جستجوی استاد راهنمای دانشگاهی

Hassan Qudrat-Ullahمشاهده پروفایل

استاد

• Sustainability
• Global/Climate Change
• System Dynamics
• +۳ مورد دیگر

Dr. Hassan Qudrat-Ullah is a Professor at the School of Administrative Studies, York University, and Coordinator of the Certificate in Logistics Management. He holds a PhD in Decision Sciences from NUS Business School and completed a post-doctoral fellowship at Carnegie Mellon University. His research focuses on dynamic decision making, system dynamics modeling, energy planning, and interactive learning environments. He teaches courses on quantitative methods, logistics, and decision analysis, informed by global industry experience across 20+ countries. Research interests include sustainability, climate change, systems thinking, and educational applications of decision sciences. He serves as Editor-in-Chief of the International Journal of Complexity in Applied Science and Technology and is a member of IEEE, DSI, and the International System Dynamics Society. His work has been published in Energy , Decision Support Systems , and others. Key projects include studies on 'structured-debriefing in dynamic decision making' and renewable energy policies in Africa. His recent articles (2023–2025) address AI integration in energy governance, system dynamics for supply chain resilience, and education for sustainability. Hassan advocates for systems thinking in K-12 education and enjoys traveling (visited 129 countries) and bird-watching.

Kimon Fountoulakisمشاهده پروفایل

دانشیار

• Machine Learning on Graphs
• Numerical Optimization
• Graph Algorithms
• +۲ مورد دیگر

Kimon Fountoulakis is an Associate Professor at the University of Waterloo. His research focuses on Machine Learning on Graphs and Numerical Optimization, with a strong emphasis on algorithmic methods for graph-structured data. He holds a Ph.D. from The University of Edinburgh (2015), an M.Sc. from The University of Edinburgh (2010), and a B.Sc. from Athens University of Economics and Business (2009). His work spans theoretical foundations and practical applications in graph algorithms, optimization, and machine learning. Research interests include graph neural networks, local graph clustering algorithms, and algorithmic reasoning. His contributions address challenges in graph representation learning, message-passing architectures, and scalable optimization methods. Notable themes in his publications include improving counting abilities of vision-language models, analyzing graph convolutions, and developing flow-based clustering techniques with statistical guarantees. His work often bridges theory and practice, with applications in network analysis, pandemic containment strategies, and high-performance computing. While no specific grants or awards are listed, his research demonstrates significant contributions to graph-based machine learning and optimization. He maintains a research group at the University of Waterloo, with a focus on developing open-source tools and frameworks for graph algorithms. His lab’s work emphasizes local graph clustering methods and their scalability in real-world networks.

David Duvenaudمشاهده پروفایل

دانشیار

• Machine Learning
• Generative Models
• Bayesian Inference
• +۵ مورد دیگر

David Duvenaud is an Associate Professor at the University of Toronto , holding a Canada Research Chair in Generative Models and a Schwartz Reisman Chair in Technology and Society . He is cross-appointed to the Department of Computer Science and Department of Statistical Sciences . A Sloan Research Fellow and founding member of the Vector Institute , his work bridges deep probabilistic models , AI safety , and scientific computing . PhD in Machine Learning (University of Cambridge, 2014) Postdoc in Hyperparameter Optimization (Harvard University, 2016) Co-founded Invenia (energy forecasting company) His research spans foundational Neural Ordinary Differential Equations (NeurIPS 2018 Best Paper) and Automatic Chemical Design (ACS Central Science 2018) to recent work on AGI governance (2025) and AI safety (2024). Key contributions include stochastic variational inference , implicit differentiation frameworks , and antisymmetrization layers for quantum Monte Carlo. Recent publications (2024-2025) focus on systemic existential risks from AI , many-shot jailbreaking attacks , and epistemic uncertainty quantification . His group trains energy-based models with scalable MCMC samplers and develops invertible neural architectures (e.g., Residual Flows NeurIPS 2019). He also explores human-AI alignment through LLM Processes (NeurIPS 2024) and Sycophancy in Language Models (ICLR 2024). Canada Research Chair (2025) NSERC Grant (2025) Sloan Research Fellow (2021) Schwartz Reisman Chair (2021) Best Paper Award (NeurIPS 2018) Distinguished Paper Award (ICFP 2021) His students include James Requeima , Jesse Bettencourt , and Raymond Douglas . He teaches courses on Statistical Methods for Machine Learning and Differentiable Inference . Current work (2025) investigates systemic human disempowerment through incremental AI capabilities and sabotage risk mitigation via hyperparameter-aware evaluations.

