Find Academic Supervisors

• Mathematical Statistics
• Applied Statistics
• Probability Theory
• +6 more

Holger Dette is a Professor and Chair Holder of Stochastics (specializing in Statistics) at the Faculty of Mathematics, Ruhr University Bochum. He leads the prominent Group Dette within the Institute of Statistics, overseeing a team of researchers, doctoral students, and administrative staff including Birgit Tormöhlen as team assistant. His research group is deeply integrated within the university's mathematical ecosystem, collaborating with other research groups across algebra, analysis, numerics, and topology. Dette's research spans mathematical statistics with strong applications in real-world problems. His primary interests include optimal experimental design, time series analysis, functional data, change point problems, nonparametric regression, biostatistics, special functions, goodness-of-fit tests, and random matrices . His work bridges theoretical statistics with practical applications, particularly evident in his collaborations with pharmaceutical giants Novartis and Bayer AG in biostatistics, as well as Quasol, a spin-off company from his statistics institute. His recent publications (2024-2025) reveal a research program increasingly focused on high-dimensional and functional data analysis, privacy-preserving statistics, and novel methodological approaches to longstanding statistical problems. Dette's work shows strong interdisciplinary connections, particularly with biomechanics (analyzing joint angles during fatigue phases) and data science (addressing challenges in the era of big data). His research group is actively involved in multiple DFG-funded projects including the newly established 'Small Data' collaborative research center (Sonderforschungsbereich 1597) and the Spatio-temporal Statistics for the Transition of Energy and Transport (Transregio 391). Dette has received significant recognition including the prestigious Humboldt Research Award . His paper 'With Great Power Come Great Side Channels: Statistical Timing Side-Channel Analyses with Bounded Type-1 Errors' achieved second place at the CSAW'24 Applied Research Competition MENA. His research group has also secured multiple significant funding awards from the German Research Foundation (DFG). As an advisor, Dette supervises numerous doctoral and master's students including Pascal Quanz, Marius Kroll, and Carina Graw. His group offers statistical consulting services for scientists and students across bachelor's, master's, and doctoral phases. The group maintains strong industrial partnerships, particularly in biostatistics applications, demonstrating Dette's commitment to translating theoretical statistics into practical solutions for real-world challenges.

Stefano NoventaView profile

Research Fellow

• Mathematical Psychology
• Psychometrics
• Psychophysics
• +7 more

Stefano Noventa is a Research Fellow at the Methods Center, Department of Social Sciences, Faculty of Economics and Social Sciences, University of Tübingen. He has held multiple postdoctoral positions at the University of Tübingen and previously at the University of Verona and the University of Padova. Education: Ph.D. in Cognitive Psychology, University of Padova (2011) M.Sc. in Physics, University of Padova (2006) Studies in Physics, University of Padova (1999–2006) International Visiting Graduate Student, University of Toronto (2009, 2010) Dr. Noventa's research lies at the intersection of mathematical psychology, psychometrics, and psychophysics, with a focus on developing and unifying quantitative models of human cognition and assessment. His work integrates Item Response Theory (IRT) and Knowledge Space Theory (KST) to create more robust frameworks for educational and psychological measurement. He investigates latent variable models, probabilistic knowledge structures, and the identifiability of complex psychometric models, often applying these to domains such as education, organizational psychology, and entrepreneurship. His recent publications (2020–2024) demonstrate a strong trend toward theoretical integration, particularly in bridging cognitive diagnosis models with traditional psychometric frameworks. The articles emphasize mathematical rigor, model generalization, and empirical validation, with applications in both cognitive science and applied psychology. Topics include the unification of assessment models, parameter estimation under local dependence, and the modeling of intuitive physical reasoning. Scientific Awards: No awards or honors listed in the provided text. Dr. Noventa has not been explicitly mentioned as an advisor to students, but he has served as a corresponding author and collaborator on multiple research projects, indicating a leadership role in research teams. He has been involved in a DFG-funded project (GLI NON-NORM) since 2019, suggesting active grant participation. His work is highly collaborative, involving researchers from Germany, Italy, Austria, and Canada. Labs and Research Groups: Methods Center, University of Tübingen Hector Institute of Education Science and Psychology, University of Tübingen Center of Assessment, University of Verona Department of General Psychology, University of Padova

• Causal Inference
• Qualitative Research Methods
• Mixed-Method Research
• +4 more

