Find Academic Supervisors

• Natural Language Processing
• Legal Informatics
• Multilingual NLP
• +3 more

Ilias Chalkidis is an Assistant Professor specializing in Natural Language Processing at the Department of Computer Science, University of Copenhagen. He is actively affiliated with the Natural Language Processing research section, contributing to both theoretical and applied advancements in the field. His research spans multiple high-impact domains with particular emphasis on: Legal natural language processing and multilingual legal reasoning Large language model applications in political and social contexts Fairness-explainability trade-offs in AI systems Innovative representation learning techniques for textual data Analysis of his recent publications reveals a strong focus on bridging legal informatics with cutting-edge NLP methodologies. His work on multilingual legal corpora (including the 689GB MultiLegalPile dataset) and legal decision influence prediction demonstrates practical applications for judicial systems. Simultaneously, his investigations into LLMs as voting assistants and European political spectrum analysis showcase innovative intersections between computational social science and language technology. His technical contributions to contrastive learning and hyperbolic embeddings provide foundational advances for document representation. Chalkidis actively participates in the research community through workshop organization (Natural Legal Language Processing Workshop 2023-2024) and conference presentations. His research has been published in top-tier venues including ACL, EMNLP, and ECAI, with significant citations reflecting community impact. While specific advising relationships aren't documented in the provided materials, his collaborative work patterns suggest active mentorship within the NLP research ecosystem.

Ai-Chun PangView profile

Professor

• Fog/Edge Computing
• Wireless Networking
• Mobile Computing
• +5 more

Professor Ai-Chun Pang is affiliated with the National Taiwan University , serving in both the Department of Computer Science and Information Engineering and the Graduate Institute of Networking and Multimedia . He held leadership roles including Associate Dean (2018-2022) and Director (2013-2016) within the College of Electrical Engineering and Computer Science. His research spans Fog/Edge Computing , Wireless Networking , Mobile Computing , and AIoT Systems , with recent advancements in federated learning security, energy-efficient network design, and 5G/6G optimization. Collaborative work includes applications in vehicular networks, industrial control systems, and non-terrestrial connectivity. Key publication themes: Edge Intelligence and Privacy (2024) Federated Learning for Heterogeneous Devices (2023-2024) 5G Backhaul Optimization (2017-2021) Wireless Energy Transfer (2022) Awarded IEEE Fellow 2021 for contributions to mobile edge networks, he has received multiple IEEE Vehicular Technology Society awards, the CES 2019 Innovation Award , and teaching accolades including National Taiwan University Distinguished Teaching Award (2010) . His lab has produced 16 PhD students now in academia and industry. As Editor-in-Chief of IEEE Wireless Communications Letters and active in conference organization, he shapes global research directions. Current projects focus on GenAI for Networking and Non-Terrestrial Networks , with recent 2024 admissions for new students.

Lingjia LiuView profile

Professor

• 5G/6G Networks
• Deep Learning in Communications
• Internet of Things (IoT)
• +4 more

Lingjia Liu is a Professor and Bradley Senior Faculty Fellow at Virginia Tech's Bradley Department of Electrical and Computer Engineering. Her research focuses on enabling technologies for 5G/6G networks, including massive MIMO systems, dynamic spectrum access, and AI-driven communication networks. She holds a Ph.D. from Texas A&M University (2008). Research Interests : 5G/6G Network Architectures (3D MIMO, cloud-RAN, ultra-low latency) AI in Communications (Reservoir Computing, federated learning) IoT & Cyber-Physical Systems (energy harvesting, privacy protection) Non-Terrestrial Networks (satellite-based connectivity) Recent work emphasizes generative AI for network simulation, explainable AI in communication systems, and secure dynamic spectrum sharing. Her research spans theoretical foundations (e.g., OTFS modulation analysis) and practical implementations (e.g., FPGA-based reservoir computing). Awards : Bradley Senior Faculty Fellow (Virginia Tech). Her contributions bridge communication theory and AI, addressing 6G challenges through innovative algorithmic and architectural solutions. Current projects explore agentic protocol learning, federated multi-agent RL for spectrum access, and resilient ML under adversarial conditions.

