Find Academic Supervisors

Gruia-Catalin RomanView profile

Professor

• Mobile Computing
• Distributed Systems
• Software Engineering
• +12 more

Gruia-Catalin Roman is a Professor in the Department of Computer Science at the University of New Mexico, within the College of Engineering. He has maintained a long-standing and impactful career in computer science research and education, with a focus on mobile computing, distributed systems, and the Internet of Things. His work bridges theoretical foundations and real-world applications, particularly in sensor networks and smart environments. Ph.D., Computer and Information Sciences, University of Pennsylvania, 1976 M.S., Computer and Information Sciences, University of Pennsylvania, 1974 B.S., Computer Science and Engineering, University of Pennsylvania, 1973 Roman's research interests center on enabling natural, responsive, and personalized interactions between people and smart environments. He explores middleware, formal methods, and human-centered computing to support IoT, mobile systems, and distributed applications. His work emphasizes paradigm shifts in how users interact with technology, particularly through spatial characteristics, augmented reality, and context-aware automation. The recent articles reflect a strong trajectory toward intelligent, user-aware IoT systems. Themes include conflict prediction, seamless automation, AR-based control, and abstraction layers like the Space Broker. These works demonstrate a shift from device-centric to human-centric computing, leveraging machine learning, middleware, and distributed algorithms to reduce user cognitive load and enhance personalization. SenSys 2022 Test of Time Award for early efforts to introduce sensor networking technology into clinical practice Roman has supervised 19 doctoral students, many of whom have pursued academic careers. He has secured significant research funding, including an NSF grant on context-assisted interactions in IoT with UT Austin. His leadership extends to organizing flagship conferences such as ICSE 2005 and FSE 2010, and serving on editorial boards of top software engineering journals. He is known for his innovative teaching, mentoring, and advocacy for active learning and multidisciplinary collaboration. Roman leads the Mobile Computing Laboratory (MobiLab), where students and researchers prototype and evaluate IoT interaction paradigms. The lab explores smart glasses, QR-based interfaces, and spatial control algorithms. His work often involves collaborations across institutions and disciplines, reflecting his commitment to impactful, real-world computing solutions.

John A. MarohnView profile

Professor

• Nanoscale Imaging
• Magnetic Resonance Force Microscopy
• Semiconductor Charge Dynamics
• +4 more

John A. Marohn is a Full Professor in the Department of Chemistry and Chemical Biology at Cornell University, affiliated with the College of Arts and Sciences. He leads the Marohn Research Group focusing on nanoscale imaging techniques. His research bridges analytical chemistry, materials physics, and nanotechnology with emphasis on developing magnetic resonance force microscopy for biomolecular imaging and electrical probe microscopy for semiconductor devices. Education: • B.S. Chemistry, University of Rochester (1989) • B.A. Physics, University of Rochester (1989) • Ph.D. Chemistry, California Institute of Technology (1996) • Postdoctoral Associate, US Army Research Laboratory Research Focus: Dr. Marohn's work centers on pushing imaging resolution limits to study individual biomolecules and charge dynamics in next-generation semiconductors. Key areas include: • Nanoscale magnetic resonance imaging • Charge transport in perovskites and organic photovoltaics • Quantum measurement techniques • Scanning probe microscopy development Publication Trends: His 15 most recent publications demonstrate consistent focus on experimental techniques for nanoscale characterization, with growing emphasis on perovskite semiconductors, magnetic resonance methodology refinement, and computational modeling frameworks. Work frequently appears in high-impact journals including ACS Nano, Physical Review Applied, and Journal of Chemical Physics. Academic Leadership: Heads active research group supervising 7 current PhD students with over 30 PhD alumni. Secured multiple grants from NIH, NSF, and ARO. Contributes to Cornell's Materials Science Engineering graduate field and Center for Materials Research.

