surfaces. These topics lie at the heart of modern arithmetic dynamics and have strong ties to Diophantine geometry and number theory. The most recent publications show a sustained focus on canonical height variation, equidistribution, and the geometry of post-critically finite and preperiodic loci in parameter spaces. Collaborations with leading mathematicians such as Laura DeMarco, Harry Schmidt, and Hexi Ye reflect her central role in current developments in arithmetic dynamics. Her work combines algebraic, analytic, and arithmetic techniques to solve deep conjectures and establish foundational results. Her research is supported by an NSERC Discovery Grant and an Early Career Supplement (2024–2029), and previously by an NSF grant (DMS-2200981). She has mentored or collaborated with several prominent researchers and is likely supervising graduate students, though none are explicitly named. She does not list formal awards, but her publication record in top journals and prestigious fellowships indicate high recognition in the mathematical community. Mavraki held the Benjamin Peirce Fellowship at Harvard (2020–2023), a highly competitive postdoctoral position, and prior positions at the University of Basel and Northwestern University. She earned her PhD from the University of British Columbia under Dragos Ghioca.

Jian ZhaoView profile

Associate Professor

• Information Visualization (InfoVis)
• Human-Computer Interaction (HCI)
• Data Science
• +6 more

Jian Zhao is an Associate Professor at the University of Waterloo's School of Computer Science, specializing in Information Visualization (InfoVis), Human-Computer Interaction (HCI), and Data Science. With a Ph.D. from the University of Toronto (2016), his research emphasizes interactive visualization techniques, AI integration in design processes, and socio-technical systems. He explores how human-AI collaboration can enhance data analysis, presentation, and user experience in complex systems. Key research areas include: 1) AI-Driven Design (e.g., code generation via sketching, infographic creation), 2) Health Informatics (therapeutic AI tools for autism support), 3) Immersive Technologies (VR/AR interfaces for presentations and education), and 4) Social Computing (remote family communication, multi-modal emoticons). His work bridges technical innovation with human-centered design principles. His publications (2021–2025) reflect a focus on interactive visualization frameworks (e.g., iTrace for cross-view data analysis), AI-human collaboration (CoLadder for hierarchical code editing), and specialized applications like TherAIssist for art therapy and EMooly for autism support. Zhao frequently explores novel interaction modalities , including gesture-based VR interfaces and sketch-based programming tools. He leads projects in computational notebooks (EDAssistant, Slide4N), visual analytics (MissBin for bipartite networks), and neurofeedback training games (Eggly). His work often emphasizes systematic design considerations for missing data, cross-view analysis, and contextual visualization in spatial AR environments.

Arno BergerView profile

Professor

• Dynamical Systems
• Ergodic Theory
• Benford's Law
• +5 more

Dr. Arno Berger is a Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. He holds a Dipl.Ing. (ME) and Dipl.Ing. (MSc) in Mechanical Engineering and Applied Mathematics from TU Wien (Vienna University of Technology), followed by a Dr. techn (PhD) and Habilitation in Applied Mathematics from the same institution. His research focuses on dynamical systems, ergodic theory, Benford's Law, nonautonomous dynamics, bifurcation theory, applied probability, and dimensional analysis. He has held visiting positions at prestigious institutions including Georgia Tech, University of Warwick, Goethe University Frankfurt, and University of Canterbury. His recent work includes studies on Saint-Venant-Polya inequalities, planar curves with position-dependent curvature, and distributions of logarithmic functions. He co-authored the seminal book An Introduction to Benford's Law (2015), and maintains the Benford Online Bibliography. His teaching spans courses like Differential Equations and Real Variables. Dr. Berger’s research has explored Benford’s Law in diverse contexts, from stochastic processes to finite-time dynamics. His articles often bridge theoretical insights with practical applications, emphasizing the ubiquity of Benford’s Law in mathematical systems.

Lev KirischianView profile

Associate Professor

• Task-adaptive reconfigurable computing systems
• Automated architectural synthesis of data-flow parallel computers
• FPGA-based stream processors
• +3 more

Lev Kirischian is an Associate Professor at the Department of Electrical and Computer Engineering , Toronto Metropolitan University. He established the Embedded and Reconfigurable Systems Laboratory in 1999 for research and graduate studies. His expertise spans reconfigurable computing, parallel systems, and embedded design. BSc/MASc in Aerospace Control Systems, Moscow Institute of Aviation Technology (MAI) PhD in Parallel and Reconfigurable Computing Systems, Moscow Power Engineering Institute (MPEI) Research Interests : Task-adaptive reconfigurable computing systems Automated architectural synthesis of data-flow parallel computers FPGA-based stream processors Article Trends : His recent research focuses on reconfigurable computing architectures, modular system optimization, and FPGA applications in aerospace and industrial systems. Work includes radiation protection for FPGAs, multi-parametric architecture optimization, and frameworks for parallel multi-tasking environments.

