جستجوی استاد راهنمای دانشگاهی

Trevor Campbellمشاهده پروفایل

دانشیار

• Bayesian Inference
• Bayesian Nonparametrics
• Machine Learning
• +۵ مورد دیگر

Trevor Campbell is an Associate Professor in the Department of Statistics at the University of British Columbia (UBC), Vancouver. He holds a Ph.D. in Machine Learning and Statistics from MIT and a B.A.Sc. in Aerospace Engineering from the University of Toronto. His research focuses on automated, scalable Bayesian inference algorithms, Bayesian nonparametrics, and streaming data analysis. Campbell is a core contributor to probabilistic programming tools like Pigeons.jl and has developed influential methods for coreset-based Bayesian inference. Education : Ph.D. in Machine Learning and Statistics (2016), MIT M.S. in Aeronautics and Astronautics (2013), MIT B.A.Sc. in Aerospace Engineering (2011), University of Toronto Research Interests : His work emphasizes scalable Bayesian computation, including variational inference, MCMC optimization, and coresets. He develops algorithms that balance statistical accuracy with computational efficiency, particularly for large-scale datasets. His recent work explores adaptive samplers (e.g., AutoStep, autoMALA) and theoretical guarantees for coreset methods. Applications span astrophysics, materials science, and network analysis. Awards & Grants : NSERC Discovery Grant (2025) Blackwell-Rosenbluth Award (2021) PIMS Early Career Award (2023) Google Perception Academic Funding (2020) Advising & Labs : He supervises a team of PhD and M.Sc. students at UBC, focusing on Bayesian methodology and computational tools. His lab collaborates with institutions like SFU and MIT on projects involving distributed sampling and probabilistic modeling. He co-organizes workshops on Bayesian computation and serves on editorial boards for Bayesian Analysis and TMLR.

• Machine Learning
• Deep Learning
• Gaussian Processes
• +۵ مورد دیگر

Geoffrey Pleiss is an Assistant Professor in the Department of Statistics at the University of British Columbia's Faculty of Science. He is also a CIFAR AI Chair at the Vector Institute and an inaugural member of CAIDA's AIM-SI (AI Methods for Scientific Impact) cluster. His work bridges statistical theory, machine learning, and computational methods with applications across various scientific domains. Pleiss received his PhD from the Computer Science department at Cornell University in 2020, where he was advised by Kilian Weinberger and worked closely with Andrew Gordon Wilson. Prior to his faculty position at UBC, he was a postdoctoral researcher at Columbia University with John P. Cunningham. His research focuses on the intersection of deep learning and probabilistic modeling, particularly on developing heuristic and approximate notions of uncertainty from machine learning models. His work has significant implications for reliable and optimal decision-making in experimental design and scientific discovery. Major research thrusts include neural network uncertainty quantification, Bayesian optimization, Gaussian processes, and ensemble methods. Pleiss develops theoretical frameworks while maintaining strong connections to practical applications across scientific domains. An analysis of his recent publications reveals a strong focus on uncertainty quantification in deep learning models, with particular attention to the limitations and capabilities of ensemble methods in the era of overparameterized models. His work increasingly addresses practical challenges in Bayesian optimization for scientific discovery, especially in materials science. There's also a growing emphasis on computational efficiency in Gaussian process methods, reflecting his commitment to making advanced statistical techniques accessible for real-world applications. CIFAR AI Chair Pleiss currently advises several graduate students including Donney Fan (PhD, Computer Science), Tim G. Zhou (MSc, Computer Science), Zachary Lau (MSc, Statistics), Nathan Cantafio (BSc, Statistics), and Tristan Cinquin (Research Intern at Vector Institute). His research is supported by multiple funding sources including his CIFAR AI Chair position, which provides significant research resources for advancing machine learning methodologies with scientific impact. Pleiss co-created and maintains GPyTorch, a highly efficient and modular implementation of Gaussian processes in PyTorch designed for speed, modularity, and prototyping. He is also involved with CoLA (Compositional Linear Algebra), a library for structured linear algebra operations in JAX and PyTorch that enables fast linear algebra computations by automatically exploiting matrix structure.

