Find Academic Supervisors

Alejandro Laguna SanzView profile

Assistant Professor

• Diabetes Technology
• Glucose Monitoring
• Artificial Pancreas Systems
• +7 more

Alejandro Laguna Sanz is an Assistant Professor in the Department of Electronic Engineering at the School of Engineering, Universitat de València. His work bridges biomedical engineering and clinical diabetes care, focusing on technological solutions for glucose regulation. His research interests include diabetes technology, artificial pancreas systems, glucose monitoring accuracy, and the impact of exercise on glycemic control. He is particularly active in developing and evaluating predictive algorithms for glycemic management, especially in dynamic conditions such as physical activity and post-bariatric hypoglycemia. His work often combines control theory with clinical validation. The recent publications reveal a strong focus on real-world performance of continuous glucose monitors (CGMs), sex-specific responses in women with type 1 diabetes, and innovative interventions like elastic band resistance training and dual-hormone closed-loop systems. His research increasingly integrates population modeling, in silico studies, and virtual education platforms for artificial pancreas training. He is a member of the CLIDET research group (Clinimetry and Technological Development in Therapeutic Exercise), where he contributes to the design and clinical evaluation of advanced monitoring and control systems for metabolic diseases. Alejandro Laguna Sanz earned his PhD from Universitat Politècnica de València with a thesis on uncertainty in postprandial glucose modeling in type 1 diabetes, supervised by Dr. Paolo Rosseti and Dr. Jorge Bondía Company. He has not received any explicitly listed scientific awards in the provided text. He advises students within his research group, though no specific names are listed. His work is supported by collaborative projects in diabetes technology and engineering, particularly in the development of adaptive control systems and clinical validation of glucose monitoring devices. He is involved in both experimental and simulation-based studies.

• Next Generation Internet
• Human-centric Networking
• Ontology and Knowledge-based Management
• +13 more

David Palma is an Associate Professor at the Department of Information Security and Communication Technology , Faculty of Information Technology and Electrical Engineering , Norwegian University of Science and Technology (NTNU). His research focuses on next-generation networking paradigms, including intent-based networking , knowledge-driven management , and IoT device automation . Key research interests include: Human-centric Internet of Things (IoT) Ontology-based network management Cloud/Edge/Fog computing for IoT Arctic and satellite communication 5G integration in smart grids His recent publications (2024-2019) highlight trends in knowledge graphs for network compliance, XR applications in critical sectors, UAV-based emergency networks , and 5G-enabled smart grid protection . Articles also explore Arctic connectivity via satellite swarms and energy-efficient IoT management.

• Reliability Engineering
• Maintenance Optimization
• Risk and Safety Analysis
• +8 more

Jørn Vatn is a Professor in reliability, availability, maintainability, and safety at the Norwegian University of Science and Technology (NTNU) , specifically within the Faculty of Engineering and the Department of Mechanical and Industrial Engineering . His work focuses on integrating risk assessment and maintenance strategies for critical infrastructure. MSc courses: TPK4120 Safety and Reliability Analysis , RAMS optimization PhD course: PK8200 Risk Influence Modelling and Risk Indicators Continuing Education: Maintenance Optimization , Safety and Reliability in Railway Applications , Project Risk Management , Risk Reducing Measures Research Interests span maintenance management, reliability modeling, condition-based maintenance, risk influence analysis, and digital twin applications in infrastructure. His methods emphasize statistical analysis and Markov processes to model degradation and optimize maintenance scheduling. The 15 most recent articles (2025-2022) demonstrate trends in applying Markov models , digital twins , and data-driven optimization to offshore wind turbines, railways, hydrogen pipelines, and cyber-physical systems. Subfields include predictive maintenance, stochastic degradation modeling, and infrastructure resilience. He leads the ROSS Gemini Centre and the RAMS group , advising on risk modeling and maintenance strategies. Collaborations involve SINTEF, industry partners, and international researchers.

