جستجوی استاد راهنمای دانشگاهی

Carlos Cinelliمشاهده پروفایل

استادیار

• Causal Inference
• Statistics
• Machine Learning
• +۴ مورد دیگر

Carlos Cinelli is an Assistant Professor in the Department of Statistics at the University of Washington. He is also a Data Science Fellow at the eScience Institute and Affiliate Faculty at the Center for Statistics and the Social Sciences. He obtained his Ph.D. in Statistics from the University of California, Los Angeles, where he was advised by Chad Hazlett and Judea Pearl. His research focuses on developing new causal and statistical methods for transparent and robust causal claims in empirical sciences. Key interests include: Causal inference challenges in social and health sciences Intersections of causality with machine learning and AI Sensitivity analysis for omitted variable bias Robust statistical methods for observational studies Instrumental variables and Mendelian randomization Generalizability of experimental findings His publications demonstrate a consistent focus on developing practical sensitivity analysis tools and advancing causal methodology, with recent work emphasizing applications in machine learning and econometrics. Honors include: Best Paper Award at SBE 2024 in Econometrics UCLA Dissertation Year Fellowship (2020) He actively advises PhD students and has received research funding from: NSF/MMS Royalty Research Fund He leads development of several open-source software packages for sensitivity analysis and maintains active collaborations with researchers at UCLA and other institutions.

• Probability Theory
• Mathematical Finance
• Stochastic Analysis
• +۴ مورد دیگر

Peter K. Friz is Einstein Professor in Mathematics at Technische Universität Berlin (TU-Berlin), where he leads research in the Probability Theory and Mathematical Finance group. He is also affiliated with the Weierstrass Institute for Applied Analysis and Stochastics (WIAS), a major research institute in Berlin focused on applied mathematics. Professor Friz's research spans several interconnected areas of mathematics and finance: Stochastic analysis and rough path theory Quantitative finance and volatility modeling Partial differential equations driven by rough paths Applications of rough path theory to financial mathematics His recent publications demonstrate a continued focus on advancing rough path theory and its applications. The research trends show increasing sophistication in handling nonlinear rough differential equations, connections to machine learning through signature methods, and deeper applications to financial modeling, particularly in volatility analysis. His work bridges pure mathematical theory with practical financial applications, maintaining relevance to industry problems while advancing theoretical foundations. Professor Friz has received significant recognition for his work: ERC Starting Grant "Rough path theory, differential equations and stochastic analysis" (2010-2016) ERC Consolidator Grant "Geometric aspects in pathwise stochastic analysis and related topics" (2016-2021) Einstein Professorship at TU-Berlin He actively mentors students and researchers, with a list of former PhD students available through his academic profile. Professor Friz coordinates the DFG research unit "Rough paths, stochastic partial differential equations and related topics" (2016-2019) and has secured substantial funding from the European Research Council, DFG, and other sources. His research group regularly hosts seminars and collaborates with institutions worldwide, including recent invited courses at Cambridge, Paris (IHP), and Bonn (HIM). Professor Friz maintains active research collaborations through his position at TU-Berlin and affiliation with WIAS, contributing to Berlin's status as a hub for mathematical finance and stochastic analysis in Europe.

• Probability Theory
• Stochastic Processes
• Mathematical Physics
• +۳ مورد دیگر

Lisa Hartung is a Full Professor (W3) at Johannes Gutenberg University Mainz's Department of Mathematics since 2025, leading research in stochastic processes. She previously held Associate (2019-2025) and Assistant Professor (2019) positions at the same institution and served as a Courant Instructor at NYU's Courant Institute (2016-2018). Her educational background includes a PhD from Bonn University (2016) under Prof. Anton Bovier in Probability Theory. Key research interests span branching Brownian motion , Gaussian free fields , random energy models , and stochastic processes on evolving networks , with applications in statistical mechanics and mathematical physics. Her work frequently addresses extremal processes, phase transitions, and complex temperature phenomena. Analysis of her recent publications reveals dominant trends in log-correlated random fields, variable-speed branching processes, and hard-wall boundary conditions. Articles consistently explore asymptotic behavior, maximum processes, and structural properties of stochastic systems, with increasing interdisciplinary connections to computer science through data stream modeling. Scientific recognition includes: Förderpreis der Fachgruppe Stochastik (2018) Hartung secures substantial research funding as PI for multiple high-impact projects: the ANR-DFG grant Random Energy Models (2020), DFG's SPP 2265 Random Geometric Systems (2020), TRR146 Multiscale Simulation Methods (2020), and Carl-Zeiss project TOPML (2022). She also serves on the executive board of DMV Fachgruppe Stochastik (since 2023) and coordinates Jugend trainiert Mathematik for grades 8/9. Her research group participates in the DFG-funded network Stochastic Processes on Evolving Networks and collaborates internationally through workshops on interacting particle systems and stochastic population models.