Venkat N. KroviView profile

Professor

• Robotics
• Autonomous Vehicles
• Human-Robot Interaction
• +5 more

Prof. Venkat N. Krovi serves as the Michelin Endowed Chair Professor of Vehicle Automation in the Departments of Automotive Engineering and Mechanical Engineering at Clemson University's College of Engineering, Computing and Applied Sciences (CECAS). He directs the Automation, Robotics and Mechatronics Laboratory (ARMLab) at the International Center for Automotive Research (CU-ICAR), focusing on smart embedded systems for autonomy in challenging environments. He earned his Ph.D. in Mechanical Engineering and Applied Mechanics from the University of Pennsylvania in 1998. His research leverages distributed autonomy and human-robot synergy to extend human capabilities, with applications spanning plant automation, consumer electronics, automobile, defense, and healthcare. The work emphasizes lifecycle treatment (design through verification) of robotic systems under uncertainty. Recent publications (2024-2025) demonstrate strong trends in digital twin frameworks for autonomous vehicle validation, sim2real transfer via reinforcement learning, and integration of large language models for editable simulations. Key themes include scalable cloud-based architectures, Koopman operator theory for robustness, and containerization for reproducible robotics development. His accolades include: National Science Foundation (NSF) CAREER Award Petro-Canada Young Innovator Award Multiple best paper awards at conferences and journals ASME Dedicated Service Award (2024) Prof. Krovi has advised doctoral students including Dr. Srivatsan Srinivasan (2024). His research receives substantial funding from NSF, DARPA, ARO, and industrial partners like Michelin. He leads the NSF I/UCRC RoSeHuB center and the AutoDRIVE ecosystem for autonomous driving education. As ARMLab director, he oversees projects including OpenCAV, the Robotics for AV Systems Bootcamp, and containerized terramechanics simulations. The lab specializes in mechatronic design, verification/validation frameworks, and human-autonomy coexistence studies for next-generation mobility solutions.

• Computational Neuroscience
• Neural Networks
• Deep Learning
• +4 more

Prof. Sander M. Bohte holds a part-time appointment as a Professor of Computational Neuroscience at the Swammerdam Institute for Life Sciences (SILS), University of Amsterdam, and is a researcher at the CWI Machine Learning group. His research focuses on computational models of neural information processing, emphasizing spiking neural networks, predictive coding, and reinforcement learning. He bridges computational neuroscience and machine learning, exploring how biological insights can improve neural network designs and vice versa. Key collaborations include work with Cyriel Pennartz (UvA), Pieter Roelfsema (NIN), and Steven Scholte (B&C). His applied research spans scientific machine learning applications in finance and genomics. He actively supervises MSc thesis students, prioritizing those from UvA, with projects ranging from biologically inspired neural architectures to efficient spiking network simulations. Research highlights include developing biologically plausible learning rules for deep networks, predictive coding models for sensory data, and spiking network models for working memory tasks. His work also addresses challenges in temporal dynamics and scalable neural computation, leveraging both theoretical and applied perspectives.

