Neuromorphic Computing Professors and Supervisors in Canada | ApplySharif

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نمایش ۱-۱۵ از ۳۹ استاد راهنما

اطلاعات این پروفایل‌ها از صفحات عمومی دانشگاه‌ها جمع‌آوری شده و ممکن است به‌روز نباشد.

Manoj Sachdevمشاهده پروفایل

استاد

University of WaterlooCanada

Manoj Sachdev is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, Faculty of Engineering. His research focuses on semiconductor devices, low-power electronics, and secure hardware systems. He leads projects in flexible electronics, nanotechnology, and radiation-hardened circuits, with applications in displays, memory technologies, and biomedical sensors. His work integrates advanced materials science with circuit design to address challenges in energy efficiency and security. Education: Not explicitly stated in provided text. Research interests span thin-film transistors (TFTs), resistive switching memories, neuromorphic computing, and physically unclonable functions (PUFs). Recent efforts include developing low-power circuits for flexible substrates and secure microprocessors. His contributions to semiconductor device physics and integration techniques have advanced applications in wearable electronics and medical diagnostics. Publications highlight innovations in low-power flip-flops, energy-efficient display drivers, and memristor-based systems. He collaborates on interdisciplinary projects combining photonics, nanoelectronics, and biomedical engineering. Awards: None explicitly listed in provided text. Grants and advising: Advises on semiconductor fabrication, secure hardware design, and radiation effects in electronics. Leads research groups focused on next-generation memory technologies and flexible integrated systems. Labs/Teams: Active in the University of Waterloo's semiconductor and flexible electronics research clusters, contributing to both academic and industry partnerships.

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Lan Weiمشاهده پروفایل

دانشیار

University of WaterlooCanada

Lan Wei is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, Canada. She leads the Waterloo Emerging Integrated Systems Group, focusing on device-circuit co-optimization, cryogenic CMOS for quantum computing, and emerging technologies like GaN, RRAM, and low-dimensional materials. Her work bridges nanoelectronics and system-level applications, with notable contributions to the MIT Virtual Source GaN HEMT (MVSG) compact model, an industry-standard tool. Education: B.S. in Microelectronics and Economics, Peking University (2005) M.S. and Ph.D. in Electrical Engineering, Stanford University (2007, 2010) Research Interests: Nanoelectronic devices Cryogenic CMOS for quantum computing GaN-based circuits and systems RRAM-based neuromorphic computing Device-circuit interactive design Publications reflect her expertise in GaN modeling, quantum computing hardware, and RRAM applications. Recent work emphasizes scalable quantum control circuits and error-resilient neural networks using emerging technologies. Awards include the 2019 Ontario Early Researcher Award and the 2020 UWaterloo President's Excellence Award in Research. She has served on technical committees for IEDM, DATE, and ICCAD, and contributed to the ITRS roadmap. Teaching includes courses like ECE 240 (Electronic Circuits) and ECE 730 (Solid State Devices). Her group actively seeks graduate students with interest in integrated systems and nanoelectronics.

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Stefanie Czischekمشاهده پروفایل

استادیار

University of OttawaCanada

Dr. Stefanie Czischek is an Assistant Professor in the Department of Physics at the University of Ottawa, leading the APRIQuOt research group focused on artificial and physically realizable intelligence for quantum applications. She joined uOttawa in 2022 after postdoctoral work at the University of Waterloo. Her research bridges quantum technologies and neural networks, with expertise in quantum simulation, neuromorphic computing, and machine learning applications in quantum physics. Research Interests: Quantum computation/simulation using neural networks Neuromorphic hardware implementations Quantum many-body systems Machine learning for quantum control and tomography Her publications demonstrate strong interdisciplinary focus, combining quantum physics with cutting-edge ML techniques. Recent works explore transformer models for quantum simulation, neural network quantum states, and quantum sensing applications. The research shows consistent evolution toward hardware-algorithm co-design for quantum problems. Awards: Springer Thesis Award (2020) for doctoral research on neural-network simulation of quantum systems. Research Group & Advising: Leads the APRIQuOt lab with 1 postdoc, 6 graduate students, and 1 undergraduate. Current projects include large language models for quantum states, quantum optimal control via reinforcement learning, and neuromorphic quantum simulations. The group collaborates with experimental teams and maintains strong industry-academia partnerships.

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Bryan Trippمشاهده پروفایل

دانشیار

University of WaterlooCanada

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.