Alexandre Bouchard-Côtéمشاهده پروفایل

استاد

• Computational Statistics
• Bayesian Inference
• Monte Carlo Methods
• +۴ مورد دیگر

Alexandre Bouchard-Côté is a Professor of Statistics at the University of British Columbia (UBC), affiliated with the Department of Statistics within the Faculty of Science. His research focuses on computational statistics, Bayesian methods, and Monte Carlo techniques, with applications in evolutionary biology, cancer genomics, and computational linguistics. Education : PhD in Computer Science (with Designated Emphasis in Statistics) from UC Berkeley (2010), BSc in Mathematics and Computer Science from McGill University (2005). Affiliations : Director of the Blang probabilistic programming project and leader of the Bouncy Particle Sampler research group. Research Interests : Bouchard-Côté develops scalable Bayesian computational methods, including non-reversible Monte Carlo algorithms like the Bouncy Particle Sampler, and applies these to problems in cancer phylogenetics, evolutionary dynamics, and historical linguistics. His work emphasizes bridging theoretical foundations with practical tools for data science. Publications Trends : Recent work spans distributed sampling frameworks (e.g., Pigeons.jl), variational phylogenetic inference, and cancer clonal evolution modeling. His articles often address algorithmic scalability and interdisciplinary applications in biology and astronomy. Awards : CRM-SSC Prize in Statistics (2024) PIMS-UBC Mathematical Sciences Young Faculty Award (2018) Tweedie New Researcher Award (2016) Advising & Grants : Supervises graduate students (e.g., Son Luu, Nikola Surjanovic) and leads funded projects on distributed MCMC and cancer genomics. Collaborates with institutions like the Simons Foundation and the Canadian Statistical Sciences Institute (CANSSI). Labs/Teams : Core member of the UBC Statistical Machine Learning group, contributing to open-source tools like Blang and the Bouncy Particle Sampler implementation.

Mark Crowleyمشاهده پروفایل

دانشیار

• Reinforcement Learning
• Deep Learning
• Manifold Learning
• +۱۲ مورد دیگر

Mark Crowley is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, with a cross-appointment in the Cheriton School of Computer Science. He is a member of the Waterloo Artificial Intelligence Institute (WAII) and the Waterloo Institute for Complexity and Innovation (WICI), and serves as National Secretary of the Canadian Artificial Intelligence Association (CAIAC). His educational background includes a Ph.D. and M.Sc. in Computer Science from the University of British Columbia, where he worked in the Laboratory for Computational Intelligence, and a B.A. in Computer Science from York University. He completed a postdoctoral fellowship at Oregon State University working with Tom Dietterich's machine learning group. Crowley's research focuses on developing dependable and transparent algorithms to augment human decision-making in complex domains with multiple agents, spatial structure, or uncertainty. His work spans Reinforcement Learning , Deep Learning , Ensemble Methods , and Manifold Learning . He frequently collaborates with researchers in applied fields including Computational Sustainability, Sustainable Forest Management, Autonomous Driving, Medical Imaging, and Material Design. His research is motivated by both theoretical opportunities and real-world challenges such as forest fire management, automotive applications, and medical imaging. His recent publications demonstrate a strong focus on addressing challenges in reinforcement learning, particularly around observation costs, multi-agent systems, and causal representation learning. His work on ChemGymRL provides a significant contribution to digital chemistry and material design through reinforcement learning frameworks. The textbook Elements of Dimensionality Reduction and Manifold Learning represents a major contribution to the theoretical foundations of machine learning. Crowley actively supervises graduate students, with recent thesis completions including Shayan Shirahmadi Gale Bagi (PhD, Feb 2025) and Oleksandra Nahorna (MASc, Dec 2024). His lab, UWECEML (Waterloo ECE Machine Learning Lab), focuses on developing new algorithms at the intersection of Machine Learning, Optimization, and Probabilistic Modeling. He teaches courses including ECE 457C (Reinforcement Learning), ECE 657A (Data & Knowledge Modelling & Analysis), and ECE 457B (Fundamentals of Computational Intelligence). His blog Computationally Thinking explores AI, machine learning, and the societal impact of these technologies.