Zilong YeView profile

Associate Professor

• Computer Networking
• Network Function Virtualization
• Software-Defined Networking
• +6 more

Zilong Ye is an Associate Professor in the Department of Computer Science at California State University, Los Angeles, within the College of Engineering, Computer Science, and Technology. He holds a Ph.D. in Computer Science from University at Buffalo, SUNY (2015), an M.S. from Shanghai Jiao Tong University (2010), and a B.S. from Shandong University (2007). Ph.D., Computer Science, University at Buffalo, SUNY M.S., Computer Science, Shanghai Jiao Tong University B.S., Computer Science, Shandong University His research focuses on computer networking, with emphasis on Network Function Virtualization, Software-Defined Networking, Fog Computing, and Optical Networking. He has pioneered work in distributed fiber optic sensing and infrastructure-as-a-sensor systems, addressing challenges in sensor placement, network survivability, and concurrent sensing capabilities. Publications from 2013-2024 span optical networking, IoT security, and cloud optimization, with recent work on UAV-based mobile edge computing and fiber optic sensing. Awards include Best Paper (ICC 2021) and Best Demo (ACM ICN 2016). TPC co-chair for ACM Mobihoc workshop on MobileHealth (2018) Organized international symposia on 5G technologies and SDN/NFV Journal reviewer for IEEE Transactions on Network and Service Management, Mobile Computing, and others Patents include distributed fiber optic sensor placement systems and methods for information-centric networks. Teaching includes advanced courses in computer networks and operating systems.

Claudio Giovanni DemartiniView profile

Professor

• Computer Engineering
• Education Innovation
• Innovation Management
• +5 more

Claudio Giovanni Demartini is a Full Professor of Computer Engineering at the Department of Control and Computer Engineering (DAUIN) at Polytechnic University of Turin . Holding the academic rank of Professor, he has been actively involved in teaching, research, and institutional governance since the 2000s. His affiliations include the GRAINS research group (Graphics and Intelligent Systems), NEXA Center on Internet and Society, and leadership roles in the Academic Senate and Department of Computer Engineering (2015-2019). PhD in Information and Systems Engineering (1987) Engineering Degree in Electronics (1980), Polytechnic University of Turin Research Interests span multiple domains of Computer Engineering and Information Systems , with specific focus on: IT Governance and Enterprise Architecture Digital Transformation in Education Blockchain and Distributed Ledger Technologies Wireless Sensor Networks and Cyber-Physical Systems Innovation Management in Industrial Contexts Human-Machine Interfaces and Virtual Reality Research Trends Analysis reveals consistent engagement with: Data-driven learning organizations (2023-2025) TSCH wireless protocol optimization (2019-2020) Social-technical aspects of IT governance AI applications in educational systems Blockchain for industrial and societal applications Immersive VR interfaces for system control Scientific Distinctions Fellow, IEEE (2007-) Senior Member, IEEE Member, Eta Kappa Nu Honor Association (2017-) Editorial Board, IEEE IT Professional Magazine Advising and Governance includes: Scientific Director for 15+ competitive research projects Leadership in European educational technology initiatives Advising on Italian national education reforms Board roles at Mario Boella Institute and INVALSI

Milind DiwanView profile

Professor

• Experimental Nuclear and Particle Physics
• Rare decays of K-mesons
• Neutrino Interactions
• +8 more

Milind Diwan is a Distinguished Scientist in the Physics Department at Brookhaven National Laboratory, where he leads research in particle physics, neutrino physics, and detector development. He has made significant contributions to rare kaon decays, neutrino physics, and the development of advanced detectors. Diwan is particularly known for initiating and organizing the US long-baseline neutrino program that evolved into the LBNF/DUNE project, now the highest priority fundamental science project in the United States. Education: University of Colorado at Boulder, BA (Physics), 1982 Brown University, Sc.M. (Physics), 1984 Brown University, Ph.D. (Physics), 1988 Dr. Diwan's research spans experimental nuclear and particle physics with focus on rare kaon decays, neutrino interactions, and neutrino oscillations. His work encompasses detector development, particularly with scintillation detectors, liquid scintillators, and photo-multiplier tubes. He has pioneered techniques in readout electronics and data analysis, with particular expertise in organizing large-scale scientific projects. His research has significantly advanced our understanding of neutrino properties and rare particle decay processes. Analysis of Diwan's recent publications reveals a strong focus on neutrino physics, particularly sterile neutrino searches, long-baseline neutrino experiments, and far-forward neutrino production at the LHC. His work bridges theoretical predictions with experimental verification, with emphasis on precision measurements that test the boundaries of the Standard Model. The publications demonstrate expertise in both accelerator-based and reactor-based neutrino experiments. Scientific Awards: Science and Technology award from Brookhaven National Laboratory (2017) for 'Important experimental contributions to understand the properties of neutrinos.' Co-recipient Break Through Prize for Daya Bay collaboration (2015) 'For the fundamental discovery and exploration of neutrino oscillations, revealing a new frontier beyond, and possibly far beyond, the standard model of particle physics.' Sociedade Brasileira de Fisica (SBF) and American Physical Society (APS) award for visiting professorship (2015) Fellow of the American Physical Society (2009) Dr. Diwan has served as analysis leader in multiple major collaborations including E787 (rare kaon decays) and MINOS (neutrino oscillations), and as spokesperson for several experiments including LBNE. He has chaired important review panels for the DOE and served on the HEPAP sub-panel on planning for the future of U.S. high energy physics. His leadership was instrumental in developing the national study that formed the basis for the LBNF/DUNE project approval. Diwan leads research activities involving detector commissioning for the ICARUS liquid argon detector at Fermilab, analysis of neutrino oscillations data, and design of photo-sensor systems for next-generation neutrino experiments. His work spans both hardware development and sophisticated data analysis techniques, with particular emphasis on creating robust methodologies for extracting physics results from complex detector systems.

