جست‌وجوی استاد و پژوهشگر

Tuan Trangمشاهده پروفایل

استاد

• Chronic Pain
• Opioid Pharmacology
• Neuroscience
• +۳ مورد دیگر

Dr. Tuan Trang is a Professor in the Faculty of Veterinary Medicine and Cumming School of Medicine at the University of Calgary, with full memberships in the Hotchkiss Brain Institute, Mathison Centre for Mental Health Research and Education, and Alberta Children's Hospital Research Institute. His research focuses on chronic pain mechanisms and opioid pharmacology, employing multidisciplinary approaches to bridge basic science and clinical applications. Dr. Trang earned his BSc (Honours) in Life Sciences and PhD in Pharmacology & Toxicology from Queen's University, followed by a fellowship at SickKids Hospital. His educational background established the foundation for his current work in neuropharmacology and pain research. His laboratory investigates fundamental molecular and cellular processes in chronic pain, with emphasis on opioid-nervous system interactions and safety optimization. Using behavioral, biochemical, molecular, and single-cell imaging techniques in animal models, his team explores how genetic, cellular, and circuit-level mechanisms contribute to pain persistence and opioid complications. This work directly informs clinical pain management strategies for pediatric and adult patients. Analysis of recent publications (2022-2025) reveals dominant themes in opioid withdrawal mechanisms, pannexin-1 channel modulation, sex-specific pain pathways, and novel therapeutic development. Key trends include translational studies moving from rodent models to human trials, sex-dimorphic mechanisms in pain and opioid responses, and innovative approaches targeting microglial signaling and neural circuitry. Research Excellence Award, Faculty of Veterinary Medicine (2019) Young Investigator Award - International Narcotics Research Conference (2018) Peak Scholar, University of Calgary (2018) Young Investigator Award, Canadian Association for Neuroscience (2017) Piafsky Young Investigator, Canadian Society for Pharmacology & Therapeutics (2017) Award in Pain, Rita Allen Foundation & American Pain Society (2014) New Investigator Salary Award, Canadian Institutes of Health Research (2014) Dr. Trang mentors PhD and MSc students while leading the Spinal Cord, Nerve Injury, and Pain (SCNIP) NeuroTeam. His lab secures significant research funding through CIHR and industry partnerships, participating in strategic university initiatives including Brain and Mental Health, Child Health and Wellness, Infections/Inflammations, and One Health programs. As CEO of AphioTx, he drives commercialization of biotherapeutics targeting chronic pain. The Trang Lab operates within the Health Sciences Centre (HSC2123), integrating wet-lab neuroscience with computational approaches for behavioral analysis. Current projects focus on pannexin-1 channel therapeutics, sex-specific pain mechanisms, cannabinoid-opioid interactions, and neural circuit mapping for pain modulation, positioning the team at the forefront of addressing the opioid crisis through novel biological targets.

• Human-AI Interaction
• Artificial Intelligence
• Human Factors Engineering
• +۳ مورد دیگر

Junho Park is an Assistant Professor at the University of Calgary, holding cross-appointment across three academic units: Schulich School of Engineering (Biomedical Engineering), Cumming School of Medicine (Community Health Sciences), and Faculty of Arts (Psychology). He also serves as Faculty Researcher at W21C Research and Innovation Centre, Member at O'Brien Institute for Public Health, and Child Health and Wellness Researcher at Alberta Children's Hospital Research Institute. PhD in Industrial and Systems Engineering (Texas A&M University) MSc in Industrial Engineering (Seoul National University) BSc in Computer and Industrial Engineering with Psychology minor (Yonsei University) His research focuses on Human-AI Interaction, Human Factors Engineering, and Ergonomics, with specific applications in biosignal processing for prosthetics, workload assessment in assistive technologies, and cognitive support systems for aging populations. Current projects include augmented reality biofeedback systems and autonomous vehicle interfaces for individuals with cognitive impairments. Recent publications demonstrate expertise in applying machine learning to biomedical engineering challenges and human factors assessments. The HERO Lab actively seeks postdoctoral researchers and graduate students in areas spanning human-AI interaction, ergonomics, and computational modeling. Scientific Awards Student Member with Honours, Human Factors and Ergonomics Society (2023) IEEE International Conference on Human-Machine Systems Travel Grant (2022) Institute for Applied Creativity Student Fellowship, TAMU (2022) Korean-American Scholarship Foundation (2021) HFES Best Student Paper Award (2021)

