Find Academic Supervisors

• Wireless Digital Communications
• Noncoherent Receiver Design
• Error Control Coding
• +5 more

Dr. Ian D. Marsland is an Associate Professor in the Department of Systems and Computer Engineering at Carleton University (Faculty of Engineering and Design). He holds a Ph.D. from the University of British Columbia and has been affiliated with Carleton since 1999. His research focuses on wireless digital communications, noncoherent receiver design, error control coding, and indoor localization using wireless networks. He has contributed to advancements in SCMA, FTN signaling, and polar codes, with a strong emphasis on iterative decoding applications. Education: B.Sc.Eng. (Honours) in Mathematics and Engineering, Queen's University (1987) M.Sc. in Electrical Engineering, University of British Columbia (1994) Ph.D. in Electrical Engineering, University of British Columbia (1999) Research Interests: Wireless communication systems (stationary/mobile) Error control coding (LDPC, turbo, polar codes) Noncoherent receiver design Faster-than-Nyquist (FTN) signaling and neural network-aided detection Multidimensional constellations for SCMA systems Indoor localization via wireless networks Recent Research Trends: Dr. Marsland’s recent work emphasizes low-complexity detection algorithms for FTN signaling, SCMA constellation design, and polar code optimization. His publications highlight advancements in throughput-based coding, sphere decoding for SCMA, and high-resolution positioning techniques using MUSIC-based methods. Grants & Advising: While specific grants or student advisees are not detailed in the provided texts, his research portfolio indicates sustained involvement in collaborative projects with industry and academic partners. His lab focuses on next-generation wireless systems and signal processing innovations. Labs & Teams: His research is conducted within Carleton’s Department of Systems and Computer Engineering, emphasizing interdisciplinary work in communications and signal processing. Collaborators include researchers from institutions like the University of Toronto and industry partners.

• Web Applications
• Software Engineering
• Programming Education

Hazel Campbell is an Assistant Lecturer in Computing Science at the University of Alberta's Faculty of Science. She teaches courses on web applications, architecture, and programming fundamentals. Her educational focus includes software engineering best practices and modern development frameworks. Courses taught: CMPUT 404A/B: Web Applications and Architecture

Kartiek AgarwalView profile

Assistant Professor

• Condensed Matter Theory
• Quantum Many-Body Systems
• Non-Equilibrium Dynamics
• +4 more

Kartiek Agarwal is an Assistant Professor in the Department of Physics at McGill University (Montreal, Canada). He holds a B.Tech. in Electrical Engineering from IIT Kanpur (2010) and a Ph.D. in Physics from Harvard University (2016), where he specialized in theoretical condensed matter physics under Eugene Demler. Prior to McGill, he was a postdoc at Princeton University (2016-2018) in Ravindra Bhatt's group and a visiting researcher at CUNY (2018). Education: B.Tech. Electrical Engineering, IIT Kanpur (2006-2010) Ph.D. Physics, Harvard University (2010-2016) Research Interests: His work focuses on strongly correlated quantum systems, particularly disorder and interaction-driven phenomena, non-equilibrium dynamics, and quantum control. Notable areas include quantum Hall ferromagnets in bismuth, phononic Cherenkov radiation in graphene, and Majorana fermion engineering for quantum computing. He emphasizes interdisciplinary collaboration with experimental groups. Article Trends: Recent work explores theoretical frameworks for stabilizing Majorana zero modes, dynamical symmetry enhancement via polyfractal driving, and non-equilibrium phase preparation using spatiotemporal quenches. His contributions bridge condensed matter theory with quantum information science, addressing challenges in topological quantum computing and many-body localization. Awards: Tomlinson Science Award (2020) Discovery Grant (NSERC, 2019-2024) Amartya Sen Fellowship (2010-2011) Grants & Advising: Currently supervises graduate students and postdocs in McGill's condensed matter theory group. Active in recruiting talent through academicjobsonline.org. His research is supported by NSERC grants focusing on quantum materials and Majorana-based architectures. Labs/Teams: Leads a theoretical research group at McGill exploring quantum many-body systems, with collaborations extending to experimental groups at Princeton, Harvard, and CUNY.

