Find Academic Supervisors

Katherine FlaniganView profile

Assistant Professor

• Cyber-Physical Systems (CPS)
• Digital Twins
• Infrastructure Resilience
• +6 more

Katherine Flanigan is an Assistant Professor in the Department of Civil and Environmental Engineering at Carnegie Mellon University (CMU), with a courtesy appointment in the Department of Electrical and Computer Engineering. She holds a Ph.D. in Civil Engineering from the University of Michigan (2020) and earlier degrees from the same institution (M.S.E. in Electrical and Computer Engineering, 2018, and M.S.E. in Civil Engineering, 2016) and a B.S.E. in Civil and Environmental Engineering from Princeton University (2014). Her research focuses on transforming civil infrastructure and urban systems into intelligent cyber-physical systems (CPS) by integrating sensing, computing, and actuation technologies. Key areas include developing wireless sensor networks for smart cities, modeling infrastructure resilience, and leveraging digital twins for data-driven decision-making. She extends this work to cyber-physical-social systems (CPSS), emphasizing human-infrastructure interactions and equity in policy-making through community-driven data. Flanigan is particularly noted for projects combining technical and social dimensions, such as human-in-the-loop control solutions and privacy-preserving urban sensing technologies. Flanigan has received prestigious awards including the NSF Graduate Research Fellowship, the Towner Prize for Outstanding PhD Research, and the Wimmer Faculty Fellow award for teaching innovation. She advises PhD student Lindsay Graff, whose work addresses transportation equity and multimodal networks. Her research also involves collaborations with industry leaders via the Manufacturing Futures Institute, and she leads the Autonomous Infrastructure Systems Lab at CMU. Her teaching contributions include course redesigns focused on hands-on projects, such as constructing habitats for pollinators and Little Free Libraries to blend engineering with community impact. She emphasizes preparing students for real-world challenges through project courses that teach sensing, data analysis, and infrastructure system design under constraints like risk and resource limitations.

• Optoelectronics
• Liquid Crystal Technologies
• Visible Light Communication (VLC)
• +4 more

Juan Carlos Torres Zafra is a Visiting Professor at Universidad Carlos III de Madrid. His research focuses on optoelectronics, liquid crystal technologies, visible light communication (VLC), and sensor systems. His work spans interdisciplinary areas including energy-harvesting IoT nodes, indoor positioning systems, and optical communication interfaces for high-definition media transmission. Torres Zafra has contributed to advancements in semiconductor materials (e.g., perovskites), low-cost sensor networks, and hybrid RF-VLC positioning systems. His research also extends to educational technology, exploring tools like Telegram and Google Workspace for improving student engagement in cybersecurity engineering programs. Notable projects include the GUTI group's work on optical vortices using liquid crystal devices and the development of tunable resonators based on liquid crystal capacitance. His publications from 2020–2025 highlight trends in VLC system optimization, AI-driven disinformation detection (SmartVote-AI initiative), and medical studies on amyloidosis therapy outcomes. Torres Zafra’s work often emphasizes practical applications in robotics, automotive systems, and sustainable energy solutions. While no formal awards are listed, his extensive publication record (over 80 entries from 2004–2025) reflects sustained contributions to photonics, sensor engineering, and liquid crystal device innovation. His research integrates hardware design, algorithm development, and material science to address challenges in modern communication systems and assistive technologies for visually impaired patients.

Zhang YanView profile

Professor

• Internet of Vehicles
• Smart grid
• Mobile edge computing
• +4 more

Zhang Yan is a Full Professor at the Department of Informatics, University of Oslo, Norway. He previously served as Head of Department and Chief Scientist at Simula Research Laboratory (2014–2016). His research focuses on advanced communication technologies including Internet of Things (IoT), 5G/6G networks, mobile edge computing, and blockchain applications. He has held significant roles such as IEEE VTS Distinguished Lecturer (2016–2020) and Chair of IEEE TCGCC (2019–2021). His honors include IEEE Fellow (2020), election to Academia Europaea (2020), and recognition as a Web of Science Highly Cited Researcher (2018–2019). Research interests span interdisciplinary areas like network dynamics, socio-economic systems, and algorithmic design. His work bridges theoretical foundations with practical applications in smart grids, vehicular networks, and global trade systems. Recent publications emphasize network science methodologies applied to economic complexity and information diffusion. Professional contributions include editorial roles for top journals and leadership in EU-funded projects. His awards reflect impactful contributions to both technical innovation and scientific leadership in informatics and communications.