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Salem Lahlouمشاهده پروفایل

استادیار

University of MontrealCanada

Salem Lahlou is an Assistant Professor in the Machine Learning Department at the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), having joined in September 2024. He previously served as a Senior Researcher at the Technology Innovation Institute (TII) in 2024. His academic background includes a PhD from Mila and Université de Montréal (UdeM) under Yoshua Bengio (2023), with prior studies in applied mathematics at École Polytechnique and statistical learning at École Normale Supérieure Paris-Saclay. His research focuses on developing more capable and reliable AI systems through three interconnected pillars: Novel Method Development : Core contributions to Generative Flow Networks (GFlowNets), uncertainty estimation techniques (DEUP), and curriculum learning frameworks Large Language Model Advancement : Enhancing reasoning capabilities and alignment through preference optimization and trace-based learning Community Tooling : Creation of torchgfn library for GFlowNets and benchmarks including BabyAI, FinChain, and LLM-BabyBench Core research areas span Machine Learning, GFlowNets, Uncertainty Estimation, LLM Reasoning, Reinforcement Learning, and AI for Science. Recent publications (2023-2025) demonstrate strong emphasis on GFlowNet theory/improvements (8+ papers), LLM reasoning evaluation (FinChain, LLM-BabyBench), uncertainty quantification, and societal AI impacts. Key application domains include mathematical reasoning, financial systems, privacy preservation, and cognitive science. He currently advises graduate students including Junyi (privacy risks in SNNs) and Abhijith (LLM reasoning). His group collaborates with MBZUAI faculty (Nils Lukas, Alham Fikri, Mingming Gong, Martin Takac) and industry partners on projects involving Conversational AI, Personalization, and Affective AI.

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Ian C. Bruceمشاهده پروفایل

استاد

McMaster UniversityCanada

Dr. Ian Bruce is a Professor in the Department of Electrical and Computer Engineering at McMaster University, Hamilton, ON, Canada. He has been with the department since 2002, conducting interdisciplinary research that bridges electrical engineering with auditory neuroscience. His work has significant implications for hearing technologies and auditory rehabilitation. Education: B.E. (electrical and electronic) from The University of Melbourne (1991) Ph.D. from the Department of Otolaryngology, The University of Melbourne Dr. Bruce's research program focuses on auditory modeling, hearing aids, cochlear implants, tinnitus, neural coding of speech, and digital speech processing. His work centers on understanding the physiological mechanisms of auditory processing and applying this knowledge to develop improved hearing technologies. He has pioneered computational models of the auditory periphery that accurately predict speech intelligibility for hearing-impaired listeners, directly informing hearing aid and cochlear implant design. Analysis of Dr. Bruce's recent publications (2019-2025) reveals a consistent focus on cochlear implants and auditory nerve modeling, with increasing integration of machine learning techniques. His work demonstrates a sophisticated balance between physiological accuracy and computational efficiency, with recent papers exploring WaveNet-based approximations of cochlear models and DNN-based auditory processing. A significant portion of his research examines the relationship between neural responses and perceptual outcomes in hearing-impaired individuals, particularly regarding temporal processing and speech understanding. Scientific Awards and Recognitions: Fellow of the Acoustical Society of America Member of the Association for Research in Otolaryngology Registered Professional Engineer in Ontario Associate Editor of the Journal of the Acoustical Society of America Dr. Bruce has mentored numerous graduate students through various capstone design projects across multiple engineering disciplines including biomedical, electrical, mechanical, and software engineering. His teaching portfolio includes specialized courses in biomedical signals and systems, cellular bioelectricity, models of the neuron, and advanced signal processing. He has consistently supervised M.Eng. projects and independent studies, demonstrating commitment to training the next generation of engineers in auditory technology development. Dr. Bruce's research is conducted within McMaster University's interdisciplinary biomedical engineering framework, collaborating with clinicians and researchers in otolaryngology and audiology. His laboratory work focuses on developing and validating computational models that simulate auditory nerve responses to both natural and prosthetic stimulation, with direct applications to improving cochlear implant performance and hearing aid algorithms for real-world listening environments.

