Find Academic Supervisors

Eve M. SchoolerView profile

Visiting Professor

• Networking
• Distributed Systems
• Sustainable Computing
• +7 more

Dr. Eve M. Schooler is a Visiting Professor of Sustainable Computing at the University of Oxford , sponsored by the Royal Academy of Engineering. She is an IEEE Fellow and co-recipient of the IEEE Internet Award (2020), with expertise in Networking , Distributed Systems , and Carbon-aware Networking . Her work bridges industry-academia partnerships, focusing on edge-cloud infrastructure and AI for cybersecurity . BS, MS, PhD in Computer Science (Yale, UCLA, Caltech) Board of Directors, Computing Research Association (US) Advisory Council, University of Delaware College of Engineering Her research spans IoT security , smart grids , reverse CDNs , and data-centric networking . She co-founded the IETF's SUSTAIN research group on sustainability and chairs standards initiatives in fog computing and open footprints. Recent trends in her publications include carbon-aware networking , edge-cloud convergence , and AI-driven cybersecurity , with over 100 papers and 35 patents. IEEE Fellow (2021) IEEE Internet Award (2020) N2Women Stars in Networking (2023) Dr. Schooler champions STEM outreach , serving organizations like Grace Hopper Conference and Sally Ride Science. She leads industry-academia collaborations through projects like EU H2020 SPATIAL and NSF-Intel ICN-WEN.

Ahmad LotfiView profile

Professor

• Computational Intelligence
• Ambient Intelligence
• Robotics
• +7 more

Ahmad Lotfi is a Professor of Computational Intelligence and Head of Department of Computer Science at Nottingham Trent University , with a Visiting Professor role at Tokyo Metropolitan University . He leads the Computational Intelligence and Applications (CIA) research group and has supervised over 30 PhD students to completion. PhD in Learning Fuzzy Systems (University of Queensland, 1995) MTech in Control Systems (Indian Institute of Technology, India) BSc in Control Systems (Isfahan University of Technology, Iran) His research spans computational intelligence , ambient intelligence , robotics , and machine learning , with applications in dementia monitoring , smart environments , and healthcare technology . Recent work focuses on using thermal sensor arrays for privacy-preserving human activity analysis. He has secured funding from Innovate UK , EPSRC , The Royal Society , and Horizon 2020 , with projects like iCarer (assistive living), SmartBerry (agricultural AI), and BigSpark (financial data augmentation). His 15 most recent articles demonstrate expertise in Wi-Fi-based activity recognition , EEG fall detection , and thermal sensor fusion . Senior Member IEEE Member of British Computer Society (MBCS) Editorial roles in Soft Computing and Journal of Ambient Intelligence and Smart Environments He has served as Program Chair for conferences like PETRA and ICCRT , and as Keynote Speaker at PETRA 2023 . His 28+ years of academic leadership include organizing UKCI and UKRAS conferences.

Yupeng ZhangView profile

Assistant Professor

• Cybersecurity
• Applied Cryptography
• Zero-Knowledge Proofs
• +4 more

Yupeng Zhang is an Assistant Professor at the University of Illinois Urbana-Champaign in the Department of Electrical and Computer Engineering, with an affiliate appointment in Computer Science. His research focuses on cybersecurity and applied cryptography , particularly zero-knowledge proofs , secure multiparty computations , and their applications in blockchain and machine learning. Education: Ph.D., Electrical and Computer Engineering, University of Maryland (2018) M.Phil., Information Engineering, Chinese University of Hong Kong (2013) Bachelor of Engineering, Information Engineering, Chinese University of Hong Kong (2011) Research Highlights: Developed scalable zero-knowledge proof systems for blockchain and machine learning Created verifiable computation frameworks for SQL and RAM Advancing privacy-preserving ML and cross-chain blockchain bridges Grants: NSF CAREER award Air Force Research Lab DARPA Google Research Scholar Award Facebook Research Award Latticex Foundation Teaching: CS 461/ECE 422: Computer Security I CS 591 SP: Security and Privacy ECE 407/CS 407: Cryptography ECE 598 YPZ: Advanced Topics in Applied Cryptography Co-taught MOOC on Zero-Knowledge Proofs (Spring 2023) Professional Service: Program Vice Co-Chair, USENIX Security 2024 Program Committee, Crypto 2025, S&P 2025 Reviewer for major journals and conferences

