جستجوی استاد راهنمای دانشگاهی

Dinesh Manochaمشاهده پروفایل

استاد

• Computer Graphics
• Robotics
• Virtual Reality
• +۴ مورد دیگر

Dinesh Manocha is a Distinguished University Professor of Computer Science at the University of Maryland, with joint appointments in the Department of Electrical and Computer Engineering and the University of Maryland Institute for Advanced Computer Studies (UMIACS). He is also affiliated with the Maryland Robotics Center and the Institute for Systems Research. His educational background includes a Ph.D. in Computer Science from the University of California at Berkeley (1992) and a B. Tech in Computer Science and Engineering from the Indian Institute of Technology, Delhi, India (1987). Professor Manocha's research spans multiple domains with significant emphasis on: Computer Graphics and Visualization Robotics and Motion Planning Virtual and Augmented Reality Systems Geometric Computing Algorithms AI Applications for Autonomous Systems High Performance Computing His extensive publication record shows consistent innovation in multi-agent navigation, collision avoidance algorithms, and applications in virtual environments. Recent work focuses on trajectory prediction for autonomous vehicles and physics-based simulation for immersive experiences, with algorithms integrated into industry-standard systems like ROS (Robot Operating System). Among his numerous honors, Professor Manocha is recognized as: ACM, IEEE, AAAS, and AAAI Fellow Member of the IEEE VGTC Virtual Reality Academy Recipient of the Pierre Bézier Award from the Solid Modeling Association University of Maryland Distinguished University Professor Multiple best paper awards across premier conferences He has supervised 54 PhD students throughout his career and currently advises numerous graduate researchers. His research has attracted significant funding from NSF, Google, Amazon, Facebook, and industry partners. Notably, he co-founded Impulsonic, a company developing physics-based audio simulation technologies acquired by Valve Corporation in 2016. Professor Manocha leads the GAMMA research group, which continues to advance geometric algorithms with applications across multiple disciplines.

Giorgia Ramponiمشاهده پروفایل

استادیار

• Machine Learning
• Mathematical Modelling
• Reinforcement Learning
• +۳ مورد دیگر

Giorgia Ramponi is an Assistant Professor with Tenure Track at the Faculty of Business, Economics and Informatics at the University of Zurich. She is also an affiliated professor at the ETH AI Center and the Data Science and AI, Computer Science and Engineering department at Chalmers University of Technology. Her educational background includes a Ph.D. in Information Technology from Politecnico di Milano (completed June 2021 with honors), advised by Marcello Restelli, and a Master of Science in Computer Science with Honours Programme (110/110 cum laude) from la Sapienza (July 2017), advised by Flavio Chierichetti and Alessandro Panconesi. Dr. Ramponi's research focuses on machine learning and mathematical modeling, with particular emphasis on reinforcement learning and multiagent learning. Her work bridges theoretical foundations with practical applications, exploring how learning algorithms can make optimal decisions in complex environments. She has made significant contributions to areas including inverse reinforcement learning, multi-agent systems, constrained Markov decision processes, and human-AI interaction through preference learning. Her recent publications demonstrate a strong trend toward addressing fundamental challenges in reinforcement learning, particularly in multi-agent settings, constrained optimization, and learning from human feedback. Her work combines theoretical rigor with practical applications across robotics, economics, and decision-making systems. Hassler Research Grant for "Unified Feedback Integration Framework for Reinforcement Learning" Dr. Ramponi actively contributes to the academic community through conference participation, invited lectures (including at the Mediterranean Machine Learning Summer School), and teaching. She designed and taught the "Data Science and Machine Learning" course for the ETH-Ashesi Master program. She is also a member of the ELLIS community, which connects excellence in AI research across Europe. Her research group focuses on developing frameworks for reinforcement learning with various feedback types, including preferences, rewards, and demonstrations. The group aims to advance the theoretical understanding of learning algorithms while addressing practical challenges in real-world applications.

