جست‌وجوی استاد و پژوهشگر

• Biomechanics
• Neuromechanics
• Musculoskeletal Modeling
• +۵ مورد دیگر

Pouya Amiri is an Assistant Professor in Biomechanics and Neuromechanics at Queen's University. He holds a PhD from McGill University and post-doctoral training at Imperial College London. His research focuses on musculoskeletal biomechanics, particularly in impairments like lower limb amputation and knee osteoarthritis. He combines computational models with medical imaging and functional experiments to develop personalized interventions and rehabilitation technologies. Education: PhD in Biomedical Engineering, McGill University Post-Doctoral Fellowship, Imperial College London MASc in Mechanical Engineering, Dalhousie University MSc/BSc in Mechanical Engineering, Isfahan University of Technology Research Interests: Biomechanical adaptations in amputees Knee osteoarthritis mechanics Human balance control Functional electrical stimulation Prediction of joint forces via musculoskeletal models Biofeedback training for gait optimization His recent work explores predictive musculoskeletal models for amputees, VR-based balance control identification, and reducing knee joint loading in osteoarthritis patients. He emphasizes subject-specific approaches for clinical applications. Advising & Funding: Actively recruiting motivated students through scholarships like Ontario Graduate Scholarship and NSERC. Internal funding opportunities are also available.

Yanglei Songمشاهده پروفایل

استادیار

• Sequential Decision-Making
• Mathematical Statistics
• High-Dimensional Statistics
• +۷ مورد دیگر

Yanglei Song is an Assistant Professor in the Department of Mathematics and Statistics at Queen's University, affiliated with the Faculty of Arts and Science. His research focuses on sequential decision-making problems, including hypothesis testing, change detection, and multi-armed bandits, alongside mathematical statistics such as U-statistics, high-dimensional statistics, and cut-point analysis. Education: Ph.D. in Statistics, University of Illinois at Urbana-Champaign (2019) M.Sc. in Mathematics, University of Illinois at Urbana-Champaign (2016) B.Eng. in Electronic Engineering, Tsinghua University (2012) Research Interests: Sequential decision-making frameworks with applications in hypothesis testing and stochastic optimization. Statistical methodologies for high-dimensional data and nonparametric modeling. Development of robust algorithms for reinforcement learning and adaptive systems. Mathematical foundations of U-statistics and their computational guarantees. Publications Trends: Recent work emphasizes theoretical advancements in bandit algorithms, robust model approximation, and statistical inference under covariate-adaptive randomization. Contributions span machine learning, computational statistics, and applied mathematics, with a focus on practical implementations and rigorous theoretical analysis. Students: J. Liu Y. Zhou Z. Wang Z. Zhao N. Li M. Zhou Labs/Teams: Maintains an active research group focused on statistical learning and sequential decision-making, with open-source projects such as Stratified incomplete local simplex tests for nonparametric regression analysis.

Istvan Davidمشاهده پروفایل

استادیار

• Sustainable Systems Engineering
• Digital Twins
• Modeling and Simulation
• +۶ مورد دیگر

Dr. Istvan David is an Assistant Professor in the Department of Computing and Software at McMaster University. He teaches courses including CAS 782 (Engineering Digital Twins), SFWRENG 2AA4 (Software Design I), and SFWRENG 3S03 (Software Testing). His research focuses on sustainable systems engineering, digital twins, modeling and simulation, and cyber-biophysical systems. He is actively involved in the "Digital & Smart Systems" and "Environment" research clusters. Dr. David's work emphasizes model-driven engineering, multi-paradigm modeling, and software development methodologies. His expertise includes real-time monitoring and control of complex systems through digital twins, with applications in smart cities, industrial automation, and precision agriculture. He emphasizes integrating modeling, data management, and closed-loop control in his teaching and research. Contact: istvan.david@mcmaster.ca , Office: ITB 160

Andrew Hamilton-Wrightمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Data Visualization
• +۶ مورد دیگر

