Find Academic Supervisors

• Mathematics Education
• Interactive Multimedia in Teaching
• Educational Technology
• +5 more

Marina Milovanović is a Professor at the University of Singidunum, Faculty of Informatics and Computing, Department of Mathematics. She holds dual doctoral degrees from the Faculty of Science, University of Kragujevac (Department of Mathematics, 2014) and Faculty of Entrepreneurial Business, Union University (2008), along with Master's and Bachelor's degrees from the Faculty of Mathematics, University of Belgrade (2000-2005 and 1995-2000 respectively). Faculty of Science, University of Kragujevac, Department of Mathematics (PhD, 2014) Faculty of Entrepreneurial Business, Union University (PhD, 2008) Faculty of Mathematics, University of Belgrade (Master's, 2000-2005) Faculty of Mathematics, University of Belgrade (Bachelor's, 1995-2000) Svetozar Marković High School, science and mathematics major (1991-1995) Professor Milovanović specializes in Mathematics Education and Educational Technology, with particular expertise in interactive multimedia applications for teaching mathematics. Her research consistently bridges theoretical mathematics with practical educational technology solutions, evolving from traditional multimedia approaches to incorporating cutting-edge AI and machine learning techniques. She has authored multiple books including 'Interactive multimedia in mathematics teaching' (2015) and collections of solved mathematics problems for entrance exams. Her recent publication record through 2025 demonstrates active engagement in interdisciplinary research, particularly at the intersection of educational technology, artificial intelligence, and practical applications in fields ranging from software engineering to medical diagnostics. Her work shows a clear trajectory from foundational educational technology research toward more sophisticated AI-enhanced learning systems. Professor Milovanović has made significant contributions to semantic web applications in education, particularly through Moodle LMS enhancements, and has explored SCADA applications in industrial contexts. Her collaborative research spans multiple countries and institutions, reflecting an international scholarly network. She has extensive experience developing computer tools for engineering education and has published on diverse topics including petroleum industry processes, environmental management, and financial mathematics. Her work demonstrates consistent application of computational approaches to solve domain-specific problems across multiple disciplines.

Bradley SchmerlView profile

Researcher

• Software Engineering
• Software Architecture
• Self-Adaptive Systems
• +5 more

Bradley Schmerl serves as a Principal Systems Scientist in the Software and Societal Systems Department (S3D) within Carnegie Mellon University's School of Computer Science. His research advances software engineering practices for modern challenges in distributed heterogeneous systems, self-adaptation, and cyber-physical integration. He leads the ABLE research group and actively mentors students in the Masters in Software Engineering program while teaching core courses like Software Architecture and Software Engineering Practicum. Dr. Schmerl's work addresses critical challenges in composing continuously evolving software systems, including components from untrusted third parties and on-the-fly recomposition for environmental changes. His research develops reusable, analyzable tools for software composition with emphasis on model-based adaptation, uncertainty management, and cross-language integration. Key projects include Rainbow for runtime architecture reflection, Acme for formal architectural foundations, and Cyber-physical Systems research linking software models with physical dynamics. Analysis of his 2023-2025 publications reveals intensifying focus on robotics software architecture (particularly ROS-based systems), explainable AI for architectural tradeoff analysis, and configuration management in adaptive systems. Trends show growing integration of machine learning for auto-tuning, empirical studies of misconfigurations, and dimensionality reduction techniques for visualizing design spaces—consistently bridging theoretical rigor with practical tool development for real-world applications. Scientific Awards: No specific awards were documented in the source materials. Dr. Schmerl serves as Practice Area Lead and mentor in CMU's Masters in Software Engineering program, guiding client projects including Rainbow UI for self-adaptive framework interfaces, CoBot UI for telepresence robots, and Educational Telepresence Tasking Language development. His research receives support through ABLE group projects funded by grants targeting software architecture foundations, adaptation mechanisms, and cyber-physical system validation. As a core member of the ABLE research group, he directs investigations into architecture-based self-adaptation with active projects spanning Rainbow (runtime architecture models for dynamic adaptation), Acme (formal architectural styles and tools), and Cyber-physical Systems (software-physical model integration). The group also maintains legacy work in End-User Architecting, Architecture Evolution, and service-oriented platforms for intelligence analysis through SORASCS.

