Find Academic Supervisors

Arash Habibi LashkariView profile

Associate Professor

• Cybersecurity
• Network Security
• Threat Hunting
• +7 more

Dr. Arash Habibi Lashkari is an Associate Professor and Canada Research Chair in Cybersecurity at York University's School of Information Technology. He holds a PhD from the University of Technology Malaysia and completed a Post-Doc at the University of New Brunswick. With over 25 years of teaching experience, he specializes in cybersecurity risk management, malware analysis, and threat hunting. He pioneered Canada’s first cybersecurity Capture the Flag (CTF) competition for post-secondary students and has authored 10 books and over 110 academic articles. Research Interests: Cybersecurity, Network Security, Threat Hunting, Malware Analysis, Cybersecurity Risk Management, and Blockchain Security. He leads the Behaciour-Centric Cybersecurity Center (BCCC), focusing on vulnerability detection technologies and cybersecurity dataset generation. Awards: Mitacs 150 Top Researchers (2017), University of New Brunswick Teaching Innovation Award (2020), Gold Medal at 2020 Canadian Online Publishing Awards, and multiple international security competition awards. His work has been featured in CBC, CIC, and global cybersecurity conferences. Key Projects: Development of intrusion detection datasets (e.g., CIRA-CIC-DoHBrw-2020), malware analysis frameworks, and AI-driven cybersecurity tools. He actively collaborates with organizations like Aviva and NICT on cybersecurity resilience strategies. Teaching: Offers courses in Digital Forensics, Network Security, and Cybersecurity Management. His 'Think-Que-Cussion' teaching methodology emphasizes interactive learning.

Kemal TepeView profile

Professor

• Vehicular Networks
• Cognitive Radio
• Wireless Communication
• +6 more

Professor Kemal Tepe is a faculty member in the Faculty of Engineering at the University of Windsor, specializing in wireless communication and information processing. His research focuses on vehicular networks, cognitive radio systems, and smart grid technologies. He leads the Wireless Communication and Information Processing Lab , where he develops solutions for autonomous driving systems, cybersecurity in vehicle-to-infrastructure communication, and spectrum sensing techniques. In 2016, he was awarded the Medal of Excellence by the Faculty of Engineering for his dedication and service. Tepe’s work bridges theoretical advancements and practical applications, addressing challenges in autonomous systems, machine learning for anomaly detection, and IoT security. His contributions include pioneering methods for detecting adversarial behavior in vehicular networks and improving spectrum utilization through probabilistic modeling. Collaborations with industry partners like Ford Motor Company and involvement in initiatives such as the Perspective Magazine automotive research highlight his industry-relevant research. His research interests span a wide range of domains including: Autonomous vehicle safety and communication protocols Cognitive radio networks and spectrum management Machine learning for network security and anomaly detection Wireless sensor networks and energy-efficient protocols Smart grid integration and communication architectures Tepe’s publications emphasize practical implementations, such as real-time routing protocols for wireless sensor networks and hardware designs for cognitive radio systems. His lab’s innovations have been showcased in industry-relevant platforms, demonstrating the real-world impact of his work.

Mohamed AtiaView profile

Associate Professor

• Sensor Fusion
• Autonomous Systems
• Embedded Systems
• +12 more

Mohamed Atia is an Associate Professor in the Department of Systems and Computer Engineering at Carleton University's Faculty of Engineering and Design. He conducts research in sensor fusion for autonomous systems, including robotics, autonomous vehicles, and the Internet of Things, with an emphasis on real-time embedded implementation. PhD, Electrical and Computer Engineering, Queen’s University (2013) M.Sc., Computer Systems, Ain Shams University (2006) B.S., Computer Systems, Ain Shams University (2000) His research focuses on sensor fusion , integrating data from heterogeneous sensors such as GNSS, IMU, Vision, Radar, and LiDAR under real-time constraints on embedded platforms like FPGAs and microcontrollers. He applies advanced signal processing, estimation theory, machine learning, and AI to solve challenges in state estimation, observability, fault tolerance, and concurrency management. Dr. Atia’s work has applications in intelligent vehicles, indoor navigation, mobile robotics, smart buildings, medical devices, and remote sensing . He leads a research lab focused on embedded multi-sensor systems, where students develop practical solutions for autonomous navigation and SLAM. His teaching emphasizes hands-on learning through labs and projects. He teaches undergraduate courses in computer systems, embedded systems, and signal processing, and a graduate course (SYSC 5807) on Advanced Topics in Computer Systems, specifically Sensor Fusion Systems . Scientific Awards: Alberta Innovate Association Award (2011) IEEE Excellence in PhD Research (2013) Mitacs Elevate Industrial Postdoc Award (2014) NSERC PDF Award (2015) Queen’s University Teaching Award (2016) Dr. Atia has advised graduate students such as Hamza Sadruddin and Alan Zhang , who have presented at IEEE Sensors and ION GNSS+ conferences, with Zhang winning Best Presentation. He has no listed grants in the provided text but has received prestigious fellowships. His research lab supports innovation in real-time embedded sensor fusion with links to industry and open-source tools.

