Find Academic Supervisors

• Cognitive Neuroscience
• Computational Modeling
• Predictive Coding
• +4 more

Ryszard Auksztulewicz serves as Visiting Professor and Group Leader of the Prediction and Memory unit within the Department of Education and Psychology at Freie Universität Berlin. Based at Habelschwerdter Allee 45, Room JK 25/220 in Berlin, he leads research at the intersection of cognitive and systems neuroscience. His research focuses on neural mechanisms of prediction, attention, and memory , employing multimodal approaches including EEG, fMRI, MEG, ECoG, TMS, electrophysiology, and calcium imaging. He integrates these with computational modeling (Bayesian models of neural/behavioral data) and multivariate decoding techniques. Key research themes include: Predictive coding frameworks for understanding sensory processing Neural gain modulation in temporal expectation Auditory cortex representations of memory and prediction Task-dependent modulation of sensory predictions Recurrent processing in perceptual awareness Analysis of his 34 publications (h-index: 18) reveals consistent focus on computational neuroscience approaches to memory systems , with recent work emphasizing cross-species auditory processing and neural representation dynamics. His 2022-2018 publications particularly highlight mechanistic distinctions between "what" and "when" predictions in sensory cortex. Teaching responsibilities include: Probabilistic and Statistical Modeling (Lecture) Introduction to Programming (Seminar) Research Colloquium for Qualification Theses

Johannes BohnView profile

Researcher

• Attachment Theory
• Structural Equation Modeling
• Online Therapy Interventions
• +4 more

Dr. Johannes Bohn serves as a Research Associate & Internship Coordinator at Freie Universität Berlin's Department of Clinical-Psychological Intervention within the Department of Education and Psychology. He completed his PhD in 2021 with research on attachment patterns in emerging adulthood and currently coordinates practical training components for the Master's program in Psychology with a focus on Clinical Psychology and Psychotherapy. His educational background includes: PhD (2021) from Freie Universität Berlin: 'On the Consistency and Specificity of Patterns of Attachment in Emerging Adulthood' Master of Science in Work, Organizational, and Economic Psychology (2011-2014) from Freie Universität Berlin Bachelor of Science in Psychology (2007-2010) and Bachelor in Philosophy and History (2010-2011) from Freie Universität Berlin Bohn's research primarily focuses on attachment theory in emerging adulthood, structural equation modeling for multitrait-multimethod data analysis, online therapy interventions for depression, and defense mechanisms in personality psychology. His methodological expertise bridges clinical applications with advanced statistical techniques, particularly in longitudinal and multimethod assessment approaches. He has contributed significantly to understanding how attachment patterns develop and stabilize during the transition to adulthood. His publication record shows a clear trajectory from methodological development to clinical application, with recent work emphasizing digital mental health interventions. The majority of his research examines psychopathology through sophisticated statistical models while maintaining strong clinical relevance, particularly in depression treatment and attachment research. His work often involves interdisciplinary collaboration across psychology subfields. Bohn actively contributes to university governance as a deputy member of the Quality Assurance Board for Teaching and Learning (since 2021) and the Departmental Council (since 2020). He has previously served on committees focused on curriculum development for psychology programs at both bachelor's and master's levels. As an educator, he has taught numerous courses since 2013, with recent focus on structural equation modeling, clinical psychology, psychotherapy with children and adolescents, and legal frameworks of psychotherapy. His teaching combines theoretical foundations with practical research applications, reflecting his dual expertise in methodology and clinical practice.

• Applied Mathematics
• Project Management
• Construction Engineering
• +3 more

Dieter Beckötter serves as a Lecturer in Mathematics and Project Management at the Faculty of Building and Preservation, HAWK University of Applied Sciences and Arts. His role bridges theoretical mathematics with practical project execution in construction contexts. Research and teaching focus on quantitative methodologies for building processes, emphasizing: Mathematical modeling for structural preservation Risk assessment in construction project management Resource optimization in heritage building restoration Statistical quality control for civil engineering projects His instruction directly supports the faculty's mission to train professionals in sustainable building practices through rigorous technical foundations. While no active research grants or publications are documented in available sources, his expertise shapes curricula for building engineering students.

