Dr. Anwar Haque is an Associate Professor in the Department of Computer Science at Western University, Canada, and a Faculty of Science Distinguished Research Professor. He holds a Ph.D. in Electrical and Computer Engineering and an M.Sc. in Computer Science from the University of Waterloo. Prior to academia, he was Associate Director at Bell Canada. His research focuses on 5G networks, IoT, cybersecurity, AI, and autonomous systems, with over 100 peer-reviewed publications and $15M in collaborative grants. Dr. Haque leads the Western Information & Networking Group (WING) Lab and is the founder/CEO of Bamboo Innovations Inc., a tech startup developing socially responsible smart technologies. Leadership roles include industry expert-in-residence in the Faculty of Science, Undergraduate Chair of the Computer Science Department, and member of Western’s Senate. He has delivered over 30 keynote talks and media features include BBC Earth and The Globe and Mail. Awards include the IEEE CCECE Leadership Award and multiple grants from NSERC, MITACS, and Bell Canada. His work spans network reliability, smart grids, and cybersecurity, with industry partnerships like the Bell-Western 5G Research Centre.
Dr. Mohammad Zulkernine is a Full Professor and Canada Research Chair in Cyber-Physical System Security at Queen’s University’s School of Computing (Faculty of Arts and Science). He leads the Queen’s Reliable Software Technology (QRST) research group and directs the Queen’s Centre for Security & Privacy. His research focuses on secure software systems for cyber-physical systems, including autonomous vehicles, IoT, and cloud computing. Dr. Zulkernine holds a cross-appointment in Electrical and Computer Engineering and is a licensed Professional Engineer in Ontario. Education: BSc (Bangladesh BUET), MEng (Japan), PhD (University of Waterloo). He joined Queen’s in 2003 and has held sabbaticals at University of Trento, Italy and Irdeto Canada. He has published over 250 papers, led 35+ research projects, and supervised 120+ students. Awards include Canada Research Chairs (Tier I and II), Queen’s Excellence in Graduate Supervision Award, and Distinguished Supervision Award from the School of Computing. Research Interests: Cyber-Physical System Security, Software Reliability, IoT Security, Vehicle Networks, Secure Software Engineering, and Cybersecurity Risk Assessment. Active industry collaborations include EU-funded projects and Canada-Africa initiatives.
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 .
Dr. Hiren Patel is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. He holds a Doctorate in Computer Engineering from Virginia Tech and previously worked as a postdoctoral fellow at UC Berkeley under Edward A. Lee. His research focuses on real-time embedded systems, computer architecture, machine learning hardware, and cybersecurity. He teaches courses like ECE 150 (Programming), ECE 320/429 (Computer Architecture), and ECE 327 (Digital Systems). Research Interests: Cyber-physical systems and hybrid architectures Hardware/software co-design methodologies Predictable cache coherence protocols IoT and edge computing systems Security in embedded and real-time systems Recent work emphasizes cache coherence solutions for safety-critical systems and GPU acceleration strategies. His publications address challenges in multicore predictability, FPGA bandwidth optimization, and autonomous robotics orchestration. No specific awards are listed, though his extensive publication record indicates significant contributions to embedded systems research. He currently oversees graduate student applications focusing on his core research areas.
Paul Lu is a Professor in the Department of Computing Science at the University of Alberta, Faculty of Science. His research focuses on high-performance computing, parallel and distributed systems, cloud computing, and bioinformatics. He holds a B.Sc. (1991), M.Sc. (1993) in Computing Science from the University of Alberta, and a Ph.D. in Computer Science from the University of Toronto (2000). His research explores software systems, including operating systems, virtual machines, and parallel programming. Recent work emphasizes high-performance data transfers and IaaS cloud computing. He teaches courses such as MINT 706: Internet Application and Programming, covering internet protocols and client-server programming. Publications highlight contributions to network optimization, machine learning-driven protocol selection, and distributed systems. His work bridges theoretical advancements with practical applications in cloud infrastructure and wide-area networks.
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.
