جستجوی استاد راهنمای دانشگاهی

• Sustainable Supply Chain Management
• Logistics and Humanitarian Logistics
• Operations Management
• +۴ مورد دیگر

M. Ali Ülkü is a Professor and Director of the Centre for Research in Sustainable Supply Chain Analytics at Dalhousie University's Faculty of Management, where he also serves as Acting Chair of the Management Science & Information Systems Department. He holds cross-appointments in the College of Sustainability, Department of Industrial Engineering, and School for Resource and Environmental Studies. His educational background includes: PhD in Management Sciences (University of Waterloo) MSc in Operations Research (Çukurova University) BSc in Industrial Engineering (Bilkent University) Dr. Ülkü's research centers on sustainable supply chain innovation , with expertise in circular economy logistics, humanitarian operations, and green marketing analytics. His interdisciplinary work applies big data methodologies to solve environmental challenges, emphasizing "the science and teaching of better" through student-led research initiatives. Recent publications (2022-2023) reveal a strategic focus on climate-resilient supply chains, integrating machine learning for multimodal logistics optimization and game-theoretic models for green product development. Key themes include shipment consolidation techniques, physical internet applications, and subsidy-driven sustainability in duopolistic markets. Notable recognitions include: A. Gordon Archibald Teaching Excellence Award (2023) Global Triple E Awardee (Education Champion) (2023) Research Star Award, Faculty of Management (2021) Distinguished Professor Award, IEOM Society (2019) Program Chair, Canadian OR Society Conference (2018) INFORMS Best Paper Award (runner-up, 2013) An active educator, Dr. Ülkü teaches Supply Chain Planning (COMM 4520), Logistics Management (COMM 3507), and Operations Management (BUSI 5551), championing experiential learning through his research center. While specific grant details aren't documented, his award-winning publications indicate sustained funding for sustainability analytics projects. He leads the Centre for Research in Sustainable Supply Chain Analytics, fostering cross-disciplinary collaboration on climate-adaptive logistics systems and sustainable consumption models through industry partnerships and policy-focused research.

Michael Fowlerمشاهده پروفایل

استاد

• Electrochemical Power Sources
• Fuel Cell Reliability
• Battery Degradation Analysis
• +۶ مورد دیگر

Michael Fowler is a Professor in the Department of Chemical Engineering at the University of Waterloo, cross-appointed to the Department of Mechanical and Mechatronics Engineering. His research focuses on electrochemical power systems, including battery degradation analysis, fuel cell reliability, and clean energy hubs. He leads projects like ChallengeX and EcoCar, developing fuel cell and hybrid vehicles. He teaches CHE 331 (Electrochemical Engineering) and actively supervises graduate students. His work integrates hydrogen economy concepts, renewable energy incentives, and Power-to-Gas energy storage. Key interests span polymer science, interfacial phenomena, and battery thermal management systems. Education: Ph.D. in Chemical and Materials Engineering (2003, Royal Military College of Canada), M.Sc. in Engineering Chemistry (1988, Queen's University), B.Sc. in Fuels and Materials Engineering (1986, Royal Military College of Canada). Research emphasizes modeling fuel cells, battery performance, and electrolyte materials. Recent articles explore lithium-ion battery diagnostics, aluminum-air batteries, and thermal runaway prevention. He collaborates with industries on sustainable energy solutions and advises student teams in green energy systems. His lab focuses on advanced materials for electrochemical systems and energy storage innovations. Current roles include co-faculty supervisor of vehicle design teams and director of the Waterloo Fuel Cell Research Network. He welcomes graduate applications and is involved in developing Canada’s hydrogen transition strategies. His work bridges academic research with practical applications in automotive, renewable energy, and material science sectors.

