Find Academic Supervisors

Hamam MokayedView profile

Associate Professor

• Machine Learning
• Computer Vision
• Deep Learning
• +4 more

Hamam Mokayed is an Associate Professor at Luleå University of Technology, working within the Department of Computer Science, Electrical and Space Engineering. His research focuses on Machine Learning, with specialization in the Embedded Intelligent Systems LAB division. Dr. Mokayed's research interests center around computer vision, machine learning, and deep learning, with particular applications to intelligent vehicles and visual systems operating in challenging environments such as snow, ice, and extreme weather conditions. His work in northern Sweden has uniquely positioned him to address vehicle intelligence challenges in complex, dynamic road conditions. Additionally, he actively researches AI applications in pedagogy, exploring how artificial intelligence can enhance teaching and learning methodologies. His recent publications demonstrate significant contributions across diverse fields including battery technology, biomedical engineering, quantum physics, computer vision, and medical imaging. These works showcase his expertise in applying machine learning techniques to solve complex problems across multiple disciplines. Dr. Mokayed has contributed to EU/National projects such as AI4EDU, Universeh, and REEDEAM, focusing on course development, MOOCs, and AI-supported learning environments. His professional development includes 20+ credits in higher education didactics, a Project Management Professional (PMP) certification, and specialized training in presentation and communication skills. With over 15 years of experience at the intersection of vehicle intelligence systems and academia, Dr. Mokayed has produced numerous publications in leading conferences and journals, along with 15 registered intellectual properties, demonstrating his commitment to both innovation and education in the field of machine learning and intelligent systems.

• Embedded Systems
• Neural Networks
• Hardware-Aware Neural Architecture Search
• +7 more

Rodolfo Zunino serves as a Full Professor in the Department of Naval, Electrical, Electronic and Telecommunications Engineering (DITEN) at the University of Genoa. His teaching portfolio includes core electronics courses across multiple degree programs including Master's in Electronic Engineering and undergraduate programs in Electronics Engineering and Information Technology. His research focuses on the intersection of embedded systems and neural networks, with particular emphasis on hardware-aware optimization for resource-constrained environments. Key areas include traffic classification for cybersecurity, affordance segmentation for robotics, energy-efficient structural health monitoring, and in-sensor computing architectures. His work consistently addresses the Pareto optimization between computational efficiency and accuracy in embedded AI applications. Analysis of his recent publications (2023-2025) reveals a strong trend toward hardware-software co-design for neural networks, with increasing specialization in wearable robotics and structural monitoring systems. The research demonstrates consistent innovation in neural architecture search techniques specifically tailored for microcontrollers and sensor-embedded systems. Professor Zunino maintains active research supervision with multiple advisees (though specific names aren't listed in available materials) and collaborates on projects involving hardware implementation of neural networks for security and monitoring applications. His work receives consistent publication in relevant engineering venues. His laboratory activities center around embedded electronic systems development, with focus areas in neural network acceleration for microcontrollers, sensor data processing, and real-time system implementation for robotics and infrastructure monitoring applications.

Junliang YuView profile

Research Fellow

• Recommender Systems
• Self-Supervised Learning
• Graph Learning
• +4 more

Dr. Junliang Yu is an ARC DECRA Research Fellow at the Data Science discipline , School of Electrical Engineering and Computer Science, The University of Queensland (UQ). He earned his PhD in Data Science from UQ (2023) and holds previous degrees from Chongqing University. His work focuses on self-supervised learning , recommender systems , and data-centric AI , with significant contributions to graph learning and LLM-powered applications .

