Dr. Omar Ramahi is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, leading the Advanced Concepts Research (ACR) Laboratory. He holds a PhD from the University of Illinois at Urbana-Champaign and has held academic positions at the University of Maryland and industry roles at Digital Equipment Corporation. His research focuses on electromagnetic phenomena, biomedical applications, renewable energy, and metamaterials, with over 500 publications and co-authorship of the textbook EMI/EMC Computational Modeling Handbook . Education: PhD, Electrical and Computer Engineering, University of Illinois (1990) MSc, Electrical and Computer Engineering, University of Illinois (1986) BSc (Mathematics) & BSc (Electrical Engineering), Oregon State University (1984) Research Interests: Radiating systems and antennas Biomedical imaging and sensing (e.g., breast cancer detection, bone fractures) Electromagnetic compatibility (EMC) and interference Metamaterials and metasurfaces Renewable energy harvesting and wireless power transfer Low-frequency electromagnetic tomography Awards: IEEE Electromagnetic Compatibility Society Technical Achievement Award (2012) University of Waterloo Research Excellence Award (2022) IEEE Fellow (Elected) 2010 Excellence in Graduate Supervision Award Advising & Labs: Directed over 30 PhD and MS students, many now faculty globally. ACR Lab develops wearable health sensors, radar-based medical imaging, and energy harvesting systems. Notable projects: Jewelry-based health monitors, microwave breast cancer detection, UAV classification via radar.
Prof. Jiangchuan Liu is a Professor in the School of Computing Science at Simon Fraser University (SFU), holding prestigious fellowships from the Canadian Academy of Engineering, IEEE, and NSERC. His research focuses on Internet architecture, wireless networks, cloud computing, and multimedia systems. He teaches courses in data communications and multimedia computing. Education: Ph.D. Computer Science, The Hong Kong University of Science & Technology, 2003 B.Eng. (Cum Laude), Tsinghua University, Beijing, 1999 Research Interests: His work spans energy-efficient networking, pervasive backscatter systems, edge computing, and 5G/6G mobile vision analytics. He emphasizes practical IoT applications, including wearable health monitoring and smart grid management. Article Trends: Recent publications address satellite networking (e.g., Starlink performance), battery-free wearables, and AI-driven multimedia systems. A key theme is integrating energy-efficient communication with emerging technologies like 6G and edge intelligence. Awards: Canadian Academy of Engineering Fellow, IEEE Fellow, NSERC E.W.R. Steacie Memorial Fellow Labs & Teams: Active in the Tangent Lab , exploring edge-assisted analytics and immersive media systems. Collaborates on projects like PupilHeart for mobile health monitoring and Apollo for battery-free wearables.
Boyu Wang is an Assistant Professor in the Department of Computer Science at Western University, with adjunct roles in the Department of Statistical and Actuarial Sciences, School of Biomedical Engineering, and Brain and Mind Institute. He is an affiliated faculty member at the Vector Institute. His research focuses on trustworthy machine learning, algorithm development, and applications in computer vision, NLP, biomedical engineering, and neuroscience. Education: Ph.D. in Computer Science from McGill University (2019), M.Sc. from University of Macau, B.Eng. from Tianjin University. Postdoctoral fellowships at University of Pennsylvania and Princeton University under Eric Eaton and Kenneth Norman. Research interests include domain adaptation, federated learning, bias mitigation, and neural mechanisms of perception. He has contributed to over 30 publications in top venues like NeurIPS, ICML, ICLR, and IEEE TPAMI. Currently serves as Area Chair for NeurIPS 2025, ICML 2025, ICLR 2025, and AISTATS 2025. Associate Editor for Neural Networks . Labs/Teams: Leads research groups focused on developing robust ML systems with interdisciplinary applications in healthcare and neuroscience. Collaborates with institutions across academia and industry.