Naimul Khanمشاهده پروفایل

دانشیار

• Machine Learning
• Medical Imaging
• Multimedia
• +۵ مورد دیگر

Naimul Khan is an Associate Professor and Associate Chair in Graduate Studies at Toronto Metropolitan University's Department of Electrical, Computer, and Biomedical Engineering. His research focuses on practical applications of machine learning, medical imaging, computer vision, and augmented/virtual reality technologies to solve real-world healthcare and multimedia challenges. PhD (Toronto Metropolitan University, 2014) MSc (University of Windsor, 2010) BSc (Bangladesh University of Engineering and Technology, 2008) His research interests span medical imaging automation, virtual reality systems for healthcare applications, and machine learning techniques for biomedical signal processing. He has developed innovative algorithms for ultrasound analysis, emotion recognition systems using EEG data, and segmentation approaches for diabetic foot ulcers. Publications demonstrate consistent focus on practical implementations across computer vision, medical diagnostics, and extended reality (XR) technologies. Scientific contributions include a Best Paper Award at the IEEE International Symposium on Multimedia (2017) and prestigious postdoctoral and graduate scholarships. He actively collaborates with industry partners through the TMU Multimedia Research Laboratory, teaching graduate courses like ELE 725 (Basics of Multimedia Systems) and COE 318 (Software Systems). Best Paper Award, IEEE International Symposium on Multimedia (2017) OCE TalentEdge Postdoctoral Fellowship (2014-2016) Ontario Graduate Scholarship (2013-2014) Queen Elizabeth II Scholarship in Science & Technology (2012-2013) As co-director of the TMU Multimedia Research Laboratory, Khan bridges academic research with industry applications through practical implementations of machine learning and extended reality technologies in healthcare domains.

Daniel Aloiseمشاهده پروفایل

استاد

• Data Mining
• Optimization
• Mathematical Programming
• +۵ مورد دیگر

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.

• Transportation Systems Optimization
• Operations Research
• Machine Learning
• +۷ مورد دیگر

Emma Frejinger is a full Professor at the Department of Computer Science and Operations Research, Faculty of Arts and Science, Université de Montréal. She holds the Canada Research Chair in Demand-Driven Optimization of Transport Systems and the CN Chair in Railway Operations Optimization. Her work bridges operations research and statistical learning to solve large-scale transportation challenges. Ph.D. in Mathematics, École Polytechnique Fédérale de Lausanne, Switzerland Her research focuses on transportation network optimization , demand forecasting , and discrete choice modeling . She develops data-driven methodologies for railway operations, EV charging infrastructure, and traffic prediction, emphasizing scalability and real-world applicability. Recent publications highlight trends in integrating machine learning with combinatorial optimization , particularly for MIPs , competitive facility location , and stochastic transport systems . These works span freight logistics , urban mobility , and reinforcement learning applications . Scientific Awards: Two-time INFORMS Transport Science and Logistics Society best Ph.D. thesis award 2017 Grand Prix d'excellence en transport (freight category) She has supervised 21+ graduate students in topics ranging from locomotive routing to electric vehicle adoption , with projects funded by NSERC, FRQ, and industry partners like CN Rail and Purolator. Her lab affiliations include CIRRELT and OPTIM , focusing on simulation and optimization.