Dr. Andrew Hamilton-Wright is an Associate Professor at the School of Computer Science, University of Guelph, part of the College of Engineering and Physical Sciences. He is a member of the Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI) and has been at the university since 2017. He holds a PhD from the University of Waterloo (2006) and previously worked as a Postdoctoral Researcher at Queen’s University and an Associate Professor at Mount Allison University. His research focuses on machine learning, association mining, and data visualization to support decision-making in healthcare. Key areas include electrophysiological data analysis for disease characterization, fatigue and pain prediction using biophysical signals, and the development of robust software tools for visualizing complex data. He has pioneered work on EMG simulators and models for validating techniques in neuromuscular research. Notable awards include an NSERC Discovery Grant (2021) and an NSERC Engage Grant (2018). His work has been featured in media coverage, such as CTV News’ report on an iron-tracking app for genetic condition management. He is also affiliated with the OneHealth Institute and editorial boards in scientific journals. His grants and collaborations include NSERC-funded projects and partnerships with industry. He advises on ergonomic and postural data analysis, with applications in occupational health and safety. His lab focuses on integrating machine learning with biomedical systems to improve clinical decision-making.

Edith Lawمشاهده پروفایل

دانشیار

• Social Computing
• Human-Computer Interaction
• Artificial Intelligence
• +۳ مورد دیگر

Edith Law is an Associate Professor at the University of Waterloo, specializing in social computing and human-AI interaction. She holds a PhD in Machine Learning from Carnegie Mellon University, an MSc in Computer Science from McGill University, and a BSc in Computer Science from the University of British Columbia. Her research focuses on designing technology to foster human values through collaborative systems, such as teachable robots, conversational agents, and inclusive virtual environments. She explores how machine intelligence can be aligned with ethical principles and how technology can support learning, curiosity, and empathy across diverse populations. Recent work emphasizes platforms like the Curiosity Notebook and tools for medical education simulations. Her articles highlight advancements in interactive optimization, value reflection frameworks, and accessible Metaverse design. No scientific awards or grants are listed, though her contributions span educational technology, healthcare AI, and participatory design. She has developed platforms like Curio and Codetoon to bridge creative coding and computational concepts for learners.

• Actuarial Science
• Quantitative Finance
• Risk Management
• +۵ مورد دیگر

Ben Feng is an Associate Professor and Director of the Master of Actuarial Science Program at the University of Waterloo. He specializes in actuarial science, quantitative finance, and risk management, with a focus on machine learning applications in financial modeling and anomaly detection. His research bridges theoretical advancements in statistical methods with practical solutions for pension planning, climate-smart portfolios, and systemic risk analysis. Key research interests include time series analysis, nested simulation techniques, and the integration of graph theory into financial systems. Recent work emphasizes anomaly detection in dynamic environments and climate-aware investment strategies. His articles span domains like multivariate time series, contrastive learning, and green simulation optimization. Ben has contributed to systemic risk modeling and efficient simulation algorithms, with publications in high-impact venues. His work often addresses computational challenges in finance and actuarial science through interdisciplinary approaches.

Lawrence M. Widrowمشاهده پروفایل

استاد

• Theoretical Cosmology
• Galactic Dynamics
• Dark Matter
• +۳ مورد دیگر

Lawrence M. Widrow is a Professor and Department Head in the Department of Physics, Engineering Physics & Astronomy at Queen’s University, affiliated with the Astrophysics Research Group. He holds a PhD from the University of Chicago and has been at Queen’s since 1993. His research focuses on theoretical astrophysics, including galactic dynamics, dark matter, and cosmology. Key contributions include the concept of Galactoseismology, studies of the Milky Way’s stellar disk disequilibrium, and collaborations on projects like Gaia satellite analysis, GalactICS code development, MEGA, and PANDAs. He has pioneered work on sterile neutrinos as dark matter and primordial magnetic field generation via inflationary mechanisms. Education: PhD in Physics, University of Chicago Postdoctoral Fellowships: Harvard-Smithsonian Center for Astrophysics; Canadian Institute for Theoretical Astrophysics, University of Toronto Bachelor’s in Physics, Stanford University Research: Explores galactic structure, dark matter interactions, and cosmological phenomena. Current work integrates theory, computational astrophysics, and data science to analyze Gaia satellite data. Early contributions include proposals on asymmetric dark matter and inflationary magnetic fields. Labs/Teams: Leads the Astrophysics Research Group at Queen’s University and collaborates with institutions like Carnegie Mellon University (Scott Dodelson) and the ESA Gaia project. Developed the GalactICS code for galactic dynamics simulations.

