Find Academic Supervisors

Venanzio CichellaView profile

Assistant Professor

• Control Systems
• Robotics
• Autonomous Vehicles
• +3 more

Venanzio Cichella serves as an Assistant Professor in the Department of Mechanical Engineering at the University of Iowa, specializing in advanced control systems and robotics research with applications spanning autonomous aerial vehicles, underwater systems, and soft robotics. His research portfolio prominently features Control Systems , Robotics , Autonomous Vehicles , Soft Robotics , Motion Planning , and Optimal Control . He develops theoretical frameworks for collision avoidance, coordinated path following, and motion planning, frequently employing Bernstein polynomials and Cosserat rod theory to address scalability challenges in multi-agent systems. His work on twisted and coiled artificial muscles (TCAMs) enables biomimetic soft robotic systems for underwater manipulation and rehabilitation applications. Analysis of his 2023-2025 publications reveals three dominant research threads: (1) Vision-based collision avoidance algorithms for intermittent measurement scenarios, (2) Physics-based modeling of TCAM-actuated soft robots using continuum mechanics, and (3) Bernstein polynomial-optimized trajectory generation for autonomous vehicles. These threads consistently bridge theoretical control innovations with practical implementations in UAV coordination, submarine maneuvering, and octopus-inspired robotics, demonstrating strong emphasis on environmental adaptation and computational efficiency.

Gian Pietro PiccoView profile

Professor

• Building Automation
• Pervasive Computing
• Software Engineering
• +12 more

Gian Pietro Picco is a Full Professor at the Department of Information Engineering and Computer Science (DISI) at the University of Trento, Italy. His research focuses on wireless networks, particularly ultra-wideband (UWB) technology, wireless sensor networks, and cyber-physical systems. He teaches courses including Distributed Systems, Low-power wireless networking for the Internet of Things, and Programming 2. Professor Picco's research interests span several interconnected domains in pervasive computing and networking: Ultra-wideband (UWB) technology for precise localization and communication Wireless sensor networks and Internet of Things (IoT) systems Cyber-physical systems and networked control Energy-efficient networking protocols Distributed systems and middleware Software engineering approaches for networked embedded systems His recent publications demonstrate a strong focus on ultra-wideband technology applications, particularly in localization, ranging, and concurrent transmissions. His work bridges theoretical foundations with practical implementations, often addressing real-world challenges in human-robot interaction, contact tracing, and infrastructure monitoring. A notable trend is the increasing application of UWB technology for precise positioning in complex environments, with significant contributions to understanding and mitigating human occlusion effects on ranging accuracy. Professor Picco has received numerous prestigious awards for his research contributions: Best Paper Award at IPSN'23 for "Network On or Off? Instant Global Binary Decisions over UWB with Flick" Best Paper Award at IPIN 2019 for "TALLA: Large-scale TDoA Localization with Ultra-wideband Radios" Best Paper Award at EWSN 2018 for "Concurrent Ranging in Ultra-wideband Radios" Best Paper Award at IPSN 2015 for "Geo-referenced Proximity Detection of Wildlife" Best Paper Award at IPSN 2011 for "Wireless Sensor Networks for Adaptive Lighting in Road Tunnels" Best Paper Award at IPSN 2009 for "Monitoring Heritage Buildings with Wireless Sensor Networks" Mark Weiser Best Paper Award at PerCom 2012 Professor Picco actively advises numerous PhD and Master's students, with several of his advisees becoming prominent researchers in wireless networking. His laboratory has secured significant funding for research projects in wireless sensor networks, IoT systems, and cyber-physical systems. His work has practical applications in heritage building monitoring, wildlife tracking, road tunnel lighting systems, and pandemic contact tracing. His research group maintains the Cloves large-scale ultra-wideband testbed and has developed several middleware systems for wireless sensor networks, including Lime and TeenyLIME. The group collaborates extensively with international research institutions and has made significant contributions to standardization efforts in IoT networking protocols.

