Find Academic Supervisors

• Functional Biogeography
• Plant Ecology
• Biodiversity Research
• +3 more

Jens Kattge is an Independent Research Group Leader at the Max Planck Institute for Biogeochemistry in Jena, Germany, specializing in Functional Biogeography. His career spans roles from Research Associate (2002–2005) to Senior Scientist (2010–2012) and current group leadership since 2013. He focuses on plant functional traits, their role in terrestrial ecosystems, and their integration into Earth system models. He coordinates the global TRY Initiative , a plant trait database for biodiversity research, and contributes to the German Centre for Integrative Biodiversity Research (iDiv). His research spans functional biogeography, trait-climate relationships, ecosystem modeling, and biodiversity-ecosystem functioning. Key projects include developing the rtry R package for trait data preprocessing and analyzing global datasets like CoRRE Trait Data and sPlotOpen. Recent publications investigate leaf cuticle thickness, drought responses in tree species, trait controls on biomass, global biodiversity patterns, and the integration of citizen science with Earth observation data. His work emphasizes trait data representativeness and future-proofing ecological science.

Matias E. MargulisView profile

Associate Professor

• Global Governance of Food Security
• Agricultural Trade Liberalization
• Large-Scale Land Acquisitions
• +4 more

Matias E. Margulis is an Associate Professor at the University of British Columbia, jointly appointed between the Faculty of Land and Food Systems and the School of Public Policy and Global Affairs. His research explores the intersection of global governance, food security, and international trade, with a focus on regime complexity and power dynamics in multilateral institutions. Ph.D. in International Relations, McMaster University (2012) M.A. in Public Policy and Politics, University of Toronto (2003) B.A. (Honours) in Economics and Political Science, University of Toronto (2000) Postdoctoral Fellow, Max Planck Institute (2012-2013) His academic work investigates how food insecurity stems from human-made social, economic, and political systems, analyzing the role of international organizations like the WTO, UN, and EU in shaping trade and food policy. Recent publications examine regime entanglements, human rights in trade negotiations, and climate crisis impacts on food governance. Matias has received multiple accolades, including the Distinguished Academic Award (Early in Career, 2014) and the Cadieux-Léger Fellowship (2010-2011). He previously held academic positions at the University of Edinburgh, University of Stirling, and Max Planck Institute, and served as a Canadian representative to the WTO, OECD, and FAO. His expertise extends to advising the UN Special Rapporteur on the Right to Food and consulting for NGOs and the Brookings Institution.

• Climate Science
• Hydrology
• Biogeochemistry
• +3 more

Prof. Dr. René Orth is a Professor at the University of Freiburg, leading the Modelling of Biogeochemical Systems group. He focuses on land-atmosphere interactions, climate change impacts on vegetation and water systems, and Earth system modeling, with a particular emphasis on soil moisture-vegetation-climate feedbacks and extreme weather events. Education: MSc and BSc in Atmospheric and Climate Science from ETH Zurich (2010), BSc in Earth Science (2008), Erasmus exchange at University of Leeds. His research integrates biophysical and societal perspectives to improve climate projections and hydroclimatic forecasts. Key projects include the EU H2020 MYRIAD multi-hazard framework and the DFG Emmy Noether Group on Hydrology-Biosphere-Climate Interactions. His publications highlight vegetation responses to climate change, drought impacts, and the robustness of modeling approaches. He has supervised over 15 BSc, MSc, and PhD theses and actively reviews manuscripts and proposals for leading journals and funding agencies. Scientific awards include the ETH Excellence Scholarship, SNF Advanced Postdoc Mobility Grant, and DFG Emmy Noether Group Grant. René Orth is affiliated with the Max Planck Institute for Biogeochemistry (Jena), Die Junge Akademie, and the institute's graduate school (IMPRS-gBGC), contributing to science-policy interfaces and advancing climate science through interdisciplinary research.

Matthew K. TamView profile

Associate Professor

• continuous optimisation and its applications
• monotone operator theory
• variational and convex analysis
• +3 more

Matthew K. Tam is an Associate Professor at the School of Mathematics and Statistics, The University of Melbourne, specializing in Operations Research. He is also an investigator at the Melbourne Centre for Data Science and an associate investigator in the ARC Training Centre OPTIMA. PhD in Mathematics (2016) from University of Newcastle under Jonathan Borwein Postdoctoral research at University of Göttingen with RTG-2088 and Alexander von Humboldt Foundation Junior Professor at University of Göttingen (2017-2020) His research focuses on continuous optimization, monotone operator theory, and variational analysis, with applications in wavelet construction and inverse problems. Key trends include distributed algorithms, resolvent splitting, and convergence analysis for feasibility problems. Discovery Early Career Researcher Award (DECRA) Alexander von Humboldt Fellowship He collaborates with institutions like ANZIAM, Springer, and IEEE, with publications spanning mathematical optimization, harmonic analysis, and computational mathematics. His work emphasizes algorithmic design for complex data systems and real-world applications in imaging and industrial modeling.

