Francesco Biondi is a Professor in the Department of Kinesiology at the University of Windsor's Faculty of Human Kinetics. His research focuses on driver behavior, automated vehicle systems, cognitive workload assessment, and workplace ergonomics. He leads the Human Systems Lab, collaborating with industry partners like Tesla and the Windsor Police Service on projects addressing driver distraction, semi-autonomous vehicle safety, and traffic zone analysis. Notable collaborations include studies on distracted driving in school zones and the effects of museum visits on mental clarity. Biondi has received federal research grants and contributed to over 50 peer-reviewed articles. His work emphasizes human-machine interaction challenges in automated systems, including legal liability implications and driver training requirements. Education and Training: Specialization in Human Factors Engineering Advanced training in Ergonomics and Biomechanics Research Interests: Automated vehicle human factors Cognitive workload measurement Driver distraction mitigation Workplace cognitive ergonomics Technology-assisted rehabilitation Recent Article Trends: Focus on real-world validation of automated driving systems, validation of cognitive workload metrics (e.g., pupil size tracking), and interdisciplinary approaches to vehicle automation safety. His team has pioneered camera-based monitoring systems for manufacturing environments and developed novel blink detection algorithms for driver monitoring. Labs/Teams: Leads the Human Systems Lab at UWindsor, collaborating with engineering faculty on cross-disciplinary projects through the WE-Spark Health Institute.
Anirudh Mohan Kaushik is an Assistant Professor in the Electrical Engineering & Computer Science (EECS) department at York University's Lassonde School of Engineering. He holds a Ph.D. from the University of Waterloo (2021) and has prior industry experience at Intel, AMD, and IBM in research and design roles focused on software performance analysis, compiler design, and hardware micro-architecture. Research Interests : Cyber-physical systems, high-performance computer architecture, compiler design, and software performance analysis with a focus on timing predictability in multi-core systems. Key Publications : His work addresses predictable cache coherence (2021), shared cache optimization (2024), GPU wavefront splitting (2023), and hardware prefetching for graph analytics (2021). Industry Experience : Contributions to compiler design and hardware micro-architecture at Intel, AMD, and IBM prior to academia.
David Lamb is an Associate Professor in the School of Computing at Queen’s University, affiliated with the Faculty of Arts and Science. He retired at the end of 2023 after 39 years of service. His research focuses on software engineering methodologies, including software architecture, requirements engineering, and design metrics. Lamb holds a PhD in Computer Science from Carnegie-Mellon University and a BSc from the University of Waterloo. He taught courses such as CISC/COGS499: CAS Labs and CISC500: Undergraduate Thesis. His work includes contributions to frameworks like SPRUCE for software restructuring and the development of the Charrette Ada compiler. He is a member of the ACM, IEEE, and Sigma Xi Honor Society. Post-retirement, Lamb has explored writing and systems analysis, including a planned book on academic life. He remains active in scholarly discussions through blogs and social media, focusing on topics like Canadian civics and systems thinking in phone game design.
William J. Bowman is an Assistant Professor in the Department of Computer Science at the University of British Columbia (UBC), within the Faculty of Science. His research focuses on programming languages, compilers, and type systems, particularly in areas like type-preserving compilation, dependently typed languages, and verified software. He is part of the Software Practices Lab and has supervised Master's theses on topics such as Redex-Plus (a metanotation compiler), sized dependent types, and ANF translation for dependently typed systems. Research Interests include programming languages, software engineering, compilers, type theory, dependent types, and formal verification. His work emphasizes preserving semantic guarantees through compilation and ensuring safety in low-level code. He has contributed to foundational research in dependently typed compilation and practical tools like Wasm-precheck for WebAssembly. Publications: Over 20 peer-reviewed articles in venues like POPL, PLDI, and ACM conferences. Awards: Distinguished Reviewer Award and teaching recognition as an Incredible Instructor. Interdisciplinary Collaboration: Open to collaborations in research grants and bridging theory/practice gaps. His research also addresses ethical considerations in compilation (e.g., ACM Profits Considered Harmful critique) and explores connections between type universes and memory allocation. He teaches courses such as Compiler Construction (CPSC 411) and advanced topics in programming languages.