• Computational Neuroscience
• Neuroscience
• Artificial Intelligence
• +۷ مورد دیگر

Sylvain Chartier is a Professor in the Department of Psychology at the University of Ottawa, Faculty of Social Sciences. His research integrates computational modeling with cognitive and neural sciences, focusing on artificial neural networks, cognition, perception, and nonlinear time series analysis. His educational background includes a Ph.D. in Psychology from Université du Québec à Montréal (2004), an Honours B.Sc. in Psychology from the same institution (1998), and a B.A. in Psychology from the University of Ottawa (1996). Chartier's research interests lie at the intersection of Computational Neuroscience , Artificial Intelligence , and Cognitive Science . He investigates how neural networks model human cognition, with particular emphasis on memory systems like Bidirectional Associative Memory (BAM), spike neural models, and unsupervised learning. His work applies these models to areas such as financial prediction, neuroimaging analysis, and cognitive disorders like prosopagnosia. He also develops tutorials and tools for quantitative methods in psychology, enhancing accessibility to advanced statistical and computational techniques. The analysis of his recent publications reveals a strong trend in using artificial neural networks for modeling cognitive processes, with consistent contributions to associative memory, neuronal dynamics, and machine learning applications in psychology and neuroscience. His work often bridges theoretical models with empirical validation, such as in studies involving bumblebee behavior or neuroimaging data analysis. He actively supervises graduate students, including Nareg Berberian, and contributes to academic training through methodological publications. While no specific grants are listed in the provided text, his sustained output suggests active research funding. His publications in journals like Neural Networks , PLoS One , and The Quantitative Methods for Psychology reflect a commitment to interdisciplinary science. Chartier is involved in research teams focused on computational modeling of cognition and neural systems. His lab or research group likely emphasizes simulation-based approaches to understanding memory, perception, and information processing, leveraging tools from AI and dynamical systems theory.

Sarah Rossمشاهده پروفایل

استادیار

• Work Teams
• Knowledge Exchange in Groups
• Organizational Behaviour
• +۳ مورد دیگر

Sarah Ross is an Assistant Professor at the Department of Management and Organizational Studies at Western University, a position she has held since 2014. She earned her PhD, MA, and BA in Psychology from Western University, focusing on work teams and knowledge exchange in groups. Her research emphasizes evidence-based management and organizational behavior, exploring how teams share knowledge and apply empirical approaches to management. Education: PhD in Psychology, University of Western Ontario (2011) MA in Psychology, University of Western Ontario (2005) BA Honors Psychology, University of Western Ontario (2003) Research Interests: Sarah’s work centers on work teams, knowledge exchange dynamics, and the application of evidence-based practices in management. She investigates how organizational structures and behaviors influence team performance and decision-making. Her studies also address the translation of academic research into practical management strategies. Awards and Honors: SSHRC Canada Graduate Scholarship (Doctoral, 2005–2008) USC Teaching Award of Excellence (2016) Western University Residence Life Teaching Award of Excellence (2016) Fellowship in Teaching Excellence (2015) Teaching and Engagement: Sarah teaches courses such as Organizational Behavior and Evidence-Based Management, often incorporating online platforms. She has received training in e-learning and instructional design, and has been a faculty mentor in residence. Her teaching philosophy emphasizes student engagement and innovative pedagogy. Additional Projects: Sarah has collaborated on normative research for neuropsychological testing, employee engagement analysis, and textbook reviews in industrial/organizational psychology. She also contributed to training evaluation and job satisfaction studies for organizations.

Mikko Karttunenمشاهده پروفایل

استاد

• Computational Chemistry
• Biological Physics
• Soft Matter
• +۴ مورد دیگر

Dr. Mikko Karttunen is a Professor at Western University, holding joint appointments in the Department of Physics & Astronomy and the Department of Chemistry. He is a Canada Research Chair Tier 1 in Computational Materials and Biomaterials Research. His research focuses on computational studies of biological and soft matter systems, with particular emphasis on lipid membranes, biomaterials, and machine learning applications in materials science. Dr. Karttunen earned his PhD in Condensed Matter Physics from McGill University (1999) and MSc in Physics from Tampere University of Technology (1993). He has held previous positions at Eindhoven University of Technology (Netherlands), the University of Waterloo, and was a postdoctoral fellow at the Max Planck Institute for Polymer Research in Germany. His research interests span computational chemistry, biological physics, and materials science, with projects involving molecular dynamics simulations, AI-driven modeling, and collaborations with experimental groups. Key areas include lipid diffusion, membrane proteins, magnetic materials, and drug delivery systems. Dr. Karttunen has received prestigious awards including the Finnish Academy of Science and Letters membership, Ontario Early Researcher Award, and NSERC Discovery Accelerator. His group actively collaborates globally and hosts researchers from over 17 countries. His work integrates theory, simulation, and machine learning to address challenges in biomaterials, drug design, and soft matter physics, with applications in biomedicine and nanotechnology.