Zhenke WuView profile

Associate Professor

• Bayesian Statistics
• Precision Medicine
• Latent Variable Models
• +5 more

Zhenke Wu is an Associate Professor (with tenure) in the Department of Biostatistics at the University of Michigan School of Public Health. He holds affiliate appointments at the Michigan Institute for Data and AI in Society (MIDAS) and leads the Michigan Statistics for Individualized-healthcare Lab (MiSIL). His research bridges statistical methodology and public health applications, with particular focus on precision medicine. Educational background includes: PhD in Biostatistics from Johns Hopkins University (2014) BS in Mathematics from Fudan University (2009) Dr. Wu's methodological research focuses on: Structured Bayesian latent variable models for disease subtyping and clustering Causal inference methods for sequential interventions in mobile health studies Reinforcement learning frameworks for personalized health interventions Scalable computation for high-dimensional biomedical data His applied work spans infectious diseases, mental health, autoimmune disorders, and cancer through collaborations with multiple research consortia including the Intern Health Study and UZIMA-DS project in Africa. Recent publications demonstrate strong focus on Bayesian methods, causal inference, and reinforcement learning applications in digital health. Article themes include mobile health interventions, synthetic EHR development, fair machine learning algorithms, and novel approaches for longitudinal and survival data analysis. Methodological innovations consistently address challenges in precision medicine and individualized health decision-making. Dr. Wu leads several collaborative initiatives including the Precision Health Use Case for mental health treatment (PROMPT) and partners with the Rogel Cancer Center. He advises multiple PhD students and postdoctoral researchers in statistical methodology development and health applications.

• Maritime Engineering
• Digital Twin Technology
• Marine Robotics
• +3 more

Hans Petter Hildre is Head of Department at the Department of Ocean Operations and Civil Engineering , part of the Faculty of Engineering at the Norwegian University of Science and Technology (NTNU). His work focuses on maritime engineering, digital twin technology, and marine operations. Research interests include: Digital Twin Applications in Maritime Industry Offshore Operations and Wind Turbine Installation Marine Robotics and Autonomous Systems Wave Field Estimation and Environmental Load Analysis Human-Machine Interaction in Maritime Contexts Co-simulation and Real-time Monitoring Recent publications highlight trends in: Wave shielding effects for offshore vessels Knowledge transfer from automotive/aviation to maritime Crane path planning using digital twins Visual attention zone recognition systems Hydrodynamic modeling and sensitivity analysis Smart city-maritime integration Scientific collaborations span institutions including: European Commission (Future Skills Reports) Royal Institution of Naval Architects The American Society of Mechanical Engineers (ASME) IEEE Transactions on multiple domains Springer Publishing

Federica SandroneView profile

Lecturer

• Rock Mechanics
• Tunnel Engineering
• Underground Construction
• +4 more

Federica Sandrone is a Lecturer at the School of Architecture, Civil and Environmental Engineering (ENAC) at École Polytechnique Fédérale de Lausanne (EPFL), where she also serves as a Scientist at the Laboratory of Experimental Rock Mechanics (LEMR) within the Institute of Civil Engineering. Her academic career spans over 15 years with continuous contributions to tunnel engineering and rock mechanics research. Her research focuses on the intersection of rock mechanics and tunnel engineering, with particular expertise in tunnel pathology analysis, TBM performance in challenging geological conditions, and long-term tunnel behavior. Sandrone's work bridges theoretical analysis with practical engineering applications, addressing real-world problems in tunnel infrastructure management and maintenance. Her research methodology combines field investigations, laboratory testing, and numerical modeling to understand complex geomechanical behaviors. Analysis of her recent publications reveals a consistent focus on tunnel inspection methodologies, TBM performance prediction in difficult ground conditions, and the long-term behavior of tunnel structures. Her work has evolved from fundamental tunnel pathology studies to more advanced applications involving GIS integration, probabilistic modeling, and modern inspection techniques including laser scanning and image analysis. Engineer at SBB-Infrastructure (2008-present) responsible for Tunnels Management and Maintenance Assistant for Tunnel Engineering courses (2007-present) PhD supervision including Erika Paltrinieri's 2015 thesis on TBM performance Development of tunnel inspection methodologies and condition assessment procedures Her teaching activities include courses in Rock Mechanics and Underground Construction, where students learn about the mechanical behavior of rock materials, tunnel excavation and support design, planning and management of underground works, and risk assessment in tunnel construction.