Dr. Daniel Kang is an Assistant Professor at the University of Illinois at Urbana-Champaign (UIUC) in the Department of Computer Science and Department of Electrical and Computer Engineering (by courtesy). His research focuses on integrating machine learning with data systems and zero-knowledge proofs to enhance privacy and trust in AI deployments. He previously worked as a postdoctoral researcher at UC Berkeley's Sky Lab and earned his PhD at Stanford University under Peter Bailis and Matei Zaharia. Developed frameworks like ZKML and ZK-IMG Co-creator of DawnBench and MLPerf benchmarks Research funded by Google, Open Philanthropy Project, and Emergent Ventures His research spans machine learning systems , privacy-preserving AI , and security risks in LLMs . Recent work includes optimizing zero-knowledge proofs for ML inference and studying adversarial attacks on AI agents. Articles reveal expertise in benchmark design , video analytics , and ML deployment systems . Dr. Kang actively recruits students at all levels and focuses on trustless AI verification and efficient query processing . His work addresses critical challenges in ML reproducibility , code generation , and web security .
Daniel Lemire is a full professor of computer science at the Université du Québec (TELUQ), recognized as one of the top 2% most cited scientists globally according to Stanford University's 2024 rankings. He ranks among the 0.0006% most followed programmers on GitHub, with his work adopted by major technology companies including Google, Facebook, Intel, and Shopify. Education: Ph.D. in Engineering Mathematics (University of Montreal and Polytechnique Montréal), Master's in Mathematics (University of Toronto), Bachelor's in Mathematics with High Distinction (University of Toronto) Current Role: Editor of Software: Practice and Experience journal since 2020 Professional Recognition: Co-chair of NSERC's Computer Science Discovery Grants Committee (2020-2021) Professor Lemire's research focuses on software performance optimization and data indexing techniques. His work bridges theoretical computer science with practical applications, particularly in areas where performance bottlenecks exist in real-world systems. He specializes in leveraging hardware capabilities through vectorization (SIMD instructions) to dramatically improve processing speeds for fundamental operations that have remained inefficient for decades. His approach combines deep theoretical understanding with practical implementation, resulting in algorithms that are both mathematically sound and immediately applicable in production systems. Lemire's research portfolio demonstrates a consistent pattern of identifying critical performance bottlenecks in widely used software operations and developing innovative solutions that achieve order-of-magnitude improvements. His work spans multiple domains including JSON parsing, Unicode string processing, URL parsing, base64 encoding, and bitmap indexing. A common thread through his publications is the application of hardware-specific optimizations, particularly SIMD instructions, to accelerate operations that were previously considered near-optimal. His research has evolved from foundational algorithm development to influencing major software ecosystems, with his libraries becoming integral components of industry-standard tools. Among the 2% most cited scientists globally (Stanford University, 2024) Université du Québec's Prix d'excellence 2020 for research success Most read articles at Software: Practice and Experience (2024, 2025) Best voted talk at QCon San Francisco 2019 Editor of Software: Practice and Experience journal since 2020 Numerous citations in patents held by Microsoft, LinkedIn, Oracle, and Fujitsu Professor Lemire maintains an active research group that has graduated numerous PhD students, many of whom now hold key positions at leading technology companies. He offers automatic scholarships for all students making progress on M.Sc. theses and Ph.D. programs in his lab, with tuition waivers for international Ph.D. students. His laboratory is equipped with a diverse server farm featuring multiple processor architectures (Intel Xeon, Core i7, Xeon Phi, POWER9, ARMv8) specifically designed for software performance experiments. The lab also explores virtual reality applications in data science. Lemire actively recruits students who are passionate about high-performance programming and open-source development, with special programs for Canadian undergraduate and graduate students through NSERC funding mechanisms.
Rodolfo Pellizzoni is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, Canada. His research focuses on real-time systems, embedded systems, and computer architecture, with an emphasis on time-predictable computing, multicore systems, and FPGA networks. He has contributed extensively to cache management, memory resource coordination, and security-aware scheduling in critical systems. His work includes designing frameworks like HopliteRT for FPGA NoCs, optimizing memory bandwidth regulation, and addressing challenges in mixed-criticality systems. Pellizzoni is affiliated with the Faculty of Engineering and maintains a research group focused on hardware-software co-design for real-time applications. His research bridges theoretical models (e.g., Network Calculus) with practical implementations, emphasizing latency reduction and resource predictability in heterogeneous platforms. Recent publications highlight advancements in cache partitioning, dynamic memory allocation, and scheduling algorithms for multicore processors. His work often appears in top-tier conferences like Euromicro Conference on Real-Time Systems (ECRTS) and journals focusing on embedded and real-time systems.