Florian BociortView profile

Assistant Professor

• Optical System Design
• Gradient-Index Optics
• Artificial Intelligence in Lens Design
• +3 more

Florian Bociort is an Assistant Professor at the Optics Research Group , Delft University of Technology (Faculty of Applied Sciences). He holds a PhD in Physics from TU Berlin (1994) and has dedicated his career to optical system design, gradient-index optics, and computational methods in lens design. Research Interests Bociort’s research focuses on design landscapes of optical systems , where he pioneered the use of saddle points to escape local minima in optimization. His work spans Gradient-index optics (conversion of homogeneous lenses to GRIN media) Artificial intelligence in lens design Optics education (simulation-driven learning) Academic Contributions He has supervised multiple PhD theses on topics like: A. M. Boyd (2025): Generalized gradient-index lens optimization Z. Hou (2023): Systematic lens design searches Y. Shao (2021): Imaging coherence and optimization M. Strauch (2020): Tunable optics M. Mout (2019): Ray-based diffraction simulation Recent Publications His 2025-2018 publications show a trajectory from classical optical design to modern computational approaches, including simulation-driven education, gradient-index conversions, and high-NA diffraction modeling. The 2024 paraxial reconstruction and 2025 simulation-education articles exemplify this evolution. Patents & Expertise He co-invented two ASML-related patents in lithographic design and served as expert witness in the 2018 ASML-Nikon patent lawsuit. His personal webpage details his networks of local minima and fractal basins in optimization.

Sylvain LefebvreView profile

Researcher

• Computer Graphics
• Texture Synthesis
• Additive Manufacturing
• +4 more

Sylvain Lefebvre is a permanent researcher at INRIA (Institut National de Recherche en Informatique et en Automatique) in France, where he leads the MFX research team since 2018. Previously, he was part of the ALICE group at INRIA Nancy (2009-2018) and the REVES team in Sophia Antipolis (2006-2009). His career includes a postdoctoral position at Microsoft Research Seattle (2005) following his PhD at INRIA Rhones-Alpes under Fabrice Neyret. His educational background includes a PhD in Computer Graphics from Université Joseph Fourier (Grenoble) in 2005, preceded by a Master in Computer Graphics from INP Grenoble in 2001. His habilitation thesis focused on Runtime Texture Synthesis. Lefebvre's research centers on simplifying content creation for highly detailed patterns, structures, and shapes with applications spanning Computer Graphics to additive manufacturing. He develops fast, controllable by-example synthesis approaches that generate content while enforcing user-specified constraints. His work addresses computational challenges through novel data structures and algorithms optimized for GPUs and FPGAs, including his Silice programming language. The ERC-funded ShapeForge project (2012-2017) advanced shape generation for 3D printing, leading to the IceSL software for digital modeling and fabrication. Analysis of his 15 most recent publications reveals a strong focus on additive manufacturing optimization, with recurring themes in structural integrity, material efficiency, and geometric algorithms. His work bridges computer graphics theory with practical fabrication constraints, particularly in microstructure design, slicing techniques, and mechanical metamaterials. The interdisciplinary nature spans computer science, materials engineering, and robotics. EUROGRAPHICS Young Researcher Award (2010) ERC Starting Grant for ShapeForge project (2012) Lefebvre has advised over 25 PhD students and interns including Marco Freire, Thibault Tricard, and Jimmy Etienne. His ShapeForge project received significant ERC funding, supporting research in computational fabrication. He serves on numerous program committees including SIGGRAPH, Eurographics, and SIGGRAPH Asia, reflecting his leadership in the computer graphics community. As leader of the MFX team since 2018, Lefebvre directs research in computational fabrication, focusing on IceSL software development for 3D printing workflows. The team integrates computer graphics techniques with manufacturing constraints, developing tools that simplify complex object design and fabrication while addressing real-world challenges in material usage and structural integrity.

