Find Academic Supervisors

• Quantitative Finance
• Financial Mathematics
• Structured Products
• +7 more

Holger Graf is a Professor at the Faculty of Business Administration and International Finance, Nürtingen-Geislingen University of Applied Sciences. His research focuses on financial mathematics, quantitative methods, and risk management, with prior roles at Munich University of Applied Sciences and Goldman Sachs. He earned his dissertation at the Technical University of Munich and conducted research at the Oxford-Man Institute of Quantitative Finance. Research Interests His work spans Arbitrage-free financial modeling using fractional processes Structured product transparency and risk evaluation Portfolio optimization under expected utility measures Implied volatility and correlation dynamics Regime-switching models for global equity indices Applications of long-memory processes in credit risk Selected Publications His recent publications incorporate trends in quantitative finance, including fractional Lévy-driven models, volatility forecasting, and regime-switching copulas. Notably, he explores intersections between mathematical statistics and financial engineering, though one outlier article addresses veterinary parasitology.

Gernot MüllerView profile

Professor

• Artificial Intelligence
• Bayesian Statistics
• Computational Econometrics
• +5 more

Prof. Dr. Gernot Müller is a Full Professor of Computational Statistics and Data Analysis at the University of Augsburg since 2013. Previously, he held academic positions at the University of Oldenburg (2013), TU Munich (2006-2013), and Australian National University (2002-2006). His research spans Artificial Intelligence , Bayesian Statistics , Computational Econometrics , and Medical Statistics , with particular focus on energy economics and biostatistical applications . PhD: Technische Universität München (2004) Habilitation: Technische Universität München (2010) Bachelor's: Julius-Maximilians-Universität Würzburg (2000) His research interests include: Bayesian hierarchical modeling for energy markets Stochastic volatility models in financial time series Machine learning applications in medical imaging Epidemiological analysis of pandemic impacts Statistical modeling of renewable energy integration Deep learning for cancer prognosis Recent publications (2024-2025) address: Electricity price forecasting with renewable energy integration Cryptocurrency market dynamics via stable processes Medical imaging analysis for wound healing Lymphocyte subset modeling in cancer patients Neurosurgical outcome assessment tools Stochastic models for photovoltaic power generation Scientific memberships include: Sigma Xi - The Scientific Research Honor Society (2022) International Statistical Institute (ISI) Bernoulli Society German University Association (DHV) He also contributes to public science communication through lectures on data science, climate change, and forensic statistics. His technical expertise covers generalized linear models, machine learning methods, and stochastic energy market modeling.

Katharina LoiblView profile

Professor

• Instructional Design
• Learning from Errors
• Problem-Solving Prior to Instruction (PS-I)
• +2 more

Prof. Dr. Katharina Loibl is a full Professor (W3) for Interdisciplinary Teaching and Learning Research at the Institute of Psychology, University of Education Freiburg. She serves as the Executive Director of the Freiburg Advanced Center of Education (FACE) and co-founder of the Center of Interdisciplinary Research on Digital Education (CIRDE). Her research focuses on instructional design, learning from errors, problem-solving prior to instruction (PS-I), diagnostic reasoning in teachers, and Bayesian cognition in educational settings. University of Education Freiburg – Department of Interdisciplinary Teaching and Learning Research Co-leader of DFG-funded projects on adaptive instruction and digital education Dr. Loibl’s research explores how pre-instructional problem-solving phases (PS-I) enhance conceptual understanding and transfer. She investigates cognitive processes in teacher diagnostics, error processing in mathematics and physical education, and adaptive learning environments using digital tools. Her work bridges cognitive science and educational practice, particularly in mathematics and motor skills development. Recent publications emphasize PS-I frameworks (CID model), Bayesian reasoning in teacher diagnostics, and digital education mechanisms. Awards include the ISLS Early Career Award (2020/21) and multiple DFG grants. She supervises theses on PS-I applications in physical education and digital cognition, though specific student names are not listed. Early Career Award (ISLS) 2020/21 Wilhelm-Hollenberg Dissertationspreis 2014 Best Student Paper Award (CSCL) 2013 Nachwuchswissenschaftlerpreis (PAEPS) 2011 Dr. Loibl leads collaborative projects like the DFG network on PS-I mechanisms and the Baden-Württemberg-funded Di.ge.LL Kolleg for digital didactics. She co-ordinates the Freiburg Advanced Center of Education (FACE) and integrates data literacy through initiatives like the Future Teaching Lab.