Davide Elmoمشاهده پروفایل

استاد مدعو

• Rock and Soil Engineering
• Geomechanics
• Numerical Modeling
• +۶ مورد دیگر

Davide Elmo is an Adjunct Professor at the Department of Earth Sciences, Faculty of Applied Science, University of British Columbia. He joined the NBK Institute of Mining Engineering in 2021, where he teaches and conducts research in engineering geology and rock engineering. University of Portsmouth - B.Eng (Hons) in Engineering Geology and Geotechnics University of Exeter - PhD in Geomechanics Simon Fraser University - Postdoctoral Fellow under Prof. Stead Elmo's research focuses on advanced numerical modeling of rock engineering problems, mining methods, and discrete fracture network models. Recently, he has explored applications of information theory, machine learning, and engineering philosophy to rock engineering, particularly examining cognitive biases in inductive engineering design. His work bridges computational techniques with practical challenges in geological systems. For contact, visit http://mining.ubc.ca/about/people/davide-elmo/ .

• International and Comparative Intellectual Property Law
• Law and Creative Expression
• Intellectual Property and Human Rights
• +۵ مورد دیگر

Dr. Eugene C Lim is a Lecturer at the School of Law, Queen's University Belfast, specializing in international and comparative intellectual property law. His research focuses on the intersection of law, technology, and society, particularly non-traditional trademarks, digital art, and biotechnology law. He has published extensively in top legal journals and received the Outstanding Presentation Award (2016). His teaching areas include copyright law, torts, and intellectual property in creative industries. Research interests span trademark dilution, copyright fixation, patent law equivalents, and legal theory. He employs cross-disciplinary and comparative methodologies to analyze socio-cultural impacts of legal frameworks. Recent work explores climate change's effect on geographical indications, virtual property rights in NFTs, and health-related trademark applications. Dr. Lim actively participates in academic activities, including organizing the School of Law PhD Conference and presenting at international forums. His publications address emerging challenges in intellectual property, biotechnology, and digital technologies, emphasizing practical legal solutions for evolving societal needs.

• Cognitive Neuroscience
• Machine Learning
• Neuroimaging
• +۷ مورد دیگر

Karim Jerbi is a Full Professor at the University of Montreal's Department of Psychology, within the Faculty of Arts and Sciences. He holds the Canada Research Chair (CRC) in Systems Neuroscience and Cognitive Neuroimaging (Junior 2) and leads interdisciplinary research programs. His affiliations include the CRM — Centre de recherches mathématiques, CERNEC — Centre de recherche en neuropsychologie et cognition, CRIUGM — Centre de recherche de l'Institut universitaire de gériatrie de Montréal, PhysNum — Laboratoire de physique numérique, BRAMS — Laboratoire international de recherche sur le Cerveau, la Musique et le Son, and CIRCA — Centre interdisciplinaire de recherche sur le cerveau et l'apprentissage. These affiliations emphasize his collaborative and interdisciplinary approach to neuroscience and neuroimaging. His research investigates cognitive functions and dysfunctions using advanced signal processing and machine learning applied to multimodal brain data. Key interests include neural oscillations, brain connectivity, and the interplay between cognitive processes and neuroimaging techniques like MEG and EEG. He explores topics such as sleep dynamics, decision-making, mindfulness meditation effects, and neurodegenerative disorders, bridging computational models with clinical applications. Jerbi's recent work highlights trends in AI-driven neuroimaging tools (e.g., MEEGNet, NeuroPycon), criticality in brain networks, and the impact of consciousness states on neural activity. His grants include leading the UNIQUE initiative (2019–2024) and co-directing projects like 'Partnership for Abundant Intelligences' (2024–2029), funded by organizations like FRQNT and SSHRC. These efforts aim to integrate AI, mathematics, and clinical research for transformative insights into brain function. Scientific Awards: Canada Research Chair (CRC) in Systems Neuroscience and Cognitive Neuroimaging (Junior 2). In advising, he has guided students in topics like AI-based biomarker identification, hypnosis dynamics, and motor intention decoding. His labs and teams, such as the CRM and CERNEC, focus on mathematical foundations of neural systems and neuropsychological cognition, respectively. Collaborations span diverse fields, including gériatrie, music cognition, and computational intelligence, reflecting his commitment to cross-disciplinary innovation.