Mohammad Zulkernineمشاهده پروفایل

استاد

• Cyber-Physical System Security
• Software Reliability
• Security Engineering
• +۶ مورد دیگر

Dr. Mohammad Zulkernine is a Full Professor and Canada Research Chair in Cyber-Physical System Security at Queen’s University’s School of Computing (Faculty of Arts and Science). He leads the Queen’s Reliable Software Technology (QRST) research group and directs the Queen’s Centre for Security & Privacy. His research focuses on secure software systems for cyber-physical systems, including autonomous vehicles, IoT, and cloud computing. Dr. Zulkernine holds a cross-appointment in Electrical and Computer Engineering and is a licensed Professional Engineer in Ontario. Education: BSc (Bangladesh BUET), MEng (Japan), PhD (University of Waterloo). He joined Queen’s in 2003 and has held sabbaticals at University of Trento, Italy and Irdeto Canada. He has published over 250 papers, led 35+ research projects, and supervised 120+ students. Awards include Canada Research Chairs (Tier I and II), Queen’s Excellence in Graduate Supervision Award, and Distinguished Supervision Award from the School of Computing. Research Interests: Cyber-Physical System Security, Software Reliability, IoT Security, Vehicle Networks, Secure Software Engineering, and Cybersecurity Risk Assessment. Active industry collaborations include EU-funded projects and Canada-Africa initiatives.

Zhenman Fangمشاهده پروفایل

دانشیار

• Customizable Computing
• Heterogeneous Computing
• FPGA Acceleration
• +۸ مورد دیگر

Dr. Zhenman Fang is an Associate Professor at the School of Engineering Science , Simon Fraser University (SFU) , where he founded and directs the HiAccel Lab . He also holds an associate membership in the School of Computing Science at SFU. His research focuses on customizable computing with software-defined hardware acceleration , addressing performance, energy-efficiency, and reliability in post-Moore’s law computing across domains like machine learning , big data analytics , quantum chemistry , and precision medicine . Education: Ph.D. in Computer Science from Fudan University (2014), with a visit to University of Minnesota during his studies. Postdoctoral Work: University of California, Los Angeles (UCLA) (2014-2017). Industry Experience: Staff Software Engineer at Xilinx (2017-2019). Dr. Fang’s research spans the entire computing stack , including application characterization , accelerator-rich architecture design , and programming/tool support . He has developed frameworks like HiSpMV , SyncNN , and SQL2FPGA , emphasizing FPGA acceleration for vision transformers , quantum chemistry , and spiking neural networks . His work has been recognized with 3 best paper awards (FPL 2024, TCAD 2019, MEMSYS 2017) and 3 best paper nominees (FCCM 2025, HPCA 2017, ISPASS 2018). Recent publications highlight trends in low-precision machine learning ( ShiftQuant , ESRU ), quantum chemistry acceleration ( SERI ), and vision transformer optimization ( Quasar-ViT ). His HiAccel Lab actively mentors PhD and MASc students , with notable graduates like Alec Lu (PhD 2024, now at Meta) and Philip Stachura (MASc, now with BC Graduate Scholarship). Scientific Awards: Inaugural SFU Research Excellence Award - Horizon Award (2025) FPL 2024 Stamatis Vassiliadis Best Paper NSERC Alliance Award (2020) CFI JELF Award (2019) Xilinx University Program Award (2019) IEEE Senior Member (2023) Grants: NSERC Discovery Grant (2019) CFI JELF Funding (2019) Huawei and Xilinx sponsorships Dr. Fang leads open-source initiatives like SyncNN , PASTA , and SQL2FPGA , and serves as General Chair for ASAP 2025 and Program Co-Chair for RAW 2025 . His lab collaborates globally with institutions such as UCLA , Northeastern University , and Xidian University .

