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Mikhail (Misha) Belkin is a Professor at the Halicioglu Data Science Institute (HDSI) at the University of California San Diego , with an affiliated appointment in the Department of Computer Science and Engineering . He is also an Amazon Scholar , reflecting his impactful industry collaboration. Since January 2024, he has served as the Editor-in-Chief of the SIAM Journal on Mathematics of Data Science (SIMODS) . Research Interests: Belkin's research centers on the theoretical foundations of machine learning, particularly the mathematical understanding of modern deep learning. His work investigates interpolation , over-parameterization , and feature learning in neural networks. He is renowned for introducing the double descent risk curve, which reconciles classical bias-variance trade-offs with the success of overfitted models. His recent work identifies the Average Gradient Outer Product (AGOP) as a fundamental mechanism of feature learning, applicable across architectures like CNNs and transformers. Scientific Contributions and Trends: His recent publications, appearing in Science , PNAS , and NeurIPS , demonstrate a strong trend toward unifying theories of generalization and optimization in over-parameterized systems. He explores how interpolating models can be statistically optimal, how gradient descent converges in non-convex landscapes via the PL* condition, and how kernel methods can be enhanced to perform feature learning. ACM Fellow (2023) Editor-in-Chief, SIAM Journal on Mathematics of Data Science (2024–present) Advising and Grants: Belkin actively mentors students and collaborators such as Adityanarayanan Radhakrishnan , Daniel Beaglehole , and Chaoyue Liu , who are frequent co-authors. He is a Principal Investigator (PI) in the Collaboration on the Theoretical Foundations of Deep Learning , funded by the NSF and Simons Foundation. He is also an external collaborator with the Eric and Wendy Schmidt Center at the Broad Institute and part of the NSF-funded TILOS AI Institute . Laboratories and Teams: While not explicitly named, his research group at UCSD is deeply involved in theoretical machine learning, focusing on the intersection of statistics, optimization, and deep learning. His work often involves large-scale collaborations and is closely tied to initiatives like SIMODS and TILOS.

• Machine Learning
• Deep Learning
• Theoretical Foundations of AI
• +11 more

Hossam Hassanein is a Professor and Director of the School of Computing at Queen's University. He received his B.Sc. in Electrical Engineering from Kuwait University in 1984, M.Sc. in Computer Engineering from the University of Toronto in 1986, and Ph.D. in Computing Science from the University of Alberta in 1990. He joined Queen's University School of Computing in 1999 and has established himself as a leading researcher in telecommunications and networking. Dr. Hassanein's research interests span wireless sensor networks, mobile ad hoc networks, edge computing, Internet of Things (IoT), radio resource management, and data-centric networks. His seminal contributions include pioneering work on WSN planning, load-balanced routing protocols, and energy-efficient network designs. He has championed research in IoT, developing frameworks for smart spaces that use contextual information to enhance IoT applications in healthcare, transportation, and infrastructure. His recent publications (2023-2025) demonstrate a strong focus on cutting-edge areas including extreme edge computing, vehicular networks, and AI/ML integration in networking. Research trends show increasing emphasis on practical applications in telesurgery, digital twins, and industrial IoT, addressing challenges in resource allocation, task offloading, and real-time processing in constrained environments. Dr. Hassanein has received numerous recognitions for his work: Fellow of the IEEE Queen's University School of Graduate Studies Award for Excellence in Graduate Student Supervision (2015) Multiple best paper awards from top international conferences As founder and director of the Telecommunications Research Lab (TRL), Dr. Hassanein has supervised over 75 students who have made substantial contributions in academia and industry. The TRL is one of Queen's largest research groups with extensive international collaborations. Dr. Hassanein has successfully attracted significant research funding from government and industry sources in the competitive telecommunications field. The Telecommunications Research Lab has developed innovative platforms including SPROUTS, a rugged sensor platform used in mining, steel manufacturing, and smart-grid monitoring. TRL's work has had significant impact in WSN planning, data dissemination, and resource reuse in wireless networks, with contributions featured in IEEE Wireless Communications Magazine.