Philip JohnsonView profile

Professor

• Quantum Computing
• Theoretical Physics
• Condensed Matter Physics
• +3 more

Philip Johnson is a Professor and Chair of the Department of Physics at American University (AU), where he has been since 2006. He also serves as Director of the Integrated Space Science and Technology Institute (ISSTI), supporting over 20 AU faculty and external partners like NASA's Goddard Space Flight Center. His research focuses on quantum computing, superconducting qubits, ultracold atoms, and effective interactions in few-body systems. He holds a PhD in Theoretical Physics from the University of Maryland and completed postdoctoral work at NIST and the University of Maryland's superconducting quantum computing group. His academic leadership roles include Associate Dean of Research for AU's College of Arts and Sciences and service on the American Physical Society's council. His research explores quantum control, nonequilibrium dynamics, and applications in quantum sensing and metrology. Key areas include ultracold bosons in optical lattices, nonlocal interactions, and hybrid machine learning approaches for quantum systems. He collaborates with institutions like the Joint Quantum Institute and Johns Hopkins Applied Physics Laboratory. Johnson's recent work advances theoretical frameworks for few-atom systems and superconducting qubits, with publications addressing topics like topological properties of interactions and correlations in quantum systems. His contributions span experimental and theoretical physics, emphasizing interdisciplinary applications in space science and technology through ISSTI.

Kaspar RiesenView profile

Researcher

• Pattern Recognition
• Machine Learning
• Computer Vision
• +4 more

PD Dr. Kaspar Riesen is the Head of the Pattern Recognition Group at the Institute of Computer Science, University of Bern. His research focuses on graph-based methods for pattern recognition, with applications in document analysis, environmental modeling, and healthcare. Key interests include graph matching, neural networks, and spatio-temporal modeling. His work spans structural pattern recognition, graph embeddings, and keyword spotting in historical documents. Recent projects involve river network analysis using graph regression and hypoglycemia prediction via LSTM-GNN hybrid models. Publications emphasize graph theory advancements, such as normalized graph compression and geometric similarity learning. Collaborations include developing specialized algorithms for automated error detection and improving decision-making in simulated sports. Labs/Teams: Pattern Recognition Group (PRG) at the University of Bern.

• Power electronics
• Renewable energy
• Nonlinear dynamics and chaos
• +7 more

Professor Herbert Ho Ching Iu is a distinguished academic at The University of Western Australia, serving in the School of Engineering within the Department of Electrical, Electronic and Computer Engineering. With an impressive research portfolio of over 500 publications and an h-index of 61, Prof. Iu has established himself as a leading authority in power electronics and nonlinear systems research. Prof. Iu received his BEng(Hons) in Electrical and Electronic Engineering from The University of Hong Kong in 1997, followed by a PhD in Electronic and Information Engineering from The Hong Kong Polytechnic University in 2000. After a brief research fellowship at HKPU, he joined The University of Western Australia in 2002 as a Lecturer and has since risen to the rank of full Professor. His primary research focuses on power electronics , renewable energy systems , nonlinear dynamics and chaos , current sensing techniques , and memristive systems . Prof. Iu's work uniquely bridges theoretical exploration with practical implementations, particularly in energy conversion, secure communications, and neuromorphic computing. His research has significant implications for DC microgrids, advanced encryption techniques, and next-generation computing paradigms. Analysis of Prof. Iu's recent publications reveals a strong interdisciplinary trajectory combining memristive systems with chaotic dynamics for applications in image encryption and secure communications . There's a notable emphasis on machine learning techniques applied to power electronics and energy systems , particularly for DC microgrids and battery management. His work demonstrates consistent progression from fundamental research in nonlinear systems to practical engineering solutions with real-world impact. Prof. Iu's significant contributions have been recognized with several prestigious awards: Vice-Chancellor's Award for HDR Supervision (2024) School of Engineering Award for Research Mentorship (2023) Vice Chancellor's Award in Research Mentorship (2023) With 18 supervised research students and leadership on 16 research grants, Prof. Iu has built a robust research program at the forefront of power systems innovation. His grant portfolio includes major projects like 'Mine Electrification' and 'Microgrid Battery Deployment' through the CRC for Future Battery Industry, as well as collaborations with Western Power on 'Project Symphony.' These initiatives demonstrate his ability to secure substantial funding and translate theoretical concepts into practical engineering solutions for industry. Prof. Iu leads a dynamic research team that specializes in hardware implementation of advanced theoretical concepts, particularly in memristive systems and chaotic circuits. The laboratory maintains strong industry connections, especially with energy and mining sectors, ensuring research has tangible real-world applications. Current work emphasizes DC microgrid technologies, advanced battery systems for electrified transportation, and novel applications of chaotic systems in security contexts, positioning the team at the cutting edge of power electronics research.

