جست‌وجوی استاد و پژوهشگر

Pooya Moradian Zadehمشاهده پروفایل

استاد

• Artificial Intelligence
• Public Health
• Social Network Analysis
• +۴ مورد دیگر

Prof. Pooya Moradian Zadeh is a Professor in the School of Computer Science at the University of Windsor, specializing in Artificial Intelligence and its applications in public health, social networks, and pandemic response. His research focuses on leveraging AI to address challenges like social isolation, disease surveillance, and healthcare accessibility. He has led interdisciplinary projects such as the $15 million INSPIRE initiative for pandemic preparedness and contributed to the WE-Spark Health Institute's COVID-19 Dashboard . Recognized for his teaching excellence, he received the 2024 Alumni Association Distinguished Teaching Award . His work involves collaborations with institutions like the Great Lakes Institute for Environmental Research and WE-Spark, addressing topics ranging from recommender systems to compassionate community approaches for vulnerable populations. He mentors students like Saghi Khani and Kameswara Peddada, guiding projects such as Simplified SCO on Wheels , which improves retail checkout processes. Research & Grants : Prof. Zadeh has secured grants totaling over $15M for pandemic response, AI-driven health solutions, and social network analysis. His teams develop algorithms for social isolation detection, personalized healthcare recommendations, and agent-based models for public health policy evaluation. He is also involved in advancing conversational AI for healthcare accessibility and optimizing team formation in digital networks. Labs & Collaborations : He leads projects at the University of Windsor’s 300 Ouellette Ave. campus and collaborates with industry and academic partners to bridge AI innovation with real-world health challenges. His work emphasizes ethical AI practices and community-focused solutions.

Prasad Daggupatiمشاهده پروفایل

دانشیار

• Water Resources Engineering
• Environmental Engineering
• Agricultural Management
• +۵ مورد دیگر

Prasad Daggupati is an Associate Professor in the School of Engineering at the University of Guelph, specializing in Water Resources Engineering. He holds a Ph.D. from Kansas State University and has held academic positions since 2016, including Assistant Professor and Associate Professor roles. His research focuses on watershed processes, climate change impacts, and the application of remote sensing technologies. Key areas include ephemeral gully erosion, critical source area identification, and hydrological modeling. He has been recognized with awards such as the 2022 Young Engineer Award (NABEC) and the 2021 Outstanding Associate Editor Recognition (ASABE Journal). Education includes a Ph.D. (2012) and MS (2012) in Biological and Agricultural Engineering from Kansas State University, and a BS (2004) from Acharya N.G. Ranga Agricultural University. His teaching spans undergraduate courses like ENGG 4360 (Soil-Water Conservation Systems Design) and graduate courses such as ENGG 6800 (Deterministic Hydrological Modeling). Research interests emphasize agricultural management's impact on sediment and nutrient transport, climate change adaptation, and precision agriculture. He leads a team of over 30 graduate students and has collaborated on projects funded by agencies like USDA-NIFA and NRCS. His work integrates machine learning, geospatial tools, and hydrological models to address water security and environmental sustainability challenges. Professional contributions include over 100 peer-reviewed publications, with a focus on SWAT modeling, non-point source pollution, and UAV applications. His lab develops decision frameworks for sustainable watershed management, particularly in Ontario's agricultural regions.

Brent Lieversمشاهده پروفایل

دانشیار

• Biomechanics
• Bone Structure and Modeling
• Engineering Principles Applied to Biological Systems
• +۴ مورد دیگر

