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Dr. Zhenman Fang is an Associate Professor at the School of Engineering Science , Simon Fraser University (SFU) , where he founded and directs the HiAccel Lab . He also holds an associate membership in the School of Computing Science at SFU. His research focuses on customizable computing with software-defined hardware acceleration , addressing performance, energy-efficiency, and reliability in post-Moore’s law computing across domains like machine learning , big data analytics , quantum chemistry , and precision medicine . Education: Ph.D. in Computer Science from Fudan University (2014), with a visit to University of Minnesota during his studies. Postdoctoral Work: University of California, Los Angeles (UCLA) (2014-2017). Industry Experience: Staff Software Engineer at Xilinx (2017-2019). Dr. Fang’s research spans the entire computing stack , including application characterization , accelerator-rich architecture design , and programming/tool support . He has developed frameworks like HiSpMV , SyncNN , and SQL2FPGA , emphasizing FPGA acceleration for vision transformers , quantum chemistry , and spiking neural networks . His work has been recognized with 3 best paper awards (FPL 2024, TCAD 2019, MEMSYS 2017) and 3 best paper nominees (FCCM 2025, HPCA 2017, ISPASS 2018). Recent publications highlight trends in low-precision machine learning ( ShiftQuant , ESRU ), quantum chemistry acceleration ( SERI ), and vision transformer optimization ( Quasar-ViT ). His HiAccel Lab actively mentors PhD and MASc students , with notable graduates like Alec Lu (PhD 2024, now at Meta) and Philip Stachura (MASc, now with BC Graduate Scholarship). Scientific Awards: Inaugural SFU Research Excellence Award - Horizon Award (2025) FPL 2024 Stamatis Vassiliadis Best Paper NSERC Alliance Award (2020) CFI JELF Award (2019) Xilinx University Program Award (2019) IEEE Senior Member (2023) Grants: NSERC Discovery Grant (2019) CFI JELF Funding (2019) Huawei and Xilinx sponsorships Dr. Fang leads open-source initiatives like SyncNN , PASTA , and SQL2FPGA , and serves as General Chair for ASAP 2025 and Program Co-Chair for RAW 2025 . His lab collaborates globally with institutions such as UCLA , Northeastern University , and Xidian University .

• Customizable Computing
• Heterogeneous Computing
• FPGA Acceleration
• +8 more

Dr. Samir H. Mushrif is a Professor in the Department of Chemical and Materials Engineering at the University of Alberta . Prior to this role, he served as faculty at the School of Chemical and Biomedical Engineering at Nanyang Technological University (NTU), Singapore . He holds a PhD in Chemical Engineering from McGill University and completed postdoctoral research at the University of Delaware, USA . Education : PhD (Chemical Engineering, McGill University), Postdoc (University of Delaware) His research focuses on computational catalysis , molecular modeling , and reaction engineering for biomass conversion and CO2 reduction . He develops novel catalysts, solvents, and reactor systems using integrated quantum mechanical and classical molecular simulations , synergized with experimental data to enable sustainable energy and chemical production . Recent publications highlight trends in condensed phase chemistry for biomass reactions, machine learning applications in solvent configuration prediction, and mechanistic studies of lignin-carbohydrate complex deconstruction. His work bridges methane activation on metal oxides, hydrodeoxygenation of bio-oil compounds, and polymerization pathways in lignin structures. Scientific Awards include: NSERC Doctoral and Post-doctoral Fellowships Discovery International Award 2017 (Australian Research Council) NANYANG EDUCATION AWARD 2016 (Singapore) SCBE Teaching Excellence Awards (Silver 2015, Gold 2016) Bharat Gaurav (Pride of India) Award 2014 Dr. Mushrif's NSERC Discovery Grant (2018), CFI John R. Evans Leaders Fund Grant (2022), and AcRF Tier-2 Grant (Singapore, 2015) have advanced his work. Current PhD and Master's students include José Carlos Velasco Calderón , Arul Mozhi Devan Padmanathan , and Sagar Bathla , among others. The CARES Lab (Catalysis Research for Sustainability) under his leadership combines ab initio molecular dynamics , machine learning potentials , and Density Functional Theory to design materials for renewable energy . Collaborations span institutions in France , Canada , India , and the UK .