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Bhavin Shastriمشاهده پروفایل

استادیار

Queen's University at KingstonCanada

Bhavin Shastri is Canada Research Chair in Neuromorphic Photonic Computing and Assistant Professor of Engineering Physics at Queen's University. He directs research developing light-based computing systems that mimic neural processing for AI applications. His lab designs photonic integrated circuits that implement neural network architectures on chip-scale platforms. Research focuses on overcoming limitations of conventional computing through nanophotonic physics and novel materials. Publications demonstrate advances in photonic tensor cores, quantum photonic neural networks, and microwave photonic processors. Recent work achieves orders-of-magnitude improvements in processing speed and energy efficiency over electronic systems. Awards include: Alfred P. Sloan Research Fellowship (2025) Royal Society of Canada College Member (2024) Science News SN10 Scientist to Watch (2024) SPIE Early Career Award (2022) As Scientific Co-Director of NSERC's NUCLEUS program, he leads national efforts in photonic computing. Guides 12+ graduate students researching silicon photonics, neuromorphic architectures, and quantum photonics.

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Bhavin J. Shastriمشاهده پروفایل

استادیار

Queen's University at KingstonCanada

Bhavin J. Shastri is an Assistant Professor in the Department of Physics, Engineering Physics and Astronomy at Queen's University in Canada. His research explores the physics of light for computing , pushing frontiers in information and signal processing through photonic computing and quantum/neuromorphic photonics . He is affiliated with the Centre for Nanophotonics and NUCLEUS , a pan-Canadian photonic computing program funded by NSERC CREATE, bridging artificial intelligence and quantum information . Canada Research Chair & Principal Investigator Faculty Affiliate at Vector Institute (2020-) Editorial Board Member of JPhys Photonics (2019-) Member of IEEE Photonics Society Technical Affairs Council (2019-) Visiting Researcher Scholar at Princeton University (2018-) Shastri Lab members have access to world-class shared facilities, including the Centre for Nanophotonics (CFI-Innovation Fund), Nanofabrication Kingston , the Centre for Advanced Computing , and the Digital Research Alliance of Canada . The lab takes an interdisciplinary approach combining nanophotonics with complex systems on emerging substrates. His research focuses on silicon photonics , nanophonic processors , and photonic integrated circuits with applications to deep learning , nonlinear programming , and quantum information science . His articles show consistent exploration of quantum photonic neural networks , photonic memory systems , and optical signal processing for machine learning and quantum technologies . 2020 IUPAP Young Scientist Prize in Optics 2014 Banting Postdoctoral Fellowship 2012 D. W. Ambridge Prize 2011 IEEE Photonics Society Graduate Student Fellowship 2011 NSERC Postdoctoral Fellowship Multiple Best Student Paper Awards Shastri's lab supervises Ph.D. candidates and postdoctoral fellows working on quantum photonics , neuromorphic computing , and photonic AI systems . His recent work includes photonic tensor cores for scientific computing , quantum photonic neural networks , and all-optical memory systems. Shastri Lab designs programmable nanophotonic processors with potential to outperform microelectronic processors in energy efficiency and computational speeds by seven and four orders of magnitude respectively. Their work spans from device design to system-level implementations in optical computing for machine learning and quantum information processing .

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Woo Soo Kimمشاهده پروفایل

استاد

Simon Fraser UniversityCanada

Woo Soo Kim is a Professor and Associate Director in the School of Mechatronic Systems Engineering at Simon Fraser University. As a Graduate Student Supervisor, he leads the SFU Additive Manufacturing Laboratory, focusing on advanced 3D printing, sensing robotics, and agritech applications. His research integrates additive manufacturing with smart systems for healthcare, agriculture, and IoT. Education: Ph.D. in Materials Engineering (KAIST, 2006), Postdoc at MIT (2009) Research Themes: 3D printed devices, bio-medical sensors, AI-driven robotics, and sustainable manufacturing His work emphasizes practical applications, such as wireless pressure sensors for helmets, AI-based crop monitoring, and portable health diagnostics. The lab collaborates on projects like the Agritech 4.0 Bootcamp and develops 3D printed neuromorphic systems. Kim’s publications span advanced materials, sensor integration, and robotics, with a focus on solving real-world challenges through additive manufacturing. He supervises graduate students in mechatronic design and teaches courses like MSE220 (Engineering Materials) and MSE812 (Advanced 3D Printing). The lab’s innovations include 3D printed disposable sensors, smart robotic grippers, and flexible electronics. His research bridges academia and industry, addressing sustainability and technological advancement in multiple sectors.