Richard S. Sutton is a Professor in the Department of Computing Science at the University of Alberta , a Research Scientist at Keen Technologies, and the Principal Investigator at the Reinforcement Learning and Artificial Intelligence Lab . He also serves as the Canada CIFAR AI Chair at the Alberta Machine Intelligence Institute (Amii) and as Chief Scientific Advisor there. Additionally, he founded the Openmind Research Institute and is Chief Scientific Officer at ExperienceFlow. His research focuses on identifying general computational principles underlying intelligence and goal-directed behavior. He emphasizes the interaction between intelligent agents and their environment , exploring how goals, choices, and information sources emerge from this interaction. His work spans reinforcement learning (RL) , agent-environment dynamics, and ethical considerations in AI, such as robot rights and moral philosophy. His articles include technical works on RL, ethical frameworks, and philosophical essays like The Bitter Lesson (2019) and The Definition of Intelligence (2016). Canada CIFAR AI Chair Co-author of foundational RL textbook Developed Tile Coding Software for RL
Nick Koudas is a Professor in the Department of Computer Science at the University of Toronto, specializing in large-scale data management, data systems, and applied machine learning. His research integrates machine learning techniques into scalable data platforms to enhance the analysis of massive datasets. He holds a PhD from the University of Toronto, an MSc from the University of Maryland, and a Bachelor's degree from the University of Patras. Education: PhD, University of Toronto MSc, University of Maryland at College Park Bachelor's degree, University of Patras, Greece Research Interests: Relational Deep Dive (ReDD): Natural language query execution over unstructured documents Streaming Video Queries (SVQ): Interactive query processing for video streams Reliable Text-to-SQL: Generating accurate SQL queries with human-in-the-loop assistance Machine Learning Integration in Data Systems Publications: Focus on video analytics, query processing, and reliable natural language interfaces. Notable works include optimizing video queries, declarative frameworks for temporal constraints, and abstention-based SQL generation. Awards: Inventor of the Year (1st Prize), University of Toronto (2011) Best Paper Awards at international conferences Entrepreneurship & Advising: Co-founder of Sysomos (Meltwater Group), Aislelabs (Constellation Software), and Workorb Advisor to mapintent and ktau Labs & Teams: Leads research groups developing systems like ReDD and SVQ, emphasizing collaboration between academia and industry.
Dr. Thomas E. Doyle is an Associate Professor at the McMaster School of Biomedical Engineering and the Department of Electrical & Computer Engineering at McMaster University. His research focuses on biomedical signal processing, human-computer interfacing (HCI), and machine learning applications for healthcare augmentation, rehabilitation, and enhancement. He holds a Ph.D. from Western Ontario, Canada, and teaches courses like COMPENG 2DI4 (Logic Design). His work bridges cybernetics and clinical applications, emphasizing AI-driven solutions for medical diagnostics, patient monitoring, and space exploration. Education: B.E.Sc, B.Sc, M.E.Sc, Ph.D. from Western Ontario, Canada Recent Projects: Developed AI systems for remote healthcare diagnostics (2023) Collaborated with NASA on medical emergency simulators for deep space missions (2017–2023) Led ventilator development efforts for local hospitals during the pandemic (2020) His research interests span machine learning for mental health diagnostics, trust quantification in medical AI, and extended reality (XR) for medical training. He emphasizes interdisciplinary approaches, integrating computational methods with healthcare challenges. Recent publications highlight applications in pediatric emergency care, chronic pain management, and reliable medical device design. Dr. Doyle actively engages in educational initiatives, including first-year engineering pedagogy and experiential learning programs. He has received funding for projects such as the Educating the Engineer of 2025 (EtE-25) awards and contributes to initiatives like the Digital & Smart Systems and Health & Bio-innovation research clusters at McMaster.
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.
Dr. Yujie Tang is an Assistant Professor in the Faculty of Computer Science at Dalhousie University, Canada, where she has been serving since September 2022. Prior to this, she was an Assistant Professor at Algoma University (2019–2022) and a Post-Doctoral Fellow at the University of Waterloo (2017–2019). Her academic journey includes a PhD from the University of Waterloo and earlier degrees from Harbin Institute of Technology and Lanzhou Jiaotong University. PhD – University of Waterloo (2017) M.E. – Harbin Institute of Technology, Shenzhen, China B.E. – Lanzhou Jiaotong University, Lanzhou, China Her research focuses on intelligent networking and computing technologies for future IoT and 5G/6G systems. Key areas include Internet of Vehicles (IoV), AI-empowered edge computing, resource management in heterogeneous networks, software-defined networking, and UAV-assisted communications. She employs machine learning and optimization techniques to design energy-efficient and high-performance network protocols. The most recent publications reflect a strong trend in applying AI and machine learning to solve complex problems in vehicular networks, edge caching, and spectrum management. Her work spans top-tier IEEE journals such as IEEE Transactions on Vehicular Technology , IEEE Internet of Things Journal , and IEEE JSAC , with a clear emphasis on real-world deployable solutions for next-generation wireless systems. Faculty Research Startup Fund, Dalhousie University, 2022 NSERC Discovery Grant, 2021–2026 Algoma University Research Fund, 2021 Faculty Research Startup Fund, Algoma University, 2019 Best Speaker Award, University of Waterloo, 2017 Faculty of Engineering Award (4 times), University of Waterloo, 2013–2017 University of Waterloo Graduate Scholarship, 2013–2015 International Doctoral Student Award, 2012–2016 Graduate Research Studentship (twice), 2011–2012 Provost Doctoral Entrance Award for Women, 2011 Dr. Tang actively supervises graduate and undergraduate students and has secured competitive research grants, including the NSERC Discovery Grant. She serves on the technical program committees of major IEEE conferences such as INFOCOM, GLOBECOM, and ICC, and regularly reviews for top journals like IEEE JSAC , IEEE TWC , and IEEE TVT . She currently leads a research group focusing on B5G/6G networks, IoV, and edge computing, and she is actively recruiting new students and visiting scholars. Her research group operates within the Faculty of Computer Science at Dalhousie University, where she leads projects in intelligent resource management, AI-driven networking, and integration of space-air-ground networks. She is a member of IEEE, IEEE Communications Society, and IEEE Vehicular Technology Society.
