جست‌وجوی استاد و پژوهشگر

Peter Tielemanمشاهده پروفایل

استاد

• Molecular Simulation
• Biophysics
• Membrane Biology
• +۴ مورد دیگر

Peter Tieleman is a Professor in the Department of Biological Sciences at the University of Calgary and holds the NSERC Canada Research Chair (CRC I) in Molecular Simulation. His work focuses on computational biophysics, particularly studying membrane proteins, lipid bilayers, and drug-membrane interactions using molecular simulations. He leads the Biocomputing Group and the Centre For Molecular Simulation. Education: PhD in Chemistry, University of Groningen (1998) EMBO Fellowship in Molecular Biophysics, University of Oxford (2000) MSc and MA in Chemistry and Philosophy, University of Groningen (1995) Postdoctoral Fellowship in Biophysical Chemistry, University of Groningen (1999) Research Interests: Theoretical techniques linking structure and function of biomolecules, membrane protein simulations, lipid-protein interactions, and computational drug discovery. His lab develops coarse-grained models like the Martini force field for biomolecular simulations. Awards: Fellow of the Royal Society of Chemistry (2015) Killam Annual Professorship (2015) Rutherford Memorial Medal in Chemistry (2008) E.W.R. Steacie Memorial Fellowship (2009) Teaching & Leadership: Teaches courses like BCEM 471 (Physical Biochemistry) and BCEM 577 (Biomolecular Simulation). Holds leadership roles in research grants and international collaborations, including the AITF Tier 1 Strategic Chair (2012). Active in advancing FAIR principles for computational data sharing. Labs & Teams: Directs the Biocomputing Group and the Centre For Molecular Simulation , focusing on collaborative computational and experimental approaches to membrane biology.

Ratna Shresthaمشاهده پروفایل

مدرس

• Environmental Economics
• Resource Economics
• Public Finance
• +۱ مورد دیگر

Ratna Shrestha is a Sessional Lecturer (Continuing) at the Vancouver School of Economics and Sauder School of Business, University of British Columbia (UBC). She teaches graduate courses including Managerial Economics for MBA/MHA students as well as various undergraduate courses such as ECON 323 (Quantitative Economic Modelling with Data Science Applications). Prior to joining UBC, she served as an Assistant Professor at Memorial University of Newfoundland and Tribhuvan University in Kathmandu, Nepal. Dr. Shrestha earned her Ph.D. in Economics from the University of Hawaii, Manoa. She was born in Bandipur, a small hill-top town in Nepal, and remains active in Nepali community activities in the greater Vancouver area and the United States. Her research focuses on Environmental/Resource Economics , Public Finance , and Healthcare Policy . Her influential 1998 paper on citizens' participation in pollution permit trading introduced a two-sided market approach that can achieve efficient solutions even under regulatory uncertainty about pollution control costs. This work has generated numerous follow-up studies in environmental economics. Her research demonstrates a consistent thread connecting theoretical economic models with practical environmental and public policy applications. Dr. Shrestha has published in prestigious journals including the Journal of Environmental Economics and Management, FinanzArchiv, Seoul Journal of Economics, International Tax and Public Finance, Resource and Energy Economics, and Environmental and Resource Economics. Her work spans environmental regulation, healthcare policy reform in Nepal, public goods provision, and the theoretical foundations of economic policy under uncertainty. Environmental Economics: Developing innovative approaches to environmental regulation and policy instruments Public Finance: Analyzing group dynamics, burden sharing, and country size effects in public goods provision Healthcare Policy: Proposing self-financing models for healthcare insurance in developing countries Dr. Shrestha has made significant contributions to understanding how economic mechanisms can address environmental challenges and improve policy design. Her work bridges theoretical economic analysis with practical policy applications, particularly in the context of developing economies.

• Machine Learning
• Computer Vision
• Artificial Intelligence
• +۵ مورد دیگر

Eugene Belilovsky is an Assistant Professor in the Department of Computer Science and Software Engineering at Concordia University. His research focuses on machine learning, computer vision, optimization algorithms, and their applications in medical imaging and federated learning. He explores topics such as continual learning, model merging, and bias mitigation in deep learning systems. His work bridges theoretical advancements with practical applications, including healthcare diagnostics and efficient training strategies for large models. Belilovsky's recent publications emphasize innovative methods like MuLoCo for inner optimizer design, FairDropout for enhancing minority group generalization, and techniques to scale multi-task learning with sparse masks. His studies on federated learning explore incentive mechanisms for decentralized systems and robust pre-training of large language models. His contributions to 3D reconstruction and motion prediction highlight interdisciplinary applications in computer graphics and generative models. His research trends reflect a strong emphasis on improving model efficiency, fairness, and adaptability across diverse domains. While no scientific awards are explicitly listed, his prolific publication record underscores his impactful contributions to AI and machine learning.

