Find Academic Supervisors

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +6 more

Prof. Fabio Galasso is a Full Professor in the Department of Computer Science at Sapienza University of Rome, where he heads the Perception and Intelligence Lab (PINLab). His research focuses on fundamental innovation in computer vision and machine learning, with particular emphasis on distributed intelligent systems, perception frameworks, and general intelligence within sustainable and interpretable AI contexts. His research interests span multiple domains of computer vision including video segmentation , motion forecasting , distributed intelligent systems , and shape reconstruction . Galasso's work emphasizes sustainable AI approaches that operate within constrained computational resources while maintaining interpretability and verifiability. His research bridges theoretical foundations with practical applications across retail, smart cities, and industrial settings. His recent publications demonstrate a clear progression toward increasingly complex human motion understanding and forecasting, with a strong emphasis on real-world applications. The research trajectory shows movement from foundational video segmentation techniques toward sophisticated motion prediction systems and anomaly detection frameworks that integrate multiple modalities. Key themes include temporal consistency, computational efficiency, and practical deployability in resource-constrained environments. His scientific achievements have been recognized with prestigious awards: 2019 IoT/WT Innovation World Cup 2019 Digital Champions Award 2018 Deutscher Digital Award Galasso has coordinated significant research initiatives including a Marie Sklodowska-Curie Actions project (Horizon 2020) and served as Principal Co-Investigator in multiple German-funded projects from the Ministry of Education and Ministry of Economics. His industry experience includes founding and directing OSRAM's Computer Vision Department in Munich, where he led R&D efforts connecting AI research with smart lighting applications, resulting in successful innovation transfers like the award-winning VISN product. He leads the Perception and Intelligence Lab (PINLab) at Sapienza University of Rome, fostering research that spans fundamental computer vision techniques to practical implementations in retail, smart cities, and industrial applications. The lab maintains strong connections with both academic institutions (including previous collaborations with University of Cambridge and Max Planck Institute) and industry partners.

Daniel S. BergerView profile

Adjunct Assistant Professor

• Cloud Computing
• Sustainability
• System Reliability
• +7 more

Daniel S. Berger is a Principal Researcher at Microsoft's Azure Systems Research Group in Redmond, focusing on the efficiency, sustainability, and reliability of cloud platforms . He is also an Affiliate Assistant Professor at the Paul G. Allen School of Computer Science at the University of Washington, where he teaches graduate classes. His research spans systems stack innovations for sustainability , including work on memory tiering , repair operations , and cooling systems (Zissou). He leverages system prototyping , simulation , and statistical modeling in his work, often collaborating with PhD students and postdocs from institutions like Columbia, University of Toronto, CMU, and Princeton. Recent publications highlight his leadership in CXL-based memory management , carbon-efficient cloud design , and latency-aware caching . His tools, such as Belatedly and FOO , have demonstrated significant improvements in cache performance and latency optimization. Best Paper Awards: USENIX OSDI 2023, HotCarbon 2023, ACM SOSP 2021. Distinguished Paper: ASPLOS 2023. His work has been integrated into Apache Traffic Server and Microsoft production systems , with open-source tools and datasets released for reproducibility. Collaborations include hardware and OS development teams within Azure and academia.

• Automotive Systems Engineering
• Generative AI
• Model-Based Engineering
• +3 more

Fengjunjie Pan is a PhD student and research assistant at the Chair of Robotics, Artificial Intelligence and Embedded Systems at the Technical University of Munich since 2021. He holds an M.Sc. in Electrical Engineering from TU Berlin (2019) and a B.Eng. in Electrical Engineering from Hamburg University of Applied Sciences. His research focuses on automotive systems engineering and generative AI applications in model-based engineering. His publications (2022-2025) demonstrate expertise in: LLM integration for automotive software development Containerized architectures for autonomous driving Virtualization technologies in vehicular systems Constraint generation and model transformation He supervises multiple Master's and Bachelor's theses on generative AI applications and privacy-enhancing technologies in automotive contexts, working alongside Prof. Alois Knoll's team.