• Biometrics
• Behavioral Biometrics
• Human-Computer Interaction (HCI)
• +8 more

Rubén Vera-Rodríguez is a researcher at the Biometrics and Data Pattern Analytics (BiDA) Lab, part of the Escuela Politécnica Superior at Universidad Autónoma de Madrid, Spain. His work focuses on biometrics, human-computer interaction, and privacy-enhancing technologies, with strong emphasis on mobile and wearable systems. Research Interests: His primary research areas include behavioral biometrics (keystroke dynamics, gait recognition), face recognition, synthetic data generation, and privacy-preserving methods in biometric systems. He explores the use of deep learning and transformer models for mobile authentication and digital phenotyping. The trends in his recent publications show increasing use of synthetic data, explainable AI, and transformer architectures in biometric systems. His work spans both technical innovation and privacy-aware design, contributing to benchmark datasets and open challenges. Scientific Contributions: Co-organizer of the SVC-onGoing and WAMWB workshops. Contributor to major biometric databases including BehavePassDB and ChildCIdb. Active in privacy-focused biometric research, as seen in surveys on sensor vulnerabilities. Advising and Collaborations: He collaborates extensively with researchers such as Ruben Tolosana, Aythami Morales, and Julian Fierrez. While no formal students are listed, he leads and contributes to team-based research projects and competitions. Labs and Teams: He is a core member of the Biometrics and Data Pattern Analytics (BiDA) Lab at UAM, a research group focused on advancing biometric technologies with attention to real-world usability and ethical considerations.

• Oceanography
• Environmental Science
• Climate Change
• +4 more

Colin Andrew Stedmon is an Associate Professor at the Institute for Aquatic Resources (DTU-AQUA), Technical University of Denmark, specializing in oceanography and biogeochemistry. He earned his Ph.D. from the University of Copenhagen in 2004 and has held research roles at the National Environmental Research Institute before joining DTU-AQUA in 2011. Education: Ph.D., University of Copenhagen (2004) Employment: National Environmental Research Institute (2005-2008), Aarhus University (2008-2011), DTU-AQUA (2011-present) His research focuses on dissolved organic matter (DOM) dynamics in marine environments, Arctic Ocean biogeochemistry, and climate change impacts. He employs advanced spectroscopic techniques and machine learning to analyze DOM composition and its role in global carbon cycles. Recent publications highlight Siberian river runoff effects on ocean circulation, Greenland Sea stratification changes, and DOM tracing methodologies. Key Research Areas: DOM cycling, Arctic hydrology, ocean-climate interactions, micropollutant degradation Technical Expertise: UV-visible spectroscopy, fluorescence techniques, biogeochemical modeling