• Machine Learning
• Time Series Analysis
• Text Mining
• +7 more

Dr. Georgiana Ifrim is an Associate Professor at the School of Computer Science, University College Dublin , where she serves as Director of Graduate Research and Co-Lead of the SFI Centre for Research Training in Machine Learning (ML-Labs). She holds concurrent appointments as an SFI Funded Investigator at the Insight Centre for Data Analytics and VistaMilk SFI Research Centre . Her academic journey includes postdoctoral research at Insight Centre, Cork Constraint Computation Centre (4C), and Aarhus University's Bioinformatics Research Centre (BiRC). Education: BSc in Computer Science, University of Bucharest, Romania MSc and PhD in Informatics, Max-Planck Institute for Informatics, Germany Dr. Ifrim specializes in scalable predictive modeling for diverse applications including: Sequence learning (DNA analysis, time series) Real-time prediction for streaming data (news/social media, energy) Interpretable machine learning models Knowledge graph exploitation (WordNet/Yago, Naga) Wearable sensor data analysis (sports science, health monitoring) Energy price forecasting for sustainable systems Her recent publications focus on time series explainability (TSHAP, tsCaptum), multivariate analysis (scalable channel selection), and healthcare applications (fall detection, walking speed estimation). Key contributions include open-source tools like SEQL (sequence learner) and Twitter-Topics (event detection). Scientific Awards: Winner of SNOW@WWW14 Data Challenge As Director of Graduate Research, she oversees advanced academic training while leading funded projects at the intersection of machine learning , real-time analytics , and domain-specific applications in agriculture, healthcare, and digital journalism. Her research group maintains active GitHub repositories with open-source implementations.

Satyajit AmbikeView profile

Associate Professor

• Biomechanics
• Human motor control
• Dynamical systems
• +3 more

Satyajit Ambike is an Associate Professor in the Department of Health and Kinesiology at Purdue University , with courtesy appointments in the Department of Speech, Language, and Hearing Sciences. His research focuses on biomechanics , human motor control , and dynamical systems . PhD in Mechanical Engineering from The Ohio State University MS in Mechanical Engineering from The Ohio State University BE in Mechanical Engineering from University of Pune Dr. Ambike investigates human locomotion and prehension across the adult lifespan, analyzing how aging affects movement stability and the stability-agility tradeoff in daily activities. His work uses synergistic control frameworks to develop biomarkers for neuromuscular pathologies like Parkinson’s Disease. Recent publications emphasize adaptive gait analysis , prehensile control , and sensor-based mobility assessment using IMUs. Themes include aging, neuromuscular disorders, and movement optimization. Scientific Awards: Outstanding Graduate Faculty Teacher (2018) Presidential Fellowship (2009) Professional Affiliations: Advisory Board Member, International Society of Motor Control Advisory Committee Member, Center for Research on Brain, Behavior, and NeuroRehabilitation (CEREBBRAL) Faculty Associate, Center on Aging and the Life Course (CALC) Dr. Ambike’s lab, the Human Motor Behavior Group , trains PhD and MS students in biomechanical research, with graduates pursuing academic and clinical roles in physical therapy, neuroscience, and biomedical engineering.

