Find Academic Supervisors

Kevin HughesView profile

Senior Lecturer

• Fuel Combustion
• Fuel Cells
• Process Modelling in Carbon Capture and Storage (CCS)
• +3 more

Kevin Hughes is a Senior Lecturer in the Energy Engineering Group at the Department of Mechanical Engineering, School of Mechanical, Aerospace and Civil Engineering, University of Sheffield. He holds a PhD and first degree in Chemistry from the University of Leicester (1987) and focuses on fuel combustion, fuel cells, and process modelling in carbon capture and storage (CCS) systems. His research combines experimental and theoretical approaches, including planar laser diagnostics, quantum chemistry, and CFD simulations. Education: PhD and BSc in Chemistry from University of Leicester. Research Interests: Fuel combustion, pollutant chemistry, PEM fuel cells, CCS process modelling, catalyst development, and combustion in supercritical CO2. Grant Projects: FP7-ENERGY-2010-2 (RELCOM), Gas-FACTS (EPSRC), EP/J020788/1, EP/M001482/1 (Selective EGR), TEABPP (Energy Technology Institute). Scientific Contributions Publications: Over 50 papers on fuel combustion mechanisms, fuel cell optimization, CCS systems, and alternative fuels. Collaborations: Regular work with M. Pourkashanian, D.B. Ingham, S. Michailos, and M.S. Ismail. Technical Expertise Chemical Kinetics Validation Quantum Chemistry Applications Gas Diffusion Layer Analysis Surrogate Fuel Development Supercritical Combustion

• Electronic Design Automation
• Neural Networks
• Accelerators
• +3 more

Wenhao Sun is a researcher at the Chair of Design Automation at the Technical University of Munich (TUM). His work focuses on advancing neural network design and electronic design automation (EDA), particularly in areas like accuracy enhancement and class-based quantization for AI models. Research: Neural Networks, Accelerators, Analog EDA, Timing Analysis Contact: wenhao.sun@tum.de Recent publications highlight his contributions to optimizing neural networks for hardware efficiency, with two papers presented at the 2023 Design, Automation and Test in Europe (DATE) conference. His work bridges the gap between machine learning and EDA, focusing on incremental and quantization-based improvements.

Ruozhou YuView profile

Assistant Professor

• Computer Networks
• Distributed Systems
• Cybersecurity
• +10 more

Ruozhou Yu is an Assistant Professor in the Department of Computer Science and a Courtesy Assistant Professor in the Department of Electrical and Computer Engineering at NC State University. His research focuses on computer networks, distributed systems, and cybersecurity , with applications to quantum networking, blockchain, IoT, cloud/edge computing, and machine learning . He earned his PhD in Computer Science from Arizona State University (2019) and his BS from Beijing University of Posts and Telecommunications (2013). Education: PhD, Computer Science, Arizona State University, 2019 BS, Computer Science, Beijing University of Posts and Telecommunications, 2013 Yu's research spans quantum internet (high-fidelity entanglement distribution, satellite-assisted quantum networks), blockchain technologies (payment channel networks, smart contracts, layer-2 security), and edge computing (resource provisioning, SLA verification, market design). He also explores machine learning in distributed systems (LLM fine-tuning on graphs) and network security (data delay attacks, Byzantine-robust federated learning). His recent publications (2024–2025) emphasize quantum networking (LACE, QuESat), edge computing SLAs (VeriEdge, WolfPack), and blockchain security (Thor, Fence). Articles like AdaOrb (PerCom 2025) and Physics-Informed Scheduling (RTAS 2025) highlight cross-domain innovations. Awards & Recognition: NSF CAREER Award (2021) IEEE TNSE Excellent Editor Award (2024) IEEE INFOCOM Distinguished TPC Member (2024, 2022, 2020) Yu supervises PhD and Master's students in quantum networking (Huayue Gu), blockchain (Xiaojian Wang), and edge computing (Zhouyu Li). He serves as Associate Editor for IEEE Transactions on Network Science and Engineering and Area Editor for Elsevier Computer Networks .