Janina KneippView profile

Professor

• Surface-Enhanced Raman Scattering
• Plasmonic Catalysis
• Nanoscale Spectroscopy
• +4 more

Prof. Dr. Janina Kneipp is a Professor (W3) of Physical Chemistry at Humboldt-Universität zu Berlin, where she has led an active research group since 2012. She previously held positions as Assistant Professor at HU Berlin/BAM (2008-2012), Junior Researcher at BAM (2005-2008), and research appointments at Harvard Medical School, Princeton University, and Erasmus Universiteit Rotterdam. Education: Dr. rer. nat. (summa cum laude), Freie Universität Berlin (2002) Undergraduate/Graduate Studies in Biology & Physics, Freie Universität Berlin (1992-1998) Research Focus: Her interdisciplinary work bridges physical chemistry and biospectroscopy, with particular emphasis on: Surface-enhanced Raman scattering (SERS) for complex sample analysis Plasmonic catalysis and hot electron chemistry Multiphoton-excited vibrational spectroscopy Nanoscale biochemical mapping in plant and animal systems Development of advanced plasmonic substrates Publication Trends: Recent work demonstrates strong focus on multimodal spectroscopy applications, with studies combining SERS, hyper-Raman, IR, and synchrotron techniques to address questions in catalysis, nanoparticle-cell interactions, plant biochemistry, and biosensing. Publications frequently incorporate advanced nanomaterials, electrochemical methods, and machine learning-assisted spectral analysis. Scientific Awards: Fellow, European Academy of Sciences (2020) Caroline von Humboldt Professorship (2019) Wilhelm Ostwald Fellow, BAM (2012) Bunsen-Kirchhoff Award, GDCh (2010) ERC Starting Grant (2010) Academic Leadership: Currently advises 5 PhD students and leads multiple collaborative initiatives. Serves as Board Member of Einstein Center Catalysis (since 2019), Head of Chemistry Department (2014-2016), and Speaker of Graduate School SALSA (since 2012). Secured funding through DFG, EU networks, and ERC grants supporting spectroscopy infrastructure development. Lab & Team: Leads the KneippLab research group with 2 postdoctoral researchers, 5 graduate students, and technical staff. Research focuses on developing spectroscopic methods for interrogating biological and chemical processes at nanoscale resolution using plasmonic enhancement strategies.

Juri RappsilberView profile

Professor

• Proteomics
• Mass Spectrometry
• Protein Structure
• +4 more

Prof. Juri Rappsilber is a Professor at the Technical University of Berlin's Institute of Biotechnology and Group Leader of Research Unit E in the UniSysCat Cluster of Excellence. His affiliations include Visiting Professorship at Kyoto University (2017, JSPS Fellow) and Senior Research Fellowships at the Wellcome Trust (2009-2019). He leads the Rappsilber Laboratory, focused on developing mass spectrometry technologies to study protein structure and interactions. Research interests center on proteomics , crosslinking mass spectrometry , and protein-network analysis . Key areas include: Functional protein networks through co-regulation studies Chromatin dynamics and mitotic chromosome composition In-cell structural proteomics integrating deep learning Development of bioorthogonal catalysis for medical applications His publication record demonstrates consistent output in high-impact journals, with recent work emphasizing: Structural mechanisms of protein complexes (γ-tubulin, condensin) Crosslinking MS methodology innovation and error control Integration of AI with experimental structural biology Catalytic processes in bioinorganic systems Awards and honors: EMBO Membership (2021) BSPR Lectureship (2014) Einstein Professorship (2011) Wellcome Trust Senior Research Fellow (2009-2019) Marie Curie Excellence Fellow (2005-2010) He actively advises doctoral students, including DAAD Hilde-Domin Program recipients, and secures substantial funding through ERC Synergy Grants, Wellcome Trust awards, and national initiatives. The lab focuses on developing anti-cancer therapeutics and sustainable chemistry solutions while pursuing Green Lab Certification.

