Find Academic Supervisors

• Wireless Communications
• 5G/6G Networks
• Mobile Communications
• +6 more

Marina Petrova is a Professor at RWTH Aachen University, holding positions in both the Teaching and Research Area of Mobile Communications and Computing and the Chair and Institute for Networked Systems. She is also a member of the Steering Committee for the Mobility & Transport Engineering (MTE) profile area at the university. Her office is located at Kackertstraße 9, 52072 Aachen, Germany. Professor Petrova's research focuses on cutting-edge wireless communication technologies, with particular emphasis on next-generation mobile networks. Her work spans multiple dimensions of wireless systems including: 5G and 6G network architectures and protocols Cell-Free Massive MIMO systems Millimeter-wave communications Resource allocation and scheduling in wireless networks Wi-Fi sensing and coexistence analysis Integration of distributed learning services in wireless networks Beamforming and beam management techniques Ultra-Reliable Low-Latency Communications (URLLC) Her recent publications demonstrate a strong trend toward the integration of artificial intelligence and machine learning techniques in wireless network design and optimization. She has been particularly active in exploring the convergence of communication and sensing functionalities (ISAC - Integrated Sensing and Communication), which is considered a key enabler for future 6G networks. Professor Petrova's research also addresses practical implementation challenges in next-generation wireless systems, with several publications focusing on ns-3 implementations and experimental validations. Professor Petrova has received recognition for her contributions to the field through numerous publications in top-tier venues, though specific awards are not mentioned in the available information. Her work shows strong industry relevance with applications in smart industries, autonomous systems, and future communication networks.

• Artificial Intelligence
• Cognitive Science
• Human-Agent Interaction
• +5 more

Prof. Dr.-Ing. Stefan Kopp is a faculty member at Bielefeld University's Faculty of Engineering and serves as Research Group Leader of the Cognitive Systems and Social Interaction Group . He also holds administrative roles as Vice Dean and Deputy CITEC Coordinator . His work focuses on Artificial Intelligence , Cognitive Systems , and Socio-Technical World research areas. Research Group Leader: Cognitive Systems and Social Interaction Group Vice Dean: Faculty of Engineering Deputy Coordinator: Center for Cognitive Interaction Technology (CITEC) Project Manager: TRR 318 "Constructing Explainability" subprojects His research explores human-agent interaction , multimodal conversational agents , and social AI through projects like 39-Inf-11 Human-Machine Interaction and 39-M-Inf-VKI Virtual Humans and Conversational Agents . Publications address topics including adaptive explanation generation , gesture synthesis , and social cognition in dynamic environments. Current research topics span cooperative AI , explainable decision-making , and sensorimotor grounding in artificial systems.

Luis Duffaut EspinosaView profile

Assistant Professor

• Control Theory
• Estimation Theory
• Robotics
• +7 more

Summary Luis A. Duffaut Espinosa is an Assistant Professor in the Department of Electrical and Biomedical Engineering at the University of Vermont (UVM), affiliated with the College of Engineering and Mathematical Sciences. His research focuses on control theory, estimation, robotics, and nonlinear systems with applications in autonomy, quantum control, and environmental monitoring. He holds a Ph.D. in Electrical and Computer Engineering from Old Dominion University (2009) and has held academic positions at George Mason University and postdoctoral roles at Johns Hopkins University and the University of New South Wales. Education: Ph.D. in Electrical and Computer Engineering (2009), Old Dominion University M.S. in Mathematics (2005), Pontificia Universidad Católica del Perú B.S. in Physics (2003), Universidad Nacional de Ingeniería, Peru Research Interests: His work emphasizes data-driven control and estimation methodologies, including model-free approaches for power systems, environmental monitoring, and quantum control. Current projects include real-time data assimilation in harsh environments, resilient robotics in GPS-denied conditions, and SAR with small satellites. He co-directs the Autonomous and Intelligent Systems Research Laboratory (AIRLab) and is part of the CREATE center. Recognition: 2024 NSF CAREER Award for work on safety-aware data-driven control frameworks Teaching & Advising: He teaches courses in estimation theory, control systems, and signal processing. Advises a team of graduate and undergraduate students focusing on autonomy, robotics, and control systems. Notable students include Danial Waleed (Ph.D. 2024), Jacob Friz-Trillo (M.S. 2025), and Farnaz Boudaghi (Ph.D. candidate). Labs & Collaborations: AIRLab: Focuses on data-driven control for autonomy in robotics and engineered systems CREATE: Research on resilient energy and autonomous technologies