Lei WangView profile

Associate Professor

• Cyber-Physical Systems
• Aquatic Systems
• Embedded Computing with Renewable Energy
• +3 more

Lei Wang is the F. L. Castleman Associate Professor in Engineering Innovation at the University of Connecticut's School of Engineering, Department of Electrical and Computer Engineering. He holds a PhD from the University of Illinois at Urbana-Champaign (2001), an MS (1996) and BS (1992) from Tsinghua University, China. His research focuses on cyber-physical systems , embedded computing with renewable energy , and nanoscale integrated circuit design . Key areas include microbial fuel cells , memristor-based hardware security , and low-power signal processing architectures . Recent work trends involve Quantum-dot transistor applications for in-memory computing Adaptive LDPC decoder optimization Hardware security leveraging memristor properties Energy-efficient power management systems for underwater sensors Scientific recognition includes National Science Foundation CAREER Award (2010) F. L. Castleman Term Professorship in Engineering Innovation Professional roles encompass editorial and committee positions at IEEE and ACM journals. His work spans interdisciplinary domains in renewable energy integration , VLSI design , and bio-inspired computing systems .

• Internet of Things (IoT)
• Fog Computing
• Edge Computing
• +16 more

Amir Taherkordi is a Professor in the Networks and Distributed Systems group at the Department of Informatics, University of Oslo, Norway. His research focuses on resource-efficiency, scalability, adaptability, and mobility in distributed systems for emerging technologies like IoT, Fog/Edge/Cloud Computing, and Cyber-Physical Systems (CPS). University: University of Oslo Department: Informatics Academic Rank: Professor His research spans IoT, Edge/Fog Computing, and Cyber-Physical Systems, emphasizing energy efficiency, privacy preservation, and self-adaptive architectures. Key areas include network traffic classification, computation offloading, and federated learning applications in vehicular systems. Recent publications highlight advances in latency-aware IoT data transmission , federated vehicular networks , energy-efficient wireless charging , and privacy-preserving data integration . These works often integrate machine learning with network optimization. Projects include the CPS Lab at UiO, DILUTE (Fluid Service Abstraction), and the Gemini Centre on IoT . He collaborates on initiatives like PACE for energy informatics curricula development.

Anna LukinaView profile

Assistant Professor

• Artificial Intelligence Safety
• Formal Methods
• AI Interpretability
• +4 more

Anna Lukina is an Assistant Professor in the Department of Intelligent Systems at Delft University of Technology's Faculty of Electrical Engineering, Mathematics and Computer Science. She leads the Sequential Uncertainty Monitoring and Interpretability (SUMI) Lab, focusing on improving safety and interpretability of artificial intelligence through formal methods with applications in engineering, transportation, health, and finance. Her research spans the critical intersection of formal verification and machine learning, particularly in developing techniques for runtime monitoring of neural networks, safety verification of decision-tree policies, and creating verifiable reinforcement learning systems. She has established strong international collaborations with researchers across the US, Europe, Japan, and Australia. Lukina's recent publications (2021-2025) demonstrate consistent output in top AI venues including AAAI, NeurIPS, and IJCAI, with a clear trajectory toward increasingly sophisticated verification techniques for complex AI systems. Her work shows strong emphasis on practical applications while maintaining theoretical rigor, particularly in creating methods that provide formal guarantees for black-box AI systems. As part of her service commitment, she leads initiatives promoting junior computer scientists from underrepresented communities, reflecting her dedication to diversity in the field as highlighted in her DerStandard interview "Warum so wenige Frauen Den Code knacken wollen" and university magazine Delta. She currently supervises multiple PhD researchers including Sterre Lutz, Daniël Vos, Aaron Berger, and Johannes Koch, along with numerous successful MSc graduates who have completed theses on topics ranging from anomaly detection to genetic programming for explainable AI.

• Image Processing
• Machine Learning
• Artificial Intelligence
• +7 more

Dilip K. Prasad is a Professor at the Department of Informatics, UiT The Arctic University of Norway. His work bridges Artificial Intelligence and Medical Imaging , with a focus on Interpretable AI , Scalable AI , and Life Science Applications . He has contributed to Maritime Technology and Biomedical Engineering . Ph.D. and B.Tech from Nanyang Technological University and IIT Dhanbad Senior Research Fellow at NTU (2015-2019), Research Fellow at NUS (2012-2015) Industry experience at IBM, Infosys, Mediatek, Philips His research explores Image Processing , Machine Learning , and AI Applications in Biomedicine . Recent work includes Dense Video Captioning , 3D Mitochondrial Modeling , and Physics-Guided Loss Functions . Articles span Neurocomputing , Optics Express , and top AI conferences like CVPR and NeurIPS . Prasad has received the Rolls-Royce Inventor Award (2016) and Best Paper Award (IJCIE 2017) . He has reviewed for 50+ journals and 30+ conferences, serving as Area Chair for NeurIPS 2022-23 and Organizer Chair for ICCV Workshop 2023 .