Zhongguo LiView profile

Lecturer

• Distributed Control
• Optimization
• Robotics
• +5 more

Zhongguo Li is a Lecturer in Robotics, Control, Communication & AI at the University of Manchester. He holds a B.Eng. (2017) and Ph.D. (2021) in Electrical and Electronic Engineering from the University of Manchester. Prior to his current role, he was a Lecturer at University College London (2022-2023) and a Research Associate at Loughborough University (2020-2022). His research focuses on distributed control, optimization, and reinforcement learning, particularly in robotics and autonomous systems. Key areas include multi-agent coordination, networked systems, and applications in autonomous vehicles. He has authored over 40 papers in top journals/conferences and co-authored a book on Distributed Optimization and Learning (2024). Teaching responsibilities include courses such as Control Systems II, Nonlinear and Adaptive Control, and Embedded Systems Project. He serves as an Associate Editor for Drones and Autonomous Vehicles and Guest Editor for Machines and Frontiers in Control Engineering. Dr. Li actively mentors PhD students, offering guidance on funding opportunities and research projects in distributed algorithms, robotics, and control systems. His work aligns with UN Sustainable Development Goals related to innovation and infrastructure.

• 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

Zhong-Ping JiangView profile

Professor

• Nonlinear Control Theory
• Dynamical Networks
• Data-Driven Optimization
• +7 more

Dr. Zhong-Ping Jiang is a Professor in the Department of Electrical and Computer Engineering and an Affiliate Professor in the Department of Civil and Urban Engineering at the Tandon School of Engineering, New York University. He has been with NYU since 2007, previously serving as Associate Professor (2002-2007) and Assistant Professor (1999-2002) at Polytechnic University (now part of NYU). Dr. Jiang received his MSc degree in Statistics from the University of Paris XI in 1989 and his PhD degree in Automatic Control and Mathematics from the Ecole des Mines de Paris in 1993. He has maintained strong international collaborations with researchers across Europe throughout his career. Internationally recognized as a key contributor to nonlinear small-gain theory, Dr. Jiang is also well-known for his seminal work in robust adaptive dynamic programming and robust reinforcement learning for real-time safety-critical control applications. His research spans dynamical networks, nonlinear control theory, data-driven optimization, and reinforcement learning with applications to mechanical, information and biological systems. Dr. Jiang's publications have had significant impact in the field, with over 500 peer-reviewed journal and conference papers, more than 34,300 citations, and an h-index of 90 on Google Scholar. He has also published six research monographs since 2011. His notable awards and honors include: Election to European Academy of Sciences and Arts (2023) Stanford's Top 2% Most Highly Cited Scientists (2023) Election to Academia Europaea (2021) Highly Cited Researchers by Clarivate Analytics (2018) Fellow of Chinese Association of Automation (2017) Fellow of International Federation of Automatic Control (2013) Fellow of IEEE (2008) National Distinguished Professor, Chinese Ministry of Education (2009) Dr. Jiang serves as Deputy Editor-in-Chief for the IEEE/CAA Journal of Automatica Sinica and has held editorial positions for numerous prestigious journals including IEEE Transactions on Automatic Control. He has delivered more than 20 plenary, semi-plenary and keynote speeches at major international conferences and workshops.

Haibo HeView profile

Professor

• Computational Intelligence
• Neural Networks
• Reinforcement Learning
• +5 more

Dr. Haibo He is the Robert Haas Endowed Professor in the Department of Electrical, Computer, and Biomedical Engineering at the University of Rhode Island (URI). As an IEEE Fellow and NSF CAREER awardee, his research focuses on computational intelligence, neural networks, and reinforcement learning with applications to smart grids and microgrid systems. Ph.D. in Electrical Engineering, Ohio University, 2006 M.S. in Electrical Engineering, Huazhong University of Science and Technology, 2002 B.S. in Electrical Engineering, Huazhong University of Science and Technology, 1999 His research interests include: Computational Intelligence Adaptive Dynamic Programming Reinforcement Learning Deep Learning for Power Systems Distributed Control in Microgrids Imbalanced Data Learning Recent research trends from publications (2018-2025) show a focus on: Multi-agent reinforcement learning for energy systems Digital twin frameworks for grid security Event-triggered control mechanisms Finite-time convergence algorithms Cyber-attack resilient control systems Evolutionary computation in power networks Awards: IEEE Fellow (2018) NSF CAREER Award (2017) Dr. He leads the Computational Intelligence and Self-Adaptive Systems (CISA) Laboratory at URI, which conducts fundamental research on computational intelligence methods with applications to power systems, data mining, and neural networks.