Brent Lievers serves as Director of the Bharti School of Engineering & Computation and holds the position of Associate Professor at Laurentian University. Previously hired in August 2012 for a teaching-only Master Lecturer position (Permanent Sessional Lecturer), he transitioned to a tenure-track faculty position after two years. Dr. Lievers earned his PhD in Mechanical Engineering in 2009 from Queen's University, following Master of Engineering and Bachelor of Engineering degrees from Carleton University. His academic journey reflects a strong foundation in engineering principles applied to complex systems. His research primarily focuses on applying engineering principles to biological systems through experimental testing and numerical modeling. Dr. Lievers maintains an active research program with particular emphasis on bone structure analysis, biomechanics, forensic trauma analysis, and vibration exposure effects on the human body. His work spans multiple disciplines including biomedical engineering, forensic science, and sports medicine. Dr. Lievers has supervised numerous graduate students to successful thesis completion, including Maxime Hogue, Britney Lamarche, Matt Bennison, Adric Heney, and Katie Goggins, whose research has been presented at conferences such as the Ontario Biomechanics Conference. Research Trends: His recent publications (2020-2025) demonstrate a consistent focus on bone mechanics, particularly trabecular and cancellous bone structure, forensic biomechanics applications, and vibration analysis related to foot-ankle systems. His work often employs advanced imaging techniques, numerical modeling, and experimental testing approaches. Dr. Lievers maintains an active presence in both academic and professional communities, with students regularly presenting research at conferences. His laboratory work appears to focus on biomechanical testing of biological structures, particularly bone and trauma analysis, with applications in both medical and forensic contexts.

Peter Westمشاهده پروفایل

استادیار

• Natural Language Processing
• Artificial Intelligence
• Large Language Models
• +۳ مورد دیگر

Peter West is an incoming Assistant Professor at the University of British Columbia (UBC) Computer Science Department, specializing in Natural Language Processing (NLP) and AI. His research focuses on understanding the capabilities and limitations of large language models (LLMs) and generative AI systems, emphasizing their divergence from human intuition and alignment challenges. He holds a PhD from the University of Washington (2024), supervised by Yejin Choi, and a BSc (Honours Computer Science) from UBC (2017). His work has been recognized with awards including Best Method Paper at NAACL 2022 and Outstanding Paper awards at ACL 2023 and EMNLP 2023. He conducted internships at the Allen Institute for AI and Microsoft Research’s NLP group. Research interests include analyzing LLM behavior through a natural sciences lens, exploring model capabilities versus human expectations, and developing decoding algorithms to infuse models with algorithmic logic. His recent publications address generative AI paradoxes, constrained text generation, and symbolic knowledge distillation. He serves on panels for NeurIPS workshops and is beginning a postdoc at Stanford with Chris Potts. His research group at UBC seeks students interested in generative AI’s analytical frontiers.

• Computer Science
• Algorithm Design
• Parallel Computing
• +۴ مورد دیگر

Susanne Bradley is a Teaching Professor in the Department of Computer Science at the University of British Columbia (UBC). She specializes in algorithm design, parallel computation, and numerical analysis. Her teaching responsibilities include courses such as CPSC 320 (Intermediate Algorithm Design and Analysis) and CPSC 418 (Parallel Computation). Her research focuses on preconditioners for saddle-point systems, eigenvalue analysis, and innovative educational methodologies like inverted two-stage exams. Bradley’s academic contributions span numerical linear algebra, computational methods, and biomechanical simulation. She has published extensively on topics such as eigenvalue bounds for saddle-point systems and the application of machine learning in sensorimotor control. Her work emphasizes practical solutions for complex computational challenges and pedagogical innovations to enhance learning outcomes. Her office is located in ICCS 241, and she can be reached at smbrad@cs.ubc.ca .

Sid Felsمشاهده پروفایل

استاد

• Human Computer Interaction
• 3D displays
• Biomechanical modeling
• +۷ مورد دیگر

Sid Fels is a Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC), affiliated with the Faculty of Applied Science. He leads the Human Communication Technologies Laboratory and was the director of the Media and Graphics Interdisciplinary Centre (MAGIC) from 2001 to 2012. His academic rank is Professor, and he is actively involved in faculty roles without any indication of retirement or former status. Education: BASc in Electrical Engineering (University of Waterloo, 1988), MSc and PhD in Computer Science (University of Toronto, 1990 and 1994 respectively). He has been at UBC since 1998 and was recognized as a Distinguished University Scholar in 2004. Research Interests: Focuses on human-computer interaction, biomechanical modeling, neural networks, medical applications, speech synthesis, and interactive arts. His lab explores topics like 3D displays, surgical simulation, and vocal tract modeling. Key projects include mandibular reconstruction simulations and biomechanical modeling for speech production. Publications: Over 150 articles, including recent works on mandibular reconstruction efficacy, AI in education, and vocal tract acoustics. Notable articles from 2025 include studies on surgical planning and chatbot-mediated critical thinking. Awards: Distinguished University Scholar (UBC, 2004). His work bridges computer science, engineering, and biomedical applications, with a strong focus on interdisciplinary projects. Students: Supervised over 20 graduate students, including those in biomechanical modeling, medical imaging, and human-computer interaction. Notable advisees include authors of theses on video-based learning tools and surgical simulation systems. Labs/Teams: Leads the Human Communication Technologies Lab and co-founded MAGIC. Collaborates with clinical partners on projects like mandibular reconstruction and sleep apnea modeling.

