Find Academic Supervisors

Kamil ErgulerView profile

Researcher

• Climate Science
• Vector-Borne Disease Modelling
• Stochastic Dynamics
• +4 more

Kamil Erguler is a Research Scientist at the Climate & Atmosphere Research Center (CARE-C) of The Cyprus Institute. He holds an MRes in Biochemical Research, an MSc in Bioinformatics and Theoretical Systems Biology, and a PhD in mathematical modelling of biochemical systems from Imperial College London. His research focuses on dynamical modelling of biological systems, stochastic dynamics, parameter inference, and climate-driven disease modelling. Dr. Erguler’s work emphasizes the impact of climate change on vector-borne diseases, particularly ticks and mosquitoes, and their public health implications. He has developed frameworks like VEClim for early warning systems and AIMSurv for harmonized surveillance of invasive mosquitoes. His interdisciplinary approach integrates machine learning, computational biology, and environmental science to address global health challenges. Key achievements include a Web of Science h-index of 10 and invited presentations at international conferences. His research spans climate-sensitive disease dynamics, vector population models, and citizen-science initiatives like the Aedes Watch Out Network in Cyprus. Collaborations include the Mosquitoes Without Borders project and pan-European vector surveillance efforts. He contributes to policy-relevant studies on climate change impacts in the Eastern Mediterranean and Middle East, emphasizing actionable insights for public health and environmental management.

Francois St-PierreView profile

Associate Professor

• Synthetic Biology
• Neuroscience
• Biomedical Engineering
• +5 more

Dr. François St-Pierre is an Associate Professor in the Department of Neuroscience at Baylor College of Medicine and an Adjunct Faculty member in Electrical and Computer Engineering at Rice University. He is a McNair Scholar at Baylor and leads the St-Pierre Lab, dedicated to developing tools for monitoring and perturbing biological systems. His research focuses on synthetic biology, voltage imaging, brain connectivity, and high-throughput screening methods. Education: BA and MA from the University of Cambridge, PhD from MIT, and postdoctoral training at Stanford University. His lab is located in the Texas Medical Center, the world's largest medical complex, and collaborates with institutions like Rice University. Research Interests: Engineered proteins, gene circuits, voltage imaging technologies, optogenetics, and novel screening methods. Key tools include genetically encoded voltage indicators and advanced microscopy techniques. Awards: McNair Scholar (Baylor College of Medicine) Labs/Teams: St-Pierre Lab at Baylor College of Medicine Lab Website: stpierrelab.com Publications emphasize voltage indicator development, optogenetic tools, and neural circuit imaging, with a focus on enabling high-resolution, in vivo studies of brain activity.

• Precision Agriculture
• Remote Sensing
• Machine Learning
• +4 more

Joseph Mhango is a Senior Lecturer in Applied Data Science at Harper Adams University's Department of Agriculture and Environment. His research focuses on leveraging big data from satellite imagery, unmanned aerial vehicles (UAVs), and proximal soil sensors to develop predictive models for agricultural decision support. He specializes in high-throughput plant phenotyping, deep learning for precision agriculture, and automation of agricultural perception tasks. His research is conducted through the Centre for Agricultural Data Science. He actively engages with the scientific community via Twitter/X (@josemhango), sharing insights on potato research, precision agriculture, and machine learning applications in agriculture. Mhango's work bridges computer science and agriculture, emphasizing interdisciplinary approaches to solve crop productivity challenges. His publications span crop management, remote sensing, and machine learning-driven agricultural analytics.

• Artificial Intelligence
• Business Analytics
• Energy Systems Optimization
• +7 more

Dr. Nishan Mills is a Lecturer in Business Analytics and AI at La Trobe University, Melbourne, Australia. He holds a PhD from the same institution. His research focuses on advancing AI applications across energy systems, healthcare, education, and smart cities. Key areas include explainable AI, generative models, cybersecurity, and net-zero carbon solutions. He collaborates extensively with industry and academia, co-authoring over 30 publications. Dr. Mills’ work spans technical innovations like emotion analysis frameworks (EMOTION AWARE), energy forecasting systems, and AI chatbots for healthcare and education. His research emphasizes ethical AI deployment, human-centric design, and real-world impact. Notable projects include optimizing energy infrastructure, developing mental health chatbots, and designing AI literacy modules for universities. He has contributed to IEEE journals and conferences, focusing on interdisciplinary applications of AI. His work bridges theoretical advancements with practical implementations in sustainability, urban planning, and digital transformation.