Haipeng Luoمشاهده پروفایل

دانشیار

• Machine Learning
• Online Learning
• Reinforcement Learning
• +۳ مورد دیگر

Haipeng Luo is an Associate Professor at the Thomas Lord Department of Computer Science, University of Southern California, holding the IBM Early Career Chair. He previously worked as a Postdoctoral Researcher at Microsoft Research, NYC, and has held visiting roles at Google and Amazon. His research focuses on developing practical machine learning algorithms with strong theoretical guarantees, particularly in online learning, bandit problems, reinforcement learning, and game theory. PhD in Computer Science, Princeton University (2011–2016) BSc in Computer Science, Peking University (2007–2011) His work spans adversarial and stochastic environments, addressing challenges in reinforcement learning, game dynamics, calibration, and omniprediction. Recent publications highlight advancements in regret minimization, game equilibrium computation, and robust optimization frameworks. Key contributions include algorithms for zero-sum games, bandit problems with feedback graphs, and theoretical analyses of convergence properties in multi-agent systems. Scientific accolades include Best Paper Awards at COLT 2021, COLT 2018, NeurIPS 2015, and ICML 2015. He has received prestigious grants such as the NSF CAREER Award (2020), Google Faculty Research Award (2020), and NSF CRII Award (2018). His students have secured academic and industry positions, and he actively teaches graduate courses in machine learning and online optimization.

Timothy Menziesمشاهده پروفایل

استاد

• Artificial Intelligence
• Software Engineering
• Data Science
• +۵ مورد دیگر

Timothy Menzies is a full Professor in the Department of Computer Science at North Carolina State University's College of Engineering. He serves as the director of the Irrational Research lab (mad scientists r'us) and holds editorial positions as editor-in-chief of the Automated Software Engineering journal and associate editor for IEEE Transactions on Software Engineering. With over 300 publications and more than 24,000 citations, Menzies is a globally recognized leader in software engineering research. Menzies' research focuses on developing computer systems that make optimal decisions with minimal data, specializing in artificial intelligence, intelligent agents, data sciences, analytics, and software engineering. His pioneering work in data-driven, explainable, and minimal AI for software systems has redefined defect prediction, effort estimation, and multi-objective optimization. He is particularly known for his contributions to empirical software engineering, emphasizing transparency and reproducibility. As the co-creator of the PROMISE repository, he helped establish modern empirical software engineering by demonstrating that small, interpretable AI models can outperform larger, more complex ones. Menzies' recent publications reveal several key trends in his research: a growing emphasis on ethical considerations in AI deployment, particularly in sensitive domains like legal systems; continued innovation in software analytics with a focus on hyperparameter optimization tailored specifically for software engineering tasks; exploration of causal relationships in software analytics; and development of techniques that work effectively with limited data, including landscape analysis, surrogate learning, and active learning approaches. Mining Software Repositories Foundational Contribution Award (2017) Carol Miller Graduate Lecturer Award (2016) IBM Faculty Award (2016, 2017) ACM Fellow (2025) ASE Fellow (2024) IEEE Fellow Professor Menzies has advised 24 Ph.D. students throughout his career, with recent completions including Andre Motta (April 2025) and Xueqi Yang (October 2024). His research has secured over $19 million in funding from prestigious agencies including NSF, DARPA, and NASA, as well as industry partners like Meta, Microsoft, and IBM. Current grants focus on improving machine learning model efficiency, adapting empirical software engineering methods to computational science, vulnerability detection, and software analytics at scale using transfer learning across 10,000+ GitHub projects. Menzies has developed innovative approaches to help developers navigate the challenges of AI implementation while maintaining ethical standards and practical effectiveness. As director of the Irrational Research lab, Menzies leads a team focused on creating AI tools that are not only intelligent but also fair, transparent, and trustworthy. The lab's work emphasizes practical applications of AI in software engineering while addressing the human factors involved in developer-AI collaboration. Current projects include developing methods for better fuzzing with L3harris, improving vulnerability detection through smart pruning techniques, and creating AI platforms for workforce empowerment through credential gap diagnostics.

Isaac Lageمشاهده پروفایل

استادیار

• Interactive Optimization
• Machine Learning
• Interpretability in Machine Learning
• +۵ مورد دیگر

Isaac Lage is an Assistant Professor of Computer Science at Colby College since July 2023, specializing in interactive optimization methods for sociotechnical machine learning applications. Their work bridges technical rigor with societal impact, emphasizing accessibility in educational settings and algorithmic accountability. Harvard University PhD in Computer Science (NSF GRFP Fellow) Microsoft Research Intern (Adaptive Systems and Interaction Group) Research Software Engineer at MIT/NYU with David Sontag Research focuses on interpretable machine learning , human-AI collaboration , and healthcare equity analysis . Current projects explore sociotechnical implications of computing systems through EHR data patterns , fairness in predictive models , and user-driven interpretability frameworks . Recent publications show trends in explainable AI (2020-2022), with subfields including clinical decision support , policy summarization , and uncertainty communication . Key themes: healthcare disparities , robust interpretability , and human-in-the-loop learning . Scientific Awards: NSF GRFP Fellowship NeurIPS Spotlight Presentation (2018) AAAI HCOMP Honorable Mention (2019) As a Pedagogy Fellow at Harvard SEAS (2022-23), they contributed to curriculum design for CS 152 and CS 231 at Colby. Also earned a Teaching Certificate from Harvard's Derek Bok Center (Spring 2023).