Philipp WeissView profile

Researcher

• Automotive Systems
• Internet of Things (IoT)
• Fail-Operational Systems
• +5 more

Philipp Weiss is a Researcher at the Technical University of Munich (TUM), affiliated with the Department of Electrical and Computer Engineering and the Chair of Embedded Systems and Internet of Things. Holding an M.Sc. degree, he actively contributes to research and teaching in embedded systems and IoT with a strong focus on automotive applications. His research spans Automotive Systems , Internet of Things (IoT) , Fail-Operational Systems , Reliability Analysis , Agent-Based Systems , and Distributed Systems . Weiss specializes in fail-operational automotive software design, dynamic agent-based mapping methods, and run-time reliability analysis, addressing critical challenges in autonomous vehicle safety and resilience through publications in DATE and DSD conferences. Analysis of his 2020-2021 publications reveals consistent focus on fail-operational architectures for automotive systems, with recurring themes in distributed agent-based modeling, timing analysis, and energy optimization within hybrid cloud environments. His work bridges theoretical reliability frameworks with practical automotive implementations. Weiss has supervised multiple Master's theses and final projects from 2019-2021 on topics including dynamic agent-based reliability analysis and fail-over timing for neural networks. As an educator, he serves as tutor for System Design for the Internet of Things and seminar manager for Advanced Seminar Embedded Systems and Internet of Things . Embedded within Prof. Sebastian Steinhorst's research team, Weiss contributes to major initiatives including Security for IoT and Autonomous Systems , Time-Sensitive Networking , and 6G Research Hub "6G-Life" , operating within TUM's IoT Remote Lab infrastructure for hands-on experimentation with industrial IoT systems.

Fatih NayebiView profile

Lecturer

• Information Systems
• Data Science
• Machine Learning
• +5 more

Dr. Fatih Nayebi is a Faculty Lecturer in Information Systems at McGill University while also serving as Vice President of Data & AI at the ALDO Group. He bridges academic research with enterprise innovation, focusing on data science, machine learning, and AI systems. Academic Background: Ph.D. in Computer Engineering from École de technologie supérieure M.Sc. in Software Engineering from Boğaziçi University B.Sc. in Computer Engineering from Boğaziçi University Dr. Nayebi's research interests include: Information Systems Data Science Machine Learning Engineering & MLOps Deep Learning Agentic AI Human-Computer Interaction AI in Retail His recent publications focus on AI for retail, mathematical foundations of AI, information integrity in democratic systems, and best practices for technical documentation. Dr. Nayebi also teaches graduate courses at McGill University including: Enterprise Data Science Machine Learning in Production Introduction to AI and Deep Learning Applications and Architectures of Deep Learning Designing and Developing Agentic AI Systems As an active speaker and thought leader, Dr. Nayebi has participated in events such as: World Summit AI Americas RETHINK Retail NRF Nexus 2025 Supply Chain Research Forum JOPT2025 - Annual Conference of Optimization Days He is also the founder of Gradient Divergence, an advisory studio focused on advanced AI solutions for retail and consumer brands.

