Find Academic Supervisors

Vladimír KubelkaView profile

Research Fellow

• Robotics
• Sensor Fusion
• Localization
• +3 more

Vladimír Kubelka is a robotics researcher specializing in sensor fusion , localization , and state estimation for mobile ground robots operating in harsh environments . He is currently a postdoc at the Robot Navigation and Perception Lab (RNP) at Örebro University , focusing on winter conditions and radar-based navigation . Previously, he was a postdoc fellow at Laval University's NORLAB , where he worked on DARPA Subterranean Challenge systems. Education: Ph.D. in Artificial Intelligence and Biocybernetics (2018), Czech Technical University in Prague (CTU) Ing. in Cybernetics and Robotics (Air and Space Systems) (2013), CTU Bc. in Electrical Engineering (Cybernetics and Measurement) (2011), CTU His research focuses on data fusion and localization algorithms for robots in adverse conditions including darkness, smoke, unstable terrain , and deep snow . He has contributed to radio propagation modeling , point cloud registration , and robust sensor integration . Recent publications highlight advancements in GNSS satellite visibility prediction , extreme motion lidar registration , and winter navigation systems . Collaborations include the CTU-CRAS-NORLAB team in the DARPA challenge. Scientific Awards: Best Robotic Vision Paper Award (2020) He has participated in EU-funded projects NIFTi and TRADR , focusing on real-world search and rescue deployments . His work addresses challenges like sensor outages and semi-structured environments .

Bikram KoiralaView profile

Researcher

• Machine Learning
• Deep Learning
• Radiative Transfer Modeling
• +7 more

Dr. Bikram Koirala is a Postdoctoral Researcher at the Vision Lab, University of Antwerp, Belgium. His work focuses on machine learning, deep learning, and hyperspectral image processing, supported by the Research Foundation – Flanders (FWO). Research areas include radiative transfer modeling, computer vision, and geomatics. Research Interests Machine learning and deep learning for spectral analysis Hyperspectral unmixing (linear and nonlinear models) Computer vision and remote sensing applications Radiative transfer modeling and Bayesian inference Geomatics and graph theory Publications & Data Dr. Koirala has published extensively on hyperspectral unmixing, including benchmarks for intimate mixtures, spectral-spatial attention networks, and nonlinear decomposition using Bézier surfaces. His work bridges algorithm design and environmental/material science applications. Labs & Teams Vision Lab, University of Antwerp

Lucy PaoView profile

Professor

• Control Systems
• Wind Energy Systems
• Sensor Fusion
• +6 more

Lucy Pao is a Professor and Palmer Endowed Chair in the Department of Electrical, Computer and Energy Engineering at the University of Colorado Boulder. She serves as Director of the Control Systems, Sensor Fusion, and Robotics Laboratory and is a Fellow at the Renewable and Sustainable Energy Institute (RASEI). Palmer Endowed Chair Professor Control Systems, Sensor Fusion, and Robotics Laboratory Director Courtesy Professor, Aerospace Engineering Sciences Richard and Joy Dorf Professor (2009-2014) Her research focuses on control systems for wind energy, multisensor fusion, haptic interfaces, and robotics. Recent work emphasizes floating offshore wind turbines, control co-design, and optimization of wind farms. Her 15 most recent publications address advanced control frameworks for wind turbines, including Lyapunov-based stability, multi-loop control tradeoffs, and haptic interface innovations. Key sub-fields include floating offshore wind, motion control, and sensor fusion. Fellow, Renewable and Sustainable Energy Institute (RASEI) Richard and Joy Dorf Professor (2009-2014) General Chair, 2013 American Control Conference Founding Scientific Director, Center for Research and Education in Wind (CREW)

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

Amel Dechemi is a Research Fellow in the Department of Computer Science, Electrical Engineering and Mathematical Sciences. Their work focuses on robotics applications in precision agriculture, assistive robotics, and computer vision for healthcare. Research spans autonomous systems for crop monitoring, infant action recognition, and data fusion techniques. Key research interests include robotic perception, agricultural automation, and lightweight neural networks for unconstrained environments. Their robotic systems address challenges like leaf retrieval, irrigation optimization, and infant motion analysis for pediatric rehabilitation. Publishing trends highlight contributions to IEEE Robotics & Automation Magazine and specialized journals. Ongoing projects aim to bridge robotics with sustainable agriculture and medical technology.