Paulo Garcia is an Assistant Professor in the Department of Systems and Computer Engineering at Carleton University, Ottawa, Canada. He holds a Ph.D. in Computer Engineering from the University of Minho, Portugal, with research periods at Asian Institute of Technology and University of Würzburg. His academic roles include serving as a faculty member, thesis chair/examiner, and committee member in multiple university initiatives. Research Focus: Embedded real-time systems, hardware/software co-design, FPGA acceleration, and engineering pedagogy. Specific technical areas include multicore architectures, runtime systems, and hardware accelerators for embedded applications. His work emphasizes synergies between processor architectures, compilers, and FPGA-based solutions. Teaching: Courses include SYSC 3310 (Real-Time Systems), SYSC 4310 (Computer Architecture), and SYSC 5807 (Hardware/Software Co-Design). He supervises senior projects like robotic systems and eHealth wearable devices. Awards & Grants: Includes 2020 awards from General Dynamics Mission Systems and Carleton’s Rapid Response Grant, plus multiple scholarships from FCT Portugal and EU programs. His research has been supported by industry partnerships and defense collaborations. Service: Active in academic service as a thesis evaluator, member of student mental health committees, and representative in university governance bodies. Engaged in curriculum development and embedded systems program revisions.
LillAnne Jackson is an Associate Teaching Professor and Associate Dean of Undergraduate Studies in the Department of Computer Science at the University of Victoria. She holds a PhD from the University of Calgary and has dual roles in academic leadership and teaching innovation within the Faculty of Engineering and Computer Science. Dr. Jackson’s research focuses on memory consistency for multiprocessor architectures, computational geometry, and pedagogical strategies for teaching concurrency in computer science. Her educational work emphasizes improving retention in STEM fields through innovative teaching methods and curriculum design. She has contributed to accreditation-driven curricular reforms and explored the efficacy of video-based instruction in technical education. Her technical research addresses challenges in parallel computing systems, including memory coherence models for architectures like Itanium and Sparc. Earlier work includes computational geometry projects such as polygon reconstruction algorithms and visibility analysis. Her articles span both technical computer science topics and educational methodologies, reflecting her dual expertise in technical systems and academic innovation. In her administrative role as Associate Dean, she oversees undergraduate academic policies and program development. She collaborates with faculty to enhance student experiences through curriculum modernization and pedagogical research. Dr. Jackson’s work bridges theoretical computer science with practical educational strategies to strengthen undergraduate education and technical workforce development.
Marc Moreno Maza is a Professor in the Computer Science and Applied Mathematics Departments at the University of Western Ontario, and a Principal Scientist at the Ontario Research Centre for Computer Algebra (ORCCA). His research focuses on applying computer science to mathematics, particularly polynomial system solving and algorithm design. Key areas include parallel computing, high-performance algebraic algorithms, and GPU acceleration. He leads projects funded by NSERC and industry partnerships, such as 'Hardware Acceleration Technologies for Polynomial Systems' and collaborative work with IBM and CAS Research. His research spans four directions: theoretical foundations of polynomial equations, efficient algorithm development, software implementation (e.g., RegularChains library in Maple), and applications to real-world challenges. He has delivered over 100 talks worldwide on topics like cylindrical algebraic decomposition, GPU-based polynomial arithmetic, and parametric system solving. His work emphasizes optimizing algorithms for modern architectures and leveraging parallelism. Notable contributions include the BPAS and CUMODP libraries for polynomial arithmetic, and the RegularChains library for semi-algebraic set computations. He collaborates internationally, with grants supporting both theoretical and applied research. His team addresses challenges in computational algebra, from theoretical breakthroughs to practical software tools.
Dr. Christopher Anand is an Associate Professor in the Department of Computing and Software and the McMaster School of Biomedical Engineering at McMaster University. He holds roles as Graduate Advisor for the MEng in Computing and Software and is a Fellow of IBM Canada Advanced Studies. His research spans computer science education, programming languages, compilers, and high-performance computation, with collaborations at IBM and global educational initiatives. Education: PhD in Differential Geometry. He focuses on making computing education accessible globally through projects like McMaster Start Coding and the Fondation STaBL, emphasizing design thinking and innovation. His work bridges academia and industry, addressing challenges in medical imaging, scanning electron microscopy, and educational technology. Research Interests: Includes scientific computing, optimization, and the application of design thinking in education and innovation. Notable achievements include the IBM Project of the Year Award (2018) for technology impacting IBM processors. He teaches courses such as Mobile User Interface Design and Model-Based Image Reconstruction, and his outreach efforts engage students from elementary schools to global post-secondary institutions.