• Magmatic Processes
• Hydrothermal Alteration
• Porphyry Systems
• +3 more

Shaun Barker is an Associate Professor in the Department of Earth, Ocean & Atmospheric Sciences at the University of British Columbia. His research focuses on magmatic processes, hydrothermal alteration, and porphyry systems, particularly studying postsubduction magmatism and its economic implications. He supervises postdoctoral researchers such as Maria Alejandra Rodriguez Mustafa and has advised graduate theses on topics like the Dawson Range district's Late Cretaceous magmatism. Research interests include tectonomagmatic frameworks, fluid-rock interaction, and exploration methodologies using geochemical datasets and advanced analytical techniques. His work integrates field observations, geochemical analyses, and isotopic studies to unravel the genesis of ore deposits and geothermal systems. Key contributions include defining trace-element haloes around deposits, developing mineral mapping via µXRF, and applying machine learning to geological modeling. His studies span global regions like the Yukon, British Columbia, Nevada, and Peru, with a focus on Carlin-type gold, porphyry copper, and skarn systems.

Michael FriedlanderView profile

Professor

• Convex Optimization
• Machine Learning
• First-Order Methods
• +3 more

Michael Friedlander is a Professor of Computational Mathematics at the University of British Columbia (UBC), holding joint appointments in the Departments of Computer Science and Mathematics. He serves as Director of the UBC Institute of Applied Mathematics and is a Sauder School Distinguished Scholar. His research focuses on optimization theory, algorithms, and applications, particularly in convex optimization, first-order methods, and machine learning. Friedlander is a SIAM Fellow (2024) and has received multiple teaching awards, including the 2013 UBC CS Teaching Award. Education: PhD (2002) in Operations Research from Stanford University; BA (1993) in Physics from Cornell University. Research Interests: Friedlander’s work spans computational optimization, including first-order methods, gauge duality, federated learning, and applications in signal processing and quantum computing. His contributions include SPGL1, a widely used convex optimization solver, and foundational work on atomic decomposition and polar alignment in structured optimization. Key Contributions: His research bridges optimization with practical applications, such as compressed sensing and data science. Notable areas include dual methods for federated learning, low-rank spectral optimization, and algorithms for sparse recovery. He has advised numerous PhD students, including work on topics like gauge duality and federated learning contribution valuation. Awards & Honors: SIAM Fellow (2024) 2013 UBC CS Department Teaching Award CACS Outstanding Young Researcher Prize AI & Statistics 2009 Best Paper Award Advising & Grants: Supervised over 20 graduate students and postdocs. Active in grant-funded research, including projects on optimization algorithms and their industrial applications. Collaborates with institutions like Pacific Institute for Mathematical Sciences (PIMS) and the Data Science Institute at UBC. Labs & Affiliations: Affiliated with the Institute for Computing, Information and Cognitive Systems (ICICS), Data Science Institute, and Quantum Computing Research Cluster at UBC. Leads interdisciplinary efforts in applied mathematics and computational science.

