Find Academic Supervisors

• Superfluidity
• Nanomechanics
• Quantum Nanofluidics
• +4 more

John Davis is a Professor in the Department of Physics at the University of Alberta, Faculty of Science. He holds a PhD and MSc from Northwestern University and a Bachelor’s from Washington University. His research focuses on nanomechanics, superfluidity, and superconductivity, particularly in confined geometries and quantum properties of nanomechanical systems. His lab develops superfluid-based technologies for dark matter detection and precision measurement. He has held academic positions since 2010, including roles at the Canadian Institute for Advanced Research and postdoctoral training at the University of Alberta with Prof. Mark R. Freeman. Education: PhD in Physics (2008), Northwestern University MSc in Physics (2003), Northwestern University Bachelor’s in Physics with Honors (2001), Washington University Research Interests: Superfluid nanomechanical resonators Ultralow-temperature superfluid 3He Nanofluidic cavity quantum electrodynamics Quantum-limited torque magnetometry Applications in dark matter detection and gravitational wave sensing His recent work emphasizes magnomechanics and optomechanical transduction , integrating superfluid systems with quantum sensors. Articles highlight advancements in cryogenic devices, nonlinear dynamics, and hybrid quantum systems. Ongoing projects include the HElium-based Light Operated Superfluid (HELIOS) dark matter detector. Grants & Labs: His lab operates a cryogen-efficient low-temperature facility, focusing on microfluidic quantum fluid experiments. Collaborations involve advanced photonic crystal cavities and diamond-based optomechanical platforms.

Linyi LiView profile

Assistant Professor

• Deep learning
• Trustworthy machine learning
• Computer security
• +4 more

Linyi Li is an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU), leading the Trustworthy Artificial Intelligence (TAI) Lab. His research focuses on certifiably trustworthy deep learning systems, combining machine learning and computer security. He holds a PhD from the University of Illinois Urbana-Champaign (UIUC) and a B.Eng. from Tsinghua University. Affiliations: Simon Fraser University, TAI Lab Education: PhD in Computer Science, UIUC, 2023 B.Eng (Cum Laude), Tsinghua University, 2018 His research interests include deep learning , trustworthy machine learning , large language models , and software engineering . He emphasizes rigorous certification of robustness, fairness, and numerical reliability in AI systems. Recent work includes the InfiBench benchmark for evaluating code LLMs and advancements in neural network verification. Recent Research Trends: His publications span certified robustness, fairness guarantees, and scalable verification techniques for deep learning models. He also explores scientific evaluation of foundation models and adversarial defense mechanisms. Awards: Rising Stars in Data Science AdvML Rising Star Award Wing Kai Cheng Fellowship Finalist: Qualcomm Innovation Fellowship (2022) Winner: VNN-COMP'23 Competition (Team α, β-CROWN) Advising & Grants: As a PI, he oversees the TAI Lab's research. Though no specific grants are listed, his work is funded through competitive awards and university resources. Labs/Teams: Leads the TAI Lab at SFU, focusing on foundational and applied research in trustworthy AI.

Arash AramiView profile

Associate Professor

• Assistive Robotics
• Human-Robot Interaction
• Machine Learning
• +6 more

Arash Arami is an Associate Professor in the Department of Mechanical and Mechatronics Engineering at the University of Waterloo, cross-appointed in Systems Design Engineering. He directs the Neuromechanics and Assistive Robotics Laboratory and maintains affiliations with Waterloo Robohub, the Centre for Bioengineering and Biotechnology, Waterloo AI institute, and KITE institute at Toronto Rehab Institute. He earned his Doctorate in Electrical Engineering from EPFL (2014), Master of Science from University of Tehran (2009), and Bachelor of Science from University of Tabriz (2006), all in Control Engineering. His research in Assistive Robotics and Rehabilitation Engineering integrates Machine Learning with Neuromechanics to develop intelligent systems for human movement analysis. Key focus areas include exoskeleton control algorithms, wearable sensor systems, and neural control modeling for rehabilitation applications. Recent publications demonstrate interdisciplinary work spanning robotics, biomedical engineering, and materials science, with emphasis on real-time human locomotion prediction, exoskeleton-human interaction, and data-driven health monitoring solutions. Dr. Arami serves as Chair of the NSERC Scholarship Committee (2021-2023) and mentors graduate students through the Mechatronics Exchange Study program. His teaching includes core courses in control systems, robot manipulators, and biomechanical engineering. The Neuromechanics and Assistive Robotics Laboratory fosters collaborations with clinical partners at Toronto Rehab Institute, focusing on translating robotic innovations into practical rehabilitation tools through interdisciplinary teamwork.

