Find Academic Supervisors

• Artificial Intelligence
• Computer Vision
• Domain Adaptation
• +6 more

Tatiana Tommasi is an Associate Professor at the Department of Control and Computer Science (DAUIN) at Polytechnic University of Turin, actively contributing to advanced AI research and education. She serves as Deputy Coordinator of the National Doctoral College in Artificial Intelligence and Scientific Advisor for the European AI, Data, and Robotics Association (ADRA) and ELLIS Network. Her affiliations include the SmartData@PoliTO Laboratory and VANDAL Visual and Multimodal Applied Learning Laboratory. Her research focuses on algorithm fairness, computer vision, domain adaptation, autonomous driving, and robot learning. Recent work includes improving neural network reliability through fault-aware design, 3D semantic novelty detection, and visual relationship reasoning for robotic grasp planning. She has contributed to European research projects like ELSA (Secure and Safe AI) and commercial initiatives in space technologies and autonomous vehicle uncertainty quantification. Selected 2024-2025 publications demonstrate expertise in foundation models, fairness metrics, uncertainty estimation, and geometric deep learning. Her team leads commercial research projects on industrial robotics trajectory generation and satellite data analysis. She teaches core courses in Computer Engineering, Automotive Engineering, and Data Science, mentoring PhD students in Artificial Intelligence and Computer Systems Engineering. Research keywords: Artificial Intelligence, Machine Learning, Computer Vision, Domain Adaptation, Robot Learning, Transfer Learning, Fairness in AI, Uncertainty Quantification, Autonomous Driving, 3D Point Cloud Analysis, Vision Transformers, Industrial Robotics.

• Applied Cognitive Psychology
• Metacognition
• Artificial Intelligence
• +5 more

Nadia Said is a researcher at the University of Tübingen, Faculty of Mathematics and Natural Sciences, specializing in Applied Cognitive Psychology. Holding a physics diploma and psychology bachelor's from Heidelberg University, her work bridges cognitive science with environmental behavior, AI interaction, and misinformation processing. PhD in Psychology and Scientific Computing from Heidelberg University Current affiliation with University of Tübingen since 2024 Her research focuses on: Metacognitive foundations of environmental behavior AI perception and risk assessment Information processing in polarized contexts Mathematical modeling of psychological phenomena Key publication trends include Cognitive conflict in social media interactions Metacognition's role in climate action AI trust and explainability Belief polarization mechanisms Human-robot cooperation dynamics Scientific contributions recognized through Post-Doc Grant for event cognition research SAW Post-Doc Network participation HCE and ZUK 5.4 funded projects FRONTIER excellence initiative support

Xiaoxuan YangView profile

Assistant Professor

• Processing-in-Memory Systems
• Neuromorphic Computing
• Hardware Accelerators
• +3 more

Xiaoxuan Yang is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Virginia . Her research focuses on processing-in-memory-based system design , biologically plausible systems , and hardware accelerators for emerging applications. She has held postdoctoral positions at Stanford University's Robust Systems Group and served as a research scientist at the University of Virginia. Ph.D. : Electrical and Computer Engineering, Duke University M.S. : Electrical Engineering, University of California, Los Angeles B.S. : Electrical Engineering, Tsinghua University Her research integrates neuromorphic computing , LLM acceleration , and hardware-software co-design , with specific interests in ReRAM crossbars , photonic neural networks , and memristor synapses . Current projects address stochastic noise resilience , quantization optimization , and energy-efficient AI . The 15 most recent publications explore PIM architectures (38%), neuromorphic systems (30%), ML hardware (25%), and optical computing (7%). Key trends include large language model acceleration , hardware robustness , and emerging memory technologies . Third Place ACM Student Research Competition (ICCAD) Best Research Award ACM SIGDA Ph.D. Forum (DAC) Best Paper Award GLSVLSI 2025 Rising Star in EECS NSF iREDEFINE Fellow Machine Learning and Systems Rising Star Rising Scholars Postdoc Fellow

• Smart Transportation
• Sustainable Mobility
• Environmental Impact Assessment
• +4 more

Cristina Pronello is a Full Professor at the Politecnico di Torino , affiliated with the Interuniversity Department of Territorial Sciences, Planning and Policies (DIST) and the SmartData@PoliTO - Big Data and Data Science Laboratory . Her career spans international collaborations at institutions like IFSTTAR, Université Lumière Lyon 2, and École des Ponts ParisTech. Research Interests Smart transportation Environmental impact assessment Data collection via IoT and smartphones Travel behavior analysis Intelligent Transport Systems (ITS) Pro-environmental behavior Notable Scientific Awards TRA VISIONS 2020 (European Commission, 2020) 2010 award from Université Paris Est Marie Curie fellowship (INRETS-LTE, 1994) Key Contributions Her research integrates big data analysis with sustainable mobility, focusing on behavioral theories and environmental impact mitigation. She leads EU-funded projects like FORTHCOMING and WE-TRANSFORM , while contributing to editorial boards of journals such as Sustainable Cities and Society . She has supervised numerous PhD students and designed courses on smart transportation, behavioral theories, and data applications in mobility.