Shiva Nejati is a Professor at the University of Ottawa 's School of Electrical Engineering and Computer Science . He holds a PhD in Computer Science from the University of Toronto and previously worked as a Senior Scientist (2012-2019) and Scientist (2009-2012) at the SnT Centre (University of Luxembourg) and Simula Research Laboratory. Research focus: Software engineering for cyber-physical systems (autonomous vehicles, IoT), blending formal verification, machine learning, and search-based testing Key tools developed: ARIsTEO, SOCRaTEs, SimCoTest, EPIcuRus Editorial roles: Associate Editor for EMSE Journal (2025–), ASE Journal (2025–), IEEE Transactions on Software Engineering (2020–2024) His work combines formal methods , empirical software engineering , and AI/ML to address verification challenges in complex systems, particularly through evolutionary algorithms and surrogate modeling . Notable collaborations include industry partners in telecommunications, automotive, and aerospace sectors. Recent publications emphasize large language models for requirements analysis, adversarial testing of vision systems, and multi-objective optimization for test generation. His Sedna Research Lab actively trains graduate students in these cutting-edge methodologies.
Natalie Enright Jerger is a Professor in the Department of Electrical and Computer Engineering at the University of Toronto's Faculty of Applied Science and Engineering. She holds the Canada Research Chair in Computer Architecture and serves as Director of the Division of Engineering Science (2023-2028). Previously, she was the Percy Edward Hart Professor (2016-2019). She received her B.S. in Computer Engineering from Purdue University (2002), and M.S./Ph.D. in Electrical Engineering from University of Wisconsin-Madison (2004/2008). Her research focuses on: Multi/many-core architectures and on-chip networks Cache coherence protocols and memory hierarchy optimization Approximate computing and sustainable systems Intermittent computing for energy-harvesting devices Hardware acceleration for machine learning Her publications demonstrate strong emphasis on networks-on-chip (NoC) innovations, including routing algorithms, deadlock handling, power-efficient designs, and topology optimizations. Recent work expands into approximate computing, mobile architectures, and ML-driven hardware design. Major Awards: Fellow of Engineering Institute of Canada (2023) McLean Senior Fellow (2019) IEEE Micro Top Picks (2016) ACM/IEEE Microarchitecture Hall of Fame (2015) Sloan Research Fellowship (2015) Canada Research Chair (current) Distinguished Scientist, ACM Fellow, IEEE She leads the NEJ research group and collaborates with industry partners including Intel, AMD, Qualcomm, and IBM. Her work is funded by NSERC, CFI, and industrial grants. She co-chaired ASPLOS 2023 and HPCA 2014, and actively promotes diversity through WICARCH and ACM initiatives.
Olivia Di Matteo serves as an Assistant Professor in the Department of Electrical and Computer Engineering within UBC's Faculty of Applied Science, leading the Quantum Software and Algorithms Research (QSAR) group since her January 2022 appointment. Her academic foundation includes a BSc from Lakehead University and MSc/PhD in Physics (Quantum Information) from the University of Waterloo, completed in 2019. Dr. Di Matteo's research centers on quantum software engineering , with pioneering work in quantum compilation , circuit optimization , and debugging tools . She champions open-source quantum frameworks and develops accessible educational resources to democratize quantum computing. Analysis of her 15 most recent publications (2021-2025) reveals dominant trends in quantum programming infrastructure, particularly circuit analysis (33%), bug classification (20%), and qubit network optimization (15%), with strong emphasis on practical software tooling over theoretical physics. No scientific awards were documented in the source materials. She advises graduate students in the QSAR group while contributing to open-source quantum ecosystems through projects like PennyLane and The Ionizer transpiler, and teaches courses including CPEN 400Q (Gate-model quantum computing) and ELEC 221 (Signals and Systems). The QSAR group operates at the intersection of quantum software development and education, focusing on making quantum programming accessible through visual tools, real-time debugging environments, and hardware-agnostic compilation techniques.
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.