• Hypersonic Aerodynamics
• Fluid Dynamics
• Aerodynamics
• +3 more

Joseph Jewell is an Associate Professor of Aeronautics and Astronautics at Purdue University, courtesy-affiliated with the School of Mechanical Engineering. His research focuses on hypersonic aerodynamics, boundary-layer instabilities, and flow control in high-speed regimes. He leads experimental and computational studies utilizing advanced diagnostics such as focused laser differential interferometry and pressure-sensitive paint in facilities like the Boeing-AFOSR Mach-6 Quiet Tunnel. Key areas include separation bubbles, transition prediction, and hypersonic vehicle inlet design. His work addresses challenges in thermal protection systems, shock-wave interactions, and environmental impacts of hypersonic flight. Research interests span experimental and numerical analysis of hypersonic flows, with emphasis on quiet flow conditions to isolate fundamental instability mechanisms. He collaborates on projects related to HIFiRE flight experiments and advanced materials for hypersonic applications. His contributions bridge computational models with empirical data to advance understanding of hypersonic aerodynamics. Recent studies include investigations into ice crystal impacts on hypersonic vehicles, porous material effects on boundary-layer stability, and the dynamics of internal waverider inlets. His work has implications for next-generation hypersonic propulsion systems and reentry vehicles.

• Interactive Design
• Music Technology
• Human-Computer Interaction
• +3 more

Teodoro Dannemann is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science. He contributes to undergraduate teaching through the Creating Interactive Objects module, focusing on microcontrollers, sensors, and Arduino-based prototyping. His research explores interactive design, music technology, and human-computer interaction, with a particular interest in augmented instruments and participatory design methods involving disabled musicians. He actively promotes Latin American perspectives in New Interfaces for Musical Expression (NIME) research, advocating for regional academic collaboration. His work spans interdisciplinary projects, including the Sabotaging Piano initiative, which investigates auditory and haptic feedback disruption in musical improvisation. Beyond technology, he has engaged in policy-oriented research on Latin American development, urban segregation, and ecological modeling. His teaching and research emphasize creativity, accessibility, and critical approaches to technology. Education details: Not explicitly stated in available texts. Research interests include designing interactive physical systems, exploring error as a creative material, and fostering inclusive musical practices. He has contributed to tools like SonicDraw, a web-based sound-sketching platform, and collaborates on global development initiatives. His work bridges artistic innovation with technical rigor, often challenging conventional approaches in instrument design and user interaction. Recent publications highlight themes of instrument modification, participatory methods, and cultural representation in technology. While no scientific awards are noted, his contributions to NIME community-building and Latin American research networks are notable.

• Biomedical Systems Biology
• Computational Biology
• Metabolic Networks Modeling
• +4 more

Natal AW van Riel is a Professor of Biomedical Systems Biology at Eindhoven University of Technology (TU/e) and part-time Professor of Computational Modeling at Amsterdam University Medical Centers. His research focuses on computational modeling of metabolic networks, systems biology, and digital twins for cardiometabolic diseases like obesity and type 2 diabetes. He leads the Computational Biology group and the Systems Biology and Metabolic Diseases program at TU/e, emphasizing translational research integrating AI, physiology, and clinical data. Academically, van Riel holds a PhD from Utrecht University (2000) and has held positions from Assistant Professor (2003) to Associate Professor (2014) before becoming Full Professor (2019). He is also Head of the Computational Biology group (2023) and chairs TU/e's Biomedical Engineering Examination Committee. His work contributes to UN Sustainable Development Goals, particularly in health and nutrition. Research interests include bile acid metabolism, gut microbiome interactions, metabolic health indices (e.g., MHI), and digital twins for personalized medicine. Collaborations span clinicians, engineers, and AI specialists to translate models into clinical tools like the DiaGame diabetes management game. Notable achievements include the Dutch Royal Order of Orange-Nassau (2014) and leadership in projects like RESOLVE and ACACIA. He has supervised 104 research works and contributed to over 300 publications, emphasizing metabolic resilience, computational methods, and translational systems biology.

Silvia LiveraniView profile

Associate Professor

• Bayesian Statistics
• Spatial Modelling
• Computational Statistics
• +3 more

Silvia Liverani is a Reader in Statistics and Head of the Centre in Data Science, Statistics and Probability at Queen Mary University of London (School of Mathematical Sciences). Her research focuses on Bayesian methods, spatial-temporal modeling, and computational statistics, with applications in epidemiology, environmental health, and genetics. She leads projects on biodiversity analysis and spatial health data, collaborating with institutions like the Royal Botanical Gardens, Kew and the Alan Turing Institute. Education: PhD in Bayesian Clustering from the University of Warwick (2009). Grants & Initiatives: Awarded £1,000 (2025) for an LGBTQ+ Mathematics Open Day; led a Data Study Group at the Alan Turing Institute (2023). Labs/Teams: Director of the Centre in Data Science, developer of R packages PReMiuM and CARME for Bayesian clustering and spatial modeling. Key Research Themes: Dirichlet process mixture models, spatial misalignment correction, and interdisciplinary collaborations in health and environment. Current work includes predicting biodiversity loss under climate change and analyzing socio-economic health disparities through spatio-temporal frameworks.