• Machine Learning
• Deep Learning
• Theoretical Foundations of AI
• +۱۱ مورد دیگر

Mikhail (Misha) Belkin is a Professor at the Halicioglu Data Science Institute (HDSI) at the University of California San Diego , with an affiliated appointment in the Department of Computer Science and Engineering . He is also an Amazon Scholar , reflecting his impactful industry collaboration. Since January 2024, he has served as the Editor-in-Chief of the SIAM Journal on Mathematics of Data Science (SIMODS) . Research Interests: Belkin's research centers on the theoretical foundations of machine learning, particularly the mathematical understanding of modern deep learning. His work investigates interpolation , over-parameterization , and feature learning in neural networks. He is renowned for introducing the double descent risk curve, which reconciles classical bias-variance trade-offs with the success of overfitted models. His recent work identifies the Average Gradient Outer Product (AGOP) as a fundamental mechanism of feature learning, applicable across architectures like CNNs and transformers. Scientific Contributions and Trends: His recent publications, appearing in Science , PNAS , and NeurIPS , demonstrate a strong trend toward unifying theories of generalization and optimization in over-parameterized systems. He explores how interpolating models can be statistically optimal, how gradient descent converges in non-convex landscapes via the PL* condition, and how kernel methods can be enhanced to perform feature learning. ACM Fellow (2023) Editor-in-Chief, SIAM Journal on Mathematics of Data Science (2024–present) Advising and Grants: Belkin actively mentors students and collaborators such as Adityanarayanan Radhakrishnan , Daniel Beaglehole , and Chaoyue Liu , who are frequent co-authors. He is a Principal Investigator (PI) in the Collaboration on the Theoretical Foundations of Deep Learning , funded by the NSF and Simons Foundation. He is also an external collaborator with the Eric and Wendy Schmidt Center at the Broad Institute and part of the NSF-funded TILOS AI Institute . Laboratories and Teams: While not explicitly named, his research group at UCSD is deeply involved in theoretical machine learning, focusing on the intersection of statistics, optimization, and deep learning. His work often involves large-scale collaborations and is closely tied to initiatives like SIMODS and TILOS.

Peter Selingerمشاهده پروفایل

استاد

• Quantum Computing
• Category Theory
• Mathematical Logic
• +۴ مورد دیگر

Peter Selinger is a Professor in the Department of Mathematics and Statistics at Dalhousie University , with a cross-appointment in Computer Science. He specializes in mathematical methods in computer science, particularly quantum computing and combinatorial game theory . His work on quantum programming languages like Quipper and foundational research in category theory has garnered international recognition. Education : Ph.D. in Mathematics (University of Pennsylvania, 1997), undergraduate studies in Mathematics (Technische Universität Darmstadt). Research Interests span quantum computing, category theory, and combinatorial game theory. He has pioneered formalisms for quantum programming languages, developed categorical models for quantum mechanics, and analyzed game-theoretic structures in games like Hex. His recent work includes linear dependent type theory , quantum circuit synthesis , and combinatorial game classification . Publications demonstrate expertise in quantum programming languages, categorical semantics, and game theory. Key trends include Hamiltonian simulation , Clifford+T circuits , and monotone game realization . Scientific Honors include the Killam Professorship (2017–2022), Faculty of Science Award for Excellence in Teaching (2023), and fellowships from the Alfred P. Sloan Foundation and German National Scholarship Foundation . Students he has supervised include PhD graduates Xiaoning Bian , Francisco Rios , and Neil J. Ross , along with MSc students like Fahimeh Bayeh and Seth Greylyn . He has advised 16 postdoctoral researchers.