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Nir Rotenbergمشاهده پروفایل

دانشیار

Queen's University at KingstonCanada

Dr. Nir Rotenberg is an Associate Professor in the Department of Physics, Engineering Physics, and Astronomy at Queen's University. He is affiliated with the Faculty of Arts and Science, the Condensed Matter Physics & Optics group, and the Centre for Nanophotonics. He holds a PhD from the University of Toronto and focuses on quantum photonics and nanophotonics, particularly exploring nonlinear phenomena in solid-state quantum systems. His lab develops novel quantum devices and circuits using nanophotonic platforms, emphasizing single quantum emitter interactions with few photons. Research Interests: Quantum photonics, nanophotonics, quantum optics, nonlinear optics, quantum devices, and solid-state quantum emitters. His work bridges fundamental physics with applications in quantum information processing and photonic technologies. Recent publications highlight advancements in quantum dot positioning, waveguide-QED systems, and reconfigurable photonic circuits. He collaborates internationally and is a member of the Quantum Nanophotonics Lab (QNL), based in Stirling 308H. Dr. Rotenberg's research has implications for quantum computing, secure communication, and advanced optoelectronic systems.

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Tony Whiteمشاهده پروفایل

استاد مدعو

Carleton UniversityCanada

Dr. Tony White is an Adjunct Professor in the School of Computer Science at Carleton University. He holds a Ph.D. from Carleton (2000), an M.A. from Cambridge, and a B.A. in Theoretical Physics. His research focuses on complex adaptive systems, including influence measurement in social networks and swarm intelligence applications. He leads the Complex Adaptive Systems Group and has extensive industry experience, previously working at Nortel. Education: Bachelor of Theoretical Physics, Cambridge University (1981) Master of Physics, Cambridge University (1981) Master of Computer Science, Carleton University (1993) Ph.D. in Electrical Engineering, Carleton University (2000) Research Interests: Artificial Intelligence Swarm Intelligence Genetic Algorithms Neural Networks Recommender Systems Search Engines His work explores influence dynamics in social networks, adaptive information systems, and evolutionary computation. Recent publications address neural network topologies, distributed control strategies, and embedded systems administration. He has also contributed to trust models, referral networks, and traffic signal optimization. Dr. White’s research bridges theoretical computer science and practical applications in robotics, network management, and autonomous systems. Labs/Teams: Leads the Complex Adaptive Systems Group, focusing on interdisciplinary approaches to complex systems and swarm intelligence.

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Gitanjali Kolhatkarمشاهده پروفایل

استادیار

McMaster UniversityCanada

Gitanjali Kolhatkar is an Assistant Professor in the Department of Engineering Physics at McMaster University and holds a Canada Research Chair in Bioinspired Smart Materials (Tier 2). She is also an Associate Member of the McMaster School of Biomedical Engineering. Her research focuses on developing smart materials for neuromorphic computing, leveraging ferroelectric materials to mimic synaptic functions while optimizing energy efficiency. Key techniques include microwave-assisted hydrothermal synthesis, magnetron sputtering, and advanced characterization methods like aSNOM and AFM-IR. Education: BSc and MSc in Physics, University of Ottawa (2008, 2010) PhD in Electrical Engineering, University of Sherbrooke (2014) Postdoctoral Fellowship, Institut National de la Recherche Scientifique (2015-2019) Alexander von Humboldt Fellow, University of Kiel, Germany (2019-2022) Research Interests: Neuromorphic materials, piezoelectric/ferroelectric systems, III-V semiconductors, thin films, photovoltaics, and smart sensors. Her work bridges material nanostructure and macroscopic properties to enable applications like artificial synapses, energy harvesters, and tactile sensors. Scientific Awards: Alexander von Humboldt Post-doctoral Fellowship Canada Research Chair (Tier 2) Invited Professorship at Munich University of Applied Sciences Teaching & Labs: Instructs courses in semiconductor devices (ENGPHYS 3PN4) and manufacturing (ENGPHYS 4Z04). Her lab (JHE A318/A313) focuses on interdisciplinary materials research. Current projects emphasize neuromorphic systems and bio-inspired smart materials.

