Find Academic Supervisors

Georgiy KrylovView profile

Assistant Professor

• High Performance Computing
• Compilers and Runtime Environments
• FPGA CAD
• +2 more

Georgiy Krylov is an Assistant Professor at the Faculty of Computer Science, University of New Brunswick. He holds a PhD from UNB (2024), and master's and bachelor's degrees from Nazarbayev University (2018 and 2016). His research focuses on high-performance computing, compilers/runtime environments, FPGA CAD, and quantum/reversible computing. Education: PhD in Computer Science, University of New Brunswick (2024) MSc in Computer Science, Nazarbayev University (2018) BSc in Computer Science, Nazarbayev University (2016) Research Interests: His work spans compiler optimization, quantum circuit design, FPGA applications, and ahead-of-time (AOT) compilation techniques. He explores geometric refactoring for quantum circuits, heterogeneous logic implementations, and GPU acceleration in genetic algorithms. Publications Trends: Recent work emphasizes AOT compilation frameworks (e.g., Eclipse OMR), quantum computing optimizations, and FPGA-based CAD tools. Earlier studies focused on GPU-accelerated algorithms for quantum circuit synthesis. Grants/Advising: No specific grants or advisees listed. Active in open-source compiler toolchains and hardware-software co-design projects.

Banani RoyView profile

Assistant Professor

• Software Maintenance and Renovation
• Software Analytics
• Empirical Software Engineering
• +4 more

Dr. Banani Roy is an Assistant Professor in the Department of Computer Science at the University of Saskatchewan. Her research focuses on software maintenance, empirical software engineering, program comprehension, and scientific workflow management systems. She explores challenges in quantum computing applications for software engineering, AI-driven code tools, and reproducibility in computational experiments. Her work includes developing frameworks like VizSciFlow for scientific workflow visualization and Nutrient App for environmental monitoring via smartphone applications. Her research interests span topics such as code quality analysis, gender disparities in software systems, and XAI (Explainable AI) challenges. She has contributed to tools like CloneCognition for code clone validation and FSECAM for feature-to-architecture linkage. Her recent studies address developer challenges with large language models and federated learning approaches for real-time bug prediction. Dr. Roy's publications emphasize interdisciplinary applications of software engineering principles in scientific computing, quantum algorithms, and community-driven problem-solving platforms. She advocates for reproducible workflows and FAIR (Findable, Accessible, Interoperable, Reusable) data practices in collaborative scientific research. While no awards are explicitly listed, her extensive contributions to open-source tool development and empirical studies highlight her impact in advancing software engineering methodologies. She collaborates on projects involving legacy system reengineering, cloud-based code clone validation, and asynchronous collaboration frameworks for scientific teams.

Jens WeberView profile

Professor

• Software Engineering
• Safety and Security Engineering
• Health Informatics
• +5 more

Jens Weber is a Professor and Associate Dean of Professional Programs & Development at the Faculty of Engineering and Computer Science, University of Victoria. He holds a PhD from the University of Paderborn and is a Professional Engineer (P.Eng.) in British Columbia. His research focuses on software engineering with emphasis on safety and security assurance in critical systems, particularly in healthcare, transportation, and defense sectors. He also investigates systems incorporating artificial intelligence and self-adaptation. He teaches courses on security engineering, software architecture, and formal methods. His affiliations include the School of Health Information Science at UBC and the Faculty of Medicine at UBC. Key research projects include GRAPE VINE (graph transformation tools), Information Security Assurance, Medical Device Software Certification, and Medical Systems Hazard Analysis. He serves on the Board of Engineers and Geoscientists BC, chairs the Academic Integrity Committee, and organized conferences like IEEE Health Informatics 2021. His work emphasizes bridging engineering practices with real-world software deployment challenges, especially in health informatics. Awards include the 2019 Société de Biomécanique Young Investigator Award for human body model validation research. Current initiatives include developing data quality frameworks for clinical systems, tool support for functional graph programming, and safety analysis methodologies for adaptive systems.