• Wireless Sensor Networks
• Mobile Ad hoc Networks
• Edge Computing
• +5 more

Dr. Lourdes Pena-Castillo is a Professor jointly appointed in the Departments of Computer Science and Biology at Memorial University of Newfoundland's Faculty of Science. Her research focuses on applying machine learning and bioinformatics to study bacterial gene regulation, with emphasis on transcriptomics, gene expression pathways, and microbiology. She leads the Bioinformatics Lab at MUN, developing computational tools like Promotech for promoter prediction and sRNARFTarget for sRNA target identification. Education: BSc in Information Systems Engineering, ITESM-Mexico MSc in Computer Science, University of Alberta PhD in Computer Science (Doktoringenieurin), Otto-von-Guericke Universität Magdeburg Postdoc in Bioinformatics, University of Toronto Research Interests: Bioinformatics, Genomics, Machine Learning, Artificial Intelligence, Transcriptomics, Gene Regulation, Microbiology Her work integrates computational methods with biological data to address challenges in molecular biology, including analyzing bacterial sRNA functions, promoter recognition, and disease diagnostics using machine learning. She has advised numerous graduate students, including PhD candidates Purvikalyan Pallegar and Bonita McCuaig, and MSc students like Ruben Chevez-Guardado and Kratika Naskulwar. Her lab focuses on translational research with applications in both basic science and clinical contexts. Publications span computational methods for bacterial gene regulation, bioinformatics tool development, and interdisciplinary projects in VR and healthcare informatics. Her research has contributed to understanding symbiotic relationships in marine organisms, inflammatory bowel disease diagnostics, and clavulanic acid production in Streptomyces. Grants & Collaborations: Works with interdisciplinary teams across computer science and biology, supported by grants enabling projects in bacterial genomics and computational tool development. Labs & Teams: Leads the Bioinformatics Lab at MUN, fostering collaborations with researchers in microbiology, computer science, and healthcare.

• Bioinformatics
• Genomics
• Machine Learning
• +5 more

Oana Balmau is an Assistant Professor in the School of Computer Science at McGill University, where she leads the Data-Intensive Storage and Computer Systems Laboratory (DISCS Lab). She also holds a status-only appointment at the University of Toronto and serves as a working group chair for MLPerf Storage. Her research focuses on creating storage infrastructure that enables fast and energy-conscious insights from data, with particular emphasis on storage and persistent memory technologies for machine learning, data science, and edge computing workloads. Dr. Balmau's research interests span computer systems, with specific focus on: Design and implementation of efficient key-value stores Storage systems for machine learning workloads Edge computing infrastructure Persistent memory technologies Performance optimization of data-intensive systems Her recent work has led to significant contributions in storage benchmarking through the MLPerf Storage benchmark and in edge computing frameworks. The MLPerf Storage benchmark has become an industry standard for evaluating storage performance in machine learning environments, while her work on hierarchical edge computing addresses security and performance challenges in distributed edge environments. Her publications show consistent high-impact contributions to top systems venues, with recent work focusing on processing-in-memory virtualization, stream processing reconfiguration, and efficient data preprocessing pipelines. Dr. Balmau has received numerous awards for her research, including: SEC 2024 Best Paper Award for "Falcon: Live Reconfiguration for Stateful Stream Processing on the Edge" MLCommons Hero Award 2023 for leadership as MLPerf Storage working group chair ACM SIGOPS Dennis M. Ritchie Doctoral Dissertation Award 2021 Honorable Mention CORE John Makepeace Bennett Award 2021 for the best Computer Science doctoral dissertation in Australia and New Zealand USENIX ATC 2019 Best Paper Award for "SILK: Preventing Latency Spikes in Log-Structured Merge Key-Value Stores" As an educator, Dr. Balmau teaches courses on advanced computer systems, operating systems, and principles of computer systems design at McGill University. She has served on program committees for top systems conferences including SOSP, SIGMOD, FAST, and EuroSys, and has co-organized workshops on resource-efficient machine learning and edge computing. She leads the DISCS Lab, which focuses on two main research directions: Systems for ML (including the MLPerf Storage benchmark) and Edge computing (including frameworks for fast and secure edge computing in hierarchical edge environments).

• Storage Systems
• Persistent Memory
• Key-Value Stores
• +4 more

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.

• Parallel and Distributed Systems
• Operating Systems
• Cloud Computing
• +4 more