Hansong TangView profile

Professor

• Coastal Engineering
• Fluid Dynamics
• Numerical Modeling
• +5 more

Professor Hansong Tang is a faculty member in the Department of Civil Engineering at The City College of New York (CCNY), part of The Grove School of Engineering. He holds a professorial rank and is affiliated with the Earth System Science and Environmental Engineering program. His research focuses on coastal and environmental fluid dynamics, with expertise in numerical modeling of multiphysics systems, including fluid-structure interactions, sediment transport, and infrastructure resilience under extreme events. He has made significant contributions to domain decomposition methods, hybrid modeling frameworks for coastal processes, and computational approaches for complex hydrosystems. Key research areas include: Coastal hydrodynamics and morphodynamics Hybrid computational models for ocean flows Infrastructure vulnerability under climate change Fluid dynamics of energetic suspensions Machine learning applications in coupled PDE solutions His work emphasizes bridging scales in coastal systems (e.g., coupling Navier-Stokes equations with geophysical fluid dynamics models) and addressing challenges in tidal energy generation, storm surge impacts, and hydraulic fracturing fluid behaviors. Recent studies include assessments of New York City coastal bridges' vulnerability to extreme storm surges and investigations into solitary wave impacts on bridge decks. Publications reflect a focus on numerical methods, experimental validations, and environmental engineering applications, with frequent exploration of interface treatment algorithms and error correction techniques in fluid dynamics simulations.

Nektaria TryfonaView profile

Academic

• AI and Cybersecurity
• Spatiotemporal Databases
• Data Management Systems
• +4 more

Nektaria Tryfona is a Collegiate Associate Professor at Virginia Tech's Bradley Department of Electrical and Computer Engineering, located at the Virginia Tech Research Center in Arlington. She holds a Ph.D., M.Sc., and B.Sc. in Computer Science from the University of Patras, Greece. Her research focuses on experiential learning at the intersection of AI and cybersecurity, resilient ML algorithms, spatiotemporal databases, and data management in data-intensive environments. She also develops industry-collaborative graduate capstone projects and teaches courses blending engineering with business principles. Her work emphasizes resilient ML for privacy/security , data quality in spatiotemporal systems , and geographic database design . Notable contributions include models for dynamic travel time prediction, ontology-based location services, and query optimization for user groups. Teaching interests extend to m-learning frameworks for K-12 education and professional development programs. While no awards are listed, her extensive publication record (spanning 1991–2017) showcases expertise in database modeling, geographic applications, and spatiotemporal systems. Advising/grants sections remain unspecified, and no lab/team affiliations are detailed.

• Cybersecurity
• Underwater Acoustic Networks
• Hardware Security
• +3 more

Zhijie Jerry Shi is an Associate Professor at the School of Computing, University of Connecticut. His research focuses on cybersecurity, underwater acoustic networks, hardware security, and blockchain technology. He leads projects such as Ocean-TUNE, a community testbed for underwater communication, and has contributed to secure firmware updates, cryptographic algorithms, and IoT security architectures. His work integrates hardware and software solutions for resilient systems. Key research areas include cryptographic side-channel attacks, underwater acoustic communication protocols, and blockchain scalability. He has developed innovative solutions for underwater sensor networks, including Aqua-Net and Aqua-Sim simulators, and explored secure metering systems using blockchain (SMChain). His hardware security research addresses vulnerabilities in embedded systems and trustable computing education. His publications span over 15 years, with recent emphasis on hardware trojan detection, diffractive optical neural networks, and secure IoT protocols. Collaborations involve experimental deployments in underwater environments and joint work on FPGA-based acoustic modems. He advises on secure wireless protocols, cryptographic implementations, and underwater network resilience.

Bing WangView profile

Professor

• Computer Networking
• Quantum Communications
• Network Security
• +4 more

Professor Bing Wang is a faculty member in the Computer Science & Engineering Department at the University of Connecticut. He leads the Computer Networking Research Group, focusing on computer networking, ubiquitous computing, multimedia streaming, network security, and quantum communications. His research integrates machine learning for depression treatment prediction using smartphone data and explores secure quantum key distribution systems. Research interests span quantum networks, adaptive bitrate streaming optimization, wireless network routing strategies, and leveraging mobile sensory data for mental health diagnostics. His work addresses challenges in network security (e.g., BGP hijacking defenses), energy-efficient streaming, and resilient microgrid systems through programmable networks. Publications emphasize cross-disciplinary applications of networking principles, including depression symptom prediction via smartphone sensors and quantum repeater security. Notable contributions include frameworks like C2 for ABR streaming and BGP-Isec for routing security. His group actively develops methodologies for optimizing network performance while balancing resource usage and user experience.