• Distributed Signal Processing
• Wireless Communications
• Machine Learning
• +۳ مورد دیگر

Dr. Erma Perenda serves as Professor and Chair of Distributed Signal Processing at RWTH Aachen University, Germany, leading research within the Department of Distributed Signal Processing. Her contact details include email perenda@dsp.rwth-aachen.de and phone +49 241 80-27879, with office location at Kopernikusstraße 16, 52074 Aachen in the ICT Cubes facility. Her research spans: Distributed Signal Processing Wireless Communications Machine Learning (Deep Reinforcement Learning, Federated Learning) Modulation Classification AI-driven Network Optimization She focuses on solving real-world challenges in wireless systems including hardware impairments, channel variations, and energy efficiency through advanced AI techniques. Analysis of her 2018-2024 publications reveals consistent innovation in applying multi-agent deep reinforcement learning to wireless power allocation, developing robust modulation classification methods resilient to channel impairments, and implementing federated learning for industrial edge computing. Her work bridges theoretical machine learning with practical wireless communication constraints. Scientific Awards: No awards documented in available sources Advising and Grants: No student advisees or grant information provided Labs and Teams: Leads Distributed Signal Processing research group at RWTH Aachen University Based in ICT Cubes building focusing on wireless AI systems

Daniel J. Stilwellمشاهده پروفایل

استاد

• Autonomous Underwater Vehicles (AUVs)
• Marine Robotics
• Control Systems
• +۷ مورد دیگر

Daniel J. Stilwell is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Polytechnic Institute and State University (Virginia Tech), and Co-Director of the Center for Marine Autonomy and Robotics. He holds affiliations including the Seale Coastal Observatory Faculty Fellow role. His research focuses on autonomous underwater vehicles (AUVs), marine robotics, control systems, and sensor networks. He earned his Ph.D. in Electrical Engineering from Johns Hopkins University (1999), M.S. from Virginia Tech (1993), and B.S. in Computer Engineering from the University of Massachusetts (1991). His notable contributions include advancements in AUV control, underwater acoustic communication, multi-agent systems, and sensor network optimization. Key projects include the "Unconventional Marine Platforms" funded by the Office of Naval Research and collaborative subsea mapping initiatives. His work bridges theoretical control systems with practical robotic applications in marine environments. Dr. Stilwell has received prestigious awards such as the NSF CAREER Award and ONR Young Investigator Program Award. His research emphasizes robust control strategies, adaptive systems, and decentralized learning algorithms. He leads efforts in experimental validation of AUV control systems and underwater sensor networks, contributing to both academic and military applications.

Bradley Hayesمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Robotics
• Human-Robot Interaction
• +۵ مورد دیگر

Bradley Hayes is an Associate Professor in the Department of Computer Science at the University of Colorado Boulder, affiliated with the College of Engineering and Applied Science. He leads the Collaborative AI and Robotics (CAIRO) Lab, focusing on creating autonomous robots that collaborate effectively with humans through advances in explainable AI, machine learning, and human-robot interaction. His prior research includes foundational work at MIT's Interactive Robotics Group and Yale's Social Robotics Lab. Research interests span Explainable AI, Learning from Demonstration, Hierarchical Reinforcement Learning, Computer Vision, Natural Language Processing, and Cognitive Science. His work emphasizes making human-robot teams more efficient and safe through innovations like emotionally expressive robotic motion, socially aware navigation, and AR-based collaboration tools. Key contributions include techniques for robust robotic exploration, generative occupancy mapping, and systems for improving human trust through predictable robot behavior. His work has been applied to teleoperation training, surgical assistance, and space exploration scenarios. Grants and partnerships support development of assistive robotic canes and AR interfaces for collaborative tasks. Lab activities emphasize translating theoretical advancements into practical systems through close collaboration between researchers, engineers, and end-users. Education efforts include developing foundational robotics curricula addressing autonomy, perception, and control systems.

