جستجوی استاد راهنمای دانشگاهی

Jörg Vogelمشاهده پروفایل

استاد

• RNA Biology
• Infection Biology
• Bacterial Pathogenesis
• +۱۲ مورد دیگر

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
• +۷ مورد دیگر

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-Lezamaمشاهده پروفایل

دانشیار

• Software Synthesis
• High-Performance Computing
• Information Flow Security
• +۳ مورد دیگر

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 Oberleithnerمشاهده پروفایل

استادیار

• Fluid Mechanics
• Turbulent Flow Analysis
• Flow Instabilities
• +۸ مورد دیگر

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 Müllerمشاهده پروفایل

پژوهشگر

• Fluid Mechanics
• Aerospace Engineering
• Turbulence Analysis
• +۷ مورد دیگر

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 Rengerمشاهده پروفایل

پژوهشگر

• Variational calculus
• Convex analysis
• Partial differential equations
• +۶ مورد دیگر

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 Baliettiمشاهده پروفایل

پژوهشگر

• Consensus formation
• Social influence
• Polarization
• +۷ مورد دیگر

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 Ganeshمشاهده پروفایل

پژوهشگر

• Decision Making
• Reward-based Learning
• Cognitive Neuroscience
• +۲ مورد دیگر

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.

Dirk Reiserمشاهده پروفایل

پژوهشگر

• Plasma Physics
• Numerical Methods
• Plasma Transport
• +۱۰ مورد دیگر

Dr. Dirk Reiser serves as Team Leader for "Theory and Numerical Simulations" at Forschungszentrum Jülich, specifically within the Plasma Physics (IFN-1) group of the Institute of Fusion Energy and Nuclear Waste Management. His research focuses on advancing computational methods for plasma physics and fusion energy applications, with particular emphasis on plasma edge physics and divertor operation in tokamak devices. Dr. Reiser's expertise spans multiple critical areas of computational plasma physics: Numerical Methods and Partial Differential Equations for plasma modeling Plasma Transport and Turbulence analysis using advanced computational techniques Statistical approaches including Bayesian Inference and Monte Carlo Methods Machine learning applications for plasma modeling and data analysis Plasma-material interactions and surface physics His research bridges theoretical plasma physics with practical computational approaches to solve complex fusion energy problems, particularly related to impurity transport, plasma edge physics, and divertor operation. He has made significant contributions to the EMC3-EIRENE simulation framework, which is widely used in the international fusion community for modeling plasma edge and divertor physics. Analysis of his recent publications (2021-2025) reveals a strong focus on applying advanced computational methods to fusion energy challenges, with increasing integration of artificial intelligence techniques alongside traditional physics-based modeling. His work demonstrates consistent contributions to major international fusion projects including ITER, ASDEX Upgrade, and JET, addressing critical challenges in plasma exhaust, impurity control, and plasma-material interactions essential for future fusion reactors. As a team leader at one of Europe's largest research institutions, Dr. Reiser oversees research efforts that contribute significantly to the international fusion energy research community. His work helps advance the physics basis for ITER operation and future fusion power plants, with particular emphasis on solving the complex plasma physics challenges at the boundary between hot plasma and material surfaces.

• Computer Vision
• Deep Learning
• Safe AI
• +۵ مورد دیگر

Prof. Dr.-Ing. Seyed Eghbal Ghobadi is a Professor of Computer Vision and Machine Learning at the Technical University of Central Hesse (Technische Hochschule Mittelhessen), Department of Mathematics, Natural Sciences and Computer Science. He received his PhD in 2010 from the Center for Sensor Systems at the University of Siegen after studying Electrical Engineering at the Technical University of Braunschweig. Research Focus: Deep Learning for Computer Vision, Safe AI, Uncertainty Estimation, Cyber-Physical Systems, and Domain Shift Detection Key Projects: AI-supported highly automated weed control in grassland (KIhUG), AI Safeguarding for Stellantis/Opel Teaching: Courses on Deep Learning for Computer Vision, Pattern Recognition, Fundamentals of AI, and Embedded Systems His recent work explores predictive uncertainty quantification using Dirichlet networks, domain shift detection in cyber-physical systems, and real-time image segmentation with PID Net. He supervises theses on topics including signal denoising and embedded AI applications.

Remo Kretschmannمشاهده پروفایل

پژوهشگر

• Statistical Inverse Problems
• Bayesian Inference
• Uncertainty Quantification
• +۳ مورد دیگر

Remo Kretschmann is a Postdoctoral Researcher at the Institute of Mathematics , University of Potsdam, specializing in Uncertainty Quantification . He is associated with project A04 of the collaborative research centre SFB 1294 Data Assimilation . His academic journey includes doctoral studies at Universität Duisburg-Essen and a master’s/bachelor’s at Technische Universität München. Education Doctorate in Mathematics (2019), Universität Duisburg-Essen Master of Science in Mathematics (2012), Technische Universität München Bachelor of Science in Mathematics (2009), Technische Universität München His research focuses on statistical inverse problems with high-dimensional parameter spaces, non-Gaussian noise models , and Bayesian hypothesis testing . He investigates regularization methods in nonparametric Bayesian inference and Gaussian approximation of posterior distributions for inverse problems. Selected research trends include: Bayesian hypothesis testing for inverse problems Optimal regularization techniques in statistical inference Laplace approximation error analysis Posterior mode characterization Applications in imaging and deconvolution He has contributed to open-source software for sampling in inverse problems and numerical simulations for regularized hypothesis testing.