Masoud AsgharianView profile

Professor

• Survival Analysis
• Changepoint Problem
• Nonparametric Bayesian Analysis
• +5 more

Masoud Asgharian is a Professor in the Department of Mathematics and Statistics at McGill University. His research focuses on survival analysis, changepoint problems, nonparametric Bayesian methods, and data envelopment analysis. He has contributed to influential studies on dementia survival rates, censored data methodologies, and statistical efficiency measures. His work bridges biostatistics and operations research, with applications in public health and medical sciences. Key contributions include methodologies for prevalent cohort survival analysis, input relaxation efficiency measures in stochastic DEA, and causal inference techniques. Asgharian has collaborated extensively with researchers in epidemiology and biomedical engineering, as evidenced by his co-authored publications on topics ranging from tooth enamel properties to low-precision neural network quantization. His research has been published in high-impact journals such as New England Journal of Medicine , Journal of the American Statistical Association , and Biometrics . Current affiliations include leadership roles in statistical research at McGill, with ongoing projects in computational statistics and healthcare analytics.

Michael J. KatzView profile

Professor

• Porous Materials
• Metal-Organic Frameworks
• Gas Storage
• +5 more

Dr. Michael J. Katz is a Professor in the Department of Chemistry at Memorial University in St. John's, Newfoundland and Labrador, Canada. He leads an active research group focused on porous materials, particularly metal-organic frameworks (MOFs), with applications in gas storage, chemical separation, and catalysis. His work is well-recognized in the field of materials chemistry, with numerous publications in high-impact journals spanning from 2005 to 2025. Dr. Katz's primary research interests lie in the synthesis, properties, and applications of porous materials. His work specifically focuses on: Metal-Organic Frameworks (MOFs) design and synthesis Gas storage technologies, particularly low-pressure methane storage Chemical separation processes including removal of harmful molecules from air Catalysis using porous materials Adsorption properties of various porous frameworks Environmental applications of porous materials Analysis of Dr. Katz's publication record from 2017-2025 reveals a strong emphasis on zirconium-based MOFs, particularly the UiO-66 family. His research spans fundamental characterization techniques like NMR spectroscopy to practical applications in carbon capture, gas separation, and environmental remediation. A notable trend is the increasing focus on real-world implementation of MOFs, including biochar-based materials for CO 2 capture and frameworks for air pollutant removal such as nitrous acid. His work demonstrates a progression from fundamental materials science toward practical environmental applications. Dr. Katz actively supervises graduate students and postdoctoral researchers in his research group. His laboratory at Memorial University is equipped for the synthesis and characterization of novel porous materials, with particular expertise in metal-organic framework development. His research is supported by various grants that enable the exploration of structure-property relationships in porous materials and their practical applications.

Wahab Hamou-LhadjView profile

Professor

• Artificial Intelligence
• Software Engineering
• Machine Learning
• +7 more

Dr. Wahab Hamou-Lhadj is a Professor and Chair at the Department of Electrical and Computer Engineering , Concordia University, and an Affiliate Researcher at NASA JPL, Caltech . He leads research in Artificial Intelligence for IT Operations (AIOps) , Software Observability , and Model-Driven Engineering , focusing on improving the reliability of digital systems in AI-driven environments.