Dr. Ryan Grant is an Assistant Professor in the Department of Electrical and Computer Engineering at Queen’s University, Canada. He leads the Computing at Extreme Scale Advanced Research (CAESAR) lab and is affiliated with the Ingenuity Labs Research Institute. His expertise spans cloud computing, high-performance networks, low-level hardware-software interfaces, and energy-efficient supercomputing systems. Dr. Grant holds a PhD from Queen’s University (2012) and previously worked at Sandia National Laboratories (2012–2021), where he contributed to critical supercomputer communication protocols now deployed globally. He has authored over 80 peer-reviewed articles and received prestigious awards including the R&D100 Award and Queen’s University’s 125th Engineering Alumni Award. His research emphasizes advancing Canada’s supercomputing infrastructure to support AI, climate science, and national security applications. Education: PhD in Computer Engineering, Queen’s University (2012) MSc in Computer Engineering, Queen’s University (2005) BSc in Computer Engineering, Queen’s University (2004) Research Interests: Dr. Grant’s work focuses on optimizing supercomputing architectures for extreme-scale systems, with an emphasis on: High-performance networking and MPI communication protocols Power/energy management in HPC systems AI-driven network traffic prediction and resource disaggregation GPU-accelerated computing and cloud infrastructure integration National sovereignty in supercomputing for sensitive applications (e.g., defense, healthcare) Awards & Recognition: R&D100 Award (Oscars of Research) U.S. Defense Programs Awards Public Good Innovator Award Queen’s University 125th Engineering Alumni Award Grants & Labs: Dr. Grant directs the CAESAR lab, one of the world’s leading supercomputing architecture research groups. His work is supported by grants from Canadian and international agencies, focusing on sovereign supercomputing and HPC-AI convergence. Labs/Teams: CAESAR Lab (Queen’s University) Ingenuity Labs Research Institute
Beatrice Ombuki-Berman is a Professor and Chair of the Department of Computer Science at Brock University, Canada. She holds a PhD in Intelligent Systems Engineering from the University of the Ryukyus, Japan, along with an ME in Information Engineering and certifications in Japanese Language Studies. Her research focuses on computational intelligence, evolutionary computation, swarm intelligence, and optimization techniques, with recent work on complex networks and drug discovery applications. Education: PhD, Intelligent Systems Engineering, University of the Ryukyus, Japan ME, Information Engineering, University of the Ryukyus, Japan BSc, Double Major in Mathematics and Statistics, Minor in Computer Science, Jomo Kenyatta University of Agriculture and Technology, Kenya Research Interests: Development of efficient computational intelligence algorithms for optimization problems, including evolutionary algorithms, particle swarm optimization, and neural networks. Applications span combinatorial optimization, multi-objective optimization, network robustness, and bioinformatics. Grants & Funding: Her research is funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) and industry collaborations through Brock-Niagara VPMI and FedDev Ontario. Professional Activities: Co-Director of the Bio-Inspired Computational Intelligence Group. She is also a publicity chair for IEEE WCCI 2024 and IEEE SSCI 2025. Labs & Teams: Leads the Bio-Inspired Computational Intelligence Group, focusing on algorithm development and real-world applications.
Dr. Michael Horsch is a retired Associate Professor in the Department of Computer Science at the University of Saskatchewan. His research focuses on Artificial Intelligence, Reasoning Under Uncertainty, Constraint Satisfaction, and Machine Learning. He has contributed to bioinformatics through the P2IRC sub-theme, linking genotype and environment to phenotype using computational methods. Education: PhD (Computer Science, UBC, 1998), M.Sc. (Computer Science, UBC, 1990), B.Sc. (Computer Science & Physics, University of Toronto, 1988). Postdoctoral work at Simon Fraser University (2000). Research interests emphasize practical AI applications, including Bayesian networks, constraint satisfaction algorithms, and machine learning. His work bridges theoretical foundations and real-world problems, such as path planning and sensor optimization. Teaching highlights include courses on AI (CMPT 317), programming (CMPT 145), and machine learning (CMPT 423/820). He revised first-year curricula to improve student accessibility, replacing C++ with more user-friendly languages. Recognized for teaching excellence with the 2014 Provost’s Award. Publications span constraint satisfaction, probabilistic reasoning, and Bayesian networks, with notable contributions to algorithm design and optimization.
Roberta Hamme is a Professor and Graduate Advisor in the Department of Earth and Ocean Sciences at the University of Victoria's Faculty of Science. She holds a BA from Pomona College and MSc/PhD from the University of Washington. Her research focuses on chemical oceanography, dissolved gases, ocean carbon cycling, and marine productivity using advanced techniques like mass spectrometry and BGC-Argo floats. Her research investigates fundamental ocean processes including air-sea gas exchange, water mass formation, denitrification, and ocean carbon sequestration. She employs geochemical tracers and autonomous platforms to understand biogeochemical cycles in diverse environments from the subarctic Pacific to Arctic regions. Analysis of Dr. Hamme's recent publications reveals consistent focus on ocean deoxygenation, carbon flux quantification, and climate impacts on marine systems. Her work utilizes innovative approaches including neural networks for CO2 estimation, noble gas tracers for ventilation studies, and integrated ship-autonomous observation systems. Research spans from coastal zones to open ocean, addressing pressing climate-related changes in ocean chemistry.