Charlotte Gibsonمشاهده پروفایل

استادیار

• Integration of mining and processing systems
• Machine learning applications in mineral processing and metallurgy
• Process development for minerals used in energy storage applications
• +۱ مورد دیگر

Charlotte Gibson is an Assistant Professor at The Robert M. Buchan Department of Mining, Queen's University, leading the Critical Minerals Processing Lab. Her expertise spans mining-process integration, machine learning applications in mineral processing, and oxide mineral flotation. PhD in Mineral Processing (Queen's University, 2016) BEDS in Architecture (Dalhousie University, 2012) BASc in Mining Engineering (Queen's University, 2009) Her research focuses on critical minerals processing for energy storage, flotation optimization, and sustainable resource recovery from primary/secondary sources. Recent work explores virtual reality applications in mining engineering education. Scientific trends in her publications include: Lithium mineral flotation using fatty acids Virtual reality for process plant design Reprocessing of mine tailings for critical minerals Surface chemistry analysis via spectroscopic methods Charlotte previously held industry roles at SGS, Barrick Gold, and Vale, specializing in mineral processing flowsheet development and data analytics across Canada, US, and Australia.

Laurent Karim Bélandمشاهده پروفایل

دانشیار

• Computational material science
• Machine learning applied to materials science
• atom-scale simulations
• +۶ مورد دیگر

Laurent Karim Béland is an Associate Professor in Mechanical and Materials Engineering at Queen's University, Kingston, Ontario. His research employs computational methods including atom-scale simulations, machine learning, and physical chemistry to study materials for nuclear applications, bone fracture, and clay/cement systems. Education: PhD in Physics, Université de Montréal (2014) BSc in Physics, Université de Montréal (2008) Dr. Béland's expertise spans computational materials science with focus on machine learning for materials, atom-scale simulations, and physical chemistry. His work addresses nuclear materials (zirconium hydrides), biomaterials (bone fracture), and environmental materials (clay for waste containment). He develops multi-scale models from quantum mechanics to solid mechanics. Recent publications highlight trends in machine learning interatomic potentials, amorphous materials simulation, and radiation effects in nuclear materials. Key areas include high entropy alloys, zirconium alloys for nuclear cladding, and neural networks for electron microscopy analysis. Scientific Awards: No awards mentioned in the provided text. Dr. Béland leads the Béland Lab, advising graduate students in computational materials science. Specific student names and grant details were not provided. The Béland Lab collaborates with Queen's Reactor Materials Testing Laboratory and the Center for Advanced Computing (Compute Canada), utilizing supercomputing to model materials across length and time scales from nanometers to meters.

Jörg Gsponerمشاهده پروفایل

استاد

• Computational Biochemistry
• Protein Interactions
• Intrinsically Disordered Proteins
• +۵ مورد دیگر

Dr. Jörg Gsponer is Professor and Head of the Department of Biochemistry & Molecular Biology at the University of British Columbia's Faculty of Medicine, with a joint appointment at the Michael Smith Laboratories in the Faculty of Science. Appointed as Department Head in November 2024 after serving as interim Head since February 2024, he has been at UBC since 2009 and was promoted to Professor in 2021. Dr. Gsponer holds a dual degree in medicine and theoretical biophysics through the Swiss MD-PhD program. His educational background includes an MD from the University of Lausanne (1997), a PhD in Biochemistry from the University of Zurich (2002), post-doctoral training at the University of Cambridge Chemistry Department (2003), and MRC Research Fellow at the Laboratory of Molecular Biology in Cambridge (2006). His research focuses on intrinsically disordered proteins (IDPs) and their connections to cancer and neurodegeneration. Using advanced computational methods, molecular modeling, simulations, and bioinformatics approaches, his lab explores how protein structure and interactions contribute to cellular phenotypes. Recent work has incorporated generative AI tools like AlphaFold to advance protein biochemistry research. The lab has developed several computational tools including MoRFchibi, GraPES, and LIST for predicting protein features and interactions. Dr. Gsponer's research has resulted in numerous high-impact publications, particularly in areas of protein-protein interactions, intrinsically disordered regions, and computational approaches to understanding disease mechanisms. His work shows strong trends toward integrating AI with traditional biochemical approaches to solve complex problems in protein science. Michael Smith Foundation for Health Research Career Investigator Distinguished Achievement Award for Overall Excellence from UBC Faculty of Medicine As Department Head, Dr. Gsponer emphasizes team leadership and delegation, working with an executive team to advance scientific knowledge and innovate in training the next generation of scientists. He has spearheaded initiatives in data science teaching and recruitment in generative AI and nanomedicine. His lab continues to secure research funding for projects exploring protein interactions in disease contexts, with particular focus on cancer and neurodegenerative disorders. The Gsponer Lab maintains strong collaborations across multiple disciplines and institutions, operating within the Centre for High-Throughput Biology (CHiBi) at the Michael Smith Laboratories. The lab environment fosters interdisciplinary research combining computational approaches with experimental validation to address fundamental questions in protein science.