Vanessa Samouëlianمشاهده پروفایل

دانشیار بالینی

• Gynecological Oncology
• Cervical Cancer
• Endometrial Cancer
• +۳ مورد دیگر

Vanessa Samouëlian, MD, PhD, serves as an Associate Clinical Professor in the Department of Obstetrics and Gynecology at the University of Montreal. Based at the Montreal University Hospital Center (CHUM), she specializes in gynecological oncology within the Gynecological Oncology division. Her institutional roles include leading Tumor Board meetings and serving as a service representative on CHUM's medical act evaluation committee and the University of Montreal externship committee. Her educational background includes: MD from University of Montpellier 1, France (1992-1998) Certificate of Clinical and Therapeutic Synthesis and Master's in Biological/Medical Sciences (1998) IUD in Colposcopy and Cervicovaginal Pathology (1999) DES in Gynecology-Obstetrics from University of Lille 2, France (2005) PhD from Biology-Health Doctoral School, University of Lille 2 (2007) Clinical Fellowship in Gynecology Oncology at CHUM (2009-2011) Dr. Samouëlian's research centers on gynecological malignancies with emphasis on cervical, endometrial, and vulvar cancers. She investigates molecular biomarkers for nodal metastasis, treatment outcomes in high-risk endometrial carcinoma, and surgical innovation including robotic techniques. Her work bridges clinical oncology, cancer pathology, and HPV vaccination strategies through national collaborations. Her 2015-2016 publications reveal a cohesive research trajectory focused on endometrial cancer through the Canadian High Risk Endometrial Cancer (CHREC) Consortium. Key themes include adjuvant therapy optimization, pathological pattern analysis (MELF invasion), and surgical training innovation using virtual reality simulation. These works demonstrate integration of clinical practice, molecular diagnostics, and surgical education within gynecologic oncology. Within CHUM's organizational structure, Dr. Samouëlian contributes to the Historical Assignment Team and Gynecological Oncology division. She co-developed the Contemporary Clinical Questions on HPV-Related Diseases and Vaccination booklet with Gynecologic Oncology Canada and Merck Canada, endorsed by major Canadian gynecological societies including the Society of Obstetricians and Gynaecologists of Canada.

Amy R. Wuمشاهده پروفایل

دانشیار

• Human-Robot Interaction
• Human Biomechanics
• Locomotion
• +۴ مورد دیگر

Amy R. Wu serves as Associate Professor in Mechanical and Materials Engineering at Queen's University and holds the Mitchell Professorship in Bio-inspired Robotics. She leads the Biomechanics x Robotics Laboratory (BxRL) and contributes to the Ingenuity Labs Research Institute. Her academic credentials include: Ph.D. in Mechanical Engineering, University of Michigan Postdoctoral Research, Biorobotics Laboratory, EPFL, Switzerland Dr. Wu's research bridges biomechanics and robotics to enhance legged mobility through innovations in human-robot interaction , exoskeleton design , and gait assistance . Her work focuses on developing robotic systems that adapt to human movement patterns, particularly for spinal cord injury rehabilitation and stability augmentation during walking. Analysis of her 2019-2023 publications reveals consistent advancement in exoskeleton control algorithms, gait stability metrics, and human adaptation studies. Key trends include modular exoskeleton systems (Symbitron), neuromuscular controllers for ankle assistance, and haptic feedback integration for motor learning enhancement. No scientific awards are documented in the provided materials. Dr. Wu mentors graduate students through BxRL, where her team conducts human subject testing and robotic development. Research is supported by institutional resources at Ingenuity Labs, though specific grant details remain unspecified. The Biomechanics x Robotics Laboratory operates within Queen's Ingenuity Labs Research Institute, facilitating interdisciplinary collaboration on projects like outdoor stability assessment, winter walking adaptations, and teleoperation interfaces for drone control.

Arash Aramiمشاهده پروفایل

دانشیار

• Assistive Robotics
• Human-Robot Interaction
• Machine Learning
• +۶ مورد دیگر

Arash Arami is an Associate Professor in the Department of Mechanical and Mechatronics Engineering at the University of Waterloo, cross-appointed in Systems Design Engineering. He directs the Neuromechanics and Assistive Robotics Laboratory and maintains affiliations with Waterloo Robohub, the Centre for Bioengineering and Biotechnology, Waterloo AI institute, and KITE institute at Toronto Rehab Institute. He earned his Doctorate in Electrical Engineering from EPFL (2014), Master of Science from University of Tehran (2009), and Bachelor of Science from University of Tabriz (2006), all in Control Engineering. His research in Assistive Robotics and Rehabilitation Engineering integrates Machine Learning with Neuromechanics to develop intelligent systems for human movement analysis. Key focus areas include exoskeleton control algorithms, wearable sensor systems, and neural control modeling for rehabilitation applications. Recent publications demonstrate interdisciplinary work spanning robotics, biomedical engineering, and materials science, with emphasis on real-time human locomotion prediction, exoskeleton-human interaction, and data-driven health monitoring solutions. Dr. Arami serves as Chair of the NSERC Scholarship Committee (2021-2023) and mentors graduate students through the Mechatronics Exchange Study program. His teaching includes core courses in control systems, robot manipulators, and biomechanical engineering. The Neuromechanics and Assistive Robotics Laboratory fosters collaborations with clinical partners at Toronto Rehab Institute, focusing on translating robotic innovations into practical rehabilitation tools through interdisciplinary teamwork.