Macartan Humphreys serves as director of the Institutions and Political Inequality group at the WZB Berlin Social Science Center and holds honorary professorships in social sciences at both Trinity College Dublin and Humboldt University Berlin. His research focuses on methodological innovations in social science, particularly in causal inference and mixed-methods approaches. Humphreys has developed frameworks that integrate causal modeling with Bayesian updating for both qualitative and quantitative research traditions. Humphreys' recent publications reveal expertise in creating systematic approaches that connect theoretical frameworks with empirical analysis. His work emphasizes transparency in research design and the integration of information from diverse data sources. Key Contributions: Development of causal modeling frameworks for qualitative and mixed-methods research Advancement of Bayesian approaches to case-level causal inference Creation of tools for research design, diagnosis, and optimization Integration of process tracing with broader causal inference frameworks Leadership Positions: Director, Institutions and Political Inequality group, WZB Berlin Social Science Center

Jon WakefieldView profile

Professor

• Spatial epidemiology
• Spatial demography
• Small area estimation
• +7 more

Jon Wakefield is a Professor in the Department of Biostatistics at the University of Washington's School of Public Health, with additional appointments in the Department of Statistics. He maintains affiliations with the Fred Hutchinson Cancer Research Center, the Center for Statistics and the Social Sciences, and serves on technical advisory groups for the World Health Organization and United Nations on mortality assessment, child mortality estimation, stillbirths, and pre-term births. Wakefield's research focuses on spatial epidemiology, spatial demography, and small area estimation, with particular emphasis on estimating under-5 mortality in low and medium income countries. His work integrates hierarchical models for survey data, space-time models for infectious disease data, and ecological inference methods for both infectious and non-infectious disease contexts. He has made significant contributions to understanding the links between Bayesian and frequentist statistical procedures, developing innovative methods for spatial modeling and disease burden estimation. His publication record shows a strong focus on methodological development with practical applications in global health, particularly in mortality estimation, infectious disease modeling, and demographic analysis. Recent work has addressed critical issues in pandemic response, including excess mortality estimation during the COVID-19 pandemic and seroprevalence studies. His research increasingly incorporates advanced computational methods, including Template Model Builder and integrated nested Laplace approximations for spatial modeling. Fellow, American Statistical Association (2007) Guy Medal in Bronze, Royal Statistical Society (2000) Member of the National Academies of Sciences, Engineering and Medicine Wakefield leads significant research initiatives funded by NIH/NCI and NIH/NIAID, including projects on spatio-temporal epidemiology and statistical issues in AIDS research. He has developed influential software tools including SUMMER, surveyPrev, and SAE4Health, which enable sophisticated small area estimation and spatial analysis for public health applications. His work with WHO and UN technical advisory groups demonstrates the real-world impact of his methodological contributions to global health measurement.

• Medical Image Computing
• Tumor Growth Modeling
• Computational Physiology
• +3 more

Bjoern Menze is a Professor and Rudolf Mößbauer Tenure Track Chair at the Technical University of Munich (TUM), leading the Image-based Biomedical Modeling Group within the Munich School of Bioengineering. His research focuses on medical image computing, tumor growth modeling, and computational physiology, with applications in clinical neuroimaging and personalized radiotherapy design. He holds a Ph.D. in Computer Science from Heidelberg University and has held positions at ETH Zurich, INRIA Sophia Antipolis, MIT, and Harvard Medical School. His academic journey includes a postdoc at MIT’s CSAIL and Harvard Medical School, followed by roles at ETH Zurich and INRIA. His work bridges biomedical imaging with machine learning, emphasizing model-driven analysis of physiological processes. He has been a visiting professor at Maastricht University and contributes to initiatives like the Center for Translational Cancer Research at TUM. Key research areas include tumor growth modeling, quantitative imaging biomarkers, and integrating mathematical models with clinical data. His awards include the MICCAI Young Scientist Award (2014), Leopoldina Fellowship (2009), and DFG Research Fellowship (2008). He advises on medical AI, leads interdisciplinary projects, and publishes extensively in top journals like Nature Neuroscience and IEEE Transactions on Medical Imaging. His lab’s work spans applications such as glioblastoma radiotherapy optimization, whole-body bone lesion detection, and neural connectivity imaging. Collaborations include institutions like Harvard, MIT, and ETH Zurich. He emphasizes translating computational methods into clinical practice for personalized healthcare solutions.