• Wireless Communication
• Communication Systems
• Cellular Networks
• +4 more

Ki Won Sung is an Associate Professor at KTH Royal Institute of Technology, specializing in wireless communication systems and network optimization. His research focuses on 5G/6G technologies, integrated sensing-communication systems, cell-free massive MIMO, and stochastic network modeling. Research interests include: Wireless network architecture design and optimization Resource allocation in multi-user communication systems Integrated sensing and communication (ISAC) Stochastic modeling of ultra-dense networks Energy-efficient communication protocols Millimeter wave and massive MIMO systems His recent publications demonstrate strong emphasis on beyond-5G systems, particularly cell-free massive MIMO deployments and URLLC applications. Research trends show consistent focus on network optimization through advanced signal processing, geometric decomposition methods, and cross-layer protocol design. Teaching activities include course management and examination for multiple degree projects and core courses: Communication Systems (IK2200) Mathematical Statistics (IX1501) Mobile Networks and Services (IK2560) Radio Networks (IK2510) Stochastic Simulation (II2206) Wireless Systems (IK1330)

Edgar Vadimovich VatamanitsaView profile

Assistant Professor

• Client-server programming
• Java and Android pattern development
• Application information systems
• +6 more

Edgar Vadimovich Vatamanitsa serves as an Assistant Professor in the Department of Computer Science at Chernivtsi National University, specializing in cross-platform software development and cloud computing applications. His work bridges academic research with industry practice through active engagement in both university teaching and commercial software development. Education: Yuriy Fedkovych Chernivtsi National University (2013): Systems Software, Software Engineer Dr. Vatamanitsa's research centers on Java/Android pattern development, client-server architectures, and distributed systems. He pioneers practical implementations of design patterns in cloud environments, with significant contributions to optical field simulation, semiconductor modeling, and intelligent transportation systems. His methodology emphasizes cross-platform efficiency and real-world applicability of theoretical computing concepts. Analysis of his 2022-2024 publications reveals dominant themes in cloud-based optical computation (AWS infrastructure), Java pattern implementation for Android, and cross-platform solutions for traffic management and semiconductor physics. His work consistently integrates distributed systems with domain-specific challenges in optics, printing technology, and educational analytics, demonstrating exceptional versatility across engineering disciplines. Professional Activities: Member of Bukovina Information Technology Cluster (since 2022) Senior Software Developer at EPAM SYSTEMS Certificate in Cross-platform Programming and Information Security (Ternopil National Technical University, 2023) Tech Summer Bootcamp for Teachers participant (2023)

Roy SterrittView profile

Lecturer

• Autonomic Computing
• Robotics
• Machine Learning
• +5 more

Roy Sterritt is a Lecturer in Informatics at the School of Computing, Ulster University. His research focuses on autonomic computing, robotics, machine learning, and cybersecurity. He has contributed extensively to decentralized systems and fault management in autonomous environments. Research Interests: Roy’s work spans autonomic computing, robotics, and AI, with applications in cloud systems, space exploration, and drone fleets. He emphasizes self-adaptation, fault tolerance, and security protocols. Scientific Awards: Highly Ranked Scholar in Autonomic Computing (2024) Multiple Best Paper Awards (2016–2023) Recent Trends: His recent publications highlight autonomic solutions in cloud security, robot swarms, and space systems, leveraging machine learning and adaptive communication protocols. Projects & Collaborations: Roy has led projects like SPAAACE-Ware and DEL CAST AWARD, focusing on autonomic analytics and apoptotic computing. He organizes international conferences on autonomous systems and collaborates globally.

Athanasios D. PanagopoulosView profile

Professor

• Communications
• Wireless Communications
• Radio Communications
• +9 more

Athanasios D. Panagopoulos is a Full Professor at the School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), specializing in Satellite & Space Communications , Antennas and Propagation , and Quantum Communication . He leads the Division of Information Transmission Systems and Material Technology, with a focus on wireless systems, machine learning, and 5G/6G technologies. Born in Athens (1975), received summa cum laude Diploma and Dr. Engineering from NTUA (1997, 2002). Former roles: part-time Assistant Professor (2003-2007), head of Satellite Division at Hellenic Authority for Information Security (2005-2008). Research Trends emphasize Quantum Key Distribution (QKD) , Reconfigurable Intelligent Surfaces , and Deep Learning Applications in satellite networks. His work bridges atmospheric propagation effects with terrestrial-satellite convergence , including AI-driven excess attenuation prediction and UAV channel modeling. Scientific Awards include URSI General Assembly Young Scientist Award (2002, 2005) Best Paper Awards: IEEE RAWCON 2006, IEEE ISWCS 2015 Grants & Collaborations : Principal Investigator for EU/ESA R&D programs, with editorial roles at IEEE Transactions on Antennas and Propagation, and Elsevier Physical Communication. Member of ITU-R , ETSI Study Groups , and IEEE (Senior) .