• Control Systems
• Energy Management
• Functional Electrical Stimulation
• +12 more

William Holderbaum is a Professor at the Department of Electrical Engineering, School of Engineering, University of Reading. His research spans control systems, energy management, functional electrical stimulation, robotics, and wireless power transfer, with over 95 publications since 2002. Research Interests: His work integrates theoretical control theory with practical applications in renewable energy, biomedical engineering, and smart systems. He has made significant contributions to microgrid protection, energy storage control, optimal power management for electric cranes, and FES for paraplegia rehabilitation. His recent work explores soft robotics using electroactive polymers and intelligent sensing for environmental and health monitoring. Publication Trends: His recent articles (2021–2025) reflect a multidisciplinary focus, combining engineering, materials science, and healthcare. Key themes include sustainable energy systems, intelligent control, wearable sensors, and novel computing paradigms using smart materials. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: He has collaborated extensively with researchers such as F. Alasali, T. Yunusov, M. Alkowatly, and V. Becerra, suggesting a strong mentoring role. His work on energy storage, smart grids, and FES implies involvement in funded research projects, though specific grants are not listed. Labs and Teams: He is part of research teams focused on control systems and energy at the University of Reading, collaborating with the group led by B. Potter and V. Becerra. His work with biomedical applications suggests ties to interdisciplinary health-tech initiatives.

Israat HaqueView profile

Associate Professor

• Software-defined Networking (SDN)
• Cyber-physical Systems (CPS)
• Internet of Things (IoT)
• +12 more

Dr. Israat Haque is an Associate Professor in the Faculty of Computer Science at Dalhousie University, Halifax, where she leads the Programmable and Intelligent Networking (PINet) research group. She also holds an adjunct professorship in the Department of Computing Science at the University of Alberta. PhD in Computer Science, University of Alberta NSERC Postdoctoral Fellow, University of California, Riverside MSc in Computer Science, Concordia University Her research focuses on developing high-performance, secure, and dependable distributed and emerging networking systems, with key interests in Software-Defined Networking (SDN), Cyber-physical Systems (CPS), Internet of Things (IoT), 5G/6G technologies, and AI/ML applications in networking. She applies data-driven approaches to solve real-world problems in network programmability, stream processing, and edge/cloud computing. The 15 most recent publications highlight a strong trend in in-network computing, IoT security, AI-driven network reliability, and stream processing. Her work spans from theoretical surveys to practical system implementations, often leveraging programmable data planes (P4), machine learning, and hardware acceleration to address challenges in performance, security, and fairness in distributed systems. Dr. Haque has received numerous prestigious recognitions: IEEE/ACM N2Women Rising Star Award (2021) ACM FAccT Best Paper Award (2023) IEEE WICE Outstanding Mentoring Award (2025) University of Alberta Alumni Honour Award (2024) Digital Nova Scotia Thinking Forward Award (2022) Intel Fast Forward Initiative Winner (2022) President’s Research Excellence Award, Dalhousie (2021) She actively mentors PhD and MSc students and leads externally funded research projects, including a Canada First Research Excellence Fund (CFREF)-supported initiative on Securing Smart Environments. She has served on editorial boards of IEEE Transactions on Vehicular Technology and IEEE Communications Magazine, and on program committees of top conferences such as IEEE ICNP, IEEE NetSoft, and ACM SIGCOMM. Her lab, PINet, fosters innovation in networking systems and produces high-impact research with real-world applicability. Dr. Haque leads the PINet research group, which includes current PhD and MSc students working on cutting-edge topics like post-quantum cryptography, network programmability for security, and large ML model security. The group collaborates with institutions such as the University of Alberta, Laval University, and Concordia, and has strong industry ties with Intel, Meta, Amazon, and Facebook.

