Dr. Howard Margolese is an Investigator at RI-MUHC and Associate Professor of Psychiatry at the Faculty of Medicine and Health Sciences, McGill University . His research focuses on psychopharmacology , first episode psychosis , and schizophrenia , with an emphasis on medication impacts and clinical trials. He has contributed extensively to understanding antipsychotic treatments, cognitive remediation, and the application of artificial intelligence in mental health . His work spans cross-cultural studies comparing mental healthcare outcomes in Montreal, Canada and Chennai, India , addressing trust in healthcare systems, functional outcomes, and treatment personalization. He has developed clinical decision support systems integrating AI for depression management and evaluated long-acting antipsychotic formulations. Key trends in his 81 publications include antipsychotic mechanisms , cognitive health strategies , and treatment switching protocols . His recent focus on AI-enabled decision systems highlights technological innovation in psychiatry. Despite his academic rigor, the text provides no details on scientific awards.
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Elyar Pourrahimianمشاهده پروفایل
پژوهشگر ارشد
- Construction Management
- Project Management
- Artificial Intelligence Applications in Engineering
- +۳ مورد دیگر
Elyar Pourrahimian is a Postdoctoral Fellow at the University of Alberta's Faculty of Engineering, specifically within the Civil and Environmental Engineering Department. He teaches courses such as CIV E 303 - Project Management (Winter Term 2026) and CIV E 601 - Analytical Methods for Project Management (Fall Term 2025), focusing on project planning, scheduling, and control methodologies. His research interests span Construction Management Project Management Artificial Intelligence Applications in Engineering Chaos Theory in Project Planning Fuzzy Systems in Labour Productivity Bayesian Inference in Construction Simulation . Recent publications highlight trends in construction workspace optimization (2025), chaos and fuzzy systems for productivity analysis (2025-2022), and machine learning frameworks (2024) for construction monitoring. He also explores multidimensional project control (2024) and socio-technical lean management frameworks (2024).
Alex Tongمشاهده پروفایل
استادیار
Alex Tong is an incoming Assistant Professor at Duke University (as of 2025) and will join Aithyra in Vienna as a Principal Investigator. Previously, he completed postdoctoral research at Mila under Yoshua Bengio and a visiting postdoc at Oxford with Michael Bronstein. Education : PhD (2021) and MPhil (2020) in Computer Science from Yale University; BS/MS (2017) from Tufts University Research Interests span generative modeling , deep learning , optimal transport , and graph signal processing applied to protein design and single-cell biology . His work bridges mathematical formalism (e.g., SE(3) flows, Wasserstein manifolds) with biological discovery. Publication Trends (2023-2025) focus on diffusion models for protein structure prediction , optimal transport in single-cell analysis , and geometric machine learning for molecular dynamics . Collaborations include labs like Mila, Oxford, and Yale. Awards : Best Paper (Gen Bio @ ICML 2025), Outstanding Paper (DELTA @ ICLR 2025), Best Student Paper (IEEE MLSP 2020)
Artur Luczak is a Professor of Neuroscience at the University of Lethbridge, where he has been a faculty member since 2009 and is affiliated with the Canadian Centre for Behavioural Neuroscience (CCBN). His research integrates experimental and theoretical approaches to study cortical dynamics and neural computation. PhD from Jagiellonian University, Poland MSc from Wroclaw University of Technology Dr. Luczak's research focuses on how neuronal populations process information through spontaneous and sensory-evoked activity. Key interests include metabolic constraints on neural coding, predictive processing in cortical networks, and the role of neural sequences in brain function. His work bridges computational modeling with electrophysiological data to uncover fundamental principles of neural organization. His publications from 2007-2022 reveal consistent exploration of spontaneous cortical activity patterns, with increasing emphasis on energy efficiency and machine learning-inspired neural algorithms. The 2022 Nature Machine Intelligence paper demonstrating how neurons optimize metabolic energy through activity prediction represents a paradigm shift in understanding neural learning mechanisms. Scientific Awards: Fellowship in the Netherlands Fellowship in France Fellowship in Italy Dr. Luczak has trained graduate students and postdoctoral fellows through CCBN's neuroscience programs, with research supported by national and international grants. His teaching portfolio includes computational neuroscience workshops, MATLAB programming courses, and core neuroscience curriculum. He leads a research group at the CCBN utilizing multidisciplinary approaches including in vivo electrophysiology, computational modeling, and behavioral analysis to investigate neural dynamics across multiple scales.