Alexandra M. Schmidtمشاهده پروفایل

استاد

• Biostatistics
• Bayesian Inference
• Spatial Statistics
• +۳ مورد دیگر

Professor Alexandra M. Schmidt is a leading academic in Biostatistics at McGill University, holding an endowed University Chair. She specializes in spatial and spatio-temporal modeling, particularly in epidemiology and environmental health. Previously, she served as a Full Professor at the Federal University of Rio de Janeiro (2012–2016). Her research focuses on Bayesian methodologies for analyzing complex processes, including disease spread, environmental hazards, and socio-economic disparities. She has authored influential books such as Spatio-Temporal Methods in Environmental Epidemiology with R (2023) and contributed to over 150 peer-reviewed articles. Key awards include the ISBA Fellowship (2024), ASA Fellowship (2020), and the Abdel El-Shaarawi Award (2008). Education: PhD in Statistics (2001, University of Sheffield, UK), MSc and BSc in Statistics (Federal University of Rio de Janeiro, Brazil). Research interests span Bayesian inference, spatial statistics, and environmental epidemiology. She has advised numerous PhD/MSc students and collaborated on projects linking statistical methods to public health challenges, such as modeling dengue outbreaks and air pollution impacts. Active in academic service, she has chaired major conferences (e.g., 2022 ISBA World Meeting) and serves on editorial boards of top journals like Bayesian Analysis and Canadian Journal of Statistics . Teaching includes advanced courses on generalized linear models, spatial epidemiology, and Bayesian analysis. Her work bridges theoretical statistics with practical applications, addressing global health issues through innovative spatio-temporal modeling techniques.

Sharan Vaswaniمشاهده پروفایل

استادیار

• Reinforcement Learning
• Optimization
• Machine Learning
• +۳ مورد دیگر

Sharan Vaswani is an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU). His research focuses on designing algorithms for sequential decision-making under uncertainty, stochastic optimization, and their interplay with machine learning generalization. He holds a PhD from the University of British Columbia (2019) and postdoctoral experiences at the University of Alberta and Mila. His academic journey includes MSc (UBC, 2015) and BTech (BITS Pilani, 2012) degrees. Education: PhD (UBC, 2019), MSc (UBC, 2015), BTech (BITS Pilani, 2012) Postdoctoral Work: University of Alberta (2020-2021), Mila (2019-2020) Teaching includes courses on Probability and Computing (CMPT 210), Optimization for Machine Learning (CMPT 409/981), and Theoretical Foundations of Reinforcement Learning (CMPT 419/983). His research group focuses on developing scalable optimization algorithms with theoretical guarantees. He advises multiple PhD and MSc students, contributing to areas like constrained MDPs, adaptive learning rates, and reinforcement learning theory. Research Highlights: Contributions to bandit algorithms, stochastic gradient methods, and reinforcement learning theory. Notable work includes global convergence analysis of policy gradients and variance-reduced optimization frameworks.

Qingguo Liمشاهده پروفایل

استاد

• Biomechanical system design
• Energy harvesting
• Wearable sensors
• +۴ مورد دیگر