Furat Al-ObaidyView profile

Lecturer

• Machine Learning
• Intelligent Systems
• VLSI Circuits
• +7 more

Furat Al-Obaidy is a Lecturer in the Department of Electrical and Computer Engineering at the University of Michigan-Dearborn's College of Engineering and Computer Science. His expertise spans machine learning, intelligent systems, VLSI circuits, multi-core systems, FPGA architecture, and deep learning applications. PhD in Electrical & Computer Engineering from Ryerson University (2021) MSc in Electrical & Computer Engineering from Ryerson University (2016) MSc in Control & Instrumentation Engineering from University of Technology, Baghdad (1999) BSc in Control & Systems Engineering from University of Technology, Baghdad (1996) His research focuses on power-aware computing systems, thermal imaging for IC testing, hybrid cache architectures, and AI-driven network optimization. He has published extensively on topics including GPGPU power management, 3D NoC routing, and wireless sensor networks. His recent publications highlight applications of neural networks in cache optimization, FPGA architecture, and thermal imaging for hardware diagnostics. Earlier work includes control system simulations for wind turbines and power factor analysis in electrical circuits. Ryerson Graduate Development Award (2021) Ontario Graduate Scholarship Award (OGS) (2020) Ryerson Graduate Fellowship (2018) Graduate Research Excellence Award (2017) Queen Elizabeth II Graduate Scholarship in Science and Technology (2017)

• Design Methodologies
• Manufacturing Process Planning
• Machining Process Characterization
• +2 more

Professor Joaquim de Ciurana Gay is a faculty member at the University of Girona in the Department of Mechanical and Industrial Construction Engineering , leading research in Manufacturing Process Engineering since 2009. He heads the Process, Product and Production Engineering Research Group (GREP) , with over 100 peer-reviewed articles and 12 book chapters focusing on biomedical device design, additive manufacturing, and machining optimization. Education: PhD in Industrial Engineering (UPC, 1997); Postdoctoral training at Rutgers University, Cranfield University, and Université du Québec à Trois-Rivières. Research Interests include additive manufacturing , machining process characterization , design methodologies , and industrial collaboration . His recent biomedical projects emphasize cell culture systems and medical device prototyping . Publications highlight trends in hybrid manufacturing , AI-driven process optimization , and sustainable biomedical design . Scientific Awards: Award for Scientific Collaboration with Companies (ASCAMM Centre Tecnològic, 2011) Teaching Innovation: Authored educational materials and led projects integrating industry partnerships into academic training. Labs & Teams: Director of GREP, fostering knowledge transfer through collaboration agreements with industrial partners.

Maurizio LeottaView profile

Assistant Professor

• Software Engineering
• Test Automation
• Empirical Software Engineering
• +7 more

Maurizio Leotta is an Assistant Professor in Computer Science at the University of Genova, Italy, where he has been employed since 2018. He is a member of the Department of Computer Science, Bioengineering, Robotics, and Systems Engineering (DIBRIS) within the School of Mathematical, Physical and Natural Sciences. Additionally, he serves on the School Council and the Department Board of DIBRIS. Dr. Leotta received his PhD in Computer Science from the University of Genova in 2015, with a thesis on Automated Web Testing under the supervision of Prof. Filippo Ricca. His doctoral work was revised by Prof. Massimiliano di Penta (Università del Sannio, Italy) and Prof. Ali Mesbah (University of British Columbia, Canada). Before his academic career, he worked as an IT Technician in various companies and participated in research and industrial projects funded by organizations such as Finmeccanica S.p.A. and the Italian Space Agency. Dr. Leotta's primary research focuses on Software Engineering, with particular emphasis on Test Automation, which is his main research topic. He collaborates with Prof. Paolo Tonella from USI, Switzerland on this area. His other significant research interests include Empirical Software Engineering, Requirements Engineering, Business Process Modelling, and Model-Driven Software Engineering. His work often addresses practical challenges in web and mobile application testing, with a growing interest in applying AI and gamification techniques to improve software testing processes. His recent publications demonstrate a strong trend toward integrating artificial intelligence with traditional software testing methodologies, particularly in end-to-end web testing. He has made significant contributions to improving test robustness, addressing flakiness in test execution, and developing tools for better test maintenance. His work also shows increasing interest in gamification approaches to engage developers and students in software testing activities, as well as applications of large language models to enhance test automation. Dr. Leotta has received multiple prestigious awards for his research, including: Best Paper Award at ICST 2025 (Short Papers, Vision and Emerging Results) Distinguished Paper Award at ICST 2023 (Industry Papers) Best Paper Award at QUATIC 2022 (Full Papers) Best Paper Award at ICST 2022 (Demo and Testing Tool Papers) Best Paper Award at QUATIC 2020 (Full Papers) Best Student Paper Award at ICWE 2016 (Full Papers) Dr. Leotta has advised numerous graduate students, including three PhD candidates who have completed their degrees (Andrea Fasciglione in 2024, Dario Olianas in 2023, and Diego Clerissi in 2020). He currently supervises multiple Master's students working on topics related to software testing, web accessibility, and AI applications in software engineering. He has also served as a postdoctoral advisor for researchers including Diego Clerissi and Dario Olianas. Beyond advising, Dr. Leotta has secured research funding through collaborations with industry partners and has been involved in multiple research projects focused on software testing and verification. He co-directs the Software Engineering for Healthcare (SEH) Laboratory, which has been partially supported by Janssen Italia (previously by Actelion Pharmaceuticals Italia). The lab focuses on applying software engineering techniques to healthcare applications, particularly in the areas of wearable technology for patient monitoring and medical data analysis. Dr. Leotta is also active in the Gamify research community, organizing workshops on gamification in software development, verification, and validation.

