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Valentina BreschiView profile

Assistant Professor

• Data-driven control
• Control systems
• Machine learning
• +5 more

Valentina Breschi is an Assistant Professor in the Control Systems Group at the Department of Electrical Engineering, Eindhoven University of Technology (TU/e). She holds a Ph.D. from IMT School for Advanced Studies Lucca, with postdoctoral and junior faculty experience at Politecnico di Milano. Her research focuses on data-driven control, jump model learning, meta-learning for system identification, and human-centered policy design for mobility systems. She contributes to UN Sustainable Development Goals related to sustainable infrastructure and innovation. Education: B.Sc. in Electronic and Telecommunication Engineering (University of Florence, 2011) M.Sc. in Electrical and Automation Engineering (University of Florence, 2014) Ph.D. in Control Systems (IMT School for Advanced Studies Lucca, 2018) Research Interests: Her work spans data-driven control methodologies, including LPV control, predictive control, and ethical frameworks for policy design. She explores applications in sustainable mobility, energy systems, and healthcare, emphasizing fairness and social impact. Labs/Teams: She is part of the Control Systems Group, collaborating on projects like the CONSIDER study and the design of fair-MPC frameworks. Her work integrates theoretical control principles with real-world applications in smart systems and social networks.

• Computer Vision
• Autonomous Robotics
• Image-Based Localization
• +3 more

Charbel Azzi is an Assistant Professor, Teaching Stream at the University of Waterloo, specializing in Computer Vision and Autonomous Robotics. His work focuses on robotics navigation, image-based localization, and sensor technology applications. He holds a full-time faculty position within the university's engineering domain. Research interests include developing algorithms for motion estimation, global localization, and context-aware robotics systems. His contributions span both theoretical advancements and practical applications such as AR-assisted wayfinding and energy-harvesting prototypes like the Eco-Brella. Notable research trends include leveraging global descriptors and 3D keypoints for improved localization accuracy, alongside interdisciplinary projects in renewable energy. No academic awards or grants are explicitly listed in the provided information.

Mihir BalaView profile

Research Fellow

• Systems
• Computer Science
• Edge Computing
• +3 more

Mihir Bala is a Research Fellow in the Computer Science Department at Carnegie Mellon University. His research focuses on systems, edge computing, and autonomous drone technologies. He is advised by Mahadev Satyanarayanan and has contributed to projects such as SteelEagle, exploring drone video stream latency and autonomous navigation systems. His work bridges drone autonomy, edge computing, and real-time video analytics, addressing challenges in bandwidth efficiency, latency reduction, and democratizing autonomous systems for industries like construction. Recent efforts emphasize cloudlet-based architectures and OODA loop applications in drone control systems. No scientific awards are explicitly mentioned. His research involves collaborations on live video analytics, lightweight drone design, and distributed edge computing frameworks. While no formal advisees are listed, his academic contributions include advancing drone-based solutions through interdisciplinary systems research. The SteelEagle project highlights his focus on practical, real-world applications of edge computing in autonomous systems.

Guillaume A SARTORETTIView profile

Assistant Professor

• Robotics
• Multi-Agent/Multi-Robot Systems
• Distributed/Decentralized Control
• +4 more

Guillaume A SARTORETTI is an Assistant Professor in the Mechanical Engineering Department at the National University of Singapore (NUS), part of the College of Design and Engineering. He specializes in distributed/decentralized coordination of multi-agent systems, with a focus on robotics, stochastic modeling, and reinforcement learning. His work spans applications in multi-robot systems, articulated robots, and swarm intelligence. Joined NUS in 2019 after a postdoctoral fellowship at Carnegie Mellon University (CMU) and a PhD from EPFL. Education: PhD in Robotics (EPFL, 2016); MSc and BSc in Mathematics/Computer Science (University of Geneva). Research interests include: Multi-agent pathfinding Decentralized control policies Swarm intelligence Reinforcement learning applications in robotics Recent publications emphasize scalable solutions for multi-agent systems, traffic signal control, and safe robotic exploration. His 2018 MFI postdoctoral fellowship recognized work on distributed reinforcement learning for pathfinding. Current projects involve bio-inspired locomotion and collaborative learning frameworks for heterogeneous robots.