• Bayesian Statistics
• Environmental Science
• Climate Change
• +5 more

Jonathan Rougier is a Visiting Professor at Imperial College London's Faculty of Natural Sciences (Department of Mathematics) with an honorary appointment at the University of Bristol. He specializes in statistics, focusing on applications in environmental science, climate change, and geohazards. His work bridges academia and industry, emphasizing probabilistic modeling, uncertainty quantification, and decision-support systems for natural hazards. Education & Affiliations: Honorary staff at Imperial's Mathematics and Statistics departments. Collaborates with institutions on global geophysical and climate projects. Research Interests: Bayesian methods, spatio-temporal modeling, volcanic eruption prediction, climate change impacts, and risk assessment. His research addresses challenges in environmental data analysis, including unreliable historical records and complex simulation outputs. Articles Trends: Recent work focuses on volcanic hazard modeling (e.g., Aso volcano likelihoods), Antarctic ice mass evolution, and Kalman filter applications in environmental monitoring. Themes include uncertainty quantification, multi-scale computational methods, and interdisciplinary data integration. Advising & Grants: No formal advisees listed. Contributions include methodological advancements for climate projections and hazard analysis, supported by collaborative research networks. Labs/Teams: Engages in global initiatives like the VOLCORE database and Antarctic mass balance studies, emphasizing interdisciplinary teams for geophysical and environmental challenges.

Vincent LostanlenView profile

Researcher

• Machine Learning
• Audio Content Analysis
• Computer Music
• +11 more

Vincent Lostanlen is a Researcher (chargé de recherche) at CNRS, affiliated with the LS2N laboratory located at Centrale Nantes engineering school. He also serves as a visiting scholar at New York University. His research focuses on developing artificial intelligence for audio analysis, emphasizing machine listening systems, bioacoustic monitoring, and computer music. Key projects include the BirdVox platform for avian migration monitoring and the Kymatio Python library for wavelet scattering transforms. Education: Ph.D. in Mathematics from École Normale Supérieure (2017), supervised by Stéphane Mallat; MSc in Acoustics & Music Informatics from Université Pierre-et-Marie-Curie (2013); Dipl. Ing. in Signal Processing from Télécom ParisTech (2013). Research interests span audio representation learning, environmental sensor networks, and generative models for sound. Notable contributions include the SONYC-UST urban sound dataset and the PCEN signal frontend for robust acoustic sensing. Scientific awards include the 2019 AFIM Young Researcher Award, 2016 SFA Best Poster Prize, and 2015 IEEE MLSP Best Paper Award. He advises graduate students across computer science and music technology domains, with former advisees working in academia and industry. Current activities include developing green AI for audio processing and collaborating on bioacoustic sensor networks for biodiversity monitoring. His work bridges signal processing theory with practical applications in music, ecology, and biomedical engineering.

Tim HansenView profile

Associate Professor

• Electric Power Systems
• Optimization
• High-Performance Computing
• +5 more

Tim Hansen is the Harold C. Hohbach Endowed Associate Professor at the Department of Electrical Engineering and Computer Science at South Dakota State University. He holds a B.S. in Computer Engineering from Milwaukee School of Engineering (2011) and a Ph.D. in Electrical Engineering from Colorado State University (2015). B.S. in Computer Engineering, Milwaukee School of Engineering, 2011 Ph.D. in Electrical Engineering, Colorado State University, 2015 Dr. Hansen's research focuses on electric power systems and markets , with a specialization in optimization, high-performance computing, and machine learning applied to cyber-physical-social systems. His work addresses critical challenges in grid stability, including virtual inertia implementation , multi-area inertia estimation , and low-inertia microgrid control , leveraging advanced computational techniques for energy management systems. He also investigates data-driven approaches for secure electric grids in communities impacted by climate change. Recent research trends include federated learning for grid analysis, model predictive control for voltage and frequency regulation, and computationally efficient inverter modeling. His publications emphasize converter-dominated power systems , energy storage applications , and smart grid technologies across multiple spatiotemporal scales. Scientific awards include: 2024 Outstanding Researcher Award, South Dakota State University Jerome J. Lohr College of Engineering 2022 Best Paper Award for AC Optimal Power Flow research 2020 Milwaukee School of Engineering Graduate of the Last Decade 2019 IEEE-HKN Teaching Award Multiple IEEE best paper awards (2017, 2016) Dr. Hansen directs grants totaling over $2 million, including: $357,467 (DOE SETO, 2024): Transmission/Distribution Systems with Flexible Coordination $750,000 (NSF, 2023): Secure Grids for Climate-Impacted Communities $978,708 (NSF, 2022): Heterogeneous HPC Cluster Development $630,897 (DOE, 2019-2021): Dynamic Response Modeling of Converter-Dominated Systems He chairs IEEE committees including Region 4 Siouxland Section Executive Committee and co-chairs the IEEE PES Research Committee, while advising the IEEE-HKN Honor Society.