Dr. Yaser Al Mtawa is a faculty member with a focus on the Internet of Things (IoT), Wireless Sensor Networks, Cyber-Physical Systems, and Network Security. His research spans Smart Home Security, SDN-based Networking, Reliability, and Quality of Service (QoS). Education: PhD in Computer Science from Queen's University, Canada Research Interests: Dr. Al Mtawa investigates IoT and its intersections with AI/Machine Learning, Autonomous Networks, and Traffic Engineering. His work optimizes network reliability, security, and efficiency using advanced computational methods. Publication Trends: Recent articles focus on SDN optimization, anomaly detection, federated learning security, and IoT applications in agriculture. Keywords include Machine Learning, Network Reliability, and Wireless Technologies. Teaching: He teaches courses such as Operations Research in Computer Science , Wireless Networking Paradigms , and Advanced Internet Programming , emphasizing learner-centered environments and practical applications. Research Team: Supervises current MSc students (MD Imtiaz Ahmed, Sayed Saminur Rahman, Tanvir Ornob) and past students (Mohammadreza Khorramfar, Arnold Brendan Osei), fostering interdisciplinary collaboration in IoT and network security.
Patrick Pilarski is Professor of Physical Medicine and Rehabilitation at University of Alberta and Canada CIFAR AI Chair. Leads the Amii Adaptive Prosthetics Program developing intelligent artificial limbs using reinforcement learning. Pioneered real-time prediction learning for prosthetic control and co-founded DeepMind's Edmonton research office. Research focuses on human-machine interaction through reinforcement learning, prediction algorithms, and sensorimotor control. Current projects include position-aware myoelectric control, shared agency frameworks, and climate-adaptive port governance. Recognized in Stanford/Elsevier top 2% of global logistics researchers.
Enayat Rajabiمشاهده پروفایل
دانشیار
- Machine Learning
- Knowledge Graphs
- Data Analytics
- +۳ مورد دیگر
Dr. Enayat Rajabi is an Associate Professor of Business Analytics at the Shannon School of Business, Cape Breton University. Holding a PhD in Information and Knowledge Engineering from the University of Alcala (Spain) and a postdoctoral fellowship from Dalhousie University, his research focuses on the intersection of Machine Learning, Knowledge Graphs, and Data Analytics. He actively applies these technologies in healthcare, smart cities, and social media crisis response contexts. Education PhD in Information and Knowledge Engineering, University of Alcala (Spain) Postdoctoral Fellowship, Dalhousie University Research Interests His work bridges Knowledge Graphs with Machine Learning, emphasizing explainability and practical applications. Key areas include: Explainable AI for clinical decision-making Knowledge Graph applications in healthcare systems Social media analytics for emergency response Smart city data integration Generative modeling for tabular data Recent Publications Trends Recent articles highlight: Explainable AI in healthcare settings Industrial breakdown prediction systems Social media influencer detection Smart city infrastructure modeling Advanced data synthesis techniques Continued focus on Knowledge Graph applications
Jesse Perlaمشاهده پروفایل
دانشیار
Jesse Perla is an Associate Professor in the Vancouver School of Economics at the University of British Columbia, Faculty of Arts. He maintains an office in the Iona Building 201B and is actively engaged in research and teaching within the economics department. Dr. Perla received his PhD in Economics from New York University in 2013 and completed his undergraduate studies in Applied Mathematics at Columbia University in 1997. His academic journey has positioned him at the intersection of economics, mathematics, and computational methods. His research focuses on macroeconomics and growth from the firm perspective, with particular emphasis on information diffusion, heterogeneous agents, and computational approaches to economic modeling. He has developed significant expertise in applying machine learning techniques and high-dimensional methods to economic problems, with notable work on technology diffusion, financial frictions, and dynamic programming. His research agenda spans theoretical and computational economics, with increasing integration of artificial intelligence methods in recent years. Perla's publications reveal a strong trend toward computational economics, with growing emphasis on deep learning applications to overcome the curse of dimensionality in economic models. His work frequently explores how information diffusion affects firm behavior and economic growth, often employing sophisticated mathematical and computational techniques to model complex economic phenomena. He is a co-author of the influential open textbook Quantitative Economics with Julia and has made significant contributions to computational economics education. His teaching focuses on preparing students for graduate work in economics, with particular emphasis on mathematical preparation and computational skills. He has published extensive course advice for UBC undergraduates interested in pursuing graduate studies. Perla actively contributes to the development of computational tools for economists, particularly through the Julia programming language ecosystem. His work includes developing educational materials on differential equations for epidemiological modeling in economics and maintaining comprehensive data source references for economic researchers.
