Find Academic Supervisors

Mark Philip PhilipsenView profile

Assistant Professor

• Computer Vision
• Robotics
• Machine Learning
• +3 more

Mark Philip Philipsen is an Assistant Professor at the Department of Architecture, Design and Media Technology, Aalborg University. He holds a PhD (ErhvervsPhD) from 2017 to 2019. His research focuses on computer vision, robotics, and machine learning applications in environmental science, infrastructure, and industrial automation. He leads projects such as ReDoCO2 (2020–2025) addressing peatland CO2 emissions and Applications of Vision and Robotics in Meat Production (2017–2020), exploring automation in food processing. His work spans diverse areas, including semantic segmentation of underground utilities using 3D point clouds, peat mapping with graph neural networks, and anomaly detection in agricultural vehicles. Philipsen has contributed datasets like OpenTrench3D and LISA Traffic Light Dataset, advancing research in infrastructure analysis and vision systems. His publications emphasize practical applications of AI in environmental monitoring, robotics, and data-driven solutions for industry challenges. Key achievements include developing synthetic thermal human scenarios (ThermalSynth) and semi-supervised learning models for sewer systems. His research often bridges theory and real-world problems, with a focus on sustainability and automation.

Sylvester B. AboagyeView profile

Assistant Professor

• Wireless Communications
• Optical Communications
• Optimization Theory
• +3 more

Sylvester B. Aboagye is an Assistant Professor in the School of Engineering at the University of Guelph, Canada. He holds a Ph.D. in Electrical Engineering from Memorial University (supervised by Profs. Telex M. N. Ngatched and Octavia Dobre) and completed a postdoctoral fellowship at York University’s Next Generation Wireless Networks lab under Prof. Hina Tabassum. His research focuses on wireless and optical communications, optimization theory, machine learning, and emerging technologies like RIS and NOMA. He currently serves as an Associate Editor for the IEEE Open Journal of Communications Society and Editor for IEEE Communications Letters. Research interests include: Terrestrial/non-terrestrial integrated sensing-communication systems Physical layer security mechanisms Convex/non-convex optimization for resource allocation Applications of machine learning in wireless networks Recent publications emphasize energy-efficient spectrum partitioning, multi-band wireless networks, and optical STAR-RIS-aided VLC systems. His work bridges theoretical advancements with practical implementations in next-generation communication systems. Awards include the prestigious Governor General’s Gold Medal (2023), David Dunsiger Award, and Provost’s Postdoctoral Fellowship for Black and Indigenous Scholars. He advises undergraduate researchers like Zohaib Saghir and Riley Brownsdon through NSERC programs, and maintains active collaborations across academia and industry.

Michael KartmannView profile

Researcher

• Adaptive Reduced Order Modeling
• PDE-Constrained Optimization
• Optimal Control
• +3 more

Michael Kartmann is a doctoral student and Research Assistant at the Department of Mathematics and Statistics, University of Konstanz, Germany, since October 2022. His work is funded by the BMBF ElAN project, focusing on efficient local waste heat utilization in low-temperature networks, and he collaborates with the YMMOR group (Young Mathematicians in Model Order Reduction). Research Interests: His work centers on adaptive reduced-order modeling, PDE-constrained optimization, optimal control, and domain decomposition methods for nonlinear preconditioning, with applications in switched low-temperature heat networks and large-scale dynamical systems. He also explores reinforcement learning for optimization problems. Publications: Recent submissions include methods for model predictive control of switched systems and L1-regularized optimal control. His 2024 published work details adaptive trust region reduced basis approaches for parameter identification, with a 2022 master thesis on hierarchical multiobjective optimization. Scientific Contributions: He presented talks at conferences including MORE24, IFIP24, and EUCCO23. His software contributions are available on GitHub. Teaching Roles: He supervises courses such as 'Numerical Mathematics,' 'Proper Orthogonal Decomposition for Linear-Quadratic Optimal Control,' and 'PDE-constrained Optimization' at the University of Konstanz, collaborating with Professors Stefan Volkwein, Behzad Azmi, and others since 2022.