Markus CasperView profile

Professor

• Hydrology
• Physical Geography
• Water Balance Modeling
• +5 more

Markus Casper is a full professor at the University of Trier , holding a position in Physical Geography within Faculty VI . His career spans from being a Junior Professor (2003-2009) to a Universitätsprofessor (W2) since 2009. He has been involved in significant roles such as member of the University of Trier Senate, the DFG-funded Modelbildung und Simulation working group, and leadership in the German Hydrological Society. Education: Diplom in Geoökologie (University of Karlsruhe, 1995), Dr.-Ing. (University of Karlsruhe, 2002). Research Interests: Dr. Casper’s work focuses on hydrological modeling , catchment classification , and climate change impacts on water balance . Key projects include: DFG-funded mesoscale hydrological modeling with remote sensing data assimilation (2019-2022) SOFI : Smart soil information for farmers (BMEL-funded, 2019-2022) Catchment regionalization with Self-Organizing Maps (SOM) (DFG, 2012-2017) His research integrates groundwater-surface water interactions , soil erosion , and remote sensing for process identification. Scientific Trends: His recent articles emphasize multi-criteria model evaluation , machine learning (SOM) in hydrology , and uncertainty quantification in water balance simulations. Themes include climate nonstationarity , pedotransfer functions , and soil-vegetation-atmosphere modeling . Advising: He has supervised numerous doctoral students, including Gayane Grigoryan (climate impacts on water balance), Philipp Reiter (hydrological modeling), and Hadis Mohajerani (hydrological processes). Completed works address flood prediction , SVAT modeling , and land use sustainability . Labs & Teams: He leads the Modelbildung und Simulation working group at the University of Trier and collaborates with institutions like the University of Leipzig, FH Trier, and LUWG Mainz. His team utilizes GIS pools , hydrological test basins , and field laboratories for runoff and erosion studies.

Hadis MohajeraniView profile

Researcher

• Hydrology
• Water Resources Management
• Hydrological Modeling
• +2 more

Dr. Hadis Mohajerani is a Researcher at the Department of Physical Geography, Faculty VI, University of Trier. Her work bridges hydrology, environmental modeling, and resource management, with a focus on developing analytical frameworks for water systems under uncertainty. Research Focus: Her expertise spans: Hydrological process modeling using pedotransfer functions and water balance approaches Bayesian decision networks for groundwater management in data-scarce regions Multi-criteria evaluation methods for environmental parameterization Climate-water interactions and sustainability challenges Publication Trends: Recent work (2017-2021) demonstrates consistent focus on uncertainty quantification in hydrology, with methodological contributions to model parameterization, decision-making under data constraints, and integrated water cycle analysis. Awards & Recognition: No scientific awards are documented in available materials. Academic Contributions: She collaborates within the Physical Geography research team but no specific advising roles, grants, or lab leadership details are provided.