• Internet of Things
• Neural Networks
• VLSI Design
• +۴ مورد دیگر

Professor Tet Him Yeap serves as an Associate Professor and Associate Director of the System Science Program within the School of Electrical Engineering and Computer Science at the University of Ottawa. With a tenure dating back to October 1989, he maintains an active research profile from his office in STE 5025. His academic contributions are anchored in electrical engineering and computer science, particularly in advancing communication systems and neural network applications for real-world problems. Professor Yeap's research program is centered on three interconnected pillars: Internet of Things (IoT) for smart infrastructure, neural network architectures, and VLSI design. Within IoT, he explores applications in smart farming—such as precision agriculture and environmental monitoring—and predictive maintenance for transportation systems. His neural network work emphasizes Restricted Hopfield Networks, focusing on adversarial robustness and associative memory capabilities. Additionally, he investigates VLSI implementations for communication systems, including visible light communication (VLC) under challenging ambient light conditions, and wireline/wireless access technologies. Analysis of Professor Yeap's 15 most recent publications (2023-2025) reveals a strategic shift toward interdisciplinary applications of machine learning in agriculture and robust communication systems. Approximately half of these works address smart farming challenges, leveraging UAV imagery, federated learning, and neural networks to predict crop yields and nitrous oxide emissions. The remainder concentrates on enhancing neural network robustness (particularly Restricted Hopfield Networks) and optimizing visible light communication for outdoor use. This dual focus underscores his commitment to both theoretical advances in AI and practical solutions for sustainability. Professor Yeap mentors graduate students in his research areas, with Kalonji H Kalala currently under his supervision. While specific grant awards are not detailed in the provided materials, his extensive publication record in high-impact areas suggests active involvement in funded projects, likely including collaborations with agricultural technology initiatives and engineering research consortia focused on IoT and communication systems.

Farshid Agharebparastمشاهده پروفایل

دانشیار

• Data Communications
• Mobile Computing
• Wireless Networks
• +۳ مورد دیگر

Farshid Agharebparast serves as an Associate Professor Teaching in the Department of Electrical and Computer Engineering at the University of British Columbia's Faculty of Applied Science. His academic career is anchored in data communications networks and mobile computing, with active contributions to the Communication Systems Research Group specializing in Data Communications. Education: BSc, University of British Columbia MASc, University of British Columbia PhD, University of British Columbia Dr. Agharebparast's research explores stochastic network calculus applications for wireless performance evaluation, cross-layer design in fading channels, and QoS provisioning. His work bridges theoretical frameworks like min-plus algebra with practical challenges in 4G handover privacy, bandwidth allocation in WiMAX, and traffic shaping for unreliable channels. These investigations address critical needs in next-generation mobile infrastructure where latency guarantees and resource efficiency are paramount. His teaching encompasses core engineering subjects including Circuit Analysis (ELEC 201), Operating Systems (CPEN 331), Computer Networking (ELEC 331, CPEN 400N), and multiple design studio courses (ELEC 291, CPEN 291). This portfolio demonstrates deep engagement with both foundational theory and hands-on implementation across electrical and computer engineering curricula. Scientific Awards: No awards documented in source materials Through design studio courses and undergraduate teaching assistantships, Dr. Agharebparast mentors emerging engineers in practical system development. His publication trajectory from 2001-2022 indicates sustained research activity in network performance modeling, though specific grant funding details remain unreported. His work consistently targets real-world wireless challenges including video streaming optimization and privacy-preserving mobility management. Affiliated with UBC's Communication Systems Research Group, he contributes to a collaborative ecosystem focused on data communications innovation. Current investigations appear centered on stochastic performance bounds for modern wireless architectures, extending his two decades of scholarship in network calculus applications.