• Wireless Communication
• Signal Processing
• Multisensor Systems
• +3 more

Dr. Lu Yin was a Research Fellow at Lancaster University, focusing on interdisciplinary research at the intersection of wireless communication systems and biomedical data analysis. His work includes advancements in signal processing for aviation and healthcare applications. Research Interests: Dr. Yin’s primary areas of expertise lie in wireless communication , multisensor systems , and signal integration . His contributions to positioning-communication hybrid signals and cooperative localization algorithms highlight his technical depth in telecommunications. Additionally, he contributed to cardiovascular studies analyzing risk factors in myocardial infarction patients, showcasing his ability to bridge engineering and biomedical fields. Publications: His recent work includes future aviation communication systems (2025), multisensor localization frameworks (2021), and wireless positioning protocols (2019). These publications reflect a focus on optimizing signal efficiency and reliability across dynamic environments. Collaborations: As a co-author in datasets like the China Acute Myocardial Infarction registry analysis (2022), Dr. Yin demonstrated expertise in handling large-scale health data and interdisciplinary research.

Tiejun WangView profile

Associate Professor

• Remote Sensing
• Geospatial Ecology
• Wildlife Population Monitoring
• +6 more

Tiejun Wang is an Associate Professor in Remote Sensing and Geospatial Ecology at the University of Twente. He holds a PhD in Spatial Ecology from Wageningen University (2009) and an MSc in GIS and Remote Sensing from ITC (2003). His research focuses on advancing remote sensing and machine learning techniques to monitor wildlife populations, habitat dynamics, and environmental management. Collaborations include the University of Oxford and the Bavarian Forest National Park. He has supervised 19 PhD and 35 Master’s students, with current supervision of 6 PhD candidates. His work contributes to UN Sustainable Development Goals related to environmental conservation and sustainable resource use. Key research areas include satellite-based biodiversity monitoring, LiDAR applications in forest ecology, and the impact of climate change on ecosystems. His awards include the ITC Research Award (2019, 2016) and recognition as an outstanding reviewer for ISPRS journals. Media engagements highlight his work on giant pandas, African elephants, and eDNA biodiversity studies.

• Earth and Planetary Sciences
• Remote Sensing
• Optical Measuring Instruments
• +3 more

Bastian Siegmann is a Researcher in the Department of Water Resources and affiliated with the Digital Society Institute, focusing on remote sensing and environmental monitoring. His work spans Earth and Planetary Sciences, with a particular emphasis on optical measuring instruments and data quality in ecological studies. His research explores crop stress detection using unmanned aerial vehicles (UAVs), synthetic spatial scaling exercises, and Sentinel-2 satellite data analysis. Key methodologies include sensor synergies, preprocessing techniques, and sub-pixel resolution modeling for chlorophyll fluorescence. Recent projects involve UAV-based remote sensing, synthetic data validation, and satellite-derived metrics for agricultural monitoring. Collaborations span multiple institutions across Europe, focusing on precision agriculture and environmental data acquisition.

Manmeet SinghView profile

Research Fellow

• Climate Science
• AI/ML in Earth System Modeling
• Numerical Weather Prediction
• +3 more

Manmeet Singh is a Distinguished Postdoctoral Fellow at the Jackson School of Geosciences, University of Texas at Austin, and a former Staff Scientist at the Indian Institute of Tropical Meteorology (IITM). His expertise spans climate modeling, AI/ML applications in Earth System Science, and numerical weather prediction. He holds a PhD from IIT Bombay and a B.E. in Civil Engineering from Thapar University. Key roles include: Associate Editor, Journal of Indian Society of Remote Sensing Editorial Board Member, Discover Cities (Springer Nature) Member, IPCC CMIP7 ScenarioMIP Advisory Group Mentor, Geoscience Hackathon 2024 Research interests focus on: Climate solutions using mathematical models and deep learning Aerosol impacts on monsoons and land-atmosphere coupling High-resolution urban digital twins for flood/wildfire prediction Physics-inspired ML algorithms for weather downscaling Awards include the Best Poster Award (2024), Distinguished Postdoctoral Fellowship (2024–2026), and Prof DR Sikka Award (2020). He has contributed to IPCC AR6 reports through IITM-ESM simulations and developed novel AI tools like UT-MeteoGAN for weather forecasting. Grants and projects include: Development of high-resolution urban gridded datasets AI-driven cloudburst prediction systems Global building height mapping (UT-GLOBUS) Led initiatives such as the Workshop on Atmospheric and Urban Digital Twins and maintains active collaborations with NASA, Microsoft, and the Ministry of Earth Sciences (India).