Javier Perez AlvaroView profile

Associate Professor

• Numerical Linear Algebra
• Matrix Analysis
• Numerical Analysis
• +8 more

Javier Perez Alvaro is an Associate Professor in the Department of Mathematical Sciences at the University of Montana, College of Humanities and Sciences. His research is centered on numerical linear algebra and matrix analysis, with applications in scientific computing and data science. He teaches courses such as Data Science Analytics, Numerical Analysis, and Linear Algebra. Research Interests: His primary focus lies in Numerical Linear Algebra , particularly Nonlinear Eigenvalue Problems , Matrix Polynomials , Linearizations , and Conditioning Analysis . He explores the theoretical and computational aspects of polynomial and rational eigenvalue problems, with applications in mechanical systems, fluid dynamics, and photonic crystals. The recent publications reflect a strong trend in structured linearizations, perturbation theory, and rational approximations for nonlinear eigenvalue problems. His work often involves developing and analyzing new families of linearizations (e.g., Fiedler-like, block-Kronecker) that preserve matrix structure and ensure numerical stability. The focus is on backward error analysis, eigenvector accuracy, and efficient solution methods for large-scale problems. Advising and Grants: He actively mentors undergraduate students in research projects related to Data Science and Machine Learning, including generative modeling, deep neural networks, and tiny machine learning. He is involved in the Master of Science in Data Science program. While specific grants are not listed, his collaborative work with researchers from institutions like KU Leuven, University of Manchester, and UC Santa Barbara indicates active research funding and partnerships. Labs and Teams: His research is conducted in collaboration with a broad network of mathematicians and computational scientists. Key collaborators include María C. Quintana, Maribel Bueno, Froilán M. Dopico, Paul Van Dooren, Karl Meerbergen, and Vanni Noferini. These collaborations span topics in structured linearizations, rational matrix functions, and numerical stability.

Nakul GargView profile

Assistant Professor

• Ambient Intelligence
• Wireless Sensing
• Healthcare
• +4 more

Nakul Garg is an Assistant Professor in the Department of Electrical & Computer Engineering at Rice University. He leads research in ambient intelligence, wireless sensing, and embedded AI, focusing on energy-efficient and scalable solutions for IoT systems, robotics, and healthcare. His work bridges the gap between sensing, computing, and communication in everyday environments. Education includes a PhD in Computer Science from the University of Maryland, College Park, and a B.Tech in Electronics & Communication Engineering from Indraprastha University, Delhi. His research has been recognized with awards such as the Marconi Society Young Scholar Award (2024) and the Best Paper Award at MobiSys 2022. Research interests span spatial sensing, low-power IoT localization, and integrating AI into wearable devices. Key projects include LiTEfoot (cellular-based asset tracking), FreshSense (food quality monitoring), and SPiDR (acoustic navigation for micro-robots). He actively collaborates with industry (e.g., Microsoft Research, NEC) and has advised multiple students in developing innovative systems. Awards and recognitions include the Cyber-Physical Systems Rising Star (2024), NSF I-Corps support, and multiple best paper/demo awards at top conferences. His work has been featured in ACM GetMobile and highlighted by the ACM SIGMOBILE community.

Jeffrey YackleyView profile

Assistant Professor

• Software Engineering
• Software Testing
• Refactoring
• +12 more

Jeffrey Yackley is an Assistant Professor of Computer Science in the Department of Computer Science, Engineering, and Physics at the College of Innovation and Technology, University of Michigan-Flint. He holds a Ph.D. in Computer & Information Science from the University of Michigan-Dearborn (2022), where he also earned his M.S. and B.S. in the same field, along with a B.Sc. in Biochemistry from the University of Michigan (2009). Ph.D. in Computer & Information Science, University of Michigan-Dearborn (2022) M.S. in Computer & Information Science, University of Michigan-Dearborn (2019) B.Sc. in Computer & Information Science, University of Michigan-Dearborn (2016) B.Sc. in Biochemistry, University of Michigan (2009) Dr. Yackley's research spans software engineering, software testing, refactoring, AI in education, cybersecurity, gamification, and educational technology. His work emphasizes both empirical software engineering and innovative pedagogical approaches, particularly active learning and gamification in computing education. He investigates refactoring practices, software quality, and trustworthiness in open-source software supply chains, while also exploring the impact of AI, VR, and digital platforms on learning outcomes. His recent publications in IEEE Access, IEEE FIE, ASEE, and Springer reflect a strong trend toward interdisciplinary research at the intersection of software engineering and education. He frequently publishes on AI-assisted learning, student engagement, and the ethical use of generative AI in computing education. His technical work includes refactoring detection, software testing, and cybersecurity tools, often developed through student-led projects. Golden Apple Award (2024, University of Michigan-Flint) Dara Knot Award (2024, UM-Flint Honors Program) MWIN Fellowship (2023, UM-Flint) Dr. Yackley actively mentors undergraduate and graduate students, advising numerous research projects and capstone teams. He has secured significant external funding, including an NSF REU site on digital accessibility and a Google Research Scholar award on software testing in open-source supply chains. His grants total over $2.5 million, supporting student research, innovation, and entrepreneurship. He leads the 'SSTEM: BlueShirting' project and contributes to initiatives on lean business models and secure authentication systems. He advises student teams on projects such as the TCP Cybersecurity Game, Automated Security Scanner, and Critical Path Tool, fostering hands-on learning and real-world problem solving. His lab environment emphasizes collaborative, student-centered research with practical impact.