• Offshore Renewable Energy
• Hydrogen Safety
• Risk Assessment
• +7 more

Ehsan Arzaghi is a researcher at the University of Tasmania, affiliated with the College of Sciences and Engineering, School of Engineering. His work focuses on offshore renewable energy systems, hydrogen safety, corrosion modeling, and risk assessment in marine environments. His research interests include offshore renewable energy, hydrogen safety, risk assessment, marine infrastructure reliability, corrosion modeling, process safety, offshore wind energy, green hydrogen production, computational fluid dynamics, and machine learning applications in safety engineering. These areas are reflected in his extensive publication record in high-impact journals such as Renewable Energy , Ocean Engineering , and International Journal of Hydrogen Energy . The recent articles highlight a strong trend toward integrating risk assessment with renewable energy systems, particularly in offshore and hydrogen-related applications. His work combines computational modeling, machine learning, and multi-criteria decision-making frameworks to address safety and reliability challenges in marine and energy infrastructure. There are no scientific awards mentioned in the provided text. Dr. Arzaghi has collaborated extensively with researchers such as Rouzbeh Abbassi, Vikram Garaniya, and Mohammad Mahdi Abaei on numerous projects related to offshore safety and renewable energy systems. No specific grants are mentioned, but the collaborative nature and scope of the research suggest involvement in funded projects. There is no mention of labs or research teams in the provided text.

• Health Management
• Public Administration
• Healthcare Innovation
• +4 more

Prof. Kim Putters is a Visiting Professor at the Erasmus School of Health Policy & Management (Health Care Governance department), Erasmus University Rotterdam. He holds a PhD in Public Administration and has extensive experience across academic, governmental, and advisory roles. Previously, he worked at Tilburg University (until 2008) and advised the Dutch Ministry of Health (1997–2003). His research focuses on healthcare governance, hybrid organizational models, and the interplay between market, government, and civil society in healthcare. Key themes include healthcare innovation barriers, policy-practice integration, and intersectoral collaboration between health, housing, mobility, and welfare sectors. Recent publications explore normativity in hospital prevention, digital welfare state transformations, and population health management strategies. His work contributes to UN Sustainable Development Goals related to health and well-being (SDG 3) and sustainable cities (SDG 11). Prof. Putters has received the 2015 Academy of Management Best Article Award. He has supervised 10 academic works and actively engages in policy-oriented research with practical implications for healthcare systems globally.

Chao LanView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Data Science
• +4 more

Chao Lan is an Assistant Professor at the School of Computer Science , University of Oklahoma. His research focuses on Machine Learning, particularly in fairness, multi-view learning, kernel methods, and privacy-preserving techniques. He holds a Ph.D. in Computer Science from the University of Kansas, and M.S. and B.S. degrees from Nanjing University of Posts and Telecommunication, China. Key professional roles include Area Chair for ICML'25 and NeurIPS'24, and Senior Program Committee member for PAKDD'25. He serves as an Associate Editor for ACM Transactions on Probabilistic Machine Learning (2024–present) and has received recognition as a Top Reviewer for multiple conferences. Education: Ph.D., Computer Science, University of Kansas M.S., Computer Science, Nanjing University of Posts and Telecommunication, China B.S., Computer Science, Nanjing University of Posts and Telecommunication, China His research emphasizes theoretical and applied machine learning, including fairness-aware algorithms, efficient kernel methods, and distributed systems with privacy guarantees. Recent work explores randomized learning frameworks and debiasing techniques in semi-supervised and multi-view contexts. Lauded for contributions, he has earned awards such as the NSF CRII Award (2019), Distinguished Paper Award at ACSAC 2021, and multiple scholarship recognitions. His publications span top venues like ICML, NeurIPS, KDD, and IJCAI.