• Construction Engineering
• Discrete-Event Simulation
• Industrial Construction
• +6 more

Yasser Mohamed is a Professor in the Civil and Environmental Engineering Department at the University of Alberta . His academic and professional focus revolves around construction engineering, discrete-event simulation, and process optimization for industrial and tunneling operations. He has also explored knowledge engineering techniques and the application of TRIZ (Theory of Inventive Problem Solving) to construction processes. Email: yaly@ualberta.ca Location: 7-269 Donadeo Innovation Centre For Engineering, Edmonton, AB Courses Taught: CIV E 603 (Construction Informatics), CIV E 606 (Design and Analysis of Construction Operations) His research emphasizes modeling construction processes using discrete-event simulation to optimize performance and develop synthetic environments for construction operations. Recent publications, however, indicate a shift toward power systems, focusing on DC microgrids , grid-forming converters , and renewable energy integration . Scientific Awards: None explicitly mentioned in the provided data. Advising and Grants: No formal advisees listed. A co-applicant on a CRD grant (2007–present) for synthetic environments in construction simulation.

Penghui CaoView profile

Associate Professor

• Materials Science
• Mechanical Engineering
• Extreme Environments
• +7 more

Penghui Cao is an Associate Professor at the Samueli School of Engineering, University of California, Irvine, with a joint appointment in Materials Science and Engineering. His research focuses on the fundamental mechanisms of material deformation and failure under extreme conditions. Key affiliations: Mechanical and Aerospace Engineering, Materials Science and Engineering Research areas: Plastic deformation, phase transitions, radiation damage tolerance, and machine learning applications in materials science Recent publications highlight his work on high-entropy alloys, nanomechanics, and additive manufacturing processes. His studies integrate atomistic simulations and experimental validations to understand material behavior at extreme scales. His laboratory, the Laboratory for Extreme Mechanics & Materials , investigates phenomena such as shock compression, chemical ordering, and grain boundary dynamics.

Zhiqiang TaoView profile

Assistant Professor

• Machine Learning
• Computer Vision
• Computational Imaging
• +7 more

Zhiqiang Tao is an Assistant Professor in the School of Information at the Golisano College of Computing and Information Sciences, Rochester Institute of Technology (RIT). Prior to RIT, he served as an Assistant Professor at Santa Clara University (2020–2022). He holds a Ph.D. from Northeastern University (ECE Department) and B.E. and M.E. degrees from Tianjin University. Research Focus: His research group develops smart, scalable, and secure machine learning algorithms for healthcare, AI education, computational imaging, and data science. Key areas include large vision-language models (LVLMs), calibrated uncertainties in low-level vision, ranking fairness, and multimodal understanding. Recent works explore LLM fairness, medical question-answering systems, and diffusion models for text-video retrieval. Awards & Honors: RIT GCCIS Emerging Scholar Award (2025) Teaching: IDAI-780: Capstone Project ISTE-780: Data-Driven Knowledge Discovery Advising & Team: Actively seeking motivated graduate/undergraduate students to join his research group. His team has produced impactful work in NeurIPS, CVPR, ECCV, and IEEE TPAMI/TKDE. Labs & Collaboration: Collaborates across disciplines to advance AI applications in healthcare and education, leveraging computational imaging and fair ML principles.

Farhan TanvirView profile

Lecturer

• Graph Mining
• Bioinformatics
• Computational Biomedicine
• +3 more

Farhan Tanvir is a Lecturer in the Department of Computer Science at Georgia State University. His research focuses on graph mining applications in bioinformatics and computational biomedicine, particularly addressing challenges in drug-drug interaction prediction, drug repurposing, and disease modeling. He holds a PhD in Computer Science from Oklahoma State University and a BSc in Computer Science and Engineering from the Islamic University of Technology. His work emphasizes heterogeneous networks to model relationships between biological entities like drugs, proteins, and diseases. Recent research trends include tackling oversmoothing in graph neural networks (GNNs) through sparsification techniques and developing attention-based models for biomedical applications. His publications appear in top-tier venues such as KDD, DSAA, and ACM BCB. Tanvir has contributed to projects leveraging graph-based methods for computational biology, with a focus on predictive modeling and network analysis. His office is located at 1 Park Place, room 717.