• Machine Learning
• Embedded Systems
• Neural Architecture Search
• +4 more

Paolo Gastaldo serves as Associate Professor in the Department of Naval, Electrical, Electronic and Telecommunications Engineering (DITEN) at the University of Genoa's School of Engineering. His academic appointment focuses on electronic engineering (IINF-01/A classification) with teaching responsibilities spanning undergraduate and graduate programs including Electronic Engineering and Information Technologies. His research centers on machine learning for embedded systems, specializing in neural architecture search techniques optimized for resource-constrained environments. Key investigation areas include hardware-aware AI deployment on microcontrollers, energy-efficient traffic classification, and affordance segmentation for wearable robotics. His work bridges theoretical machine learning with practical hardware constraints, targeting applications in IoT security, edge computing, and smart wearable systems. Analysis of his recent publications reveals a strong trend toward Pareto-optimal solutions balancing computational efficiency and accuracy in neural network design. His research demonstrates particular expertise in adapting deep learning models for microcontroller deployment while maintaining cybersecurity functionality. The publications consistently address the challenge of implementing complex AI tasks within strict memory and power limitations of embedded devices. Gastaldo maintains active research supervision with regular publication output, primarily through the University of Genoa's institutional repository (IRIS). His work shows strong interdisciplinary connections between computer science, electrical engineering, and robotics applications.

Andrea ResminiView profile

Senior Lecturer

• Blended space and blended experiences
• Game making for the exploration of wicked problems
• Information architecture and artificial intelligence
• +5 more

Andrea Resmini is a Senior Lecturer at Halmstad University's School of Information Technology, where he teaches and conducts research at the intersection of information architecture, blended spaces, and game design. With a distinguished career spanning academia and professional practice, Resmini has made significant contributions to the field through his publications, leadership roles, and community building initiatives. His work bridges theoretical research with practical applications in urban environments and digital-physical experiences. Resmini's research focuses on blended spaces and blended experiences, exploring how digital and physical environments intertwine to create meaningful interactions. He investigates game making as a method for exploring wicked problems and developing innovative solutions. His work on information architecture and artificial intelligence examines the ethical and practical implications of AI systems on information organization and user experience. Resmini also studies placemaking in blended environments, seeking to understand how people create meaning and connection across digital and physical contexts. His recent publications reveal a strong focus on the application of information architecture principles to emerging challenges in urban planning, AI systems, and community engagement. Resmini increasingly explores the potential of game-based methods for addressing complex societal issues and facilitating co-creation processes. There's a clear trajectory toward examining the ethical dimensions of information systems, particularly in relation to AI classification systems and their social impacts. His research demonstrates how information architecture can contribute to building more resilient, inclusive, and human-centered technological environments. While specific awards aren't detailed in available sources, Resmini's leadership roles indicate significant recognition in his field. As a two-time past president of the Information Architecture Institute and co-founder of several key organizations including the Journal of Information Architecture, Architecta (the Italian Society for Information Architecture), the Academic/Practitioner Information Architecture Roundtable, and World IA Day, his contributions have been instrumental in shaping the discipline. Resmini employs innovative, participatory methods in his work, including 'tiny pop-up living labs' and workshop-based approaches to design research. His practice demonstrates a commitment to bridging theory and practice, engaging with communities, and exploring the potential of game-based methods for addressing complex challenges in both academic and professional contexts.