Sarah K. CarmichaelView profile

Professor

• Fluid-rock reactions
• Paleoenvironmental reconstruction
• Manganese geomicrobiology
• +2 more

Dr. Sarah K. Carmichael is a Professor in the Department of Geological and Environmental Sciences at Appalachian State University , where she also serves in the Appalachian Studies Faculty . Her interdisciplinary research focuses on fluid-rock reactions , manganese geomicrobiology , and paleoenvironmental reconstruction of mass extinction events through Devonian period studies . B.A. from Smith College M.A. and Ph.D. from Johns Hopkins University Her research combines geochemistry of mass extinctions , microbe-mineral interactions in caves , and Appalachian manganese ore formation through fieldwork from Mongolian mountains to Vietnamese basins . She leads the DAGGER (Devonian Anoxia, Geochemistry, Geochronology, and Extinction Research) Team and collaborates with Dr. Suzanna Bräuer on cave geomicrobiology projects. Recent publications focus on Devonian-Mississippian stratigraphy in Mongolia , mercury isotope evidence for Late Devonian ocean anoxia , and human impact assessment in Appalachian caves . Her work has been recognized with National Geographic Explorer status, inclusion in the Explorers Club 50 , and the UNC Board of Governors Teaching Award . Scientific awards include National Geographic Explorer (2018, 2022) Explorers Club 50 (2022) Board of Governors Teaching Award (2020) Academy of Outstanding Teachers induction (2022) KWI Distinguished Service Award (2020) William C. Strickland Outstanding Junior Faculty Award (2014) She mentors numerous student researchers and has secured over $1.2 million in external funding from organizations like the National Science Foundation and National Geographic Society . Her laboratory work utilizes advanced techniques such as electron microscopy and isotope analysis to decode micron-scale mineral textures that reveal Earth's ancient environmental changes.

Haoyu WangView profile

Researcher

• Event-Centric NLP/NLU
• LLM Reasoning and Planning
• Knowledge Graph
• +5 more

Haoyu Wang is a Researcher in the Computer and Information Science department at the University of Pennsylvania . He previously held research positions at Shanghai Jiao Tong University and interned at Google DeepMind , Amazon AWS , ByteDance , AI2 , Tencent AI Lab , and Goldman Sachs . Education : PhD in Computer and Information Science (2021–Present), MS in Computer and Information Science (2019–2021), BS in Computer Science (2015–2019). His research focuses on Event-Centric NLP/NLU , LLM Reasoning and Planning , Knowledge Graph , and Pose Estimation in Computer Vision . His work includes event causality identification, semantic classification in context, and synthetic control for temporal reasoning. He has contributed to multimodal hallucination analysis and safety in reasoning models through projects like RESIN-11 and Devil's Advocate . His publications span venues like EMNLP , EACL , and ACL . His recent articles analyze LLM limitations in NP-hard problems , clinical trial prediction , event causality , and hallucination in vision-language models . He has served as PC Member for conferences including ACL , NAACL , NeurIPS , and EMNLP since 2019.

Kaiming BiView profile

Assistant Professor

• Infectious Disease Modeling
• Epidemic Forecasting
• Public Health Decision-Making
• +12 more

Kaiming Bi, Ph.D., is an Assistant Professor in the Department of Management, Policy & Community Health at the University of Texas Health Science Center at Houston (UTHealth Houston) School of Public Health . As an affiliated member of the Center for Health Care Data , his research bridges quantitative methods and public health, focusing on infectious disease modeling , epidemic forecasting , and data-driven health policy . B.S. in Mathematics from Northeastern University (2015) Ph.D. in Industrial Engineering from Kansas State University (2020) Postdoctoral training at University of California San Diego School of Medicine (2020-2021) and University of Texas at Austin (2021-2024) His methodological expertise spans mathematical modeling , machine learning , and optimization , applied to diverse public health challenges including respiratory infections , vector-borne diseases , STIs , and the opioid epidemic . Recent work includes modeling SARS-CoV-2 Omicron subvariants , population immunity dynamics , and multi-pathogen burden projections for the 2023-2024 US winter season. Dr. Bi has received prestigious accolades such as the Pencis Best Researcher Award (2021) and IISE Best Paper (2018). He previously taught graduate courses in Integer Programming , Information Systems , and Industrial Simulation at Kansas State University. Currently leading the Big-data and Infectious Disease Modeling Lab (BI Lab) , he seeks STEM-motivated PhD students to develop computational solutions for epidemic control.