• Latent Variable Models
• Dynamic Latent Variable Models
• Latent Class Methods
• +۸ مورد دیگر

Augustin Kelava is a Professor at the Department of Quantitative Methods, Eberhard Karls University of Tübingen. He has held this position since 2018 and leads the Methods Center as Managing Director. Previously, he was Professor at the Hector Institute for Empirical Educational Research (2013-2018) and Junior Professor at Technical University of Darmstadt (2011-2013). PhD in Psychology (Goethe University Frankfurt, 2009) Diploma in Psychology (Goethe University Frankfurt, 2004) Kelava specializes in latent variable modeling, machine learning in social sciences, and educational research. His work spans dynamic latent class models, Bayesian regularization techniques, and prediction of human behavior using intensive longitudinal data. He contributes to psychometric theory (e.g., item response theory extensions) and applies these methods to diverse fields including sports science and emotion regulation. Editor of "Testtheorie und Fragebogenkonstruktion" (3rd ed., Springer, 2020) Key researcher in the Cluster of Excellence "Machine Learning in Science" Active in methodological conferences (FGME 2017, SEM 2019) Review activities for 20+ journals and foundations including Psychometrika, DFG, and SNSF His recent publications focus on integrating machine learning with psychometrics, addressing identifiability in complex models, and evaluating personality assessment validity for large language models. He collaborates with researchers across psychology, education, and computational fields.

• Invasion Ecology
• Forest Ecology
• Computational Modelling
• +۴ مورد دیگر

Samuel M. Fischer is a Research Fellow at the Helmholtz Centre for Environmental Research (UFZ) in the Department of Ecological Modelling, where he has been conducting postdoctoral research since 2023. He holds a PhD in Applied Mathematics from the University of Alberta and a Bachelor of Science in Applied Systems Science from Osnabrück University. His research integrates mathematical modeling, algorithm design, and statistics to address ecological challenges. Primary interests include: Modeling invasive species spread using mobile app data and network analysis Mechanistic forest modeling for productivity assessment and stress detection via remote sensing Developing computational methods for ecological epidemiology and carbon sequestration Advancing standardization in ecological modeling through the Open Modelling Foundation Fischer's publications demonstrate a strong focus on integrating computational methods with ecological applications. Recent work emphasizes forest carbon dynamics, species persistence modeling, and optimization algorithms for environmental management. Methodological innovations in statistical computing and cross-disciplinary model integration are recurring themes across his research portfolio. He actively mentors students in computational ecology and promotes open science standards. Current projects involve linking hyperspectral satellite data with forest models to monitor ecosystem health in German forests.

Christian Holmمشاهده پروفایل

استاد

• Computer simulations of soft matter
• Theoretical modeling of charged systems
• Polyelectrolyte solutions
• +۷ مورد دیگر

Christian Holm is a Professor at the Institute for Computational Physics of the University of Stuttgart. His research focuses on computational modeling of soft matter systems with electrostatic and magnetic interactions, including polyelectrolytes, colloids, and hydrogels. Member of collaborative research centers SFB 716, SFB 1313, SFB 1333, FOR 2811, and MultiXscale Develops machine learning potentials and advanced simulation methods for complex molecular systems Co-developer of the ESPResSo simulation package for soft matter research His recent work explores: Mechanisms of polyelectrolyte complexation and gel swelling Machine learning approaches for quantum-accurate interatomic potentials Dynamics of active colloids and biofilm formation in porous media Multi-agent reinforcement learning for smart active systems He maintains affiliations with CECAM's Soft Matter and Statistical Mechanics node and contributes to open science initiatives through software development.