• Entrepreneurial Resilience
• Family Business
• Strategic Flexibility
• +7 more

Danilo Brozovic is a Senior Lecturer in Business Administration at the University of Skövde, specifically within the School of Business and the Department of Commerce and Entrepreneurship. He maintains his office in Room E219 and serves as a course coordinator for multiple undergraduate and graduate business courses, each carrying 7.5 credits. His academic work spans teaching, research, and project leadership within the business administration domain. Brozovic's research interests focus on entrepreneurial resilience, particularly in family businesses and small-to-medium enterprises (SMEs), strategic flexibility in business growth, value creation processes, and service ecosystems. He has extensively examined organizational resilience during crises, including the COVID-19 pandemic's impact on retail and tourism sectors. His work also explores sustainability in business models, food tourism, societal collapse scenarios, and the evolution of service systems. Brozovic employs diverse methodologies including literature reviews, scenario-based analyses, and empirical studies to investigate how organizations adapt to multiple crises and changing market conditions. His publication record demonstrates a strong trend toward interdisciplinary research connecting business administration with futures studies, sustainability science, and crisis management. Recent work shows increasing focus on entrepreneurial resilience in the face of multiple concurrent crises, with projects examining how SMEs navigate complex challenges. His research bridges theoretical frameworks like service-dominant logic with practical business applications, particularly in tourism, retail, and family business contexts. Brozovic leads the research project 'Entrepreneurial resilience in small and medium-sized enterprises in the face of multiple crises' (July 2023 - June 2025), which develops knowledge about how SMEs manage multiple concurrent crises. His collaborative approach is evident in numerous co-authored publications with researchers like Börje Boers, Marco Tregua, and Hiroaki Saito across various business disciplines.

• Control Systems
• Electric Vehicles
• Hybrid Electric Vehicles
• +4 more

Nikolce Murgovski is an Assistant Professor at Chalmers University of Technology, specializing in Mechatronics . He focuses on electric and hybrid vehicle energy management , autonomous driving systems , and optimization algorithms for powertrain design. His work bridges control theory , battery technology , and transport electrification . Current projects include CHARGE (2023–2026) for charging and trip planning , and EcoPilot (2022–2026) for energy-efficient autopilot development. Collaborates with institutions like Volvo Cars , Swedish Electromobility Centre , and VINNOVA on autonomous vehicle control and thermal energy systems . His recent publications emphasize convex optimization , eco-driving strategies , and collision avoidance in complex environments. He has contributed to tools like CONES for electromobility studies and has led research on hybrid powertrains and predictive energy management .

Andreas DuitView profile

Professor

• Comparative Environmental Politics
• Climate Policy
• Environmental Governance
• +4 more

Andreas Duit is a Professor of Political Science at Stockholm University and also serves as a Guest Professor at the Political Science Unit, Luleå University of Technology. His academic work focuses on comparative environmental politics with special emphasis on the role of the state in addressing environmental challenges. Department of Political Science, Stockholm University Guest Professor, Political Science Unit, Luleå University of Technology Former visiting scholar at the Workshop in Political Theory and Policy Analysis at Indiana University, Bloomington Former visiting scholar at the Department of Political Science, University of Washington Professor Duit's research centers on comparative environmental politics, particularly examining how states address environmental challenges. His work employs quantitative methods and cross-national panel data analysis, with additional engagement in complex system analysis and survey research. His primary research interests include climate policy, environmental governance, sustainability transitions, and the relationship between welfare states and environmental policy. Analysis of Professor Duit's recent publications reveals a strong focus on climate governance mechanisms, environmental state development, and the interplay between political institutions and environmental outcomes. His research spans multiple analytical levels from international climate agreements to domestic policy implementation, with particular attention to how different governance structures affect environmental performance across countries. GREENTRANS: The Great Green Transformation – Politics, Markets and Civil Society in the Anthropocene (funded by Marcus and Marianne Wallenberg Foundation) THERAPY: The Electoral Ramifications of Environmental Policy (funded by FORMAS) GLEAN: A Global Survey of Learning, Participation and Ecosystem Management in Biosphere Reserves GRACE: Governing the Anthropocene – Environmental Policy and Outcomes in a Comparative Perspective Professor Duit has secured significant research funding from major Swedish research organizations including FORMAS, MISTRA, the Swedish Research Council, and the Marcus and Marianne Wallenberg Foundation. His research portfolio demonstrates a consistent focus on understanding the political dimensions of environmental challenges and identifying effective governance approaches for sustainability transitions.