Jonathan BaughView profile

Professor

• Quantum Information Science
• Nanoelectronics
• Semiconductor Devices
• +4 more

Jonathan Baugh is a Professor in the Department of Chemistry at the University of Waterloo, serving as Director of the Quantum Information Graduate Program. His research focuses on quantum devices, nanoelectronics, and molecular electronics with affiliations at the Institute for Quantum Computing and Waterloo Institute for Nanotechnology. He leads the Baugh Research Lab, exploring quantum control, semiconductor spin qubits, and superconducting hybrid systems. Research interests include quantum information processing, nanoscale charge transport, and the development of next-generation photonic sources. His work bridges quantum physics and materials science, with recent breakthroughs in dopant-free semiconductors and single-molecule transistors. Publications emphasize scalable quantum architectures, noise mitigation in quantum control, and phase-coherent molecular electronics. Current projects involve cryogenic CMOS device modeling and topological quantum computing in silicon-based systems. No awards are explicitly listed, though his work has been highlighted in invited reviews and special sessions on quantum systems. Advising focuses on graduate students in quantum nanotechnology and condensed matter physics. His lab collaborates on integrated quantum networks and III-V/Si nanowire photodetectors. Labs/Teams: Baugh Research Lab (Quantum Nanoelectronics Group), Institute for Quantum Computing (IQC), Waterloo Institute for Nanotechnology (WIN).

Amin HammadView profile

Professor

• BIM and Digital Twins
• Construction Automation & Robotics
• Sustainable Infrastructure Management
• +4 more

Amin Hammad is a Professor at the Concordia Institute for Information Systems Engineering, with an additional appointment as Affiliate Professor in Building, Civil, and Environmental Engineering at Concordia University. His research focuses on advancing construction technology through digital transformation, automation, and AI integration. He leads work in BIM applications, 4D simulation, robotic systems, and sustainable infrastructure management. His interdisciplinary approach bridges civil engineering with computer science and data analytics. Key research areas include: Automation and robotics in construction (Construction 4.0) BIM and digital twin lifecycle management AI-driven defect detection and inspection systems Occupational safety through exoskeleton performance evaluation Multi-purpose utility tunnel optimization Energy-efficient building systems Recent work emphasizes applying machine learning to construction equipment activity recognition, UAV path optimization for infrastructure inspection, and ontology development for integrated systems. His research addresses industry challenges in productivity, safety, and sustainability through data-driven solutions.

Mohammadreza KaramadView profile

Assistant Professor

• Electrochemistry
• Heterogeneous Catalysis
• Material Science
• +3 more

Mohammadreza Karamad is an Assistant Professor in the School of Sustainable Energy Engineering at Simon Fraser University (SFU), with a joint appointment in the Sustainable Energy Engineering department. His research focuses on computational materials discovery, leveraging quantum-mechanical methods (e.g., DFT) and machine learning (ML) to design advanced energy materials for clean technologies like hydrogen storage and catalysis. He holds a Ph.D. from the Technical University of Denmark (DTU) and completed postdoctoral research at Stanford University. His academic background includes leadership roles in the CMD Lab (Computational Materials Discovery), where he explores novel materials for electrochemical energy conversion processes. Key research areas include electrochemistry, heterogeneous catalysis, and material science, with a particular emphasis on CO2 reduction, ammonia synthesis, and sustainable energy storage solutions. Dr. Karamad collaborates with industry and academic partners to advance materials discovery through high-throughput computational screening and AI-driven approaches. He actively seeks motivated students (undergraduate and graduate) to join his research program, focusing on developing next-generation energy materials. His lab is located in room B8220, and he can be reached at mkaramad@sfu.ca. Notable technical contributions include pioneering work on transition metal nitrides for CO2 reduction, single-atom catalysts for ammonia synthesis, and machine learning frameworks for predicting material properties. His research bridges fundamental theory with practical applications, addressing global challenges in sustainable energy and environmental technology.