• Renewable Energy
• Statistical Data Analysis
• Energy Management Systems
• +3 more

Yu Fujimoto is a Professor at Waseda University's Advanced Collaborative Research Organization for SmartSociety. Previously, he served as an Associate Professor at Waseda University's Advanced Collaborative Research Organization for Smart Society (2015-2023) and Institute for Nanoscience & Nanotechnology (2012-2015), and as an Assistant Professor at Aoyama Gakuin University's Department of Integrated Information Technology (2009-2012). He holds a Doctorate from Waseda University's Graduate School, Division of Science and Engineering and is a member of IEEE and the Information Processing Society of Japan. His research focuses on statistical science applied to energy systems, with specific interests in renewable energy forecasting, statistical data analysis for energy management systems, and statistical machine learning theory. Fujimoto has published 109 papers with 1,191 citations and maintains an h-index of 16, demonstrating significant impact in his field. His recent publications show a strong trend toward integrating machine learning techniques with power system applications, particularly focusing on renewable energy integration, electric vehicle charging optimization, smart grid technologies, and carbon reduction strategies. His work frequently addresses the challenges of grid stability with high renewable penetration and develops innovative solutions using advanced data analytics. Best Paper Award for Big Earth Data (2025) for wind generation dataset research Best Paper Award in International Conference on Power, Energy and Electrical Engineering (2025) Second Best Paper Award in International Conference on Renewable Energy Research (2024) Top Downloaded Article in IET Smart Cities (2023) Top Downloaded Article in IEEJ Transactions (2022) Fujimoto's research bridges theoretical statistical methods with practical energy system applications, contributing significantly to Japan's efforts in smart grid development and renewable energy integration. His work often involves collaboration with industry partners and government research organizations to address real-world power system challenges.

R. JayakrishnanView profile

Professor

• Transportation Systems Engineering
• Dynamic Traffic Assignment
• Urban Traffic Simulation
• +3 more

R. Jayakrishnan , a Professor in the Department of Civil and Environmental Engineering at the Samueli School of Engineering , University of California, Irvine, is a leading researcher in transportation systems engineering. Ph.D., University of Texas, Austin, Civil Engineering, 1992 M.S., University of Texas, Austin, Civil Engineering, 1987 B.S., Indian Institute of Technology, Madras, India, 1985 His research focuses on dynamic traffic assignment , urban traffic simulation , and real-time information systems to improve congested traffic corridors. He is developing advanced dynamic simulation-assignment models for urban traffic networks. Recent publications highlight his contributions to: Crowdsourced delivery optimization using decomposition heuristics Eco-driving algorithms with V2I communication Multi-furniture placement applications via augmented reality Subscription mobility services cost-benefit analysis Agent-based lane-changing coordination systems These works demonstrate his interdisciplinary approach combining transportation engineering, optimization algorithms, and emerging technologies like AR and connected vehicles.

Mihaela GirtanView profile

Associate Professor

• Thin Films
• Solar Cells
• Optoelectronics
• +3 more

Mihaela Girtan is an Associate Professor in the Faculty of Sciences at the University of Angers, heading the Thin Films for Photovoltaic Applications research group. Her work spans thin-film technologies, solar cells, and optoelectronic devices, with expertise in physical/chemical deposition methods and nanomaterials. Education: PhD in Physics, University of Stuttgart (1995) Her research investigates charge transport in oxides, organic/perovskite solar cells, transparent conducting films, plasmonics, and fluid dynamics in CVD reactors. She develops innovative materials for energy conversion, including oxide/metal/oxide electrodes and polymer-based photovoltaics. Recent publications focus on climate-agriculture interactions, including drought risk modeling, irrigation dynamics, and crop yield sustainability. Her work integrates remote sensing, machine learning, and climate modeling to address food security challenges. Scientific Awards: Consistently ranked in top 2% of researchers worldwide since 2020 She leads international collaborations and advises PhD students in materials science. Her group maintains advanced thin-film deposition and characterization facilities at Angers Photonics Laboratory.