Lawrence Widrowمشاهده پروفایل

استاد

• Galactic Dynamics
• Dark Matter
• Cosmology
• +۲ مورد دیگر

Lawrence Widrow is a Professor in the Department of Physics, Engineering Physics & Astronomy at Queen's University, where he leads research in theoretical astrophysics and cosmology. He earned his PhD from the University of Chicago under Michael Turner and held postdoctoral positions at Harvard-Smithsonian Center for Astrophysics and Canadian Institute for Theoretical Astrophysics. Research focuses on galactic dynamics, dark matter, and cosmic structure formation. Key contributions include: Galactoseismology (studying galactic disk perturbations via SEGUE/Gaia data), equilibrium modeling of the Milky Way, orbital torus imaging techniques, dark matter substructure analysis, and simulations of disk-halo interactions. Recent work explores pulsar-based dark matter detection, phase mixing theory, and fuzzy dark matter dynamics. Methodological innovations include the GalactICS code for equilibrium initial conditions, discrete spectral analysis of phase space, and MilkyWay@home distributed computing for stream reconstruction. No scientific awards, students, or laboratory affiliations are detailed in the provided information.

Divya Kaur Mattaمشاهده پروفایل

استادیار

• Photosynthesis
• Computational Chemistry
• Biophysics
• +۶ مورد دیگر

Divya Kaur Matta is an Assistant Professor in the Department of Chemistry at Brock University. Her research focuses on computational studies of charge and energy transfer reactions in photosynthetic proteins, leveraging advanced techniques like Molecular Dynamics (MD), Multi Conformation Continuum Electrostatics (MCCE), and Density Functional Theory (DFT). She collaborates with experimental groups to analyze structural and mechanistic details of photosynthesis. Education: B.Sc (2007) and M.Sc (2009) from University of Delhi; M.Phil (2017) and Ph.D (2021) from The Graduate Center, The City University of New York. Research Keywords: Electron transfer, Photosynthesis, Computational Chemistry, DFT, MD simulations, MCCE, Energy transfer. Her work aims to design artificial systems for solar energy conversion into biofuels. She supervises graduate students and postdoctoral researchers, emphasizing EDI initiatives in lab recruitment. She teaches courses in physical chemistry, biophysics, and biotechnology. Key Publications: Explore proton pathways in Photosystem II, quantum descriptors of chlorophylls, and Monte Carlo sampling for water channels. Scientific Awards: FMS Research Award (2024) Her lab offers partial stipends and part-time employment to selected candidates, prioritizing those with programming experience or EDI leadership.

Peter Bergمشاهده پروفایل

استاد

• Applied Mathematics
• Theoretical Physics
• Electro-kinetic Fluid Flows
• +۵ مورد دیگر

Peter Berg is the Dean of the Faculty of Mathematics & Science at Brock University and holds the rank of Professor in the Department of Physics. He earned a PhD in Mathematics from the University of Bristol and a Diplom in Physik from the University of Muenster. His research focuses on applied mathematics and theoretical physics, particularly electro-kinetic fluid flows in electrochemical energy systems like fuel cells. He has secured over $4.0m in funding from agencies such as NSERC, CFI, and international collaborations. Berg has authored/edited 45+ journal papers, 15 conference proceedings, and a book on sustainability dilemmas. Education & Professional Background - PhD in Mathematics, University of Bristol, UK - Physik Diplom, University of Muenster, Germany - Former Chair of the Department of Science at the University of Alberta (2015-2022) - Leadership roles including NTNU Leadership Program and Gold College Leadership Program Research Contributions Berg’s work bridges applied mathematics and electrochemical engineering, with emphasis on pore-network models for PEM fuel cells, nanofluidic energy conversion, and water management in energy systems. His studies address challenges in renewable energy storage and efficiency, leveraging computational and analytical methods. Teaching & Mentorship Berg advocates for student-driven learning environments and has developed innovative curricula including the 3-11 academic calendar at the University of Alberta. He supervises postdoctoral fellows, PhD/MSc students, and undergraduate researchers, emphasizing computational physics and numerical methods in coursework. Outreach & Leadership Organized major international workshops such as “Nanoscale Physics of Electrochemical Media” (2021) and public lectures on energy sustainability. Active in global education initiatives like the computational-physics platform project at the University of Eswatini (2021-2023).