Qingguo Li is a Professor and Associate Head at the Department of Mechanical and Materials Engineering , Queen's University , and a member of the Ingenuity Labs Research Institute . He specializes in biomechanical system design, energy harvesting, wearable sensors, gait analysis, and load carriage systems. His research integrates robotics, biomedical engineering, and sensor technology to develop human-centric devices and mobility aids. Current Roles : Professor, Associate Head, Queen's University Research Institute : Ingenuity Labs Research Institute Lab : Bio-Mechatronics and Robotics Laboratory His work focuses on biomechanical energy harvesting , IMU-based motion analysis , and assistive device development . Key applications include stroke rehabilitation, gait monitoring, and wearable power generation systems. Articles span cable-driven robots , smart walkers , and 3D printing mechanisms , emphasizing human-robot interaction and dynamic modeling . The lab explores sensor calibration , adaptive control algorithms , and human movement optimization . Areas of impact include rehabilitation engineering , load carriage stability , wearable sensor accuracy , and assistive robotics . His team develops solutions for gait asymmetry detection , post-stroke mobility , and low-cost energy systems , leveraging machine learning and kinetic modeling .

Peter F. Driessenمشاهده پروفایل

استاد

• Communication
• Signal Processing
• Control Systems
• +۶ مورد دیگر

Professor Peter F. Driessen is a faculty member in the Department of Electrical and Computer Engineering at the University of Victoria, with a cross-appointment in the School of Music. He holds a BSc and PhD from the University of Victoria and is a Professional Engineer (PEng). His research focuses on communication systems, signal processing, control, and interdisciplinary projects in computer music and wireless technologies. Key areas include audio/video signal processing, radio propagation, sound recording, and multimedia systems. He leads the University of Victoria Propagation Laboratory, which explores radio wave propagation and Amateur radio integration with engineering education. His work spans theoretical research and applied projects like ECOSat satellite systems, software-defined radio (SDR), and innovative musical instruments such as the Radio Drum. He supervises undergraduate and graduate projects in these domains through ELEC 499 courses. Notable contributions include the APEGBC Editorial Board Award for Best Paper (2002) and patents in wireless networking and signal processing. His teaching includes courses in signal analysis and electromagnetics, and he collaborates on interdisciplinary programs like the Music/Computer Science degree. Education: BSc in Electrical Engineering, University of Victoria PhD in Electrical Engineering, University of Victoria Research Interests: Audio and video signal processing for music and media Software-defined radio and Amateur radio technologies Satellite communication and ground station development Gesture-based interfaces and musical instrument design Error mitigation in streaming audio/video Optical and microwave-photonic systems Labs & Collaborations: Propagation Laboratory (radio wave research) UVic Experimental Radio Group (Amateur radio club) UVic Satellite Design Team (ECOSat projects) UVic Centre for Aerospace Research Grants & Awards: APEGBC Editorial Board Award (2002) Multiple US patents in wireless systems and signal processing

• Computational Imaging
• Artificial Intelligence
• Inverse Problems
• +۷ مورد دیگر

Aviad Levis is an Assistant Professor at the University of Toronto's Department of Computer Science, starting July 2024. He is affiliated with the Dunlap Astronomical Data Science and Technology Group (DADDAA) and collaborates with the Toronto Computational Imaging Group alongside Kyros Kutulakos and David Lindell. Previously, he was a postdoctoral researcher at Caltech's Computing + Mathematical Sciences department under Katherine Bouman, working with the Event Horizon Telescope (EHT) collaboration. PhD in Electrical Engineering from the Technion (supervised by Yoav Schechner) Research focuses on computational imaging tools at the intersection of AI and physics Develops algorithms for 3D tomography in both cloud physics and black hole imaging Recipient of ERC Synergy grant for CloudCT space mission His research spans two major domains: Computational Climate Imaging through cloud tomography to improve climate models, and Black Hole Imaging with the EHT collaboration. He pioneered methodologies for 3D cloud structure recovery using scattered sunlight and contributes to dynamic 3D reconstructions of black hole environments. Current interests include non-linear inverse problems, equation discovery from data, and ML-accelerated scientific simulations. Recent publications highlight advancements in atmospheric tomography and black hole emission modeling. His work on CloudCT involves coordinated nano-satellites for 3D cloud imaging, while EHT contributions include first images of Sagittarius A* (2022) and ongoing development of algorithms for 3D structure recovery. The ERC Synergy grant underscores his impact on climate imaging technology. Personal Website Work Email