Safa OtoumView profile

Assistant Professor

• Network Security
• Blockchain Applications
• Artificial Intelligence
• +5 more

Safa Otoum is an Assistant Professor at the College of Technological Innovation (CTI), Zayed University, UAE, and holds an adjunct role at the School of Computer Science and Electrical Engineering. She is a licensed Professional Engineer (P.Eng.) in Ontario and a member of IEEE and ACM. Her expertise spans network security, blockchain, AI, and IoT, with a focus on intrusion detection and prevention systems. Education: She earned a M.A.Sc. (2015) and Ph.D. (2019) in Computer Engineering from the University of Ottawa, Canada, followed by postdoctoral research there. She has held roles as a data scientist at Cheetah Networks and as a researcher in reputable institutions. Research Interests: Her work emphasizes AI-driven security solutions, blockchain applications in IoT, federated learning, and sustainable smart city infrastructure. She explores machine/deep learning for cybersecurity and has pioneered architectures for secure vehicular networks and healthcare systems. Publications: Over 15 peer-reviewed articles in top journals/conferences like IEEE Transactions on Network, ACM TOIT, and GLOBECOM. Notable contributions include highly cited blockchain surveys and award-winning intrusion detection frameworks. Awards: Recipient of prestigious scholarships (NSERC, Canada Graduate Scholarship) and grants (RIF, TII). Honored with a Best Paper Award for intrusion detection research in critical infrastructure. Service Roles: She serves as Area Editor for Springer's Cluster Computing, chairs international workshops on securing healthcare systems, and organizes conferences on network security and intelligent transportation. She also guest-edits special issues on AI-driven healthcare in journals like Electronics.

Zhongguo LiView profile

Lecturer

• Distributed Control
• Optimization
• Robotics
• +5 more

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

• Machine Learning
• Deep Learning
• Video Classification
• +3 more

Reza Bosagh Zadeh is an Adjunct Professor at the Institute for Computational and Mathematical Engineering (ICME) at Stanford University. His research focuses on machine learning, deep learning, and their applications in video classification, healthcare analytics, and distributed algorithms. He specializes in developing scalable computational methods for real-time data processing and has contributed to advancements in neural networks and optimization techniques. Reza's work spans theoretical and applied domains, with notable contributions to TensorFlow frameworks, video summarization systems, and medical imaging analysis. His research often integrates interdisciplinary approaches, leveraging both academic and industrial collaborations. Notable projects include developing machine learning models for glaucoma detection and creating efficient algorithms for large-scale data processing in environments like Apache Spark. His publications emphasize real-time video stream analysis, distributed computing architectures, and practical implementations of deep learning. Reza holds a strong presence in both academic and tech sectors, with contributions to platforms like Twitter's Who-to-Follow system and innovations in edge computing for video surveillance.

Nashid ShahriarView profile

Assistant Professor

• Computer Networks
• 5G Mobile Networks
• Resource Allocation
• +5 more

Nashid Shahriar is an Assistant Professor in the Department of Computer Science at the University of Regina, Faculty of Science. His research addresses resource allocation challenges in next-generation networks including 5G, elastic optical networks, cloud infrastructures, and IoT systems. He holds a Ph.D. in Computer Science from the University of Waterloo, an M.Sc. from Bangladesh University of Engineering and Technology (BUET), and a B.Sc. from BUET. His work leverages optimization, machine learning, and AI for network management. Recent publications focus on 5G network slicing, intrusion detection, and NFV security. Research emphasizes practical AI-driven solutions for telecommunications and cloud systems.