Christopher CrickView profile

Associate Professor

• Artificial Intelligence
• Cognitive Science
• Human-Robot Interaction
• +7 more

Christopher Crick is an Associate Professor in the Department of Computer Science at Oklahoma State University. He leads the Robotic Cognition Laboratory, focusing on grounding developmental psychology and cognitive science in embodied AI systems, while improving robotics through human cognition-inspired models. University: Oklahoma State University Department: Computer Science Academic Rank: Associate Professor Email: chris.crick@okstate.edu, chriscrick@cs.okstate.edu Research Interests: Artificial Intelligence Cognitive Science Human-Robot Interaction Atmospheric Sciences (via UAV applications) Machine Learning Medical Informatics Scientific Activities: NSF-funded research in robotics, UAVs, and AI Professional memberships: Cognitive Science Society, ACM, AAAS Editorial roles and conference reviewing in robotics and AI Lab: Robotic Cognition Laboratory

Sangtae HaView profile

Associate Professor

• Machine Learning
• Deep Learning Systems
• Networks
• +6 more

Sangtae Ha is an Associate Professor in the Computer Science Department at the University of Colorado Boulder. His research focuses on building practical computer systems spanning multiple disciplines, including machine learning/deep learning systems, networks and distributed systems, internet protocols, wireless networks, video streaming, storage systems, and security. He specializes in creating efficient and scalable solutions for real-world computing challenges. His work emphasizes interdisciplinary approaches, bridging theoretical computer science with practical system design. Key areas of exploration include optimizing neural network execution for edge computing, developing adaptive streaming protocols, and enhancing wireless network performance through novel signal processing and spectrum management techniques. Recent projects include frameworks for semantic offloading in neural networks and reinforcement learning-based cloud scheduling. No scientific awards or notable grants are explicitly mentioned in the provided materials. While no formal advisees are listed, his research team likely involves graduate students and collaborators. No dedicated labs or teams are named, though his work aligns with broader university initiatives in computer systems and networking.

Emrah AkyolView profile

Associate Professor

• Game Theory
• Communications
• Control Systems
• +3 more

Emrah Akyol is an Associate Professor in the Electrical and Computer Engineering Department at Binghamton University (SUNY). He joined in 2017 after postdoctoral research at the University of Illinois at Urbana-Champaign (UIUC) and the University of Southern California (USC). He holds a PhD from UC Santa Barbara (2011) and prior industry experience at HP and NTT Docomo. Education: PhD in Electrical and Computer Engineering, UC Santa Barbara (2011) Postdoctoral Researcher at USC (2013–2014) and UIUC (2014–2017) Research: Focuses on game theory, control systems, and secure cyber-physical systems. Key areas include strategic communication, opinion dynamics in social networks, and stealthy attacks in multi-agent systems. Recent work addresses bounded rationality in data gathering and network topology inference. Awards: NSF Career Award (communication over human networks), Binghamton's Interdisciplinary Collaboration Award (with Economics/Political Science), and CoCo Seed Grant (CPS security). Advising & Grants: Supervised PhD/Master’s students Anju Anand, Xilin Zhang, and Griffin Rule (recipient of ECE MS Research Award). Active grants include NSF Career and CoCo Seed. Labs/Teams: Leads research on secure cyber-physical systems and game theory applications. Collaborates with UIUC and organizes conferences like DSAA’20.

• Ultrasonic Technology
• Nondestructive Testing
• Smart Materials and Structures
• +5 more

Professor Ningqun Guo holds the dual roles of Professor in Mechanical Engineering and Head of School for both the Malaysia School of Engineering and School of Information Technology at Monash University Malaysia. He previously served at Nanyang Technological University, Singapore. His academic journey includes a B.Eng from Nanjing University of Aeronautics and Astronautics (China) and a PhD from Imperial College London (UK). His research focuses on stress wave propagation, ultrasound applications, smart materials, nondestructive testing, and civil infrastructure analysis. He has published over 130 papers, secured S$2 million in research grants, and supervised over 10 PhD students. Key research areas include ultrasonic technology for material characterization, smart material systems, and image processing for infrastructure monitoring. His work aligns with UN Sustainable Development Goals, emphasizing sustainable infrastructure and innovation. Notable collaborations span global institutions, with recent projects exploring AI-driven pavement crack detection, transparent object reconstruction, and 3D imaging systems. His contributions to magnetorheological fluid applications and nanofluidics further underscore his interdisciplinary impact. Grants and funding have supported projects in sensor development, structural health monitoring, and advanced imaging technologies. His advisory work has produced impactful PhD graduates in mechanical engineering and materials science. Prof. Guo leads research teams focused on smart materials, nondestructive evaluation, and computational imaging. His lab integrates experimental and theoretical approaches to solve challenges in infrastructure, photonics, and nanotechnology.