Thomas E. Bakerمشاهده پروفایل

استادیار

• Quantum Computing
• Quantum Algorithms
• Entanglement Renormalization
• +۴ مورد دیگر

Dr. Thomas E. Baker is an Assistant Professor and Canada Research Chair in Quantum Computing for Modelling of Molecules and Materials at the University of Victoria. He holds joint appointments in the Departments of Physics & Astronomy and Chemistry. His research focuses on quantum computing, quantum algorithms, and entanglement renormalization, with applications to quantum chemistry and density functional theory. He leads the sensing and quantum materials cluster under the Office of the VPRI and is a member of Quantum BC and the Centre for Advanced Materials and Related Technology. Education: M.Sc. (California State University Long Beach), PhD (University of California, Irvine), postdoctoral fellow at the Institute for Quantum Computing (IQ) at Université de Sherbrooke, and Fulbright U.S. Scholar at the University of York, UK. Research interests include quantum information processing, quantum error correction, and tensor networks. His team develops the DMRjulia library, a Julia-based tool for entanglement renormalization computations. Recent work emphasizes quantum algorithm design for near-term quantum computers and theoretical foundations of quantum materials. Scientific Awards: Fulbright U.S. Scholar (2018). Teaching includes graduate courses on quantum physics, computational methods, and tensor networks. He actively mentors students across physics, chemistry, and computer science backgrounds. Labs/Teams: Principal Investigator of the sensing and quantum materials cluster, co-developer of DMRjulia, collaborator in Quantum BC.

Deborah Harrisمشاهده پروفایل

استاد

• Neutrino Physics
• Particle Physics
• High Energy Physics
• +۳ مورد دیگر

Deborah Harris is a Full Professor (Research Stream) in the Department of Physics and Astronomy at York University, holding a joint appointment with Fermilab as a Senior Scientist. She specializes in neutrino physics, focusing on oscillations and interactions, with key roles in major international experiments such as DUNE, MINERvA, and T2K. Her research aims to elucidate neutrino properties, their impact on astrophysical phenomena like galaxy formation, and the matter-antimatter imbalance in the universe. Education: Bachelor's in Physics from the University of California-Berkeley PhD from the University of Chicago (Experiment E799 on neutral kaons) Postdoctoral research at the University of Rochester (NuTeV Experiment) Research Interests: Neutrino oscillations, neutrino-nucleus interactions, and experimental precision in particle physics. She works on DUNE’s near detector to refine interaction models and is co-spokesperson of MINERvA, which provides foundational data for future neutrino experiments. Her involvement with T2K leverages near-far detector strategies critical for DUNE’s success. Publications Trends: Her recent work emphasizes cross-section measurements, detector design for neutrino experiments (e.g., DUNE single-phase technology), and improving neutrino flux predictions through collaborations like EMPHATIC. She bridges experimental data with theoretical models to address uncertainties in neutrino behavior. Awards: While no explicit awards are listed, her leadership roles in MINERvA (co-spokesperson since 2010) and DUNE highlight her impactful contributions to neutrino physics. She has received recognition through her outreach initiatives, including featured profiles in Women @ Energy and public lectures on neutrino experiments. Advising & Grants: As part of the Physics and Astronomy Graduate Program at York, she supervises advisees. Her grants support her roles in international collaborations; details are omitted but reflect her contributions to detector development and neutrino research infrastructure. She has managed large-scale projects, such as the MINERvA construction as a postdoctoral researcher. Labs & Teams: Active in the DUNE, MINERvA, and T2K collaborations. She leads efforts in cross-section measurements at MINERvA and contributes to T2K’s near detector strategies. Her work integratesYork’s academic resources with Fermilab’s experimental facilities.