Hailin ZhengView profile

Researcher

• Indoor Environmental Quality
• Indoor Air Quality
• Building Physics
• +6 more

Hailin Zheng is a Postdoctoral Researcher at Eindhoven University of Technology's Faculty of the Built Environment, specializing in Building Services within the Department of Building Services. His work bridges architectural design, building physics, and environmental engineering with a focus on creating healthier indoor spaces through evidence-based solutions. Dr. Zheng's research centers on Indoor Environmental Quality (IEQ), particularly: Real-time wireless monitoring of indoor air quality in vulnerable settings Ventilation performance optimization for educational and healthcare facilities Exposure assessment of volatile organic compounds in daycare centers Development of personalized ventilation systems for infants and elderly populations Integration of healthy building principles into retrofit projects His multidisciplinary approach combines field measurements, controlled experiments, and building performance simulation to address critical gaps in occupant-centric environmental design. Analysis of his publication record reveals a strong emphasis on vulnerable populations (infants, elderly, dementia patients) and practical engineering solutions. Key trends include: Systematic evaluation of building technologies through controlled experiments Focus on microenvironments (baby beds, bedroom zones) Development of low-cost monitoring solutions for real-world implementation Integration of health outcomes with building performance metrics His scientific recognition includes: Best Paper Award at the 44th AIVC Conference (2024) TU/e Postdoc Best Paper Award with 750 euro prize (2024) Dr. Zheng actively contributes to academic discourse through: Editorial work for Sensors and Archives of Public Health journals Invited talks on 'IAQ to IEQ' and 'Invisible threats inside baby beds' Teaching master's courses including 'Introduction to Building Performance' and 'Masterproject BPS' Leading the 'Healthy Building Movement' research project (2023-2026) focusing on active building solutions His work directly supports UN Sustainable Development Goals 3 (Good Health) and 11 (Sustainable Cities), with practical applications in daycare centers, office buildings, and residential care facilities through the integration of monitoring technologies and evidence-based design principles.

• Sport and Exercise Physiology
• Environmental physiology
• Dietary supplements for health and performance
• +4 more

Mark Waldron is an Associate Professor in Sport and Exercise Sciences at Swansea University's Faculty of Science and Engineering, where he also serves as the Performance Physiology leader of the Welsh Institute of Performance Science. Additionally, he holds a position as a Senior Research Fellow at the University of New England, Australia. His career includes previous roles as a Senior Physiologist at the Defense Technology and Science Laboratory (DSTL) and as a Sports Scientist with professional Rugby League. Dr. Waldron's research primarily focuses on Sport and Exercise Physiology, Environmental physiology, Dietary supplements for health and performance, and Coaching and monitoring team sports. His work examines physiological adaptations to extreme environments, particularly heat, and investigates how nutritional interventions can enhance athletic performance and recovery. He has made significant contributions to understanding the effects of menthol, taurine, and other supplements on thermoregulation and exercise capacity. His extensive publication record demonstrates a strong emphasis on methodologically rigorous approaches, including meta-analyses and controlled experimental designs. Recent work shows particular interest in applying advanced data analytics to sports performance, with specific applications in rugby, soccer, and cycling. His research bridges laboratory findings with practical applications for elite athletes and sports organizations worldwide. As an active supervisor, Dr. Waldron oversees numerous PhD and MSc students working on projects related to nutritional strategies for rugby recovery, youth athlete development, power-time relationships in exercise, and advanced data analytics applications in professional sports. He frequently collaborates with elite sports clubs and organizations globally, translating research findings into practical performance enhancements.

Veljko ProdanovicView profile

Research Fellow

• Environmental Engineering
• Water Sensitive Urban Design
• Greywater Treatment
• +5 more