• Program Analysis
• Program Synthesis and Transformation
• Machine Learning for Programming Systems
• +4 more

Kihong Heo is an Associate Professor at the School of Computing and Graduate School of Information Security, KAIST, Republic of Korea. He leads the Programming Systems Laboratory and has held academic positions since 2017, including Assistant Professor (2017-2020) and Associate Professor (2020-2024). His research focuses on developing program reasoning systems for safe and reliable software, with key contributions in AI-based program analysis, program simplification, and scalable synthesis systems. His educational background includes a Ph.D. and B.S. in Computer Science & Engineering from Seoul National University (2009 and 2005 respectively). He has served as a post-doctoral researcher at the University of Pennsylvania (2009-2017) before transitioning to KAIST. Research interests span AI-driven program analysis for deep semantic bug detection, program debloating systems for security optimization, and synthesis frameworks using probabilistic models. His work bridges undecidability in static analysis with external information integration. Recent publications analyze logic-based verification for mobile agents, compiler fuzzing , and data dependency guided fuzzing . Trends show a focus on security-critical systems, reinforcement learning for code reduction, and probabilistic models. Scientific awards include the ACM SIGSOFT Distinguished Paper Award (FSE 2025) , Amazon Research Award (2024) , and multiple KAIST teaching honors . He has also received ACM SIGPLAN Distinguished Paper (PLDI 2019) and Best Artifact Awards. He has mentored numerous Ph.D. and Master’s students and contributes to program committees for leading conferences like SOAP (PC Co-Chair) , ICSE , and SAS . His work is supported by grants and collaborations with institutions including Facebook and Seoul National University.

Lars-Henrik ErikssonView profile

Senior Lecturer

• Formal Methods
• Logic in Computer Science
• Programming Language Theory
• +7 more

Lars-Henrik Eriksson is a Senior Lecturer in the Department of Computer Science at Uppsala University, part of the Department of Information Technology. He holds a PhD and is recognized as an Excellent Teacher and educational mentor. He currently serves as the program director for the Master's program in Computer Science and has previously held leadership roles, including Head of the Department of Computer Science from 2004 to 2018. PhD in Computer Science Excellent Teacher Award Pedagogical Mentor at Uppsala University His research focuses on the applications of logic in computer science, particularly formal methods for software development. His work spans formal specification, verification, and synthesis, with strong emphasis on logic programming, interactive theorem proving, and logical frameworks. He has made significant contributions to the use of formal methods in safety-critical domains such as railway signaling. His current research includes modeling application domains within formal methods and formalizing concurrency theory using the Isabelle proof assistant. The recent publications reflect a sustained focus on modal logics for nominal transition systems, formal verification tools (e.g., GTO), and domain-specific applications of formal methods. These works demonstrate deep integration of theoretical logic and practical software engineering, especially in distributed and safety-critical systems. The recurring themes include concurrency, verification, and the use of domain models to enhance formal reasoning. Scientific Awards and Recognitions: Excellent Teacher Pedagogical Mentor Lars-Henrik Eriksson has advised numerous students through project supervision, thesis reviews, and course mentorship, though no formal list of advisees is provided. He has been involved in significant research management, including leading the department and directing a master’s program. He has also collaborated with industry, particularly in railway signaling, contributing to safety analysis and formal verification projects with Trafikverket and through companies he co-founded. He is a board member of Formal Methods Europe and served as program committee chair for the FME Symposium 2002 (part of FLoC’02). His work bridges academia and industry, especially in the application of formal methods to real-world engineering challenges.