Titus Theodorus RobroekView profile

Researcher

• Machine Learning
• Deep Learning
• Resource-aware Computing
• +4 more

Titus Theodorus Robroek is a Postdoctoral Researcher at IT University of Copenhagen, working within the Data, Systems, and Robotics section. His research focuses on resource-aware data systems and data-intensive systems and applications, with affiliations to both the Resource-Aware Data Systems group and the Center for Climate IT. Dr. Robroek's research interests span multiple areas in machine learning systems, including: Resource-aware Machine Learning Deep Learning Training Optimization GPU Computing and Collocation ML Benchmarking Frameworks Data Management for Machine Learning Scientific Visualization for Training Systems His recent publications demonstrate a strong focus on optimizing machine learning workflows, particularly in resource-constrained environments. His work addresses critical challenges in data selection, pipeline orchestration, and efficient utilization of hardware resources for deep learning training. The research shows a progression from foundational work on data management to more comprehensive frameworks for resource-aware machine learning. Dr. Robroek has received research funding through two major projects: MOTH: Machine Learning on Tiny Hardware (Novo Nordisk Foundation, 2023-2026) RAD: Extremely Parallel and Incredibly Diverse Data Processing on Many Heterogeneous Cores (Independent Research Foundation of Denmark, 2021-2025) His PhD thesis "Resourceful Learning: Training More Models with Fewer Resources" (2024) represents a significant contribution to the field of efficient machine learning systems.

Theocharis TheocharidesView profile

Associate Professor

• Embedded Systems
• Hardware Acceleration
• Computer Vision
• +3 more

Theocharis (Theo) Theocharides is an Associate Professor and Department Chair at the University of Cyprus's Department of Electrical and Computer Engineering. He serves as Director of Research at the KIOS Research and Innovation Centre of Excellence. Holding a Ph.D. in Computer Science and Engineering from Pennsylvania State University, his research focuses on designing intelligent embedded systems with specialization in domain-specific architectures, reconfigurable hardware, and low-power embedded processors. He leads the Embedded and Application-Specific System-on-Chip Lab, conducting projects in mobile systems, tinyML, embedded computer vision, pattern recognition architectures, and intelligent system monitoring. His pioneering work includes hardware acceleration for real-time object detection and depth estimation in embedded vision systems for robotics and cyber-physical applications. Current investigations focus on hardware-friendly ML algorithms for tinyML, visual information extraction, and distributed embedded vision systems involving drone swarms and robotic networks. Professor Theocharides has authored over 130 publications and serves as Associate Editor for prestigious journals including ACM's Computing Surveys and IEEE Transactions on Computer-Aided Design. As Technical Programme Committee Chair for DATE 2025, he maintains active involvement in the research community.

Alexey TumanovView profile

Assistant Professor

• Machine Learning
• Distributed Systems
• Resource Management
• +4 more

Alexey Tumanov is an Assistant Professor in the School of Computer Science at Georgia Institute of Technology, part of the College of Computing. His research focuses on systems for machine learning, resource management, and scheduling in distributed environments. He holds a PhD from Carnegie Mellon University and conducted postdoctoral research at UC Berkeley under Ion Stoica. Previously, he worked at the University of Toronto and in industry on cloud computing and datacenter systems. Education: PhD in Computer Science, Carnegie Mellon University (2019) Postdoc, UC Berkeley RISELab (2019) MSc in Computer Science, University of Toronto (2012) BSc in Computer Science, University of Toronto (2010) Research Interests: Tumanov's work addresses challenges in distributed machine learning, including efficient resource management for inference and training pipelines, scheduling algorithms for soft-real-time systems, and federated learning. His lab (SAIL) develops systems like SARATHI-SERVE and RocketKV to optimize LLM inference and co-scheduling. He emphasizes practical deployments in healthcare (HOLMES) and cloud environments. Key Contributions: Ray distributed framework for AI (OSDI 2018) ESCHER scheduler for ephemeral cloud resources (SoCC 2022) InferLine ML pipeline orchestration (SoCC 2020) Awards: NSERC CGS-D3 Scholarship (2016) Best Paper at EuroSys 2016 (TetriSched) Google PhD Fellowship Nominee (2018) Lab & Teaching: Leads the Systems for Artificial Intelligence Lab (SAIL) at Georgia Tech. Teaches advanced courses on operating systems (CS3210) and systems for ML (CS8803-SMR). Supervises 15+ graduate students across PhD and MS programs.

Lihong ZhengView profile

Associate Professor

• Artificial Intelligence
• Computer Vision
• Data Analytics
• +6 more

**Dr. Lihong Zheng** is an Associate Professor in the School of Computing, Maths and Engineering at Charles Sturt University (CSU), specializing in Data Science and Engineering. Her research focuses on applying Artificial Intelligence, Computer Vision, and Machine Learning to solve real-world problems in agriculture, viticulture, and environmental management. She has secured over $7.4 million in industry grants since 2018, leading projects such as IoT-based agricultural solutions, cyber security for farms, and robotic systems for highway monitoring. Dr. Zheng is a recipient of prestigious awards including the Cisco Women in IT Academia Award (2019) and two Charles Sturt Research Awards. She actively contributes to IEEE committees, organizes international conferences, and serves on editorial boards. Her work emphasizes translating research into practical applications, such as the IoT Spartans Challenge 2017 (Runner-up with a global team of 250 universities) and developing the Vitidoc smartphone app for vine health assessment. Key research areas include intrusion detection in robotics, livestock data management, and precision agriculture through AI-driven systems. She collaborates with industry partners to enhance STEM awareness and mentors students in national/international competitions.