Ahmad Mohammadpanahمشاهده پروفایل

مدرس

• Dynamics
• Vibrations
• Modal Analysis
• +۵ مورد دیگر

Ahmad Mohammadpanah serves as a Lecturer in the Department of Mechanical Engineering within the Faculty of Applied Science at the University of British Columbia (UBC). Holding a Professional Engineer (P.Eng.) designation, he earned his PhD and MASc from UBC alongside an MSc from Sharif University of Technology. His academic profile is anchored in mechanical engineering with specialized expertise in dynamic systems and manufacturing technologies. His educational qualifications include: PhD in Mechanical Engineering, University of British Columbia MASc in Mechanical Engineering, University of British Columbia MSc in Mechanical Engineering, Sharif University of Technology Dr. Mohammadpanah's research centers on Dynamics, Vibrations, and Modal Analysis , with pioneering work in Spherical Parallel Robots and Light Weight Lattice Design for Additive Manufacturing . His exploration of Generative Design & Topology Optimization bridges computational methods with practical manufacturing solutions. Current investigations focus on thermal management in metal extrusion additive manufacturing and vibration control in industrial saw systems, demonstrating strong alignment with industry needs in wood processing and advanced manufacturing. Analysis of his 10 publications (2012-2024) reveals consistent focus on vibration phenomena in rotating machinery and additive manufacturing defect mitigation. His work shows progressive evolution from fundamental dynamics research (2012-2018) toward applied industrial solutions (2021-2024), particularly in metal additive manufacturing and smart lumber production. The integration of machine learning in wood quality prediction (2023) highlights his adaptation to emerging computational techniques. No scientific awards were documented in the provided materials. Regarding academic mentorship, Dr. Mohammadpanah actively supervises undergraduate capstone projects (MECH 45X) and laboratory courses, though formal graduate student advisement isn't specified. His teaching portfolio spans foundational courses like MECH 223 (Introduction to Mechanical Design) through advanced topics including MANU 465 (AI in Manufacturing), reflecting comprehensive engagement with mechanical engineering education. His research operates through the "Intelligent Engineering" initiative, focusing on practical industrial applications rather than dedicated laboratory facilities. Current work emphasizes real-world implementation of vibration control systems and additive manufacturing optimization, with strong industry connections evident through Fpinnovations collaboration and patent development.

Nick Brodyمشاهده پروفایل

دانشیار

• Social Implications of Technology
• Bystander Behavior in Cyberbullying
• Relational Communication
• +۱ مورد دیگر

Nick Brody is an Associate Professor of Communication Studies at the University of Puget Sound. His research focuses on the social implications of technology, particularly how digital artifacts affect emotional recovery after romantic breakups. He teaches courses in communication theory, quantitative research methods, communication and technology, and relational communication. 2019: Thomas A. Davis Teaching Award 2016: Puget Sound Faculty Research Award Research Interests Brody's work explores: Bystander behavior in cyberbullying episodes Communication patterns in on-again/off-again relationships Digital memory persistence and its psychological impact The cognitive parallels between social and physical pain Recent Article Trends His recent publications analyze how social media algorithms and network connections complicate post-breakup recovery, emphasizing the need for intentional digital curation. The Virtual Relational Memory model (2022) highlights the interplay of digital objects, narrative control, and social networks. Scientific Contributions Developed the Virtual Relational Memory framework Published in Communication Monographs , Computers in Human Behavior , and other top-tier journals Identified correlations between nostalgic tendencies and post-breakup adjustment

Serpil Kocabiyikمشاهده پروفایل

استاد

• Fluid Mechanics
• Computational Science
• Theoretical Fluid Dynamics
• +۳ مورد دیگر