• Bioinformatics
• Cancer Research
• Model Organism
• +۷ مورد دیگر

Edwin Wang is a Professor at the University of Calgary holding concurrent appointments across three key departments: the Department of Medical Genetics, Department of Biochemistry & Molecular Biology, and Department of Oncology. His research program bridges computational and experimental biology to advance precision medicine applications. His primary research interests include: Bioinformatics and genomic data analysis Cancer biomarker discovery through machine learning/deep learning Predictive modeling of diseases using molecular network algorithms Model organism studies for translational insights Development of novel data interpretation frameworks for hypothesis generation Professor Wang emphasizes creating innovative computational methods that transform genomic data into clinically actionable knowledge for personalized treatment strategies. While specific scientific awards aren't documented in available sources, his active research program in computational oncology and systems biology indicates ongoing grant support. Prospective students should contact him directly regarding current advising capacity and research opportunities. Professor Wang leads an integrated research group developing computational tools for precision medicine, though the specific lab name isn't provided in source materials. His team's work focuses on translating molecular network analyses into clinical applications for cancer and other complex diseases.

Ethan MacDonaldمشاهده پروفایل

دانشیار

• Magnetic Resonance Imaging (MRI)
• Data Acquisition
• Big Data Analytics
• +۴ مورد دیگر

Dr. Ethan MacDonald is an Associate Professor with joint appointments in Biomedical Engineering and Electrical & Software Engineering at the University of Calgary's Schulich School of Engineering. He also serves as Adjunct Associate Professor in Radiology at the Cumming School of Medicine and holds full memberships at the Hotchkiss Brain Institute and Alberta Children's Hospital Research Institute. Educational background includes: Postdoctoral Fellowship in Radiology (UCalgary, 2020) PhD in Biomedical Engineering (UCalgary, 2014) MSc in Biomedical Engineering (UCalgary, 2010) BEng in Electrical Engineering (Lakehead University, 2008) Diplomas in Electronics Engineering (Nova Scotia Community College, 2005-2006) His research program centers on three interconnected themes: MRI hardware/software development for image acquisition/reconstruction Big Data analytics and machine learning for biomedical applications Brain circuit modeling for next-generation intelligent algorithms This work intersects biomedical engineering, medical imaging, and artificial intelligence. Notable scientific awards include: Dean Brawn Medal for academic excellence Professional Engineers of Ontario Medal Dr. MacDonald leads the Brain Image Team and mentors students through skill development in programming, technical writing, and presentation. The lab prioritizes work-life balance while preparing trainees for leadership positions in academia and industry.

Dominique Orbanمشاهده پروفایل

استاد

• Numerical Optimization
• Nonlinear Programming
• Numerical Linear Algebra
• +۴ مورد دیگر