Kihong HeoView profile

Associate Professor

• Program Analysis
• Programming Language
• Machine Learning
• +3 more

Kihong Heo is an Associate Professor in the School of Computing and Graduate School of Information Security at KAIST (Korea Advanced Institute of Science and Technology) in South Korea. His academic career includes serving as an Assistant Professor at KAIST from 2017-2019 before being promoted to Associate Professor in 2020, following his postdoctoral research at the University of Pennsylvania. He earned both his Ph.D. and B.S. in Computer Science & Engineering from Seoul National University. Dr. Heo's research focuses on developing program reasoning systems for safe and reliable software, with specific interests in AI-based program analysis systems for detecting deep semantic software bugs, general-purpose program simplification systems for secure and efficient software, and scalable program synthesis systems for automatic software generation and repair. His work bridges the gap between programming languages, program analysis, and machine learning techniques to create next-generation programming systems. Analysis of his recent publications reveals a strong trend toward integrating machine learning techniques with traditional program analysis methods, with significant contributions in compiler validation, software security, fault localization, and program debloating. His research has practical impact, with some of his work incorporated into Facebook's Infer static analyzer. ACM SIGSOFT Distinguished Paper Award, FSE 2025 Amazon Research Award, 2024 The Soo-Young Lee Teaching Innovation Award, KAIST, 2024 Prize for Excellence in Teaching, KAIST, 2024 Best Artifact Award, ICSE 2022 ACM SIGPLAN Distinguished Paper Award, PLDI 2019 ACM SIGSOFT Distinguished Paper Award, ICSE 2019 Dr. Heo actively mentors graduate students, currently advising several Ph.D. candidates including Yeonhee Ryou, Taeeun Kim, and Sujin Jang, as well as master's students. He has served on program committees for major software engineering and programming language conferences including PLDI, ICSE, POPL, and SPLASH, demonstrating his active role in the academic community. His laboratory, the Programming Systems Laboratory at KAIST, focuses on creating innovative programming systems that leverage both semantic-based program analysis and AI techniques.

Matthias BreuerView profile

Professor

• Corporate Reporting
• Sustainability Reporting
• Financial Regulation
• +3 more

Matthias Breuer serves as Heisenberg-Professor for Corporate Reporting & Regulation at Goethe University Frankfurt's Faculty of Economics and Business, where he investigates corporate transparency mechanisms and regulatory solutions for sustainability reporting challenges. His interdisciplinary work bridges historical accounting contexts with modern ESG assurance frameworks. His academic foundation includes: PhD from the University of Chicago Master’s degree from the London School of Economics and Political Science Bachelor’s degree from WHU Breuer's research program centers on information verification dynamics in corporate reporting, with particular emphasis on how financial regulation shapes resource allocation, innovation incentives, and disclosure practices. He examines historical precedents (e.g., 1890s streetcar industry) to inform contemporary sustainability assurance systems, employing advanced econometric techniques to analyze regulatory impacts across public and private firms. His scholarship consistently addresses the tension between voluntary and mandatory disclosure regimes in capital markets. Analysis of his 15 most recent publications reveals escalating focus on sustainability regulation, with 60% directly addressing ESG-related disclosure challenges. Methodologically, he integrates Bayesian statistics, fixed effects modeling, and historical analysis to dissect reporting anomalies, while recent work increasingly explores spillover effects in peer disclosure networks and minority representation dynamics within corporate environments. Key recognitions include: DFG Heisenberg Fellowship TRR 266 Research Fellowship (Accounting for Transparency) Fellowship at the Centre for Advanced Studies on Law and Finance Affiliate Fellowship at Chicago Booth's Stigler Center As principal investigator of the DFG-funded Heisenberg project, Breuer directs substantial research resources toward transparency regulation while maintaining active collaboration with Columbia University and Chicago Booth through prior appointments. His grant portfolio reflects sustained interest in regulatory economics, with current projects examining auditing mandates and corporate innovation under disclosure requirements. Breuer contributes to Goethe University's Accounting Research Seminar series and is embedded in the TRR 266 collaborative research center, where he coordinates interdisciplinary teams analyzing transparency standards. His methodological laboratory combines archival financial data with historical industry records to model disclosure dynamics under varying regulatory regimes.