Serpil Kocabiyik is a Professor of Mathematics at Memorial University of Newfoundland, leading research in fluid mechanics and computational science. She holds a Ph.D. from Western Ontario (1987) and has held academic positions at Manitoba and Western Ontario. Her work focuses on unsteady separated flows, vortex-induced vibrations, and numerical simulation methodologies. Education: B.Sc. & M.Sc. (Middle East Technical University, 1979/1981), Ph.D. in Applied Mathematics (University of Western Ontario, 1987). Postdoctoral fellowships followed before joining Memorial in 1999 as Associate Professor, promoted to Full Professor in 2005. Research Interests: Interdisciplinary applied mathematics, theoretical fluid mechanics, computational science. Specializes in fluid-bluff body interactions, numerical methods for PDEs, and validation across computational/experimental studies. Key Contributions: Over 80 refereed papers, $2M+ in grants, and mentorship of >30 students/postdocs. Pioneered studies on free surface flows with moving bodies and developed parallelized CFD algorithms. First woman in Canada to win Petro-Canada Young Innovator Award (2000) CAIMS Arthur Beaumont Distinguished Service Award (2006) Teaching: Courses include Numerical Algorithms, Fluid Mechanics, Partial Differential Equations. Advocated for STEM education equity, training 17→50 graduate students in her department (1999–2009).

Patricia Evansمشاهده پروفایل

استاد

• Computational Biology
• Bioinformatics
• Algorithm Design and Analysis
• +۵ مورد دیگر

Patricia Evans is a Professor and Associate Dean at the Faculty of Computer Science , University of New Brunswick. She holds a B.Sc. from the University of Alberta and M.Sc./Ph.D. from the University of Victoria. Research Focus : Computational Biology, Bioinformatics, Algorithm Design and Analysis, Graph Theory, Parameterized Complexity Current Projects : RNA structure comparison, haplotype inference, biological network analysis, parallelization of dynamic programming Collaborations : Eric Aubanel (UNB), Todd Wareham (Memorial University), Ken Kent (UNB), Jackie Rice (University of Lethbridge) Grants : NSERC, Genome Atlantic, AIF, NBIF Her research applies computer science theory to molecular biology problems, including RNA structure analysis and phylogenetic network modeling. Past work includes FPGA implementations for bioinformatics algorithms and parameterized complexity analysis for motif finding. Publications span 1999-2008, with key topics in RNA pseudoknot detection, haplotyping, and computational complexity. Her lab has mentored 13 graduate students, including 2 Ph.D. graduates.

Przemyslaw Pochecمشاهده پروفایل

دانشیار

• Data Communication
• Image Processing
• Three-dimensional Computer Vision

Przemyslaw Pochec is an Associate Professor at the Faculty of Computer Science , University of New Brunswick, where he has held a faculty position for approximately 27 years. He holds a Master of Science in Computer Science and a Ph.D. from the University of New Brunswick. Research Interests: Data Communication, Image Processing, Three-dimensional Computer Vision His recent research focuses on queueing models for parallel computer systems , data communication system modeling, and stereo vision matching algorithms . While specific grants, awards, or student advisement records are not detailed in the provided text, his work bridges theoretical and applied aspects of computer science, particularly in communication systems and computer vision.

Ruppa Thulasiramمشاهده پروفایل

استاد

• Computational Finance
• Cloud Computing
• Blockchain Technology
• +۳ مورد دیگر

Ruppa Thulasiram is a Professor in the Department of Computer Science at the University of Manitoba, affiliated with the Faculty of Science. Her research and teaching focus on the intersection of computing and finance, with a particular emphasis on computational finance and cloud computing. University: University of Manitoba Department: Computer Science Academic Rank: Professor Email: Tulsi.Thulasiram@umanitoba.ca Office: E2-576 EITC Phone: 204-474-6538 Her research involves designing and analyzing parallel algorithms for finance problems like derivative pricing and portfolio optimization, leveraging cloud computing and blockchain technology for financial smart contracts. She also explores the multiscale mathematical and computational challenges inherent in financial systems. Ruppa leads the Computational Financial Derivatives and Cloud Computing Laboratory , focusing on robust computational methods and security analysis in blockchain-based financial applications. Her work bridges advanced mathematics, computer science, and finance.