Rong Zheng is a Professor in the Department of Computing and Software and a member of the School of Biomedical Engineering at McMaster University, Canada. She holds a Tier-1 Canada Research Chair in Mobile Computing and serves as Acting Chair of the Computing and Software department from July to December 2025. She is also an Associate Member of the Electrical & Computer Engineering department. Education: Ph.D. in Computer Science, University of Illinois, Urbana-Champaign, USA Master of Engineering (thesis) in Electrical Engineering, Tsinghua University, Beijing, China Bachelor of Engineering in Electrical Engineering, Tsinghua University, Beijing, China Dr. Zheng's research lies at the intersection of mobile computing, wireless networking, and machine learning, with a strong focus on applications for aging populations. She directs the NSERC Smart Mobility for the Aging Population CREATE program. Her work encompasses sensor development, wireless network design, and mobile data analytics to address real-world challenges in healthcare, mobility, and data center monitoring. She has developed innovative solutions like the MacQuest campus navigation app and has captured first prize in indoor localization competitions. Her recent publications demonstrate a clear trajectory toward applying wireless sensing technologies (particularly acoustic, Wi-Fi, and mmWave) to health monitoring and mobility assessment for older adults. There's a strong emphasis on developing efficient edge computing solutions that can process data in real-time on resource-constrained devices, as exemplified by her TeamNet framework for collaborative inference on the edge. Her work bridges theoretical advances with practical applications that have social impact. Scientific Awards: Tier-1 Canada Research Chair in Mobile Computing US National Science Foundation CAREER Award (2006) Joseph Ip Distinguished Engineering Fellow (2015-2018) Dr. Zheng leads the Wireless System Research Group (WiSeR) at McMaster University, which has secured significant funding including a $1.65M NSERC CREATE grant for smart mobility research for older adults. Her research has been supported by multiple funding agencies including NSERC, NSF, UH GEAR, and DURIP. She actively mentors graduate students and has developed specialized courses including CAS 772 (Mobile Data Analytics) and CAS 781 (Mobility in the Aging Population). The WiSeR group conducts impactful research on communication, networking, and data analytics issues in Cyber Physical Systems, with applications spanning healthcare, smart infrastructure, and data center monitoring. Their work on data center infrastructure monitoring networks has been featured in EurekAlert and Data Center Dynamics, and they've made significant contributions to indoor localization technology.

• Mobile computing
• Wireless networking
• Cyber physical systems
• +4 more

Nick Graham is a Professor in the School of Computing at Queen's University, Canada. He holds a PhD from TU Berlin and teaches courses like Game Architecture and Digital Game Development. His research focuses on next-generation digital games, particularly exergames (combining exercise with play) and educational games. He directs the EQUIS Lab, which develops accessible gaming solutions for populations like children with cerebral palsy and spinal cord injury patients. Graham previously served as VP Research & Development at Namzak Labs and has held academic roles at York University and Queen's University. Education: PhD (Dr.-Ing.) in Engineering - TU Berlin (1995) MSc in Computer Science - Queen's University (1988) BSc in Computer Science - University of Toronto (1985) Research Interests: Exergames promoting physical activity through gaming Accessibility in game design for persons with disabilities Game networking and distributed algorithms Augmented reality tools for education Lab & Collaborations: The EQUIS Lab develops projects such as the Liberi exergame and conversion tools for existing games like Skyrim into exergames. Techniques like 'Partial Automation' enable gameplay accessibility. Professional Roles: Director, EQUIS Lab Member, IFIP Working Group 2.7 on User Interface Engineering VP Research & Development, Namzak Labs (2000–present)

• Video Game Development
• Human-Computer Interaction
• Accessibility in Technology
• +4 more

Aviad Levis is an Assistant Professor at the University of Toronto's Department of Computer Science, starting July 2024. He is affiliated with the Dunlap Astronomical Data Science and Technology Group (DADDAA) and collaborates with the Toronto Computational Imaging Group alongside Kyros Kutulakos and David Lindell. Previously, he was a postdoctoral researcher at Caltech's Computing + Mathematical Sciences department under Katherine Bouman, working with the Event Horizon Telescope (EHT) collaboration. PhD in Electrical Engineering from the Technion (supervised by Yoav Schechner) Research focuses on computational imaging tools at the intersection of AI and physics Develops algorithms for 3D tomography in both cloud physics and black hole imaging Recipient of ERC Synergy grant for CloudCT space mission His research spans two major domains: Computational Climate Imaging through cloud tomography to improve climate models, and Black Hole Imaging with the EHT collaboration. He pioneered methodologies for 3D cloud structure recovery using scattered sunlight and contributes to dynamic 3D reconstructions of black hole environments. Current interests include non-linear inverse problems, equation discovery from data, and ML-accelerated scientific simulations. Recent publications highlight advancements in atmospheric tomography and black hole emission modeling. His work on CloudCT involves coordinated nano-satellites for 3D cloud imaging, while EHT contributions include first images of Sagittarius A* (2022) and ongoing development of algorithms for 3D structure recovery. The ERC Synergy grant underscores his impact on climate imaging technology. Personal Website Work Email

