جست‌وجوی استاد و پژوهشگر

Andrew Warfieldمشاهده پروفایل

استاد مدعو

• Computer Systems Software
• Virtualization
• Distributed Storage
• +۳ مورد دیگر

Andrew Warfield is an Adjunct Professor in the Department of Computer Science at the University of British Columbia (UBC) and a Senior Principal Engineer at Amazon. His research focuses on computer systems software, including virtualization, distributed storage, security, and high-availability systems. He previously held roles as Associate Professor at UBC, CTO at Coho Data, and Technical Director at Citrix Systems. His work has led to projects like Remus (high-availability replication), Tralfamadore (execution analysis), and secure hypervisor development with Xen. Warfield's education includes a PhD from the University of Cambridge's Computer Laboratory, where he researched I/O device virtualization under Steven Hand. He has held visiting roles at Intel Research Cambridge and internships at AT&T Research and Nortel Networks. Research Grants: Supported by Intel Research, NSERC, Network Appliance, and the Communications Security Establishment. Professional Activities: Technical Advisory Board Member at Teradici, and involvement in program committees for conferences like HotOS, EuroSys, and FAST. His research emphasizes practical systems, aiming to bridge the gap between theoretical computer science and real-world applications. Notable contributions include innovations in storage for virtualized environments, secure hypervisor architectures, and disaster-tolerant systems like SecondSite. Warfield is affiliated with UBC's Department of Computer Science and maintains active collaboration with industry partners. Though currently not actively recruiting students, his prior mentorship has influenced many in systems research.

Ahmad Afsahiمشاهده پروفایل

استاد

• Parallel Processing
• High-Performance Computing
• Network-Based Computing
• +۵ مورد دیگر

Dr. Ahmad Afsahi is a Professor in the Department of Electrical and Computer Engineering at Queen's University, Canada. He leads the Parallel Processing Research Laboratory (PPRL) and chairs the Graduate Studies committee in ECE. His research focuses on parallel processing, high-performance computing (HPC), and network-based systems, with emphasis on communication runtime systems, accelerated computing, and deep learning infrastructure. Education: Ph.D. (Electrical Engineering, 2000) from University of Victoria; M.Sc. (Computer Engineering, Sharif University of Technology); B.Sc. (Computer Engineering, Shiraz University). Research interests include parallel programming models, MPI optimization, GPU-aware communication, network-aware algorithms, and power-efficient HPC systems. He is a Senior Member of IEEE, ACM member, and licensed Professional Engineer in Ontario. Key Awards: Canada Foundation for Innovation Award, Ontario Innovation Trust Award. Over 50 publications in top venues like SC, EuroMPI, IPDPS, and IEEE journals. Current teaching includes cluster computing and digital systems. Labs/Groups: PPRL, Queen's Collaborative Graduate Specialization in Computational Science and Engineering, Data, Analytics, and Computing (DAC) Research Group.

Bryan Trippمشاهده پروفایل

دانشیار

• Computational Neuroscience
• Deep Learning
• Robotics
• +۱ مورد دیگر

Bryan Tripp is an Associate Professor at the University of Waterloo, specializing in computational neuroscience, deep learning, robotics, and medical AI. He leads the BRAIN Lab, which focuses on developing neural system models that interact with the physical world through robots. His research integrates neurobiological models with advanced machine learning techniques to study visuomotor processes and robotic applications. Tripp teaches courses such as Computational Neuroscience (SYDE 552), Deep Learning (SYDE 577), and Biomedical Engineering Design Workshops (BME 461/462). His lab has achieved milestones including the OREO robotic head, the first spiking neural network model for complex action planning, and comprehensive datasets for robotic grasping. His recent work emphasizes Medical AI applications, with graduate positions available. The BRAIN Lab is affiliated with the Centre for Theoretical Neuroscience and Waterloo.AI, contributing to interdisciplinary AI research initiatives.