• Computational Catalysis
• Molecular Modeling
• Reaction Engineering
• +7 more

Gordon Geoffrey J. is a Professor at Carnegie Mellon University, specializing in Machine Learning , Artificial Intelligence , and Computer Science . His research spans Reinforcement Learning , Domain Adaptation , Graph-based Planning , and Relational Learning , with significant contributions to Adversarial Learning , Deep Learning Dynamics , and Mobile Sensor Networks . He has pioneered algorithms like ARA* and Anytime Dynamic A* for efficient planning under constraints. Key research areas: Transfer Learning , Bayesian Knowledge Tracing , Multi-agent Systems , Database Optimization His work has been cited thousands of times across foundational topics in AI and robotics, demonstrating sustained impact in algorithm design and applications to complex systems.

• Machine Learning
• Artificial Intelligence
• Computer Science
• +5 more

Mohammad Hamdaqa is an Associate Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal, where he leads the Laboratory of Software and Emerging Technologies. His academic journey includes a Ph.D. in Electrical and Computer Engineering from the University of Waterloo (2016), a Master's in Electrical and Computer Engineering from Concordia University, an MBA from the New York Institute of Technology, and a Bachelor's in Computer Engineering from Jordan University of Science and Technology. His research focuses on the intersection of software engineering and emerging technologies, particularly examining how software engineering approaches can be adapted for complex new platforms like cloud computing and blockchain. His work spans model-driven software engineering, cloud application architecture, smart contract development, and infrastructure as code. He investigates both how traditional software engineering practices can evolve to address the challenges of modern distributed systems and how emerging technologies can transform software development processes themselves. Analysis of his recent publications reveals a strong emphasis on blockchain technologies (particularly smart contracts), cloud-native applications, and the application of AI to software engineering tasks. His work shows a consistent thread of empirical research combined with practical tool development, with increasing focus on sustainability aspects of software systems in recent years. Much of his research bridges theoretical foundations with practical implementation concerns. Professor Hamdaqa serves as a thesis supervisor for multiple graduate students, with recent completed Master's theses focusing on smart contract auditing, prompt engineering for OCL generation, model-driven epidemiology, and security practices in infrastructure as code. He actively recruits students for research projects in his laboratory. He is a member of both the IEEE Computer Society and the Association for Computing Machinery (ACM), has served on program committees for major software engineering conferences, and is on the editorial board of Service Transaction on Internet of Thing. His laboratory, the Laboratory of Software and Emerging Technologies, serves as the hub for his research activities in blockchain, cloud computing, and model-driven engineering.

• Software Engineering
• Model-Based Software Engineering
• Cloud Computing
• +7 more

Raouf Boutaba is a Professor at the University of Waterloo , serving as Director of the David R. Cheriton School of Computer Science since July 2020. He holds prestigious fellowships including FRSC , FIEEE , FIEC , and FCAE . 2024: Inaugural Rogers Chair in Network Automation 2024: Ontario Research Fund–Research Excellence (ORF–RE) $2M grant for next-gen mobile networks 2021: University Professor title, University of Waterloo Research Interests span network automation, resource management in wired/wireless networks, network function virtualization (NFV), software-defined networking (SDN), cloud computing, blockchain, future Internet architecture, and cybersecurity. His work focuses on zero-touch networks, 5G/B5G slicing, and AI-driven orchestration. Scientific Contributions include 15+ recent publications on topics like reinforcement learning for RAN slicing, encrypted traffic classification, quantum network optimization, and self-driving infrastructure. His projects 5G LEAP and 5G ELITE explore network isolation and Open RAN principles. 2024: IFIP/IEEE CNOM Test of Time Paper Award 2024: Graduate Supervision Excellence Award 2021: Kenneth C. Sevcik Outstanding Student Paper Award (advisor) Teaching includes co-developing the NSERC CREATE Network Softwarization program, offering courses like Network Softwarization: Principles and Foundations (Winter 2024) and Technologies and Enablers since 2018. He emphasizes hands-on training in SDN, NFV, Open RAN, and 5G. Students and Collaborations : Supervised PhD students such as Shihabur R. Chowdhury (2021), Nashid Shahriar (2020), and undergrad Leni Aniva (2022 Gov. Gen. Silver Medal ). His team includes researchers working on 5G, blockchain, and AI-driven network management. Professional Leadership : Organized Rogers TEP Workshops (2024-2025), delivered keynotes at IEEE Globecom, ColCom, and BalkanCom, and served on expert panels for AI orchestration and 5G cybersecurity at major symposia.