Sherry Suyuمشاهده پروفایل

دانشیار

• Observational Cosmology
• Gravitational Lensing
• Dark Energy
• +۵ مورد دیگر

Prof. Sherry Suyu is an Associate Professor of Observational Cosmology at the Department of Physics, Technical University of Munich (TUM), School of Natural Sciences . She leads the H0LiCOW and HOLISMOKES programs, focusing on measuring the Hubble constant (H₀) through gravitational lensing and studying dark energy, galaxy evolution, and supernovae via lensed systems. Research Focus : Observational cosmology, gravitational lensing, dark energy, supernova progenitors, galaxy dark matter halos Key Projects : H0LiCOW (H₀ measurement), HOLISMOKES (supernova studies) Her recent publications (2010–2020) analyze time-delay distances in lensed quasars to constrain cosmological parameters like H₀ (73.3 ± 1.7–1.8 km/s/Mpc in flat ΛCDM) and probe tensions between early- and late-Universe observations. She employs tools such as Hubble Space Telescope , Keck adaptive optics , and lenstronomy/GLEE lensing software. She teaches courses including Gravitational Lensing , Experimental Physics , and Supernova Seminars , with mentoring in TUM's Bachelor’s Physics Program . Her work involves collaborations with institutions in Germany, the U.S., Switzerland, Japan, and other countries.

• Econometrics
• Statistics
• Nonparametric Methods
• +۳ مورد دیگر

Christoph Breunig is a Professor at the Department of Economics, University of Bonn, with research focused on Econometrics. He is affiliated with the Institute for Financial Economics & Statistics at the university. His primary research interests include Econometrics, Statistics, Nonparametric Methods, Instrumental Variables, Treatment Effects, and Missing Data Analysis. Professor Breunig's work demonstrates a strong focus on methodological developments in econometric theory with applications to economic questions. His publication record shows a consistent output of high-quality research in top econometrics journals including Econometrica, Journal of Econometrics, and Quantitative Economics. His research trajectory demonstrates progression from foundational work on nonparametric methods and instrumental variables toward more complex problems involving treatment effects, missing data, and high-dimensional settings. Professor Breunig has established himself as a contributor to the field of econometric theory with particular expertise in nonparametric and semiparametric methods. His work often addresses identification and estimation challenges in complex economic models.

• Economics
• Game Theory
• Microeconomics
• +۴ مورد دیگر

Prof. Dr. Stephan Lauermann is a faculty member in the Department of Economics at the University of Bonn , affiliated with the Institute of Microeconomics , ECONtribute , Hausdorff Center for Mathematics , and CRC TR 224 . His research focuses on Bargaining, Auctions, Search Theory, and Matching Theory , with applications to market design and information aggregation. Research Trends: Recent articles (2022-2025) emphasize information aggregation in elections and frictions in matching and auction models . Older works (2012-2017) address adverse selection in auctions, price discovery in search markets, and foundational matching theory. Subfields span dynamic matching , strategic voting , common-value auctions , and equilibrium analysis . Contact: Email: s.lauermann@uni-bonn.de , Phone: +49 228 73-6327, Room 3.004, Adenauerallee 24-42, Bonn.

• Dynamic Decision Problems
• Equilibrium Models and Games
• Optimal Learning
• +۳ مورد دیگر

Sven Rady is a Professor of Economics at the University of Bonn, holding the Hausdorff Chair for Mathematical Economics since 2011. He is affiliated with both the Department of Economics and the Hausdorff Center for Mathematics, where he serves as an Associate Director. His research primarily focuses on game theory, strategic experimentation, and real estate economics, with significant contributions to the understanding of bandit games and information dynamics in economic settings. Rady received his Ph.D. in Economics from the London School of Economics in 1995, following studies in mathematics and physics at the universities of Konstanz, Bonn, and Paris VII. Prior to his position at Bonn, he was a Professor of Economics at the University of Munich (1999-2011) and an Assistant Professor at Stanford University's Graduate School of Business (1996-2001). His research explores dynamic decision problems, equilibrium models and games, optimal learning, and strategic experimentation. A significant portion of his work examines how agents learn in strategic environments, particularly through bandit game frameworks where players face trade-offs between exploration and exploitation. His research also extends to real estate markets, analyzing how contracts and market structures affect aggregate economic fluctuations. Rady's publications reveal a consistent focus on strategic experimentation across different contexts, from pure game-theoretic settings to applied economic problems. His work often bridges theoretical game theory with practical economic applications, particularly in housing markets and financial economics. The evolution of his research shows increasing sophistication in modeling information dynamics and strategic behavior in complex economic environments. Fellow of the Econometric Society (2023) Teaching award, Department of Economics, University of Bonn (2021 & 2022) Teaching award of the State of Bavaria (2005) As an academic leader, Rady has served in numerous editorial roles, including as Co-Editor of the Journal of Mathematical Economics and the B.E. Journals in Theoretical Economics. He is currently Secretary of the European Standing Committee of the Econometric Society and serves as Deputy Spokesperson for the Collaborative Research Centre SFB/TR 224. His research team includes postdocs Gregorio Curello, Lucas Pahl, Miguel Risco, and Shaofei Jiang, with whom he collaborates on various projects related to game theory and economic dynamics.