Ilker HacihalilogluView profile

Associate Professor

• Artificial Intelligence in Healthcare
• Medical Image Processing
• Machine Learning Applications
• +8 more

Dr. Ilker Hacihaliloglu is an Associate Professor in the Department of Radiology and Medicine at the University of British Columbia's Faculty of Medicine. He holds a PhD from UBC (2010) and has held faculty positions at Rutgers University (2014-2021). His research focuses on applying machine learning to medical imaging, aiming to bridge clinical and engineering research for practical healthcare solutions. Key areas include AI-driven diagnostics, ultrasound imaging, and personalized medicine. Education: B.Sc. and M.Sc., Istanbul Technical University (2001, 2004) Ph.D., University of British Columbia (2010) Postdoctoral Fellow, Vancouver General Hospital (2010-2013) Research Interests: Artificial Intelligence in Medical Imaging Ultrasound-Based Diagnostics Image-Guided Surgery Computer-Aided Diagnostics for Orthopedics and Neurosurgery His work emphasizes explainable AI, standardization in machine learning metrics, and clinical translation of technologies. Scientific Contributions: Over 35 peer-reviewed articles (2019-2025) highlight innovations in medical image segmentation, AI-driven radiomics, and cross-modality imaging. Recent trends focus on synthetic data augmentation, diffusion models for medical imaging, and curriculum development in AI for healthcare. Grants and Advising: No explicit grants mentioned, but active mentorship in interdisciplinary research teams. Collaborations span clinicians, engineers, and computer scientists to address real-world healthcare challenges. Labs/Teams: Engaged in computational ultrasound and AI-driven diagnostics initiatives, though specific lab names are not provided in the text.

Gilles PeslherbeView profile

Professor

• Computational Chemistry
• Molecular and Reaction Dynamics
• Nanomaterials and Materials Engineering
• +8 more

Gilles Peslherbe is a Professor at Concordia University, cross-appointed in the departments of Chemistry and Biochemistry , Physics , and Chemical and Materials Engineering . He leads the Centre for Research in Molecular Modeling (CERMM) and supervises graduate students in programs spanning Chemical Engineering, Chemistry, Physics, and Nanoscience. PhD in Physical Chemistry with Minor in Computer Engineering (Wayne State University, USA) Diplôme d'Ingénieur Chimiste (Ecole Supérieure de Chimie Industrielle de Lyon, France) His research integrates computational chemistry , quantum mechanics , and machine learning to model chemical processes in extreme conditions, drug delivery systems, and environmental impact assessments. Collaborative projects include studies on spin catalysis , nanostructured materials , and RNA structure fundamentals . Recent publications highlight applications of artificial intelligence in therapeutics development , density-functional theory in nanomaterials , and ultrasfast spectroscopy in electron solvation . His work bridges quantum computing and biological simulations . Teaching includes courses on quantum theory , computational chemistry , and statistical mechanics . He actively seeks students for ongoing projects in chemical interactions in extreme conditions and environmental remediation .

W. Kendal BusheView profile

Associate Professor

• Combustion
• Turbulence
• Numerical Simulation
• +3 more

W. Kendal Bushe is an Associate Professor at the Faculty of Applied Science , University of British Columbia , within the Department of Mechanical Engineering . His research focuses on turbulent combustion, numerical simulation, and computational fluid dynamics (CFD) with applications to internal combustion engines and thermal power generation. Developed the Conditional Source-term Estimation (CSE) method for turbulent combustion modeling. Conducted experimental studies on methane/natural gas ignition in engines. Expertise in Reynolds-Averaged Navier-Stokes (RANS) and Large Eddy Simulation (LES) for reacting flows. Current projects involve autoignition modeling in HCCI engines using Stochastic Particle Models . Teaching Activities : Courses on heat transfer, energy conversion systems, computational fluid dynamics, combustion, and turbulent shear flows. Scientific Recognition : Fellow of the Combustion Institute.

Jessica GronsbellView profile

Assistant Professor

• Biostatistics
• Data Science
• Electronic Health Records
• +6 more

Jessica Gronsbell is an Assistant Professor at the University of Toronto in the Department of Statistical Sciences . Her work focuses on developing statistical and machine learning methods for electronic health records (EHRs) and mobile health (mHealth) data, addressing challenges like measurement error, missing data, and fairness in algorithmic applications. BA in Applied Mathematics, UC Berkeley PhD in Biostatistics, Harvard University Postdoctoral work, Department of Biomedical Data Science, Stanford School of Medicine Her research spans semi-supervised learning , data integration , and critical data studies , with applications in EHRs, biobanks, and mobile health technologies. Recent work includes evaluating fairness in semi-supervised settings and improving statistical inference with machine learning-derived data. Current trends in her publications reflect a focus on machine learning ethics , health disparities , and computational methods in biostatistics. She actively mentors students, including participation in the Florence Nightingale Day, and contributes to open-source tools like the fairness evaluation R package on CRAN.