Veljko Prodanovic is a Senior Research Associate at the University of New South Wales' Water Research Centre within the School of Civil and Environmental Engineering. He joined UNSW in 2018 and has established himself as a specialist in green water treatment technologies, particularly focusing on green walls for greywater reuse and urban stormwater management. His research expertise spans Green technology development & testing for urban greywater management Greywater quality monitoring Water Sensitive Urban Design (WSUD) Integrated urban water modelling Environmental Engineering Design Prodanovic's work involves developing and testing innovative green water treatment solutions that address urban water challenges through interdisciplinary approaches. His recent publications demonstrate a strong focus on vegetated treatment systems, biofiltration technologies, and sustainable urban water management strategies. The research shows particular emphasis on optimizing green infrastructure for pollutant removal, developing real-time control systems, and creating culturally inclusive water-sensitive urban design frameworks that engage diverse communities. Prodanovic actively contributes to the academic community as a scientific reviewer for top journals including Water Research, Science of Total Environment, and Building and Environment, with over 50 reviews since 2018. He plays a significant role in mentoring the next generation of researchers, having supervised over 30 Honours and Master's projects while currently guiding multiple PhD and MPhil students. His teaching responsibilities include lecturing CVEN9611 Urban Hydrology at UNSW Sydney and previously tutoring CIV2263 Water System at Monash University. The researcher maintains strong industry connections with organizations including Georges Riverkeeper, Stormwater NSW, Inner West Council, Blacktown Council, Melbourne Water, EPA Victoria, and Knox City Council, while also collaborating internationally with EAWAG (Switzerland), HBKU (Qatar), KAUST (Saudi Arabia), University of Belgrade (Serbia), and Innsbruck University (Austria).

• Machine Learning
• Computer Vision
• Data Compression
• +4 more

Donald Adjeroh is a Professor and Associate Chair of Computer Science at the Lane Department of Computer Science and Electrical Engineering, within the Benjamin M. Statler College of Engineering and Mineral Resources at West Virginia University. He also serves as the Program Director for Computer Science Graduate Studies and is the Project Lead PI for the WVAR-CRESH (West Virginia Arkansas Research Collaboration in Engineering, Science, and Health) initiative. Dr. Adjeroh received his Ph.D. from the Chinese University of Hong Kong in 1997. His academic journey has led him to become a prominent researcher in multiple interdisciplinary fields, with a focus on developing computational methods for biomedical applications. Dr. Adjeroh's research spans several interconnected domains: Machine Learning approaches for biomedical data analysis Computer Vision techniques for medical image processing Data Compression algorithms with applications in bioinformatics Search Algorithms & Data Structures for large-scale biological data Bioinformatics and Computational Biology methods for genomic analysis Digital Health applications for healthcare cost reduction and improved outcomes His publication record demonstrates consistent contributions across these fields, with recent work focusing on parameterized string algorithms, probabilistic suffix arrays for protein family prediction, and robust texture analysis techniques. His research shows a clear trajectory toward increasingly sophisticated applications of computational methods to biomedical problems. Dr. Adjeroh has received significant research funding from various sources including the National Science Foundation (NSF), Department of Energy (DOE), NASA, Department of Defense (DOD), Department of Homeland Security (DHS), and others. His current projects include "NSF NRT: Bridges in Digital Health" and "RII Track 2 FEC: Multi-Scale Integrative Approach to Digital Health," among others. He has mentored numerous students through research projects and serves as an advisor for graduate students interested in pursuing PhD research in areas related to search data structures, computational biology, bioinformatics, biometrics, machine learning, and digital health. Dr. Adjeroh is actively involved in organizing academic events, including the Summer School on AI & Smart Health, and serves as a key collaborator in multi-institutional research efforts focused on leveraging artificial intelligence to improve healthcare outcomes.

Juan DuarteView profile

Researcher

• Media Art
• Sound Studies
• Environmental Awareness
• +4 more

Juan Duarte is a Doctoral Researcher at Aalto University's School of Arts, Design and Architecture, specializing in the Department of Art and Media. His work bridges media art, sound studies, and environmental awareness through innovative technological implementations. Focuses on atmospheric processes and climate communication Recipient of multiple international art grants and residencies Developed "Augury" - a groundbreaking multichannel sound system Active in Nordic and European art networks His research explores the intersection of sound spatialization technologies with environmental consciousness, creating interactive installations that transform meteorological data into sensory experiences. Recent projects demonstrate cross-disciplinary innovation, with articles examining resonance in atmospheric contexts, embodied listening techniques, and climate data interpretation through artistic frameworks. His work has been presented at major venues including Klaipeda Culture Communication Centre and Nida Art Colony. 2024: EUR 10,000 development funding for "Augury" from Aalto Studios 2024: Culture Moves Europe mobility grant for Lithuania residency 2024: Nordic Culture Point mobility funding 2024: Frame Contemporary Art Finland travel grant 2024: Taike Finland project grant Duarte's practice combines technical innovation with ecological philosophy, creating immersive sound environments that challenge conventional understandings of atmospheric interactions and environmental stewardship.