Tobias NeckelView profile

Associate Professor

• Uncertainty Quantification
• Random Differential Equations
• Numerical Algorithms
• +4 more

Tobias Neckel is an Associate Professor at the Institute for Informatics at the Technical University of Munich (TUM), where he leads research projects and coordinates academic programs. He has been the project team leader of the IGGSE Project ExaNIML since 2018, main coordinator of the Ferienakademie since 2014, and Program Coordinator of the Bavarian Graduate School of Computational Engineering (BGCE) since 2009. Diploma in Technomathematik from TU München (2005) Dr. rer. nat. in Informatics from TU München (2009) Neckel's research focuses on Uncertainty Quantification, Random Differential Equations, and High Performance Computing. His work develops efficient numerical algorithms using hierarchic and adaptive methods such as octrees/spacetrees and sparse grids, with applications in fluid-structure interactions and incompressible fluid flow simulation. His research bridges theoretical mathematics with practical computational science, emphasizing robust and efficient implementations. His recent publications demonstrate a strong trajectory in multi-fidelity modeling, uncertainty quantification, and high-performance computing. Neckel has made significant contributions to scalable hierarchical approximation methods, dynamic resource management in HPC, and the application of machine learning techniques to computational science problems. His work spans diverse application domains including plasma physics, hydrology, and computational engineering. Lehrfonds prize of the TUM (2014) Ernst Otto Fischer prize of the TUM (2011) Promotionspreis des Bunds der Freunde der TU München (2009) Neckel has supervised numerous graduate students and has been actively involved in curriculum development and teaching innovation. His book "Bits and Bugs: A Scientific and Historical Review of Software Failures in Computational Science" (2019) represents a significant contribution to understanding software reliability in scientific computing. He has organized minisymposia at major conferences including SIAM CSE and SIAM UQ, and serves on program committees for various computational science conferences. As coordinator of the Ferienakademie and the BGCE, Neckel plays a central role in advanced computational engineering education in Bavaria. His research group develops software for exascale computing and contributes to the Transregional Collaborative Research Centre 89 on Invasive Computing. Neckel also maintains international collaborations, with research stays at institutions including the Australian National University and Tokyo Institute of Technology.

Katharina EggenspergerView profile

Researcher

• Automated Machine Learning (AutoML)
• Hyperparameter Optimization (HPO)
• Tabular Machine Learning
• +1 more

Katharina Eggensperger is an Early Career Research Group Leader at the University of Tübingen , leading the AutoML for Science group within the Cluster of Excellence Machine Learning for Science . She previously completed her Ph.D. at the University of Freiburg under Frank Hutter and Marius Lindauer (2022), and actively contributes to the AutoML community through open-source tool development and competition leadership. Co-developer of AutoML.org tools Faculty member of IMPRS-IS Chair for multiple AutoML workshops/conferences (2019-2025) Her research focuses on automated machine learning (AutoML) with specific attention to: AutoML Systems Hyperparameter Optimization Tabular Machine Learning Scientific Applications of ML She has organized multiple AutoML schools and conferences, including serving as Program Chair for AutoML 2024 and Non-archival Track Chair for AutoML 2025. Her work emphasizes making machine learning accessible through automation while maintaining scientific rigor and interpretability, particularly for tabular data applications. Katharina actively recruits PhD students through IMPRS-IS and collaborates with institutions like the University of Freiburg and Cyber Valley .

Yi-Chi LiaoView profile

Researcher

• Human-Computer Interaction
• Human-in-the-Loop Optimization
• Reinforcement Learning
• +4 more

Yi-Chi Liao is a postdoc researcher at ETH Zürich under the SIP Lab, funded by the ETH Zürich Postdoc Fellowship Programme. He holds a PhD from Aalto University (supervised by Prof. Antti Oulasvirta) and Master's/Bachelor's degrees from National Taiwan University. His research focuses on computational interaction, human-in-the-loop optimization, and biomechanical simulation. He has contributed to over 14 top-tier publications in venues like CHI, UIST, and TiiS. Education: PhD in Computer Science, Aalto University (Finland) Master's in Computer Science, National Taiwan University Bachelor's in Computer Science, National Taiwan University Research Interests: Advances in HILO frameworks for adaptive systems, human-AI co-design, and biomechanical motion simulation. His work integrates reinforcement learning, Bayesian optimization, and computational modeling to enhance interactive systems. Publications: Over 14 papers in top HCI venues, emphasizing optimization techniques, tactile interfaces, and 3D interaction. Recent work includes meta-Bayesian optimization for wrist-based interactions and real-time target inference via biomechanical simulation. Awards: CHI 2022 Honorable Mention, multiple outstanding review recognitions, and the ETH Postdoc Fellowship. He serves on program committees for CHI, UIST, and TEI. Teaching: Delivered lectures on Bayesian statistics, deep learning, and computational design at Aalto and NTU. Served as a teaching assistant for HCI and computer architecture courses. Labs: Active in the Human-Computer Interaction Lab (Saarland University), SIP Lab (ETH Zürich), and collaborations with Meta Reality Labs.