• Network Management
• Systems and Services
• Resource Management in Wired/Wireless Networks
• +9 more

Ebrahim Bedeer Mohamed is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Saskatchewan. He joined in July 2019, following roles as an Assistant Professor (Lecturer) at Ulster University, UK, and postdoctoral fellowships at Carleton University and the University of British Columbia. He holds a Ph.D. (Distinction) from Memorial University of Newfoundland (2014), with expertise in signal processing and wireless communications. His research focuses on optimizing communication systems through advanced signal processing techniques, including faster-than-Nyquist signaling, IoT network design, AI integration, and energy-efficient protocols. Key areas include next-generation communication networks, non-orthogonal modulation, and MIMO systems. Notable contributions include work on channel estimation for FTN signaling, RIS-aided wireless systems, and LR-FHSS protocols in IoT. His publications span spectral efficiency, interference minimization, and energy management in 5G/6G contexts. He actively seeks Ph.D. students with strong backgrounds in signal processing fundamentals. Awards and grants are not explicitly listed in the provided texts. His work emphasizes practical applications, such as UAV trajectory optimization for IoT data collection and energy-efficient caching strategies in dynamic networks.

• Signal Processing
• Wireless Communications
• Artificial Intelligence
• +3 more

Patrick Mitran is a full-time Professor at the University of Waterloo's Department of Electrical and Computer Engineering, within the Faculty of Engineering. His research focuses on advanced wireless communication systems, including 5G/6G technologies, millimeter-wave and sub-THz communication, digital predistortion techniques, MIMO systems, and beamforming architectures. He leads projects addressing challenges in transmitter linearization, network resource allocation, and hardware-efficient signal processing. Key research interests include optimizing frequency multiplier-based transmitters, mitigating inter-cell interference in massive MIMO networks, and developing algorithms for reconfigurable intelligent surfaces (RIS). His work often intersects hardware design, signal processing, and network optimization, with applications in next-generation wireless infrastructure. Recent publications highlight innovations in ultrawideband signal generation for 6G testing, practical RIS configurations, and FPGA-based real-time digital predistortion implementations. His contributions emphasize both theoretical advancements and practical system-level solutions. Dr. Mitran's research group collaborates on cutting-edge topics such as hybrid NOMA in multi-cell networks, adaptive coding modulation for Gaussian channels, and interference decoding strategies. His work has been published in top-tier journals and conferences, reflecting a sustained impact on modern wireless communication technologies.

• 5G/6G Communication Systems
• Millimeter-wave and Sub-THz Technologies
• Digital Predistortion
• +3 more

Kevin Leyton-Brown is a Professor of Computer Science at the University of British Columbia (UBC), holding a Canada CIFAR AI Chair at the Alberta Machine Intelligence Institute (Amii). He is also an Associate Member of the Vancouver School of Economics and a Fellow of the Royal Society of Canada, ACM, and AAAI. His research focuses on AI, machine learning, computational economics, and game theory, with notable contributions to algorithmic market design, heuristic algorithms, and large language models. He co-authored influential textbooks on multiagent systems and game theory, and his work has been recognized with prestigious awards including the INFORMS Franz Edelman Award and the Killam Teaching Prize. Education: PhD (Computer Science), Stanford University; MSc (Computer Science), Stanford University; BSc (Computer Science), McMaster University. Research Interests: Artificial Intelligence, Machine Learning, Game Theory, Computational Economics, Algorithmic Game Theory, Market Design, and Large Language Models. He has developed impactful tools like SATzilla, AutoWEKA, and Mechanical TA, and contributed to high-stakes projects such as spectrum auction design and Ugandan agricultural market platforms. Awards & Recognition: Royal Society of Canada Fellow (2023), ACM SIG-KDD Research Track Test of Time Award (2023), INFORMS Franz Edelman Award (2018), ACM Fellow (2020), AAAI Fellow (2018), Killam Teaching Prize (UBC), and numerous paper awards from top conferences like AAAI, ICML, and ACM-EC. Leadership & Affiliations: Director of UBC’s CAIDA and AIM-SI research clusters, former Chair of ACM SIG-Ecom, and advisor to companies like AI21 Labs and Auctionomics. He has held visiting roles at institutions including MIT, Harvard, and the Simons Institute.