• Reinforcement Learning
• Robotics
• Deep Learning
• +۳ مورد دیگر

Glen Berseth is an Associate Professor in the Department of Computer Science and Operations Research at the University of Montreal and a Senior Academic Fellow at Mila – Quebec Institute for Artificial Intelligence. He is also a Canada CIFAR Chair in AI and Co-Director of the Montreal Robotics and Integrative AI Laboratory (REAL). His work focuses on reinforcement learning, robotics, and deep learning applied to autonomous systems. He holds a postdoctoral background from Berkeley Artificial Intelligence Research (BAIR), working under Sergey Levine. His research emphasizes real-world applications, including human-robot collaboration, continual learning, and multi-agent systems. He teaches courses on robot learning at the University of Montreal and Mila, covering cutting-edge techniques for general-purpose robots. Key research interests include reinforcement learning for robotics, adaptive interfaces, and sim-to-real transfer. His recent work addresses challenges in autonomous learning systems, such as robust locomotion control and efficient exploration strategies. Notable awards include the Canada CIFAR AI Chair. He has supervised numerous students, including PhD candidates Ozgur Aslan and Siddarth Venkatraman, and Master’s students like Roger Creus-Castanyer and Léa Demeule, focusing on topics like reinforcement learning and robotic control. Berseth leads research projects funded by organizations like the CRSNG, FCI, and MITACS, addressing topics such as modular lifelong learning and generalization in robotics. His lab, REAL, explores embodied AI and robotics integration.

Biniam Kebedeمشاهده پروفایل

استادیار

• Food Bioprocessing
• Data Science
• Fermentation Technology
• +۹ مورد دیگر

Dr. Biniam Kebede is an Assistant Professor in the Department of Food Science at the University of Guelph. His research focuses on integrating bioprocessing, food analytics, and data science to enhance food functionality and sustainability. He leads the Food Bioprocessing and Data Science Research Group, which explores fermentation, enzymatic treatments, and machine learning for food innovation. Academic History: B.Sc. in Food and Biochemical Technology, Bahir Dar University (Ethiopia) M.Sc. in Food Technology, Ghent University (Belgium) Ph.D. in Bioscience Engineering, KU Leuven (Belgium) Research Interests: Dr. Kebede's work addresses sustainable food systems through: Optimizing fermentation to unlock bioactive compounds from plant-based sources Mitigating off-flavors in pulses via value-chain analysis Engineering food structures to improve nutrient bioaccessibility Developing portable machine learning tools for food authenticity Valuing indigenous food ingredients and traditional processing methods His approach combines multi-omics, imaging, and AI to create predictive models for food innovation. Awards & Affiliations: 2022 IUFoST Young Scientist Award 2014 EFFoST PhD Student of the Year Award Member of IFT, CIFST, and IUFoST Honorary affiliation with University of Otago (New Zealand) Training Philosophy: Dr. Kebede emphasizes personalized mentoring through Individual Development Plans (IDPs), biweekly lab meetings, and interdisciplinary collaboration. Students engage in all research phases, including conference presentations and industry partnerships to build technical and professional skills.

Fred Popowichمشاهده پروفایل

استاد

• Natural Language Processing
• Artificial Intelligence
• Big Data
• +۴ مورد دیگر