Dr. Alvine Boaye Belle is an Assistant Professor in the Department of Electrical Engineering & Computer Science at Lassonde School of Engineering, York University. She leads the DARE! research group and serves on multiple international committees, including ICSE and RE conferences. Her work bridges software engineering with equity, diversity, and inclusion (EDI) initiatives. PhD in Software Engineering (École de Technologie Supérieure, University of Quebec) 2-year Industrial Postdoctoral (University of Ottawa) Graduate Diploma in Public Administration & Governance (McGill University) Dr. Belle's research focuses on system assurance for autonomous systems, generative AI applications in software engineering, and EDI in computing . She applies machine learning to safety case automation and vulnerability detection, as shown in her publications with high-impact journals. Her recent work explores deep learning and SVM models for Android malware detection with 99% accuracy. She mentors a diverse group of students across Bachelor's, Master's, and PhD levels, emphasizing accessibility and social impact in technology. Keynote speaker at Black History Month events Moderator of EDI-focused panels at ICSE conferences Editorial board member for journals like IEEE Software and Information and Software Technology
Hui Jiang is a Professor in the Department of Electrical Engineering and Computer Science at the Lassonde School of Engineering, York University in Toronto, Canada. He holds the professional engineering designation (P.Eng) and maintains an active research program in machine learning and artificial intelligence with an office located in Room 3014 of the Lassonde Building at 4700 Keele Street. Dr. Jiang's research focuses on machine learning and artificial intelligence, with particular emphasis on deep learning theory and methods, as well as their applications in speech and language processing and computer vision. His work spans from fundamental machine learning concepts to cutting-edge AI technologies including transformers, diffusion models, and neural network architectures. He has developed methods such as the Fixed-size Ordinally Forgetting Encoding (FOFE) for named entity recognition and contributed significantly to convolutional neural networks for speech recognition. His publication timeline shows consistent contribution to the field, beginning with foundational work in speech recognition and progressing to comprehensive frameworks in machine learning. His recent work focuses on explaining complex AI concepts through his blog and textbook, demonstrating a commitment to both research advancement and education in the AI community. IEEE SPS Best Paper Award (2016) for "Convolutional Neural Networks for Speech Recognition" Dr. Jiang has authored the textbook "Machine Learning Fundamentals" published by Cambridge University Press in 2021, which provides a comprehensive introduction to both traditional machine learning methods and modern deep learning techniques. He maintains an active technology blog where he shares detailed technical insights on machine learning concepts, with recent posts covering diffusion models, transformers, and GPT architecture. His complete publication list is available on his Google Scholar profile, and he can be reached via email at huijiang@yorku.ca for academic and research inquiries.
Dr. Marzieh Ahmadzadeh is an Associate Professor (Teaching Stream) at the Department of Electrical Engineering & Computer Science, York University. She holds a Ph.D. and MSc in Information Technology (Software Engineering) from the University of Nottingham, UK, and a BSc in Computer Engineering from Isfahan University. A certified Professional Engineer (P.Eng.) in Ontario, she has held academic positions at Shiraz University of Technology, University of Toronto, and University of Georgia, USA before shifting her focus to education research in 2015. Education: Ph.D., Information Technology (Software Engineering), University of Nottingham (2006) MSc, Information Technology (Software Engineering), University of Nottingham (2002) BSc, Computer Engineering, Isfahan University Her research intersects Computer Science Education and Human-Computer Interaction , with a focus on Applied Data Mining for educational analytics and security applications. She has published in prestigious venues like ACM SIGCSE, IEEE Transactions, and Future Generation Computer Systems. Recent publications demonstrate expertise in: Exam design and cognitive load optimization Ransomware detection in fog computing environments Breast cancer survivability modeling with imbalanced data Gender preferences in e-commerce UX design Academic integrity analysis in programming education
Dr. Manos Papagelis is an Associate Professor in the Department of Electrical Engineering & Computer Science at York University's Lassonde School of Engineering. He serves as the Graduate Program Director for the MSc and MScAI programs. His research focuses on data science and machine learning, particularly in data mining, graph mining, big data analytics, mobility analytics, and knowledge discovery. Education: PhD in Computer Science (University of Toronto), MSc and BSc in Computer Science (University of Crete, Greece). Prior to York, he held postdoctoral and research roles at UC Berkeley, Yahoo! Labs Barcelona, and FORTH, Greece. Research emphasizes trajectory analysis, emotion recognition, and scalable systems. Recent work includes TrajLearn (2025) and Disease Outbreak Detection (2025), highlighting contributions to mobility and health informatics. He has filed three U.S. patents and designed systems like Confious (conference management) and Green2.0 (socio-technical building analysis). Honors include the Lassonde Educator of the Year (2021) and IEEE MDM Best Paper Awards (2020, 2018). His advising spans interdisciplinary teams in AI and data science, with active involvement in grants related to mobility analytics and healthcare technologies. Labs/Teams: Director of the Data Mining Lab, collaborating on projects like trajectory prediction and emotion-aware systems.
