جستجوی استاد راهنمای دانشگاهی

Sivakumar Rathinamمشاهده پروفایل

استاد

• Motion Planning
• Autonomous Vehicles
• Collaborative Decision Making
• +۳ مورد دیگر

Dr. Sivakumar Rathinam is a Professor in the Department of Mechanical Engineering at Texas A&M University, affiliated with the College of Engineering and the Computer Science & Engineering department. He holds certifications as a Fellow of ASME (2021) and Senior Member of IEEE (2019). His research focuses on motion planning for autonomous vehicles, collaborative decision-making, combinatorial optimization, and vision-based control systems. He leads the Autonomy Lab, addressing challenges in multi-agent systems, path planning, and rural autonomous vehicle accessibility. Education: Ph.D., Civil Systems Engineering, University of California, Berkeley (2007) M.S., Electrical Engineering & Computer Science, UC Berkeley (2006) M.S., Mechanical Engineering, Texas A&M University (2001) B.Tech., Mechanical Engineering, Indian Institute of Technology Madras (1999) Research Highlights: Dr. Rathinam's work spans autonomous vehicle navigation, UAV coordination, and sensor fusion for adverse conditions. His lab develops algorithms for multi-agent pathfinding and persistent monitoring missions. Recent efforts include rural road detection datasets (R2D2) and thermal/LIDAR sensor fusion for safety in challenging environments. Awards: Outstanding Faculty Contribution Award (2021) Best Paper Runner-Up, ICAPS (2021) Teaching Excellence Award (2012) Labs & Teams: Directs the Autonomy Lab, collaborating with industry partners through the Mechanical Engineering Industry Advisory Council. Active in NSF-funded projects on equitable rural autonomy and multi-UAV recharging frameworks.

Lingzhou Xueمشاهده پروفایل

استاد

• High-Dimensional Statistics
• Nonparametric Statistics
• Statistical Learning
• +۹ مورد دیگر

Lingzhou Xue is a Professor of Statistics at The Pennsylvania State University, affiliated with the Eberly College of Science. He holds dual roles as a faculty member and the Associate Director of the National Institute of Statistical Sciences (NISS). His research focuses on high-dimensional statistics, nonparametric methods, statistical learning, and optimization, with applications in biomedical, environmental, and social sciences. He leads the SLDM (Statistical Learning and Data Mining) Lab and MDS (Microbiome Data Science) Lab. Education: B.Sc. in Statistics from Peking University (2008), Ph.D. in Statistics from the University of Minnesota (2012), postdoctoral training at Princeton University (2012–2013). Professional roles include Associate Editorships at the Journal of the American Statistical Association, Annals of Applied Statistics, and ACM Transactions on Probabilistic Machine Learning. Research Interests: Federated learning, causal inference, graphical models, reinforcement learning, optimal transport, and large-scale optimization. Recent work emphasizes theoretical guarantees for sparse PCA and federated Q-learning algorithms. Awards: IMS Fellow (2024), ASA Fellow (2023), COPSS Emerging Leader Award (2021), and Bernoulli Society New Researcher Award (2019). He has mentored 17 Ph.D. students and 3 postdocs, with four students securing tenure-track faculty positions. Service: Organized multiple NISS writing workshops, co-chaired the Ingram Olkin Statistics Serving Society Forum on Gun Violence, and contributed to the ASA whitepaper 'Discovery with Data' (2014).

• Digitalization
• Control Systems
• Optimization
• +۴ مورد دیگر

Guoyuan Li is a Professor at the Department of Marine Operations and Engineering Technology at NTNU, affiliated with the Faculty of Engineering. His research focuses on digitalization, control systems, robotics, maritime operations, and human-machine interaction. He holds IEEE Senior Membership (2019) and editorial roles in IEEE Journal of Oceanic Engineering and IEEE Transactions on Intelligent Transportation Systems . Education includes a Ph.D. in Computer Science from Hamburg University (2013), M.S. and B.S. from Chongqing University (2009/2006). Key projects include EU-funded RoboSAPIENS (robotic adaptation), Digital Twin for Green Ship Operations, and TwinShip (vessel lifecycle services). Recent awards include 2024 IEEE Robotics & Automation Magazine Best Paper and multiple IEEE conference recognitions. His work spans AI-driven maritime safety, digital twin applications, and autonomous systems. Visit Intelligent Systems Lab for more.