Siamak KhorramView profile

Adjunct Professor

• Remote Sensing
• Geospatial Analysis
• Image Processing
• +1 more

Siamak Khorram is an Adjunct Professor in the Department of Environmental Science, Policy, & Management at the University of California, Berkeley. His research focuses on remote sensing, geospatial analysis, and computational methods for land cover classification and environmental monitoring. He developed foundational techniques in neural network-based image classification and correspondence analysis fusion, with applications spanning urban mapping, land use change detection, and water quality modeling. His publications demonstrate consistent innovation in remote sensing methodologies since the 1980s. Professor Khorram established the Computer Graphics Center (later Center for Earth Observation) at North Carolina State University and held leadership roles at the International Space University, including Dean and Vice President for Academic Programs. He currently serves on ISU's Board of Trustees.

• Greenhouse Gas Emissions
• Agricultural Soil Management
• Climate Change Mitigation
• +3 more

Stephanie Jones is a Research Fellow in the Crop and Soils department at Scotland's Rural College (SRUC). Her work focuses on agricultural soil management, greenhouse gas emissions, and sustainable land use. She has contributed to projects like the RESAS Strategic Research Programme and the Soils Programme Research Partnership. Her research spans topics such as nitrous oxide mitigation, methane dynamics, and carbon sequestration in grasslands. Jones has authored over 40 peer-reviewed publications and collaborates internationally on climate change mitigation strategies. Projects: RESAS 22-27: Agriculture Climate and Carbon (2022-2027) Soils Programme Research Partnership: Management for Soil Biology and Health (2017-2021) Research Interests: Her work integrates field measurements and modeling to understand soil-ecosystem interactions. Key areas include emissions from agricultural soils, grassland carbon dynamics, and the impacts of land-management practices on climate. Impacts: Her findings inform policy on agricultural emissions reduction and sustainable farming practices, with contributions to environmental policy frameworks.

Elias SymeonakisView profile

Associate Professor

• Remote Sensing
• GIS
• Environmental Science
• +5 more

Dr. Elias Symeonakis is a Reader in Earth Observation and GIS at Manchester Metropolitan University, specializing in Remote Sensing of Land Degradation. His work integrates Earth Observation technologies, AI, and open data to inform policy and achieve UN Sustainable Development Goals. He holds a PhD in Geography (King’s College London) and has held academic roles since 2001, including postdoctoral fellowships at CSIRO and the University of Valencia. Research & Impact: Focuses on land degradation in sub-Saharan Africa and Mediterranean regions. Projects include the EU-funded SAV-EO and PantEOn initiatives, and collaborations with UNDP on Lake Tanganyika sedimentation. His research informs policy at local and international levels, including contributions to UN meetings and intergovernmental reports. Teaching & Supervision: Coordinates courses like 'Earth Observation & GI Technologies' and supervises 5 current PhD students. Recent completions include studies on glacial volcanic interactions and habitat modeling. Post-doc researchers include EU Marie Curie Fellows Dr. Eva Arnau-Rosalén and Dr. Christina Karakizi. Awards & Grants: Recipient of Marie Curie and Leverhulme Fellowships, plus EU H2020 grants. His work has been featured in BBC and TRT World reports on wildfires, climate migration, and biodiversity. Labs & Affiliations: Leads the Remote Sensing Research Group (www.remote-sensing-mmu.org). Active in global initiatives like the UN’s Land Degradation Neutrality framework. Office: E410a John Dalton Building; available Tues 1-3pm and Wed 1-2pm.