• Computational Imaging
• Artificial Intelligence
• Inverse Problems
• +7 more

Michael Feeley is an Associate Professor in the Department of Computer Science at the University of British Columbia (UBC), affiliated with the Institute for Computing, Information and Cognitive Systems (ICICS). His research focuses on operating systems, distributed systems, and their applications in scalable file systems, cloud storage, and mobile computing. He leads projects such as the Unico file system, Parallax storage system, and Remus virtual machine replication framework. His research interests include peer-to-peer file systems, mobile ad hoc networks, interference mitigation in wireless networks, and system software for workstation clusters. He has supervised numerous graduate students, contributing to advancements in distributed systems and networking. His work often addresses challenges in resource management, fault tolerance, and performance optimization. Publications span topics like interference detection in WiFi networks, dynamic program analysis (Tralfamadore), and distributed storage solutions. Feeley teaches courses such as CPSC 213, emphasizing operating systems fundamentals. His office is located in CISR 393, with contact details available via email and phone.

• Operating Systems
• Distributed Systems
• File Systems
• +7 more

Bilal Farooq is an Associate Professor and Program Director for the Master of Engineering in Interdisciplinary Engineering (MEIE) at Toronto Metropolitan University, holding the Canada Research Chair in Disruptive Transportation Technologies and Services within the Department of Civil Engineering. His educational background includes a PhD from the University of Toronto (2011), MASc from Lahore University of Management Sciences (2004), and BSc from the University of Engineering and Technology (2001). Dr. Farooq's research pioneers disruptive transportation solutions through cyber-physical systems, AI/machine learning applications, behavioral modeling, and optimization techniques. His work specifically targets on-demand multimodal systems, sustainable urban transportation, urban air mobility, automated vehicles, and extended reality applications, addressing critical urban mobility challenges with human-centered approaches. Analysis of his recent publications reveals a strong trend toward quantum-enhanced computational methods, privacy-preserving federated learning frameworks, and sustainability-focused decarbonization strategies across transportation domains, with increasing emphasis on human factors and real-world implementation. Notable scientific awards include: Ontario Early Researcher Award (2018) Canada Research Chair (2017) MassMotion Academic Pedestrian Modelling Project of the Year (2016) Québec Early Researcher Award (2014) Dr. Farooq actively supervises graduate students and secures significant research funding through his Canada Research Chair position and Early Researcher Awards. He directs the Laboratory of Innovations in Transportation (LiTrans), which develops interdisciplinary solutions integrating mathematics, engineering, computer science, and economics to address emerging transportation challenges. LiTrans focuses on disruptive transportation technologies, complete streets design, cyber-physical systems, pedestrian dynamics, resilience, and climate change impacts, collaborating with industry and government partners to translate research into practical urban mobility innovations for smart cities worldwide.

• Disruptive transportation technologies and services
• Cyber-physical transportation systems
• Artificial intelligence and machine learning
• +4 more

Dr. Gary Scavone is a Professor and Department Chair in the Music department at McGill University's Schulich School of Music. He holds a PhD in Computer-Based Music Theory & Acoustics and MS in Electrical Engineering from Stanford University, alongside degrees from Syracuse University in Music and Electrical Engineering. His research focuses on music technology, including acoustic modeling, sound synthesis, and instrument design. He directs the Computational Acoustic Modeling Laboratory (CAML), which explores advanced techniques for simulating musical instruments and developing software tools. As a saxophonist, he specializes in contemporary concert music performance. Research interests include physically-based sound synthesis, wind instrument acoustics, and digital waveguide modeling. He has contributed to studies on brass and woodwind impedance measurements, violin soundpost dynamics, and free-reed instrument modeling. His work bridges engineering and artistry, with applications in music pedagogy, instrument design optimization, and virtual acoustic replication. Key contributions include open-source projects for wind instrument modeling and the development of tools for automated timbre assessment. His research often combines experimental methods with computational simulations, addressing challenges in both theoretical and applied music acoustics. Current projects focus on deep learning for friction modeling, impedance measurement systems, and cross-cultural instrument analysis.