• Energy Efficiency in Software
• Multicore Memory Systems
• Hardware/Software Co-Design
• +۳ مورد دیگر

Arrvindh Shriraman is an Associate Professor and Program Director of Software Systems at Simon Fraser University's School of Computing Science in Surrey. His research focuses on energy-efficient software, multicore memory systems, and optimizing hardware/software interfaces for parallel programming. He holds a Ph.D. (2010) and M.S. (2006) in Computer Science from the University of Rochester, and a B.Eng. (2004) from the University of Madras. He teaches courses on parallel programming and energy-conscious software design. His research interests include synchronization mechanisms for domain-specific architectures, cache optimization, and FPGA-based acceleration. He has contributed to frameworks like Mu-grind for HLS-generated RTL instrumentation and TAPAS for parallel accelerator generation. Notable projects include RANGE-BLOCKS for synchronization in domain-specific systems and TapeFlow for gradient computation in neural networks. His work emphasizes real-time verification of autonomous systems and safety-critical trajectory planning for underwater vehicles. Shriraman collaborates closely with the Tangent Lab, exploring cutting-edge solutions in hardware-software co-design and embedded systems. His teaching and research bridge theoretical computer science with applied engineering challenges, addressing scalability, efficiency, and safety in modern computing systems.

Alaa Alameldeenمشاهده پروفایل

دانشیار

• Computer Architecture
• Memory Systems
• Hardware Security
• +۵ مورد دیگر

Alaa Alameldeen is an Associate Professor in the School of Computing Science at Simon Fraser University (SFU), part of the Faculty of Applied Sciences. Previously, he worked as a Research Scientist at Intel Labs (2006–2020) and held an Adjunct Faculty position at Portland State University (2008–2018). He earned a PhD in Computer Sciences from the University of Wisconsin-Madison (2006), and earlier degrees from Alexandria University, Egypt. His research focuses on computer architecture, including memory systems (processing-in-memory, cache/memory compression, security), energy-efficient architectures, and hardware-software co-design for machine learning. He advises PhD and MSc students in these areas and teaches advanced computing science courses. Key contributions include innovations in memory hierarchies, cache compression techniques, and mitigating hardware vulnerabilities. His work has been published in top conferences (e.g., ISCA, MICRO, HPCA) and patented in areas like near-memory processing and error correction. Alameldeen currently leads a research group exploring secure and high-performance memory architectures. He has supervised multiple graduate students, with many progressing to roles at leading tech companies and academic institutions.

• Data Center Networking
• Software Defined Networks
• Congestion Control
• +۱ مورد دیگر

Yashar Ganjali is Professor in the Department of Computer Science at the University of Toronto, where he leads research on computer networks and distributed systems. His work focuses on improving efficiency in data center operations through innovative algorithmic approaches. Research areas include: Machine learning applications for network optimization SDN controller architectures and load migration Congestion control mechanisms for high-speed networks Data center traffic engineering and resource allocation Recent projects explore joint time-space scheduling for distributed ML training, network-aware transport protocols, and reinforcement learning for congestion management. His team collaborates with industry partners including Google to develop practical solutions for cloud infrastructure challenges.

John R. Longمشاهده پروفایل

استاد

• High Speed Wireline Circuits
• High Frequency Circuits
• Low Power Circuits
• +۲ مورد دیگر

John R. Long is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, specializing in high-speed wireline and high-frequency circuit design for integrated wireless communications systems. His work bridges theoretical innovation with practical applications in mobile transceiver technologies. His academic credentials include: Doctorate in Electronics from Carleton University, Canada (1996) Master of Engineering in Electronics from Carleton University, Canada (1992) Bachelor of Science in Electrical Engineering from the University of Calgary, Canada (1984) Dr. Long's research concentrates on energy-efficient circuit architectures for next-generation wireless systems. His expertise spans millimeter-wave frequency doublers, low-voltage RF design, and wideband transceivers, addressing critical challenges in mobile communications bandwidth and power consumption. Recent work demonstrates significant advancements in DC-100 GHz circuit operation and autonomous system integration. Publications from 2015-2016 reveal a consistent focus on pushing the boundaries of solid-state circuit performance, particularly in high-frequency analog design and system-on-chip implementations for wireless applications. No scientific awards were documented in the provided materials. As an active educator, Dr. Long mentors graduate students and teaches core courses including Radio Frequency Integrated Devices (ECE 432) and Integrated Analog Electronics (ECE 444), emphasizing hands-on circuit design principles. His leadership extends to administrative roles, having previously chaired the Electronics Research Laboratory at Delft University of Technology.