Bin HuView profile

Assistant Professor

• Resilient Cyber-Physical Systems (CPS)
• AI-Human Collaboration Safety & Security
• Big Data & Machine Learning-Based Control
• +5 more

Dr. Bin Hu is an Assistant Professor in the Department of Engineering Technology at the University of Houston (2022–present) and previously held the same position at Old Dominion University (2019–2022). He earned a Ph.D. in Electrical Engineering from the University of Notre Dame (2016), an M.S. in Control Science from Zhejiang University (2010), and a B.S. in Electrical Engineering from Hefei University of Technology (2007). His research focuses on resilient Cyber-Physical Systems (CPS), AI-human collaboration safety, machine learning-driven control, and vehicular networks. Key projects include NASA-funded work on adaptive human-autonomy responsibility allocation (2023–2026) and NSF-supported AI-human collaboration in autonomous vehicles (2020–2023). He has secured over $1.2 million in grants, including a $700k NASA grant (pending) and an ONR Rapid Solutions grant ($42k). Dr. Hu’s work bridges control theory, machine learning, and human factors. Recent contributions include safer leader-follower robotic systems, fault-tolerant UAV control, and studies on driver trust in AI-driven vehicles. His research has been recognized through awards like the 2024–2025 APeX Excellence Speaker Series. Teaching experience includes courses on C++ programming and electrical engineering fundamentals at both the University of Houston and Old Dominion University. He advises multiple undergraduate and graduate students in projects ranging from LiDAR systems to cyber-physical security. Key Labs/Teams : NAIL Lab (Networked Autonomous Intelligence & Learning), part of the University of Houston’s Cybersecurity and Robotics initiatives. Grants : NASA TTT Grant ($700k), NSF CHS Grant ($500k), ONR RSLP Project ($42k).

• Chemical Engineering
• Pharmaceutical Processing
• Catalytic Combustion
• +3 more

Antonello Barresi is a Full Professor of Transport phenomena at the Department of Applied Science and Technology (DISAT), Politecnico di Torino. He leads the Process development and design course and serves on the PhD board for Chemical Engineering. His research focuses on drying, combustion processes, and pharmaceutical freeze-drying optimization. He has authored over 300 papers and holds patents for freeze-drying monitoring systems. His awards include the AFSIA Award (2018) and Arun S. Mujumdar Medal (2021). Roles: Full Professor, PhD Program Member, Course Coordinator Research Interests: Pharmaceutical Freeze-Drying, Catalytic Combustion, Process Scale-Up, Nanotechnology Key Projects: Multi-hazard industrial infrastructure vulnerability modeling, SMART VIAL freeze-drying monitoring Labs: Lyophilization Lab, Molecular Engineering Lab (MolE), Process System Engineering Recent work includes multi-risk vulnerability frameworks for industrial systems and advancements in freeze-drying process analytical technology (PAT). He actively contributes to editorial boards like Pharmaceutics and Drying Technology.

Adrian FilipescuView profile

Professor

• Control Systems
• Robotics
• Mechatronics
• +5 more

Prof. Adrian Filipescu is a full professor and PhD supervisor at the Department of Automation and Electrical Engineering, Dunărea de Jos University of Galati. He holds a PhD in Control Systems (1993) and has over 40 years of academic and research experience. His expertise spans numerical calculus, adaptive control, robotics, and Industry 4.0/5.0 applications. He has authored 12 books and over 110 indexed articles with an h-index of 11 (SCOPUS), and his work earned two UEFISCDI awards (2014, 2019). Education: Engineer diploma in Automatic Control (Polytechnic University of Bucharest, 1981) and PhD in Control Systems (Dunărea de Jos University, 1993). His research focuses on mechatronics systems integration, robotic control, digital twins, and industrial automation. Notable contributions include developing a mechatronics line with integrated autonomous robots, wheelchair systems for disabled users, and multidirectional autonomous vehicles. Research Themes: Digital twin frameworks, IoT-cloud robotics control, Industry 4.0/5.0 systems, inverse kinematics modeling, and autonomous robotic navigation. His recent work emphasizes cloud-based remote control, HIL simulation, and SCADA integration. Awards: Recipient of the 2014 and 2019 UEFISCDI 'Award for Research Results' for highly commended papers. He has led 10+ research projects, endowing labs with advanced robotic infrastructure and PLC-controlled systems. Advising & Grants: Supervised 7 completed PhD theses (including 1 international co-supervision). Projects focused on robotic-assisted manufacturing, autonomous systems, and mechatronics line optimization. Current lab facilities include mobile robots, industrial manipulators, and PLC-based control systems. Labs/Teams: Director of a research lab developing Industry 5.0-ready systems, including robotic assistants for elderly/disabled users and flexible assembly/disassembly technologies. Collaborations include NATO internships at Politecnico di Torino (Italy), INPG Grenoble (France), and ISR Coimbra (Portugal).