• Robotics
• Machine Learning
• Computer Vision
• +۴ مورد دیگر

Kuan Fang is an Assistant Professor of Computer Science at Cornell University, specializing in robotics, machine learning, and computer vision. His research focuses on enabling robots to perform complex tasks in unstructured environments through deep learning-based perception and control systems. Previously, he was a postdoc at UC Berkeley under Sergey Levine and earned his Ph.D. and M.S. from Stanford University under Fei-Fei Li and Silvio Savarese, with a B.S. from Tsinghua University. He has also worked at RAI Institute, Google Brain, Google X Robotics, and Microsoft Research Asia. Education: Ph.D. & M.S., Computer Science, Stanford University Bachelor's Degree, Tsinghua University Research Interests: Robot manipulation and control Reinforcement learning and policy optimization Robot perception and vision-language integration Generalization in robotics across tasks, environments, and robots Open-world robotic systems leveraging large-scale data Teaching: CS 6758: Deep Learning for Robotics (Fall 2024) CS 4756: Robot Learning (Spring 2025) Lab & Collaborations: His lab at Cornell develops scalable algorithms and systems for robotic perception and control, emphasizing data-driven methods. Notable work includes ReLIC for interlimb coordination, GLIDE for bimanual manipulation, and TRA for compositional task execution. He collaborates with institutions like Boston Dynamics AI Institute and UC Berkeley.

Francesca Pariseمشاهده پروفایل

استادیار

• Multi-Agent Systems
• Network Theory
• Control Systems
• +۶ مورد دیگر

Francesca Parise is an Assistant Professor in the School of Electrical and Computer Engineering at Cornell University. Her research focuses on multi-agent systems, particularly analyzing large networks of autonomous decision-makers in transportation, finance, and social systems, employing tools from control theory, game theory, and optimization. She holds a PhD from ETH Zurich and was a Postdoctoral Research Fellow at MIT's Laboratory for Information and Decision Systems under Prof. Asuman Ozdaglar. Her work bridges theoretical foundations with practical applications in networked systems and interventions. Education: Bachelor's and Master's from University of Padua, Italy PhD in Control Engineering from ETH Zurich (2016) Her research interests span game-theoretic models for network analysis, learning dynamics in time-varying systems, and optimal intervention strategies. Key themes include synchronization in power grids, epidemic control via testing, and dynamic resource allocation. Recent work emphasizes graphon-based frameworks for large-scale network games and computational methods for equilibrium analysis. She explores both theoretical guarantees and real-world applications in transportation and energy systems. Publications highlight contributions to networked game theory, stochastic systems, and control applications, with a focus on scalable solutions for complex systems. Her work often intersects with interdisciplinary challenges in systems biology and socio-technical networks. She currently holds an office at Frank H. T. Rhodes Hall, Cornell University.

• Control Engineering
• Networked Control Systems
• Multi-Agent Systems
• +۶ مورد دیگر

Qinglong Han is the Pro-Vice Chancellor (Research Quality) and a Distinguished Professor at Swinburne University of Technology in Melbourne, Australia. He previously held academic and leadership roles at Griffith University and Central Queensland University. His research focuses on networked control systems, multi-agent systems, time-delay systems, smart grids, and unmanned vehicles. He is a Fellow of IEEE, IFAC, and multiple other institutions, and has received prestigious awards including the IEEE Dr.-Ing. Eugene Mittelmann Achievement Award (2024) and Norbert Wiener Award (2021). His research interests span control engineering, applied mathematics, and artificial intelligence. Notable contributions include secure platooning control for autonomous vehicles, resilient control under cyber-physical threats, and optimization of industrial systems. He leads editorial roles in journals like IEEE Transactions on Industrial Informatics and IEEE/CAA Journal of Automatica Sinica. His work emphasizes interdisciplinary applications in smart grids, robotics, and industrial automation. Dr. Han has supervised numerous PhD students in areas like networked control and vehicle dynamics. He has secured grants from ARC and NSFC for projects on networked control systems and renewable energy integration. His achievements include multiple best paper awards and recognition as a Clarivate Highly Cited Researcher in Engineering and Computer Science.