Nidhi RastogiView profile

Assistant Professor

• Cybersecurity
• Artificial Intelligence
• Data Science
• +7 more

Nidhi Rastogi is an Assistant Professor at the Department of Software Engineering within the Golisano College of Computing and Information Sciences (GCCIS) at Rochester Institute of Technology (RIT). She leads the AI4Sec Research Lab, which focuses on data-driven AI solutions for cybersecurity, emphasizing interpretability and practical applications. Her research interests span Cyber Threat Intelligence, Artificial Intelligence, Graph Analytics, and Healthcare Analytics. Education: Ph.D. in Computer Science (2018), Rensselaer Polytechnic Institute M.S. in Computer Science (2008), University of Cincinnati Bachelor of Information Technology (2003), University of Delhi Research Interests: Transdisciplinary work in cybersecurity, AI, heterogeneous networks, and graph analytics. She develops systems for threat intelligence, explainable AI, and security monitoring. Notable projects include the CyNER library for cybersecurity NER, TINKER framework for open-source CTI, and personal health knowledge graphs. Awards & Mentoring: Students advised include Le Nguyen (1st place at UPSTAT23), Tanvirul Alam (IEEE SP Travel Grant), and Dipkamal Bhusal. Recent recognitions include program committee roles for ACM CCS'24 and ACSAC'24. Labs & Collaborations: The AI4Sec Lab collaborates with federal agencies, national labs, and enterprises. Current projects address autonomous vehicle security, healthcare analytics, and systemic cyberattack detection using graph-based methods.

• Software Engineering
• Reverse Engineering
• Program Understanding
• +7 more

Ken Wong is an Associate Professor in the Department of Computing Science at the University of Alberta's Faculty of Science. He also serves as Associate Chair within the same department. Holding a PhD in Computer Science from the University of Victoria (1999), his research focuses on software engineering challenges such as reverse engineering, program understanding, and software visualization. He emphasizes improving software evolution through tools like architecture recovery and root cause analysis, with applications in web/mobile platforms and diverse system understanding. Teaching highlights include developing Massive Open Online Courses (MOOCs) via Coursera, including the 'Software Product Management Specialization' and courses on Agile practices, client needs analysis, and software metrics. His recent publications (2023–2025) span AI-driven healthcare innovations (e.g., medical imaging, photoacoustic tomography) and advanced computer vision techniques (e.g., diffusion models, video inpainting). Notable collaborations include EVAREST studies on heart failure management and lung transplantation outcomes. His work bridges software engineering theory and practical applications in healthcare technology, with contributions to federated learning frameworks (e.g., FedLPPA) and AI-augmented clinical decision support systems. Research also extends to autonomous driving (DriveGPT4-V2) and 3D human avatar generation (DreamAvatar), showcasing interdisciplinary impact.

Andreas MalikopoulosView profile

Professor

• Cyber-Physical Systems
• Transportation Systems
• Control Theory
• +4 more

Andreas Malikopoulos is a Professor at Cornell University's School of Civil & Environmental Engineering and Director of the Information and Decision Science Lab (IDS Lab). Previously, he held roles as the Terri Connor Kelly and John Kelly Career Development Professor at the University of Delaware (UD) and founding Director of UD's Sociotechnical Systems Center. He also served as the Alvin M. Weinberg Fellow at Oak Ridge National Laboratory (ORNL), Deputy Director of ORNL's Urban Dynamics Institute, and Senior Researcher at General Motors R&D. His research focuses on cyber-physical systems (CPS), stochastic control, and learning-driven approaches for optimizing energy efficiency and sustainable mobility in smart cities and transportation systems. Education: PhD (Mechanical Engineering, University of Michigan, 2008), M.S. (Mechanical Engineering, University of Michigan, 2004), Diploma (National Technical University of Athens, 2000). Research Interests: Analysis and control of CPS, stochastic scheduling, game theory, and mechanism design applied to emerging mobility systems (e.g., autonomous vehicles, electric vehicles). He emphasizes integrating learning and control for socially optimal solutions in transportation networks. Awards: IEEE ITS Young Researcher Award (2019), UD’s Outstanding Junior Faculty Award (2020), Alvin M. Weinberg Fellowship (2010), and recognition as a NAS Kavli Frontiers of Science Scholar (2012). He is an IEEE Senior Member, ASME Fellow, and serves on editorial boards of leading journals. Teaching: Focuses on optimal decision-making, control theory, and emerging mobility systems. Courses include stochastic optimal control and game theory at Cornell. Labs: Leads the IDS Lab, which develops scalable frameworks for CPS and smart city applications. Current projects include coordinated routing for mixed-traffic systems and AI-driven recommendations for autonomous vehicles.