• Music Technology
• Musical Acoustics
• Acoustical Signal Processing
• +3 more

Xuemin (Sherman) Shen is a University Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), the Royal Society of Canada, the Canadian Academy of Engineering, and the Engineering Institute of Canada. Professor Shen serves as Editor-in-Chief of multiple prestigious journals including the IEEE Internet of Things Journal and Springer Peer-to-Peer Networking and Applications. Dr. Shen received his Doctorate in Electrical Engineering from Rutgers University in 1990, following a Master of Applied Science from the same institution in 1987. His undergraduate degree is a Bachelor of Applied Science in Electrical Engineering from Dalian Marine University, China (1982). Professor Shen's research spans wireless communications and networking, with particular expertise in resource allocation, mobility management, wireless network security, and privacy preservation. His work extends to IoT applications, connected and automated vehicles, network digital twins, and satellite-terrestrial networks. His research has been applied to vehicular networks, wireless body area networks, remote e-healthcare systems, and smart grid technologies, demonstrating both theoretical depth and practical impact across multiple domains. His recent publications reveal strong trends in AI-assisted networking, security and privacy preservation for IoT applications, and energy management in vehicular and smart grid systems. The research shows an increasing focus on integrating AI techniques with traditional networking approaches, addressing critical challenges in security, privacy, and resource management for next-generation wireless systems. R.A. Fessenden Award (2019) from IEEE Canada James Evans Avant Garde Award (2018) from the IEEE Vehicular Technology Society Joseph LoCicero Award (2015) from the IEEE Communications Society Education Award (2017) from the IEEE Communications Society West Lake Friendship Award from Zhejiang Province (2023) President's Excellence in Research from University of Waterloo (2022) Canadian Award for Telecommunications Research (2021) Professor Shen has mentored over 100 graduate students and postdoctoral fellows throughout his career, with many now holding prominent academic positions at top universities worldwide. His supervision has been recognized with multiple awards including the Award of Excellence in Graduate Supervision (2006) from the University of Waterloo. He has served in numerous leadership roles including Past President of the IEEE Communications Society and has chaired major international conferences including IEEE Globecom 2024.

• Wireless communications and networking
• Resource allocation and mobility management
• Wireless network security and privacy preservation
• +6 more

WonSook Lee is a tenured Full Professor in the School of Electrical Engineering and Computer Science at the University of Ottawa’s Faculty of Engineering. Her expertise spans medical imaging, machine/deep learning, computer graphics, and computer vision. She earned her Ph.D. in Computer Science from the University of Geneva (Switzerland) and holds degrees from POSTECH (Korea) and NUS (Singapore). Before academia, she worked at Korea Telecom, Samsung Advanced Institute of Technology, and Eyematic Interfaces Inc. (USA). Her research focuses on applications such as virtual/augmented reality, MRI/CT/Ultrasound analysis, and 3D mesh modeling. She has authored over 130 publications, including 30+ journal papers, and serves on conference committees and editorial boards. Lee has secured major grants (NSERC, CFI, ORF) as Principal Investigator and contributed to global initiatives like South Korea’s National Research Foundation. Her lab explores cutting-edge techniques in medical imaging, AI-driven object detection, and multimodal systems. Notable projects include adversarial perturbation analysis for model robustness, cross-domain GANs for semantic segmentation, and real-time ultrasound-enhanced pronunciation training. She actively promotes interdisciplinary research in healthcare technology and autonomous systems.

• Medical Imaging
• Machine Learning
• Deep Learning
• +4 more

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.

• Computer Architecture
• Interconnection Networks
• Approximate Computing
• +5 more

Kaiyang Liu is an Assistant Professor at the Department of Computer Science, Memorial University of Newfoundland. He holds a Ph.D. from Central South University (2019) and was a Postdoctoral Fellow at the University of Victoria, Canada. His research focuses on distributed cloud/edge computing, data center networks, and distributed machine learning, emphasizing optimization for data-intensive services. He is an IEEE Senior Member and has received prestigious awards, including the NSERC Discovery Grants and IEEE TCCLD Outstanding Ph.D. Thesis Award. Education: Ph.D. in Information Science and Technology, Central South University (2014–2019) M.Sc. in Information Science and Technology, Central South University (2012–2014) B.Eng. in Information Science and Technology, Central South University (2008–2012) Research Assistant at the University of Victoria (2016–2018) Research Interests: Kaiyang’s work bridges AI and cloud computing, exploring optimization strategies for next-generation systems. Key areas include learning-based congestion control, energy-efficient resource management, and scalable distributed storage solutions. His research has been published in top-tier journals like IEEE Transactions on Parallel and Distributed Systems and conferences such as IEEE ICDCS. Awards & Grants: NSERC Discovery Grants & Discovery Launch Supplement (2024) IEEE TCCLD Outstanding Ph.D. Thesis Award (2020) CSC-UVic Fellowship (2016–2018) Teaching: He teaches courses on Computer Networks, Advanced Computer Networks, and Operating Systems at Memorial University and previously at the University of Victoria. Labs & Teams: His research group focuses on Space Edge Computing, leveraging LEO satellites for resilient, low-latency networks. Ongoing projects include optimizing distributed systems for AI workloads and satellite-based data centers.

• Distributed Cloud/Edge Computing
• Data Center Networks
• Distributed Machine Learning
• +3 more