Find Academic Supervisors

• Image Processing
• Video Compression
• Computer Vision
• +4 more

Fengqing Maggie Zhu is an Associate Professor at the Elmore Family School of Electrical and Computer Engineering within Purdue University , West Lafayette campus. Her research spans image processing , video compression , computer vision , and smart health , with notable contributions to learned image compression , 3D reconstruction , and nutrition analysis via computer vision . Educational background: BS in Electrical Engineering, Purdue University (2004) MS in Electrical and Computer Engineering, Purdue University (2006) PhD in Electrical and Computer Engineering, Purdue University (2011) Her work focuses on developing machine learning-based compression techniques for 2D/3D images and videos, with applications in food portion estimation , wearable dietary monitoring , and virtual reality facial expression tracking . She explores structured pruning , mixed precision quantization , and continual learning to create efficient, robust systems for edge-cloud collaboration. The 2025-2024 article collection reveals concentrated efforts in learned image compression (with 8 papers on quantization, pruning, hierarchical VAEs), food-related computer vision (12+ papers on portion estimation, databases, classification), and 3D reconstruction (MetaFood3D dataset, ICP-3DGS algorithm). Emerging themes include privacy-preserving AI for wearable cameras and class-incremental learning frameworks. Contact: zhu0@purdue.edu

David AtienzaView profile

Professor

• Embedded Systems
• Low-Power Computing
• Edge AI
• +3 more

David Atienza is a Professor in the Department of Electrical Engineering at the School of Engineering, Swiss Federal Institute of Technology in Lausanne (EPFL), renowned for pioneering embedded systems education and research in ultra-low power computing. His innovative teaching methods, including using Nintendo DS consoles and smartphones to teach embedded systems, earned him the 2015 EPFL Teaching Award in Electrical Engineering. His research focuses on Embedded Systems , Edge AI , and Wearable Healthcare , with breakthroughs in energy-efficient hardware-software co-design for biomedical applications. Key contributions include open-source platforms like X-HEEP and HEEPocrates for ultra-low power edge computing, and frameworks like SzCORE for seizure detection benchmarking. His work bridges computer architecture with real-world healthcare challenges, emphasizing privacy-preserving algorithms and sustainable computing. Recent publications (2023-2025) reveal a dominant trend toward biomedical edge AI and sustainable computing , with 70% of articles targeting healthcare wearables (seizure detection, cough monitoring) and 30% addressing energy efficiency in data centers and edge devices. His research consistently integrates open-hardware principles (RISC-V) with novel algorithm-hardware co-design. Awards include: 2015 EPFL Teaching Award in Electrical Engineering section While specific advising details are unreported, his extensive publication record and leadership in multi-partner projects like Sustainable Textile Electronics (STELEC) indicate active graduate supervision and significant research funding. His group develops open-source hardware frameworks used globally in academia and industry. He leads the Embedded Systems Laboratory at EPFL, driving projects in ultra-low power RISC-V architectures, biomedical wearables, and sustainable computing. Current initiatives include carbon-aware data center frameworks and multi-modal health monitoring systems deployable on commercial wearables.

Sadegh TalebiView profile

Assistant Professor

• Reinforcement Learning
• Markov Decision Processes
• Online Learning
• +2 more

Sadegh Talebi is a Tenure Track Assistant Professor in the Machine Learning Section at the Department of Computer Science, University of Copenhagen . His research focuses on theoretical aspects of reinforcement learning, Markov decision processes, online learning, stochastic multi-armed bandit problems, and resource allocation in networks. Education BSc in Electrical Engineering (minor: Electronics) from Iran University of Science and Technology (IUST) (2004) MSc in Electrical Engineering (minor: Communication Systems) from Sharif University of Technology (2006) PhD in Electrical Engineering from the Department of Automatic Control at KTH Royal Institute of Technology (supervised by Alexandre Proutiere and Mikael Johansson) Research Specializes in theoretical foundations of reinforcement learning and online learning Key contributions in stochastic optimization, MDPs, and bandit algorithms Collaborates on applications in resource allocation and quantum computing Publications include high-impact work on offline RL, differentially private exploration, and scalable MDP solutions in journals like Neural Processing Letters and conferences such as NeurIPS and UAI.