Kaidi YangView profile

Assistant Professor

• Intelligent Transportation Systems
• Traffic Flow Theory and Control
• Multimodal Transportation Systems
• +5 more

Kaidi Yang is an Assistant Professor at the National University of Singapore (NUS) in the Department of Civil and Environmental Engineering, specializing in Intelligent Transportation Systems and related fields. He holds a PhD from ETH Zurich (2019), an M.Sc. in Control Science and Engineering from Tsinghua University (2014), and dual bachelor’s degrees in Automation and Mathematics from Tsinghua University (2011). His research focuses on advancing traffic control, connected/automated vehicles, shared mobility systems, and data privacy in transportation. He has contributed to developing algorithms for efficient traffic signal control, platooning coordination, and privacy-preserving data sharing in transportation networks. Education: Ph.D., Civil and Environmental Engineering (Transportation), ETH Zurich, 2019 M.Sc., Control Science and Engineering, Tsinghua University, 2014 B.Sc./B.Eng., Dual Degrees in Pure/Applied Mathematics and Automation, Tsinghua University, 2011 Yang has received prestigious awards including the Swiss National Science Foundation’s Postdoc Mobility Fellowship (2021–2022) and the IEEE ITS Conference Best Student Paper Award (2020). He serves as an Associate Editor for the IEEE Conference on Intelligent Transportation Systems (2024). His work bridges theoretical advancements in operations research, robotics, and machine learning with practical applications in urban mobility systems. Recent efforts emphasize integrating privacy-preserving techniques into traffic management and optimizing mixed-autonomy platoon control.

Li SongView profile

Professor

• Building Energy System Optimization
• HVAC System Fault Detection
• Energy Efficiency Technology
• +3 more

Li Song is a Professor and holds the Lesch Centennial Chair & Lloyd G. and Joyce Austin Presidential Professor at the University of Oklahoma's Aerospace & Mechanical Engineering Department. He leads the Building Energy Efficiency Lab and serves as AME Associate Director for Research. His expertise spans building energy systems, HVAC optimization, and fault detection technologies. Education: Ph.D. (Thermal/Fluid Science, 2004) from University of Nebraska-Lincoln; M.S. (Thermal/Fluid Science, 1996) from Harbin Institute of Technology; B.S. (Thermal Energy Systems, 1993) from Shengyang University of Civil Engineering and Architecture. Research focuses on energy-efficient HVAC systems, fault detection algorithms, and building performance analytics. Notable contributions include the ULEM-FDD system for high-performance buildings and virtual sensor technologies for airflow/water flow measurement. Awards include the ConocoPhillips Energy Prize (2011 finalist) and Bes-Tech Innovation Award (2006). Publications emphasize HVAC control strategies, energy modeling, and IoT-enabled diagnostics. Courses taught include Thermodynamics, Energy Efficient Building Systems Design, and HVAC Systems Engineering.

Mahsa GhasemiView profile

Assistant Professor

• Reinforcement Learning
• Control Theory
• Active Perception
• +3 more

Mahsa Ghasemi is an Assistant Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University, leading the AKADEMI Group. Her research focuses on theoretical advancements in trustworthy sequential decision-making for autonomous systems, emphasizing human-aware collaboration and adaptation to dynamic environments. She is affiliated with the Institute for Control, Optimization and Networks (ICON). Education: PhD in Electrical and Computer Engineering from The University of Texas at Austin (2021), MSE in Mechanical Engineering (2017), and BSc in Mechanical Engineering from Sharif University of Technology (2014). Research Interests: Reinforcement learning, control theory, active perception, multi-agent systems, robotics, and online learning. Applications span disaster response, healthcare, and autonomous systems design. Key methodological directions include compositional learning, human-AI collaboration, and adaptive decision-making under uncertainty. Teaching: Courses include Reinforcement Learning Theory (ECE 59500), Introduction to Reinforcement Learning (ECE 49595), and Python for Data Science (ECE 20875). Awards: Finalist for Student Best Paper Award at the 2018 American Control Conference (ACC). Students: Current advisees include Maheed H. Ahmed, Jayanth Bhargav, and Somtochukwu Oguchienti. Past members include Lai Wei and Juan Sebastian Mateo Ruiz Bulla. Service: Editorial roles at ICRA, ICCPS, and IFAC workshops. Reviewer for top conferences (NeurIPS, ICML) and journals (Automatica, IEEE TAC). Labs/Teams: Leads the AKADEMI Group, focusing on algorithmic and theoretical research in autonomous decision-making systems.