Dominique Orban is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He holds a Ph.D. from FUNDP Namur and INP Toulouse and has established himself as a leading researcher in numerical optimization. His academic affiliations include the Institute for Data Valorization (IVADO) and the Decision Analysis Study and Research Group (GERAD). Professor Orban's research focuses on numerical mathematics, particularly continuous nonlinear optimization, nonlinear systems of equations, and numerical linear algebra. His work involves designing specialized numerical algorithms for optimization problems, with particular interest in degeneracy and ill-posed problems. His research spans theoretical development of algorithms, their implementation in software, and applications to real-world problems such as image reconstruction, optimal structure design, and optimization under differential constraints. Analysis of his recent publications (2021-2025) reveals a strong focus on developing practical optimization algorithms with theoretical guarantees. His work spans multiple areas including nonsmooth optimization, iterative methods for linear systems, regularization techniques, and software implementation in Julia. A notable trend is his increasing focus on developing open-source software tools that make advanced optimization methods accessible to practitioners. Over 160 publications including journal articles, conference papers, and technical reports Multiple publications in top optimization journals each year through 2025 Strong emphasis on both theoretical foundations and practical implementation Increasing focus on Julia-based optimization software development Professor Orban has successfully supervised 9 doctoral students and 13 master's students to completion, demonstrating his commitment to mentoring the next generation of researchers. His supervision style appears to balance theoretical depth with practical implementation skills, preparing students for both academic and industry careers. His research has been supported through various institutional and collaborative grants, enabling him to maintain an active research program with multiple ongoing projects. His contributions to the field include significant software developments such as Krylov.jl, JSOSuite.jl, and DCISolver.jl, which have made advanced optimization techniques more accessible to the broader scientific community. These tools reflect his philosophy of bridging theoretical optimization with practical computational implementation.

Serge Prudhommeمشاهده پروفایل

استاد

• Scientific Computing
• Finite Element Method
• Error Estimation
• +۷ مورد دیگر

Serge Prudhomme is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. With a background including a Dipl. Eng. from École Centrale de Lille, an M.Sc. from the University of Virginia, and a Ph.D. from UT Austin, he has established himself as a leading researcher in computational mathematics and mechanics. His primary research interests span scientific computing, finite element methods, error estimation and adaptive methods, multi-scale modeling, verification and validation of numerical calculations, uncertainty quantification, and model reduction. Professor Prudhomme's work demonstrates a consistent focus on developing mathematically rigorous computational frameworks with practical engineering applications. His recent publications show an expanding interest in integrating machine learning techniques with traditional numerical methods. Analysis of his recent publications reveals a strong trend toward interdisciplinary research that bridges computational mathematics with mechanics and increasingly machine learning. His work on peridynamics, proper generalized decomposition, and neural network applications to scientific computing represents cutting-edge developments in computational methods. The publications demonstrate a consistent focus on improving the accuracy, efficiency, and reliability of computational models. Professor Prudhomme has successfully supervised 10 graduate students to completion (4 doctoral theses and 6 master's theses), indicating a strong commitment to student mentorship. His supervision record spans topics including wave equation approximations, goal-oriented calibration, contact detection for ellipsoids, and PGD reduced-order modeling. His research is supported by active collaborations with institutions worldwide, as evidenced by his co-authorship patterns and conference participation across North America and Europe. The breadth of his work, from fundamental mathematical developments to practical engineering applications, positions him as a significant contributor to the field of computational science and engineering.

Matthew Joseph Kusnerمشاهده پروفایل

دانشیار

• Machine Learning
• Artificial Intelligence
• Pattern Analysis
• +۵ مورد دیگر

Matt J. Kusner is an Associate Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. He holds additional affiliations as a Senior Academic Member at Mila - Quebec Artificial Intelligence Institute and as a Member at the Institute for Data Valorization (IVADO). Previously, he served as an Associate Professor at University College London and the University of Oxford. Professor Kusner's research spans machine learning, with particular focus on causal inference, fairness in algorithms, representation learning, and domain adaptation. His work bridges theoretical foundations with practical applications in natural language processing, algorithmic fairness, and scientific computing domains including plasma physics and renewable energy systems. His research interests include Pattern Analysis and Artificial Intelligence, Algorithms, and Learning and Inference Theories. His publication record shows consistent output across top machine learning venues including NeurIPS, ICML, and ICLR, with recent work trending toward causal machine learning methods and applications in scientific domains. Professor Kusner has 42 publications spanning from 2014 to 2025, demonstrating sustained research productivity. Turner Dissertation Award for best doctoral dissertation in Computer Science & Engineering Professor Kusner received his PhD in Computer Science from Washington University in St. Louis in 2016 under Kilian Weinberger. His work has been featured in major media outlets including The Guardian, Forbes, and the Harvard Business Review, and he has presented at prestigious institutions including the Federal Reserve Banks, Cambridge Centre for Mathematical Sciences, and the Royal Society.