Hye-Chung KumView profile

Professor

• Health Informatics
• Data Science
• Privacy-Preserving Record Linkage
• +4 more

Hye-Chung Kum is a Professor in the Department of Health Policy & Management at Texas A&M University, where she pioneers Population Informatics to transform digital data into evidence-based health policy solutions. Her work bridges computer science, public health, and social work to address critical data challenges in healthcare systems. Education: PhD, University of North Carolina at Chapel Hill (UNC-CH), 2004 MSW (Master of Social Work), UNC-CH, School of Social Work, 1998 MS, UNC-CH, Department of Computer Science, 1997 BS, Yonsei University, Seoul, Korea, Department of Computer Science, 1995 Research Vision: Dr. Kum develops privacy-preserving human-computer hybrid systems for cleaning and integrating chaotic real-world data (e.g., EHRs, administrative records). Her Population Informatics framework enables ethical large-scale analysis while addressing data genocide in marginalized communities through innovations like the Secure Decoupled Linkage (SDLink) system. Publication Trends: Recent work (2024-2025) reveals three dominant threads: (1) Mental health service utilization dynamics during/post-pandemic, (2) Racial/ethnic disparities in emergency department use and cancer outcomes, and (3) Ethical data sharing frameworks for vulnerable populations. Her Texas-focused studies on Medicaid payment models and AI-driven record linkage demonstrate practical policy applications. Research Infrastructure: As director of the Population Informatics Lab, she leads cross-disciplinary teams developing Privacy-by-Design tools that balance public health needs with data sovereignty—particularly for American Indian and Alaska Native communities. Her grant-funded work consistently addresses Medicaid transformation and healthcare cost drivers through secondary data analytics.

A. Lynn AbbottView profile

Professor

• Computer vision
• Pattern recognition
• Artificial intelligence
• +5 more

A. Lynn Abbott is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech , specializing in computer vision, biometrics, and AI-driven sensing systems. His work bridges theoretical and applied domains, including autonomous vehicle perception, physiological signal analysis, and secure healthcare monitoring. Education: Ph.D., University of Illinois, 1990 M.S., Stanford University, 1981 B.S., Rutgers University, 1980 Research Interests focus on computer vision for autonomous systems, biometrics using physiological signals, and deep learning applications in transportation safety and healthcare. Recent projects include neural networks for intersection safety modeling and vision-based cardiovascular signal recovery. Publications highlight advancements in graph neural networks for traffic analysis, spatiotemporal filtering for 3D object detection, and privacy-preserving biometric authentication. His work spans disciplines like transportation safety, biomedical signal processing, and computer architecture. Labs & Teams: Affiliated with the Center for Embedded Systems for Critical Applications , contributing to real-time vision systems and hardware-software co-design for safety-critical domains.

Ming JinView profile

Assistant Professor

• Trustworthy AI
• Cyber-physical systems
• Machine Learning
• +4 more

Ming Jin is an Assistant Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. He holds a PhD from UC Berkeley and a B.Eng. from Hong Kong University of Science and Technology. His research focuses on trustworthy AI, CPS security, and energy systems, with affiliations to the Power and Energy Center and Autonomy and Robotics @ VT. Education: PhD in Electrical Engineering and Computer Science (UC Berkeley, 2017), B.Eng. (Honors) in Electronic and Computer Engineering (HKUST, 2012). Postdoc in Industrial Engineering and Operations Research at UC Berkeley. Research interests include safe reinforcement learning, foundation models, cybersecurity, and power systems. Awards include the Siebel Scholarship (2018) and first place in the 2021 CityLearn Challenge. Active in conference organization (e.g., ICML, AAAI) and tutorial development on topics like Safe RL and CPS security. Grants include NSF support for embodied optimization (2025), Amazon-VT Initiative (2023), and Commonwealth Cyber Initiative projects. Involved in labs focused on AI, robotics, and energy systems. Publications span AI safety, RL frameworks, and CPS resilience, with over 50 peer-reviewed articles since 2015.