Remy DehaanView profile

Researcher

• Remote Sensing
• Agricultural Innovation
• Environmental Science
• +5 more

Remy Dehaan is a Spatial Scientist and Researcher at Charles Sturt University (CSU), affiliated with the Agricultural, Environmental and Veterinary Sciences School and the Cropping, Horticulture and Soil Health Department. He specializes in remote sensing, drone technology, and precision agriculture. His research focuses on hyperspectral imagery for weed mapping, stubble burning effects, vegetation changes in national parks, and soil health improvements. As Chief Pilot of CSU's drone fleet, he integrates drone data to enhance decision-making in agriculture and environmental management. Key research areas include: Hyperspectral and multispectral imaging Drone-based environmental monitoring Weed detection and management River system geomorphic processes Soil amelioration techniques Dr. Dehaan has been awarded grants for projects such as developing drone technology for weed control, national vegetation mapping frameworks, and creating a 'Weed Managers Guide to Remote Detection.' His work bridges remote sensing innovations with practical agricultural and environmental applications. He is also a registered supervisor, guiding research students in these interdisciplinary areas. His contributions include over 35 peer-reviewed publications, emphasizing precision agriculture, environmental monitoring, and AI-driven solutions for land management challenges.

Kakarountas AthanasiosView profile

Associate Professor

• Computer Architecture
• Embedded Systems
• VLSI Circuits & Systems Design
• +7 more

Dr. Athanasios Kakarountas is an Associate Professor at the University of Thessaly, specializing in Embedded Computing Systems. He leads the Intelligent Systems Laboratory (iSL) and manages ParICT, Central Greece's parallel computing node. His roles include Head of the Department of Computer Science and Biomedical Informatics, Deputy Chairman of GRNET (2019–2023), and Chair of the Central Greece Research & Innovation Committee (2018–2022). He holds an MBA from Cyprus Open University and has extensive industry experience in tech policy, innovation, and entrepreneurship. Education: BSc & PhD in Electrical & Computer Engineering (University of Patras, 1998/2004), MBA (Cyprus Open University, 2015). Research focuses on Computer Architecture, VLSI Design, TinyML, IoT Security, and Healthcare Technologies . Key projects include seizure detection systems, blockchain in supply chains, neuromorphic circuits, and cybersecurity solutions for smart cities. He has published 150+ articles, 2 e-books, and 7 book chapters, with 1,600+ citations. Awards include IEEE's Outstanding Branch Counselor Award (2013). Leadership: IEEE Greece Section Secretary (4 years), Vice-Chair (6 years), and co-Chair of the IEEE eGOV Special Committee. Editor-in-Chief of IEEE Potentials magazine, and active in organizing IEEE conferences and hackathons. Founder of A2rTEST creative team and mentor for robotics/tech competitions. Grants & Projects: Over 30 EU/Greek-funded projects. Labs/Teams: iSL, ParICT, and partnerships with Patras Science Park. Future work emphasizes TinyML for healthcare, IoT security, and smart city infrastructure.

Daniele PalossiView profile

Researcher

• Autonomous Systems
• Deep Learning
• Nano Drones
• +4 more

Dr. Daniele Palossi is a Researcher at ETH Zürich's Department of Digital Integrated Circuits and Systems. His work focuses on autonomous robotics, particularly nano-drones and embedded AI systems. He specializes in developing energy-efficient, vision-based navigation frameworks and deep learning architectures for resource-constrained platforms. Research interests include autonomous systems, computer vision for robotics, and hardware-software co-design for UAV applications. His recent work advances swarm robotics, sim-to-real transfer learning, and ultra-low-power neural networks for nano-scale drones. Key contributions include the FünfiBër-Drone modular platform and secure SOC designs for UAV navigation. His research bridges theoretical AI advancements with practical implementations in miniature robotic systems.