• Artificial Intelligence
• Machine Learning
• Game Theory
• +5 more

Travis Wiens is an Associate Professor in the Department of Mechanical Engineering at the University of Saskatchewan. His research focuses on fluid power systems, acoustic sensing, dynamic modeling, mining mechatronics, and artificial intelligence applications in engineering. He holds a B.Sc., M.Sc., and Ph.D. in Mechanical Engineering from the same institution, completed in 2002, 2004, and 2008 respectively. His research interests emphasize innovative solutions for hydraulic control systems, pipeline resonance reduction, and mining safety through acoustic technologies. He has developed low-cost electrohydrostatic actuators and explored additive manufacturing in valve design. Notable contributions include vibrational data communication tools for extractive industries and machine learning models for mine roof stability assessment. Dr. Wiens teaches ENGC 01A - Introduction to Fluid Power Components . His work bridges theoretical fluid dynamics with practical applications in energy efficiency, structural health monitoring, and biomimetic systems. Ongoing research trends include integrating AI with hydraulic systems and advancing non-destructive testing methods for infrastructure integrity. His publications highlight interdisciplinary approaches, combining mechanical engineering principles with computational modeling, neural networks, and mechatronics. Collaborative efforts focus on mining safety, oil and gas infrastructure, and sustainable hydraulic technologies.

• Fluid Power (Hydraulics)
• Acoustic Sensing
• Dynamic Modelling
• +4 more

Dr. Goetz Bramesfeld serves as a Professor in the Department of Aerospace Engineering at Toronto Metropolitan University, where he leads research in applied aerodynamics and unconventional flight systems. His expertise spans flight vehicle design, small UAV development, and motorless flight dynamics, with particular emphasis on energy harvesting from atmospheric phenomena. Bramesfeld's educational background includes a PhD (2006) and MS (1999) from The Pennsylvania State University, and a BEng (1998) from Technische Universität Braunschweig. His research interests focus on applied aerodynamics , flight dynamics , and energy-efficient aircraft design , with notable contributions to sailplane optimization, gust energy extraction, and microwave-powered UAV concepts. His work bridges theoretical aerodynamics with practical applications in both terrestrial and planetary exploration contexts. Analysis of his publication record reveals consistent innovation in energy harvesting flight systems, particularly through gust energy extraction and unconventional propulsion methods. His research evolves from traditional sailplane optimization toward cutting-edge concepts like microwave-powered aircraft and planetary exploration gliders, maintaining strong connections between fundamental aerodynamics and real-world flight applications. Bramesfeld actively supervises graduate students through the Applied Aerodynamics Laboratory of Flight (AALF) and maintains significant professional engagement as a Senior Member of the American Institute of Aeronautics and Astronautics (AIAA), member of the Canadian Aeronautics and Space Institute (CASI), Associated Editor for the Technical Soaring Journal, and board member of the Organisation Scientifique et Technique du Vol à Voile (OSTIV).

• Applied Aerodynamics
• Flight Vehicle Design
• Small UAV Design
• +5 more

Dr. Zia Saadatnia is an Assistant Professor at Ontario Tech University's Department of Mechanical and Manufacturing Engineering, part of the Faculty of Engineering and Applied Science. He holds affiliations with the KITE Research Institute (University Health Network) as an Affiliate Scientist and the University of Toronto's Department of Mechanical and Industrial Engineering as an Assistant Professor (Status-only). His research focuses on advanced materials, energy harvesting, and biomedical devices, with notable contributions to aerogel composites, triboelectric nanogenerators, and functional electrical stimulation technologies. Education: Ph.D., Mechanical Engineering, University of Toronto (2019) M.Sc., Mechanical Engineering, Ontario Tech University (2015) B.Sc. (Hons), University of Science and Technology, Iran (2007) Research Interests: Smart structures and materials Nonlinear vibration dynamics Energy harvesting systems Sensors and actuators Biomedical devices Polymer composites and aerogel fabrication Awards and Honors: Mitacs Accelerate Fellowship (2021-2024) William Dunbar Memorial Scholarship (2019) Pierre Rivard Hydrogenics Graduate Fellowship (2018) Ranked First in Undergraduate Class (2011) Advising and Grants: Dr. Saadatnia has led research projects supported by grants from Mitacs and the Government of Canada. His work integrates interdisciplinary approaches to address challenges in energy systems, biomedical engineering, and material innovation. Labs and Teams: Collaborates with KITE Research Institute and University of Toronto teams on advanced materials and biomedical applications. His lab focuses on experimental and computational studies of energy harvesting and smart materials.