Yuhong YanView profile

Associate Professor

• Service Computing
• Service-Oriented Architecture
• Cloud Computing
• +4 more

Yuhong Yan is an Associate Professor at Concordia University's Department of Computer Science and Software Engineering. Her research focuses on Service Computing, emphasizing flexibility and interoperability in software systems. She advises graduate students in master's and PhD programs, though currently not recruiting new students. Her work includes service-oriented architecture, cloud computing, and workflow management, with notable contributions to service composition optimization and QoS-aware systems. Her research has been recognized through awards such as the Best Student Paper at CLOSER 2017 and SCC 2014, and her paper on service-oriented network virtualization was the most cited in CSCWD (2001-2011). She has led and participated in multiple IEEE conferences, including serving as Chair for the IEEE Cloud Cup in 2013 and 2014. Her work often involves collaborations with industry, funded by NSERC Engage and MITACS grants. Yuhong emphasizes practical industrial experience and maintains a lab focused on adaptive service composition, failure recovery, and graph-based algorithms. She has authored or co-authored over 50 peer-reviewed articles and serves on program committees for leading conferences in services computing.

• Big Data Management
• Scalable Data Science
• Spatial and Spatio-Temporal Data Processing
• +5 more

Suprio Ray is an Associate Professor in the Faculty of Computer Science at the University of New Brunswick, Canada, leading the Big Data Systems and Analytics Lab. He holds a PhD from the University of Toronto, an M.Sc. from the University of British Columbia, and a B.E. from NIT Trichy. Previously, he worked in industry roles at Oracle, Bell Labs, and Webtech Wireless, and completed a PhD internship at SAP. His research focuses on scalable data systems, spatial/spatio-temporal data management, and modern hardware utilization. Key projects include the Jackpine spatial database benchmark , DaskDB (a scalable data science system), and NUMA-aware query processing . He collaborates across disciplines with ECE and GGE departments, supported by grants from NBIF, NSERC, and industry partners. Research interests span Big Data systems, privacy/security in databases, blockchain analytics, and parallel/distributed computing. He has pioneered techniques like STILT multi-dimensional indexes , privacy-preserving spatial queries (Pystin) , and learned spatial indexes . Over 60 publications appear in top venues like SIGMOD, ICDE, and IEEE BigData. Education: PhD (Computer Science), University of Toronto (2015) M.Sc. (Computer Science), University of British Columbia (2003) B.E. (Computer Science & Engineering), NIT Trichy (2000) Teaching includes courses on big data systems (CS4545/6545), database foundations (CS6585), and data science (CS2545). He advises graduate students in scalable analytics and spatial systems, emphasizing industry-relevant software skills. Key awards include the 2024 ACM SIGSPATIAL best poster award , IBM Best Student Paper (2014) , and Harrison McCain Foundation award (2016) . His work has been recognized with 7 best paper awards and 3 patents. Current projects explore FPGA-accelerated joins , serverless data analytics , and privacy-preserving blockchain queries . Past contributions include the GEMM mobility model (2003) and foundational spatial benchmarks.

• Compiler Optimization
• High Performance Computing
• Computer Architecture
• +2 more

Dr. J Nelson Amaral is a Professor in the Department of Computing Science within the Faculty of Science at the University of Alberta. His research focuses on compiler optimization techniques that enhance performance in modern computing architectures through clever code transformations. Educational background includes B.Sc. in Electrical Engineering (PUCRS), M.Sc. in Electrical Engineering (ITA), and Ph.D. in Electrical and Computer Engineering (University of Texas at Austin). Research interests span compiler optimizations for resource utilization, learning technology applications in compilation processes, and feedback-directed optimization efficiency improvements. Current investigations include instruction-level parallelism exploitation, code transformations for heterogeneous architectures, and machine learning-enhanced compilation. Publication analysis reveals strong focus on compiler techniques for performance optimization, particularly for matrix operations, convolution algorithms, and memory hierarchy management. Recent work emphasizes hardware-software co-design for specialized processors and auto-vectorization methods. University of Alberta Faculty of Science Excellence in Teaching Award (2015) Distinguished Engineer, Association for Computing Machinery (2014) Distinguished Speaker, Association for Computing Machinery (2012-2014) Interdepartmental Science Students' Society Award for Excellence in Teaching (2014) IBM Center for Advanced Studies Research Faculty Fellow of the Year (2012) Research includes industry collaborations through the IBM-CAS partnership. Contributes to computing education through curriculum development and student mentoring. Leads compiler optimization research at the IBM Center for Advanced Studies, focusing on performance improvements for enterprise workloads and specialized hardware.