• Software Engineering
• Data Analytics
• Mining Software Repositories
• +5 more

Ahmed E. Hassan is a Professor and Canada Research Chair in Software Analytics at the School of Computing, Queen's University. He serves as NSERC RIM Industrial Research Chair and leads the Software Analysis and Intelligence Lab (SAIL). Dr. Hassan pioneered the Mining Software Repositories (MSR) conference and co-edited special issues in the IEEE Transactions on Software Engineering and the Journal of Empirical Software Engineering on MSR topics. Education Ph.D. in Computer Science, University of Waterloo (2005) MMath in Computer Science, University of Waterloo BMath in Computer Science, University of Waterloo His research focuses on software analytics, mining software repositories, and systems engineering. Projects at SAIL include analyzing version control systems, predicting software defects, and improving software quality through empirical methods. The lab's work spans software evolution, architecture analysis, and debugging of distributed systems. Dr. Hassan has taught courses like CISC 322: Software Architecture and CISC 880: Mining Software Engineering Data at Queen's University, University of Victoria, and University of Waterloo. His teaching philosophy emphasizes hands-on learning with tools like WEKA and R, and includes project-based assignments using MSR Challenge datasets. Scientific Awards NSERC RIM Industrial Research Chair in Software Engineering Canada Research Chair in Software Analytics He has industrial experience from RIM (Blackberry platform), IBM Research (Almaden Lab), and Nortel Networks. Dr. Hassan is a named inventor on patents in the US, Europe, Canada, Japan, and India. SAIL lab is funded by NSERC, ORF, CFI, and industry partners.

Jacques ArchambaultView profile

Professor

• Human Papillomavirus (HPV)
• Polyomavirus
• Viral DNA Replication
• +4 more

Dr. Jacques Archambault is a Professor in the Department of Microbiology and Immunology at McGill University , and an associate member of the Division of Experimental Medicine since 2016. His research focuses on the molecular biology and pathogenesis of human papillomaviruses (HPVs) and polyomaviruses (HPyVs), with an emphasis on their replication mechanisms as episomes in host cells. The Archambault laboratory employs functional genomics, proteomics, and chemical biology approaches to identify cellular pathways exploited by these viruses and develop high-throughput assays for screening small molecule inhibitors of viral replication. Analysis of his recent publications reveals a strong focus on HPV and HPyV replication machinery, including studies on the E1 helicase, UAF1-USP1 interactions, and structural characterization of viral proteins involved in DNA replication. His work bridges virology, oncology, and drug discovery, particularly targeting oncogenic HPV types implicated in anogenital and oropharyngeal cancers, as well as HPyVs like BKPyV and JCPyV that cause pathologies in immunosuppressed patients. Current efforts in the lab aim to elucidate the molecular mechanisms by which HPVs and HPyVs replicate their genomes and to develop antiviral therapies targeting these processes. Techniques such as fluorescence anisotropy, NMR spectroscopy, and crystallography are frequently employed to study protein-DNA and protein-protein interactions critical to viral replication.

Abhijit SarkarView profile

Professor

• Dynamics
• Probabilistic Mechanics
• Uncertainty Quantification
• +5 more

Abhijit Sarkar is a Professor in the Department of Civil and Environmental Engineering at Carleton University, Ottawa. His work centers on computational dynamics and probabilistic modeling, with office MC 3076 in the Minto Centre for Advanced Studies in Engineering and contact details including phone (613) 520-2600 x6320 and email abhijit_sarkar@carleton.ca . Education: D.Phil. from University of Oxford M.Sc. from Indian Institute of Science (IISc) B.E. from Calcutta University Professional Engineer (P.Eng.) designation His research drives innovation in uncertainty quantification for complex engineering systems. Core interests include dynamics of nonlinear structures, probabilistic mechanics for stochastic finite element methods, and Bayesian inference frameworks for parameter estimation. He pioneers scalable high-performance computing solvers for large-scale systems and sparse learning algorithms to address overfitting in statistical modeling. Recent publications (2022-2024) reveal three dominant trends: (1) Bayesian model calibration for stochastic compartmental systems applied to epidemiology and aerospace, (2) domain decomposition techniques for scalable uncertainty quantification in stochastic PDEs, and (3) sparse learning methods for nonlinear aerodynamic encoding. Key applications span wind turbine vibration analysis, flutter margin prediction, MEMS resonator optimization, and geospatial pandemic modeling. Scientific awards: No awards, fellowships, or medals listed in the source material Graduate supervision includes 6 current students (Ajay Kumar, John Clarabut, Nastaran Dabiran, Sakhi Mittal, Michael Pantano, Brandon Robinson) and 18 graduated students across 17 years (2006-2023). His research leverages high-performance computing for projects in structural dynamics, aeroelasticity, and computational epidemiology, frequently co-supervised with Dominique Poirel and Chris Pettit. Notable grants focus on wind tunnel validation for nonlinear systems and pandemic spread modeling. Based in the Minto Centre for Advanced Studies in Engineering, his computational mechanics group develops algorithms for stochastic dynamics using Carleton University's high-performance computing infrastructure. Collaborations span aerospace engineering (flutter analysis), civil infrastructure (seismic wave propagation), and public health (Covid-19 modeling).