Mario Badrمشاهده پروفایل

استادیار

• Computer Architecture
• Parallel Computing
• Heterogeneous Computing
• +۳ مورد دیگر

Mario Badr is an Assistant Professor (Teaching Stream) in the Department of Computer Science at the University of Toronto, part of the Faculty of Arts and Science. His research focuses on computer architecture, with an emphasis on simulation tools for evaluating multi-core, heterogeneous, and energy-harvesting systems. He has contributed to methodologies for analyzing memory behavior, synchronization performance, and cache coherence in modern architectures. Key research areas include parallel computing, performance analysis of GPU offloading, and energy-efficient computing models like the EH framework. His work often involves developing synthetic traffic models (e.g., SynFull) to assess architectural designs under realistic conditions. These tools enable early-stage exploration of trade-offs in processor architectures, particularly for mobile and embedded systems. Dr. Badr’s publications span topics such as cache behavior modeling, multi-core scalability, and intermittent computing. His research bridges theoretical analysis and practical implementation, with applications in both academic and industry contexts. While no specific awards or grants are listed, his contributions to architecture evaluation frameworks reflect a strong focus on advancing computational efficiency and system design.

• Computational Biology
• Systems Biology
• Genomics
• +۷ مورد دیگر

Gary Bader is a Professor at The Donnelly Centre, University of Toronto, specializing in Computational Biology and Systems Biology. His research focuses on integrating molecular interaction networks, pathway analysis, and 'omics data to understand disease mechanisms and develop clinical models. He leads the Bader Lab, which develops computational tools like Cytoscape and Pathway Commons, and explores single-cell genomics to study tissue function, cancer, and regenerative processes. Education: BSc Biochemistry (McGill University), PhD Biochemistry (University of Toronto), Postdoc at Memorial Sloan-Kettering Cancer Center. Affiliations: Donnelly Centre, Department of Molecular Genetics, Department of Computer Science, Lunenfeld-Tanenbaum Research Institute, Princess Margaret Cancer Centre, and CIFAR MacMillan Multiscale Program. Research interests include causal mechanistic modeling of disease (e.g., pediatric ependymoma therapies), ecosystem theories of tissue function, and open-source bioinformatics tools. Recent work emphasizes single-cell and spatial transcriptomics to map cellular heterogeneity in diseases like cancer, liver pathologies, and neurological disorders. Collaborations span computational methods, stem cell biology, and clinical translation.

• Formal methods
• Formal verification
• Model checking
• +۸ مورد دیگر

Summary Alan J. Hu is a Professor in the Department of Computer Science at the University of British Columbia (UBC), affiliated with the Faculty of Science. His primary research interests include formal methods, formal verification, model checking, and software/hardware co-design. He leads research in areas such as post-silicon validation, cloud resource scheduling, and concurrency verification. Hu teaches courses like CPSC 513 (Formal Verification) and CPSC 320 (Algorithm Design). Education & Roles: Ph.D. in Computer Science (Stanford University), current roles include supervision of graduate students (e.g., Malte Schwerin, Stuart Hoad) and leadership in research groups like ICICS and CAIDA. Research Contributions: Key projects include BackSpace (post-silicon debug framework), MonoSAT (SMT solver), and contributions to formal verification of embedded systems. His work on cloud resource scheduling (e.g., Gridiron, Cospot) addresses network bandwidth guarantees in datacenters. Awards & Recognition: Recipient of the IEEE Outstanding Service Award, IBM Faculty Award, and UBC CS Teaching Award. His research is supported by industry (Intel, Microsoft) and grants (NSERC, SRC). Labs & Collaborations: Active in UBC's Institute for Computing, Information and Cognitive Systems (ICICS) and the CAIDA lab for AI-driven decision-making.

Gabriel A. Wainerمشاهده پروفایل

استاد

• Discrete-Event Modeling and Simulation
• Real-Time Systems
• Cellular Models
• +۴ مورد دیگر