Hao ZhangView profile

Associate Professor

• Robotics
• Artificial Intelligence
• Computer Vision
• +6 more

Hao Zhang is an Associate Professor in the Department of Computer Science at the Manning College of Information and Computer Sciences (CICS), University of Massachusetts Amherst. He directs the Human-Centered Robotics Laboratory (HCRLab), focusing on lifelong collaborative autonomy, robot adaptation, and human-robot teaming. His research integrates robotics, AI, and machine learning to develop algorithms for real-world applications like manufacturing, autonomous driving, and environmental monitoring. He holds an NSF CAREER Award and DARPA Young Faculty Award, among other recognitions. Dr. Zhang earned a PhD from the University of Tennessee, Knoxville (2014) and an MS from the Chinese Academy of Sciences (2009). His work addresses challenges in unstructured environments through innovations like self-reflective terrain adaptation and graph-based perception systems. He actively promotes equity in robotics through his PROGRESS outreach program. His research sponsors include NSF, DARPA, and industry partners such as Toyota. Publications span conferences like RSS, ICRA, and IROS, with best paper awards. He serves on editorial and program committees for top-tier journals/conferences including RA-L, NeurIPS, and AAAI.

• Linked Data
• Data Management
• Ontology Engineering
• +4 more

Dr. Ronald Maria Siebes serves as Assistant Professor at the Faculty of Science, Vrije Universiteit Amsterdam, with affiliations to the Network Institute and Business Web and Media department. His research focuses on knowledge organization systems, semantic web technologies, and FAIR data principles. He leads projects involving interoperability frameworks for restricted data access, IoT-enabled smart buildings, and historical chronicle analysis. Current roles include managing Open Data Infrastructure initiatives and guiding PhD research in data governance. Education details are not explicitly provided in the text. Research interests emphasize ontology engineering, knowledge graph applications, and data management standards. Recent work explores FAIR Implementation Profiles for research data, IoT sensor integration in office environments, and dynamic knowledge graph embeddings. Active in 6 collaborative projects including smart grid integration and historical source analysis. Labs/Teams: Involved in Network Institute initiatives and Open PHACTS Foundation projects. Supervised 2 PhD theses (names not specified in text). Grant activities span €2.1M in EU Horizon and national funding for data infrastructure and knowledge engineering research.

Carmela TroncosoView profile

Associate Professor

• Privacy Engineering
• Security Systems
• Cryptography
• +3 more

Carmela Troncoso is an Associate Professor at EPFL heading the SPRING Lab focused on Security and Privacy Engineering. Her work addresses technology's societal impact through machine learning security, privacy-enhancing technologies, and privacy engineering frameworks. She leads significant research in decentralized privacy systems, including contributions to the DP-3T contact tracing protocol adopted by Google/Apple during COVID-19. Her lab develops tools for privacy evaluation including Synthetic Data Evaluation frameworks and anonymous authentication libraries. Awards include the ERCIM Best Ph.D. Thesis Award, CNIL Privacy Protection Award, and IEEE Distinguished Paper Award. She was recognized as a Fortune 40 Under 40 Technology Leader in 2020.

Wei BaoView profile

Associate Professor

• Networking
• Distributed Machine Learning
• AI for Networked Systems
• +4 more

Dr. Wei Bao is an Associate Professor in the School of Computer Science at the University of Sydney, part of the Faculty of Engineering. He leads the I-Net (Intelligent Networking) Group and holds a B.Eng. from Beijing University of Posts and Telecommunications (2009), M.A.Sc. from the University of British Columbia (2011), and Ph.D. from the University of Toronto (2015). His research focuses on distributed machine learning, AI-driven network systems, and intelligent network optimization, with industrial collaborations at companies like Link Group. Education: Bachelor of Engineering, Beijing University of Posts and Telecommunications (2009) Master of Applied Science, University of British Columbia (2011) Ph.D., University of Toronto (2015) Research Interests: Dr. Bao's work addresses challenges in distributed machine learning, AI integration into network systems, and optimizing network performance. He emphasizes practical applications through industry partnerships, aiming to bridge academic research with real-world impact. His current projects include federated learning frameworks, IoT communication sharing architectures (e.g., sTube+), and edge computing optimizations. Publications Trends: His recent work focuses on federated learning algorithms (e.g., Federated Learning with Nesterov Accelerated Gradient ), edge computing optimizations ( SOAR: Smart Online Aggregated Reservation ), and partial label learning techniques. These reflect a blend of theoretical advancements and applied systems research. Awards: Multiple Distinguished TPC Member awards (INFOCOM 2020-2024) Best Paper Awards at ACM MSWiM (2019), IEEE NCA (2016), and others Advising & Grants: Dr. Bao supervises PhD candidates in distributed systems and machine learning. His grants include projects like Pioneering Federated Real-Time Video Analytics (ARC DP 2025) and industry collaborations via the University of Sydney's Industry Program. He also leads the Master of Computer Science program at the University of Sydney. Labs & Teams: He directs the I-Net Group, focusing on intelligent networking and distributed systems. Collaborations span institutions like The Hong Kong Polytechnic University (Dr. Dan Wang) and York University (Dr. Uyen Trang Nguyen).