Christian MöllmannView profile

Professor

• Marine Ecosystem Dynamics
• Climate Change Impact on Fisheries
• Ecosystem-based Management
• +3 more

Christian Möllmann is a Professor at the University of Hamburg within the Faculty of Mathematics, Computer Science and Natural Sciences , specifically in the Department of Biology and its Institute of Marine Ecosystem and Fisheries Sciences . His research focuses on climate and fisheries impacts on marine ecosystems, with a transdisciplinary approach integrating scientific and non-scientific stakeholders. 1989-1996: University of Giessen/Kiel (Biology, Fisheries Biology, Marine Zoology) 1996: M.Sc. thesis on zooplankton consumption by herring/sprat 2002: Ph.D. on mesozooplankton dynamics in the Baltic Sea Research areas include: Climate change effects on marine ecosystems Fisheries management and trophic dynamics Gill microbiota responses to environmental stress Adaptive capacity of social-ecological systems Ecosystem-based marine policy frameworks Recent publications analyze: Estuarine food web restructuring under climate change Trophic plasticity in eutrophic environments Bayesian modeling of fisheries adaptive capacity Multi-omics integration for stressor detection Current projects funded by: BMBF : SpaCeParti, balt_ADAPT, SeaUseTip, marEEshift City of Hamburg : AuTagBeoFisch (autonomous underwater monitoring) EU Horizon 2020 : COMFORT, MISSION ATLANTIC DFG : RTG 2530 on biota-mediated carbon cycling His work emphasizes transdisciplinary collaboration, combining natural sciences with socio-economic factors to develop sustainable marine management strategies.

Jacob M. GravingView profile

Researcher

• Animal Behavior
• Computer Vision
• Deep Learning
• +5 more

Jacob M. Graving is a Research Scientist at the Max Planck Institute of Animal Behavior, working in the Advanced Research Technology (ART) Unit within Central Scientific Services. Based in Konstanz, Germany, he focuses on developing advanced computational methods for measuring and analyzing animal behavior in both laboratory and field environments. Dr. Graving's research spans the intersection of artificial intelligence, data science, and animal behavior. His work combines computer vision, deep learning, and Bayesian statistical methods to create scalable, interpretable tools for behavioral analysis. He collaborates extensively with neuroscientists, biologists, physicists, and engineers to develop end-to-end pipelines from experimental design to computational analysis. A central theme in his research is building AI systems that go beyond prediction to test hypotheses and reveal scientific mechanisms. His publication record shows consistent contributions to understanding collective animal behavior, with recent work on locust swarming mechanisms challenging traditional alignment-based theories. His research demonstrates how AI can be used not just for prediction but for hypothesis testing and revealing underlying biological mechanisms. Dr. Graving leads the development of open-source tools like DeepPoseKit, which has been featured in prestigious outlets including Quanta Magazine, Nature Methods, and Nature News & Views. His work on drone-based tracking systems and automated barcode systems for animal tracking demonstrates his commitment to developing practical, field-deployable technologies. He maintains active engagement with the scientific community through GitHub, Bluesky, and academic conferences, reflecting his dedication to open science and interdisciplinary collaboration.

Jörg VogelView profile

Professor

• RNA Biology
• Infection Biology
• Bacterial Pathogenesis
• +12 more

Prof. Dr. Jörg Vogel is Director of the Helmholtz Institute for RNA-based Infection Research (HIRI) in Würzburg and Professor at the Institute for Molecular Infection Biology (IMIB), University of Würzburg. He is a leading figure in RNA biology and infection research, recognized with the 2017 Leibniz Prize. His work bridges molecular microbiology, high-throughput sequencing, and therapeutic development. His research focuses on the RNA biology of bacterial infections, particularly regulatory and small RNAs, host-pathogen interactions, and the development of antisense-based antibiotics. He has pioneered methods like microbial MATQ-seq for single-cell transcriptomic profiling in bacteria. The recent publications highlight a consistent research trajectory in bacterial transcriptomics, phage biology, and RNA-targeting therapeutics. Themes include antisense oligomers, small RNA function, ribosome hijacking by phages, and RNA stability dynamics in pathogens like Salmonella and Fusobacterium. Scientific awards include: Gottfried Wilhelm Leibniz Prize (2017) He mentors several students and postdocs, including Suyen Espinoza and Giorgia Danti, and leads collaborative projects, such as with the Maric Lab. His team is involved in major initiatives like the Single-Cell Center Würzburg and graduate programs in RNA & Infection. He also organizes scientific symposia such as ASOBIOTICS. His research group is based at the Helmholtz Institute for RNA-based Infection Research (HIRI) in Würzburg, Germany, a leading institution combining RNA research with infection biology.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +7 more