Vlachos Kostasمشاهده پروفایل

استادیار

• Robotics
• Control of Robotic Mechanisms
• Microrobotics
• +۷ مورد دیگر

Kostas Vlachos is an Assistant Professor in the Department of Computer Science and Engineering at the University of Ioannina, Greece. He has been in this position since 2014, following prior teaching roles at the University of Thessaly (2007–2013). He is a member of the Information Processing and Analysis (I.P.AN.) research group and actively supervises PhD, MSc, and diploma students in robotics and control systems. PhD, School of Mechanical Engineering, National Technical University of Athens, 2004 MSc, Interdepartmental Postgraduate Program in Automation Systems, National Technical University of Athens, 2000 Diploma in Electrical Engineering, Technical University of Dresden, Germany, 1993 His research focuses on robotics and control, with emphasis on microrobotics , haptic mechanisms , medical simulators , and autonomous navigation . He has made significant contributions to over-actuated marine platforms, reinforcement learning for navigation, and tactile robotic systems. His work bridges mechanical engineering and computer science, particularly in intelligent robotic control. The 15 most recent publications highlight a strong trend in autonomous marine robotics , multi-agent reinforcement learning , and intelligent control systems . Key themes include energy-efficient control, obstacle avoidance, sensor fusion, and learning-based navigation. The research spans from theoretical control design to real-world implementation in unmanned surface vehicles and microrobots. Best Student Paper Award, 9th Hellenic Conference on AI (SETN 2016) Vlachos has supervised over 30 students at various levels and has participated in multiple national and European research projects in robotics and automatic control. His teaching includes courses such as Computational Mathematics, Robotics, and Robotic Systems. He collaborates extensively with researchers like E. Papadopoulos and K. Blekas. He leads research within the Information Processing and Analysis (I.P.AN.) group, focusing on intelligent perception and control of robotic systems. His lab works on mobile manipulators, haptic devices, mini-robots, and marine platforms, integrating simulation (ROS/Gazebo) with real-world experimentation.

Gianluca Aloiمشاهده پروفایل

دانشیار

• Wireless Networks
• Cellular Networks
• Sensor Networks
• +۷ مورد دیگر

Gianluca Aloi serves as an Associate Professor in Telecommunications (IINF-03/A) at the Department of Computer Engineering, Modeling, Electronics and Systems (DIMES) of the University of Calabria, Italy. He holds the position of scientific director for the Telecommunications and Information Theory for Advanced Networking Laboratory (TITAN Lab.). Dr. Aloi earned his PhD in Systems Engineering and Computer Science from the University of Calabria in 2003 and became a University Researcher in Telecommunications (ING-INF/03) in 2004 before advancing to his current academic rank. His research expertise spans wireless networks, cellular networks, sensor networks, Internet of Things systems and their interoperability, management of resources and services in the Cloud/Edge and IoT (CEI) Continuum, and management and orchestration of network resources using Artificial Intelligence. His work particularly focuses on UAV-assisted IoT systems for industrial applications, geological hazard monitoring, and maritime environments. Analysis of Dr. Aloi's recent publications (2023-2025) reveals a strong emphasis on applying reinforcement learning and deep learning techniques to solve complex networking challenges. His research shows a clear trajectory toward developing intelligent network architectures that optimize data collection, improve system resilience, and enhance resource management across the edge-to-cloud continuum. Key application areas include smart factories, disaster monitoring, and urban vehicular systems. Dr. Aloi teaches courses including Fundamentals of Telecommunications Networks and Telecommunications Networks for both Electronic Engineering and Computer Engineering programs. He maintains regular reception hours every Tuesday from 9am to 11am or by appointment via email. As scientific director of TITAN Lab, Dr. Aloi leads research initiatives focused on advanced networking technologies, with particular emphasis on developing solutions for next-generation communication systems that integrate artificial intelligence with traditional networking paradigms to address real-world challenges in telecommunications and IoT applications.

Lydia Tapiaمشاهده پروفایل

استاد

• Robotic Motion Planning
• Computational Biology
• Machine Learning
• +۷ مورد دیگر

Lydia Tapia is a Professor and the Department Chair of the Department of Computer Science at the University of New Mexico, School of Engineering. She is a leading researcher in motion planning, robotics, and computational biology, with a strong commitment to diversity and leadership in computing. Education: PhD in Computer Science, Texas A&M University, 2009 BS in Computer Science, Tulane University, 1998 Her research focuses on the development of efficient algorithms for high-dimensional motion simulation in both robotics and molecular systems. She applies adaptive learning techniques to solve computationally intensive problems in robotic navigation, molecular docking, and antibody aggregation. Her work bridges robotics and computational biology, with applications in immunology, neurodegenerative diseases, and human-automation systems. Her recent publications highlight trends in reinforcement learning for motion planning, multi-agent navigation, human-robot collaboration, and the use of deep learning for geometric prediction. These works frequently appear in top venues such as IROS, ICRA, IEEE RA-L, and MIG, reflecting her interdisciplinary impact. Scientific Awards: 2016 Denice Denton Emerging Leader ABIE Award 2016 NSF CAREER Award 2017 CRA-W Borg Early Career Award She actively advises students and postdocs, leads the Tapia Lab, and contributes to major research centers including the Adaptive Motion Planning Research Group, the Center for Evolutionary & Theoretical Immunology, and the New Mexico Center for the Spatiotemporal Modeling of Cell Signaling. She has secured significant research funding and has been instrumental in organizing workshops and initiatives to promote diversity in robotics and computing. Her lab, the Tapia Lab, focuses on complex robotic systems, including molecular robots like proteins, using adaptive learning to solve planning problems. Current projects include human-automation collaboration, resilient learning, robotic task learning, and molecular docking games that leverage human intuition through crowdsourcing.

