جستجوی استاد راهنمای دانشگاهی

Marc Parizeauمشاهده پروفایل

استاد

• Pattern Recognition
• Evolutionary Computation
• Neural Networks
• +۶ مورد دیگر

Marc Parizeau is a Professor at Université Laval, affiliated with the Faculty of Science and Engineering and the Department of Electrical and Computer Engineering. His office is located at PLT-1138-B, and he can be reached at (418) 656-2131 ext. 407912 or via email at marc.parizeau@gel.ulaval.ca. Academic Background: Ph.D., École Polytechnique de Montréal, 1992 M.Sc.A., École Polytechnique de Montréal, 1987 B.Eng., École Polytechnique de Montréal, 1984 His research focuses on Pattern Recognition, Evolutionary Computation, Neural Networks, 2D and 3D Computer Vision, and parallel and distributed systems . He integrates these areas to develop scalable computational models and tools for intelligent systems. His work bridges theoretical AI with practical software engineering for high-performance environments. His teaching includes courses such as Programmation parallèle et distribuée (GIF-4104) , Réseaux de neurones (GIF-21410) , and various algorithm and programming courses in Python and engineering. His recent publications reflect a strong trend in distributed evolutionary algorithms and concurrent programming frameworks, particularly using Python-based tools like DEAP and SCOOP, emphasizing scalability and real-world deployment. Scientific Leadership and Software Contributions: Director, Calcul Québec Creator, Distributed Evolutionary Algorithms in Python (DEAP) Creator, Scalable Concurrent Operations in Python (SCOOP) Contributor, Portable Agile Classes in C++ (PACC) Contributor, Open Beagle Marc Parizeau has supervised multiple collaborative projects and students, though specific names are not listed. He has secured research support through leadership roles and software development. His work is supported by institutional and provincial computing infrastructure initiatives. Conference Involvement: Organizing Committee, High Performance Computing Symposium (HPCS'08) International Workshop on Frontiers in Handwriting Recognition (IWFHR'02) International Conference on Pattern Recognition (ICPR'02) Vision Interface (VI'99) Conférence Internationale sur l'Écrit et le Document (CIFED'98) He leads the Computer Vision and Systems Laboratory at Université Laval, a research group focused on intelligent systems, machine learning, and high-performance computing applications in vision and optimization.

Malcolm Heywoodمشاهده پروفایل

استاد

• Genetic Programming
• Coevolution
• Reinforcement Learning
• +۱۲ مورد دیگر

Dr. Malcolm Heywood is a Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He leads the Network Information Management and Security (NIMS) Lab and is actively involved in research on genetic programming, coevolution, reinforcement learning, and big data analytics. His research interests span: Genetic Programming and Evolutionary Computation Coevolution and Competitive Learning Problem Decomposition and Hierarchical Models Streaming Data Analysis and Anomaly Detection Network Security and Insider Threat Detection Reinforcement Learning in Games (Atari, ViZDoom, Dota 2) Dr. Heywood's recent publications focus on emergent behaviors in reinforcement learning using Tangled Program Graphs (TPG), benchmarking genetic programming for streaming data, and applications in cybersecurity and computational finance. His work demonstrates a strong trend toward scalable, efficient evolutionary models for complex, real-world problems. His scientific awards include: Silver placed at Human-Competitive (Humies) Competition (2018) Best Paper at EuroGP (2017) Best Paper at DETA track, ACM GECCO (2017) Best Paper at RWA track, ACM GECCO (2018) Nomination for Best Paper at DETA track, ACM GECCO (2019) He has supervised numerous graduate students, including PhD and Master's candidates, many of whom have continued research in evolutionary computation. His lab has developed open-source code distributions for Tangled Program Graphs and Symbiotic Bid-Based GP. Dr. Heywood teaches courses in Computer Organization, Introduction to AI with Gaming Applications, and Genetic Algorithms and Programming.

• Machine Learning
• Surgical Research
• Biomedical Informatics
• +۴ مورد دیگر

Stephen Kelly is an Assistant Professor in the Department of Computing and Software at McMaster University. His interdisciplinary work spans surgical research methodology, biomedical informatics, and machine learning. Affiliation: McMaster University Roles: Faculty in Computing and Software, Surgical Research Collaborator Research Interests: Dr. Kelly’s work focuses on applying evolutionary computation and reinforcement learning to medical and educational challenges, such as detecting mind wandering via EEG and optimizing surgical training programs. His research bridges clinical practice and software innovation. Recent Publications: Trends include automated program synthesis, neural memory frameworks for RL, and evidence-based surgical education tools. Key areas: AI for healthcare, algorithmic design, and educational technology. Contact: Email: kellys32@mcmaster.ca

Jeff Cluneمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Neuroevolution
• +۵ مورد دیگر