Jamie SikoraView profile

Assistant Professor

• Quantum Computing
• Quantum Information
• Quantum Cryptography
• +3 more

Dr. Jamie Sikora is an Assistant Professor in the Department of Computer Science at Virginia Polytechnic Institute and State University (Virginia Tech), within the College of Engineering. His research focuses on quantum computing, quantum information, quantum cryptography, semidefinite programming, and convex optimization. He holds a Ph.D. in quantum computing from the University of Waterloo, Canada. His work addresses foundational and applied challenges in quantum information science, including cryptographic protocols, error mitigation, and optimization techniques. Recent research has explored quantum state exclusion, secure sampling, and device-independent protocols. His contributions span theoretical frameworks for quantum algorithms and cryptographic systems, with implications for post-quantum security and quantum communication. He has published extensively in top venues, including the Conference on the Theory of Quantum Computation and the Physical Review series. His articles often bridge quantum theory with practical cryptographic and computational applications, emphasizing rigor in protocol design and analysis.

James MickensView profile

Professor

• Computer Security
• Cloud Computing
• Systems Architecture
• +3 more

James Mickens is the Gordon McKay Professor of Computer Science at Harvard University's School of Engineering and Applied Sciences (SEAS). He specializes in systems security, cloud computing, and web technologies. His research focuses on fundamental advancements in secure systems design, including isolation mechanisms for AI, privacy-preserving protocols, and fault-tolerant storage solutions. Notable contributions include groundbreaking work on hypervisor security (Guillotine), TinyML vulnerability analysis, and resilient cloud storage systems (Carbink). He has been recognized with the Harvard College Professorship (2022) and tenure (2019). His work spans theoretical breakthroughs and practical implementations, often addressing critical gaps in system reliability and user privacy. Mickens' research trends emphasize proactive security measures, efficient resource management, and balancing performance with safety in distributed environments. His articles reflect a focus on real-world applicability, from browser optimization (Crom, Vesper) to cloud infrastructure resilience (Blizzard, Oort). Awards include the Harvard College Professor title, signaling exceptional teaching and scholarly impact. While specific grant details are not provided, his prolific publication record suggests sustained funding in systems research. Mickens' work often critiques conventional approaches, exemplified by his humorous yet incisive keynote critique of 'security as an afterthought.'

• Cognitive engineering
• Interface design
• Trust in automation
• +4 more

John D. Lee serves as the Emerson Electric Professor in the Department of Industrial & Systems Engineering at the University of Wisconsin-Madison College of Engineering, with additional affiliation in Civil & Environmental Engineering. His pioneering research examines human-automation interaction and trust dynamics across domains including UAVs, maritime operations, automated vehicles, and deep space exploration. He co-authored the foundational textbook Designing for People: An Introduction to Human Factors Engineering and edited the Oxford Handbook of Cognitive Engineering , with research funded by NSF, ONR, NHTSA, NASA, and major automotive manufacturers. His educational background includes: PhD, University of Illinois, Urbana-Champaign (1992) MS, University of Illinois, Urbana-Champaign (1989) BS, Lehigh University (1988) BA, Lehigh University (1987) Year Abroad Program, University of York (1985) Dr. Lee's research centers on cognitive engineering and trust calibration in human-AI systems. He investigates how interface design, automation transparency, and human adaptation mechanisms affect team performance in safety-critical environments. His work develops frameworks for appropriate reliance on automation through modeling human behavior and designing responsive systems that maintain operator situational awareness. Recent publications (2023-2025) reveal expanding focus on conversational trust metrics, prosocial automation design, and explainable AI interfaces. The research increasingly integrates machine learning for dialogue analysis while maintaining core human factors principles, with growing applications in healthcare automation and environmental AI systems alongside traditional transportation domains. His major recognitions include: Byron Bird Research Publication Award (2022) Paul M. Fitts Education Award (2014) Human Factors and Ergonomics Society Fellow (2011) Emerson Electric Professorship (2009) Dr. Lee mentors graduate researchers through I SY E 699, 790, 890, and 990 courses while leading funded projects from: Federal Agencies: NSF, ONR, NHTSA, NASA Industry Partners: Nissan, Toyota, GM His laboratory develops experimental methodologies for measuring trust dynamics in real-world settings, focusing on driver-automation collaboration and AI explainability challenges in complex operational environments.