Hassan Rivazمشاهده پروفایل

استاد

• Medical image analysis
• Deep learning
• Machine learning
• +۵ مورد دیگر

Professor Hassan Rivaz is a Full Professor and Concordia University Research Chair in Medical Imaging with Deep Learning at Concordia University's Gina Cody School of Engineering and Computer Science. He holds appointments in the Department of Electrical and Computer Engineering and is cross-appointed to the Department of Computer Science & Software Engineering. Dr. Rivaz serves as the Founding Director of the IMPACT Lab and actively supervises PhD students in Electrical and Computer Engineering and Computer Science programs. Dr. Rivaz received his PhD from Johns Hopkins University in 2011, Master's degree from the University of British Columbia, and Bachelor's degree from Sharif University, followed by postdoctoral training at McGill University. His academic journey includes prestigious awards such as the NSERC Post-Doctoral Fellowship and Jeanne Timmins Costello Post-Doctoral Award. His research focuses on advancing medical image analysis through deep learning techniques, particularly in ultrasound imaging applications. Dr. Rivaz has made significant contributions to quantitative ultrasound, cancer detection, lymphedema assessment, and ultrasound elastography. His work bridges theoretical algorithm development with practical clinical applications, addressing challenges in medical image denoising, segmentation, registration, and tissue characterization. The IMPACT Lab under his direction develops innovative solutions for medical imaging problems with direct clinical relevance. Analysis of his recent publications reveals a strong emphasis on deep learning applications for ultrasound image processing, with particular focus on denoising techniques, elastography improvements, and segmentation algorithms. His work consistently addresses the challenge of working with real clinical data rather than simulated environments, contributing to more practical medical imaging solutions. Dr. Rivaz has received numerous prestigious awards including: Concordia University Research Chair in Medical Imaging with Deep Learning (2023–2028) QBIN/RBIQ Rising Star in Bio-Imaging in Quebec (2022) Concordia University Research Chair in Medical Image Analysis (2018-2023) Petro-Canada Young Innovator Award (2016–2018) He actively mentors graduate students, with many recipients of competitive scholarships including NSERC CGS, FRQNT, and FRQS awards. Dr. Rivaz serves on editorial boards for top journals including IEEE Transactions on Medical Imaging (since 2017), Medical Image Analysis (since 2025), and IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control (since 2018). He has organized major conferences including IEEE EMBC 2020, ISBI 2021, and IEEE IUS 2023, and served as Area Chair for MICCAI from 2017 to 2024. As Founding Director of the IMPACT Lab, Dr. Rivaz leads a multidisciplinary research team focused on innovative medical imaging solutions. The lab maintains strong collaborations with hospitals and research institutions to translate imaging technologies into clinical practice. Current projects include developing AI-powered ultrasound analysis tools, quantitative imaging biomarkers for cancer diagnosis, and advanced techniques for ultrasound elastography with applications in tissue characterization and disease detection.

Abbas Milaniمشاهده پروفایل

استاد

• Textile Composites
• Biocomposites
• Materials Constitutive Relations
• +۱۰ مورد دیگر