Sourav MedyaView profile

Assistant Professor

• Machine Learning
• Graph Mining
• Data Science
• +3 more

Sourav Medya is an Assistant Professor in the Department of Computer Science at the University of Illinois at Chicago (UIC). Prior to joining UIC, he was a research assistant professor at the Kellogg School of Management and the Northwestern Institute on Complex Systems (NICO) at Northwestern University. He earned his Ph.D. in Computer Science from the University of California, Santa Barbara (2019) and a Master of Engineering in Computer Science and Automation from the Indian Institute of Science, Bangalore, India. Ph.D., University of California, Santa Barbara, 2019 M.Eng., Indian Institute of Science, Bangalore, India His research lies at the intersection of graphs, machine learning, and data science, with a focus on bias, fairness, and interpretability in AI. He also develops machine learning techniques with applications in healthcare, infrastructure, and computational social science. His recent publications address topics such as global counterfactual explainers for GNNs, game-theoretic node selection, and interpretable logical rule extraction from graph models. Medya has advised students in areas like multimodal learning, graph neural networks, explainable AI, and efficient AI. His scientific awards include the UIC Outstanding MS Thesis Award (2024) and Best Paper Award at the Machine Learning on Graphs (MLoG) Workshop at WSDM'23. He has reviewed for top conferences including NeurIPS, ICML, KDD, and AAAI, and recently taught courses on Machine Learning on Graphs (CS 519) and Introduction to Data Science (CS 418) at UIC.

João Pedro Machado VitorinoView profile

Part-Time Lecturer

• Machine Learning
• Artificial Intelligence
• Cybersecurity
• +3 more

João Pedro Machado Vitorino is a part-time lecturer and researcher at the Polytechnic Institute of Porto's School of Engineering, affiliated with GECAD. He is pursuing a Ph.D. in Informatics Engineering (Artificial Intelligence) at the University of Porto and holds an MSc in Artificial Intelligence Engineering. Education : Ph.D. (in progress), MSc, and BSc in Informatics/Artificial Intelligence Engineering Research : Focuses on machine learning robustness, explainability, and adversarial attack simulation for cybersecurity applications Projects : Involved in 7 EU/National R&D initiatives (2021-2025), including BEHAVIOR (2024-2025) and CYDERCO (2023-2025) His recent publications (2023-2025) span cybersecurity datasets , adversarial learning , malware detection , LLM security , and energy-efficient AI . He co-organized the 36th European Simulation & Modelling Conference (2022) and peer-reviewed for Pattern Recognition and Computers & Security . Awards : IEEE Outstanding MSc Thesis (2023), Engineers Association Innovation Award (2024), multiple merit certificates Skills : Machine Learning, AI Security, Network Analysis, and 3 professional certifications (Cisco, Microsoft, Airbus)

Jon M. HouckView profile

Associate Professor

• Translational Neuroscience
• Substance Use Disorders
• Neuroimaging
• +7 more

Jon M. Houck is an Associate Professor of Translational Neuroscience at the University of New Mexico and a Research Associate Professor at the Center on Alcoholism, Substance Abuse, and Addictions (CASAA) . His work integrates neuroimaging (MEG, fMRI) with behavioral therapy to study mechanisms of behavior change in substance use disorders and schizophrenia . Dr. Houck has developed MEG-based neuroimaging protocols to investigate motivational interviewing efficacy and neuromodulation for alcohol treatment. Research Interests : Translational neuroscience, neurobiological mechanisms of behavior change, neuroimaging (MEG/fMRI), psychotherapy process research, substance use disorders, schizophrenia, neuromodulation. Publications : 15+ peer-reviewed articles spanning 2015–2024, focusing on motivational interviewing , brain connectivity in schizophrenia, adolescent addiction , and neuroimaging methods . Contributions : Pioneering dynamic functional network connectivity analysis in schizophrenia, client language modeling for addiction treatment outcomes, and test-retest reliability assessments in clinical populations. Affiliation : University of New Mexico, CASAA, with collaborations across neuroimaging , addiction science , and clinical psychology disciplines.