Yann Strozeckiمشاهده پروفایل

دانشیار

• Enumeration Complexity
• Algorithmic Game Theory
• Simple Stochastic Games
• +۷ مورد دیگر

Yann Strozecki is an Associate Professor (Maître de Conférences HDR) at the University of Versailles Saint-Quentin, where he is based in the DAVID Laboratory and leads the ALMOST research team focused on algorithms and stochastic models. He is currently on a part-time assignment at LIGM, Gustave Eiffel University, and has previously held positions at LIP6 (RO team), Paris-Sud University (ALGO team), and completed a postdoctoral fellowship at the University of Toronto's Theory Group. He earned his PhD from Paris Diderot (Paris 7) under Arnaud Durand. His research lies at the intersection of theoretical computer science and discrete mathematics, with core interests in: Enumeration complexity, especially delay and space constraints Algorithmic game theory, particularly simple stochastic games (SSGs) Graph and matroid algorithms Cheminformatics and molecular structure generation Sparse polynomials and algebraic complexity Analysis of his recent publications reveals a strong trend in developing efficient enumeration algorithms with provable delay and space bounds, advancing the theoretical foundations of output-sensitive computation. He also contributes to practical algorithms for Cloud RAN scheduling and cheminformatics, often combining theoretical rigor with real-world applications. His work on geometric amortization and strategy improvement in SSGs demonstrates innovation in algorithm design. Notable scientific contributions include: Generic strategy improvement methods for SSGs Polynomial-delay enumeration via closure operations Efficient deterministic scheduling for low-latency networks Tools for molecular cage generation in chemistry Yann Strozecki actively supervises PhD and master’s students, including Noé Demange, Maël Guiraud, and Xavier Badin de Montjoye. He co-organizes the ALMOST team seminar and has advised numerous interns in algorithmics and game theory. His research has been supported through collaborations with Nokia Bell Labs (CIFRE thesis) and interdisciplinary projects in cheminformatics and networking.

• Logic in computer science
• linear dynamical systems
• automated verification
• +۳ مورد دیگر

James Worrell is a Professor of Computer Science at the University of Oxford , with a focus on logic in computer science , linear dynamical systems , and automated verification . He is also a Fellow of Green Templeton College. His research spans theoretical computer science and formal methods , including work on metric temporal logic , probabilistic semantics , and category theory . His publications address decision problems , verification of linear dynamical systems , and automata theory . James has received the EPSRC Established-Career Fellowship for his work in linear dynamical systems verification . His recent papers include polynomial invariants , polyhedral escape problems , and skolem problem solutions . He has advised students such as Mehran Hosseini , Pascale Gourdeau , and Ventsislav Chonev , and teaches courses like Computational Learning Theory and Logic and Proof . His work appears in venues like ICALP , LICS , and SODA .

• Artificial Intelligence
• Automated Planning
• Symbolic Reasoning
• +۶ مورد دیگر

Dr. Peter Kissmann is a researcher in the Computer Science Department at Universität des Saarlandes, Germany, affiliated with the Foundations of Artificial Intelligence (FAI) Group. His work focuses on automated planning systems, particularly in optimal and net-benefit planning domains. Research Interests: His primary research areas include Artificial Intelligence, Automated Planning, Symbolic Reasoning, Heuristic Search, and Knowledge Representation. He specializes in BDD-based planning, variable ordering, and symbolic search algorithms for optimal planning. The 15 most recent publications reflect his sustained contributions to the International Planning Competitions (IPC) from 2008 to 2014, showcasing evolution in planner design, including support for conditional effects, bidirectional search, improved variable ordering, and integration of modern BDD libraries like CUDD. These works demonstrate deep technical innovation in planner efficiency, robustness, and scalability. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: There is no explicit information about students or grant funding in the provided material. However, his leadership in developing and releasing competitive planning systems suggests involvement in research projects, possibly with student contributors or collaboration within the FAI group. Labs and Teams: He is a member of the Foundations of Artificial Intelligence (FAI) Group at Universität des Saarlandes, which develops advanced planning systems and participates in international competitions like the IPC.