Stef De SabbataView profile

Associate Professor

• Geographical Artificial Intelligence (GeoAI)
• Urban Analytics
• Generative Artificial Intelligence
• +3 more

Stef De Sabbata is an Associate Professor of Geographical Information Science at the University of Leicester, holding dual affiliations with the School of Geography, Geology and the Environment and the Institute for Digital Culture. She serves as Chair of the Geographic Information Science Research Group at the Royal Geographical Society and previously chaired GISRUK 2018. Her research focuses on GeoAI, urban analytics, and cultural informatics, with notable contributions to graph neural networks in geodemographic classification and urban form analysis. Education includes a PhD in Geography from the University of Zurich (2013), and BSc/MSc in Computer Science from the University of Udine. Prior to Leicester, she held roles at the Oxford Internet Institute (OII) including Researcher (2013-2015), Junior Research Fellow at Wolfson College (2014-2015), and Research Associate (2015-2021). Her work explores spatially-explicit approaches to urban systems and the application of large language models in geographic contexts. Professional roles include membership in the International Cartographic Association's Commission on Location-Based Services and leadership in interdisciplinary initiatives like the GeoNet project examining Sub-Saharan Africa's knowledge economies. No specific awards are explicitly listed in the provided materials.

Fabrício de Oliveira OuriqueView profile

Lecturer

• Digital Signal Processing
• Machine Learning
• Internet of Things (IoT)
• +4 more

Fabrício de Oliveira Ourique is a Lecturer in Digital Signal Processing at Queen Mary University of London's School of Physical and Chemical Sciences. He holds a Ph.D. from the University of New Mexico, USA. His academic roles include former Associate Professor positions at Brazil's Federal University of Santa Catarina, where he supervised Master's and Ph.D. students. Research focuses on Digital Signal Processing (DSP), machine learning, and IoT applications. Key areas include image watermarking, time series forecasting, energy monitoring systems, and wearable cardiac signal acquisition devices. He has developed embedded systems for smart meters and semi-automatic music transcription techniques. Publications emphasize healthcare analytics (e.g., excess death estimation in Brazil) and optimization in cancer treatment pathways using perishable inventory models. His work on Markov chains introduced multi-cluster time aggregation methods published in Automatica. No scientific awards explicitly listed. Advised students during prior academic roles but no names provided. Active in interdisciplinary projects combining signal processing with healthcare and IoT.

• Bioinformatics
• Computational Genomics
• Public Health Informatics
• +5 more

Dr. Michael Mapundu is a Research Fellow at Northumbria University's Applied Sciences Department, specializing in AI-driven bioinformatics and public health informatics. He holds a PhD in Bioinformatics from Wits University, an MSc in Information Technology from Vaal University of Technology, and a BSc (Hons) in Information Systems from Midlands State University. His research focuses on leveraging AI/ML for genomic data analysis and public health systems, including NLP for verbal autopsy analysis and scalable cloud solutions (AWS). Key areas include healthcare decision-making, epidemiological insights from clinical narratives, and improving healthcare informatics efficiency. He has published extensively on machine learning applications in public health and contributed to UN Sustainable Development Goals through data-driven healthcare solutions. Mapundu has experience in curriculum development, research funding acquisition, and mentoring. His work emphasizes interdisciplinary approaches to address healthcare challenges, combining computational methods with real-world healthcare data.

• Information Visualization
• Visual Analytics
• Time-Oriented Data Analysis
• +5 more

Wolfgang Aigner is a Professor at TU Wien's Department of Visual Analytics, part of the Faculty of Computer Science. His main affiliation is with the Visual Analytics Research Department (E193-07). He focuses on advancing information visualization, time-oriented data representation, and knowledge-assisted visual analytics. His work emphasizes user onboarding methods for unfamiliar visualizations, integrating explicit domain knowledge into analytic workflows, and developing systems for malware analysis and healthcare informatics. Key research areas include: Design of intuitive visualization onboarding systems Temporal data visualization and pattern detection Integration of explicit knowledge in visual analytics Malware behavior visualization and forensic analysis Healthcare data exploration focusing on temporal patient trajectories Notable contributions include the KAMAS malware analysis system, task cube conceptual framework for visualization design evaluation, and methodologies for visually guided time series imputation. He has supervised 14+ graduate theses exploring topics like glyph editors, temporal data visualization, and bias detection in language models. Recent work emphasizes educational theories in visualization onboarding, with comparative studies on user guidance effectiveness and system scaffolding approaches. His research bridges cognitive science principles with practical visual analytics tools for domains like cybersecurity and healthcare.