Seung-Kyum ChoiView profile

Associate Professor

• Additive Manufacturing
• Uncertainty Quantification
• Robust Design Optimization
• +4 more

Dr. Seung-Kyum Choi is an Associate Professor at the School of Mechanical Engineering, Georgia Institute of Technology. He joined Georgia Tech in 2006 and currently directs the Center for Additive Manufacturing Systems (CAMS), focusing on advancing additive manufacturing technologies and educational programs. His research emphasizes robust design optimization, uncertainty quantification, and probabilistic mechanics applied to engineered systems, including additive manufacturing, metamaterials, and aerospace components. Education: Ph.D., Mechanical Engineering, Wright State University (2006) M.S., Mechanical Engineering, Ajou University, South Korea (2001) B.S., Mechanical Engineering, Ajou University, South Korea (1996) Research interests span additive manufacturing, structural reliability, and multidisciplinary design optimization. He develops simulation tools for uncertainty management in complex systems, such as 3D-printed lattice structures and aerospace components. His work integrates machine learning, meta-learning, and surrogate modeling to enhance decision-making in engineering design. Notable awards include the 2009 Lockheed Martin Aeronautics Dean’s Award for Teaching Excellence. He contributes to journals like International Journal of Materials and Product Technology and Structure & Infrastructure Engineering , and actively mentors students through CAMS and his research labs.

Mary HenryView profile

Associate Professor

• Remote Sensing
• GIS
• Landscape Ecology
• +3 more

Mary Henry is an Associate Professor in the Department of Geography at Miami University, affiliated with the Geospatial Analysis Center. She holds a B.A. in Geography & Environmental Studies from the University of California, Santa Barbara (1992), an M.A. in Geography from San Diego State University (1996), and a Ph.D. in Geography from the University of Arizona (2002). Her research focuses on remote sensing applications, GIS techniques, and landscape ecology, with a particular emphasis on environmental monitoring, wildfire patterns, and invasive species dynamics. Dr. Henry’s work involves satellite and multispectral data analysis to study land-use changes, fire history reconstruction, and vegetation dynamics. She has contributed to studies of burn scars in Kenya, American beech distribution in Ohio, and the impact of agricultural expansion on Midwestern ecosystems. Her methods often integrate optical and microwave remote sensing with GIS modeling to address ecological and conservation challenges. Her publications highlight advancements in detecting invasive shrubs using remote sensing, modeling dissolved organic carbon in alpine lakes, and analyzing wildfire occurrence in diverse landscapes. She has mentored graduate students who have authored or co-authored several of her papers, particularly in fire ecology and GIS applications. Mary Henry has no explicitly listed scientific awards but has maintained a prolific publication record since the late 1990s, demonstrating sustained contributions to geospatial science and environmental research. Her teaching includes courses on physical environments, natural hazards, landscape ecology, and remote sensing techniques.

• Robotics
• Agricultural Robotics
• Adaptive Control Systems
• +6 more

Antonio Candea Leite is an Associate Professor at the Department of Mechanical Engineering and Technology Management, Norwegian University of Life Sciences (NMBU). His research focuses on adaptive and robust control systems, visual servoing, robot manipulators, and agricultural robotics applications. His work emphasizes: Development of autonomous navigation systems for agricultural robots Integration of computer vision for precision agriculture tasks Control strategies for uncertain robotic systems Automation in food quality measurement and pest management Advanced sensor integration for manufacturing processes Recent research trends show strong emphasis on: CNN-based crop row detection for autonomous navigation (2024) Human-robot collaboration frameworks for fruit picking (2024) Robotics solutions for fatty acid measurement in food production (2023-2022) Precision pest control systems using smart automation (2023) No scientific awards or grants are explicitly listed in the provided information. He is actively involved in advising and developing robotic platforms for agricultural and industrial applications without specific student names mentioned here.

• Machine Learning
• Manufacturing Systems
• Production Scheduling
• +4 more

Sohyung Cho is a Lecturer in the Department of Industrial Engineering at the University of Miami's College of Engineering. His research focuses on integrating machine learning techniques with manufacturing processes to improve quality control, tool monitoring, and operational efficiency. Key areas include SVM-based algorithms for geometric tolerance analysis, multisensor fusion systems for real-time tool condition assessment, and control-theoretic approaches to production scheduling. Cho's work emphasizes predictive modeling in manufacturing contexts, such as tool wear progression under semi-dry/dry conditions and capacity investment decisions under exchange rate uncertainty. He collaborates closely with industry stakeholders to develop solutions that reduce production costs and enhance consistency in manufacturing inspections. Notable projects include web-based open-source systems for cylindricity evaluation and ensemble machine learning frameworks for abnormality detection in milling processes. His research has been applied to improve the robustness of manufacturing systems through quantitative complexity measures and sensor data integration. Computational experiments highlight his focus on algorithm performance metrics like accuracy and CPU efficiency. While no explicit awards are listed, Cho's contributions have been referenced in patents and widely read on platforms like Mendeley.