Ann Sychterzمشاهده پروفایل

استادیار

• Structural Engineering
• Sustainable and Resilient Infrastructure Systems
• Adaptive Structures
• +۳ مورد دیگر

Ann Sychterz is an Assistant Professor at the University of Illinois Urbana-Champaign in the Civil and Environmental Engineering department. She leads the SMARTI Lab, focusing on adaptive structures, tensegrity systems, and machine learning for civil infrastructure. Her research integrates computational design with experimental validation to create deployable systems for climate resilience. PhD, Swiss Federal Institute of Technology Lausanne (2018) M.A.Sc, University of Waterloo (2014) B.A.Sc, University of Waterloo (2012) Her research interests span structural dynamics, form-finding, machine learning, and adaptive infrastructure. Recent work involves MLRAM materials for tensegrity structures, origami-based coastal anchors, and sensor-actuator optimization. 2024-2025 highlights include a Visiting Professorship at University of Stuttgart and SEAOI Outstanding Young Engineer Award . Key trends across her 2023-2025 publications include: Advancements in tensegrity and origami structural modeling Integration of machine learning for damage detection Functionally graded materials for geotechnical applications Experimental validation of full-scale deployable systems Scientific recognition includes: ASCE SEI Future Leaders Award (2024) Best Paper/Poster Awards (2021-2025) NSERC and Ontario Graduate Scholarships Leadership roles in ASCE Technical Committees She advises PhD students in structural control, oversees the SMARTI Lab, and collaborates with the Beckman Institute and National Center for Supercomputing Applications. Her work addresses climate-driven infrastructure challenges through deployable, biomimetic designs.

Ralph Neiningerمشاهده پروفایل

استاد

• Probability Theory
• Algorithms
• Stochastic Analysis
• +۳ مورد دیگر

Ralph Neininger is a Professor at the Department of Computer Science and Mathematics, Goethe University Frankfurt. His research focuses on Probability Theory, Algorithms, Stochastic Analysis, and Data Structures, with a particular emphasis on the analysis of randomized algorithms and combinatorial structures. Key research areas: Randomized algorithms, stochastic processes, data structures, and distributional analysis. Recent publications address complexity fluctuations in algorithms, analysis of satisfiability problems, and probabilistic models for trees and urns. His methodological work leverages advanced probabilistic techniques to derive limit theorems and convergence rates for algorithms. Though no scientific awards are explicitly mentioned, his extensive publication record underscores significant contributions to theoretical computer science and applied probability. He has not been listed with advisees or students in the provided materials.

• Plasmonics
• Electron Energy Loss Spectroscopy
• Ultra-fast Electron Optics
• +۴ مورد دیگر

Dr. Johannes Schultz is a PostDoc researcher at the Leibniz Institute for Solid State and Materials Research Dresden (IFW Dresden), affiliated with the Institute for Integrative Nanosciences. He leads the Advanced Methods of Electron Microscopy Group, focusing on cutting-edge electron microscopy techniques and plasmonics research. His scientific focus spans magnetic textures and dynamics at micro-to-nano scales , development of ultra-fast electron optical elements with sub-nanosecond response times, Electron Energy Loss Spectroscopy in TEM (particularly low-loss excitations), and plasmonics in nanoparticle assemblies and correlated materials. His work bridges fundamental physics with nanotechnology applications. Dr. Schultz has published 16 journal papers since 2018 in high-impact venues including Nature Communications , Physical Review Research , and Advanced Optical Materials , with significant contributions to understanding plasmon localization, electron optics, and nanomaterial characterization. His research demonstrates consistent technical innovation in electron microscopy methodologies. As an active contributor to the microscopy community, he has delivered 8 invited talks at major international conferences including the European Microscopy Society Congress and PICO 2024. His collaborations span multiple institutions including Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, and international partners in Belgium, France, and Austria. The Advanced Methods of Electron Microscopy Group develops specialized techniques for nanoscale characterization, with particular expertise in spectral field mapping, plasmon dynamics analysis, and computational modeling of electron-matter interactions. Current work emphasizes disordered plasmonic systems and ultrafast electron optical components.