Natalija Vlajic is an Associate Professor in the Department of Electrical Engineering and Computer Science at York University's Lassonde School of Engineering. She holds a PhD in Electrical Engineering from the University of Ottawa and an M.Sc. in Electrical and Computer Engineering from the University of Manitoba. Dr. Vlajic is a highly accomplished researcher with over 80 publications in international conferences and journals, specializing in cybersecurity with a focus on industrial control systems. Her primary research interests include network and information security, communication systems and network protocols, cybersecurity of industrial systems, machine learning applications in security, and system performance evaluation. She has made significant contributions to the understanding of security risk management, bot and DDoS attacks, user privacy, IoT security, and sensor networks. Her work bridges theoretical security concepts with practical applications in critical infrastructure protection. Dr. Vlajic's recent publications demonstrate a strong focus on Industrial Control Systems security, particularly addressing vulnerabilities through innovative approaches like risk-based cryptoperiod optimization, attack tree modeling using MITRE ATT&CK framework, and advanced bot detection techniques. Her research combines traditional security methodologies with machine learning and data analytics to develop more robust protection mechanisms for critical infrastructure. NSERC University Faculty Award Faculty-Wide Excellence in Teaching Award Departmental Mildred Baptist Teaching Award Best Poster Award at ACM/IEEE ICCPS (2023) Best Paper Award at HoTSoS (2018) Dr. Vlajic actively mentors graduate students including Gabriele Cianfarani, Melina Najimi, Stefan Petrovic, Shadi Sadeghpour, Daniel Brown, and Jazdeep Sarai. Her research group has received significant recognition, with students presenting at major conferences like GradCon hosted by Waterloo's Cybersecurity and Privacy Institute. She serves as a co-editor for the IEEE Communications Magazine special issue on Security of Communication Protocols in Industrial Control Systems. Her research is conducted through the Security Research at York (SecRAY) initiative, focusing on practical security solutions for industrial systems, web applications, and IoT environments. The group maintains strong industry connections and collaborates on real-world security challenges, particularly in the domain of critical infrastructure protection.