Fred Popowich is a Professor in the School of Computing Science at Simon Fraser University (SFU), Canada. He holds adjunct positions at Dalhousie University's Faculty of Graduate Studies and is an Associate Member of SFU's Department of Linguistics and Cognitive Science Program. His academic career began post-PhD (Cognitive Science/Artificial Intelligence, University of Edinburgh, 1989) and has spanned over three decades at SFU. Education: PhD in Cognitive Science/Artificial Intelligence (University of Edinburgh, 1989); M.Sc. and B.Sc. in Computing Science (Simon Fraser University and University of Alberta, 1985/1982). Research focuses on natural language processing (NLP), machine translation, intelligent systems, and big data applications. He directs SFU’s Big Data Initiative and leads the Natural Language Laboratory, supervising MSc/PhD students in computing science. His work includes developing systems for smart homes, toxic language detection in social media, and energy grid analysis. Industry roles include co-founding Axonwave Software (as CTO/President) and contributing to technology commercialization. Current projects address EV charging impacts, personalized learning systems, and real-time load monitoring. Publications span machine translation, sentiment analysis, and NLP applications in education and energy systems. His work bridges theoretical computer science with practical applications in healthcare, smart cities, and education.

Arash Habibi Lashkariمشاهده پروفایل

دانشیار

• Cybersecurity
• Network Security
• Threat Hunting
• +۷ مورد دیگر

Dr. Arash Habibi Lashkari is an Associate Professor and Canada Research Chair in Cybersecurity at York University's School of Information Technology. He holds a PhD from the University of Technology Malaysia and completed a Post-Doc at the University of New Brunswick. With over 25 years of teaching experience, he specializes in cybersecurity risk management, malware analysis, and threat hunting. He pioneered Canada’s first cybersecurity Capture the Flag (CTF) competition for post-secondary students and has authored 10 books and over 110 academic articles. Research Interests: Cybersecurity, Network Security, Threat Hunting, Malware Analysis, Cybersecurity Risk Management, and Blockchain Security. He leads the Behaciour-Centric Cybersecurity Center (BCCC), focusing on vulnerability detection technologies and cybersecurity dataset generation. Awards: Mitacs 150 Top Researchers (2017), University of New Brunswick Teaching Innovation Award (2020), Gold Medal at 2020 Canadian Online Publishing Awards, and multiple international security competition awards. His work has been featured in CBC, CIC, and global cybersecurity conferences. Key Projects: Development of intrusion detection datasets (e.g., CIRA-CIC-DoHBrw-2020), malware analysis frameworks, and AI-driven cybersecurity tools. He actively collaborates with organizations like Aviva and NICT on cybersecurity resilience strategies. Teaching: Offers courses in Digital Forensics, Network Security, and Cybersecurity Management. His 'Think-Que-Cussion' teaching methodology emphasizes interactive learning.

Kemal Tepeمشاهده پروفایل

استاد

• Vehicular Networks
• Cognitive Radio
• Wireless Communication
• +۶ مورد دیگر

Professor Kemal Tepe is a faculty member in the Faculty of Engineering at the University of Windsor, specializing in wireless communication and information processing. His research focuses on vehicular networks, cognitive radio systems, and smart grid technologies. He leads the Wireless Communication and Information Processing Lab , where he develops solutions for autonomous driving systems, cybersecurity in vehicle-to-infrastructure communication, and spectrum sensing techniques. In 2016, he was awarded the Medal of Excellence by the Faculty of Engineering for his dedication and service. Tepe’s work bridges theoretical advancements and practical applications, addressing challenges in autonomous systems, machine learning for anomaly detection, and IoT security. His contributions include pioneering methods for detecting adversarial behavior in vehicular networks and improving spectrum utilization through probabilistic modeling. Collaborations with industry partners like Ford Motor Company and involvement in initiatives such as the Perspective Magazine automotive research highlight his industry-relevant research. His research interests span a wide range of domains including: Autonomous vehicle safety and communication protocols Cognitive radio networks and spectrum management Machine learning for network security and anomaly detection Wireless sensor networks and energy-efficient protocols Smart grid integration and communication architectures Tepe’s publications emphasize practical implementations, such as real-time routing protocols for wireless sensor networks and hardware designs for cognitive radio systems. His lab’s innovations have been showcased in industry-relevant platforms, demonstrating the real-world impact of his work.