Gyula Mate KovácsView profile

Research Fellow

• Remote Sensing
• Wetlands
• Artificial Intelligence
• +6 more

Gyula Mate Kovács is a Research Fellow (Postdoctoral Researcher) at the Department of Geosciences and Natural Resource Management, Faculty of Science, University of Copenhagen. His research is funded by the Novo Nordisk Foundation through the Global Wetland Center. Education Ph.D. in Remote Sensing of Wetlands, University of Copenhagen (2020–2024) M.Sc. in Geography and Geoinformatics, University of Copenhagen (2017–2019) B.Sc. in Environmental Management, Birkbeck University of London (2013–2017) Research Focus Dr. Kovács specializes in AI-driven remote sensing for wetland ecosystem analysis. His work integrates machine learning, deep learning, and satellite data fusion to quantify natural/anthropogenic impacts on wetlands at global scales. Key methodologies include time series analysis, cloud computing, and convolutional neural networks for applications like carbon mapping, water body detection, and land-use impact assessment. Publication Trends His 7 recent publications demonstrate a strong focus on wetland dynamics using satellite remote sensing, with themes spanning deep learning applications (CNN U-Net algorithms), greenhouse gas emissions in croplands, continental-scale wetland inventories, and ecosystem change detection. Research consistently employs advanced AI techniques to address environmental challenges in diverse regions like the Sahel and Europe. Funding & Affiliation Supported by the Novo Nordisk Foundation via the Global Wetland Center, his work advances wetland monitoring capabilities. He collaborates with international teams on projects involving satellite data processing and ecological modeling.

• Orthodontic Biomechanics
• 3D Craniofacial Imaging
• Ultrasound Imaging
• +3 more

Paul Major is Professor and Chair of the School of Dentistry, Senior Associate Dean (Dental Affairs), and ACFD Project Lead at the University of Alberta's Faculty of Medicine & Dentistry. He leads the Orthodontic Biomechanics Research Group and co-founded the Inter-disciplinary Airway Research Clinic (I-ARC), driving innovation across dental academia and clinical practice. His educational background includes a Doctorate of Dental Surgery (DDS) from the University of Alberta (1980) followed by MSc and Orthodontic Specialty training at the same institution (1988). He joined the academic staff in 1989 and served as Director of the TMD/Orofacial Pain Program (1991-2001) and Orthodontic Graduate Program (2001-2010). Dr. Major's research centers on Orthodontic Biomechanics , 3D Craniofacial Imaging , and Ultrasound Imaging . His Orthodontic Biomechanics Research Group developed the OSIM system for 3D force measurement on dental appliances, while his imaging work pioneers reconstruction of craniofacial structures and periodontal ultrasound diagnostics. Through the I-ARC, he leads interdisciplinary studies on pediatric sleep-disordered breathing, examining craniofacial morphology and orthodontic interventions. Analysis of his 190+ publications reveals consistent innovation in biomechanical analysis of orthodontic appliances, machine learning for dental image processing, and hydrogel development for intraoral imaging. Recent work bridges dentistry with engineering through projects on dental aerosols, clear aligner mechanics, and airway measurement software. Dr. Major has supervised over 75 graduate students while maintaining clinical teaching duties despite administrative leadership roles. His research is supported by grants enabling the OSIM system development and interdisciplinary I-ARC projects. He directs the Orthodontic Biomechanics Research Group's experimental biomechanics work and the I-ARC's clinical research team, which integrates pediatric ENT, pulmonology, radiology, and biomedical engineering specialists to advance treatment of pediatric sleep apnea through craniofacial analysis and innovative imaging techniques.