Marta ZanioloView profile

Assistant Professor

• Water Resources Planning and Management
• Drought Modeling
• Climate Change Adaptation
• +5 more

Marta Zaniolo serves as an Assistant Professor in Civil and Environmental Engineering at Duke University since January 2024, addressing critical water sustainability challenges through integrated hydrology, machine learning, and systems modeling approaches. Her work focuses on ensuring equitable water access amid climate change, scarcity, and competing demands across diverse spatial scales. Her academic foundation includes: B.S. in Environmental Engineering from Politecnico di Milano (2014) M.S. in Environmental Engineering from Politecnico di Milano (2017) Ph.D. in Information Technology (Environmental Intelligence Lab) from Politecnico di Milano (2020) Postdoctoral Research at Stanford University (2020-2023) Dr. Zaniolo's research integrates water resources planning , drought modeling , and climate adaptation with cutting-edge machine learning techniques including reinforcement learning and feature extraction. The ZEDD Lab develops computational models that synthesize climate, hydrological, and socio-economic data to enhance decision-making for water security. Her work emphasizes real-world applicability through stakeholder engagement and participatory design, particularly in vulnerable regions like the Omo-Turkana basin where mismanaged infrastructure operations can trigger socio-ecological conflicts. Analysis of her 2021-2025 publications reveals three dominant research trajectories: (1) development of synthetic weather generators (e.g., FIND model) for precise drought characterization, (2) neuro-evolutionary algorithms for multi-objective reservoir control under climate uncertainty, and (3) quantification of flexibility value in water infrastructure planning. These studies consistently bridge technical innovation with equity considerations, particularly in transboundary water systems. She leads the Zaniolo Lab for Environmental Data and Decisions (ZEDD Lab), which operates at the intersection of hydrology, systems modeling, and machine learning. The lab's framework combines computational water system dynamics with stakeholder-inclusive approaches to develop decision support tools for extreme event preparedness. Current projects focus on urban drought resilience, dam reoperation strategies for climate extremes, and heat-substance use risk intersections in rural communities, all grounded in case studies spanning California, Ethiopia, and the American South.

Dinsha VinodView profile

Researcher

• Control Systems
• Artificial Intelligence
• Robotics
• +3 more

Dinsha Vinod is a Postdoctoral Researcher at the University of Agder, affiliated with the Department of Engineering Sciences. His research focuses on control systems, fog computing, and autonomous robotics, with a particular emphasis on integrating artificial intelligence and event-triggered control mechanisms. He has contributed to advancements in fog-cloud platforms for mobile robotics applications, model predictive control (MPC), and robust control design. Education details are not explicitly provided in the text, but his work spans academic and industrial collaborations. He is part of the Robotics and Automation research group at UiA. Key research areas include: Event-based control strategies for LPV systems Fog computing autoscaling with service guarantees MPC applications in mobile robotics Integration of AI inference into control systems Publications since 2017 reflect a trajectory from aerospace control systems to fog computing and robotics. Recent work emphasizes real-time systems and autonomous decision-making frameworks.

Hamed Nabizadeh RafsanjaniView profile

Lecturer

• IoT
• AI in Architecture, Engineering, and Construction (AEC)
• Digital Twin Technology
• +5 more

Hamed Nabizadeh Rafsanjani, Ph.D., P.E., ENV SP is a Lecturer at the University of Georgia (UGA) College of Engineering since 2018. He holds a Ph.D. and master’s degrees in Construction Engineering and Management from the University of Nebraska-Lincoln (UNL). Prior to UGA, he served as an assistant professor and visiting lecturer at various universities, earning accolades such as the Best Teacher Award and Best Researcher Award. His research focuses on IoT, AI, and Digital Twin technologies applied to Architecture, Engineering, and Construction (AEC) industries. As director of iSC-LAB, he develops IoT-based platforms to analyze building occupants’ energy-use behaviors and improve learning environments. His research interests span smart building systems, non-intrusive energy monitoring, and occupant behavior analysis. Key projects include a global occupant behavior database and an IoT-based smartphone energy assistant (iSEA). He has secured grants from agencies like the National Science Foundation (NSF), contributing to studies on energy efficiency, construction project management, and sustainability in built environments. Rafsanjani’s publications (2018–2023) emphasize AI-driven solutions for AEC, IoT applications in energy management, and adaptive learning systems. His work bridges technology and human behavior to optimize building performance and educational outcomes. Awards reflect his dual excellence in pedagogy and research, with ongoing contributions to the Sustainable Human-Building Ecosystems field.