Professor Gabriel A. Wainer holds a Ph.D. from the Universidad de Buenos Aires and is a Full Professor at Carleton University's Department of Systems and Computer Engineering, Faculty of Engineering and Design. His research focuses on Discrete-Event Modeling and Simulation (DEVS), Real-Time Systems, and Parallel/Distributed Simulation. He leads the Advanced Real-Time Simulation (ARS) Lab at Carleton's V-Sim Centre, collaborating with industry partners like Ericsson Canada on projects such as channel reconstruction for wireless networks and AI-driven spectrum sharing. Wainer is a Fellow of the Society for Modeling and Simulation International (SCS) and an ACM Distinguished Speaker, with over 450 publications and significant grant funding. He has mentored numerous students and researchers, contributing to Carleton's academic and research excellence. Education: Ph.D. (Computer Science, Universidad de Buenos Aires, 1998), M.C.S. (Universidad de Buenos Aires, 1993). He has held visiting roles at institutions worldwide, including INRIA and the University of Nice. Research interests include DEVS formalism, cellular models, and real-time operating systems. His work bridges theory and application, addressing challenges in embedded systems, IoT, and pandemic modeling. Awards include the McLeod Founder Award (2022), SCS Outstanding Professional Achievement (2020), and IEEE Outstanding Engineering Award (2019). Labs/Teams: Advanced Real-Time Simulation Lab (ARS), part of Carleton's V-Sim Centre. Grants: Over $4.3M as PI and $1.2M as co-applicant, totaling $30M+ in collaborative funding. Advising: Supervised 12 postdocs, 19 PhDs, 16 RAs, 71 MSc students, and 150+ undergrads since 1997.

Tristan Glatardمشاهده پروفایل

دانشیار

• Neuroinformatics
• Reproducibility in Science
• Big Data Infrastructures
• +۴ مورد دیگر

Tristan Glatard is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University . He holds a Canada Research Chair (Tier II) on Big Data Infrastructures for Neuroinformatics . His work focuses on reproducible research methodologies, neuroimaging analysis pipelines, and open science frameworks. Key contributions include developing tools like NeuroCI for continuous integration of neuroimaging results and VIP (Virtual Imaging Platform) for collaborative research. Research interests span neuroinformatics , reproducibility in computational science , big data architectures , and machine learning applications in healthcare . His projects address challenges in analytical flexibility, software dependency management, and numerical stability in neuroimaging workflows. He actively promotes open data sharing through initiatives like the Neuroimaging Data Model (NIDM) and Brain Imaging Data Structure (BIDS). Recent work emphasizes cross-platform reproducibility, leveraging containerization (Docker/Guix) and cloud-based solutions. He collaborates with international teams to validate neuroimaging pipelines and benchmark computational tools for large-scale biomedical data analysis. Notably absent from the profile are explicit mentions of academic awards or named graduate students, though his work has significant impact on research methodologies. He is deeply involved in open science advocacy through platforms like Brainhack and the Montreal Neuroinformatics Ecosystem.

Simon Lacoste-Julienمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +۶ مورد دیگر

Simon Lacoste-Julien is a Full Professor at the Université de Montréal's Department of Computer Science and Operations Research, part of the Faculty of Arts and Sciences. He is affiliated with the Centre de recherches mathématiques (CRM) and Mila – Quebec AI Institute. His research focuses on machine learning, deep learning, optimization, and theoretical foundations of AI. Notable contributions include advancements in constrained optimization algorithms, game-theoretic learning, and sparsity-aware models. Education: Doctorate in Machine Learning (PhD), specialized in optimization and graphical models. Research Interests: His work bridges theory and practice, exploring topics such as: - Optimization for Machine Learning : Developing efficient algorithms for large-scale problems. - Game-Theoretic Approaches : Applying game theory to prediction and mechanism design. - Disentangled Representations : Learning structured latent variable models for better interpretability. - Adaptive Gradient Methods : Improving convergence and stability of training processes. Grants & Projects: Lead researcher on multiple grants including: - Model-based Machine Learning with Guarantees (2025-2031, CRSNG) - Robust and Efficient Structured Prediction (2017-2025) - Projects on few-shot learning and algorithmic fairness funded by MITACS and IVADO. Labs & Teams: Active member of Mila, leading research in optimization and theoretical aspects of deep learning. Collaborates widely with industry (e.g., Samsung Electronics) and contributes to open-source tools like the Cooper library for constrained optimization.