Julien Arinoمشاهده پروفایل

استاد

• Mathematical Epidemiology
• Mathematical Population Dynamics
• Data Science
• +۳ مورد دیگر

Julien Arino is a Professor in the Department of Mathematics at the University of Manitoba , specializing in Mathematical Epidemiology and Mathematical Population Dynamics . He is an active member of the Mathematical Biology group and focuses on the role of movement in disease transmission, with applications to both human and animal health. Department of Mathematics Faculty of Science University of Manitoba His research spans: Metapopulation dynamics with explicit movement Multi-pathogen and multi-species transmission modeling Computational epidemiology with data science integration Evaluation of travel control measures for disease variants Recent work includes publications on: Cholera transmission in Chad Multi-species bovine tuberculosis dynamics Measles resurgence patterns Software tools for R compilation and data visualization He maintains a public YouTube channel with lecture videos on Mathematical Epidemiology and contributes open-source code for scientific computing in R and Python. His methodological contributions include novel approaches to plot formatting and computational efficiency in epidemic modeling.

Ralf Meyerمشاهده پروفایل

استاد پژوهشی

• Computational Materials Science
• Nanomaterials
• High-Performance Computing
• +۲ مورد دیگر

Ralf Meyer holds the SHARCNET Research Chair position in the Department of Mathematics and Computer Science at Laurentian University, where he has been since July 2008, with a cross-appointment in the Department of Physics. His academic role combines research in computational materials science with extensive teaching across computer science and mathematics disciplines. Dr. Meyer earned his Ph.D. (Dr. rer. nat.) in theoretical physics from the University of Duisburg in 1998, establishing the foundation for his interdisciplinary research approach. His research specializes in computational modeling of materials, particularly investigating the structural and dynamic properties of nanoparticles and nanostructured materials. He leverages the high-performance computing resources of the Shared Hierarchical Academic Computing Network (SHARCNET) to conduct complex simulations, bridging computer science, physics, and materials engineering. This work contributes to advancements in nanotechnology through computational methodologies. Dr. Meyer has maintained a consistent teaching record since 2009, instructing courses such as Computer Organization (COSC 3406E), Parallel Computing (COSC 4456E), Numerical Methods (MATH/COSC 3416), and Object-Oriented Programming (COSC 2947E) at both undergraduate and graduate levels. His pedagogical focus spans foundational programming concepts to advanced high-performance computing applications. No scientific awards were documented in the provided information. Similarly, no formal advisees or grant funding details were specified, though his SHARCNET affiliation indicates institutional support for computational research infrastructure. His research operations are centered around SHARCNET resources, a consortium providing high-performance computing facilities across Ontario academic institutions, enabling large-scale computational experiments in materials science without dedicated on-campus laboratory facilities.

• Phase-field modeling
• Computational materials science
• Solidification
• +۵ مورد دیگر

Nikolas Provatas serves as an Adjunct Professor in the Department of Materials Science and Engineering within the Faculty of Engineering at McMaster University. His academic career spans over three decades with extensive contributions to computational materials science, particularly in phase-field modeling techniques. Provatas' research focuses on phase-field modeling, solidification phenomena, microstructure evolution, and computational materials engineering. His work bridges theoretical modeling with practical applications in additive manufacturing, metallurgy, and materials processing. He has developed advanced computational frameworks for simulating solidification processes, phase transformations, and microstructural development in various materials systems including metals, alloys, and nanomaterials. His research has significant implications for understanding fundamental materials phenomena and improving industrial manufacturing processes. Analysis of Provatas' extensive publication record reveals a strong trend toward increasingly sophisticated computational modeling approaches, particularly the phase-field crystal method. His work spans fundamental theoretical developments to practical applications in additive manufacturing, welding, and casting processes. The research shows particular emphasis on understanding microstructure evolution during solidification, phase transformations, and the relationship between processing conditions and final material properties. Provatas has mentored numerous students and collaborated extensively across the materials science community, though specific details of his advising record are not provided in the available information. His research has attracted significant attention, as evidenced by the substantial readership metrics across multiple platforms. His laboratory work centers on computational materials science, developing advanced simulation frameworks to model solidification phenomena, phase transformations, and microstructure evolution across multiple length and time scales. These computational approaches enable detailed investigation of materials behavior that would be challenging to observe experimentally.