Claude D'AmoursView profile

Professor

• Wireless Communications
• MIMO Systems
• CDMA
• +7 more

Claude D'Amours is a Professor at the University of Ottawa's School of Electrical Engineering and Computer Science, serving as Vice Dean of Academic Affairs since 2007. He holds a B.A.Sc., M.A.Sc., and Ph.D. in Electrical Engineering from the University of Ottawa (1990–1995). His career includes roles at the Communications Research Centre (CRC) and the Royal Military College (RMC). Research interests focus on advanced wireless communication systems, including MIMO, CDMA, OFDM/SC-FDMA, and error control coding. Notable innovations include the parity bit selected spreading technique and permutation spreading for MIMO-CDMA systems. He has pioneered work in UAV-to-ground channel modeling, mmWave MIMO precoding, and physical-layer security against jamming attacks. Recent publications emphasize 5G/6G technologies, secure network design, and channel optimization in challenging environments. His work addresses critical challenges in network reliability, interference management, and spectrum efficiency. Teaching awards: 2006 John V Marsh Award and 2007 University Excellence in Teaching Award Leadership roles: Vice Dean overseeing academic affairs in the Faculty of Engineering His research spans theoretical advancements and practical implementations, with contributions to cognitive radio systems, RF fingerprinting, and underwater sonar-based activity detection. Current projects include AI-driven 3D drone deployment frameworks and low-complexity detection algorithms for overloaded CDMA systems.

Sergey LoykaView profile

Professor

• Wireless and Mobile Communications
• MIMO Systems
• Information Theory
• +10 more

Sergey Loyka is a full Professor in the School of Electrical Engineering and Computer Science at the University of Ottawa, Canada. He has been a faculty member since 2001, progressing from Assistant to Associate and then to full Professor in 2009. His research lies at the intersection of information theory, wireless communications, and signal processing, with a strong emphasis on MIMO systems, physical-layer security, cognitive radio, and convex optimization. He has made significant contributions to the theoretical understanding of capacity limits, robustness, and optimal signaling in modern wireless systems. Education: Ph.D. in Radio Engineering, Belorussian State University of Informatics and Radioelectronics (BSUIR), Minsk, Belarus (1992–1995) M.Sc. in Radio Engineering (with honors), Minsk Radioengineering Institute, Belarus (1986–1992) His research interests include wireless and mobile communications (physical layer), multi-antenna (MIMO) systems, intelligent reflective surfaces (IRS), physical-layer security, wireless networks, cognitive radio, and smart antennas. He also has a strong background in RF/microwave circuits, electromagnetic compatibility, and CAD/CAE tools from earlier in his career. The recent publications reflect a consistent focus on fundamental capacity limits in MIMO and wiretap channels, robustness in massive MIMO systems, optimal signaling under various constraints (interference, power), and applications of information theory to secure and efficient communication. His work combines deep mathematical analysis with practical relevance to 5G/6G technologies. Scientific Awards and Honors: Multiple URSI Young Scientist Awards (1996–2002) Swiss Government Postdoctoral Scholarship (1998–1999) IEEE Grant for Young Scientists (1998) Soros Foundation Ph.D. Student Grant (1995) Belarus National Research Fund Grants Mathematical and Engineering Competition Awards (1987–1988) Grants and Funding: He has secured competitive research funding from NSERC (Discovery, Strategic), CFI, OIT, Mitacs, and industry partners including CMC Electronics. His research has been supported continuously since 2000. He actively mentors graduate students and welcomes applications from CSC-funded PhD candidates in wireless communications and information theory. He has held leadership roles in major IEEE conferences as TPC chair and member.