Nicola MarsdenView profile

Research Professor

• Gender and IT
• Design Science Research
• Participatory Processes
• +5 more

Prof. Nicola Marsden is a Research Professor of Socioinformatics at Heilbronn University's Faculty of Computer Science. She leads the Socioinformatics Lab and holds roles such as spokesperson for the Research Unit on Social, Behavioral, and Economic Sciences in the Baden-Württemberg Graduate School. Her expertise spans Gender and IT, Design Science, and participatory processes. She has served as dean, gender equality officer, and consultant in tech management. Education: PhD from Saarland University (2001), Diplom in Psychology (1994). Research focuses on gender equity in tech, human-centered design, and autonomous systems. She leads projects like MAKEitREAL (makerspaces for girls) and GEWINN (gender equality in informatics). Over 200 publications address HCI, AI ethics, and inclusive tech design. Active in committees like BW-CAR and the Gi's Participation Group. Key initiatives include feminist living labs, co-design for AI fairness, and virtual reality gender stereotypes. Advises startups like Edkimo and serves on international boards for gender equity in tech. Her work bridges academia and industry, emphasizing ethical innovation and societal impact.

Paul RuvoloView profile

Professor

• Machine Learning
• Robotics
• Computer Vision
• +3 more

Dr. Paul Ruvolo is a Professor of Computer Science at Olin College in Needham, MA. His research focuses on developing assistive technologies for people with sensory and motor impairments, leveraging machine learning, robotics, and computer vision. He holds a Ph.D. and M.S. in Computer Science and Engineering from the University of California San Diego, and a B.S. in Computer Science from Harvey Mudd College. Key research areas include creating systems that learn through imitation and experience, such as navigation aids for the visually impaired and educational tools for orientation and mobility. He leads projects like Co-Designing Assistive Apps with Students Who Are Blind, emphasizing participatory design. His work integrates Bayesian statistics, numerical optimization, and linear algebra to solve complex sensorimotor tasks. Education: Ph.D., Computer Science and Engineering, UC San Diego M.S., Computer Science and Engineering, UC San Diego B.S., Computer Science, Harvey Mudd College Awards: NSF IGERT Fellowship for 'Learning and Vision in Humans and Machines' Recent publications highlight innovations in AR navigation systems, smartphone-based SLAM for indoor environments, and educational tools for blind users. His work bridges computational methods with real-world accessibility challenges, emphasizing interdisciplinary collaboration and user-centric design. Dr. Ruvolo’s lab, linked at occam.olin.edu , focuses on assistive technologies. He actively contributes to Teach Access and other initiatives promoting inclusive technology education. His research has applications in robotics, healthcare, and educational technology.

Alexander BrownView profile

Lecturer

• Robotics
• Vehicle Dynamics
• Human-Robot Interaction
• +5 more

Alexander Brown is a Lecturer in the Department of Mechanical Engineering at Lafayette College, specializing in dynamic systems and controls. His research bridges robotics, vehicle dynamics, and bio-inspired modeling, with a focus on human-robot and animal-robot interactions. B.S., Pennsylvania State University (2008) M.S., Pennsylvania State University (2012) Ph.D., Pennsylvania State University (2013) Research interests include robotics, vehicle dynamics, and mathematical modeling of physical systems. His work explores interactions between robots, vehicles, humans, and animals, with applications in autonomous driving and ethorobotics. Alexander's recent publications highlight trends in fish-robot interaction studies, autonomous vehicle safety, and simulation platforms like Webots. He emphasizes interdisciplinary collaboration and hands-on student engagement in his research and teaching. Outside academia, he builds and restores motorcycles, surfs in Monmouth County, N.J., and plays music with his band 'Imposters In Rome'.

Eike SchneidersView profile

Lecturer

• Human-Computer and Robot Interaction
• Human-AI Interaction focusing on generative AI and its impact on society
• Multidisciplinary research in collaboration with artists, medical professionals, and lawyers

Dr. Eike Schneiders is a Lecturer at the School of Electronics and Computer Science, University of Southampton. He holds a PhD in Computer Science from Aalborg University and is a recipient of the UKRI Trustworthy Autonomous Systems Hub Early Career Researcher Award. His research focuses on autonomous and AI-infused systems, investigating how to ensure their trustworthiness and responsible interaction with humans and other agents through interdisciplinary collaborations across art, law, and medicine. His recent work explores the societal impact of Generative AI, particularly examining its influence on legal services accessibility for non-experts through the REGALS project (Principal Investigator) and its effects on creative communities via the ART-I project (Co-Investigator). Dr. Schneiders emphasizes public engagement to enhance AI literacy and employs both qualitative and quantitative methods to study human-computer interaction and ethical implications. Dr. Schneiders teaches courses including COMP6251 (Web and Cloud Application Development), ELEC6259 (Research Methods and Project Preparation), and ELEC6200 (Group Design Project). His pedagogical approach integrates foundational technical skills with critical research methodologies. He has led several interdisciplinary projects including ARTificial Intelligence: Show Me a Story about AI (ART-I) and Responsible Employment of Generative AI for Legal Services (REGALS), and contributes to the Agents, Interaction and Complexity research group. His Cat Royale project received the 2024 Webby Award for Best Integrated Experience. Honors include the Best Paper Award at ACM CHI 2024, multiple Honourable Mentions at CHI and TAS Symposiums, and membership in the Foundation for Science and Technology's Future Leaders Programme.