Jiaqi Liu is an Associate Professor in the Department of Computer Science and Engineering at Southern University of Science and Technology in Shenzhen, China. His research spans multiple domains within artificial intelligence, with a particular focus on autonomous systems, multimodal learning, and computer vision. He maintains active collaborations with researchers across China and internationally, particularly with Jian Sun and Peng Hang on autonomous driving projects. Dr. Liu's research interests encompass a broad spectrum of AI and computer science topics. His work in autonomous driving involves developing sophisticated decision-making frameworks for cooperative vehicle systems. In multimodal learning, he has pioneered approaches for handling missing modalities and creating adaptive fusion networks. His signal processing research includes innovative methods for sleep staging and EEG analysis using dynamic mode decomposition techniques. Additional research areas include photonic computing accelerators, graph neural networks, and quantum neural networks. The publication trend shows significant growth in output quality and quantity, with numerous papers in top-tier venues including IEEE Transactions, CVPR, AAAI, and IJCAI. His work demonstrates a strong interdisciplinary approach, bridging computer science with applications in healthcare, robotics, and telecommunications. Recent publications indicate increasing focus on the integration of large language models with reinforcement learning for autonomous systems. Dr. Liu has received recognition through publications in high-impact journals and conferences including: IEEE Transactions on Intelligent Transportation Systems IEEE Robotics and Automation Letters Expert Systems with Applications CVPR (Computer Vision and Pattern Recognition) AAAI Conference on Artificial Intelligence His research program involves collaborations across multiple domains, with current projects focusing on language-guided autonomous driving, multimodal sentiment analysis, and advanced signal processing techniques. He leads research efforts in developing novel deep learning architectures for complex real-world problems, particularly in the domains of autonomous systems and healthcare applications.

Armando Solar-LezamaView profile

Associate Professor

• Software Synthesis
• High-Performance Computing
• Information Flow Security
• +3 more

Armando Solar-Lezama is an Associate Professor at the Massachusetts Institute of Technology (MIT) where he leads the Computer Aided Programming Group within CSAIL. His research bridges programming languages, formal methods, and machine learning to advance program synthesis techniques for real-world applications. His primary research focuses on software synthesis with applications spanning high-performance computing, information flow security, and probabilistic programming. He investigates how to automatically generate correct and efficient programs from specifications, with recent work emphasizing neurosymbolic approaches that combine neural networks with symbolic reasoning for complex synthesis tasks. Analysis of his publication trends (2021-2025) reveals a strategic evolution from foundational synthesis algorithms toward practical applications in probabilistic programming and security-critical systems. His work consistently integrates machine learning with formal verification, demonstrating growing emphasis on coarse-to-fine synthesis methods and knowledge compilation techniques for discrete probabilistic models. Award highlights: SIGPLAN Milner Award As leader of MIT's Computer Aided Programming Group, Solar-Lezama mentors graduate researchers and collaborates across institutions. His extensive service as Program Chair for POPL 2025 and committee roles at premier conferences (PLDI, POPL, SPLASH) demonstrates significant community leadership. While specific grant details aren't provided, his sustained publication output in top venues indicates robust funding support. He directs the Computer Aided Programming Group at MIT CSAIL, which develops tools for program synthesis, verification, and analysis. Current projects focus on neurosymbolic programming frameworks, probabilistic program synthesis, and security-aware compilation techniques that integrate information flow control with high-performance computing optimizations.