• Quantum Magnetometry
• Precision Electromagnetic Measurements
• Plasma Immersion Ion Implantation (PIII)
• +7 more

Michael P. Bradley is a Professor in the Department of Physics and Engineering Physics at the University of Saskatchewan, affiliated with the College of Arts and Science. He holds a Ph.D. from MIT and is a Professional Engineer (P.Eng.). His research focuses on precision measurement techniques, plasma-based nanofabrication, and quantum metrology, including work on diamond NV-centre magnetometers and superconducting watt balance systems. He leads the University of Saskatchewan Plasma Physics Laboratory (U of S PPL) and has received a Canada-UK Joint Quantum Technology grant for quantum sensor development. Education BSc (Honours) in Applied Physics, University of New Brunswick Ph.D. in Physics, Massachusetts Institute of Technology (MIT) Research Interests Bradley specializes in quantum magnetometry , plasma processing , semiconductor nanostructures , and precision electromagnetic measurements . His lab develops novel techniques for materials characterization and fabrication, including plasma immersion ion implantation (PIII) for micro- and nano-scale engineering, graphene doping, and silicon photonics. Recent work includes advancements in diamond NV-centre magnetometry for quantum technologies. Grants & Collaborations Recipient of a prestigious Canada-UK Joint Quantum Technology grant (2023). Collaborated internationally, including at the Bureau International des Poids et Mesures (BIPM) in France, where he contributed to superconducting watt balance prototypes for redefining mass standards. Teaching Teaches courses in optics, thermodynamics, and planetary astronomy, including EP421: Optical Systems & Materials and ASTR104: Planetary Astronomy .

• Computational Materials Science
• Theoretical Materials Science
• Defects in Materials
• +9 more

Hao Zhang is a Professor and Associate Dean for Graduate Students at the Faculty of Engineering, Department of Chemical and Materials Engineering, University of Alberta. Prior to joining the University of Alberta in September 2007, he held a postdoctoral research associate position at Princeton University. He earned his B.E. and M.S. in Materials Science and Engineering from Tsinghua University and a PhD in Mechanical and Aerospace Engineering from Princeton University. Area of Focus: Computational Materials Science, Surface Science, and Thermodynamics Key Projects: Defect properties in materials, hydrogen embrittlement in pipeline steel, mechanical response in nanostructured materials, CO2 capture via hydrotalcite design Courses Taught: CME 900 (Directed Research), MAT E 374 (Computational Methods in Materials Engineering) Email: hao7@ualberta.ca

Nabil BassimView profile

Associate Professor

• Materials Science
• Electron Microscopy
• Nanomaterials
• +3 more

Nabil Bassim is an Associate Professor in the Department of Materials Science and Engineering at McMaster University and serves as Scientific Director of the Canadian Centre for Electron Microscopy (CCEM). His research focuses on advanced electron microscopy techniques, ion microscopy, nanofabrication, and beam-sample interactions, applied to nanomaterials, 2D materials, and structural materials like concrete and alloys. He holds a B.S. in Mechanical Engineering from the University of South Florida, and M.Sc. and Ph.D. degrees from the University of Florida. Research interests include: Development of novel electron/ion microscopy techniques Nanomaterial synthesis and characterization Beam-induced damage and doping mechanisms Structural materials analysis Machine learning optimization for microscale processes Recent publications demonstrate strong focus on semiconductor characterization, nanomaterials synthesis, and advanced microscopy techniques. Article trends highlight innovative approaches to nanoscale analysis, materials for energy applications, and correlative microscopy methods. As Faculty Lead for McMaster Engineering's Aerospace and Defense Initiative, Dr. Bassim coordinates interdisciplinary research. He co-founded the FIB-SEM User Meeting and teaches graduate courses in electron/ion microscopy characterization techniques.