Saibal MukhopadhyayView profile

Professor

• VLSI Systems
• Nanotechnology
• Low-Power Electronics
• +5 more

Dr. Saibal Mukhopadhyay is a Professor in the Department of Electrical and Computer Engineering at the Georgia Institute of Technology, where he joined in 2007. He holds the Joseph M. Pettit Professorship and is recognized as an IEEE Fellow for his contributions to low-power and reliable VLSI systems. Education: BEng (Jadavpur University, India), Ph.D. (Purdue University) Labs: Gigascale Reliable Energy Efficient Nanosystem (GREEN) Lab His research focuses on VLSI Systems , Nanotechnology , and Low-Power Electronics , with emphasis on technology-circuit co-design for energy-efficient computing. Recent work explores Compute-in-Memory (CIM) architectures and Spiking Neural Networks for edge AI. Key article themes include Transformer Model Acceleration , Quantum Computing Calibration , 3D Object Detection , and Device Aging Analysis , reflecting his interdisciplinary approach bridging hardware design and machine learning. Scientific Awards IEEE Fellow (2018) ONR Young Investigator (2012) NSF CAREER Award (2011) IBM Faculty Awards (2009, 2010) Best Paper Awards (IEEE-Nano 2003, ICCD 2004)

Sirojan TharmakulasingamView profile

Lecturer

• Machine Learning
• Artificial Intelligence
• Data Science
• +6 more

Dr. Sirojan Tharmakulasingam serves as a Lecturer and Research and Development Coordinator at the Signals, Information & Machine Intelligence lab within the Faculty of Engineering at the University of New South Wales (UNSW) Sydney. His work bridges theoretical machine learning with practical applications in edge computing and high-performance systems. His research spans multiple cutting-edge domains including machine learning, artificial intelligence, data science, edge computing, and high-performance computing. Dr. Tharmakulasingam specializes in developing next-generation inference models by integrating machine learning, signal processing, mathematical modeling, and computing across diverse data types including images, video, audio, and quantum molecular data. His work has significant implications for scientific computing, telecommunications, and healthcare applications. Analysis of his publication trends reveals a strong focus on practical AI implementations, with increasing emphasis on edge computing solutions, quantum applications, and energy-efficient models. His recent work demonstrates progression from foundational machine learning techniques toward specialized applications in scientific computing and real-time systems. Dr. Tharmakulasingam holds a Doctor of Philosophy from UNSW Sydney and a Bachelor of Science of Engineering from the University of Moratuwa in Sri Lanka. His academic journey reflects a strong foundation in both theoretical and applied engineering principles. As Research and Development Coordinator for the Signals, Information & Machine Intelligence lab, he oversees critical research infrastructure and collaborations. His work location in Room 447 of the EE&T Building (G17) places him at the heart of UNSW's engineering research ecosystem, with access to the Mark Wainwright Analytical Centre's extensive facilities.

Kathryn Seigfried-SpellarView profile

Adjunct Professor

• Cyberforensics
• Digital Forensics
• Child Sexual Exploitation
• +5 more

Kathryn Seigfried-Spellar serves as an Adjunct Professor in the cyberforensics program within the School of Applied and Creative Computing at Purdue University's Polytechnic Institute. She holds a prominent international reputation as an expert in the psychosocial and technological factors associated with cybercriminal behavior, with particular expertise in the criminological characteristics and grooming strategies of online child sex offenders. Her professional appointments include membership in the Tippecanoe High Tech Crime Unit and Special Deputy status for the Tippecanoe County Prosecutor's Office. Dr. Seigfried-Spellar completed her academic training at Purdue University, earning a PhD in Technology with concentration in Cyberforensics and Psychology (2011), an MA in Forensic Psychology from City University of New York, John Jay College of Criminal Justice (2007), and a BA in Psychology and Law and Society from Purdue University (2005). Her research interests focus on the psychological aspects of cybercrime, particularly the personality and motivation of cyberdeviants, child sexual exploitation, and behavioral analysis of digital evidence. Her work bridges the gap between psychological theory and digital forensics practice, with significant contributions to understanding online child sex offenders' behaviors and characteristics. She has conducted international research including a 2022-2023 Fulbright Scholar position at the University of Valencia, Spain, studying criminological differences in online child sex offenders, and has been awarded the International Fellows Award for Summer 2024 at the University of Adelaide, Australia. Dr. Seigfried-Spellar's scholarly contributions have been recognized with numerous awards including the 2023 Jefferson Award, 2022 University Faculty Scholar designation at Purdue, the 2022 Outstanding Early Career Achievement in Forensic Science Award from the American Academy of Forensic Sciences, and multiple Outstanding Research Awards from the Digital and Multimedia Sciences Section of the American Academy of Forensic Sciences (2015-2018). She serves as the Chair of the Digital and Multimedia Sciences section of the American Academy of Forensic Sciences and is a Fellow of this organization. Her professional affiliations also include membership in the American Psychological Association, American Psychology-Law Society, and International Association of Law Enforcement Intelligence Analysts. In December 2023, she co-founded and co-organized the Child Sexual Abuse Reduction Research Network workshop at the University of Adelaide, bringing together academics, nonprofit institutions, and law enforcement professionals focused on combating online child sexual abuse.