Wenwu ZhuView profile

Professor

• Big data analysis
• Multimedia communication and networking
• Multimedia computing
• +5 more

Wenwu Zhu is a Professor and Vice Chair of the Department of Computer Science and Technology at Tsinghua University. He has held prominent positions at Microsoft Research Asia, Intel Research China, and Bell Labs, establishing himself as a leading figure in multimedia computing and networking with international recognition as a FOREIGN member of the Academy of Europe (elected 2018). His educational background includes: Ph.D. in Electrical and Computer Engineering from New York University (1996) Professor Zhu's research focuses on the intersection of multimedia systems, networking, and big data. His work has pioneered advancements in internet video streaming, multimedia cloud computing, and social-aware content distribution. He has made significant contributions to understanding how multimedia content can be efficiently delivered across diverse network environments, from traditional wired networks to modern mobile and social platforms. His research bridges theoretical computer science with practical applications, with his work on social-aware video content distribution being transferred to Tencent company. His publication record shows a clear evolution from foundational work on internet video streaming in the early 2000s, through multimedia cloud computing in the early 2010s, to more recent work on social-aware multimedia and network embedding using deep learning approaches. This progression reflects the changing landscape of multimedia computing from infrastructure-focused to socially-aware and AI-driven systems. Professor Zhu has received numerous prestigious honors: AAAS Fellow (2016) SPIE Fellow (2013) IEEE Fellow (2010) Minister of Education's Natural Science Award, 1st prize (2017) Chinese Institution of Electronics's Natural Science Award, 1st prize (2015, 2012) National Natural Science Award, 2nd prize (2012) Chief Scientist for NSFC Major Project (2016) Chief Scientist for Ministry of Science and Technology's 973 Project (2014) Multiple Best Paper Awards including ACM Multimedia 2012 As Editor-in-Chief of IEEE Transactions on Multimedia since 2017 and through leadership roles as General Co-Chair for ACM CIKM 2019 and ACM Multimedia 2018, Professor Zhu has significantly shaped the multimedia research community. His research has been supported by major grants including NSFC Major Projects and Ministry of Science and Technology's 973 Projects, demonstrating both academic and national strategic importance. He has published over 300 referred papers with an H-Index of 55, including 6 Best Paper Awards and 7 books or book chapters. Professor Zhu leads a research group at Tsinghua University focused on multimedia big data computing, with strong industry connections. His team has made pioneering contributions to structural network embedding using deep learning and social contextual recommendation systems, bridging theoretical advances with practical applications in social media platforms.

Kemal TepeView profile

Professor

• Vehicular Networks
• Cognitive Radio
• Wireless Communication
• +6 more

Professor Kemal Tepe is a faculty member in the Faculty of Engineering at the University of Windsor, specializing in wireless communication and information processing. His research focuses on vehicular networks, cognitive radio systems, and smart grid technologies. He leads the Wireless Communication and Information Processing Lab , where he develops solutions for autonomous driving systems, cybersecurity in vehicle-to-infrastructure communication, and spectrum sensing techniques. In 2016, he was awarded the Medal of Excellence by the Faculty of Engineering for his dedication and service. Tepe’s work bridges theoretical advancements and practical applications, addressing challenges in autonomous systems, machine learning for anomaly detection, and IoT security. His contributions include pioneering methods for detecting adversarial behavior in vehicular networks and improving spectrum utilization through probabilistic modeling. Collaborations with industry partners like Ford Motor Company and involvement in initiatives such as the Perspective Magazine automotive research highlight his industry-relevant research. His research interests span a wide range of domains including: Autonomous vehicle safety and communication protocols Cognitive radio networks and spectrum management Machine learning for network security and anomaly detection Wireless sensor networks and energy-efficient protocols Smart grid integration and communication architectures Tepe’s publications emphasize practical implementations, such as real-time routing protocols for wireless sensor networks and hardware designs for cognitive radio systems. His lab’s innovations have been showcased in industry-relevant platforms, demonstrating the real-world impact of his work.