Hossein SayadiView profile

Assistant Professor

• Cybersecurity
• Hardware Security and Trust
• Applied Machine Learning
• +7 more

Dr. Hossein Sayadi is an Assistant Professor and Associate Chair in the Department of Computer Engineering and Computer Science at California State University, Long Beach (CSULB). He holds a Ph.D. in Electrical and Computer Engineering from George Mason University, an M.S. from Sharif University of Technology, and a B.S. from K. N. Toosi University of Technology. His research focuses on hardware security , AI/ML applications , cybersecurity , and computer architecture . He leads the iSEC Lab , exploring topics like hardware trust, malware detection, and edge computing security. His work is supported by NSF grants and CSU awards, including the 2024-25 CSU STEM-NET Faculty Fellowship. Education: Ph.D., Electrical and Computer Engineering (George Mason University) M.S., Computer Engineering (Sharif University of Technology) B.S., Computer Engineering (K. N. Toosi University of Technology) His publications span conferences like IEEE ISQED, ISCAS, and DATE. He serves as Technical Program Committee Chair for IEEE ISQED (2024–2025). Awards include NSF ERI grants ($195,305) and the 2023 Multidisciplinary Research Grant. Research opportunities are available for students in machine learning , hardware security , and cybersecurity education .

Hendro WicaksonoView profile

Professor

• Data-Driven Approaches
• Causal AI
• Explainable AI
• +5 more

Prof. Hendro Wicaksono is a Professor of Data-Driven Industrial Systems at the School of Business, Social & Decision Sciences, Constructor University Bremen gGmbH. His expertise lies in applying AI and data-driven methods to enhance decision-making in complex industrial systems. He holds a Dr.-Ing. from Karlsruhe Institute of Technology (Germany) and M.Sc./B.Sc. degrees from German and Indonesian institutions. Research Interests : Focuses on causal AI, explainable AI, digital twins, sustainable industrial systems, and smart cities. His work integrates machine learning with domain-specific challenges in supply chains and energy management. Projects : Led over 10 funded projects including Delfine (accelerating energy transition), Talenta (digital asset management), and xAgri (agri-food supply chain analytics). Collaborates with global partners like Stadtwerke Trier and JetBrains. Teaching : Courses include Data Management in Industry 4.0, Production Planning, and Smart Cities. Recently on sabbatical in Spring 2023. Students : Supervises 20+ PhD/Master students researching topics like causal ML in software projects, EV adoption modeling, and blockchain logistics. Affiliations : Visiting Professor at University of Exeter, Adjunct Professor at Sebelas Maret/Airlangga Universities (Indonesia), and Academic Leader at Bandung Institute of Technology.

Mehdi SadiView profile

Assistant Professor

• Secure and Reliable System-on-Chip Design
• AI and Neuromorphic Hardware
• Safety-critical Hardware
• +2 more

Mehdi Sadi is an Assistant Professor of Electrical and Computer Engineering at Auburn University's College of Engineering. He holds a Ph.D. from the University of Florida, an M.S. from the University of California-Riverside, and a B.S. from Bangladesh University of Engineering and Technology. His research focuses on secure and reliable system-on-chip design, AI/ML-driven VLSI CAD/EDA, neuromorphic hardware, and emerging post-CMOS computing technologies. Notable achievements include earning the NSF CAREER Award for chiplet-based design optimization and a $175k NSF grant for magnetic RAM research. His work integrates machine learning with hardware co-design to enhance AI accelerators' performance, energy efficiency, and security. Recent projects include adversarial attack mitigation on AI hardware and reliability analysis of neuromorphic systems. Dr. Sadi's contributions span chiplet architecture, memory systems (e.g., STT-MRAM/SOT-MRAM), and fault-tolerant computing. He actively publishes on topics like skyrmion logic gates and TRNG implementations using MRAM. His work bridges theoretical machine learning advancements with practical hardware implementations, addressing critical challenges in next-generation computing systems.