Hoda Eldardiryمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۶ مورد دیگر

Hoda Eldardiry is an Associate Professor in the Department of Computer Science at Virginia Polytechnic Institute and State University (Virginia Tech), and Director of the Machine Learning Laboratory. Her research focuses on artificial intelligence, machine learning, data mining, and their applications in policy, ethics, and complex systems analysis. Education: Ph.D., Computer Science, Purdue University M.S., Computer Science, Purdue University B.E., Computer and Systems Engineering, Alexandria University, Egypt Research Interests: Eldardiry’s work spans machine learning modeling (e.g., hypergraph neural networks, time-series forecasting), AI ethics and policy education, and interdisciplinary applications in transportation, healthcare, and cybersecurity. She emphasizes socially responsible AI development through curriculum design and policy frameworks. Recent Research Themes: Her 2025 publications highlight advancements in graph-based learning, policy-aware AI education modules, and novel techniques in multi-modal data analysis. Key areas include zero-shot learning, sparse control systems optimization, and collaborative machine learning frameworks. Labs & Teams: Directs the Machine Learning Laboratory at Virginia Tech, fostering innovation in ethical AI systems and data-driven decision-making.

Hao Zhangمشاهده پروفایل

دانشیار

• Robotics
• Artificial Intelligence
• Computer Vision
• +۶ مورد دیگر

Hao Zhang is an Associate Professor in the Department of Computer Science at the Manning College of Information and Computer Sciences (CICS), University of Massachusetts Amherst. He directs the Human-Centered Robotics Laboratory (HCRLab), focusing on lifelong collaborative autonomy, robot adaptation, and human-robot teaming. His research integrates robotics, AI, and machine learning to develop algorithms for real-world applications like manufacturing, autonomous driving, and environmental monitoring. He holds an NSF CAREER Award and DARPA Young Faculty Award, among other recognitions. Dr. Zhang earned a PhD from the University of Tennessee, Knoxville (2014) and an MS from the Chinese Academy of Sciences (2009). His work addresses challenges in unstructured environments through innovations like self-reflective terrain adaptation and graph-based perception systems. He actively promotes equity in robotics through his PROGRESS outreach program. His research sponsors include NSF, DARPA, and industry partners such as Toyota. Publications span conferences like RSS, ICRA, and IROS, with best paper awards. He serves on editorial and program committees for top-tier journals/conferences including RA-L, NeurIPS, and AAAI.

David Castañónمشاهده پروفایل

استاد

• Stochastic Control
• Estimation Theory
• Game Theory
• +۶ مورد دیگر

David Castañón is a Professor of Electrical and Computer Engineering (ECE) and Systems Engineering (SE) at Boston University. He holds a PhD from MIT (1976) and has held leadership roles including Department Chair of BU ECE (2010-2014) and President of the IEEE Control Systems Society (2008). His research focuses on stochastic control, optimization, game theory, and distributed computing, with applications in sensor management, inverse problems, and autonomous systems. Education: PhD, Massachusetts Institute of Technology (1976). Key affiliations include the Center for Information and Systems Engineering, the Rafik B. Hariri Institute for Computing, and the ALERT Department of Homeland Security Center of Excellence. He teaches courses such as EC702 Recursive Estimation and EC719 Statistical Learning Theory. Research interests span stochastic control, estimation theory, optimization algorithms, and multi-agent systems. Notable contributions include work on sensor management, cooperative operations, and inverse problem solutions for medical and security imaging. His work often integrates theoretical frameworks with practical applications in autonomous systems and distributed computing. Scientific achievements include IEEE Fellow status (2006), CSS Distinguished Member Award, and leadership roles in major conferences like the IEEE Conference on Decision and Control (2007 as General Chair). He has also served on the Air Force Advisory Board and the IEEE Society Review Committee. Grants and lab affiliations include the NSF Engineering Research Center for Subsurface Sensing (2001-2013) and the SENTRY DHS Center of Excellence (2021-present). His interdisciplinary collaborations bridge robotics, medical imaging, and security systems.

Sara Merino Aceitunoمشاهده پروفایل

استاد

• Kinetic Theory
• Partial Differential Equations
• Mathematical Biology
• +۵ مورد دیگر