Mariana Resenerمشاهده پروفایل

استادیار

• Power Systems Optimization
• Distributed Energy Resources
• Renewable Energy Integration
• +۵ مورد دیگر

Mariana Resener is an Assistant Professor in the School of Sustainable Energy Engineering at Simon Fraser University (SFU). She holds a Ph.D. in Electrical Engineering (2016) from the Federal University of Rio Grande do Sul, Brazil, alongside M.Sc. (2011) and B.Sc. (2008) degrees in the same field. Her research focuses on optimizing power systems, particularly in distributed energy resources, energy storage, and volt/var control. She teaches courses like Power Electronics and Power Systems Analysis & Design. Her work emphasizes sustainable development in grid planning and energy infrastructure. As a Senior Member of IEEE and an Associate Editor for the Energy Systems Journal (Springer), she contributes to advancing smart grid technologies and renewable integration. Her research spans metaheuristic optimization, stochastic modeling, and grid resilience strategies for distributed systems. Recent projects include hybrid renewable energy systems for substations, EV charging station optimization, and fault analysis in unbalanced grids. She collaborates with industry on practical solutions for grid modernization and reliability enhancement.

Golnoosh Farnadiمشاهده پروفایل

استادیار

• Algorithmic Fairness
• Privacy-preserving Machine Learning
• Responsible AI
• +۵ مورد دیگر

Golnoosh Farnadi is an Assistant Professor at McGill University's School of Computer Science and an Adjunct Professor at the University of Montréal. She serves as a Visiting Faculty Researcher at Google, a Core Academic Member at MILA (Quebec Institute for Learning Algorithms), and holds a prestigious Canada CIFAR AI Chair. Farnadi co-directs McGill's Collaborative for AI & Society (McCAIS) and founded the EQUAL Lab (EQuity & EQuality Using AI and Learning algorithms), which focuses on advancing algorithmic fairness and responsible AI. Her educational background includes a Ph.D. in Computer Science from KU Leuven and Ghent University (2017), with postdoctoral research at the University of Montreal/MILA (2018-2020) and the University of California, Santa Cruz (2017-2018). During her doctoral studies, she was a visiting scholar at UCLA, University of Washington, Tsinghua University, and Microsoft Research. Dr. Farnadi's research centers on developing mathematical tools and algorithms for fairness-aware machine learning systems. Her work addresses bias and discrimination in AI decision-making across critical domains including healthcare, criminal justice, financial services, and social media. She has pioneered approaches to ensure fairness in deep learning models, particularly in sequential decision-making under uncertainty. Her research bridges theoretical foundations with practical applications, examining how AI systems can be designed to promote equity while maintaining performance. Analysis of her recent publications reveals a strong focus on practical implementations of fairness mechanisms across diverse AI applications. Her work spans technical domains from generative models and large language models to recommender systems and healthcare optimization. A unifying theme is the development of mathematically rigorous frameworks that balance performance with fairness considerations, with increasing attention to cultural diversity in multilingual AI systems and privacy-preserving fairness approaches. Google Scholar Award (2021) Facebook Research Award (2021) Rising Stars in AI Ethics (2021) Google Award for Inclusion Research (2023) WAI Responsible AI Leader of the Year Finalist (2023) 100 Brilliant Women in AI Ethics (2023) Canada CIFAR AI Chair Dr. Farnadi advises numerous doctoral and master's students across McGill University, University of Montréal, and MILA, with research focusing on fairness, privacy, and responsible AI. Her EQUAL Lab brings together researchers from computer science, social sciences, and policy domains to address systemic challenges in AI ethics. She has secured significant research funding from Google and other major organizations to support her work on fairness-aware AI systems, with applications spanning healthcare, social media safety, and public policy. The EQUAL Lab serves as a hub for interdisciplinary research on algorithmic fairness, bringing together computer scientists, social scientists, and policy experts. The lab's work spans theoretical foundations of fairness metrics, practical implementations in real-world systems, and policy recommendations for responsible AI deployment. Current projects include developing frameworks for fair kidney exchange programs, mitigating cultural stereotypes in multilingual language models, and creating privacy-preserving approaches for detecting online harms while protecting user data.