Jeremy BradburyView profile

Associate Professor

• Software Engineering
• Software Quality Assurance
• Testing and Analysis
• +7 more

Jeremy Bradbury is an Associate Professor in the Faculty of Science at Ontario Tech University, where he has served as Undergraduate and Graduate Program Director for Computer Science and as a member of the Board of Governors. His research focuses on software testing, analysis, and quality assurance, with a particular emphasis on human-centered approaches, empirical methodologies, and concurrency issues in software systems. He leads the Software Quality Research Lab (SQRLab) and develops tools like PIE for visualizing software design patterns and GidgetML for adaptive educational gaming. Education: PhD in Computer Science from Queen's University (2007). Research interests include software visualization, open-source software analysis, flaky test detection, and the application of machine learning to software engineering challenges. His work spans both academic contributions (e.g., combinatorial testing frameworks) and practical innovations like the Run, Llama, Run game for computational thinking education. Recent work explores AI-driven bug prediction, data augmentation bias in ML models, and cybersecurity for connected autonomous vehicles. He has organized workshops on testing configurable systems (ToCaMS) and challenges in parallel computing.

• Programming Languages
• Concurrency
• Embedded Systems
• +3 more

Dr. Emil Sekerinski is a Professor in the Department of Computing and Software at McMaster University, located at ITB 215. He serves as the Undergraduate Advisor for Computer Science (Jan 1, 2025 - June 30, 2025). His research focuses on Programming Logic , Concurrency , Embedded Systems , and Programming Languages , with specialization in Computer Systems, Software Quality, and Computation Theories. He is affiliated with the Digital & Smart Systems research cluster. Recent academic contributions include advanced compiler design, concurrent system design, and formal specification techniques. His work spans theoretical aspects of computing, such as program algebra and automata, alongside practical applications in real-time monitoring and wireless sensor networks. He actively contributes to academic communities, serving as a conference co-chair (FMTea'24) and program committee member (ICTAC'24, iFM'24). Teaching highlights include courses like CAS 781 Advanced Topics: Advanced Compiler Design and Optimization (Fall 2025) and SFWR ENG 3BB4 Concurrent System Design (Fall 2024). His publications emphasize concurrency, formal methods, and algorithm optimization, bridging theoretical foundations with real-world system implementations.

Margo SeltzerView profile

Professor

• Computer Systems
• Operating Systems
• Database Systems
• +7 more

Margo Seltzer is a Professor and Co-Head of the Department of Computer Science at the University of British Columbia (UBC). She holds the Canada 150 Research Chair in Computer Systems and the Cheriton Family Chair in Computer Science. Her research focuses on computer systems, including operating systems, databases, data provenance, graph analytics, and interpretable machine learning. She is also involved in educational initiatives and has held leadership roles in academia, including serving as Dean of Computer Science and Engineering at Harvard University. Education: PhD in Computer Science from UC Berkeley (1992), AB in Applied Mathematics from Harvard (1983). Professional roles include Chief Technology Officer at Sleepycat Software and architect at Oracle Corporation. She has received prestigious awards such as the ACM Athena Lecturer Award, Killam Teaching Prize, and membership in the National Academy of Engineering. Research Interests: Systems, databases, transaction processing, provenance capture, healthcare informatics, and cybersecurity. Her work emphasizes practical applications of systems research, including optimizing storage, improving operating system performance, and enhancing data security. She has contributed to open-source projects like Berkeley DB and CamFlow provenance system. Key Contributions: Developed systems for provenance-aware storage, sparse decision trees (e.g., GOSDT), and intrusion detection (FRAPpucino). She advocates for reproducible research through tools like Encapsulator and Rclean. Her teaching and mentorship have influenced countless students and junior faculty in computer science. Awards: ACM Athena Lecturer (2023), SIGMOD Systems Award (2020), USENIX Lifetime Achievement Award (2019), and election to the American Academy of Arts and Sciences (2021). Her work bridges theoretical computer science with real-world applications, emphasizing both technical innovation and societal impact.