• Machine Learning
• Network Performance
• IoT Security
• +3 more

Andreas Johnsson is an Adjunct Senior Lecturer at the Department of Information Technology , Uppsala University, Sweden. His research spans Machine Learning , Network Performance , and IoT Security in the context of 5G/6G Networks and Edge Computing . Research interests include federated learning, transfer learning, and network optimization techniques. His recent work (2024-2021) focuses on self-regulated learning models for 6G, multi-objective neural architecture search, IoT intrusion detection generalizability, and delay prediction in heterogeneous networks. He has co-authored over 15 publications in high-impact venues like IEEE Transactions on Machine Learning in Communications and Networking and IEEE NOMS . Andreas actively collaborates with researchers such as Jalil Taghia, Farnaz Moradi, and Hannes Larsson. His contributions extend to change detection algorithms, policy adaptation frameworks, and feature selection methodologies in dynamic network environments. No formal scientific awards or student advisement details are currently documented.

Heyuan ShiView profile

Associate Professor

• Software Quality Assurance
• Kernel Fuzz Testing
• Operating System Security
• +3 more

Heyuan Shi is an Associate Professor at the School of Electronic Information, Central South University since 2023. He earned his B.S. (2015) and Ph.D. (2020) from Central South University and Tsinghua University respectively. His research focuses on software quality assurance with emphasis on kernel fuzz testing , open source software security , and AI application security . Presided over 10+ projects including NSFC General Program (No.62472448) and National Key R&D Sub-Project Published 30+ CCF-A/B papers across software security, machine learning, and quantum testing Supervised 15+ graduate students in software quality assurance areas His recent 2024-2025 publications demonstrate expertise in: LLM-enhanced patch classification Quantum neural network verification Hypergraph adversarial attacks RTOS fuzzing techniques Scientific recognition includes: 2024 Beijing Science & Technology Progress Award (First Prize) Hunan Province Xiaohe Sci-Tech Talent (2023) China Association for Science & Technology Young Talent (2025-2027) Active in academic service as PC member for FM2024 and reviewer for IEEE Transactions journals. Leads industry collaborations with Alibaba and Beijing Institute of Aerospace Metrology.

• Wireless Communication
• Machine Learning
• Signal Processing
• +4 more

Prof. Dr. Haris Gačanin is a faculty member at RWTH Aachen University, affiliated with the Institute for Distributed Signal Processing under the College of Electrical Engineering. His research focuses on integrating machine learning with wireless communication systems, particularly in industrial IoT, edge computing, and network optimization. Current academic rank: Professor Contact: harisg@dsp.rwth-aachen.de Research Interests: Wireless systems, machine learning, signal processing, and network optimization. Key contributions include: Adaptive resource allocation in IIoT and vehicular networks AI-driven channel estimation and feedback mechanisms Security-oriented emitter identification via metric learning Federated/transfer learning for edge environments Hardware-efficient deep learning models for mmWave and THz communications Methodological Focus: Combines reinforcement learning, attention mechanisms, and robust neural architectures with practical implementations on FPGA and vehicular systems.