Axel Lubkمشاهده پروفایل

استاد

• Transmission Electron Microscopy (TEM) method development
• Charge particle optics and scattering theory
• Magnetic nanostextures
• +۳ مورد دیگر

Prof. Dr. Axel Lubk is a faculty member holding the CEOS endowed professorship for electron optics at Technische Universität Dresden and serves as a research group leader at the Leibniz Institute for Solid State and Materials Research (IFW Dresden). His work focuses on advanced electron microscopy techniques and magnetic nanotextures. Institution: TU Dresden / IFW Dresden Contact: a.lubk@ifw-dresden.de Research Interests: Explore cutting-edge developments in TEM methodology (high-resolution imaging, tomography, holography, spectroscopy) and theoretical foundations of charge particle optics. Investigate magnetic textures (skyrmions, domain walls), plasmonics in heterogeneous structures, electronic properties of oxide interfaces, and semiconductor heterostructures. Recent Research Trends: His 2025-2024 publications demonstrate expertise in nanoparticle synthesis via electron irradiation, plasmon localization in random networks, thermoelectric multilayer engineering, and 3D magnetic soliton characterization using advanced tomography. Technical Leadership: Co-developer of novel algorithms for electron tomography (WRAP) and quantitative holography techniques. Regularly contributes to international microscopy standards through invited talks at major conferences (APMC2025, IMC20, PICO 2024).

Peter Nejjarمشاهده پروفایل

استادیار

• Probability Theory
• Stochastic Processes
• Mathematical Physics
• +۳ مورد دیگر

Peter Nejjar is a Tenure-Track Juniorprofessor for Probability Theory at the University of Potsdam since January 2023. His research addresses fundamental questions of universality in stochastic systems , investigating why identical probability distributions emerge across disparate contexts like random matrices and growth models, with connections to combinatorics, mathematical physics, and numerical analysis. His primary research domains include stochastic particle systems (TASEP/ASEP), KPZ universality class phenomena, and Markov chain mixing behavior —particularly the cutoff phenomenon. Nejjar's work reveals deep structural parallels between shock fluctuations in interacting particle systems and random matrix eigenvalue distributions, leveraging connections to algebraic combinatorics through Schur processes. Recent publications demonstrate evolving focus from shock fluctuation theory (2015-2018) toward dynamical phase transitions in KPZ systems (2020-2022) and emerging interdisciplinary applications like DNA-based molecular tagging (2025). His collaborative network prominently features Patrik Ferrari across 7 publications, reflecting sustained focus on universality in exclusion processes.

Davud Rostam-Afsharمشاهده پروفایل

استاد

• Accounting
• Taxation
• Macroeconomics
• +۷ مورد دیگر

Professor Davud Rostam-Afshar is a Professor (W2) at the University of Mannheim Business School, where he serves as academic director of the German Business Panel. Previously, he held positions at the University of Hohenheim, Free University of Berlin, and University of Potsdam, with research stays at UC Berkeley and Harvard University. He also serves as a consultant to the European Commission and the OECD. Education: PhD (summa cum laude) in Economics, Free University of Berlin (2010-2015), Supervisors: Viktor Steiner (FU Berlin), Richard Blundell (UCL) Diploma (M.Sc. equivalent) in Economics, Free University of Berlin (2005-2010) Rostam-Afshar's research spans several interconnected fields with a focus on empirical analysis. His work in accounting and taxation examines corporate decision-making, regulatory effects, and transparency issues. In public finance, he investigates optimal taxation, tax incidence, and the relationship between taxation and economic behavior. His labor economics research explores occupational regulation, migration effects, and precautionary behavior. Methodologically, he specializes in econometrics, survey design, and experimental approaches, often combining theoretical modeling with empirical validation. His recent publications reveal a strong focus on business decision-making during economic disruptions (pandemics, wars), with particular attention to how firms respond to regulatory changes, taxation policies, and external shocks. The German Business Panel serves as a key data source for much of this research, providing insights into managerial perceptions and expectations that complement traditional accounting data. Scientific Awards: Principal Investigator, German Research Foundation (2023) University of Turin Fellowship (2020) Excellence in Reviewing Award, Labor Economics (2020) Best Teaching Award, University of Hohenheim (2019-2020 and 2019) Ph.D. Fellowship, German National Academic Foundation (2011-2013) Scholarship, German National Academic Foundation (2009-2010) Rostam-Afshar has advised numerous master's and PhD students, including Laura Arnemann (now at OECD) and Lisa Feil (now at SAP). His grant portfolio includes significant funding from the German Research Foundation and collaborations with major institutions including the European Commission. He has developed specialized Stata modules for econometric analysis, including BBANDITS for multi-armed bandit experiments. As academic director of the German Business Panel, Rostam-Afshar leads a major research initiative that collects semi-annual survey data from managers responsible for accounting and tax matters in German firms. This panel provides unique insights into firm-level decision-making processes that complement traditional financial reporting data.