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Richard Naudمشاهده پروفایل

استادیار

University of OttawaCanada

Richard Naud is an Assistant Professor in the Department of Cellular and Molecular Medicine at the Faculty of Medicine, University of Ottawa, with a cross-appointment in Physics at the Faculty of Science. He holds dual research positions at the Center for Neural Dynamics (CND) and Brain and Mind Research Institute (BMRI), focusing on computational approaches to decode neural signaling mechanisms and develop brain-machine interfaces. His educational background includes: PhD in Neuroscience from École Polytechnique Fédérale de Lausanne (EPFL), 2011 MSc in Physics from McGill University, 2006 BSc in Physics from McGill University, 2004 Dr. Naud's research investigates how neurons encode information through spikes, bursts, and silences using mathematical models and statistical analysis of electrophysiology data. His lab develops computational protocols for synaptic dynamics analysis, studies dendritic computation in neurological diseases, and creates neuromorphic algorithms for spiking neural networks. Current work emphasizes serotonin system dynamics, burst coding mechanisms, and neural network simulations for demyelinating conditions. Analysis of his recent publications reveals three dominant research vectors: (1) Burst coding as an independent information channel beyond firing rates, (2) Serotonin-mediated value coding in decision systems, and (3) Neuromorphic implementation of biologically plausible learning rules. His work bridges theoretical neuroscience with clinical applications in stroke recovery and neurological disorders. Dr. Naud leads the Neural Coding Lab, which actively recruits postdoctoral fellows, graduate students, and undergraduates for projects in neural coding theory, computational psychiatry, and neuromorphic engineering. The lab maintains collaborations with experimental neuroscience groups for model validation and develops open-source tools like SRPlasticity for synaptic dynamics analysis.

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Shideh Kabiri Ameriمشاهده پروفایل

دانشیار

Queen's University at KingstonCanada

Dr. Shideh Kabiri Ameri serves as Associate Professor in the Department of Electrical and Computer Engineering at Queen's University, where she joined in September 2018 after completing postdoctoral research at the University of Texas at Austin. Her interdisciplinary expertise bridges nanomaterials engineering and biomedical applications, with particular focus on developing imperceptible wearable sensors for continuous health monitoring. Her educational foundation includes: PhD in Electrical Engineering (2015) from Tufts University Master's and Bachelor's degrees in Physics (solid state) AS degree in Medical Laboratory Sciences Dr. Ameri's research program centers on 2D material-based electronic devices for wearable bioelectronics, human-machine interfaces (HMI), and mobile healthcare systems . Her lab pioneered graphene electronic tattoos (GETs) that achieve unprecedented skin conformity while recording high-fidelity physiological signals. Current work emphasizes ultrasoft hydrogel-based sensors that eliminate motion artifacts and enable months-long wear without skin irritation, representing a paradigm shift from conventional rigid medical devices toward truly imperceptible health monitors. Analysis of her 40+ publications reveals a strategic evolution from fundamental nanomaterial characterization toward clinically viable systems. Recent work (2021-2025) demonstrates increasing sophistication in multimodal sensing (simultaneous ECG/EEG/temperature), reusable sensor architectures , and wireless power integration . The trajectory shows clear progression from lab prototypes to FDA-pipeline devices, particularly in cardiac and neurological monitoring applications. Her scientific recognition includes: Rising Star in EECE 2017 award Dr. Ameri leads the Ameri Nano Research Group which operates advanced nanofabrication facilities for developing next-generation bioelectronic interfaces. Her research has attracted significant media attention from BBC, IEEE Spectrum, and Phys.Org, highlighting real-world impact in remote patient monitoring. The group actively collaborates with medical institutions to translate innovations into point-of-care diagnostics, with current projects focusing on in-ear physiological monitors and strain-neutralized neural recording systems. The research team maintains strong industry partnerships for commercializing soft bioelectronics, with particular emphasis on creating accessible health monitoring solutions for underserved communities through low-cost manufacturing approaches.

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Behraad Bahreyniمشاهده پروفایل

استاد

Simon Fraser UniversityCanada

MEMS
Micro- and Nano-systems
Sensors
+۳ مورد دیگر

Behraad Bahreyni is a Professor and Graduate Student Supervisor in the Department of Mechatronic Systems Engineering at Simon Fraser University's Faculty of Applied Sciences. His academic career includes a PhD in Electrical Engineering from the University of Manitoba (2006), a postdoctorate at Cambridge University (2007), and a B.Sc. in Electronics Engineering from Sharif University of Technology (1999). His research focuses on micro- and nano-systems, advanced sensors, and sensor signal processing, with notable contributions to MEMS technology, resonant sensing, and geophysical instrumentation. Recent work includes developing high-sensitivity gravimeters, 3D-printed computing systems, and neuromorphic processors. His publications span innovative applications in microelectromechanical systems (MEMS), including nonlinear resonators, capacitive proximity detection, and thermal control systems. He teaches courses such as MSE 251 (Electronic Circuits), MSE 311 (Introduction to MEMS), and MSE 811 (Microdevice Characterization). Bahreyni's research lab integrates cutting-edge fabrication techniques with interdisciplinary approaches to address challenges in sensor technology and microsystem engineering.

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