• Embedded real-time systems
• Computer architectures
• Hardware/software co-design for IoT
• +5 more

Tiago Manuel Ribeiro Gomes is an Assistant Professor at the Department of Industrial Electronics within the School of Engineering at the University of Minho, Portugal. He is also a Senior Researcher at Centro ALGORITMI and a member of both the IE R&D Group and the ESRG R&D Lab. Holding a Ph.D. in Electronics and Computers Engineering, his research focuses on embedded real-time systems, computer architectures, and hardware/software co-design for IoT devices. Academic Degree: Ph.D. in Electronics and Computers Engineering Current Position: Assistant Professor, School of Engineering, University of Minho Gomes has led extensive research in IoT systems over 15 years, particularly in hardware acceleration for automotive LiDAR sensors, secure embedded systems, and efficient OS frameworks for low-end devices. His work includes the EU-funded CROSSCON project and spans hardware-assisted security, dynamic binary translation, and wireless sensor networks. Recent publications highlight his expertise in automotive sensor technology, with articles like FOG-Zip for LiDAR compression, SecureQNN for TinyML security, and Hardware-Assisted Range Image Generation for LiDAR processing. His work bridges IoT, embedded systems, and cybersecurity, focusing on real-time performance and hardware-software co-design. Projects include the development of reliable/secure automotive sensor solutions and EU project CROSSCON. He contributes to open-source frameworks like UTango for IoT security and investigates heterogeneous fault tolerance architectures using Arm/RISC-V processors. Labs: IE R&D Group, ESRG R&D Lab Education: Ph.D. in Electronics and Computers Engineering, Master’s in Telecommunications Engineering (both from University of Minho)

Hongyi WuView profile

Professor

• Security and Privacy
• Intelligent Computing Systems
• Communication Systems
• +7 more

Hongyi "Michael" Wu is the Department Head and Thomas R. Brown Leadership Chair in the Department of Electrical and Computer Engineering at the University of Arizona, part of the College of Engineering. He is a full-time Professor and a Fellow of IEEE, with a strong leadership presence in both academic administration and research. Education: PhD in Computer Science, State University of New York (SUNY) at Buffalo, 2002 MS in Electrical Engineering, State University of New York (SUNY) at Buffalo, 2000 BS in Scientific Instruments, Zhejiang University, China, 1996 His research is focused on security and privacy in intelligent computing and communication systems , with applications in cybersecurity, mobile and wireless networks, Internet of Things (IoT), and distributed computing. His work bridges theoretical foundations with real-world systems, emphasizing robustness and trust in next-generation networks. While specific article titles are not listed in the provided text, his publication record exceeds 160 technical papers in top-tier journals and conference proceedings, indicating sustained contributions in cybersecurity, mobile computing, and networked systems over two decades. His editorial roles in IEEE Transactions on Mobile Computing, IEEE Internet of Things Journal, and others reflect his influence in these domains. Scientific Awards and Honors: NSF CAREER Award (2004) UL Lafayette Distinguished Professor Award (2011) IEEE Percom Mark Weiser Best Paper Award (2018) IEEE Fellow Dr. Wu has served as Principal or Co-Principal Investigator on over 50 funded research projects totaling more than $23 million from NSF, NSA, DOD, DOE, NATO, state governments, and industry partners. He actively mentors graduate students, with recent advisees securing faculty positions at Old Dominion University, Chongqing University, and the University of Hawaii at Manoa. He currently has openings for postdoctoral researchers and research assistants in AI, security, and wireless networks. He has chaired major conferences including IEEE INFOCOM 2020 and WoWMoM 2021, and has served on the editorial boards of several leading journals such as IEEE Transactions on Computers, IEEE Transactions on Mobile Computing, IEEE Internet of Things Journal, and Elsevier Parallel Computing.