Ibrahim Numanagić is an Assistant Professor in Computer Science and Canada Research Chair (Tier 2) at the University of Victoria, Canada. His research spans computational biology, programming languages, and secure computing. He leads the 0xTCG Lab , developing tools like Aldy for pharmacogenomics and Codon for high-performance Python applications. Education: PhD (Simon Fraser University, Vanier Scholar), Postdoc (MIT CSAIL), BSc (University of Sarajevo). His work focuses on integrating computational methods with genomics, including segmental duplication analysis, compiler design, and secure biomedical data sharing. Research Highlights: Innovations in genotyping tools (e.g., Geny, BISER), dynamic compiler optimization (Vectron), and secure multi-party computation frameworks (Sequre). Active in open-source projects and clinical bioinformatics. Awards: Canada Research Chair (2021–), Vanier Canada Graduate Scholarship (2015–2018) Recruitment: Seeking PhD/MSc students (2025 intake) with strong Python/C++ skills; applications must include 'Lab 0xTCG' in emails
Mirza Omer Beg is an Associate Professor in the Department of Computer Science at the National University of Computer and Emerging Sciences (NUCES) in Islamabad, where he leads the Artificial Intelligence and Machine Learning (AIM) Lab. He holds a PhD in Artificial Intelligence from the University of Waterloo and an undergraduate degree in Computer Science from the University of Texas at Austin. His academic roles include serving as the Head of the AIM Lab and contributing to interdisciplinary research initiatives. **Education:** PhD in Artificial Intelligence, University of Waterloo (2007) MS in Computer Science, University of Waterloo (2005) BEng in Computer Science, University of Texas at Austin (2001) **Research Interests:** Green Computing: Optimizing energy efficiency in software and hardware systems, particularly for mobile and embedded devices. Natural Language Processing (NLP): Emotion detection, hate speech identification, and multilingual NLP (e.g., Urdu and Roman Urdu). Compiler Optimization: Instruction scheduling, memory hierarchy optimizations, and clustered architectures. Text Mining: Sentiment analysis, aspect-based opinion mining, and keyphrase extraction. Machine Learning: Applications in healthcare, smart cities, and cybersecurity. **Awards & Recognition:** Third Place in Graduate Student Track of ACM SRC PLDI 2010 for work on instruction scheduling on multicore architectures. **Teaching:** Advanced courses in AI, data science, computer architecture, and programming languages. Recent courses include Text Mining, Natural Language Processing, and MultiAgent Systems. **Labs & Teams:** Leads the AIM Lab focusing on AI-driven solutions for energy efficiency, NLP, and computational linguistics.
Sebastian Fischmeister is a Professor and NSERC/Magna Industrial Research Chair in Automotive Software for Connected and Automated Vehicles at the Department of Electrical and Computer Engineering, University of Waterloo. His research focuses on systems at the intersection of software technology, distributed systems, and formal methods, with applications in automotive systems, avionics, and medical devices. He has pioneered frameworks for scalable location-based pervasive computing and verifiable real-time communication schedules, contributing to the ASTM F29.21 standard. Education: Dipl.-Ing. in Computer Science (Vienna University of Technology, 2000), Ph.D. in Computer Science (University of Salzburg, 2002) Research Themes: Real-time embedded systems, runtime monitoring, security analysis, data analytics for validation, and performance evaluation. Scientific Awards: APART Stipend (2005) Ontario Early Researcher Award (2014) Multiple best paper and tool awards He is an ACM Distinguished Speaker and actively participates in organizing conferences such as ESCAR, RTSS, DATE, and ICPE. His work includes significant contributions to anomaly detection, cybersecurity in automotive networks, and runtime verification techniques under unreliable conditions.
Dr. Gennady Pekhimenko is an Associate Professor in the Department of Computer Science and cross-appointed to the Electrical and Computer Engineering department at the University of Toronto. He leads the EcoSystem research group and serves as CEO/Co-Founder of CentML, with affiliations at CIFAR and Vector Institute. PhD in Computer Science from Carnegie Mellon University (2016) His research spans computer architecture, systems optimization, and applied machine learning, with a focus on memory hierarchy efficiency, hardware acceleration, and compiler design. Recent work emphasizes large language model training, GPU virtualization, and processing-in-memory systems. Key article trends include: 1) Memory optimization for transformers and sparse models (2024-2025), 2) GPU resource management (2022-2024), 3) Prompt programming languages (2025), and 4) Deep learning compilation frameworks (2021-2023). Major Awards: NVIDIA Graduate Fellowship, Microsoft Research PhD Fellowship, ISCA Hall of Fame, AWS/Google/VMware research grants Key Collaborations: Onur Mutlu (CMU), Todd Mowry (CMU), Vector Institute researchers