Edoardo SerraView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Cybersecurity
• +5 more

Edoardo Serra is an Associate Professor in the Department of Computer Science at Boise State University (BSU), a role he has held since July 2021. He previously served as an Assistant Professor at BSU from 2015 to 2021 and holds a joint appointment as a Senior Researcher at Pacific Northwest National Laboratory (PNNL) since June 2021. Since January 2023, he has co-directed the Computing Ph.D. Program at BSU and serves as General Chair of the 2024 ACM CIKM Conference. His academic journey includes a Ph.D. in Computer Science Engineering from the University of Calabria, Italy (2012), followed by postdoctoral positions at the University of Calabria and the University of Maryland. He also served as a Visiting Researcher at UCLA (2010–2011). His research focuses on AI/ML applications in cybersecurity, graph representation learning, generative AI, and robust AI systems. Notable projects include: NSF-funded cybersecurity curriculum integration Department of Defense-funded analysis of terrorist networks Idaho Department of Commerce precision agriculture initiatives Key research areas include graph neural networks, adversarial robustness, and ML-driven security solutions. His work has been recognized with awards such as Best Application Paper (2021) and Best Paper Award (2018). He actively contributes to professional service roles, including program chairs and editorial boards. Current projects emphasize AI ethics, generative models, and scalable graph algorithms. He advises on applied AI consulting for industry and government, focusing on model interpretability and cybersecurity implications.

Michael HamView profile

Associate Professor

• Cybersecurity
• Cyber Operations
• Malware Analysis
• +5 more

Dr. Michael Ham is an Associate Professor and Coordinator for Cyber Operations Initiatives at Dakota State University's Beacom College of Computer & Cyber Sciences. He leads the NSA-designated Center of Academic Excellence in Cyber Operations (CAE-CO) and oversees DSU's Cyber Operations bachelor's program and Ethical Hacking Certificate. His roles include teaching advanced cybersecurity courses, advising doctoral students, and coordinating cyber events like DakotaCon and GenCyber camps. Education: All degrees from Dakota State University — D.Sc. in Cybersecurity (202X), M.S. (20XX), and B.S. (20XX). Research focuses on cybersecurity education, penetration testing, malware analysis, and developing experiential learning tools. Key projects include open-source platforms for hardware reverse engineering and wireless security training using software-defined radios. He emphasizes cyber hygiene practices and offensive security principles in both teaching and industry consulting. Grants highlight his leadership in addressing cybersecurity workforce shortages, including NSF CyberCorps SFS programs (PI), GenCyber camps (Co-PI), and DoD scholarships. Recent publications address ransomware prevention, X.509 certificate validation, and pandemic-era ICT systems. Dr. Ham also serves as an independent security consultant, advising on infrastructure security, vulnerability remediation, and policy development for organizations. His work bridges academia and industry through practical, hands-on cybersecurity education and tool development.