Mike Domaratzki is an Associate Professor and Chair of the Department of Computer Science at Western University. His research focuses on bioinformatics, genomics, and theoretical computer science, with recent emphasis on machine learning tools for genomic prediction in crops. He has held academic roles at Western University and previously at the University of Manitoba and Acadia University. Education: (No explicit details provided in texts) Research Interests: Domaratzki’s work bridges computational methods and biological systems, including algorithm design for genomic analysis, machine learning applications in agriculture, and theoretical foundations of computing. His recent projects address challenges in imbalanced data classification and crop yield prediction using advanced neural networks. Teaching: He has taught a wide range of courses, including introductory programming, data structures, algorithms, bioinformatics, and automata theory across multiple institutions. Notable courses include COMPSCI 1026/1027 at Western, and COMP courses at the University of Manitoba/Acadia covering foundational CS topics and specialized areas like bionformatics algorithms. Publications: His work spans machine learning, genomics, and theoretical computer science, with recent trends emphasizing agricultural genomic applications and data-driven health analytics. Key themes include imbalanced data solutions, neural network architectures for genomics, and computational tools for biological data interpretation. Grants/Advising: No specific grants or student advising details are listed, though his teaching and research imply active involvement in mentoring. His lab focuses on interdisciplinary projects combining computational methods with biological datasets.
Mike Katchabawمشاهده پروفایل
دانشیار
Dr. Mike Katchabaw is an Associate Professor in the Department of Computer Science at The University of Western Ontario. His research focuses on game design and development, including adaptive game systems, believable agents, algorithmic music composition, and networked game optimization. He holds a Ph.D. from Western (2002) and has been with the department since 2002. Teaching responsibilities include courses on open-source projects, software maintenance, game development, and game design. He is affiliated with the Digital Recreation, Entertainment, Art, and Media (DREAM) Group. Key research areas include psychosocial behavior modeling in NPCs, automated difficulty adjustment, and latency management in multiplayer games. Publications span over 30 refereed journal/conference papers, book chapters, and technical reports. Notable achievements include the Best Paper Award at GameOn 2011 and a patent for a flexible music composition engine. He has contributed to commercial games like 'To The Moon' and 'Animal Planet Vet Life' as a consultant/lead programmer.
Sihang Liuمشاهده پروفایل
استادیار
Dr. Sihang Liu is an Assistant Professor in the School of Computer Science at the University of Waterloo. Prior to this role, he served as a visiting faculty member at SystemsResearch@Google. He holds a PhD from the University of Virginia, where his research was supported by the Google Fellowship Award. His academic career has been marked by contributions to computer architecture, systems, and persistent memory technologies. Dr. Liu's research interests span computer architecture, systems, and cybersecurity, with a focus on persistent memory systems, energy-efficient computing, and data center optimization. His work bridges hardware-software co-design and explores challenges in scalable computing, fault tolerance, and sustainable AI systems. His recent publications highlight advancements in adaptive memory tiering (HybridTier), carbon intensity forecasting (EnsembleCI), and securing persistent memory (Side-Channel Attacks on Optane). These contributions underscore his commitment to addressing critical issues in modern computing systems' performance, security, and environmental impact. Awards: Google Fellowship Award (PhD), NVMW Memorable Paper Award Finalist (2019), 2019 MICRO Top Picks Honorable Mention Professional Service: Program committees for ISCA, MICRO, ASPLOS; Artifact evaluation roles in leading conferences As an advisor, Dr. Liu mentors a diverse group of students, including current PhD candidates Henry Tian and Desen Sun, and numerous undergraduates. His lab focuses on innovation in sustainable computing and resilient systems design.