Sven ScheweView profile

Professor

• Automata Theory
• Game Theory
• Formal Verification
• +12 more

Sven Schewe is a Professor in the Department of Computer Science at the University of Liverpool, affiliated with the School of Electrical Engineering, Electronics and Computer Science. He leads the AI Section and is a founding member and former leader of the Verification Group. He also has secondary affiliations with the Algorithms, Complexity Theory and Optimisation Group and the Institute for Risk and Uncertainty. Research Interests: His research centers on automata theory and game theory, particularly their applications in the verification and synthesis of reactive and safety-critical systems. He investigates infinite-duration games, automata over infinite words and trees, and develops algorithms and tools for automated verification, synthesis, and learning of optimal control strategies. His work extends to reinforcement learning with formal guarantees, cyber-physical systems, and AI safety. Recent Research Trends: His recent publications demonstrate a strong integration of formal methods with machine learning, particularly in adversarial training, neural network robustness, and model-free reinforcement learning under omega-regular objectives. He also applies formal reasoning to interdisciplinary domains such as chemical space exploration and materials science. Scientific Awards: Finalist for the ERCIM Cor Baayen Award 2010 Dr. Eduard Martin Preis 2009 GI Dissertation Award 2008 Advising and Grants: He actively supervises numerous PhD students and postdoctoral researchers. He is Principal Investigator (PI) or Co-Investigator (CI) on multiple major grants, including EPSRC Programme Grants, Royal Society Fellowships, and Horizon Europe projects. His funded research spans topics such as game theory, verification, synthesis, reinforcement learning, and risk analysis. He has hosted visiting researchers and collaborated internationally with institutions in Germany, France, India, Taiwan, and the US. Labs and Teams: He co-founded and led the Verification Group and previously led the AI Section at the University of Liverpool. These groups focus on formal methods, automata, games, and their applications in AI and safety-critical systems.

• Network Performance
• Cybersecurity
• Congestion Control
• +4 more

Simone Ferlin is an Adjunct Senior Lecturer at Karlstad University and a Senior Performance Engineer. She holds a PhD in Computer Science from Simula Research Lab and Universitetet i Oslo (2017), focusing on robustness in multipath transport protocols like MPTCP. Her research spans network performance, security, and congestion control in mobile/5G networks and the Internet. She collaborates actively with academia and industry, co-supervising students in areas such as edge computing, container orchestration, and distributed systems. Affiliations: Department of Informatics, Karlstad University; Red Hat Research; Ericsson R&D. Education: PhD (2017), Simula/UiO; Master’s and Undergraduate studies emphasized networking and electronics. Her work includes projects like AIDA (AI-driven edge networking) and DRIVE (latency-sensitive mobile services). She has published over 50 papers on topics like QUIC, eBPF, and containerized microservices. Awards include the Best Paper at IEEE ICIN 2021 and ANRP 2025 Prize. Teaching responsibilities include Future Internet Design and Service Quality . Advising spans 15+ students across institutions like TU Berlin, KTH, and Unifesp. She chairs conferences (e.g., ACM SIGCOMM 2025) and serves on editorial boards (IEEE Communications Magazine). Key interests: network observability, low-latency protocols, and sustainability in networking.

• Resource Optimization
• Telecommunication Networks
• VLSI Circuit Design
• +5 more

Professor Arunabha Sen is a faculty member at Arizona State University (ASU), affiliated with the School of Computing and Augmented Intelligence and the College of Health Solutions as a Health Solutions Ambassador. He joined ASU in 1987 and holds a Ph.D. in Computer Science from the University of South Carolina (1987). His research focuses on resource optimization in telecommunication networks, VLSI circuits, hardware-software co-design, and network security. Key areas include algorithm design, combinatorial optimization, and network processor systems. His work spans wireless, optical, and sensor networks, with contributions to video transmission over mobile ad-hoc networks and interference-aware channel assignment. Notable projects include robust network design against WMD attacks and tools for resilient communication networks. He has served on multiple technical committees for conferences like IEEE and IFIP, and contributed to academic initiatives such as capstone courses on network processors. Grants include NSF, DOD-DTRA, and Motorola Labs funding, emphasizing interdisciplinary research in network science and communications. Teaching responsibilities include courses on algorithms, game theory, and network design. His service roles include Associate Editor for IEEE Transactions on Mobile Computing and leadership in graduate program committees. Research outputs include over 30 peer-reviewed publications, with recent work in algorithmic network design and social computing data mining.