Mohamed Hassanمشاهده پروفایل

استادیار

• Computer Architecture
• Embedded Systems
• Real-Time Systems
• +۱۲ مورد دیگر

Dr. Mohamed Hassan is an Assistant Professor in the Department of Electrical & Computer Engineering at McMaster University. His research focuses on Cyber-Physical Systems-on-Chip (iCPSoCs) , emphasizing design, analysis, and deployment for critical domains like Unmanned Aerial Vehicles (UAVs), Autonomous Cars, and healthcare systems. Key research areas include hardware/software codesign, real-time systems, embedded systems, and security. He teaches courses such as COMPENG 4DM4 (Computer Architecture) and COMPENG 4DS4 (Embedded Systems) . His work bridges foundational theories (e.g., scheduling, AI) with infrastructure-level innovations (e.g., compilers, memory systems). The Fanos Research Lab he leads explores interdisciplinary solutions for efficient CPS-on-Chip, addressing challenges in multicore predictability, memory latency, and edge computing. Recent contributions include frameworks for explainable memory-centric workloads and techniques to accelerate TinyML inference. Dr. Hassan serves on Technical Program Committees for conferences like RTAS and OSPERT, highlighting his role in advancing real-time embedded systems research.

Ting Huمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Bioinformatics
• Data Mining
• +۷ مورد دیگر

Dr. Ting Hu is an Associate Professor in the School of Computing at Queen's University, affiliated with the Faculty of Arts and Science. She leads the Machine Intelligence & Biocomputing (MIB) Laboratory, focusing on bio-inspired AI and bioinformatics. Her research bridges evolutionary computing, machine learning, and biomedical data analysis. Dr. Hu holds a PhD in Computer Science from Memorial University and completed postdoctoral training at Dartmouth College. She teaches courses with strong student evaluations, winning the Howard Staveley Teaching Award (2019-2020) and recognition as a Mental Health Champion (2023). Education: B.Sc. in Computational Mathematics, Wuhan University M.Sc. in Computer Science, Wuhan University PhD in Computer Science, Memorial University Postdoctoral Fellowship, Geisel School of Medicine, Dartmouth College Research Interests: Evolutionary algorithms and genetic programming Interpretable and explainable AI Biomedical data mining (metabolomics, genomics) Complex network analysis Applications in precision medicine and disease prediction Awards & Recognition: Queen's AMS Undergraduate Mentorship Award (2025) IEEE CIBCB Best Student Award (2022) Howard Staveley Teaching Award (2019-2020) NSERC Discovery Grant Reviewer (2019) Memorial University's Best Professor Award (2016) Lab & Collaborations: MIB Lab develops tools like geneDRAGNN (graph neural networks for gene-disease prioritization) Active roles in IEEE Computational Intelligence Society and EuroGP Advances include vaccination strategies via graph-RL and interpretable clustering methods

Andrew Rau-Chaplinمشاهده پروفایل

استاد

• High Performance Computing
• Parallel Algorithms
• Data Warehousing
• +۱۴ مورد دیگر

Andrew Rau-Chaplin is a Professor and Dean of the Faculty of Computer Science at Dalhousie University, where he leads the Risk Analytics Lab and contributes significantly to research in high performance computing, parallel algorithms, and risk analytics. He is affiliated with the Institute for Big Data Analytics and has a strong academic and administrative presence. Education: Postdoc - DIMCS (Princeton, Rutgers, Bell Labs) PhD - Carleton University (1993) MCS - Carleton University (1990) BCS - York University (1986) His research focuses on applying parallel and high performance computing to data-intensive domains such as data warehousing, OLAP, catastrophe modeling, and risk analytics. He emphasizes both algorithmic design and practical system implementation, with a strong grounding in experimental evaluation. His work spans theoretical studies and real-world applications in finance, bioinformatics, and geospatial systems. The 15 most recent publications reflect a consistent focus on parallel data processing, OLAP optimization, indexing techniques (e.g., Hilbert curves), and risk modeling. Key themes include scalable data cube computation, view selection, adaptive coding, and spatial analytics, demonstrating expertise in both algorithmic innovation and systems-level performance. He has served on numerous scientific committees and grant panels, including NSERC and Compute Canada, and has been a journal editor for JPDC and DMTCS. Dr. Rau-Chaplin has supervised a wide range of graduate students in areas including risk analytics, GPU computing, text analytics, and parallel algorithms. His lab has received funding for postdoctoral, graduate, and undergraduate research positions. He teaches courses such as Parallel Computing, Software Engineering, Data Structures, and Risk Analytics, and has developed software tools like LaHave, Clustal XP, and Digital Coliseum. His lab, the Risk Analytics Lab, focuses on integrating analytics, risk management, and HPC for challenges in catastrophe modeling and financial risk. The lab leverages technologies such as stochastic simulation, optimization, and spatial OLAP.