Mohammad ShahradView profile

Assistant Professor

• Cloud Computing
• Sustainability
• Large-Scale Computing Systems
• +3 more

Mohammad Shahrad is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC) and an Associate Faculty Member of UBC's Computer Science Department. He leads the UBC Cloud Infrastructure Research for Reliability, Usability, and Sustainability (CIRRUS) Lab. His research focuses on sustainable and efficient large-scale computing systems, particularly in cloud resource management and serverless systems. Education: Ph.D. in Electrical Engineering, Princeton University (2020) M.A. in Electrical Engineering, Princeton University (2016) B.Sc. in Electrical Engineering, Sharif University of Technology, Tehran (2014) Research Interests: Shahrad's work emphasizes energy-efficient cloud computing, serverless architecture optimization, and sustainability in distributed systems. His projects address challenges like carbon footprint reduction, resource allocation, and cold-start delays in serverless platforms. He employs interdisciplinary approaches, combining system design, algorithm development, and empirical analysis to tackle real-world cloud infrastructure problems. Recent Contributions: His research trends include carbon-aware scheduling, geospatial shifting for sustainability, and developer-centric compliance tools for serverless applications. These efforts aim to balance performance, cost, and environmental impact in modern cloud ecosystems. Scientific Awards: Community Award at USENIX ATC '20 for Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider Advising & Grants: As CIRRUS Lab lead, Shahrad mentors researchers in cloud infrastructure and sustainability. While specific grant details are not listed, his work reflects substantial industry collaboration (e.g., Microsoft Research, Azure). Labs/Teams: The CIRRUS Lab focuses on creating sustainable cloud solutions through open-source frameworks like OpenPiton and collaborative industry partnerships.

• Computer Architecture
• Computer Systems
• Parallel Computer Architecture
• +4 more

Jeffrey Mark is an Assistant Professor in the Department of Computer Engineering at the University of Toronto , within the College of Engineering. He earned his PhD from the Massachusetts Institute of Technology in 2019, alongside MASc and BASc degrees from the University of Toronto in 2011 and 2009, respectively. Research Interests: His work focuses on computer architecture and computer systems , with specific emphasis on parallel computer architecture , accelerator architectures , parallel programming models , compilers , and hardware/software co-design to optimize parallelization, performance, and efficiency. Scientific Awards: Facebook Fellowship (2017) IEEE Micro Top Picks Honourable Mention (2016) IEEE Micro Top Picks (2015) Prior to his academic role, Dr. Mark conducted postdoctoral research as a Research Scientist at Facebook AI Research and gained industry experience in software engineering at Google , Epson , and a Y-combinator startup, AeroFS .

Tor AamodtView profile

Professor

• Computer Architecture
• GPU Architecture
• Deep Neural Networks
• +4 more

Tor Aamodt is a Professor in the Department of Electrical and Computer Engineering at the University of British Columbia within the Faculty of Applied Science. He has been a faculty member at UBC since 2006 and maintains an active research program in computer architecture with a focus on GPU systems. His academic journey includes a BASc, MASc, and PhD from the University of Toronto, followed by industry experience at NVIDIA where he worked on the memory system architecture of the GeForce 8 Series GPU. Professor Aamodt's research spans computer architecture with particular expertise in accelerators for deep neural networks and graphics processor unit architecture for non-graphics computing. His work has evolved from foundational GPU architecture research to cutting-edge applications in ray tracing, robotics acceleration, and energy-efficient computing. He has made significant contributions to understanding GPU memory systems, interconnect architectures, and the application of GPUs to machine learning workloads. His research group has developed influential tools including the first widely used GPGPU architecture simulator and the Vulkan-Sim ray tracing simulator. His recent publications reveal a strong trend toward real-time ray tracing acceleration and robotics applications, with substantial work on optimizing tree traversal algorithms for ray tracing workloads. The research shows a clear evolution from general GPU architecture studies to specialized hardware-software co-design for specific application domains, particularly in rendering and robotics. His group consistently publishes at top-tier architecture venues including MICRO, ISCA, and ASPLOS. IEEE Micro Top Picks (3 papers) Communications of the ACM Research Highlight MICRO Conference Hall of Fame NVIDIA Academic Partnership Award (2010) NSERC Discovery Accelerator (2016-2019) Google Faculty Research Award (2016) SAMSUNG Global Research Outreach Award (2023) IISWC 2023 Best Paper Award Professor Aamodt has advised numerous graduate students who have gone on to positions at leading technology companies including Google. His research has been supported by major grants from NSERC, NVIDIA, Google, and Samsung. He has served in leadership roles including Program Chair for ISPASS 2013 and General Chair for ISPASS 2014, and was a Visiting Associate Professor at Stanford University during his 2012-2013 sabbatical. His group maintains active collaborations with industry partners and has produced influential open-source tools including GPGPU-Sim and Vulkan-Sim.