Kilian OberleithnerView profile

Assistant Professor

• Fluid Mechanics
• Turbulent Flow Analysis
• Flow Instabilities
• +8 more

Kilian Oberleithner is an Assistant Professor and group leader of Dynamics of Unsteady Flows at the Institute of Fluid Mechanics and Technical Acoustics (ISTA) , Technical University of Berlin since 2018. He earned his doctorate in 2012 under Prof. Oliver Paschereit (TU Berlin) and Prof. Israel Wygnanski (University of Arizona), followed by a postdoc at Monash University with Prof. Julio Soria . His work bridges fluid mechanics and interdisciplinary applications in renewable energy and propulsion systems. Research interests focus on flow instabilities , coherent structures , and their control in thermofluid dynamic turbulent flows . He integrates data-driven and analytical methods with experimental and numerical data, targeting real-world applications in wind power , gas turbine combustion , and hydropower . Recent publications highlight interdisciplinary approaches: machine learning for multiphase flow metrology, physics-informed neural networks in medical fluid dynamics, and linear stability analysis for combustion optimization. His group develops active flow control strategies for vortex dynamics and Bayesian optimization frameworks for industrial systems. Key collaborations include Monash University , Stanford Center for Turbulence Research , and industry partners like Miele and Honda . Current projects emphasize cross-energy systems and AI-integrated fluid dynamics , aligning with TU Berlin's strategic goals in renewable energy and advanced propulsion .

Jens MüllerView profile

Researcher

• Fluid Mechanics
• Aerospace Engineering
• Turbulence Analysis
• +7 more

Jens Müller is a Researcher at the Technical University of Berlin , affiliated with the Faculty of Transportation and Mechanical Systems and the Institute of Fluid Mechanics and Technical Acoustics (ISTA) . He completed his M.Sc. in Aerospace Engineering at TU Berlin in 2018 and is currently pursuing his doctoral research as a Research Assistant in the Dynamics of Unstable Flows group. His research focuses on theoretical and data-driven analysis methods for flow control applications, including: Suppression of global flow instabilities in gas turbines and Francis turbines Reduction of trailing edge noise in wind turbine rotor blades Development of linear stability theory and adjoint-based optimization tools Dr. Müller has contributed to 15+ publications across fluid mechanics, turbulence modeling, and renewable energy applications, with a particular emphasis on: Bayesian optimization and resolvent analysis (2024) PIV-based modeling of combustor flows (2021) Multi-physics CFD solvers like FELiCS (2023) Vortex breakdown modes in jet flows (2020) He serves as a supervisor and teaching assistant for the course 'Methods of Data Analysis in Thermofluid Dynamics' at TU Berlin. His work bridges fundamental fluid dynamics with industrial applications in: Wind energy (noise reduction) Hydroelectric power (vortex precession control) Aerospace engineering (combustion instability mitigation)

Dingenis Roelant Michiel RengerView profile

Researcher

• Variational calculus
• Convex analysis
• Partial differential equations
• +6 more

Dr. D.R. Michiel Renger is a mathematical researcher at the Technical University of Munich (TUM), affiliated with the School of Computation, Information and Technology in the Department of Mathematics. He also maintains connections with the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) in Berlin, where he has presented lectures on advanced mathematical topics. Position: Researcher in the Department of Mathematics Institution: Technical University of Munich (TUM) Location: Boltzmannstraße 3, 85748 Garching bei München Contact: d.r.m.renger@tum.de Dr. Renger's research spans multiple areas of mathematical analysis and theoretical physics, with particular expertise in variational calculus, convex analysis, partial differential equations, and non-equilibrium thermodynamics. His work bridges pure mathematics with applications in physics and chemistry, especially through his investigations of gradient flows, Hamiltonian systems, large deviations theory, and chemical reaction networks. His research has significant implications for understanding complex systems far from equilibrium. His publication record shows consistent scholarly output from 2013 through 2025, with recent work focusing on the connections between macroscopic fluctuation theory and large deviations principles. His research often involves collaborative work with other leading mathematicians across European institutions. Recent teaching: Modeling and Simulation with Ordinary Differential Equations (TUM, Winter 2025/26) Past specialized courses: Large Deviations (TU Berlin, 2020), Convex Analysis (TU Berlin, 2018) Dr. Renger completed his PhD at Eindhoven University of Technology in 2013 with a dissertation titled "Microscopic interpretation of Wasserstein gradient flows," establishing his foundation in the mathematical theory of gradient flows which continues to inform his current research directions.