Abbas Milani is a tenured Professor of Mechanical Engineering at the University of British Columbia's Okanagan campus, where he holds the Tier 1 Principal's Research Chair in Sustainable & Smart Manufacturing and serves as Director of the Materials and Manufacturing Research Institute (MMRI). He also serves as Technical Director of the Composites Research Network (CRN), Lead of the Canadian-International Biocomposites Research Network, and leads multiple major initiatives including the UBC-Pacific Economic Development Canada-Advancing Circular Economy (ACE) program and the UBC-NRC IRAP National Circular Economy CtO Program. Dr. Milani's primary research focuses on advanced modeling, simulation, and multi-criteria design optimization of composite and biocomposite materials, structures, and manufacturing processes. His expertise spans Textile Composites/Biocomposites, Materials Constitutive Relations, Finite Element Modeling, Robust Inverse Methods, Material Selection for End-of-Life Design Strategies, Multiple Criteria Decision Making, and Industry 5.0 applications. His interdisciplinary research bridges mechanical engineering, sustainable materials science, and smart manufacturing technologies. Analysis of his recent publication record reveals a strong emphasis on sustainable materials development, with particular focus on biocomposites, life cycle assessment methodologies, and optimization of manufacturing processes. His work integrates computational modeling with experimental validation across diverse application areas including medical devices, sustainable packaging, and circular economy strategies. The publications demonstrate increasing integration of artificial intelligence and machine learning approaches with traditional engineering methods. 2015 UBC Okanagan Researcher of the Year Award Killam Faculty Research Award (2016) Inducted into Royal Society of Canada - College of New Scholars (2020) Gold Medal Service Contribution Award by Academics World Reviewer Contribution Award by ASM International Multiple teaching excellence awards from UBC Dr. Milani has successfully mentored over 100 students and postdoctoral fellows who have secured positions in both industry and academia. His research program has been supported by more than $15 million in funding from government and industrial organizations. He leads the NSERC CREATE in Immersive Technologies (CITech) program and co-leads the Advanced Materials and Fabrication Core Competency within the Survive and Thrive Applied Research (STAR) program, demonstrating his commitment to training the next generation of engineers and advancing applied research.

Frank Rudziczمشاهده پروفایل

دانشیار

• Natural Language Processing
• Machine Learning
• Healthcare
• +۱۲ مورد دیگر

Dr. Frank Rudzicz is an Associate Professor in the Faculty of Computer Science at Dalhousie University. His research lies at the intersection of artificial intelligence, natural language processing, and healthcare, with a focus on developing machine learning systems that improve clinical decision-making, patient outcomes, and accessibility in medicine. He holds a BSc from Concordia University (2004), an MEng from McGill University (2006), and a PhD from the University of Toronto (2011). His research interests include Natural Language Processing, Machine Learning, Healthcare, Speech Technologies, Explainable AI, and Fairness in ML. Dr. Rudzicz's recent publications span a wide range of topics, including Alzheimer's detection through speech analysis, surgical outcome prediction, mental health monitoring, privacy in AI, and the application of large language models in clinical settings. His work consistently emphasizes ethical AI, patient privacy, and real-world clinical integration. He has received several awards, including a Best Paper award at EMNLP 2020, a Best Student Paper award at ICASSP 2021, and the ISCA Best Student Paper award in 2013. His research has been published in top-tier journals such as Nature Scientific Reports , JAMA Network Open , IEEE Access , and Frontiers in Human Neuroscience , as well as leading conferences including NeurIPS, ACL, ICML, and Interspeech. Dr. Rudzicz supervises a dynamic research group working on AI for health, with active projects in voice-based diagnostics, ambient clinical documentation, explainable AI for surgery, and wearable-based monitoring for chronic diseases. He collaborates widely across disciplines, including with clinicians, neuroscientists, ethicists, and public health experts. He is also involved in major initiatives such as the Genetics Navigator study and Bridge2AI-Voice, aiming to build ethically sourced, diverse biomedical datasets. His lab actively explores the societal implications of AI in healthcare, including fairness, trust, and resistance to malicious fine-tuning.

Qingguo Liمشاهده پروفایل

استاد

• Biomechanical system design
• Energy harvesting
• Wearable sensors
• +۴ مورد دیگر

Qingguo Li is a Professor and Associate Head at the Department of Mechanical and Materials Engineering , Queen's University , and a member of the Ingenuity Labs Research Institute . He specializes in biomechanical system design, energy harvesting, wearable sensors, gait analysis, and load carriage systems. His research integrates robotics, biomedical engineering, and sensor technology to develop human-centric devices and mobility aids. Current Roles : Professor, Associate Head, Queen's University Research Institute : Ingenuity Labs Research Institute Lab : Bio-Mechatronics and Robotics Laboratory His work focuses on biomechanical energy harvesting , IMU-based motion analysis , and assistive device development . Key applications include stroke rehabilitation, gait monitoring, and wearable power generation systems. Articles span cable-driven robots , smart walkers , and 3D printing mechanisms , emphasizing human-robot interaction and dynamic modeling . The lab explores sensor calibration , adaptive control algorithms , and human movement optimization . Areas of impact include rehabilitation engineering , load carriage stability , wearable sensor accuracy , and assistive robotics . His team develops solutions for gait asymmetry detection , post-stroke mobility , and low-cost energy systems , leveraging machine learning and kinetic modeling .