Shuvra S. BhattacharyyaView profile

Professor

• Signal Processing
• Embedded Systems
• Biomedical Engineering
• +3 more

Shuvra S. Bhattacharyya is a Professor at the University of Maryland, College Park (UMD), Department of Electrical and Computer Engineering, with a joint appointment in UMIACS. He is also a part-time visiting professor at Tampere University (Finland) and INSA Rennes (France). His research focuses on signal processing systems, embedded systems, biomedical engineering, and machine learning. He has authored 7 books and over 300 papers, and directs the Maryland DSPCAD Research Group. He holds an NSF Career Award and is an IEEE Fellow. His honors include the Fulbright Senior Specialist and FiDiPro Professorship. He advises graduate students and leads projects in neural decoding, cyber-physical systems, and criminal justice AI. Education: B.S., University of Wisconsin-Madison Ph.D., University of California, Berkeley Research Interests: Signal processing architectures, biomedical circuits, embedded software, hardware/software co-design, and AI for healthcare and criminal justice. His work emphasizes real-time systems, energy efficiency, and interdisciplinary applications. Grants & Awards: NIH Grant for real-time neural decoding (2023) IEEE Fellow (2011) Jimmy Lin Entrepreneurship Award (2012) Labs & Teams: Maryland DSPCAD Research Group, collaborating on dynamic data-driven systems, neural interface technologies, and AI ethics in criminal justice. His work integrates academic and industrial partnerships, including roles at Hitachi America and Kuck & Associates.

• Communication and Information Theories
• Wireless Communications
• Energy-Efficient Wireless Networks
• +1 more

Dr. Nghi Tran is a Professor in the Department of Electrical and Computer Engineering at the University of Akron's College of Engineering. His research focuses on communication and information theories, wireless communications, energy-efficient wireless networks, and security. He has published over 160 papers and served as an editor for multiple IEEE journals. Education: Ph.D. in Electrical & Computer Engineering (University of Saskatchewan, 2008) Education: M.Sc. in Electrical & Computer Engineering (University of Saskatchewan, 2004) Education: B.Sc. in Electrical Engineering (Hanoi University of Technology, 2002) Dr. Tran's research spans wireless communication systems, with special emphasis on physical layer security, energy-efficient networks, full-duplex technologies, and channel coding. His work bridges theoretical foundations with practical implementations in next-generation wireless systems. His recent publications (2023-2025) demonstrate expertise in physical layer security, full-duplex antenna design, polar codes for GNSS, and machine learning applications in MIMO systems. Key trends include optimization of wireless networks, robust error correction, and cross-layer security approaches. Scientific Awards: NSERC Postdoctoral Fellowship Professional Recognition: IEEE Senior Member As an ABET coordinator for ECE and frequent journal editor, Dr. Tran contributes to academic leadership. His research has received funding from the US National Science Foundation, Office of Naval Research, Air Force Research Laboratory, and industry partners.

• Optimization methods
• Metaheuristics
• Heuristics
• +12 more

Mina Mirhosseini is a Research Fellow at the Faculty of Logistics, Molde University College, Norway. She holds a PhD in Computer Science from Shahid Beheshti University of Tehran, Iran, and has prior academic experience as a faculty member and lecturer in Iran and as a remote teaching assistant at the University of Hertfordshire, UK. Her primary research interests include Optimization Methods, Metaheuristics, Heuristics, Linear Integer Programming, Parallel Processing, Machine Learning, Artificial Intelligence, and Logistics. She has made significant contributions to solving complex computational problems such as the n-similarity problem and Mixed Integer Linear Programming (MILP) models using hybrid and parallel algorithms, particularly in the context of high-level synthesis and wireless sensor networks. The analysis of her recent publications reveals a strong focus on developing and applying advanced optimization techniques, especially quantum-inspired gravitational search algorithms and parallel genetic algorithms, to real-world engineering and computational challenges. Her work consistently emphasizes performance improvement, scalability, and load balancing in distributed and heterogeneous computing environments. Mina Mirhosseini has an extensive publication record in high-impact journals such as IEEE Transactions on Parallel and Distributed Systems, Journal of Parallel and Distributed Computing, Journal of Supercomputing, and Computers and Electrical Engineering. Her research has been published across a range of venues, reflecting interdisciplinary work at the intersection of computer science, electrical engineering, and applied optimization. She has actively contributed to the academic community through roles such as program committee member and executive committee member for conferences on fuzzy systems, swarm intelligence, and evolutionary computation. Her academic journey includes teaching and research roles in Iran, demonstrating a sustained commitment to higher education and scientific inquiry. Mina Mirhosseini is part of the research group focused on Planning, Optimization and Decision Support at Molde University College. Her current work continues to advance the state-of-the-art in parallel and metaheuristic optimization methods, with applications in logistics, synthesis, and sensor network design.