Stefano BaliettiView profile

Researcher

• Consensus formation
• Social influence
• Polarization
• +7 more

Stefano Balietti is a postdoctoral researcher at the Chair of Data Science in the Economic and Social Sciences at the University of Mannheim's Business School, specializing in Information Systems. Previously, he held research positions at the Center for European Social Science Research at Mannheim University (MZES), the Alfred-Weber Institute of Economics at Heidelberg University, Microsoft Research Computational Social Science, Northeastern Network Science Institute, Harvard Institute for Quantitative Social Science, and ETH Zurich Computational Social Science group. His research spans consensus formation, social influence, and polarization; governance in blockchain systems; incentives schemes for collective intelligence, particularly peer review systems; optimal experimental designs; socio-economic inequality; and philosophy of science with a focus on Paul Feyerabend's work. He employs methodologies combining computer simulations, data analytics, and online behavioral experiments to investigate complex social phenomena. His work often bridges computational methods with social science theory to address pressing societal issues. His publication record shows a strong trajectory in computational social science, with recent work focusing on blockchain governance, DAOs, climate perception, and polarization reduction. His research demonstrates a consistent pattern of applying rigorous experimental methods to understand collective behavior, with increasing emphasis on decentralized systems and their social implications. His 2025 paper on climate perception divergence and 2024 work on DAO governance represent his current research frontiers. Special mention in the Exeter Prize for Research in Experimental Economics, Decision Theory and Behavioral Economics 2017 Best Lightening Talk Award The Market Structure For Innovation SocInfo 2016 Dr. Balietti actively supervises master's students at the University of Mannheim, with recent theses focusing on blockchain applications, DAOs, and decentralized systems. He has developed significant research infrastructure including nodeGame, an open-source JavaScript platform for real-time online group-behavior experiments, and co-founded the State of Crypto initiative. His EduCERT project aims to enable innovation and increase digital teaching at higher education institutions through blockchain-based academic credentialing. His research program demonstrates strong continuity from his PhD work at ETH Zurich through his current focus on blockchain governance and decentralized systems.

Prashanti GaneshView profile

Researcher

• Decision Making
• Reward-based Learning
• Cognitive Neuroscience
• +2 more

Prashanti Ganesh is a post-doctoral researcher at Brown University working in Matt Nassar's laboratory, having recently defended her PhD at Freie Universität Berlin's Berlin School of Mind and Brain program. Her doctoral research was conducted within the Department of Education and Psychology's Neurocognitive and Experimental Psychology group and Learning Lab under Dr. Rasmus Bruckner's supervision. Education: PhD in Psychology (2020-2025), Freie Universität Berlin, Berlin School of Mind and Brain MSc in Cognitive Science (2018-2020), Indian Institute of Technology Gandhinagar BA in Psychology (2015-2018), St. Xavier’s College, Mumbai Her research centers on perceptual and economic decision-making mechanisms under uncertainty, investigating how individuals integrate perceptual and reward information during value-based choices. Using behavioral experiments, fMRI, and computational modeling, she examines neural correlates of prediction errors, counterfactual feedback processing, and adaptive learning strategies in uncertain environments. Recent publications demonstrate consistent focus on computational mechanisms of learning and decision-making, particularly how pupil-linked arousal encodes uncertainty, limitations of learning rates as performance predictors, and adaptive information integration. Her work bridges cognitive psychology, neuroscience, and computational modeling with applications in understanding healthy and pathological decision processes. No scientific awards were documented in available sources. Student advising activities and research grant details were not specified in source materials. Previously affiliated with FU Berlin's Learning Lab, she now conducts research in Matt Nassar's laboratory at Brown University, advancing cognitive and computational neuroscience approaches to human decision architecture.