Florian Shkurtiمشاهده پروفایل

استادیار

• Robotics
• Machine Learning
• Computer Vision
• +۶ مورد دیگر

Florian Shkurti is an Assistant Professor in the Department of Computer Science at the University of Toronto Mississauga (UTM), affiliated with the UofT Robotics Institute, Vector Institute, and Acceleration Consortium. His research focuses on robotics, machine learning, and computer vision, emphasizing safe and effective autonomous systems in dynamic environments. He directs the Robot Vision and Learning (RVL) lab, exploring areas like environmental monitoring, autonomous navigation, and mobile manipulation. Research Interests: His work spans robotics, machine learning, and computer vision. Key areas include robot perception, planning under uncertainty, safe exploration, imitation learning, and applications in field robotics, autonomous vehicles, and chemistry lab automation. He develops methods enabling robots to perceive, reason, and act safely in collaboration with humans. Publications: Recent work includes advancements in safe multitask learning, interactive crowd navigation, and diffusion models for trajectory planning. His research bridges theoretical foundations with real-world applications in environmental science and autonomous systems. Affiliations: Faculty Member, UofT Robotics Institute; Faculty Affiliate, Vector Institute; Faculty Member, Acceleration Consortium. He also holds positions at UTM's Mathematical & Computational Sciences department. Teaching: Courses include Imitation Learning for Robotics, Neural Networks, and Mobile Robotics. He emphasizes hands-on experience with autonomous systems through projects involving RC cars and simulation tools. Labs & Teams: Leads the RVL lab, collaborating on projects like RoboCulture (automated biological experimentation) and SICNav (safe crowd navigation systems). The lab focuses on cross-disciplinary robotics solutions for real-world challenges.

Thomas E. Doyleمشاهده پروفایل

دانشیار

• Biomedical Engineering
• Human-Computer Interfacing (HCI)
• Machine Learning
• +۶ مورد دیگر

Dr. Thomas E. Doyle is an Associate Professor at the McMaster School of Biomedical Engineering and the Department of Electrical & Computer Engineering at McMaster University. His research focuses on biomedical signal processing, human-computer interfacing (HCI), and machine learning applications for healthcare augmentation, rehabilitation, and enhancement. He holds a Ph.D. from Western Ontario, Canada, and teaches courses like COMPENG 2DI4 (Logic Design). His work bridges cybernetics and clinical applications, emphasizing AI-driven solutions for medical diagnostics, patient monitoring, and space exploration. Education: B.E.Sc, B.Sc, M.E.Sc, Ph.D. from Western Ontario, Canada Recent Projects: Developed AI systems for remote healthcare diagnostics (2023) Collaborated with NASA on medical emergency simulators for deep space missions (2017–2023) Led ventilator development efforts for local hospitals during the pandemic (2020) His research interests span machine learning for mental health diagnostics, trust quantification in medical AI, and extended reality (XR) for medical training. He emphasizes interdisciplinary approaches, integrating computational methods with healthcare challenges. Recent publications highlight applications in pediatric emergency care, chronic pain management, and reliable medical device design. Dr. Doyle actively engages in educational initiatives, including first-year engineering pedagogy and experiential learning programs. He has received funding for projects such as the Educating the Engineer of 2025 (EtE-25) awards and contributes to initiatives like the Digital & Smart Systems and Health & Bio-innovation research clusters at McMaster.

Septimiu E. Salcudeanمشاهده پروفایل

استاد

• Medical Robotics
• Medical Imaging
• Biomedical Engineering
• +۴ مورد دیگر

Septimiu E. Salcudean is a Professor at the University of British Columbia's Department of Electrical and Computer Engineering, holding the C.A. Laszlo Chair in Biomedical Engineering and a Canada Research Chair. His research focuses on medical robotics, image guidance systems, and ultrasound elastography. He has contributed to advancements in haptic interfaces, teleoperation, and needle insertion modeling. Education: B.Eng and M.Eng from McGill University (1979-1981), Ph.D. from UC Berkeley (1986). He has held positions at IBM T.J. Watson Research Center and was a Killam Research Fellow at ONERA in France. His work spans robotics, biomedical engineering, and surgical systems. Research interests include medical robotics, real-time imaging, and surgical navigation. Notable projects involve ultrasound-guided surgery, vibro-elastography for tissue characterization, and haptic feedback systems. His lab, the Robotics and Control Laboratory (RCL), develops technologies like the da Vinci surgical system integration with ultrasound imaging. Awards include the NSERC Synergy Award, IEEE Fellowship, and UBC Killam Research Prize. He has advised over 30 graduate students and published extensively in robotics and biomedical journals.