Mohamed Atiaمشاهده پروفایل

دانشیار

• Sensor Fusion
• Autonomous Systems
• Embedded Systems
• +۱۲ مورد دیگر

Mohamed Atia is an Associate Professor in the Department of Systems and Computer Engineering at Carleton University's Faculty of Engineering and Design. He conducts research in sensor fusion for autonomous systems, including robotics, autonomous vehicles, and the Internet of Things, with an emphasis on real-time embedded implementation. PhD, Electrical and Computer Engineering, Queen’s University (2013) M.Sc., Computer Systems, Ain Shams University (2006) B.S., Computer Systems, Ain Shams University (2000) His research focuses on sensor fusion , integrating data from heterogeneous sensors such as GNSS, IMU, Vision, Radar, and LiDAR under real-time constraints on embedded platforms like FPGAs and microcontrollers. He applies advanced signal processing, estimation theory, machine learning, and AI to solve challenges in state estimation, observability, fault tolerance, and concurrency management. Dr. Atia’s work has applications in intelligent vehicles, indoor navigation, mobile robotics, smart buildings, medical devices, and remote sensing . He leads a research lab focused on embedded multi-sensor systems, where students develop practical solutions for autonomous navigation and SLAM. His teaching emphasizes hands-on learning through labs and projects. He teaches undergraduate courses in computer systems, embedded systems, and signal processing, and a graduate course (SYSC 5807) on Advanced Topics in Computer Systems, specifically Sensor Fusion Systems . Scientific Awards: Alberta Innovate Association Award (2011) IEEE Excellence in PhD Research (2013) Mitacs Elevate Industrial Postdoc Award (2014) NSERC PDF Award (2015) Queen’s University Teaching Award (2016) Dr. Atia has advised graduate students such as Hamza Sadruddin and Alan Zhang , who have presented at IEEE Sensors and ION GNSS+ conferences, with Zhang winning Best Presentation. He has no listed grants in the provided text but has received prestigious fellowships. His research lab supports innovation in real-time embedded sensor fusion with links to industry and open-source tools.

• Magmatic Processes
• Hydrothermal Alteration
• Porphyry Systems
• +۳ مورد دیگر

Shaun Barker is an Associate Professor in the Department of Earth, Ocean & Atmospheric Sciences at the University of British Columbia. His research focuses on magmatic processes, hydrothermal alteration, and porphyry systems, particularly studying postsubduction magmatism and its economic implications. He supervises postdoctoral researchers such as Maria Alejandra Rodriguez Mustafa and has advised graduate theses on topics like the Dawson Range district's Late Cretaceous magmatism. Research interests include tectonomagmatic frameworks, fluid-rock interaction, and exploration methodologies using geochemical datasets and advanced analytical techniques. His work integrates field observations, geochemical analyses, and isotopic studies to unravel the genesis of ore deposits and geothermal systems. Key contributions include defining trace-element haloes around deposits, developing mineral mapping via µXRF, and applying machine learning to geological modeling. His studies span global regions like the Yukon, British Columbia, Nevada, and Peru, with a focus on Carlin-type gold, porphyry copper, and skarn systems.

Vincent Wadeمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۸ مورد دیگر

Professor Vincent Wade is a prominent academic and co-founder of the ADAPT SFI Research Centre, holding the Professorial Chair of Computer Science (established 1990) and a Personal Chair in Artificial Intelligence at Trinity College Dublin's School of Computer Science and Statistics. He co-directs the DREAL Centre for Research Training and leads ADAPT, a globally recognized centre for digital media technology and AI research. His work spans intelligent systems, personalisation, machine learning, and ethical AI applications in healthcare and education. Research interests include AI-driven personalisation, multimodal interaction, knowledge graphs, and ethical considerations in digital technologies. He has published over 350 peer-reviewed papers, earned the prestigious Provost Innovation Award (2018), and holds patents in personalisation technologies. He co-founded EmpowerTheUser, a TCD spin-out focused on simulation-based learning analytics. Major Achievements: 2018 Provost Innovation Award (Trinity College Dublin) 2010 European Language Label Award Fellow of Trinity College Dublin Over 350 scientific publications Key Contributions: Developed the ADELE corpus for social conversation analysis Pioneered cross-site personalisation frameworks Advanced adaptive e-learning systems through platforms like Slicepedia and AMASE His research bridges technical innovation with societal impact, addressing challenges in healthcare, education, and digital ethics.