• Machine Learning
• Artificial Intelligence
• Reinforcement Learning
• +۴ مورد دیگر

Joelle Pineau is a Professor at the School of Computer Science, McGill University, Montreal, Canada. Her work spans Machine Learning , Artificial Intelligence , and Reinforcement Learning , with significant contributions to causal inference , ethics in AI , and continual learning . She has led initiatives like the NeurIPS 2019 Reproducibility Program and co-authored over 315 publications. Key Research Themes : Algorithmic fairness, robust policy learning, interpretable models, and AI ethics Recent Trends : Focus on uncertainty-aware systems, multi-task learning, and societal implications of foundation models Scientific Awards : NeurIPS 2019 Reproducibility Program Leadership Advancements in AI Ethics Review Practices Her work intersects Computer Science , Biomedical Research , and Societal Policy , with applications in healthcare, robotics, and knowledge graphs.

• Algorithmic Sequential Decision-Making
• Robust AI
• Safe AI
• +۱۱ مورد دیگر

Ilija Bogunovic is an Assistant Professor (UK Lecturer) in the Department of Electronic and Electrical Engineering at University College London (UCL), Faculty of Engineering Sciences. His research focuses on algorithmic sequential decision-making for robust, reliable, and safe artificial intelligence, with applications in large language model alignment, reinforcement learning, and human feedback integration. His research interests lie at the intersection of machine learning, optimization, and decision theory. He investigates robustness in Bayesian optimization, bandits, and reinforcement learning under adversarial attacks, model misspecification, and distributional shifts. His work spans both theoretical foundations and real-world applications in energy systems, mobility, and AI safety. A central theme is developing algorithms that are provably robust and efficient in uncertain and potentially corrupted environments. The recent publications highlight a strong trend in robust decision-making, particularly in reinforcement learning and preference optimization. Key themes include adversarial robustness in large models, distributional robustness in RL, safe multi-agent systems, and robust Bayesian optimization. The work frequently involves theoretical analysis of regret and sample complexity, combined with practical algorithm design for high-impact applications. Scientific Awards: Google Research Scholar Program Award (Machine Learning & Data Mining), 2023 EPSRC New Investigator Award, 2023 Ilija Bogunovic actively supervises PhD and MSc students, with several of his advisees leading papers accepted to top venues like NeurIPS and ICLR. He has secured competitive research grants, including the EPSRC New Investigator Award, to support his work on robust decision-making. He is building a research team focused on robust and safe AI. He leads a research team and has initiated an online reading group on modern adaptive experimental design and active learning, fostering a collaborative research environment.

• Artificial Intelligence
• Machine Learning
• Artificial Neural Networks
• +۱۹ مورد دیگر

Professor Stephan Chalup is a leading academic in Artificial Intelligence and Machine Learning at the University of Newcastle , affiliated with the School of Information and Physical Sciences and the Data Science and Statistics department. He leads the Interdisciplinary Machine Learning Research Group (IMLRG) and the Newcastle Robotics Lab , where his team has achieved global recognition, including two RoboCup world championships. PhD in Computing Science , Queensland University of Technology (2002) Diplom in Mathematics with Neuroscience , University of Heidelberg His research focuses on artificial neural networks , deep learning , and high-dimensional data analysis , with applications in robotics, computer vision, medical imaging, and architectural analysis. He investigates how biological neural systems inspire robust AI models, particularly in topological data analysis and 4D vision . The recent publications highlight a strong trend in topological and geometric machine learning , with a focus on 4D data analysis , multi-agent reinforcement learning , and robot perception . His work bridges theoretical AI with practical industry solutions in transport, healthcare, and robotics. RoboCup World Champion (2008, 2006) Leadership Excellence, CESE (2024) Supervision Research Excellence Award (2015) Multiple Best Student Paper Awards (2019, 2018, 2011) Chalup has supervised numerous students and led significant research projects, including the ARC Discovery Project on estimating topology of low-dimensional data. His lab fosters interdisciplinary collaboration and innovation, with alumni working in top global tech roles. He is an active keynote speaker and program committee member in major AI conferences. His labs, including the Newcastle Robotics Lab , are equipped with state-of-the-art robots and computing systems, supporting cutting-edge research in humanoid robotics, autonomous navigation, and AI-driven data analysis.