Jeff Clune is a Professor in the Department of Computer Science at the University of British Columbia (UBC), Canada, and a Canada CIFAR AI Chair at the Vector Institute. He also serves as a Senior Research Advisor at DeepMind. His work focuses on advancing artificial intelligence through evolutionary computation, neuroevolution, and open-ended systems. Clune's research explores topics such as continual learning, generative models (e.g., Genie), and automated scientific discovery. He is known for developing algorithms like Go-Explore and pioneering methods in neuroevolution to create robust and adaptable AI systems. His academic affiliations include the UBC Faculty of Science and collaborations with industry leaders like DeepMind. Clune teaches courses on machine learning and data mining, emphasizing practical applications and theoretical foundations. His awards include the SIGEVO Impact Award and recognition for outstanding publications in AI research. Notable projects include the development of the Go-Explore algorithm and contributions to AI safety and ethical frameworks. Clune's work bridges biological inspiration with machine learning, aiming to create systems that evolve and learn like natural organisms. Research interests span AI safety, generative AI, robotics, and scalable learning algorithms. He has published extensively on topics like neural network dynamics, open-endedness, and automated experimentation. Current projects include enhancing AI's ability to self-improve and exploring ethical implications of advanced AI systems.

Howard M. Schwartzمشاهده پروفایل

استاد

• Adaptive and Learning Systems
• Multi-Robot Learning and Adaptation
• Cooperative Control of Autonomous Robots
• +۴ مورد دیگر

Howard M. Schwartz is a Professor at the Department of Systems and Computer Engineering, Carleton University, within the Faculty of Engineering and Design. He holds a Ph.D. from MIT and specializes in adaptive control systems, multi-robot learning, and reinforcement learning applications in autonomous systems. His work integrates machine learning with robotics, focusing on cooperative control, swarm intelligence, and real-time systems. Education: Ph.D. in Engineering, Massachusetts Institute of Technology (MIT) Research Interests: Adaptive and Learning Systems Multi-Robot Learning and Cooperation Reinforcement Learning for Autonomous Vehicles Control of Robot Manipulators Cooperative Multi-Agent Systems Advising & Research: Supervised over 40 graduate students, with notable projects in drone navigation, swarm robotics, and game-theoretic control. Collaborates with Ericsson on 5G-enabled autonomous systems in the Ericsson Carleton Partnership Program. Labs & Facilities: Ericsson 5G Autonomous Control Laboratory for multi-vehicle research. Swarm robotics experimentation facilities focusing on decentralized control and adaptive algorithms.

Judith Mankمشاهده پروفایل

استاد

• Evolutionary Genomics
• Sexual Dimorphism
• Sex Chromosome Evolution
• +۷ مورد دیگر

Dr. Judith Mank is a Professor and Canada 150 Research Chair in Evolutionary Genomics at the University of British Columbia (UBC), Department of Zoology. She leads the Mank Lab, focusing on evolutionary genomics, sexual dimorphism, and sex chromosome evolution. Her work integrates genomic, transcriptomic, and ecological approaches to study how selection shapes phenotypic diversity. Education: Ph.D. in Genetics, University of Georgia (2006) M.S. in Forest Resources, Pennsylvania State University (2001) B.A. in Anthropology, University of Florida (1997) Research Interests: Her lab investigates the genetic and genomic basis of sexual dimorphism, sex chromosome evolution, and dosage compensation. Key areas include: Genetic mechanisms underlying sexual conflict and adaptation Evolutionary dynamics of sex chromosomes (e.g., Y and W chromosomes) Role of transposable elements and epigenetics in genome evolution Behavioral genomics and social behavior in fish Awards & Honors: 2020 Honorary Doctorate, Uppsala University 2016 Royal Society Wolfson Fellowship 2013 Zoological Society of London Scientific Medal Labs & Collaborations: The Mank Lab is part of UBC’s Biodiversity Research Centre and collaborates with global institutions. They maintain state-of-the-art facilities for molecular genetics, including single-cell RNA-Seq and fish behavior experiments. Current projects involve guppies, willows, and other model organisms to study sex chromosome evolution and sexual selection.

• Insider Trading
• Financial Disclosure
• Stock Repurchases
• +۱ مورد دیگر

William McNally is a Professor at Wilfrid Laurier University's Lazaridis School of Business and Economics. His research focuses on insider trading, financial disclosure, and stock repurchases. He teaches finance pedagogy and explores interdisciplinary applications of machine learning in sports science and geophysics. McNally's recent work integrates physics-based models with deep learning for sports simulations (golf, hockey) and environmental systems. His articles emphasize practical computational solutions, from optimizing golf equipment to climate model calibration, reflecting a strong cross-disciplinary approach.