• Visible Light Communication
• Optical Wireless Communication
• Mobile Ad Hoc Networks
• +3 more

Thomas D. C. Little is a Professor at Boston University, USA, specializing in Visible Light Communication (VLC), Optical Wireless Communication, and Mobile Ad Hoc Networks. His research focuses on hybrid RF/VLC systems, interference mitigation, and dynamic network optimization under illumination constraints. Recent work includes 3D localization via zone-based positioning Dynamic FOV receiver optimization Multi-tier transmission for 5G Li-Fi Security-aware OFDM modulation Research interests center on integrating optical wireless with traditional RF networks, developing energy-efficient communication protocols, and creating positioning systems for smart spaces. Publications analyze spectral efficiency, channel modeling, and coexistence strategies in dense optical environments. Collaborations span institutions in the USA and Germany, with applications in Industry 4.0 and coastal monitoring systems. His team has contributed to ns-3 simulator extensions for VLC, beam control in FSO systems, and interference analysis in optical networks. Current projects address reconciling SNR models and optimizing handover parameters via Q-learning for heterogeneous deployments.

• Wireless Sensor Networks
• Low-Power Communication
• Backscatter
• +6 more

Ambuj Varshney is a researcher specializing in low-power wireless communication, visible light networking, and embedded systems. His work explores tunnel diodes for non-contact sensing, backscatter technologies, and edge-based language models. Key contributions include AudioCast for audio-broadcast connectivity, TunnelSense for tunnel diode sensing, and PiXelGen for mixed-reality cameras. Core Research Areas: Wireless Sensor Networks, Backscatter, Tunnel Diodes, IoT, Embedded Systems Recent Trends: Integration of large language models (LLMs) in edge devices, visible light communication, low-power AR networking

Ilker HacihalilogluView profile

Associate Professor

• Artificial Intelligence in Healthcare
• Medical Image Processing
• Machine Learning Applications
• +8 more

Dr. Ilker Hacihaliloglu is an Associate Professor in the Department of Radiology and Medicine at the University of British Columbia's Faculty of Medicine. He holds a PhD from UBC (2010) and has held faculty positions at Rutgers University (2014-2021). His research focuses on applying machine learning to medical imaging, aiming to bridge clinical and engineering research for practical healthcare solutions. Key areas include AI-driven diagnostics, ultrasound imaging, and personalized medicine. Education: B.Sc. and M.Sc., Istanbul Technical University (2001, 2004) Ph.D., University of British Columbia (2010) Postdoctoral Fellow, Vancouver General Hospital (2010-2013) Research Interests: Artificial Intelligence in Medical Imaging Ultrasound-Based Diagnostics Image-Guided Surgery Computer-Aided Diagnostics for Orthopedics and Neurosurgery His work emphasizes explainable AI, standardization in machine learning metrics, and clinical translation of technologies. Scientific Contributions: Over 35 peer-reviewed articles (2019-2025) highlight innovations in medical image segmentation, AI-driven radiomics, and cross-modality imaging. Recent trends focus on synthetic data augmentation, diffusion models for medical imaging, and curriculum development in AI for healthcare. Grants and Advising: No explicit grants mentioned, but active mentorship in interdisciplinary research teams. Collaborations span clinicians, engineers, and computer scientists to address real-world healthcare challenges. Labs/Teams: Engaged in computational ultrasound and AI-driven diagnostics initiatives, though specific lab names are not provided in the text.

• Integrated Circuits
• Wireless Communications
• CMOS
• +7 more

Jussi Ryynänen is a Professor and Dean at Aalto University's School of Electrical Engineering, leading the Electronic Circuit Design (ECD) unit within the Department of Micro and Nanosciences. His research focuses on integrated circuits, wireless transceivers, and millimeter-wave systems, with a strong emphasis on RF, analog, and digital IC design. The ECD unit is internationally recognized for its work in nanoscale IC technologies and advanced measurement infrastructure. Key research areas include time-based integrated circuits, beam-steering/MIMO systems, antenna-IC co-design, and RF DAC/ADC development. His work combines cutting-edge semiconductor technologies with innovative circuit architectures for high-speed communication and sensor applications. The group maintains extensive collaborations and funding networks supporting its interdisciplinary projects. Ryynänen's research has been honored with awards such as the ECCTD Best Student Paper (2015), Crowncom Student Award (2011), and a best paper at the Advances in Cognitive Radio Conference (2012). His group actively publishes in top-tier venues like IEEE Transactions on VLSI Systems and IEEE Access, addressing challenges in mmWave circuits, neural network accelerators, and reconfigurable DSP hardware. Current projects span automated design frameworks for analog circuits, passive CMOS circulators for nonreciprocal systems, and energy-efficient signal processing architectures for 5G/6G transmitters. His work bridges fundamental circuit theory with practical implementations for next-generation communication systems.