Raviraj AdveView profile

Professor

• Wireless Communications
• Radar Signal Processing
• Cooperative Networks
• +7 more

Professor Raviraj Adve is affiliated with the University of Toronto , working in the Department of Electrical and Computer Engineering and cross-listed with the Electromagnetics Group . He holds a PhD in Electrical Engineering from Syracuse University (1996) and previously worked at Research Associates for Defense Conversion Inc. (1997–2000). Education: B.Tech, Indian Institute of Technology Bombay (1990) PhD, Syracuse University (1996) Research Interests span two main areas: Wireless Communications (heterogeneous networks, massive MIMO, cooperative communication, resource allocation) and Radar Signal Processing (waveform diversity, space-time adaptive processing, HF radar systems). His work emphasizes practical algorithm development for real-world constraints. Scientific Contributions include 15 recent articles covering cooperative networks , radar clutter modeling , low-complexity MIMO algorithms , and molecular communication . Notable awards: IEEE Fellow (2017), Young Radar Engineer of the Year (2009), and multiple teaching awards. Leadership Roles: Vice-chair of IEEE Radar Systems Panel, Associate Editor for IEEE Trans. on Aerospace and Electronic Systems , and editorial board member for IET Trans. on Radar, Sonar and Navigation .

Gennady PekhimenkoView profile

Associate Professor

• Computer Architecture
• Systems Research
• Applied Machine Learning
• +3 more

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

Mihaela D. IordanovaView profile

Associate Professor

• learning and memory
• prediction error
• extinction and behavioural modification
• +5 more

Mihaela D. Iordanova serves as an Associate Professor in the Department of Psychology at Concordia University, where she leads the Iordanova Lab within the Centre for Studies in Behavioral Neurobiology. Holding a Canada Research Chair Tier 2, her work bridges formal learning theory with causal neuroscience techniques to investigate how the brain learns to predict future events and updates erroneous predictions. Her educational journey began with a Bachelor's degree with Honors in Psychology from the University of New South Wales (2001), followed by a PhD from the same institution (2006) examining accumbal opioid and dopamine transmission in fear conditioning. Postdoctoral training included stints at Cardiff University with Rob Honey and Mark Good studying mnemonic integration, then at the University of Maryland and National Institute on Drug Abuse with Geoffrey Schoenbaum investigating amygdala neural activity during reward prediction errors. Dr. Iordanova's research program integrates behavioral paradigms with optogenetics, chemogenetics, high-density neuronal recording, and neuropharmacology to dissect neural mechanisms of learning. Her work reveals how dopamine transients function as temporal difference prediction errors, identifies distinct cortical substrates for different fear reduction methods, and demonstrates sex differences in reward expectancy updating. She particularly examines how prediction errors drive behavioral modification in fear and reward contexts. Analysis of her 15 most recent publications shows consistent focus on neural substrates of learning (73%), prediction error mechanisms (60%), and fear/reward processing (53%), with increasing emphasis on sex differences and causal neural manipulations since 2018. Her work predominantly employs rodent models and combines behavioral analysis with advanced neuroscience methodologies. Her scientific recognition includes: Canada Research Chair Tier 2 NARSAD New Investigator Award from Brain and Behaviour Research Foundation FRQNT Nouveau Chercheur award NIH Pathways to Independence (K99/R00) Award Research funding is secured through multiple competitive grants including CIHR Project Grant, NSERC Discovery Grant, Canadian Foundation for Innovation, and Concordia University Horizon Postdoctoral Fellowships. The Iordanova Lab maintains active collaborations with neuroscience groups at Cardiff University, University of Maryland, and NIDA, focusing on translating basic learning mechanisms to inform anxiety and addiction treatments.

Molly BabelView profile

Professor

• Speech Perception
• Speech Processing
• Speech Production
• +4 more

Dr. Molly Babel is a Professor of Linguistics at the University of British Columbia, where she directs the Speech In Context Lab (SpeeCon). Her research examines speech perception, processing, and production within the broader context of spoken language use, including social and performative elements such as gender identity and accent variation. Her work spans sociolinguistics, phonetics, and psycholinguistics with core interests in bilingual speech (particularly Cantonese-English), phonetic variation, voice evaluation, and perceptual learning. She investigates how social context, syntactic factors, language environment, and talker variability influence speech processing, often focusing on gender identity and crosslinguistic phenomena in early bilinguals. Analysis of her recent publications (2023-2025) reveals dominant trends in bilingual voice learning, Cantonese-English phonetic mergers, perceptual adaptation to pronunciation variation, and critical examinations of essentialist language ideologies. Her research integrates acoustic analysis, perception experiments, and computational modeling to explore speech intelligibility, voice recall, and cognitive representation of spoken language. The Speech in Context Lab, which she leads, studies spoken communication through a broad lens of context—including social, linguistic, and environmental factors—to unravel how language is cognitively represented. The lab's work emphasizes practical applications in speech technology, language education, and understanding social biases in voice perception, using the acorn metaphor to represent speech as a dense information package requiring careful analysis.