Sandro BartoliniView profile

Associate Professor

• High-Performance Computing (HPC)
• Computer Architecture
• Embedded Systems
• +5 more

Sandro Bartolini serves as Associate Professor in the Department of Information Engineering and Mathematical Sciences at the University of Siena, Italy, where he teaches advanced courses in computer architecture and parallel programming while leading cutting-edge research in high-performance computing systems. His academic journey began with a cum laude Laurea in Computer Engineering followed by a PhD in Computer Science and Engineering from Università di Pisa. Education: PhD in Computer Science and Engineering, Università di Pisa Laurea in Computer Engineering (cum laude), Università di Pisa Research Focus: His work centers on photonic interconnects for chip multiprocessors , energy-efficient software optimization for multi-core/GPU architectures, and performance-portable parallel programming models . Current investigations span cryptographic acceleration, blockchain algorithms, and hardware/software co-design for emerging computing paradigms, with strong emphasis on practical implementations bridging theoretical advances and real-world applications. Publication Trends: Recent publications (2019-2023) reveal three dominant threads: (1) Photonic network innovations addressing energy bottlenecks in chip multiprocessors, (2) The PHAST library ecosystem enabling seamless CPU/GPU programming across domains from autonomous vehicles to UAV navigation, and (3) Hardware accelerator designs for convolutional networks and cryptographic workloads. These works consistently target performance-portability challenges in heterogeneous computing environments. Grants and Collaborations: As principal investigator for the Italian Ministry-funded PHOTONICA project, he established international research partnerships with Murcia University, Columbia University, and Hong Kong University of Science and Technology, while securing industry collaborations with STMicroelectronics, Intel Munich, IBM, and IMEC. He has also managed complex IT system deployments for Siemens Italy, RAI (Italian public broadcasting), and SpaceDys. Academic Leadership: Bartolini serves as Associate Editor for the Eurasip Journal of Embedded Computing and actively contributes to the European HiPEAC network. His research group at Siena maintains strong industry ties for technology transfer, particularly in photonic interconnect validation and parallel programming frameworks for next-generation computing systems.

Jacob MotaView profile

Assistant Professor

• Muscle Function Assessment
• Neuromuscular Adaptations
• Occupational Health (Firefighters/Law Enforcement)
• +4 more

Jacob Mota, Ph.D. , is an Assistant Professor in the Department of Kinesiology & Sport Management at Texas Tech University. His research focuses on neuromuscular adaptations and muscle function assessment across diverse populations including firefighters, law enforcement officers, and athletes. Ph.D. in Human Movement Science (2020), University of North Carolina Chapel Hill School of Medicine M.S. in Kinesiology (2016), Texas Tech University B.S. in Exercise and Sport Sciences (2014), Texas Tech University Dr. Mota's work examines the relationship between chronic health conditions and musculoskeletal injury risk in occupational athletes, with emphasis on resistance training adaptations and muscle quality analysis. His Neuromuscular and Occupational Performance Laboratory develops evidence-based strategies for injury prevention and performance optimization. Recent publications analyze firefighter health standards, muscle asymmetry impacts on occupational performance, and technological advancements in muscle assessment. His research integrates exercise physiology, biomechanics, and public safety health metrics. Outstanding Mentorship of Undergraduate Students in Research – University of Alabama (2022) Apple Polishing Award – TTU Mortar Board (2023) Dr. Mota teaches courses in applied exercise physiology and advanced strength conditioning, contributing to Texas Tech's Kinesiology undergraduate and graduate programs.