• Veterinary Medicine
• Dairy Cattle Health
• Reproductive Health
• +4 more

Prof. Jocelyn Dubuc is a Full Professor at the Faculty of Veterinary Medicine (Université de Montréal), specializing in bovine population health and reproductive medicine. He leads the Department of Clinical Sciences , focusing on dairy cow uterine health, metabolic diseases, and epidemiology. His work bridges clinical practice and research, emphasizing evidence-based management strategies. Education: 2005: DVM, Université de Montréal 2007: MSc in Veterinary Sciences, Université de Montréal 2010: PhD in Veterinary Science, University of Guelph Research Focus: Uterine infections and postpartum disorders Metabolic diseases (e.g., ketosis) Antimicrobial stewardship On-farm diagnostic tools Reproductive tract disease epidemiology His studies have redefined understanding of postpartum endometritis and shaped clinical guidelines for dairy herd management. Awards: 2013: Prix Carl J. Norden for Teaching Excellence 2015: Journal of Dairy Science Loyalty Program Grants & Collaborations: Leading projects funded by MAPAQ, FRQNT, and industry partnerships. Key member of RQR (Reproduction Network) and Op+lait (Dairy Quality Optimization). Developed courses on uterine health monitoring and metabolic diagnostics. Teams: Collaborates with over 100 researchers in networks like Vetmétabo (AI-driven veterinary medicine) and the Québec Dairy Health Benchmarking Initiative.

Daler N. RakhmatovView profile

Associate Professor

• Reconfigurable architectures
• Energy-efficient computing
• Electronic design automation
• +9 more

Daler N. Rakhmatov is an Associate Professor in the Electrical and Computer Engineering Department at the University of Victoria . His research focuses on energy-efficient computing architectures for biomedical imaging applications, including ultrasound systems and embedded systems optimization. He is also involved in hardware/software co-design and battery management systems. Education: Bachelor of Science (BSc) in Engineering, University of Rochester Master of Science (MSc) in Engineering, University of Arizona Doctor of Philosophy (PhD) in Engineering, University of Arizona Professional Engineer (PEng) designation Research Interests: Rakhmatov's work emphasizes four core areas: 1. Low-complexity biomedical ultrasound imaging techniques for energy efficiency. 2. Configurable hardware/software architectures for computation-intensive tasks. 3. Analytical models for battery dynamics in embedded systems. 4. Model-driven configuration algorithms for energy-constrained applications. Teaching: Rakhmatov teaches courses such as ECE 242 (Discrete Structures) , ECE 355 (Microprocessor Systems) , and advanced topics like ECE 568 (System-on-Chip Engineering) . Students & Alumni: Rakhmatov supervises PhD and MASc/MEng students in biomedical imaging and embedded systems. Notable alumni include: - Hamad Alrimeih (PhD, Cryptographic Hardware Acceleration) - Mohammed Albulayli (PhD, Ultrasound Imaging Reconstruction) - Solmaz Khezerloo (MASc, Gradient-Driven Beamforming) Labs & Collaborations: His research integrates with the University of Victoria's biomedical engineering and embedded systems communities. He is a member of the Cadence University Program .

Salma EmaraView profile

Assistant Professor

• Machine Learning
• Computer Networking
• Natural Language Processing
• +3 more

Salma Emara is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Toronto within the Faculty of Applied Science & Engineering. Her academic journey includes a B.Sc. in Electronics and Communications Engineering from the American University in Cairo (2018) and a Ph.D. in Computer Engineering from the University of Toronto (2022), supervised by Professor Baochun Li. Her research spans two domains: (1) technical work in reinforcement learning for computer networking , including adaptive bitrate selection, edge caching, and congestion control; and (2) pedagogical work focused on debugging skill development for beginner programmers and leveraging natural language processing in engineering education . She emphasizes hands-on learning through in-class activities and problem-solving assignments. Publication trends reveal a focus on reinforcement learning in networking (2018–2023) and a parallel interest in educational technology (2024). Her recent work (e.g., TextCraft ) explores NLP-driven resource recommendation for textbooks, while earlier projects (e.g., Cascade , Pareto ) address network optimization through machine learning. Scientific awards include: Faculty of Applied Science & Engineering Early Career Teaching Award (2025) Departmental Teaching Awards (2022–2024) Shortlisted for TATP Teaching Assistant Excellence (2022)