Tse-Hsun ChenView profile

Associate Professor

• Software Engineering
• Performance Engineering
• DevOps & AIOps
• +3 more

Tse-Hsun (Peter) Chen is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University in Montreal, Canada. He serves as Director of the SPEAR lab (Software Performance, Analysis, and Reliability lab), which focuses on improving the quality of large-scale software systems through research in log analysis and AIOps, software performance analysis, software testing, and mining software repositories. His research group maintains extensive collaborations with industry partners including ERA Environmental, Ericsson, Microsoft, and BlackBerry. Dr. Chen received his PhD and MSc in Computer Science from Queen's University and his BSc in Computer Science from the University of British Columbia. Dr. Chen's research addresses critical challenges in modern software engineering, including leveraging Large Language Models to assist developers with development, debugging, and maintenance; helping developers debug production systems by utilizing rich software data; providing optimization suggestions by analyzing user usage data; improving software quality assurances in DevOps environments; and mining software development history for useful developer suggestions. His work spans Software Engineering, Performance Engineering, DevOps & AIOps, Software Testing, and Mining Software Repositories, with a strong emphasis on practical applications that bridge academic research and industrial practice. His recent publications (2024-2025) demonstrate a pronounced shift toward integrating Large Language Models into various aspects of the software engineering lifecycle, particularly in log analysis, fault localization, code generation, and performance testing. This trend reflects the growing importance of AI in software engineering research and practice. Gina Cody Research award (2022) Ranked as one of the most active software engineering researchers worldwide by an independent study published in JSS Dr. Chen has successfully advised numerous PhD and Master's students, many of whom have secured prestigious academic positions. Several of his graduated PhD students now hold tenure-track assistant professor positions at institutions including York University, University of Alberta, DePaul University, and IIT Gandhinagar. His SPEAR lab has developed research tools that have been integrated into industrial practice for ensuring the quality of large-scale enterprise systems. The SPEAR lab, under Dr. Chen's leadership, has established itself as a leading research group in software engineering, with particular expertise in software performance analysis, log analysis, and AI applications for software engineering. The lab maintains strong industry connections and has produced numerous high-impact publications in top-tier software engineering venues including ICSE, FSE, ASE, and TSE.

Pascal HitzlerView profile

Professor

• Neurosymbolic Artificial Intelligence
• Semantic Web
• Knowledge Graphs
• +7 more

Pascal Hitzler is a University Distinguished Professor and holds the endowed Lloyd T. Smith Creativity in Engineering Chair at Kansas State University's Department of Computer Science, Carl R. Ice College of Engineering. He directs the Center for Artificial Intelligence and Data Science (CAIDS) and the Institute for Digital Agriculture and Advanced Analytics (ID3A). Previously, he held roles at Wright State University, Karlsruhe Institute of Technology, and TU Dresden. His research focuses on neuro-symbolic AI, semantic web technologies, knowledge graphs, and ontology engineering. Education: PhD in Mathematics (2001, University College Cork), Diplom in Mathematics (1998, University of Tübingen). Academic achievements include over 400 publications, founding editor roles for journals like Neurosymbolic Artificial Intelligence , and leadership in organizations like the Neural-Symbolic Learning and Reasoning Association. Research interests include AI explainability, knowledge representation, and interdisciplinary applications of semantic technologies. He leads the DaSe Lab for Data Semantics, advancing projects like the KnowWhereGraph and Enslaved.org Hub Knowledge Graph. His work bridges symbolic AI with neural networks, emphasizing practical applications in agriculture, environmental science, and historical data preservation. Grants and collaborations span academic, industrial, and international partners. He has advised numerous students and researchers, contributing to both theoretical advancements and real-world semantic systems deployments.

Nicholas V. SarlisView profile

Professor

• Solid State Physics
• Seismology
• Geophysics
• +3 more

Nicholas V. Sarlis is a Professor of Experimental Solid State Physics at the Department of Physics , National and Kapodistrian University of Athens since 2017. With an h-index ≥36, he has authored 2 monographs, >155 peer-reviewed publications, and 80 conference communications. B.Sc. in Physics (1991), National and Kapodistrian University of Athens Ph.D. in Physics (1997), National and Kapodistrian University of Athens His research applies Natural Time Analysis to: Earthquake precursor identification Space weather and cosmic ray studies Climate phenomenon prediction (El Niño) Fracture mechanics and complex systems Seismicity and geoelectric field correlations Non-extensive statistical mechanics applications Recent work includes analyzing seismic entropy changes under time reversal and developing earthquake nowcasting systems. Publications span 2025's Statistical mechanics in geophysical contexts to 2024's cross-disciplinary disaster prediction tools.