• Sustainable Construction
• Construction Engineering and Management
• Project Management
• +۵ مورد دیگر

Ahmed Hammad is an Associate Professor in the Department of Civil and Environmental Engineering at the University of Alberta's Faculty of Engineering, where he also serves as Director of Academic Integrity, ENG WIL, Co-op and Career Connections. With 25 years of industry experience as a Project Planning & Control Manager on global mega-projects (including Oil Sands, LNG, and Infrastructure across Canada, UAE, Australia, and Egypt), he brings extensive practical expertise to academia. Education: Doctorate of Philosophy, Construction Engineering & Management, University of Alberta (2009) Master of Science, Construction Engineering & Management, University of Alberta (1999) Master of Engineering, Construction Engineering and Management, Cairo University (1996) Bachelor of Science, Civil Engineering, Mansoura University (1989) Research Focus: Dr. Hammad's work centers on applying smart tools to achieve sustainable construction through maximizing efficiency and minimizing waste . His research employs Machine Learning , Multi-Criteria Decision Making , Knowledge-Based Decision Support Systems , and digital twin technologies to optimize project planning, resource allocation, and sustainable material selection. He investigates the integration of BIM and Augmented Reality to enhance construction processes while reducing environmental impact. Publication Trends: Recent publications (2023-2025) emphasize sustainable construction methodologies, with 60% focusing on GHG reduction, resource optimization, and decision support systems. Key themes include machine learning for labor estimation, TOPSIS/MCDM for sustainable material selection, and digital twins for production planning, reflecting his NSERC-funded projects on KBDSS and construction-oriented digital twins. Scientific Recognition: Best Paper Award at 8th International Conference on Industrial Engineering and Operations Management (2018) Best Paper Award at HBRC Green Smart Sustainable Buildings Conference (2024) Research Leadership: Dr. Hammad secures major industry-academic partnerships, including NSERC Mission Alliance Grants ($1.2M+) with 16 industry partners for GHG reduction projects and NSERC Alliance Grants with 9 partners for digital twin development. His completed projects include collaborations with the City of Edmonton and Alberta Ministry of Infrastructure on resource allocation models. Research Ecosystem: As leader of the Sustainable Construction Research Group (SCRG), he fosters industry-academia collaboration through regular workshops with construction firms and government agencies, focusing on translating research into practical tools for sustainable project delivery.

Noman Mohammedمشاهده پروفایل

دانشیار

• Data Security
• Privacy-Preserving Techniques
• Healthcare Data Privacy
• +۷ مورد دیگر

Noman Mohammed is an Associate Professor of Computer Science at the University of Manitoba’s Faculty of Science, leading the Data Security & Privacy (DSP) laboratory. He specializes in privacy-preserving techniques for data sharing, addressing challenges in healthcare, genomic, and financial data. In 2020, he received the Terry G. Falconer Memorial Rh Institute Foundation Emerging Researcher Award for his contributions to bridging privacy and data utility gaps. His research focuses on balancing data accessibility and individual privacy through technical solutions like federated learning, differential privacy, and secure genomic data processing. He emphasizes integrating policy guidelines with advanced technologies to mitigate privacy risks from interconnected data sources. Notable achievements include developing toolkits for data anonymization and federated learning frameworks, as well as advancing methods to secure cloud-based data storage and analysis. His work aligns with societal needs for robust privacy mechanisms in an era of expanding personal data collection. Future objectives involve addressing privacy challenges in emerging technologies, such as heterogeneous data integration and scalable systems for personal data management. Despite his research focus, he notably avoids social media platforms.