Elizabeth MaroonView profile

Assistant Professor

• Climate Science
• Oceanography
• Climate Change
• +3 more

Elizabeth Maroon is an Assistant Professor in the Department of Atmospheric and Oceanic Sciences at the University of Wisconsin-Madison (College of Letters and Science). Her research focuses on ocean-climate interactions, particularly how ocean dynamics influence climate variability and predictions. She holds a PhD from the University of Washington. Research interests include large-scale ocean dynamics, climate change, and decadal climate predictability. Her work uses climate models, observational data, and statistical methods to improve understanding of coupled ocean-atmosphere processes. Key areas include North Atlantic circulation, marine heatwaves, and Antarctic sea ice responses to extreme events. Her recent publications analyze North Atlantic climate trajectories, ocean carbon storage variability, and nuclear war impacts on sea ice. She leads the Oceans and Climate Group, mentoring PhD students and a research scientist addressing topics like thermohaline processes and tropical-extratropical interactions. She also collaborates with the Mead Witter School of Music on the Earth Signals project, translating ocean data into music for public outreach. Labs/Teams: Oceans and Climate Group at UW-Madison. NSF CAREER Award supports transdisciplinary work like the 2025 Earth Signals concert on El Niño, blending science with jazz-classical performances.

C. Jessica E. MetcalfView profile

Professor

• Demography
• Infectious Disease Dynamics
• Vaccine Policy
• +6 more

C. Jessica E. Metcalf is a Professor of Ecology and Evolutionary Biology and Public Affairs at Princeton University, with an affiliation to the Princeton School of Public and International Affairs. She serves as Co-Director of the Program in Global Health and Health Policy and is part of the Climate Change and Infectious Disease Initiative and Grand Challenges Program. Her research integrates demography, infectious disease dynamics, and policy, with a focus on vaccine equity, immune function, and disease elimination. She holds a PhD from Imperial College London, where she studied evolutionary demography, followed by postdoctoral research at the Max Planck Institute for Demographic Research, Duke University, and Penn State University. Her work spans theoretical and applied aspects of epidemiology, including climate-driven disease shifts, pathogen evolution, and public health policy. Her recent articles emphasize climate impacts on disease seasonality, vaccine trial design, and microbial decline in Africa. She collaborates globally, addressing challenges in Madagascar’s health systems and advancing tools like the RSero R package for pathogen circulation analysis. Her research bridges ecological, immunological, and policy dimensions to combat global health inequities.

Wen HuaView profile

Research Fellow

• Data Science
• Machine Learning
• Data Mining
• +5 more

Dr. Wen Hua is an Honorary Senior Research Fellow at the University of Queensland's School of Electrical Engineering & Computer Science. His research focuses on Data Science, Machine Learning, and Knowledge Graphs, with significant contributions to privacy-preserving techniques, neural network architectures, and trajectory analysis. He has co-authored over 80 peer-reviewed publications, including influential works on entity linking, meta-learning, and spatiotemporal data privacy. His work bridges theoretical advancements with practical applications in areas like recommendation systems, sentiment analysis, and information resilience. Key research interests include: Machine Learning and Deep Learning Privacy-Preserving Data Publishing Knowledge Graph Construction Text Mining and Natural Language Processing Spatiotemporal Trajectory Analysis Information Retrieval Systems Recent publications highlight trends in: Neural architectures for meta-learning and transformer-based systems Efficient algorithms for relation extraction and entity disambiguation Temporal and spatial data anonymization techniques Integration of knowledge graphs with text corpus analysis

• Metacognition AI
• Reinforcement Learning
• Robotics
• +3 more

An Nguyen An Nguyen is a Researcher in the Department of Computer Science at the University of Oxford. Their work focuses on advancing AI systems through metacognition principles, reinforcement learning, and robotics applications. Key research themes include human-AI collaboration in robotic teleoperation, decision-making frameworks, and spatiotemporal task coordination. Nguyen's recent studies explore confidence calibration mechanisms in joint human-AI systems and their implications for autonomous decision support. Research interests span: Metacognition AI architectures for self-aware systems Reinforcement learning in dynamic environments Robotic teleoperation interfaces Human-robot trust dynamics Publications from 2020-2025 reflect a progression from transportation modeling to cutting-edge AI-robotics integration. Work combines unsupervised learning techniques with real-world robotics applications, demonstrating expertise in both theoretical and applied computer science domains.