• Probabilistic Machine Learning
• Physics-Informed Machine Learning
• Industrial Applications of Data Science
• +4 more

Markus Lange-Hegermann serves as Professor of Mathematics and Data Science at Ostwestfalen-Lippe University of Applied Sciences (TH OWL) since 2018 and holds a board position at the Institute for Industrial Information Technology (inIT). His career bridges academic research and industrial applications, with expertise in translating machine learning theory into practical engineering solutions for automation and manufacturing sectors. His educational foundation includes a Diplom (Master equivalent) in Computer Mathematics from RWTH Aachen University (2004-2008) followed by a Dr. rer. nat. (PhD equivalent) in algorithmic differential algebra (2008-2014). Prior to academia, he gained industry experience at FEV GmbH as an R&D engineer (2014-2017) and P3 automotive GmbH as a Data Science Consultant (2017-2018). Lange-Hegermann's research centers on probabilistic machine learning with distinctive emphasis on physics-informed approaches. He develops Gaussian process methodologies that incorporate differential equations to model time dependencies, uncertainties, and physical constraints in industrial systems. His work enables robust data-based modeling and optimization for cyber-physical systems, with applications spanning predictive maintenance, process control, and quality assurance in manufacturing. Analysis of his 15 most recent publications (2024-2025) reveals consistent innovation in physics-integrated machine learning, particularly using Gaussian processes to solve partial differential equations and optimal control problems. The research demonstrates strong industrial applicability across domains including medical imaging, material science, automotive engineering, and brewing processes, with recurring themes of anomaly detection in time-series data and uncertainty-aware decision making. His scientific contributions have earned significant recognition: Forschungspreis TH OWL (2024) Top reviewer award at NeurIPS (2023) Outstanding reviewer award at NeurIPS (2021) Best poster award at Bosch AI CON (2019) Borchers Plakette for outstanding dissertation (2014) Springorum Denkmünze for outstanding diploma (2009) As chairman of the Data Science study program and vice chairman of undergraduate examination boards, Lange-Hegermann actively shapes academic curricula while supervising graduate theses. His governance roles include serving on professorship search committees at multiple institutions and contributing to examination regulations. He maintains active research funding through collaborations with industrial partners and reviews proposals for initiatives like It's OWL and 3IA Côte d’Azur. Lange-Hegermann leads the Mathematics and Data Sciences research group within inIT, fostering collaboration between theoretical machine learning and industrial automation. He co-founded AICOmmunityOWL and the Informatics Europe working group on Data Analysis and Reporting, while organizing machine learning reading groups and data science hackathons to bridge academic research with industrial problem-solving.

Zeev RudnickView profile

Professor

• Number Theory
• Mathematical Physics
• Quantum Chaos

Zeev Rudnick is a Professor of Mathematics at Tel Aviv University, holding the Cissie and Aaron Beare Chair in Number Theory since 2012. He is affiliated with the School of Mathematical Sciences and the Department of Theoretical Mathematics. Education: Ph.D., Yale University, 1990 M.Sc. summa cum laude, The Hebrew University, 1985 B.Sc. summa cum laude, Bar-Ilan University, 1984 Research Interests: Professor Rudnick's work focuses on the interface of Number Theory and Mathematical Physics, particularly Quantum Chaos. His research includes eigenvalue distribution, zeros of L-functions, quantum unique ergodicity, arithmetic problems in function fields, lattice point counting, and spectral statistics. Recent publications explore zeros of modular forms, quantum models, sparse exponential sums, and spectral properties of geometric domains. Awards and Honors: Heilbronn Distinguished Visiting Professor (2019) David Rees Distinguished Visiting Fellowship (2017) Aisenstadt Chair (2014) Invited Speaker at ICM 2014 ERC Advanced Grants (2013–2024) Fellow of the AMS (2012–) AHP Distinguished Paper Award (2011) Erdős Prize (2001) Alon Fellowship (1995) Sloan Dissertation Fellowship (1989/90) Advising and Grants: He has supervised 28 Ph.D. and M.Sc. students in number theory and mathematical physics. His research is supported by an ERC Advanced Grant (RMAST, 2019–2024), following a previous ERC grant (2013–2018). He currently teaches graduate/undergraduate seminars and analytic number theory.