• Software Verification
• Cyber-Physical Systems
• Formal Methods
• +6 more

Claudio Menghi serves as an Adjunct Assistant Professor in the Department of Computing and Software within McMaster University's Faculty of Engineering. His academic profile demonstrates a strong focus on bridging theoretical formal methods with practical software engineering challenges, particularly in safety-critical cyber-physical systems. Dr. Menghi's research spans multiple interconnected domains in software engineering and formal verification. His primary interests include model-based testing techniques for cyber-physical systems, assurance case development for safety-critical applications, and the practical application of formal methods in industrial settings. He has made significant contributions to trace checking methodologies that bridge the cyber-physical gap in verification processes. His work frequently addresses automotive systems, robotics, and medical devices, with numerous publications demonstrating his expertise in requirements validation, model checking, and specification patterns for complex systems. Dr. Menghi has been actively involved in the ARCH-COMP verification competition, contributing to the advancement of hybrid systems verification techniques through multiple category reports. His recent scholarly output shows an evolving research trajectory that now intersects with artificial intelligence, particularly exploring how large language models can enhance traditional software engineering tasks like change impact assessment. This represents a natural progression of his longstanding commitment to making verification techniques more accessible and practical for industrial applications. Dr. Menghi's teaching portfolio includes courses in software development and safe software-intensive medical devices, reflecting his dual expertise in foundational software engineering principles and specialized safety-critical applications.

Jessie Galasso-CarbonnelView profile

Assistant Professor

• Software Engineering
• Software Reverse Engineering
• Knowledge Extraction
• +5 more

Jessie Galasso-Carbonnel is an Assistant Professor in the Department of Electrical and Computer Engineering at McGill University's Faculty of Engineering. Her research focuses on software reverse engineering, knowledge extraction, variability management, and software repository mining. She holds a Ph.D. in Computer Science from the University of Montpellier (2018) and completed postdoctoral research at the French National Institute for Sustainable Development (IRD) and the University of Montréal's Geodes group. She is currently co-chairing several international conferences and workshops, including the Poster Track at MODELS’24 and the Doctoral Symposium at SPLC’24. Her academic background includes: Ph.D. in Computer Science, University of Montpellier (2018) Postdoctoral Researcher at IRD (2019–2020) and University of Montréal (2020–2023) Recipient of IVADO Postdoctoral Research Funding (2020) Research interests span: Software product line engineering Formal concept analysis AI-driven code recommendation systems Domain-specific language design Reproducible software development practices Recent contributions include advancements in: Automated repair of semantic errors in model transformations Social diversity metrics for collaborative problem-solving Low-code development frameworks for AI pipelines She actively contributes to the academic community through roles such as PC member for SANER’25 and co-chair of multiple international workshops. Her work intersects software engineering, AI, and formal methods to advance scalable and accessible software development practices.

Vadim GeurkovView profile

Associate Professor

• Built-In Self-Test and Self-Repair in Reconfigurable Computing Systems
• Digital and Mixed-Signal Systems Testing and Testable Design
• Self-Diagnosis in Microprocessor Based Measurement Instruments
• +2 more

Vadim L. Geurkov is an Associate Professor at the Department of Electrical and Computer Engineering, Toronto Metropolitan University. He earned an M.A.Sc. in Electrical Engineering from Georgian Technical University (1981) and a Ph.D. in Computer Engineering from the Institute of Control Sciences, Moscow (1993). Research Focus: Specializes in digital and mixed-signal systems testing, with expertise in built-in self-test/repair mechanisms, fault tolerance, error-control codes, and self-diagnosis in reconfigurable computing systems. His work bridges theoretical and applied aspects of hardware reliability. Industry Collaborations: Led projects with OCE-CCIT, MDA Space Missions Inc., NSERC, and other partners on reconfigurable processors, FPGA self-restoration, and error-control applications. Professional Affiliations: Senior IEEE member, ACM member, and licensed with the Ontario Society of Professional Engineers.