Mohamed Youssefمشاهده پروفایل

دانشیار

• Propulsion Systems
• Power Electronics
• Railway Electromagnetic Compatibility (EMC)
• +۴ مورد دیگر

Dr. Mohamed Youssef is an Associate Professor in the Department of Electrical, Computer and Software Engineering at Ontario Tech University. He holds a PhD (Electrical and Computer Engineering) from Queen’s University (2005). His primary affiliation is the Faculty of Engineering and Applied Science, with research focusing on propulsion systems, power electronics, railway systems, and renewable energy technologies. Education: PhD, Electrical and Computer Engineering, Queen’s University (2005) MSc, Power Electronics, Concordia University (2001) MSc, Electric Power and Machines, Ain Shams University (1999) BSc, Electric Power and Machines, Ain Shams University (1995) Research Interests: Dr. Youssef’s expertise spans propulsion systems for automotive and hyperloop technologies , power electronics for IoT and renewable energy , railway electromagnetic compatibility , and power system stability . His work emphasizes practical applications in electric vehicles, smart grid integration, and sustainable energy systems. He leads the PEDAL (Power Electronics and Drives Laboratory) at Ontario Tech. Awards and Recognition: Recipient of the NSERC Post-doctorate Scholarship (2006) Best Paper Award at IECON 2004 Award of Merit from Ontario Center of Excellence (2006) Nominated for the Howard Alper Prize (2007) Professional Activities: He serves as a reviewer for IEEE Transactions on Power Electronics , IEEE Transactions on Industrial Electronics , and others. He has held roles as Technical Chair at IEEE SEGE 2015 and Track Chair at IEEE SEGE 2016. Current affiliations include Senior Member of IEEE and Chair of the IEEE Power Electronics Chapter in Toronto. Labs and Teams: He directs the PEDAL Lab , focusing on advanced power electronics and electric vehicle technologies. Collaborations include Bombardier Transportation and Armstrong Pumps.

• Wind-Driven Rain
• Hygrothermal Performance
• Building Envelopes
• +۷ مورد دیگر

Hua Ge is a Professor in the Department of Building, Civil and Environmental Engineering at Concordia University's Faculty of Engineering and Computer Science. She holds a Tier II Concordia University Research Chair in High Performance Building Envelope for Climate Resilient Buildings and leads extensive research in building science and climate adaptation. Her research focuses on wind-driven rain analysis , hygrothermal performance of building envelopes , advanced building facades , innovative wood-frame construction , and low-energy buildings . Current work examines climate change impacts on wind-driven rain loads, urban micro-climate effects, climate-resilient building envelopes, dynamic facades, and low-carbon healthy buildings. Her methodology combines large-scale laboratory testing, field monitoring, and computational modeling. Her 15 most recent publications demonstrate strong trends in nature-based climate resilience solutions , overheating risk mitigation in educational buildings , advanced hygrothermal modeling of wood-frame systems , and carbon sequestration strategies for buildings. The work spans multiple sub-disciplines including computational fluid dynamics, life cycle assessment, stochastic modeling, and field validation studies across Canadian climates. Tier II Concordia University Research Chair (CURC) in High Performance Building Envelope for Climate Resilient Buildings Professional Engineers of Ontario American Society of Heating, Refrigerating and Air-conditioning Engineers ASHRAE TC4.4 Building materials and building envelope performance (Subcommittee Chair) Professor Ge has supervised 42 graduate students (26 PhD, 16 MASc), including current advisees working on nature-based solutions, climate-resilient envelopes, and building integrated photovoltaics. Her research is supported by Concordia University Research Chair funding and collaborative projects with institutions like BCIT. She directs activities at Concordia's Building Envelope Test Facility and contributes to national standards through ASHRAE.