• Mixed Methods Research
• Education Science
• Research Methodology
• +2 more

Judith Schoonenboom is a Professor at the University of Vienna and Deputy Head of the Department of Education. She teaches courses in quantitative and interpretive methodologies, research design, and PhD/master's thesis supervision, including seminars like 'Methodology and Research Design' and 'Quantitative Methodologies in Education Science'. Her academic responsibilities reflect a focus on advanced research methodologies in educational contexts. Schoonenboom's research centers on mixed methods and multimethod approaches in education science, emphasizing methodological innovation, data integration, and theoretical development. Key interests include the interplay between qualitative and quantitative research, design patterns in mixed methods, and strategies for enhancing inferential rigor in social science studies. Her work bridges epistemological frameworks with practical research applications. Her scholarly publications demonstrate a consistent focus on advancing mixed methods research, particularly through innovations in design, integration techniques, and theoretical reflection. Recent works explore causal inference in qualitative research, visualization of methodological interactions, and performative approaches, highlighting trends toward interdisciplinary synthesis and practical methodology refinement.

Zukui LiView profile

Professor

• Mathematical Optimization
• Machine Learning
• Process Systems Engineering
• +4 more

Zukui Li is a Professor in the Department of Chemical and Materials Engineering at the University of Alberta's Faculty of Engineering, where he leads a research group focused on mathematical optimization, machine learning, and process systems engineering. His work spans oil sands extraction, steel production, biomedical applications, and advanced optimization methods. Education: Ph.D. in Chemical Engineering, Rutgers University (2010) M.Sc. in Control Theory and Control Engineering, University of Science and Technology of China (2005) B.Sc. in Automatic Control, University of Science and Technology of China (2002) Postdoctoral Training: Princeton University (2010-2012) Research Focus: Dr. Li's research integrates mathematical optimization and machine learning for complex process systems. His primary areas include: Advanced optimization techniques (robust, stochastic, and distributionally robust optimization) Machine learning applications in process monitoring and biomedical systems Industrial applications in energy, manufacturing, and resource extraction Specific innovations include physics-informed ML for anemia treatment, adaptive optimization for steel production, and distributionally robust methods for uncertainty management. Publication Trends (2019-2023): Recent articles demonstrate a strong focus on uncertainty-aware optimization methods, with increasing integration of machine learning techniques. Dominant themes include distributionally robust optimization, adaptive decision-making under uncertainty, neural network approximations for complex constraints, and applications in industrial process control and biomedical systems. Theoretical advancements are consistently coupled with practical implementations in energy and manufacturing sectors. Research Group: Leads an active team developing optimization frameworks and machine learning solutions for process engineering challenges. Group website: Dr. Zukui Li's Research Group

Le-Nam TranView profile

Researcher

• Wireless Communications
• 5G/6G Network Optimization
• Signal Processing
• +4 more

Dr. Le-Nam Tran is a researcher at the UCD School of Electrical & Electronic Engineering , University College Dublin. His work focuses on optimizing the last hop of 5G/6G wireless networks through mathematical programming, with emphasis on energy efficiency, interference management, and security against eavesdropping. Develops low-cost, low-complexity transmission techniques Projects supported by Science Foundation Ireland Career Development Award Author of over 80 peer-reviewed publications Research Keywords: Wireless Communications Network Security Signal Processing Energy-Efficient Systems Beamforming Optimization Interference Mitigation

Joris M. MooijView profile

Professor

• Causality
• Causal Modeling
• Causal Discovery
• +5 more

Joris M. Mooij is a Professor of Mathematical Statistics at the Korteweg-De Vries Institute of the University of Amsterdam, Netherlands. His research focuses on causality, spanning causal modeling, discovery, and inference with applications in biology, medicine, fairness, and business analytics. He combines mathematical modeling with statistical and algorithmic approaches in his work. Dr. Mooij received his PhD with honors from Radboud University Nijmegen in 2007, focusing on approximate inference in graphical models. After postdoctoral work at the Max Planck Institute for Biological Cybernetics in Tübingen, Germany, he obtained an NWO VENI grant in 2011 for further postdoctoral research at Radboud University. He became Assistant Professor at the University of Amsterdam's Informatics Institute in 2013, was promoted to Associate Professor in 2017, and became a full Professor of Mathematical Statistics in 2020. Dr. Mooij's research centers on causal inference, with particular expertise in structural causal models, cyclic causal systems, and causal discovery algorithms. His work addresses fundamental questions about when causal relationships can be identified from observational data and how to develop robust causal discovery methods that work in complex real-world settings with latent variables, cycles, and selection bias. He has made significant contributions to understanding the limitations of existing causal discovery approaches and developing new methods that overcome these limitations. His research group organizes the Amsterdam Causality Meeting series and develops theoretical frameworks for causal modeling that encompass both acyclic and cyclic systems. Dr. Mooij has collaborated extensively on applications of causal methods in biological systems, including protein signaling networks and gene expression data. The group's recent work explores performative predictions, causal domain adaptation, and robust causal discovery methods that account for selection bias and missing data. Dr. Mooij has received numerous awards for his research, including: Best paper award at UAI for "Establishing Markov equivalence in cyclic directed graphs" IEEE Geoscience and Remote Sensing Society 2011 Letters Prize Paper Award ICML Test of Time Honorable Mention Best student paper award at UAI 2010 He has secured competitive research funding through an NWO VENI grant, NWO VIDI grant, and an ERC Starting Grant, which supported the establishment of his research group consisting of 3 PhD students and 3 postdocs focused entirely on causality. Dr. Mooij has supervised several PhD students, including Tineke Blom, whose work on "Causality and Independence in Perfectly Adapted Dynamical Systems" significantly influenced his thinking about causality in complex systems. He has co-taught the MasterMath course on Causality and published lecture notes titled "A Mathematical Introduction to Causality." His research continues to push the boundaries of causal inference methodology and its applications across diverse scientific domains.