• Computer Vision
• Robotics
• 3D Reconstruction
• +4 more

El Mustapha Mouaddib is a Professor in the Perception and Robotics department at Universite de Picardie Jules Verne, affiliated with Laboratory Heudiasyc (UMR CNRS 7253). His research bridges advanced robotics with cultural heritage preservation, focusing on developing novel computer vision techniques for complex documentation challenges. His primary research interests include omnidirectional vision systems , hyperspectral imaging , and 3D reconstruction methodologies , with significant emphasis on applications for cultural heritage documentation. Mouaddib's work particularly addresses challenges in temporal illumination compensation , laser scanning registration , and multi-scale digitization of historical structures, as evidenced by his extensive Notre-Dame de Paris cathedral research. Analysis of his 15 most recent publications reveals a consistent trajectory toward heritage robotics - developing specialized computer vision algorithms for cultural preservation. His work demonstrates increasing sophistication in integrating multi-modal sensor data (TLS, hyperspectral, RGB-D) solving illumination challenges in historical documentation developing adaptive robotic systems for complex environments Notably, his Notre-Dame research forms a cohesive body of work examining structural changes through advanced 3D analysis. Mouaddib actively participates in major interdisciplinary projects including SAMURAI , ASSIDUITAS , SCANBOT , ADAPT , and SUMUM , which focus on heritage digitization and robotic exploration. His collaborative approach is evident through extensive co-authorship with institutions like CNRS and international partners in Japan and Italy. His laboratory work centers on the E-Cathedrale initiative, creating comprehensive digital twins of Gothic cathedrals through multi-temporal and multi-scale documentation. This involves developing specialized hardware (like the HDROmni camera system) alongside novel algorithms for processing challenging heritage environments.

Caleb StanfordView profile

Assistant Professor

• Programming Languages
• Formal Methods
• Distributed Systems
• +4 more

Caleb Stanford is an Assistant Professor in the Department of Computer Science at the University of California, Davis. He specializes in programming languages and formal methods with applications to distributed systems, data processing, and computer security. His research focuses on developing practical tools and theoretical foundations for secure and reliable systems. Research interests span programming languages theory, formal verification techniques for distributed systems, Rust ecosystem security, stream processing verification, and automata theory extensions. Specific projects include securing Rust crates through automated auditing tools, extending derivative-based abstractions for regular expressions, and developing verification frameworks for dataflow programming. His publications demonstrate consistent focus on formal methods applications, with recent emphasis on Rust program safety (2025), stream processing verification (2020-2024), and incremental graph algorithms (2023). Article keywords frequently include formal verification, programming languages, distributed systems, and automata theory. Dr. Stanford actively recruits students for research positions: Undergraduates should demonstrate excellence in relevant coursework Prospective PhD students must apply through UC Davis admissions Current openings focus on Rust security, formal methods, and distributed systems verification He leads the Guided Incremental Digraphs (GID) research project and collaborates with UCSD on Rust security initiatives. Community contributions include serving as Artifact Evaluation Co-Chair for POPL 2024 and committee member for PLDI and OOPSLA conferences.

Han DongView profile

Assistant Professor

• Operating Systems
• Distributed Systems
• Datacenter Sustainability
• +3 more

Han Dong is an Assistant Professor in the Department of Computer Science at Hamilton College, appointed to the faculty in 2025. His research bridges software and hardware systems to address sustainability challenges in large-scale computing environments. His educational background includes: Ph.D. in Computer Science from Boston University M.S. in Computer Science from University of Maryland Baltimore County B.S. in Computer Science from University of Maryland Baltimore County Dong specializes in optimizing datacenter-scale systems through machine learning-driven approaches to energy-performance trade-offs. His work targets fundamental improvements in software-hardware heuristics for sustainable computing infrastructure, with applications in distributed systems and operating system design. This research addresses critical industry needs for reducing the environmental impact of cloud-scale computing while maintaining performance. His professional recognition includes: Oak Ridge Institute for Science and Education (ORISE) Fellowship at the Food and Drug Administration Prior to joining Hamilton College, Dong held research positions at Red Hat (3 years) and served as a postdoctoral associate at Boston University. His technical contributions are reflected in active open-source projects related to network drivers and systems optimization, as evidenced by his GitHub repository activity.