Hugh ChipmanView profile

Professor

• Statistical Learning
• Data Mining
• Bayesian Methods
• +5 more

Hugh Chipman is a Professor in the Department of Mathematics and Statistics at Acadia University , Canada. He holds a BScH (Acadia), MMath and PhD (University of Waterloo), and is a P.Stat. member of the Statistical Society of Canada. Education: BScH, Acadia University MMath, University of Waterloo PhD, University of Waterloo P.Stat. (Professional Statistician) Research Interests: Dr. Chipman specializes in statistical learning and data mining with applications to complex data structures. Key areas include Bayesian ensemble methods , tree-based models , design of experiments , network analysis , and high-dimensional data problems. He has contributed to drug discovery and functional data monitoring through computational statistics. Recent Talks: His 2024 presentation at UNC Chapel Hill focused on simulation studies for experimental design. Earlier work (2015) addressed big data education at Dalhousie University, while 2011-2014 talks covered active learning and network data mining at institutions like Harvard and MITACS. Scientific Awards: Co-organizer of NSERC-funded workshops Contributed to major statistical learning summer schools (e.g., AARMS 2014) Teaching: Courses include Statistics I and Design and Analysis of Experiments . He maintains software tools like BART (Bayesian Additive Regression Trees) and hybrid hierarchical clustering algorithms.

Steven M CarrView profile

Professor

• Molecular Evolution
• Genome Evolution
• Phylogeography
• +7 more

Dr. Steven M. Carr is Professor of Biology at Memorial University of Newfoundland with a cross-appointment in Population Genetics to the Faculty of Medicine and affiliations with the Department of Computer Science. His research laboratory focuses on molecular and genome evolution within vertebrate species, with particular expertise in phylogeographic analysis using complete mitochondrial DNA genomes. Dr. Carr earned his BSc in Biology from California Polytechnic State University (1975), followed by a CPhil (1978) and PhD (1983) in Genetics from the University of California, Berkeley. His research methodology combines traditional molecular genetics with computational approaches, including Monte Carlo simulations and neural network analysis of genomic data. His primary research areas include: phylogeographic genomics of fisheries and wildlife populations (Atlantic Cod, Wolffish, Harp Seals, Newfoundland Caribou); biogeographic evolution of cods and pollocks; population genomic structure of Newfoundland's founding human populations; and development of novel biotechnologies including DNA sequencing microarrays. His work bridges evolutionary biology, conservation genetics, and computational genomics. Analysis of Dr. Carr's publication record reveals a clear progression from traditional phylogeographic studies toward increasingly sophisticated genomic analyses incorporating machine learning techniques. His recent work on honey bee evolution challenges established hypotheses about their biogeographic origins, while his research on ancient DNA lineages connects Newfoundland's Indigenous history with contemporary populations. Genome Editor's Choice Award, April 2020 Genome Editor's Choice Award, August 2018 Dr. Carr has successfully mentored numerous graduate students through PhD, MSc, and honors thesis programs, with research topics spanning conservation genetics, population genomics, and computational biology. His teaching portfolio includes Principles of Genetics (Biol 2250), Advanced Genetics (Biol 4241), Principles of Biotechnology (Biol 3950), and Evolutionary Genetics (Biol 4250), where he integrates theoretical concepts with practical genomic applications. The Carr Lab operates at the intersection of biology, computer science, and statistics, maintaining active collaborations with fisheries scientists, wildlife biologists, and medical researchers to address complex questions in evolutionary genomics and conservation biology.

Dake ZhangView profile

Professor

• Information Retrieval
• Health Informatics
• Artificial Intelligence
• +3 more

Dake Zhang is a faculty member at the David R. Cheriton School of Computer Science, University of Waterloo. His research focuses on reducing health misinformation through search systems, leveraging artificial intelligence and information retrieval techniques to improve online health information quality. Key collaborations with Mark D. Smucker, Amir Vakili Tahami, and Mustafa Abualsaud Active participant in TREC Health Misinformation Tracks (2021-2023) Developed ReadProbe system for lateral reading support using OpenAI models and Bing search His work demonstrates expertise in: Information retrieval systems for health domains Mitigating cognitive biases in search Transformer-based document analysis Automated answer prediction from web sources Search engine credibility modeling Human-AI interaction in information verification