• Cybersecurity
• Machine Learning
• Data Analytics
• +5 more

Dr Zhiyuan (Thomas) Tan is an Associate Professor at Edinburgh Napier University’s School of Computing, Engineering and the Built Environment . He is internationally recognised for his cybersecurity research and has been listed among Stanford University’s Top 2% Scientists for 2021–2023. Education BEng (2005) with high distinction – North-eastern University, China MEng (2008) – Beijing University of Technology, China PhD in Computer Systems (2014) – University of Technology Sydney, Australia Research Interests Dr Tan’s research integrates cybersecurity with machine learning and data analytics. His core areas include: Intrusion detection and defence of critical service systems Adversarial machine learning for malware and anomaly detection Virtualisation security through non-parametric behaviour modelling IoT and vehicular network security—cloud/edge/cloudlet frameworks Privacy-preserving AI and federated machine unlearning Smart-city digital forensics and cyber-physical system resilience Research Output Trends His recent articles (2020–2025) reveal a strong focus on federated learning, edge & mobile computing, UAV coordination, and AI-driven security. Topics span advanced steganography, metamorphic malware, graph injection attacks, and trustworthiness in vehicular platoons, all anchored in real-world IoT and transportation applications. Awards & Distinctions Stanford University Top 2% Scientists List (2021, 2022, 2023) National Research Award 2017 – Research Council of the Sultanate of Oman Best Paper Awards (three instances) Kaspersky Lab Student Cyber Security Conference – Finalist Award SICSA Supervisor of the Year 2019 – Honourable Mention Grants & Leadership Dr Tan has attracted over £200k in external funding including: Carnegie Trust (£73,564) – Federated Machine Unlearning ENU Development Trust (£29,998) – Machine Unlearning Royal Society (£12,000) – VANET Security & Privacy SICSA & other awards for MemoryCrypt, AI Secrets, behaviour biometrics, and visiting-fellow schemes. Supervision & Mentoring Since 2013 he has supervised or co-supervised 16 doctoral candidates and several master’s students; six PhDs have successfully graduated. Roles range from Director of Studies to additional supervisor across diverse topics from malware evolution to VR olfactory interfaces. Research Groups & Collaboration He is affiliated with the Centre for Artificial Intelligence and Robotics , the Centre for Distributed Computing, Networking and Security , and the Centre for Cybersecurity, IoT and Cyber-physical Systems at ENU, fostering interdisciplinary collaboration with national and international partners.

• Data Quality
• Database Systems
• Data Profiling
• +7 more

Felix Naumann is a Professor at the Hasso Plattner Institute in Potsdam, Germany, specializing in data management and database systems. His research focuses on data quality, data profiling, entity resolution, functional dependencies, and database optimization. He has authored over 300 publications in top-tier conferences and journals, including VLDB, SIGMOD, and ICDE. His work bridges theoretical foundations with practical systems, such as TASHEEH for data cleaning and PRISMA for privacy-preserving schema matching. He serves as an editor for the ACM Journal of Data and Information Quality and has led initiatives on hybrid conference formats and inclusive computing. Key contributions include: Data Quality: Pioneered studies on data quality's impact on machine learning and developed tools like AutoTSAD for anomaly detection. Data Dependency Discovery: Advanced functional and inclusion dependency mining algorithms, including Hitting Set Enumeration methods. Schema Matching & Integration: Created systems like BrewER and Frost for entity resolution and schema alignment. Educational Impact: Led massive open courses in data engineering with over 10,000 participants. His work emphasizes practical applications in cultural heritage data (ReCLAIM), Wikipedia table analysis, and cross-platform data systems (RHEEMix). He collaborates extensively with industry and academia, addressing challenges in dynamic datasets and data governance.

• Artificial Intelligence
• Recommender Systems
• Information Systems
• +2 more

Dietmar Jannach is a Full Professor at the University of Klagenfurt, Austria, affiliated with the Institute for Artificial Intelligence and Cybersecurity where he leads the Research Group for Information Systems. His academic roles include membership in the university's Senate and Curricular Commissions for Liberal Arts and Information Management. His research spans: Core Areas : Artificial Intelligence, Recommender Systems, and Software Engineering. Methodological Focus : Algorithm reproducibility, fairness in AI, sequential recommendations, and hybrid learning models. Emerging Interests : Generative AI for group decision support, ethical recommender systems, and foundation model applications. Jannach's recent publications critically evaluate reproducibility challenges in AI research, advocate for calibrated recommendations to mitigate bias, and explore agentic paradigms in group recommender systems. He emphasizes real-world validation, with studies on deployment challenges and developer experiences in software processes. He actively contributes to academic governance and mentors through research groups, though specific student advisees are not listed. Contact via Dietmar.Jannach@aau.at .