Tom Townsendمشاهده پروفایل

مدرس ارشد

• Cyber Security
• IT Infrastructure
• Data Centers
• +۷ مورد دیگر

Dr. Tom Townsend serves as a Senior Lecturer at UNSW Canberra within the School of Professional Studies. With 20 years of experience in the higher education sector and federal government consulting, he bridges professional IT leadership with academic instruction in cybersecurity. His educational background includes doctoral research taking a Design Science approach to IT infrastructure and data centers within the context of services availability. His research methodology combines qualitative and quantitative approaches including modeling, scenarios, interviews, and thematic analysis. Townsend's research interests span Cyber Security, Artificial Intelligence (particularly Reinforcement Learning), Serious Games, Wargaming, and IT Infrastructure. His recent work bridges AI, Simulation, and Military Wargaming through interdisciplinary projects connecting Computer Science with Design Science. His publication record demonstrates consistent contributions to fields including data center efficiency (2017-2019) and AI applications in board games (2021-2023), showing an evolving research trajectory from infrastructure to AI applications. As an Education Focused academic, Townsend teaches in the Graduate Certificate in Cyber Security Leadership and Master of Cyber Security (Online) programs, with particular interest in the intersection of Computer Science, Social Sciences, and Management within cybersecurity. Professional highlights include designing recovery programs after cyber incidents at Australian National University, developing strategies for universities and federal government departments, and leading digital transformation programs.

Maryam Kamgarpourمشاهده پروفایل

استادیار

• Control Theory
• Game Theory
• Mechanism Design
• +۶ مورد دیگر

Maryam Kamgarpour is a Tenure Track Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL), School of Engineering. She previously held faculty positions at the University of British Columbia and ETH Zürich. Her work bridges stochastic control , multiagent learning , and game theory , focusing on safety-critical systems. Education: PhD in Engineering from UC Berkeley, BSc in Applied Science from University of Waterloo. Research Interests: Control under uncertainty, game theory, mechanism design, mixed-integer optimization, and applications to transportation, robotics, power grids, and healthcare. Her recent publications emphasize safe reinforcement learning , multirobot coordination , and stochastic trajectory planning , with applications to aircraft navigation and energy systems. She has received the European Union ERC Starting Grant, NASA High Potential Individual Award, and IEEE Transactions on Control of Network Systems Outstanding Paper Award. Scientific Awards: ERC Starting Grant (2016-2021) NASA High Potential Individual Award (2010) NASA Excellence in Publication Award IEEE Outstanding Paper Award (2022) PhD Students: Jordan Philip Christopher Maddux Anna Maria Ni Tingting Ren Kai Salizzoni Giulio Schlaginhaufen Andreas Vaishampayan Saurabh Dilip Vallat Gabriel Rémi Former EPFL student: Guo Baiwei

Christopher Palمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Computer Vision
• Pattern Recognition
• +۶ مورد دیگر

Christopher Pal is a Full Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. With a Ph.D. from the University of Waterloo, he has held academic positions at the University of Rochester and the University of Toronto, and industry roles at Interval Research and Microsoft Research's Interactive Visual Media Group. Fields of Expertise: Artificial Intelligence, Computer Vision, Pattern Recognition, Machine Learning, and Natural Language Processing Affiliations: CIFAR Chair in Artificial Intelligence, Institute for Data Valorization (IVADO) Member His research focuses on deep learning applications in visual question answering , medical image segmentation , and generative models . Recent work involves multimodal data analysis for climate modeling and vision-language systems for code generation. Key projects include CarbonSense for climate flux modeling and GeoCoder for geometry problem-solving AI. His 15 most recent publications (2023-2025) span topics from diffusion models to multi-agent systems , with emphasis on video generation , 3D animation , and environmental applications . Scientific recognition includes: CIFAR Chair in Artificial Intelligence IVADO Institute Membership Top-2% cited researcher (2021) He has supervised 22 Ph.D. and Master's students, with recent graduates working on generative AI , reinforcement learning , and medical imaging . Current research grants include MITACS-funded projects in software engineering agents and drone imagery analysis for tropical forest conservation.