• Global Navigation Satellite Systems (GNSS)
• Autonomous Systems
• Sensor Fusion
• +4 more

Patrick Henkel is a Professor at the Technical University of Munich (TUM) affiliated with the TUM School of Engineering and Design and the Chair of Communication and Navigation. He holds a professorship in Satellite Geodesy under Prof. Hugentobler. His research focuses on advanced positioning technologies, including Global Navigation Satellite Systems (GNSS), autonomous systems, and sensor fusion. He develops algorithms for precise positioning in challenging environments such as urban areas, alpine regions, and indoor spaces. His work also extends to environmental applications, such as snow hydrology and climate monitoring using GNSS signals. Henkel’s contributions include innovations in real-time kinematic (RTK) positioning, UAV navigation, and multi-sensor integration for robotics and autonomous vehicles. His research is supported by collaborations with industry and academic partners, addressing both theoretical and applied challenges in geodesy and navigation. Henkel leads projects on GNSS signal processing, satellite-based environmental monitoring, and autonomous driving technologies. He has contributed to the Galileo HAS service and developed methodologies for snow water equivalent estimation using multi-frequency GNSS signals. His expertise spans hardware-software co-design for navigation systems and algorithm optimization for high-precision positioning in dynamic environments. He actively publishes in top-tier journals and conferences, with a focus on advancing the reliability and accuracy of navigation systems across various domains. His advising and grants include funding for projects on sensor fusion, UAV-based measurements, and satellite receiver development. He collaborates with teams at TUM’s Navigation Lab and the Professur für Satellitengeodäsie, contributing to both academic and industrial applications. His work on low-bandwidth RTK dissemination and laser-tracker verified UAV positioning highlights his commitment to bridging theoretical advancements with real-world implementation.

Manoj KarkeeView profile

Professor

• AI in Agriculture
• Agricultural Robotics
• Precision Agriculture
• +3 more

Dr. Manoj Karkee is the Norman R. and Sharon R. Scott Professor of Agriculture and Life Sciences at Cornell University's Department of Biological and Environmental Engineering. He leads the #AgRobotics Lab, focusing on AI, robotics, and automation for precision agriculture. His research includes robotic systems for crop monitoring, harvesting, and sustainable farming practices. Education: PhD in Agricultural Engineering and Human-Computer Interaction, Iowa State University (2009) ME in Remote Sensing and GIS, Asian Institute of Technology (2005) BE in Computer Engineering, Tribhuvan University (2002) Associate Degree in Civil Engineering, Tribhuvan University (1997) Research Interests: AI-driven robotic solutions for fruit harvesting and crop management Soft robotics for orchard operations Autonomous crop monitoring with sensor integration Modular robotic systems for small-scale and urban farming Awards: 2020 Rain Bird Engineering Concept of the Year 2019 Pioneer in AI and IoT (Connected World) CIGR Next Generation Leader (2015) Advising & Collaboration: Dr. Karkee leads a multidisciplinary lab collaborating with industry partners and global institutions to translate research into scalable technologies. He previously directed the Washington State University Center for Precision and Automated Agricultural Systems. Labs/Teams: The #AgRobotics Lab at Cornell develops AI and robotics solutions for labor-intensive agricultural tasks, emphasizing precision, sustainability, and economic viability.

Barzan MozafariView profile

Associate Professor

• Database Systems
• Data Management
• Machine Learning
• +3 more

Barzan Mozafari is an Associate Professor of Computer Science and Engineering at the University of Michigan, Ann Arbor, and leads a research group focused on scalable database systems and approximate computing. He holds a PhD from UCLA (2011) and was a Postdoctoral Associate at MIT. His research emphasizes data-intensive systems, combining statistical models, optimization, and machine learning to enhance database performance and predictability. Notable projects include BlinkDB (approximate query processing), DBSeer (database diagnosis), and VerdictDB (platform-independent AQP). He co-founded Keebo, advancing data learning technologies, and contributed to SnappyData (acquired by TIBCO). His work spans technical innovations like CATS/VATS scheduling algorithms (adopted in MySQL/MariaDB) and grants such as ConFlux (NSF-funded supercomputing-big data integration) and a smart black-box for autonomous vehicles. Awards include the NSF CAREER Award and Best Paper recognitions at SIGMOD and EuroSys. Teaching includes EECS 484/584 (Database Management Systems) and advanced topics courses. He advises students like Yongjoo Park (SIGMOD Dissertation Runner-Up), Jiamin Huang, and Boyu Tian.