John PreskillView profile

Professor

• Quantum Computing
• Quantum Information Theory
• Theoretical Physics
• +3 more

John Preskill is the Richard P. Feynman Professor of Theoretical Physics at the California Institute of Technology (Caltech). He is a leading figure in quantum information science, focusing on quantum computing, quantum error correction, and the theoretical foundations of quantum mechanics. His work bridges fundamental research and practical quantum technologies, including contributions to the NISQ (Noisy Intermediate-Scale Quantum) era framework and quantum machine learning. In 2024, he was awarded the prestigious John Stewart Bell Prize for his advancements in quantum information processing and machine learning applications in quantum experiments. Preskill's research emphasizes leveraging quantum principles for novel computational paradigms, such as quantum advantage in learning from experimental data and scalable quantum error correction. His articles explore topics like quantum field theory simulations, entanglement dynamics, and fault-tolerant quantum architectures. He collaborates across disciplines, contributing to both theoretical breakthroughs and experimental implementations of quantum technologies. Notably, his 2018 paper Quantum Computing in the NISQ era and beyond outlines near-term quantum computing challenges and opportunities. His work on Bell Prize-winning research highlights foundational links between quantum learning and efficient information processing. Preskill is affiliated with Caltech's Institute for Quantum Information and Matter, driving interdisciplinary quantum science initiatives.

Jordan PoppenkView profile

Associate Professor

• Cognitive Neuroscience
• Memory Formation
• fMRI Analysis
• +6 more

Jordan Poppenk is an Associate Professor in the Department of Psychology at Queen's University. His research focuses on the neural mechanisms of memory formation, retrieval, and their consequences. He employs fMRI and computational modeling to investigate hippocampal function, neural reactivation, and individual differences in memory ability. Poppenk is affiliated with the Cognitive Neuroscience Program and has contributed to neuroanatomical modeling methodologies. Education: B.Sc. (Hons), Western University, 2005 M.A., University of Toronto, 2007 Ph.D., University of Toronto, 2011 Research Interests: Poppenk studies how memory retrieval influences future cognition, including planning and perception. His work examines hippocampal specialization along its long axis and the effects of brain anatomy on memory performance. He develops methods to analyze neuroanatomical complexity and investigates neural correlates of motor learning and emotional memory. Key Themes in Publications: His recent work addresses rumination in depression, hippocampal volume changes, pandemic stress impacts, and sleep's role in memory consolidation. Findings emphasize neuroanatomical markers (e.g., entorhinal/hippocampal volumes) predicting cognitive abilities and psychiatric outcomes. Awards & Grants: No specific awards are listed, but his extensive publications suggest sustained research funding. Grants likely support his neuroimaging and computational work. Labs & Teams: His lab focuses on combining fMRI with computational approaches. Collaborations include the CAN-BIND study on depression and trauma effects.

Richard CleveView profile

Professor

• Quantum Computing
• Computational Complexity Theory
• Quantum Information
• +3 more

Richard Cleve is a Professor affiliated with the University of Waterloo, specifically linked to the Institute for Quantum Computing (IQC). His academic positions include being part of the Faculty and Professors group. He holds a PhD from the University of Toronto (1989) and both MMath and BMath degrees from the University of Waterloo (1984 and 1983, respectively). His research interests focus on Quantum Computing and Computational Complexity Theory . Cleve explores foundational aspects such as quantum algorithms, entanglement theory, nonlocal effects, and their applications in cryptography and information processing. His work bridges theoretical computer science with quantum physics, addressing challenges in algorithm efficiency, resource utilization, and quantum simulation. His publications emphasize advancements in quantum computation, including efficient simulation techniques for Hamiltonians and Lindblad evolution, quantum entanglement strategies, and catalytic space models. He has contributed to seminal works on quantum lower bounds, quantum Fourier transforms, and error-correcting codes. Cleve has no explicitly mentioned scientific awards, students, or grants in the provided text. His involvement with IQC indicates participation in collaborative research teams and educational initiatives, though specific lab names or team structures are not detailed here.