• Tort Law
• Intellectual Property Law
• Data Privacy
• +6 more

Michael L. Rustad is the Thomas F. Lambert Jr. Professor of Law and Co-Director of Suffolk University Law School's nationally ranked Intellectual Property Concentration. A member of the American Law Institute and former ABA Business Law Section Subcommittee chair, he has shaped legal discourse through extensive publications and empirical research. BA - University of North Dakota MA - University of Maryland PhD - Boston College JD - Suffolk University LLM - Harvard University His research focuses on tort law's evolution in the digital age, including: Platform liability under CDA Section 230 Global data privacy frameworks Software licensing and cloud computing contracts Medical malpractice and gender bias in awards Cybercrime and negligent enablement theories Empirical analysis of arbitration clauses Article trends reveal expertise in: • Digital contract enforceability • AI liability frameworks • Cross-border data governance • Tort reform and empirical studies • Cybersecurity legal standards Scientific Recognition: Four-time recipient of Suffolk's teaching excellence awards Published in top journals like Harvard Journal of Law & Technology and Wake Forest Law Review Cited as authority in U.S. Supreme Court amicus briefs Rustad's work combines theoretical innovation with practical applications, influencing both academic debates and real-world legal reforms.

• Cybersecurity
• Cyber Analytics
• Digital Forensics
• +4 more

Nicole L. Beebe is a Professor at the Alvarez College of Business, The University of Texas at San Antonio , specializing in cybersecurity, cyber analytics, and digital forensics. With over two decades of experience spanning academia, government, and industry, she has contributed extensively to research on insider threats, IoT security, and threat hunting. Ph.D. in Business Administration (Information Technology), UTSA MS in Criminal Justice, Georgia State University BS in Electrical Engineering, Michigan Technological University Her research explores cybersecurity challenges in emerging technologies, including quantum computing, IoT, and large language models. She has pioneered studies on cyberbullying dynamics, forensic automation, and AI-driven threat detection. Recent publications focus on adversarial image obfuscation , VR for security operations , IoT forensic methodologies , and deepfake detection frameworks , reflecting interdisciplinary work at the intersection of security, AI, and digital evidence. 2022 Best Paper Award, Journal of Network & Computer Applications Senior Member, IEEE and ACM Senior Fellow, Information Systems Security Association As an Associate Editor for Computers & Security , she shapes the field through peer review. Her $14M+ in funding from NSF, DHS, and DoD underscores her impact on advancing cybersecurity research and education.

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.

Haining WangView profile

Professor

• Cybersecurity
• Networking Systems
• Cloud Computing
• +5 more

Haining Wang is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. His research focuses on cybersecurity, networking systems, cloud computing, and cyber-physical systems. He holds a Ph.D. from the University of Michigan (2003). His work addresses critical challenges in network security, IoT device fingerprinting, drone navigation security, and 5G/6G infrastructure vulnerabilities. Notable contributions include developing frameworks for detecting deceptive reviews, securing industrial IoT devices, and enhancing geofencing systems with 6G technologies. Wang's IEEE Fellow award (2020) recognizes his contributions to network and cloud security. His research also explores cloud gaming security, data center thermal vulnerabilities, and DNS privacy risks. He actively publishes on topics like container registry typosquatting, acoustic indoor localization, and encrypted DNS censorship analysis. Education: Ph.D., University of Michigan, 2003 Awards: IEEE Fellow (2020) Key Research Areas: Cybersecurity, Network Measurement, IoT Security, 5G/6G Systems Wang's recent work emphasizes securing emerging technologies like drone navigation systems and optimizing sensor placements in indoor environments. His projects often bridge theoretical frameworks with practical implementations in real-world networks and cloud infrastructures.