Rita BorgoView profile

Professor

• Data Visualization
• Computer Science
• Human-Computer Interaction
• +4 more

Dr Rita Borgo is a Professor in Data Visualization and Head of the Human Centred Computing Group at King's College London's Department of Informatics. She holds a leadership role within the Faculty of Natural, Mathematical & Engineering Sciences and is affiliated with the Centre for Urban Science and Progress (CUSP) London. Her research focuses on interdisciplinary visualization challenges, including human-computer interaction, AI trust calibration, and epidemiological modeling. Education details are not explicitly stated in the provided text. Her work spans over 58 publications, emphasizing visualization techniques for large datasets, trust in AI systems, and urban science applications. Key projects include RAMPVIS (visual analytics for pandemic response) and Trusted Autonomous Systems Hub (AI ethics and human-machine partnerships). Research interests include data visualization, human factors, generative AI, and policy simulation. Recent articles explore trust calibration in AI, time-series visualization, and ethical clinical decision support systems. She has led grants totaling £multi-million, including EPSRC-funded initiatives. Collaborations with organizations like ContactEngine Limited highlight her industry engagement. Labs/Teams: Leads the Human Centred Computing Group and contributes to CUSP's urban data initiatives. Supervised student work includes a notable BSc thesis by Munkhtulga Battogtokh. Current projects address visualization in nuclear policy, social media mental health correlations, and trustworthy autonomous systems.

Young-Jin ChaView profile

Professor

• Deep Learning
• Structural Health Monitoring (SHM)
• Autonomous UAVs
• +5 more

Dr. Young-Jin Cha is a tenured full Professor in the Department of Civil Engineering at the University of Manitoba, affiliated with the Price Faculty of Engineering. He holds a PhD from Texas A&M University and has postdoctoral experience at MIT. His research focuses on deep learning-based structural health monitoring (SHM), autonomous UAVs for infrastructure inspection, and smart transportation systems, with over 100 peer-reviewed publications and $1.2M in grants. He is a Fellow of ASCE and has received notable awards including the 2021 Merit Award and 2022 International Association of Advanced Materials Scientist Award. His work has been cited over 9,200 times globally. Research interests include automated SHM with UAVs, nonlinear system identification, unsupervised deep learning for damage detection, and sustainable infrastructure design. He serves as an editor for journals like Structural Control & Health Monitoring and Engineering Reports . His lab, the Laboratory for Infrastructure Science and Technology (LIST), develops advanced technologies for infrastructure resilience. Key achievements include pioneering deep learning-based SHM with UAVs, top-cited papers in civil engineering journals, and leadership in organizing international conferences. He actively seeks graduate students for research in AI-driven infrastructure solutions.

Jie XuView profile

Researcher

• Polymer Electronics
• Flexible Electronics
• Sustainable Materials
• +4 more

Jie Xu is a Scientist at Argonne National Laboratory and a CASE Affiliated Scientist at the University of Chicago, Pritzker School of Molecular Engineering . Her research focuses on engineering durable, scalable, and sustainable polymer semiconductors for skin-like electronics and autonomous material discovery. Education : PhD in Chemistry (Nanjing University), Postdoctoral Fellow (Stanford University) Her research bridges polymer physics , self-driving laboratories , and AI-guided material synthesis to address challenges in stretchable electronics, recyclable polymers, and energy-efficient manufacturing. She pioneered polymer circuits that remain conductive under extreme deformation and developed the first roll-to-roll mass-production method for stretchable semiconductors. Her 15 most recent articles highlight advancements in AI-driven polymer discovery , biodegradable electronics , and multi-modal energy dissipation . Key themes include autonomous experimentation , hydrogen-bonded polymer systems , and machine learning for conjugated polymers , with applications in wearable medical sensors , soft robotics , and human-computer interfaces . Scientific accolades include the Materials Research Society Postdoctoral Award , MIT Technology Review’s Innovators Under 35 , and recognition as a Scialog Fellow . She serves on editorial boards for APL Machine Learning and Flexible Electronics , and her team at Argonne includes postdocs and students working on self-driving labs and degradable polymers .