• Business Process Management
• Process Mining
• Process Analytics
• +4 more

Hajo A. Reijers is a Professor at the University of Utrecht, Netherlands, with a former affiliation at Vrije Universiteit Amsterdam. His research focuses on Business Process Management (BPM), Process Mining, and Robotic Process Automation (RPA), emphasizing practical applications in healthcare, organizational processes, and human-computer interaction. He contributes to developing tools like SWORD for detecting workarounds and DEUCE for auditing electronic health records. His work spans algorithm development for process discovery, predictive analytics, and optimization techniques. Key areas include analyzing event logs, modeling workplace behavior, and enhancing process transparency. Reijers collaborates extensively with industry partners, addressing challenges in process automation, employee acceptance of AI, and ethical monitoring. His contributions to conferences like BPM, CAiSE, and ICIS highlight interdisciplinary approaches, combining computer science with organizational studies. Notable projects include frameworks for task mining, reinforcement learning in care processes, and pattern recognition in government transparency assessments. Research initiatives often involve cross-disciplinary teams, exploring topics like workplace well-being through process mining, decision-making support systems, and overcoming barriers to BPM adoption. His work bridges theoretical advancements with real-world impact, influencing both academic discourse and practical business solutions.

• Artificial Intelligence
• Robotics
• Autonomous Systems
• +5 more

Nisar Ahmed is an Associate Professor at the University of Colorado within the Aerospace Engineering Sciences department. His research focuses on the intersection of Artificial Intelligence , Robotics , and Autonomous Systems , emphasizing decision-making under uncertainty, sensor fusion, and human-machine collaboration. Key research interests include: Active Inference for autonomous planning Decentralized Data Fusion in multi-robot systems Machine Self-Confidence and competency assessment Reinforcement Learning for spacecraft and robotic guidance Uncertainty Quantification in dynamic environments Recent publications highlight trends in Pareto-optimal decision-making , Bayesian optimization , contextual bandits , and trust calibration for UAS and planetary rovers. His work integrates probabilistic modeling with real-time autonomy , ensuring robustness in applications like search-and-rescue missions and lunar exploration. Contact: Nisar.Ahmed@Colorado.EDU

Burak SarsilmazView profile

Assistant Professor

• Distributed Control
• Adaptive Control
• Trajectory Optimization
• +2 more

Dr. Burak Sarsilmaz is an Assistant Professor in the College of Engineering at Utah State University, specializing in Electrical and Computer Engineering. His research focuses on distributed control of multi-agent systems, adaptive control, and trajectory optimization for aerospace applications. Education: PhD in Mechanical Engineering, University of South Florida (2020) MA in Mathematics, University of South Florida (2020) MS in Aerospace Engineering, Middle East Technical University (2016) BS in Aerospace Engineering, Middle East Technical University (2013) Research: His work bridges control theory and practical applications in autonomous systems, with emphasis on robustness under uncertainty. Recent publications explore novel approaches to cooperative control and optimization in constrained environments. Students: Actively mentors graduate students including Akalu Desta Teklu and Kursad Metehan Gul. Leads the Autonomy, Robotics, Control, and Optimization (ARCO) research group. Awards: Holds a Canada Research Chair in Translational Cancer Research.

• Intelligent Transportation Systems
• Traffic Network Optimization
• Stochastic Demand Modeling
• +3 more

Kun An is a Professor at the Department of Traffic Information and Control Engineering within the College of Transportation Engineering at Tongji University. She has previously held academic positions at Monash University (Senior Lecturer, 2018–2019; Lecturer, 2016–2018) and conducted postdoctoral research at the University of Illinois at Urbana-Champaign (2015–2016) and The Hong Kong University of Science and Technology (2014–2015). Her academic journey includes a PhD in Civil Engineering from HKUST (2014) and a Bachelor's degree from Tongji University (2009). PhD: Civil Engineering, The Hong Kong University of Science and Technology (2014) Bachelor's: Transportation Engineering, Tongji University (2009) Dr. An specializes in intelligent transportation systems, focusing on optimizing complex traffic networks, characterizing traveler behavior in stochastic environments, and advancing electric vehicle sharing infrastructure. Her research spans urban transit planning, carsharing logistics, battery electric bus deployment, and real-time traffic signal optimization, with applications to mitigate rail disruptions and enhance multimodal connectivity. Her publications emphasize solving transport challenges through stochastic programming, game theory, and behavioral analysis. Key themes include autonomous vehicle integration, demand uncertainty modeling, and sustainable mobility solutions. Awards include the Hong Kong PhD Fellowship and multiple best paper recognitions. Hong Kong PhD Fellowship (2010–2014) TRBADB30 Best Paper Nomination (2014) Best Paper at 18th Hong Kong Transportation Annual Meeting (Second Author, 2013)