Amanda BarnardView profile

Professor

• Applied machine learning and artificial intelligence
• Data science and eResearch
• Computational methods for physical sciences
• +2 more

Professor Amanda Barnard is a Senior Professor of Computational Science at the ANU College of Engineering and Computer Science. She leads research in computational modeling, high-performance supercomputing, and AI applications in materials science. With a BSc (Hons) in applied physics (2000) and PhD in theoretical condensed matter physics (2003) from RMIT University, she has held prestigious roles including Distinguished Postdoctoral Fellow at Argonne National Lab (USA) and Violette & Samuel Glasstone Fellow at Oxford University (UK). Board member at BioViS (Garvan Institute), CTCMS (AIBN), Our Health in Our Hands (ANU), and NeSI (New Zealand eScience). Former Chair of the Australian National Computational Merit Allocation Scheme (NCMAS) and current Chair of the Australasian Leadership Computing Grants (ALCG). Research focuses on materials informatics, nanoinformatics, and AI-driven material discovery. Awards include the 2009 Malcolm McIntosh Physical Scientist of the Year, 2014 Feynman Prize in Nanotechnology, and 2019 AMMA Medal. Her work bridges computational science with real-world applications in energy storage, carbon removal, and hydrogen economy technologies. Collaborates with industry through ChoiceFlows Inc. and Data61 (CSIRO).

• Data Science
• Machine Learning
• Natural Language Processing

Michael Bloodgood is an Associate Professor of Computer Science in the Department of Computer Science at The College of New Jersey (TCNJ), part of the School of Science. He has been actively contributing to the academic community at TCNJ since joining as one of six new full-time faculty members in 2016, as noted in a TCNJ Science news article from August 2016. Dr. Bloodgood earned his Ph.D. in Computer Science from the University of Delaware in 2009. His academic journey has led him to become a respected researcher and educator specializing in data-intensive computational fields. Dr. Bloodgood's research primarily focuses on data science, machine learning, and natural language processing . His work bridges theoretical computer science with practical applications, particularly in text classification, active learning methodologies, and computational linguistics. He has developed innovative approaches to stopping criteria in active learning systems, translation lexicon acquisition for low-resource languages, and methods for improving cognates detection through global constraints. His research often involves undergraduate student collaborators, reflecting TCNJ's emphasis on mentored research experiences. Dr. Bloodgood has received multiple Mentored Undergraduate Student Experience (MUSE) Awards (2024, 2022) and a Support of Scholarly Activities (SOSA) Award (2023) from TCNJ, recognizing his commitment to undergraduate research mentorship. His papers have been finalists for Best Paper at the IEEE International Conference on Semantic Computing (ICSC) in 2022 and 2020, demonstrating the quality and impact of his scholarly work. As an educator, Dr. Bloodgood teaches a range of courses including Machine Learning (CS 426), Natural Language Processing (CS 427), and Mentored Research. He serves on multiple departmental and college committees, including the Computer Science Promotion and Reappointment Committee (as Chair for 2024-2025) and the Mentored Research and Internships Council. His dedication to student research is evident through his active recruitment of TCNJ students for mentored research projects, with dedicated web pages for research projects, mentees, and funding opportunities.