• Sequential Decision Making
• Bandit Problems
• Theoretical Reinforcement Learning
• +6 more

Claire Vernade is a Group Leader at the University of Tübingen in the Cluster of Excellence Machine Learning for Science. She leads an active research group focused on theoretical aspects of sequential decision making, with particular expertise in bandit problems and reinforcement learning theory. Her work bridges theoretical foundations with practical applications in scientific discovery. Her research interests span sequential decision making, bandit problems, theoretical Reinforcement Learning, Learning Theory, and principled learning algorithms. She has made significant contributions to understanding non-stationary environments, lifelong learning frameworks, and the theoretical foundations of bandit algorithms. Her work on "Eigengame: PCA as a Nash Equilibrium" received an Outstanding Paper Award at ICLR 2021. Dr. Vernade has been awarded prestigious grants including an Emmy Noether award (2022) for her FoLiReL project and an ERC Starting Grant (2024) for her ConSequentIAL project. Her current ERC project explores the role of Reinforcement Learning in developing Continual Learning agents, with applications to scientific domains like drug discovery and micro-chemistry. Emmy Noether award under the AI Initiative call (2022) ERC Starting Grant (2024) Outstanding Paper Award at ICLR 2021 She currently supervises three PhD students and actively recruits postdocs and PhD candidates through the IMPRS-IS and ELLIS doctoral programs. Her group collaborates extensively with the broader machine learning community, organizing workshops like FoRLaC at ICML 2024 and serving as co-chairs for tutorials at major conferences. Dr. Vernade is also deeply committed to diversity and inclusion in machine learning, co-leading initiatives like Women in Learning Theory and Tübingen Women in Machine Learning.

Jürgen CitoView profile

Associate Professor

• Software Engineering
• Explainable AI
• Performance Engineering
• +3 more

Jürgen Cito is an Associate Professor with tenure at Vienna University of Technology (TU Wien), specializing in software engineering, explainable AI, and performance engineering. He leads research at the IPA Lab (as indicated by his personal website) and maintains a visiting researcher position at Google. His academic journey began with joining TU Wien as an Assistant Professor in Spring 2020, with promotion to Associate Professor announced in April 2024. His research interests span multiple critical areas of modern software development, with particular focus on developer experience, program comprehension, and the intersection of AI with software engineering practices. His work bridges theoretical foundations with practical industrial applications, as evidenced by collaborations with major technology companies. Analysis of his recent publications reveals a strong emphasis on practical tools and methodologies that enhance software quality, performance, and security. His research trajectory shows increasing focus on explainable AI techniques applied to software engineering problems, performance prediction from source code, and automated security testing approaches that leverage large language models. best teaching award for distance learning for Web Engineering (2020) Cito actively contributes to the software engineering community through numerous conference committee roles, including program committee positions at ASE, ICSE, ESEC/FSE, and other major venues. His lab appears to focus on developer tools, program analysis, and AI-assisted software engineering, with connections to both academic and industrial research environments.

• Cognitive Neuroscience
• Information Seeking
• Belief Formation
• +4 more

Professor Tali Sharot is a leading cognitive neuroscientist at University College London, holding dual appointments in the Department of Experimental Psychology (since 2018) and the Max Planck UCL Centre for Computational Psychiatry and Ageing Research (since 2020). She directs the Affective Brain Lab, focusing on neural mechanisms of human cognition and emotion. Education B.A. in Psychology and Economics, Tel Aviv University (1998) Ph.D. in Cognitive Neuroscience, New York University (2006) Research Focus Her interdisciplinary research bridges neuroscience, psychology, and computational modeling to investigate: How motivation shapes evidence accumulation in decision-making Neural basis of belief formation and confirmation bias Information-seeking behaviors and their impact on mental health Pharmacological and neuroimaging approaches to affective disorders Awards & Fellowships Wellcome Trust Senior Research Fellow (2019-2024) Wellcome Trust Career Development Fellow (2011-2017) British Academy Postdoctoral Fellow (2007-2011) Leadership She leads the Affective Brain Lab and serves as Group Leader at the Max Planck UCL Centre, integrating computational methods with neuroimaging to study psychiatric conditions.