Ettore Merloمشاهده پروفایل

استاد

• Cybersecurity
• Artificial Intelligence
• Software Engineering
• +۵ مورد دیگر

Ettore Merlo is a Full Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal, where he leads research in cybersecurity, artificial intelligence, and software systems. He holds an M.Sc. from the University of Turin and a Ph.D. from McGill University. His affiliations include membership in the Institute for Data Valorization (IVADO), focusing on data science and AI innovation. His research integrates software engineering with AI, emphasizing: Software artifact analysis (static/dynamic/symbolic) AI-driven security solutions for clone detection, malware analysis, and access control Fairness and robustness in machine learning systems Evolutionary analysis of software vulnerabilities Recent publications (2022-2025) demonstrate a strong focus on ethical AI, including bias mitigation in neural networks, automated anomaly detection, and certification of safety-critical ML systems. His work frequently applies graph neural networks, unsupervised learning, and formal verification methods to industrial and cybersecurity challenges. Professor Merlo has supervised 25 graduate students (10 PhD, 15 Master's), with projects ranging from avionics software to phishing kit analysis. While no scientific awards are listed, his extensive publication record includes 136 works spanning journals, conferences, and technical reports. Collaborations include partnerships with industrial telecommunication firms and international academia. No dedicated lab is specified, but his research aligns with Polytechnique Montréal's 'New Frontiers in Information and Communications Technologies' center.

Sean Kauffmanمشاهده پروفایل

استادیار

• Formal Methods
• Real-time Systems
• Software Engineering
• +۴ مورد دیگر

Sean Kauffman is an Assistant Professor in the Department of Electrical and Computer Engineering at Queen's University, Faculty of Engineering and Applied Science. He holds his office in Walter Light Hall, Room 611, and can be reached at sean.k@queensu.ca or by phone at 613-533-6000 ext. 77360. Dr. Kauffman earned his Ph.D. in Electrical and Computer Engineering from the University of Waterloo before completing a two-year postdoctoral position at Aalborg University in Denmark. Notably, he returned to academia after accumulating over a decade of industry experience as a software engineer, with his final industry role being Principal Software Engineer at Oracle. His research expertise spans several critical areas in computer science and software engineering, with a particular focus on safety-critical software systems. His work significantly contributes to the fields of Formal Methods, Runtime Verification, Anomaly Detection, and Explainable AI. Dr. Kauffman has established productive research collaborations with prestigious organizations including NASA's Jet Propulsion Laboratory, the Embedded Systems Institute, QNX, and Pratt and Whitney Canada. Dr. Kauffman's research output demonstrates a consistent focus on event stream analysis, formal verification techniques, and the development of practical tools for system monitoring. His most notable contribution is the nfer language and toolset, which has become influential in the runtime verification community for its ability to abstract event streams into meaningful temporal hierarchies. His publications reveal a progression from theoretical foundations to practical implementations, with applications spanning spacecraft telemetry, autonomous vehicles, and embedded systems. Among his scientific contributions, Dr. Kauffman has received recognition for his work on the complexity analysis of nfer evaluation, developing methods for annotating control-flow graphs for formalized test coverage criteria, and creating frameworks for anomaly detection in embedded systems. His research has been published in top-tier venues including Science of Computer Programming, International Journal on Software Tools for Technology Transfer, and proceedings of major conferences like Runtime Verification and NASA Formal Methods. As an educator, Dr. Kauffman employs active learning techniques, productive failure approaches, and peer instruction to foster student engagement. His industry background informs his teaching approach, providing students with practical insights into real-world software engineering challenges, particularly in safety-critical domains. Dr. Kauffman leads the CritLab research group at Queen's University, which focuses on critical systems research. The lab develops tools and techniques for analyzing and verifying systems where failures could have severe consequences, with applications in aerospace, automotive, and other safety-critical domains. His work on the nfer language has spawned related projects including nvis for visualizing temporal interval hierarchies.