Mohamed Hibat-Allah is an Assistant Professor in the Department of Applied Mathematics at the University of Waterloo, where he researches the intersection of natural language processing and quantum many-body physics. His work focuses on using language models to describe quantum systems and developing machine learning solutions for combinatorial optimization problems applicable to scientific and industrial challenges. His research integrates techniques from natural language processing, quantum physics, and statistical mechanics to advance computational methods for simulating quantum materials and solving complex optimization problems. This interdisciplinary approach bridges theoretical physics with cutting-edge AI methodologies. Analysis of his recent publications reveals a strong thematic focus on quantum machine learning, neural network applications in physics, and variational methods. Key trends include the development of quantum-inspired algorithms, benchmarking of quantum models against classical counterparts, and novel approaches to simulating quantum systems using recurrent neural architectures.
![](https://www.queensu.ca/mathstat/people/faculty/profiles/"/mathstat/sites/mastwww/files/styles/staggered_tiles_image/public/2021-01/gharesifard-bahman.jpg?h=10d202d3&itok=2vakrgj4")
Bahman Gharesifard is a Professor in the Department of Mathematics and Statistics at Queen's University, Canada. He holds a Ph.D. from Queen's University (2009) and advanced degrees from Shiraz University (B.Sc., 2002; M.Sc., 2005). His research focuses on systems and control theory, with emphasis on distributed control, optimization, geometric control, and their intersections with network sciences, machine learning, and game theory. He has been recognized with the First Year Instructor Teaching Award in Engineering & Applied Science (2014 & 2016). His academic journey includes postdoctoral research at the University of California, San Diego (2009–2012) and the University of Illinois, Urbana-Champaign (2012–2013). His work bridges theoretical foundations of control systems with practical applications in distributed optimization, neural networks, and contagion models on networks. Recent research trends include advancing Lyapunov-based methods for reinforcement learning, analyzing structural controllability in sparse systems, and developing models for network dynamics using Pólya urn frameworks. His articles explore topics like averaged controllability, flexible-step MPC, and stability in distributed algorithms. Education: Ph.D., Queen's University (2009) M.Sc., Shiraz University (2005) B.Sc., Shiraz University (2002) Awards: Engineering & Applied Science First Year Instructor Teaching Award (2014) Engineering & Applied Science First Year Instructor Teaching Award (2016) He collaborates on projects involving secure distributed optimization, epidemic modeling via Pólya contagion networks, and neural network approximation guarantees. His lab contributes to theoretical control advancements with practical implications in robotics, energy systems, and AI.
Ian Churchمشاهده پروفایل
دانشیار
Dr. Ian Church is an Associate Professor in the Department of Geodesy and Geomatics Engineering within the Faculty of Engineering at the University of New Brunswick. He specializes in ocean mapping using acoustics, with primary research interests in integrating hydrodynamic numerical modeling with ocean mapping, marine habitat mapping, acoustic water column interpretation, and evaluating autonomous mapping platforms. His research aims to better understand the marine environment using acoustics and minimize uncertainty in ocean mapping products. Dr. Church received all his degrees (B.Sc.Eng, M.Sc.Eng, Ph.D.) from UNB while working with the Ocean Mapping Group. His professional experience includes operating and managing multibeam sonar systems onboard the coast guard ship Amundsen in the Canadian Arctic (2005-2013), and positions at the University of Southern Mississippi before returning to UNB in 2016. His publications demonstrate consistent focus on hydrographic surveying, ocean mapping technologies, and marine data processing. Recent trends show increased emphasis on AI applications in seabed mapping, advanced sensor technologies, collaborative training programs, and hydrodynamic modeling for coastal management. Dr. Church also contributes to educational initiatives like the COMREN International School on Hydrographic Surveying.
Ruohong Jiaoمشاهده پروفایل
استادیار
Dr. Ruohong Jiao is an Assistant Professor in Earth and Ocean Sciences at the University of Victoria, currently on leave. Specializing in fission-track and (U-Th)/He thermochronology, his research investigates orogenic processes and landscape evolution through field studies and computational modeling. Research focuses on tectonic exhumation patterns, mountain building processes, and fluvial system responses to tectonic forcing. Recent methodological innovations include applications of physics-informed neural networks for thermochronological data inversion. Field areas span Vancouver Island, Tibetan Plateau, Tian Shan, and Yangtze River regions. Publications demonstrate expertise in reconstructing tectonic histories using low-temperature thermochronometry, with emphasis on coupling between surface processes and deep crustal dynamics. Professional service includes graduate student committee supervision.
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