Xingche GuoView profile

Assistant Professor

• reinforcement learning
• functional data analysis
• variable selection
• +2 more

Xingche Guo serves as an Assistant Professor in the Department of Statistics at the University of Connecticut, Storrs Campus. His research program develops advanced statistical methodologies with critical applications in behavioral decision-making and neuroscientific data analysis. His primary research domains include: Reinforcement Learning and Inverse Reinforcement Learning Functional Data Analysis in high-dimensional contexts Variable Selection techniques for complex models Markov Chain Monte Carlo (MCMC) methods State-space modeling for dynamic systems Statistical applications in mental health and neuroscience Analysis of recent publications reveals concentrated innovation in modeling decision dynamics through reinforcement learning frameworks applied to mental disorders and behavioral experiments. His methodological contributions frequently integrate hierarchical modeling and semiparametric approaches to address complex data structures in neuroscience, particularly for EEG analysis and biomarker-guided treatment development. The publication pattern demonstrates consistent output in top-tier statistical journals and growing interdisciplinary impact.

• Machine Learning
• Data Mining
• Computer Vision
• +7 more

Guansong PANG is a full-time Assistant Professor at the School of Computing and Information Systems , Singapore Management University (SMU) , where he leads the Machine Learning & Applications (MaLA) Lab . He holds a PhD from University of Technology Sydney (2019) and has previously served as a Research Fellow at the Australian Institute for Machine Learning , University of Adelaide. Research Interests : His work centers on machine learning and data mining , particularly focusing on anomaly detection , open-world learning , and foundation models for graphs, time series, and tabular data. He investigates security and safety in foundation models , including hallucination mitigation and AI-generated content detection , with applications spanning network intrusion detection , fraud detection , industrial defect detection , and biometric anti-spoofing . Recent Awards : Lee Kong Chian Fellowship (2024-2026) World's Top 2% Scientists (2022-2024) DSAA 2023 Best Paper Award (Applications Track) Most Influential KDD 2023 Paper Professional Roles : Area Chair for NeurIPS, ICLR, ICML, CVPR, KDD, PAKDD, IJCAI, and AAAI; Associate Editor for IEEE Transactions on Neural Networks and Learning Systems and Pattern Recognition ; Editorial Board member for IEEE Intelligent Systems and International Journal of Data Science and Analytics .

Gary PattiView profile

Professor

• Metabolomics
• Cancer Biology
• Computational Biology
• +12 more

Gary Patti is a Professor of Chemistry and of Genetics and Medicine at Washington University in St. Louis, with appointments in the Department of Chemistry, Department of Genetics, and Department of Medicine (School of Medicine). He serves as Senior Director of the Center for Metabolomics and Isotope Tracing, Director of the Clinical Research Core in Medicine, and Director of Faculty Affairs in Chemistry. His research focuses on the intersection of cancer biology , metabolomics , and computational biology . The Patti laboratory investigates how cancer cells rewire metabolism to support proliferation, how tumors manipulate host metabolism across tissues, and develops cutting-edge tools in isotope tracing, mass spectrometry imaging, and metabolomics data analysis. His work spans cancer metabolism , single-cell metabolomics , spatial omics , and bioinformatics tool development . The recent publications highlight a strong trend in quantitative metabolic imaging , isotope-based flux analysis , and machine learning for metabolomics . His lab integrates stable isotope tracing with LC/MS , MALDI/DESI imaging , and spatial transcriptomics to resolve metabolic dynamics at the cellular and tissue level. A major focus is overcoming data annotation bottlenecks through tools like DecoID and PeakDetective . Selected scientific awards include: Alfred P. Sloan Award (2014) Pew Biomedical Scholars Award (2015) Camille Dreyfus Teacher-Scholar Award (2015) Mallinckrodt Scholar Award (2016) Inaugural NIEHS Award for Visionary Research (2017) Blavatnik National Finalist (2020) ACS Midwest Award (2023) Professor Patti advises a multidisciplinary research team and leads multiple institutional cores. His grants support technology development in metabolomics, cancer metabolism studies using zebrafish and mouse models, and software tools for omics data integration. He collaborates with instrument manufacturers to improve data acquisition strategies and promotes inclusive research practices. He leads the Center for Metabolomics and Isotope Tracing and the Clinical Research Core in Medicine , fostering collaboration across chemistry, genetics, and medicine. His lab is at the forefront of advancing single-cell metabolomics and in vivo metabolic imaging .