Abdellah SebbarView profile

Professor

• Number Theory
• Algebraic Geometry
• Arithmetic Geometry
• +7 more

Abdellah Sebbar is a Full Professor in the Department of Mathematics and Statistics at the University of Ottawa. He holds a PhD from Stony Brook University (1993-1997) and prior degrees from Rabat and Strasbourg. His research focuses on number theory, algebraic geometry, and modular forms, with specialties in elliptic curves, moonshine theory, and quantum groups. He has authored over 40 publications, including works on Schwarzian equations and equivariant functions. His career includes roles as CRM-ISM Postdoctoral Fellow (1997-1999), CMS Instructor (1999-2001), and Associate Professor (2004-2013) before attaining his current rank. He advises graduate students and collaborates on projects involving modular subgroups and automorphic forms. Education: 1992: BSc in Pure Mathematics, Rabat 1992-1993: DEA (Master's), Strasbourg 1993-1997: PhD in Mathematics, Stony Brook (Fulbright Scholar) Research Interests: Modular forms and functions Elliptic curves and surfaces Discrete groups and moonshine Quantum groups and mathematical physics Schwarzian differential equations Professional Timeline: 2013–Present: Full Professor, UOttawa 2004–2013: Associate Professor, UOttawa 2001–2004: Assistant Professor, UOttawa His recent work emphasizes applications of Schwarzian equations to modular forms and automorphic differential equations. Collaborative efforts with Hicham Saber and others explore equivariant functions and vector-valued modular forms. He has supervised multiple PhD/MSc students, including co-supervision with Damien Roy.

Sara NabilView profile

Assistant Professor

• Human-Computer Interaction (HCI)
• E-Textiles
• Smart Materials
• +3 more

Sara Nabil is an Assistant Professor at Queen's University's School of Computing within the Faculty of Arts and Science. She leads the HCI Design Studio and previously held a postdoctoral position at Carleton University's Creative Interactions Lab. Her research focuses on integrating interior design, fashion, and product design with interactive technologies, emphasizing e-textiles, smart materials, and shape-changing interfaces. She holds a PhD from Newcastle University and prior experience as an HCI Lecturer, interior designer, and senior software developer. Education: PhD in Computing, Newcastle University (2019) MSc in Computing BSc in Computing Research interests include designing computational spaces, wearable technology, and e-textile interactions. Her work explores sustainable fabrication methods, user-centered design for smart environments, and bridging traditional crafts with digital technologies. Notable projects include exhibitions like 'Living with Adaptive Architecture' and 'Persuasive Pharmacy Space,' showcasing interactive wearables and spaces. Her recent articles (2023–2025) emphasize e-textile innovations, sustainable design, and human-building interactions. Key themes include smart materials in fashion, modular wearables, and community-driven interaction systems. Awards: None explicitly mentioned. Advising/Grants: No details provided. Labs/Teams: Head of HCI Design Studio and part of iStudio Lab, focusing on interioraction design.

Jonathan CantView profile

Associate Professor

• Cognitive Neuroscience
• Visual Perception
• Object-Ensemble Perception
• +6 more

Dr. Jonathan Cant is an Associate Professor at the Department of Psychology, University of Toronto Scarborough (UTSC). His research focuses on cognitive neuroscience, particularly visual perception and cognition using fMRI and behavioral techniques. Born in Hamilton, Ontario, educated at Western University (Psychology) and Harvard University (postdoctoral) Expertise: Object perception, ensemble processing, texture/shape perception, scene recognition The Cant Lab investigates high-level human visual cortex organization, with a focus on the ventral stream's role in perception. Key research includes dissociating shape and surface property processing, material properties in cognition, and object-scene interactions in neural systems. Recent publications highlight ensemble encoding of facial expressions (2025), cross-modal ensemble coding (2024), and texture-shape relationships in ventral cortex (2015). His work bridges modular/distributed cortical organization theories and explores neural substrates for 3D shape vs. material compliance. Teaching includes PSY5222 (Visual Cognitive Representation), PSYC75 (Cognitive Psychology Laboratory), and PSYC51 (Cognitive Neuroscience of Vision). The lab welcomes motivated students and researchers through direct email applications.