Fan LongView profile

Assistant Professor

• Programming Languages
• Software Engineering
• Systems Security
• +3 more

Fan Long is an Assistant Professor in the Department of Computer Science at the University of Toronto, affiliated with the Bahen Centre for Information Technology. His research focuses on programming languages, software engineering, systems security, and blockchain technology, with particular emphasis on smart contract security and automatic patch generation. He leads the Conflux project, a high-throughput blockchain platform, and has contributed to systems like Shrec (bandwidth-efficient transaction relay), Gosig (scalable Byzantine consensus), and CodePhage (cross-application code transfer). Education background includes a PhD in Computer Science from MIT (as evidenced by his former MIT homepage). His work spans foundational blockchain scalability improvements, runtime validation for smart contracts (Solythesis), and input sanitization techniques (SIFT/SOAP). Notable awards include a Distinguished Paper at ICSE 2024 and Best Paper at IEEE ICBC 2022. Research highlights include developing Prophet (machine learning-driven patch generation), RCV (crash recovery tool), and LVMT (authenticated storage for blockchains). His lab collaborates with industry partners to address challenges in decentralized systems, including transaction finality, oracle security, and gas optimization in DeFi applications. Grants and collaborations include projects funded by NSERC, MIT-IBM Watson AI Lab, and industry partnerships with blockchain firms. Active in open-source communities, maintaining repositories for Conflux, Prophet, and CodePhage. Recent work explores atomic state sharding (Möbius), flash loan attack synthesis (Flashsyn), and robust front-running methodologies.

• Programming Languages
• Functional Programming
• Type Systems
• +5 more

Ningning Xie is an Assistant Professor in the Department of Computer Science at the University of Toronto. She holds a part-time Research Scientist position at Google DeepMind and is a Faculty Affiliate at the Schwartz Reisman Institute. Previously, she was a Research Associate at the University of Cambridge and earned her Ph.D. from the University of Hong Kong and B.S. from Zhejiang University. Her research focuses on foundational aspects of programming languages, including functional programming, type systems, logics, and their applications in broader domains. Key areas include algebraic effect handlers, staged compilation, type inference, and parallel computing. She has contributed to systems like MacoCaml and Perceus, and her work has been recognized with ACM SIGPLAN Distinguished Paper Awards and the Cambridge Hall of Fame Award. Dr. Xie co-chairs the Haskell Symposium 2025 and serves on committees for major programming language conferences (PLDI, ICFP, etc.). She actively organizes workshops and tutorials, including invited keynotes at Lambda Days and ZuriHac 2024. Her service roles include moderating the Types Forum and steering committees for Haskell and PLMW. Her academic contributions span over 25 peer-reviewed publications, with a focus on practical implementations and formal proofs. She advises students through her research group and emphasizes generative programming techniques in her teaching.

Roman MakarovView profile

Professor

• Mathematical Finance
• Monte Carlo Methods
• Stochastic Processes
• +3 more

Roman Makarov is a Professor in the Department of Mathematics at Wilfrid Laurier University, specializing in Mathematical Finance and Computational Methods. His research focuses on asset price models, derivative pricing, credit risk, and the application of Monte Carlo methods. He is affiliated with the Faculty of Science and teaches courses in financial mathematics and stochastic processes. Dr. Makarov's work integrates numerical methods and high-performance computing to address challenges in computational finance. His office is located at LH3049, and he is reachable via rmakarov@wlu.ca. Research Interests: His ongoing projects include developing solvable diffusion models, analyzing systemic risk components in financial securities, and advancing statistical learning strategies for trading. He explores the intersection of stochastic processes with real-world financial applications, emphasizing both theoretical rigor and computational feasibility. Publications: His recent work spans 2024-2018, covering topics like spectral expansions for credit risk, multi-asset jump-diffusion models, and high-frequency statistical modelling. These publications highlight his focus on bridging mathematical theory with practical financial applications. Awards: No specific awards are listed in the provided materials. Advising & Grants: While student advisees are not listed, his research interests suggest active involvement in supervising graduate students in mathematical finance and computational methods. Grant details are not specified here. Labs/Teams: No specific lab affiliations are mentioned, though his research likely involves collaborations within the Department of Mathematics and interdisciplinary groups at Wilfrid Laurier University.