Amir Keyvan Khandaniمشاهده پروفایل

استاد

• Information Theory
• Wireless Communications
• Optical Communications
• +۵ مورد دیگر

Dr. Amir Keyvan Khandani is a Professor and Senior Ciena-NSERC Industrial Research Chair in the Department of Electrical and Computer Engineering at the University of Waterloo. He holds prestigious research chairs including Tier 1 Canada Research Chair in Wireless Communications and former Senior NSERC Chairs with Blackberry and Nortel. His research focuses on information theory, wireless and optical communications, and signal processing, emphasizing foundational principles and practical applications. Dr. Khandani earned his BEng and MEng from Tehran University (1985) and PhD from McGill University (1992). He joined Waterloo in 1993, supervising over 45 PhD students, 35 master’s candidates, and numerous postdoctoral researchers. His alumni work globally in academia and industry. Research interests include Network Information Theory , Media-Based Modulation , Full-Duplex Systems , and Quantum-Safe Encryption . Recent work explores secure key generation, interference management, and next-generation wireless innovations. Notable awards include NSERC/Ciena Industrial Research Chair and multiple Canada Research Chairs. His publications span foundational and applied topics in communications, with recent focus on cybersecurity and 5G/6G technologies. Dr. Khandani actively contributes to conferences, consults for industry/government, and teaches ECE 307 - Probability Theory and Statistics . His lab develops cutting-edge solutions in wireless networks, optical systems, and secure communication protocols.

Stephen Brownمشاهده پروفایل

استاد

• Field-Programmable Gate Arrays (FPGAs)
• Computer Architecture
• CAD Algorithms
• +۴ مورد دیگر

Stephen Brown is a Professor at the University of Toronto within the Department of Electrical and Computer Engineering under the Faculty of Applied Science and Engineering. He earned his B.A.Sc and M.A.Sc in Electrical Engineering from the University of Toronto and New Brunswick, respectively, and a Ph.D. in Electrical Engineering from the University of Toronto (1992). His career spans over two decades in academia and industry collaboration. Education : B.A.Sc, University of New Brunswick M.A.Sc, University of Toronto Ph.D, University of Toronto Professor Brown’s research focuses on field-programmable gate arrays (FPGAs) , CAD algorithms , and computer architecture , with applications in machine learning and high-level synthesis . He is a principal investigator in the LegUp project , an open-source high-level synthesis framework that bridges software and hardware design. His work also extends to optimizing FPGA interconnect delays, physical synthesis, and logic block architectures. Key trends in his publications include advancements in high-level synthesis tools, FPGA architecture evaluation, and timing-driven design methodologies. His contributions often intersect with design automation , resource sharing , and embedded systems . Scientific Awards : NSERC 1992 Doctoral Prize Hart Professorship for Innovation in Teaching (2017) Multiple teaching excellence awards Best Paper Award at ICCAD 1990 Best Paper Award nomination at Canadian Conference on VLSI (1989) As Director of the FPGA University Program at Intel Corporation, he leads industry-academia initiatives. His teaching portfolio includes courses like ECE253 (Digital Logic) and ECE1733F (Switching Theory).

Kay C. Wieseمشاهده پروفایل

استاد

• Bioinformatics
• Computational Biology
• Evolutionary Computation
• +۴ مورد دیگر

Kay C. Wiese is a Professor and Software Systems Chair at the School of Computing Science, Simon Fraser University. His research focuses on computational intelligence and bioinformatics, particularly RNA secondary structure prediction and visualization. He leads the Bioinformatics Research Lab and has contributed to RNA design and gene finding. Wiese holds a PhD in Computer Science from the University of Regina (1999) and degrees in Computer Science and Mathematics from the Universität des Saarlandes (Germany). He has extensive editorial roles, including Associate Editor for the IEEE/ACM Transactions on Computational Biology and Bioinformatics, and has organized major conferences like the IEEE Symposium on Computational Intelligence in Bioinformatics. His teaching interests include Bioinformatics, Computational Biology, and Discrete Mathematics. Wiese has supervised numerous graduate students, including Boris Shabash, Wenbo Jiang, and Andrew Hendriks. His research group developed tools like jViz.RNA for RNA visualization and SARNA-Predict for structure prediction. His work bridges computational methods with biological applications, emphasizing algorithmic innovation and practical software solutions.