• Smart Structures and Materials
• Nonlinear Vibration and Structural Dynamics
• Energy Harvesting
• +5 more

Dr. Amir Keyvan Khandani is a Professor and Senior Ciena-NSERC Industrial Research Chair in the Department of Electrical and Computer Engineering at the University of Waterloo. He holds prestigious research chairs including Tier 1 Canada Research Chair in Wireless Communications and former Senior NSERC Chairs with Blackberry and Nortel. His research focuses on information theory, wireless and optical communications, and signal processing, emphasizing foundational principles and practical applications. Dr. Khandani earned his BEng and MEng from Tehran University (1985) and PhD from McGill University (1992). He joined Waterloo in 1993, supervising over 45 PhD students, 35 master’s candidates, and numerous postdoctoral researchers. His alumni work globally in academia and industry. Research interests include Network Information Theory , Media-Based Modulation , Full-Duplex Systems , and Quantum-Safe Encryption . Recent work explores secure key generation, interference management, and next-generation wireless innovations. Notable awards include NSERC/Ciena Industrial Research Chair and multiple Canada Research Chairs. His publications span foundational and applied topics in communications, with recent focus on cybersecurity and 5G/6G technologies. Dr. Khandani actively contributes to conferences, consults for industry/government, and teaches ECE 307 - Probability Theory and Statistics . His lab develops cutting-edge solutions in wireless networks, optical systems, and secure communication protocols.

• Information Theory
• Wireless Communications
• Optical Communications
• +5 more

Wagdi George Habashi is a Professor and NSERC-Industrial Research Chair at McGill University's Faculty of Engineering, Department of Mechanical Engineering. He leads the Computational Fluid Dynamics (CFD) Lab, focusing on aerodynamics, fluid mechanics, and icing-related simulations. His research emphasizes in-flight icing prediction, computational wind engineering, and CFD-driven optimization of aircraft and jet engine systems. Education: Ph.D., Cornell University M.Eng., McGill University B.Eng., McGill University Research Interests: Habashi's work bridges analytical and computational methods to address multi-physics/multi-scale engineering challenges. Key areas include in-flight ice crystal ingestion in jet engines, ice surface roughness modeling, supercooled droplet dynamics, and CFD-based risk management for icing. His team develops tools like FENSAP-ICE for real-time aero-icing simulations and explores mesh adaptation, parallel computing, and reduced-order modeling. Labs/Teams: Computational Fluid Dynamics Lab (CFD Lab).

• Aerodynamics
• Fluid Mechanics
• Computational Fluid Dynamics (CFD)
• +3 more

Dr. Jennifer Bauman is an Associate Professor in the Department of Electrical & Computer Engineering at McMaster University. Her research focuses on the electrification of transportation, power electronic converters, vehicle design and control, and smart-grid integration of electric vehicles (EVs). She holds a B.Sc. and Ph.D. from the University of Waterloo and has 8 years of industry experience as Director of Research at CrossChasm Technologies. Her work spans three levels: low-level power electronics optimization, mid-level vehicle system design, and high-level EV-grid interaction analysis. Education: B.A.Sc. and Ph.D. in Engineering from the University of Waterloo (2004, 2008). Professional registration: P.Eng. (Professional Engineer). Teaching: Instructors for ECE 724/MECHENG 721 on Modeling, Control, and Design of Electrified Vehicles. Research Interests: Development of efficient power electronic converters (e.g., using wide-bandgap devices), optimization of hybrid/electric powertrains, and analyzing EV impacts on smart grids. Notable contributions include a 65kW boost converter for fuel cell vehicles and studies on solar-charged EV architectures. Grants & Awards: Received $1.9M in CFI infrastructure funding (2017) as part of a team. Lab: Located in ITB A220, focusing on advanced vehicle electrification and grid-integrated systems.

• Power Electronics
• Electric Vehicle Design
• Renewable Energy Integration
• +3 more

Lianying Zhao is an Associate Professor in the School of Computer Science at Carleton University and serves as a Director of the Carleton Computer Security Lab (CCSL). His research focuses on low-level platform security, including hardware, firmware, hypervisor, and operating systems, with an emphasis on trusted computing, authentication, privacy preservation, and security metrics. He leads the CCSL research group, collaborating with interdisciplinary teams to address critical security challenges in IoT, cloud systems, and web applications. Education: Not explicitly listed in provided texts. Roles: CCSL Director, Research Supervisor, and Graduate Program Advisor. Dr. Zhao’s work spans hardware security improvements, firmware vulnerability analysis, and user-centric security metrics. Recent research highlights include studies on router configuration habits, tracker detection in web browsers, and CVSS score discrepancies. He has supervised numerous graduate students in cybersecurity domains, contributing to over 30 peer-reviewed publications since 2013. His lab affiliations include CCSL and CISL, where he collaborates on projects such as secure deletion frameworks, TLS validation vulnerabilities, and hybrid decision-making models for cloud security. Current research also explores cross-regional login throttling mechanisms and AI-driven vulnerability detection in embedded systems.

• Trusted Computing
• Hardware/Architectural Security
• Systems Security
• +3 more