Yongfeng ZhangView profile

Associate Professor

• Machine Learning
• Data Mining
• Information Retrieval
• +8 more

Yongfeng Zhang is an Associate Professor in the Department of Computer Science at Rutgers University. He is also the Director of the AIOS Foundation . His research focuses on Machine Learning, Data Mining, Recommender Systems, and Explainable AI , with notable contributions to fair and personalized AI, AI for science, and social good. Education and Experience: PhD in CS (2011–2016) from Tsinghua University Postdoc at UMass Amherst (2016–2017) Joined Rutgers as Assistant Professor (2018), promoted to Tenured Associate Professor (2024) Research Interests: Machine Learning, Data Mining, Information Retrieval, Recommender Systems, Natural Language Processing, ML Systems, AI Agents, Explainable AI, Fairness, and AI for Science. Recent Achievements: Received the 2024 ACM SIGIR Test of Time Award, 2024 Presidential Teaching Excellence Award, and multiple NSF grants. He has advised over 15 PhD students and led projects on trustworthy AI, generative recommendation, and causal inference. Awards: 2024 ACM SIGIR Test of Time Award 2021 NSF CAREER Award 2015 Microsoft PhD Fellowship Grants & Labs: Led NSF grants on explainable AI, conversational recommendation, and neural-symbolic AI. Active in the AIOS platform development and multiple research labs.

Meredith CarrollView profile

Professor

• Aviation Human Factors
• Human-Autonomy Teaming
• Urban/Advanced Air Mobility (AAM/UAM)
• +5 more

Dr. Meredith Carroll is a Professor of Aviation Human Factors and Director of the ATLAS Lab at Florida Institute of Technology's College of Aeronautics. She holds a B.S. in Aerospace Engineering from the University of Virginia, an M.S. in Aviation Science from Florida Tech, and a Ph.D. in Applied Experimental Psychology and Human Factors from the University of Central Florida. Her research focuses on human performance in aviation systems, including decision-making under stress, human-autonomy teaming, advanced air mobility (AAM), and cybersecurity education. Key research initiatives include: Investigating trust dynamics in human-agent teams funded by AFOSR User-centered design of multi-UAV ground control stations for NASA eVTOL battery display design and pilot interface studies Cybersecurity curriculum development for underrepresented high school students Adaptive flight training strategies funded by the Naval Air Warfare Center Her lab facilities include Florida Tech's Center for Aeronautics and Innovation with advanced flight simulators and UAS testing environments. Current projects explore VR training immersion effects, multi-agent team repair strategies, and AAM operational challenges. She advises 12+ graduate and undergraduate researchers and collaborates with industry partners like P17 Solutions and Rockwell Collins. Recent publications emphasize trust calibration in autonomous systems, IoT-based cybersecurity education, and adaptive training efficacy. She has secured grants from NASA, FAA, AFRL, ONR, and NAWC totaling over $5M in external funding.

• Robotics
• Control Systems
• Automation
• +3 more

Federica FERRAGUTI is an Associate Professor at the Department of Engineering Sciences and Methods, University of Modena and Reggio Emilia. Her research focuses on robotics, control systems, and automation, with a particular emphasis on collaborative robotics, surgical robotics, and industrial applications. She teaches courses such as Industrial and Collaborative Robotics, Automatic Controls, and Collaborative Robotics for Smart Industry. Her work integrates advanced control strategies, safety protocols, and human-robot interaction principles to enhance automation in both industrial and medical contexts. Research Interests: Federica’s research spans robotics control, human-robot collaboration, surgical robotics, and safety-critical systems. She develops novel algorithms for motion planning, trajectory generation, and real-time control, with applications in manufacturing and healthcare. Her recent work includes AI-driven augmented reality systems for surgery, energy-efficient safety frameworks, and intuitive robot-assisted welding solutions. Publications: Over 40 peer-reviewed articles in journals and conferences, including work on collaborative robotics safety, real-time surgical AR integration, and industrial automation. Key contributions include high-velocity walk-through programming for industrial robots and energy-based control architectures for shared autonomy. Labs/Teams: Active in the ARSControl research group, focusing on advanced robotics systems. Collaborates with industry partners on projects like MyWelder, an intuitive welding system for SMEs. Engages in EU-funded initiatives like the SARAS project for surgical teleoperation.