Song Wang is an Associate Professor in the Department of Electrical Engineering and Computer Science at York University's Lassonde School of Engineering. He joined York University as an Assistant Professor in July 2019 and was promoted to Associate Professor in May 2024. He serves as an Associate Editor of ACM Transactions on Software Engineering and Methodology (TOSEM) and has established himself as a prominent researcher at the intersection of Software Engineering and Artificial Intelligence. Dr. Wang earned his Ph.D. in Computer Engineering from the University of Waterloo in December 2018 under Prof. Lin Tan. He received his MS degree from the Chinese Academy of Sciences in June 2014 under the supervision of Prof. Ye Yang, Prof. Wen Zhang, and Prof. Qing Wang. His undergraduate education includes a BE in Software Engineering and a BHRM in Human Resource Management from Sichuan University in June 2011. Prior to academia, he gained industry experience through internships at Microsoft Research, Morgan Stanley Capital International, Yahoo, and Baidu, and co-founded a startup named QualDivine. Dr. Wang's research focuses on two main directions: (1) leveraging AI technologies to address software reliability challenges (AI for SE), and (2) developing software reliability assurance techniques for AI systems (SE for AI). His recent work has particularly focused on how Large Language Models can optimize and reshape software testing practices. His research has practical impact, with tools and techniques that have detected hundreds of true bugs in real-world software systems. His work spans multiple application areas including mobile testing, fuzz testing, and functional testing. His recent publications (2024-2025) demonstrate a strong focus on the intersection of AI and software engineering, with significant contributions in automated vulnerability detection, API recommendation, bias analysis in generated code, and mobile application testing. His research combines empirical studies with innovative technical approaches, often involving benchmarking and systematic literature reviews to establish foundations for future work. He has published over 60 papers in prestigious IEEE/ACM Software Engineering journals and flagship conferences, with over 2,600 citations. Dr. Wang has received four best paper awards: a Distinguished Paper Award at APSEC'23, an ACM Distinguished Paper Award at ICPC'22, an ACM Distinguished Paper Award at ICSE'20, and a Best Paper Award at PROMISE'19. He was recognized as one of the top-10 most impactful early-career researchers in Software Engineering by the Journal of Systems and Software in 2020 and received the TOSEM Distinguished Reviewer Award in 2023. Dr. Wang currently supervises multiple PhD and Master's students including Mohammad Abdollahi, Haoran Xue, Jiho Shin, Nima Shiri Harzevili, and Moshi Wei. He has successfully guided several students to complete their theses, including Reem Al Eithan (Master's thesis defense in April 2025), Moshi Wei (PhD thesis defense in April 2025), and Nima Shiri Harzevili (PhD thesis defense in February 2025). His research group has received funding from various sources to support their work on software engineering and AI. Dr. Wang leads an active research group focused on AI and software engineering at York University. His team includes PhD students, Master's students, and research assistants working on various projects related to software testing, reliability, and AI applications in software engineering. The group has developed tools that have detected hundreds of true bugs in real-world software systems, with some findings documented in Jira issues and GitHub repositories across numerous open-source projects.
Madhur Anand is a Professor at the University of Guelph's School of Environmental Sciences, where she leads research on global ecological change and sustainability. Her work integrates empirical studies of forest ecosystems with advanced computational modeling to address biodiversity loss, climate impacts, and human-environment interactions. She holds a Canada Research Chair in Global Ecological Change (2006–2011) and collaborates globally with institutions in Brazil, China, India, and beyond. Her research focuses on: Global Ecological Change : Assessing climate impacts on biodiversity using multi-scale models. Socio-Ecological Systems : Analyzing feedback between human behavior and environmental tipping points. Machine Learning Applications : Developing predictive tools for ecological crises (e.g., droughts, disease outbreaks). Recent publications (2022–2025) emphasize coupled human-environment dynamics, with trends in machine learning integration ( e.g., deep learning for bifurcation detection ) and interdisciplinary approaches to climate mitigation. Awards include the Premier’s Research Excellence Award (2002–2007), 'Top 40 Under 40' (Guelph Mercury, 2009), and recognition from the Indo-Canada Chamber of Commerce (2012). Her lab is funded by NSERC, CFI, and international grants, focusing on field sites in Canadian forests and tropical ecosystems. Current projects examine forest resilience, agroecosystem sustainability, and socio-climate modeling to inform conservation policy.
Sajjad Dadkhah is an Assistant Professor at the University of New Brunswick (UNB), holding the Canada Mastercard IoT Research Chair and leading the Cybersecurity Team at the Canadian Institute of Cybersecurity (CIC) within the Faculty of Computer Science. He specializes in cybersecurity, IoT security, and machine learning applications in security. His work focuses on developing robust security frameworks, intrusion detection systems, and datasets for emerging technologies like IoT and IoV. He has earned Bronze and Gold medals in international invention competitions and holds a fellowship from Kyushu Institute of Technology. Dadkhah serves as a Board Member and Managing Editor for the Applied Soft Computing (ASOC) Elsevier journal since 2016. His research includes pioneering datasets such as CICIoMT2024 and TruthSeeker, addressing critical challenges in medical IoT security, fake news detection, and vehicular network protection. His academic contributions span over 40 publications, emphasizing practical solutions for real-world cybersecurity threats. He actively collaborates with organizations like Kyushu University and IRIS Smart Technology Complex, bridging academic research with industrial applications.