Qi YuView profile

Professor

• Artificial Intelligence
• Machine Learning
• Data Science
• +6 more

Qi Yu is a Professor in the School of Information at the Golisano College of Computing and Information Sciences at Rochester Institute of Technology (RIT). He serves as the Graduate Program Director and directs the Machine Learning and Data Intensive Computing Lab. His research focuses on machine learning, deep learning, and data-driven knowledge discovery, particularly in knowledge-rich domains like medicine and bioinformatics. He holds a B.E. from Zhejiang University, an M.E. from the National University of Singapore, and a Ph.D. from Virginia Tech. His work emphasizes interpretable models, multimodal data fusion, and human-in-the-loop learning. He has secured significant grants, including a $500K NSF award and a $1.6M ONR grant, supporting projects on Bayesian learning frameworks and decision-making under uncertainty. His lab actively explores active learning, few-shot learning, and uncertainty quantification. He advises a vibrant group of Ph.D. and MS students and teaches courses such as Data-Driven Knowledge Discovery and Thesis/Project Capstones. Education: B.E., Electrical Engineering, Zhejiang University (2001) M.E., Computer Engineering, National University of Singapore (2003) Ph.D., Computer Science, Virginia Tech (2008) Research Interests: Machine Learning, Deep Learning, Vision-Language Models, Uncertainty Quantification, Active Learning, Multimodal Data Fusion, Bayesian Methods, and Applications in Healthcare and Cybersecurity. Recent Work Trends: His articles emphasize label-efficient learning, interactive systems, and applying ML to complex domains like medical imaging and anomaly detection. Notable projects include Bayesian learning for dynamic decision-making and evidential optimization for robust models. Awards/Grants: NSF IIS Award ($500K, 2018–2023); DoD/ONR Award ($1.6M, 2018–2023); multiple conference recognitions (NeurIPS, ICML, CVPR). Advising spans over 20 students, many securing roles at Amazon, Samsung, and academia. Labs/Teams: Leads the Mining Lab, collaborating on interdisciplinary projects with domain experts in medicine, cybersecurity, and material science.

Haibo YangView profile

Assistant Professor

• Distributed Learning
• Federated Learning
• Optimization Algorithms
• +3 more

Haibo Yang is an Assistant Professor in the Department of Computing and Information Sciences at Rochester Institute of Technology's Golisano College of Computing and Information Sciences. He earned his Ph.D. in Electrical and Computer Engineering from The Ohio State University under the supervision of Prof. Jia (Kevin) Liu. Rochester Institute of Technology , Golisano College of Computing and Information Sciences Ohio State University , Ph.D. in Electrical and Computer Engineering His research focuses on distributed and federated learning systems, examining how statistical and system variability affect algorithm performance under constraints like privacy and communication limitations. Key areas include optimization algorithms, communication-efficient frameworks, Byzantine robustness, and multi-modal adversarial attacks. He is actively involved in developing theoretically grounded solutions for scalable and intelligent distributed learning. Recent publications highlight advancements in multi-objective reinforcement learning, zeroth-order federated optimization, and robustness against heterogeneous client participation. His work has appeared in top venues like UAI, IJCAI, ICLR, AAAI, NDSS, ACM CCS-LAMPS, and ACM MobiHoc, with notable acceptance rates (e.g., 19.3% for IJCAI 2025). Current projects investigate exact convergence mechanisms and adaptive weighting strategies. Dr. Yang received the RIT AI Seed Funding and GWBC Award in February 2024. He supervises funded Ph.D. students and teaches advanced machine learning topics, including CSCI-635: Introduction to Machine Learning.