Christoforos HadjicostisView profile

Professor

• Fault Diagnosis
• Distributed Algorithms
• Discrete Event Systems
• +3 more

Christoforos Hadjicostis is Professor of Electrical and Computer Engineering at the University of Cyprus. He previously served as Chair of the department from 2008-2010 and held positions at the University of Illinois at Urbana-Champaign. He holds S.B. degrees in Electrical Engineering, Computer Science, and Mathematics, an M.Eng. in Electrical Engineering and Computer Science (1995), and a Ph.D. in Electrical Engineering and Computer Science (1999) from MIT. His research focuses on fault diagnosis and tolerance in distributed dynamic systems, error control coding, monitoring and control of discrete event systems, with applications in network security, anomaly detection, energy distribution, medical diagnosis, biosequencing, and genetic regulatory models. His publications primarily explore formal methods for system verification, distributed algorithms for multi-agent coordination, and privacy-preserving mechanisms in discrete event systems. Recent work emphasizes detectability, observability, and opacity enforcement in networked and cyber-physical systems.

Joohee KimView profile

Associate Professor

• Image and Video Signal Processing
• Computer Vision
• Machine Learning
• +5 more

Joohee Kim is an Associate Professor in the Department of Electrical and Computer Engineering at Illinois Institute of Technology, where she has been since 2009. She directs the Multimedia Communications Laboratory and focuses on research funded by U.S. Federal Agencies and the Korean Government. Education: Ph.D., Electrical and Computer Engineering, Georgia Institute of Technology (2003) M.S., Electrical Engineering, Yonsei University (1993) B.S., Electrical Engineering, Yonsei University (1991) Research interests include image/video signal processing, computer vision, machine learning, multimedia communication, and real-time 3D reconstruction . Her work spans applications in autonomous systems, robotics, and advanced driver assistance systems, emphasizing low-complexity algorithms and efficient coding techniques. Her recent articles highlight advancements in pedestrian detection via deep learning, 3D reconstruction fusion methods, and optimized depth map coding. These contributions address challenges in real-time systems, energy efficiency, and robust transmission over wireless networks. Awards: None explicitly listed. Grant Activity: Active in federal and international funding for projects like low-delay distributed video coding and error-resilient transmission. Advising: No students listed, though lab leadership implies mentorship roles. Labs/Teams: Director of the Multimedia Communications Lab, collaborating on 3D imaging, video coding, and intelligent systems integration.

• Episodic Memory
• Neuroscience
• Aging
• +5 more

Roberto Cabeza is a Professor of Psychology and Neuroscience, and Professor in Psychiatry and Behavioral Sciences at Duke University. He is affiliated with multiple institutes including the Center for Cognitive Neuroscience, Duke Institute for Brain Sciences, and the Center for Brain Imaging and Analysis. His research focuses on the neural correlates of memory and aging using fMRI, emphasizing episodic memory processes, cross-functional brain comparisons, and age-related changes in neural activity. He earned his Ph.D. in 1994 from the University of Tsukuba. His work investigates three core areas: neural mechanisms of episodic memory, cross-functional brain comparisons, and aging effects. Recent studies highlight age-related increases in bilaterality and functional compensation in neural activity. He leads the Mnemology Lab, exploring memory representations, networks, and aging impacts through behavioral and neuroimaging methods. Major grants include the Duke/UNC ADAR Program (NIH 2024–2029) and studies on cognitive reserve and resilience. His publications (290+ entries) span insights into memory encoding, retrieval, and creativity, with recent work linking prediction error to memory and creativity. Past roles include Director of the Center for Cognitive Neuroscience and Bass Connections faculty leadership.

• Quantum Computing
• Quantum Algorithms
• Quantum Chemistry Simulations
• +3 more

Abhinav Anand is a Researcher at Duke University's Pratt School of Engineering, focusing on quantum computing and its applications in algorithms, error correction, and molecular simulations. His work bridges theoretical physics and computational methods to address challenges in near-term quantum computing (NISQ era). Research interests include variational quantum algorithms, fault-tolerant quantum computation, quantum chemistry simulations, and quantum information theory. He has contributed to topics like noisy channel mixtures, stabilizer configuration interaction, and operator implementations for quantum systems. Key trends in his articles emphasize robustness in quantum measurements, parameter optimization in variational methods, and leveraging classical simulatable circuits for quantum compression. His work often intersects with machine learning approaches for quantum algorithm design. No scientific awards are mentioned, but his research has been published in top-tier quantum computing journals. He has advised no listed students, but collaborates on projects involving quantum software frameworks like Tequila. His research team explores quantum processor simulation and error detection techniques in molecular systems.