Ning YuView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +6 more

Ning Yu serves as an Associate Professor in the Department of Computing Sciences within the School of Arts & Science at State University of New York Brockport. He earned his Ph.D. in Computer Science from Georgia State University and joined SUNY Brockport in 2017 after serving as a Tenure-Track Assistant Professor at the University of South Carolina Upstate. Georgia State University, Computer Science, Ph.D. Southern Illinois University Carbondale, Computer Science, M.S. Dr. Yu's research spans artificial intelligence, network and information security, big data analytics, deep learning, and cloud computing with significant applications in bioinformatics. His work demonstrates a clear progression from foundational AI and security research toward specialized applications in healthcare and energy systems. The most recent publications show increased focus on graph-based deep learning approaches for biomedical problems, particularly in cancer genomics and drug response prediction. His scholarly impact includes over 40 publications in prestigious venues including ACM/IEEE Transactions, BMC, PLoS, and Information Sciences. The publication trend reveals consistent output with increasing emphasis on interdisciplinary applications, particularly at the intersection of AI and biomedical research. Teacher of The Year, School of Science and Art, SUNY Brockport 2022-2023 Influential Professor 2022, SUNY Brockport, Fall 2023 Provost Post-Tenure Scholarship Award, $3,500, SUNY Brockport, Spring 2024 Google Research Credits Grants (2018, 2020-2021) WORLDWIDE TOP 10 FINALISTS, IBM 2017 Watson Analytics Global Competition As an educator, Dr. Yu has mentored numerous undergraduate researchers who have presented at national conferences including NCUR and SURC. He founded SUNY Brockport's first ACM SIGAI Student Chapter and has secured significant funding including a multi-campus SUNY IITG grant for AI education development. His research group actively recruits students for projects involving cloud development (Azure/GCP/AWS), CI/CD, Docker/K8s, and software architecture. Prior to academia, Dr. Yu accumulated 10 years of professional experience in software development and system networking with certifications from Cisco, Microsoft, and Google.

Alessio GambiView profile

Researcher

• Software Engineering
• Software Testing
• Self-Driving Cars
• +4 more

Alessio Gambi is a Researcher at the Austrian Institute of Technology (AIT) within the Security & Communication Technologies department, specializing in software engineering for autonomous systems. His current work focuses on testing methodologies for self-driving cars, self-adaptive systems, and cloud environments. His research interests center on Software Testing for Autonomous Vehicles , where he develops novel techniques for scenario generation, safety validation, and uncertainty management. Key areas include search-based procedural content generation, simulation-based testing, and the integration of large language models for test learning. His work bridges theoretical advances with practical tools like Flexcrash and TEASER for real-world validation. Analysis of his recent publications (2023-2025) reveals a strong trend toward autonomous vehicle testing with increasing incorporation of AI techniques. Approximately 60% of his work addresses self-driving car validation, 25% focuses on general software testing methodologies, and 15% explores AI/LLM applications in testing. His subfield specialization shows consistent emphasis on critical scenario generation, mixed-traffic simulation, and safety monitoring. Gambi actively contributes to the software engineering community through program committee roles at major conferences including ASE (2023-2025), ICSE (2024-2026), ISSTA (2021-2025), and ESEC/FSE. He has served as session chair, workshop organizer, and track committee member across these venues, demonstrating leadership in software testing research. His professional activities include developing open-source testing tools (visible on GitHub), teaching engagements like the Database Systems course at AIT (2024), and industry collaborations through AIT's research infrastructure. Current projects focus on predictive safety monitoring and uncertainty management for automated driving systems.

Zaki MalikView profile

Professor

• Service Computing
• Business Analytics
• Reliable Distributed Systems
• +3 more

Dr. Zaki Malik is a Professor in the Department of Marketing and Business Analytics at the College of Business, East Texas A&M University. He holds the Sandra Lyday & Gary J. Fernandes Endowed Professorship in Business Analytics and has been with the institution since at least 2009. His educational background includes a Ph.D. and M.S. in Computer Science from Virginia Tech (2008 and 2004 respectively), and a B.S. in Computer Science from Wichita State University (2001). Research Focus: Dr. Malik's work spans interdisciplinary domains including: Service Computing architectures and reliable distributed systems Business analytics with emphasis on social media data extraction Semantic integration systems for knowledge management Social network analysis for product taxonomy development His current projects involve developing frameworks for extracting weighted product taxonomies from Twitter data to predict market success. Publication Trends: Recent works demonstrate strong focus on business analytics applications in distributed systems and social media, with increasing emphasis on ethical implications of big data. Methodological contributions include self-healing frameworks for service-oriented systems and novel approaches to cloud-based data processing. Awards and Honors: Teaching Excellence Award (College of Business, 2024) Endowed Professorship in Business Analytics (2020-present) Paul W. Barrus Distinguished Faculty Teaching Award (2020) Multiple IEEE Best Paper Awards (2014-2016) Teaching and Advising: Key courses include Cyber Security & IT Auditing (BUSA 533), Cyber Forensics (BUSA 539), and Advanced Analytics (BUSA 537). He particularly emphasizes Python programming for business analytics, focusing on real-world problem solving.