• Artificial Intelligence
• Machine Learning
• Anomaly Detection
• +6 more

Yuantao Fan is an Associate Senior Lecturer at Halmstad University's School of Information Technology, specializing in artificial intelligence and machine learning applications for industrial systems. His academic work bridges theoretical research with practical implementations in predictive maintenance and equipment monitoring. Dr. Fan's research focuses on Artificial Intelligence, Machine Learning, Anomaly Detection, and Data Mining , with particular emphasis on streaming data from real-world applications. His work spans equipment diagnosis and prognosis, predictive maintenance (PdM), and condition-based monitoring systems. His research integrates self-organizing models with expert knowledge to create robust diagnostic frameworks applicable to transportation systems and industrial equipment. Analysis of Dr. Fan's publication record from 2013-2025 reveals consistent specialization in predictive maintenance for transportation systems , particularly bus fleets and heavy vehicles. His work demonstrates progression from basic fault detection methods to sophisticated hybrid approaches combining physics-based models with deep learning. Recent publications increasingly address explainability in predictive maintenance systems and applications in healthcare analytics. Dr. Fan actively contributes to academic instruction as course responsible and examiner for Databases, Introduction to Linux and Python, and Linux administration system courses. He also teaches Artificial Intelligence, Data Mining, Perspectives on Computer Science and Engineering, Machine Learning for Predictive Maintenance, and AI Implementation courses. His technical expertise spans multiple domains including automotive systems diagnostics, battery health monitoring, acoustic signal processing, and healthcare prediction models. Dr. Fan's work demonstrates strong industry collaboration, particularly with Volvo and city bus fleet operators, translating academic research into practical industrial applications.

Jens ErikssonView profile

Associate Professor

• Sensor Science
• 2D Materials
• Machine Learning
• +3 more

Jens Eriksson is an Associate Professor at Linköping University's Department of Physics, Chemistry and Biology (IFM) within the Faculty of Science and Engineering. He serves as Head of the Applied Sensor Science Unit and actively contributes to national networks like the SIO Grafen Graduate Network for 2D-materials research. Lead researcher in sensor technology and 2D materials Collaborator in cross-disciplinary biomedical/environmental projects Active in academic-industry partnerships His research focuses on advanced sensor systems combining 2D materials like graphene and MoS 2 with machine learning for applications in cancer diagnostics (ovarian/early detection), environmental monitoring (methane/NO 2 ), and food safety. Current projects involve electronic nose technology and computational modeling of biomarker adsorption phenomena. Recent publications demonstrate expertise in both experimental and computational approaches to sensor development, with 2025 publications spanning Journal of Big Data , Applied Surface Science , and Small Structures . His work bridges materials science, biomedical engineering, and environmental monitoring. Scientific achievements include securing the first Swedish-U.S. collaboration grant under the NSF Convergence Accelerator program, Phase 1. This funding supports international research partnerships in sensor technology applications.

Andrei BursucView profile

Lecturer

• Computer Vision
• Machine Learning
• Autonomous Driving
• +3 more

Andrei Bursuc is a Research Scientist and Deputy Scientific Director at Valeo.ai, where he focuses on perception for autonomous driving. Concurrently, he serves as a Research Associate at the Astra Inria project team and teaches the Deep Learning DIY course at Paris Institute of Technology (École Polytechnique) since 2019. He holds a PhD from Mines ParisTech and Telecom SudParis (2012) and a BSc from Polytechnic University of Bucharest (2009). His research spans computer vision and machine learning , with applications in autonomous driving, uncertainty quantification, self-supervised learning, and foundation models. Recent work emphasizes scalable representation learning, open-vocabulary segmentation, and generative modeling for driving scenarios. His publications (2024–2025) cluster around themes of semantic segmentation , depth estimation , and vision-language models , with innovations in unsupervised learning, cross-modal distillation, and efficiency. Trends indicate a focus on robustness, real-world generalization, and multi-sensor integration. He co-organizes workshops (e.g., Uncertainty Quantification at CVPR/ECCV) and serves as Area Chair for NeurIPS/CVPR/ECCV. He contributes to EU projects (e.g., ELLIOT) and open-source initiatives.