Michael Friedlanderمشاهده پروفایل

استاد

• Convex Optimization
• Machine Learning
• First-Order Methods
• +۳ مورد دیگر

Michael Friedlander is a Professor of Computational Mathematics at the University of British Columbia (UBC), holding joint appointments in the Departments of Computer Science and Mathematics. He serves as Director of the UBC Institute of Applied Mathematics and is a Sauder School Distinguished Scholar. His research focuses on optimization theory, algorithms, and applications, particularly in convex optimization, first-order methods, and machine learning. Friedlander is a SIAM Fellow (2024) and has received multiple teaching awards, including the 2013 UBC CS Teaching Award. Education: PhD (2002) in Operations Research from Stanford University; BA (1993) in Physics from Cornell University. Research Interests: Friedlander’s work spans computational optimization, including first-order methods, gauge duality, federated learning, and applications in signal processing and quantum computing. His contributions include SPGL1, a widely used convex optimization solver, and foundational work on atomic decomposition and polar alignment in structured optimization. Key Contributions: His research bridges optimization with practical applications, such as compressed sensing and data science. Notable areas include dual methods for federated learning, low-rank spectral optimization, and algorithms for sparse recovery. He has advised numerous PhD students, including work on topics like gauge duality and federated learning contribution valuation. Awards & Honors: SIAM Fellow (2024) 2013 UBC CS Department Teaching Award CACS Outstanding Young Researcher Prize AI & Statistics 2009 Best Paper Award Advising & Grants: Supervised over 20 graduate students and postdocs. Active in grant-funded research, including projects on optimization algorithms and their industrial applications. Collaborates with institutions like Pacific Institute for Mathematical Sciences (PIMS) and the Data Science Institute at UBC. Labs & Affiliations: Affiliated with the Institute for Computing, Information and Cognitive Systems (ICICS), Data Science Institute, and Quantum Computing Research Cluster at UBC. Leads interdisciplinary efforts in applied mathematics and computational science.

Ilker Hacihalilogluمشاهده پروفایل

دانشیار

• Artificial Intelligence in Healthcare
• Medical Image Processing
• Machine Learning Applications
• +۸ مورد دیگر

Dr. Ilker Hacihaliloglu is an Associate Professor in the Department of Radiology and Medicine at the University of British Columbia's Faculty of Medicine. He holds a PhD from UBC (2010) and has held faculty positions at Rutgers University (2014-2021). His research focuses on applying machine learning to medical imaging, aiming to bridge clinical and engineering research for practical healthcare solutions. Key areas include AI-driven diagnostics, ultrasound imaging, and personalized medicine. Education: B.Sc. and M.Sc., Istanbul Technical University (2001, 2004) Ph.D., University of British Columbia (2010) Postdoctoral Fellow, Vancouver General Hospital (2010-2013) Research Interests: Artificial Intelligence in Medical Imaging Ultrasound-Based Diagnostics Image-Guided Surgery Computer-Aided Diagnostics for Orthopedics and Neurosurgery His work emphasizes explainable AI, standardization in machine learning metrics, and clinical translation of technologies. Scientific Contributions: Over 35 peer-reviewed articles (2019-2025) highlight innovations in medical image segmentation, AI-driven radiomics, and cross-modality imaging. Recent trends focus on synthetic data augmentation, diffusion models for medical imaging, and curriculum development in AI for healthcare. Grants and Advising: No explicit grants mentioned, but active mentorship in interdisciplinary research teams. Collaborations span clinicians, engineers, and computer scientists to address real-world healthcare challenges. Labs/Teams: Engaged in computational ultrasound and AI-driven diagnostics initiatives, though specific lab names are not provided in the text.