Prof. Sara Merino Aceituno is a Professor at the Faculty of Mathematics, University of Vienna, leading research in kinetic theory and its applications to biology, medicine, and social sciences. She holds roles as Vice-Dean of the Faculty and Head of the Institute of Mathematics. Her work bridges mathematical models with experimental data, focusing on emergent phenomena in collective dynamics, opinion formation, and cell behavior. She teaches advanced courses on kinetic theory, biomathematics, and mathematical strategies for learning. Her contributions include modeling cell delamination, nematic alignment, and swarm dynamics through PDEs and probabilistic methods. Collaborations with experimentalists drive her interdisciplinary research. She actively engages in education, advising, and public outreach, including a video series explaining mathematical patterns in nature. Her research emphasizes understanding macroscopic patterns arising from microscopic interactions in complex systems. Education: Holds a PhD in Mathematics, with expertise in kinetic theory and applied partial differential equations. Teaching and leadership roles reflect her commitment to academic excellence and student support. Her work integrates experimental and computational models to study clonal dynamics in tissues and mechanical constraints in epithelial layers. She has authored over 20 papers on topics ranging from active matter to opinion formation networks, contributing to both theoretical advancements and practical applications in biology and social sciences. Research focuses on deriving hydrodynamic and continuum models from particle systems, analyzing stability and bifurcations in collective behavior. Grants and collaborations include the Vienna Biocenter PhD Program and experimental groups in cell biology. Her lab explores how environmental factors influence particle swarms and how mechanical forces shape cell cycles in pseudostratified epithelia.

Raymond H. Cuijpersمشاهده پروفایل

دانشیار

• Cognitive Robotics
• Human-Robot Interaction
• Socially Assistive Robotics
• +۵ مورد دیگر

Raymond H. Cuijpers is an Associate Professor at Eindhoven University of Technology in the Human Technology Interaction group. His research focuses on Cognitive Robotics , Human-Robot Interaction , and Artificial Intelligence for cognitive agents, with applications in healthcare robotics and aging population support. PhD in Physics of Man from Utrecht University (2000) Postdoctoral research at Erasmus MC Rotterdam and Radboud University Nijmegen Key research areas include: Developing socially intelligent robots with proper social cue interpretation Hybrid AI approaches for real-world complexity handling Visual-haptic perception integration in human motor control Service robots for COPD patient assistance (KSERA project) Rescue robotics and tele-operation applications Recent research output (2025) includes studies on: Personalization in human-robot communication Optimal lighting for elderly visual perception Human-robot bonding mechanisms Interactive sensorized platforms for homecare (GUARDIAN) Audiovisual temporal integration in virtual environments He coordinates large-scale European projects like GUARDIAN and previously KSERA, contributes to sustainable development goals through healthcare robotics, and serves on editorial boards of leading journals including International Journal of Social Robotics . His work spans both technical robotics development and human-centric interaction studies.

Marynel Vázquezمشاهده پروفایل

استادیار

• Human-Robot Interaction (HRI)
• Artificial Intelligence
• Robotics
• +۳ مورد دیگر

Marynel Vázquez is an Assistant Professor in Yale University's Computer Science Department, leading the Yale Interactive Machines Group (IMG). Her research focuses on Human-Robot Interaction (HRI), particularly in multi-party settings, advancing perception and decision-making algorithms for socially aware robots. She holds a PhD from Carnegie Mellon University and previously worked at Stanford and Disney Research. Her research combines computer science, behavioral science, and design, emphasizing interdisciplinary approaches. Key projects include the social robots Chester and Shutter, and frameworks like SEAN-VR for evaluating robot navigation in virtual reality. Vázquez has received prestigious awards including the NSF CAREER Award (2022) and AFOSR YIP Award (2024). Teaching includes courses on interactive machines, robotics, and human-computer interaction. She actively mentors students and emphasizes ethical and socially responsible robotics development.

Damian Nale Dailisanمشاهده پروفایل

مدرس

• Computational Social Science
• Machine Learning
• Multi-Agent Systems
• +۳ مورد دیگر

Dr. Damian Nale Dailisan is a Lecturer in the Department of Humanities, Social and Political Sciences at ETH Zürich, affiliated with the Computational Social Science group. He holds a Ph.D. in Physics from the University of the Philippines, specializing in traffic modeling and machine learning applications. His research focuses on multi-agent systems, particularly in transportation and urban systems. He has held postdoctoral roles and contributed to projects like the ACCeSs@AIM lab. His work bridges computational methods with real-world challenges, including traffic control optimization, AI-driven decision-making, and smart city infrastructure. Notable projects include FAIRLANE for priority lane management and studies on democratizing traffic control systems. Dailisan’s publications span journals like Transportation Research Part C and IEEE Access, addressing topics such as reinforcement learning in traffic signals and ethical AI frameworks. He has presented at workshops like 'Back to the Future' at ETH Zurich and collaborates with interdisciplinary teams to enhance urban mobility solutions. His technical expertise includes Python, network analysis, and agent-based modeling, with contributions to open-source tools for earthquake networks and social systems analysis.