Abdelrahman Husseinمشاهده پروفایل

پژوهشگر

• Computer Architecture
• Systems
• Domain-Specific Accelerators
• +۶ مورد دیگر

Abdelrahman Hussein is a PhD student and Graduate Research Assistant at the School of Computing Science, Simon Fraser University since 2024, advised by Prof. Alaa Alameldeen. His research focuses on optimizing microarchitecture structures for heavily multithreaded workloads in data centers. PhD in Computing Science, Simon Fraser University (2024–Present) MSc in Computing Science, Simon Fraser University (2021–2024) Diploma in Digital Systems Design, Information Technology Institute, Egypt (2017–2018) BSc in Computer Engineering, Mansoura University, Egypt (2012–2017)

Alison Phinneyمشاهده پروفایل

استاد

• Dementia Care
• Aging and Elder Care
• Community Inclusion for People with Dementia
• +۳ مورد دیگر

Alison Phinney is a Professor and Associate Director of Faculty Development at the School of Nursing within the Faculty of Applied Science at the University of British Columbia (UBC), where she maintains an active research program focused on dementia care and community inclusion. Her work bridges academic scholarship with practical community engagement to transform societal approaches to aging. Her research centers on participatory action methodologies that prioritize lived experience of people with dementia, exploring dementia-friendly urban planning, digital community building, and cultural dimensions of aging (notably Ikigai among Japanese Canadians). She employs narrative and arts-based approaches to investigate oral health impacts on elder well-being, stigma reduction in healthcare, and pandemic-related disruptions to long-term care. Her signature projects—including DemSCAPE for dementia-inclusive streets and the Zeitgeist intergenerational storytelling initiative—emphasize co-design with marginalized communities to develop practical toolkits like 'Flipping Stigma on its Ear'. Analysis of her 2023-2025 publications reveals three converging trends: (1) Digital innovation for connection through apps like 'WhatMatters' and virtual communities, (2) Critical examination of urban infrastructure's role in dementia inclusion via municipal policy studies, and (3) Culturally responsive frameworks for living well with dementia through narrative and arts-based research. These strands collectively advance her mission to dismantle stigma by centering human agency in care ecosystems.

• Distributed Computing
• Algorithms
• Theoretical Computer Science
• +۳ مورد دیگر

Leqi Zhu is an Assistant Professor at the University of Manitoba, specializing in theoretical computer science and distributed computing. Previously, he held postdoctoral positions at IST Austria (ISTA) and the University of Michigan, following his PhD at the University of Toronto. University of Toronto (PhD) University of Michigan (Postdoc/Lecturer) ISTA (Postdoc) University of Manitoba (Assistant Professor) His research focuses on distributed systems , consensus algorithms , and space complexity analysis , with significant contributions to Byzantine fault tolerance and synchronization protocols. His work explores fundamental limits in concurrent computing using novel proof techniques and simulation methods. Key publication trends include: Byzantine agreement protocols with optimal resilience (SODA 2023, STOC 2022) Lower bound proofs for distributed coordination (STACS 2019, PODC 2018) Complexity hierarchies in multiprocessor synchronization (PODC 2016) Space-efficient consensus implementations (OPODIS 2015) Scientific recognition includes: Co-recipient of Best Student Paper Award at STOC 2016 Contact: jimmy.zhu@umanitoba.ca

Shohini Bhattasaliمشاهده پروفایل

استادیار

• Computational Linguistics
• Neurolinguistics
• Psycholinguistics
• +۵ مورد دیگر

Shohini Bhattasali is an Assistant Professor in the Department of Language Studies at the University of Toronto Scarborough, with a cross-appointment in the Department of Linguistics at the St. George campus. She holds a PhD in Linguistics from Cornell University (2019) with a graduate minor in Cognitive Science. Her research focuses on computational neurolinguistics and computational social science, exploring neural mechanisms of language processing and mitigating bias/misinformation through NLP tools. She has held postdoctoral roles at the University of Maryland's Institute for Advanced Computer Studies and Linguistics Department. Education: PhD in Linguistics, Cornell University MA in Linguistics, Cornell University AB in Linguistics, Bryn Mawr College (magna cum laude) Research Themes: Computational Neurolinguistics: Investigates top-down prediction during sentence processing, interaction between linguistic levels and cognitive mechanisms using fMRI/EEG data. Computational Social Science: Leverages topic models and language models to uncover bias/misinformation in social media and public health texts. Grants & Projects: Principal investigator for Data Sciences Institute grant on AI-driven mitigation of harmful social media content (2023). Co-developer of the Le Petit Prince multilingual fMRI corpus (Scientific Data, 2022). Teaching: Courses include Syntax I (LINB06), Computational Methods for Linguistics (JFL1107), and Morphology (LINB10).