David ChalmersView profile

Professor

• Philosophy of Mind
• Consciousness
• Cognitive Science
• +8 more

David Chalmers is a University Professor of Philosophy and Neural Science at New York University and co-director of the Center for Mind, Brain, and Consciousness. He is also an Honorary Professor of Philosophy at the Australian National University and co-director of the PhilPapers Foundation. His work bridges philosophy, cognitive science, and emerging technologies. Research Interests: Chalmers is best known for his work on the 'hard problem of consciousness'—the challenge of explaining subjective experience. His research spans philosophy of mind, metaphysics, epistemology, philosophy of language, and the foundations of physics and AI. He actively explores the implications of virtual reality, simulation theory, and large language models for philosophy and consciousness studies. Recent Research Trends: His recent publications focus on AI consciousness, the ethical treatment of AI systems, the simulation hypothesis, and the nature of thought in language models. These works reflect a growing engagement with artificial intelligence and digital metaphysics, positioning philosophy at the forefront of technological inquiry. Scientific Awards: While no specific awards are listed in the provided text, Chalmers is widely recognized as one of the most influential contemporary philosophers, particularly in philosophy of mind. Advising and Grants: He mentors students and postdocs, though specific names are not listed. His leadership in the Center for Mind, Brain, and Consciousness and the PhilPapers Foundation suggests active grant-funded research and academic collaboration. Labs and Teams: He co-directs the Center for Mind, Brain, and Consciousness at NYU and the PhilPapers Foundation , both of which support research, publications, and global philosophical discourse in philosophy of mind and related fields.

Francesco CellarosiView profile

Associate Professor

• Pure Mathematics
• Probability Theory
• Number Theory
• +3 more

Francesco Cellarosi is an Associate Professor in the Department of Mathematics and Statistics at Queen's University, within the Faculty of Arts and Science. His research focuses on the intersection of dynamics, probability theory, ergodic theory, number theory, and mathematical physics. He investigates how classical number-theoretic objects exhibit random features, employing dynamical methods such as spectral theory of group actions and analysis of flows on homogeneous spaces. Educational Background: PhD in Mathematics (2011), Princeton University MSc in Mathematics (2007), Princeton University Laurea Magistrale (Master's) in Mathematics (2006), Università degli Studi di Bologna Research Interests: Dr. Cellarosi explores probabilistic phenomena in number theory, including theta sums, quadratic Weyl sums, and k-free integers. His work bridges ergodic theory and quantum mechanics, analyzing autocorrelation functions and spectral properties of physical systems. Key themes include limit theorems, random processes of number-theoretic origin, and applications to statistical mechanics. Professional Profile: He teaches advanced courses such as MATH 892 and MATH/MTH 328. His office is Jeffery Hall 506, and he maintains a Google Scholar profile and personal website. No awards are explicitly listed, but his extensive publication record reflects scholarly contributions. Labs/Teams: While no specific labs are mentioned, his collaborations span pure mathematics and mathematical physics, often involving interdisciplinary dynamics and probability.

• Signal Processing
• Communications
• Bioimaging
• +5 more

Amir Asif is a Professor at the Lassonde School of Engineering, York University, and concurrently serves as Vice President, Research and Innovation. His academic leadership roles include Dean of the Gina Cody School of Engineering and Computer Science at Concordia University (2014-2020). He specializes in signal processing, communications, and their applications in healthcare, power grids, and distributed systems. Asif holds a PhD from Carnegie Mellon University and a Harvard certification in executive leadership. Education: PhD, Electrical and Computer Engineering, Carnegie Mellon University (1996) MS, Electrical and Computer Engineering, Carnegie Mellon University (1993) BSc, University of Engineering and Technology Lahore (1990) Harvard Certificate in Leadership for Senior Executives (2018) Research Interests: Asif’s work spans signal processing for medical imaging (e.g., ultrasound elastography), smart grid optimization, and cybersecurity in power systems. His recent publications address hydrogen energy systems, EMG-based gesture recognition, and resilient control frameworks against cyberattacks. Grants & Leadership: He leads NSERC-funded projects on federated learning and resilient algorithms. He chairs the Ontario Council of University Research and serves on TRIUMF Innovations and the Richmond Hill Board of Trade. His grants include SSHRC funding for equity initiatives and NSERC support for distributed signal processing. Teaching & Mentorship: Asif has supervised over a dozen graduate students and taught courses like Digital Communications and Statistical Signal Processing Theory. Notable advisees include Arash Mohammadi (PhD, 2014) and Nick Sajadi (PhD, 2017).