Nuttida RungratsameetaweemanaView profile

Assistant Professor

• Neural computations
• Cognitive neuroscience
• Computational neuroscience
• +6 more

Nuttida Rungratsameetaweemana is an Assistant Professor in the Department of Biomedical Engineering at Columbia University's Fu Foundation School of Engineering and Applied Science. She also serves as a Provost Research Fellow at Columbia University, demonstrating her significant research contributions early in her academic career. NuttidaLab, her research group, focuses on integrating computational and experimental approaches to investigate the neural computations underlying complex cognitive functions in both health and disease. The lab's research spans several key areas including learning mechanisms, decision-making processes, and social behavior, with particular emphasis on understanding how these cognitive functions become disrupted in neurological and neuropsychiatric disorders. The lab has developed several important computational neuroscience tools including NeuralDecoder for representational similarity analysis, state_space_analysis for modeling neural dynamics, MARLAX for multi-agent reinforcement learning, and dynamax for state space modeling. These tools reflect the lab's commitment to developing robust analytical frameworks that can be widely adopted by the neuroscience community. NuttidaLab maintains an active GitHub presence with multiple repositories that demonstrate the lab's focus on open science and reproducible research. The lab's work combines cutting-edge computational approaches with experimental neuroscience to develop a deeper understanding of brain function. The lab actively seeks talented students and researchers interested in interdisciplinary work at the intersection of engineering, neuroscience, and computational science. Professor Rungratsameetaweemana's research represents an important bridge between theoretical computational models and experimental neuroscience, with significant potential for advancing our understanding of both healthy brain function and neurological disorders.

René SchenkendorfView profile

Professor

• Process Systems Engineering
• Uncertainty Analysis
• System Identification
• +4 more

Prof. Dr. René Schenkendorf is a Professor and head of the Process Systems Engineering group at the Institute of Energy and Process Systems Engineering, Braunschweig University of Technology. He has held this position since 2016, following research roles at the German Aerospace Center (2013-2016) and PhD studies at the Max Planck Institute for Dynamics of Complex Technical Systems (2007-2012). His academic credentials include: Dipl.-Ing. in Engineering Cybernetics, Otto-von-Guericke-University Magdeburg (2007) Dr.-Ing. in Engineering Cybernetics, Otto-von-Guericke-University Magdeburg (2014) Prof. Schenkendorf's research centers on data-driven process engineering, with core expertise in Uncertainty Analysis, System Identification, and Design of Experiments. He pioneers hybrid methodologies merging Physics-Informed Neural Networks with traditional modeling to solve industrial challenges in manufacturing, sustainable energy, and pharmaceutical synthesis. His work emphasizes practical implementation of digital twins and statistical process control for real-world optimization. Recent publications (2021-2024) reveal a strategic shift toward AI-integrated process engineering, particularly in physics-informed machine learning for parameter identification and model inversion. This trend underscores his commitment to bridging theoretical uncertainty quantification with industrial applications like sustainable aviation fuel production. As leader of the Process Systems Engineering group, he directs research advancing data-centric methodologies for process control and optimization. His active publication record indicates ongoing supervision of graduate students and successful grant acquisition in process systems engineering, though specific project details remain undisclosed in the source material.

• Computational Science
• Statistical Mechanics
• Optimal Control
• +3 more

Marcus Weber is a Privatdozent (equivalent to Associate Professor) affiliated with the Zuse Institute Berlin (ZIB), where he conducts research at the intersection of computational science and statistical mechanics. His work is supported by active German Research Foundation (DFG) grants through Collaborative Research Centers focused on complex system dynamics. Research interests include: Optimal control methods for time scale selection in equilibrium systems Protein dynamics and scale cascade phenomena Non-equilibrium kinetics of multivalent binding processes Computational modeling of biomolecular systems As a participating scientist in the Graduate School for Analytical Sciences Adlershof, he supervises graduate students within Berlin's university consortium. His current projects demonstrate sustained DFG funding for fundamental research in computational biophysics and statistical mechanics.

Angelo VallerianiView profile

Research Professor

• Stochastic Processes
• RNA Biology
• Population Genetics
• +8 more

Dr. Angelo Valleriani serves as Group Leader for Stochastic Processes in Complex and Biological Systems at the Max Planck Institute of Colloids and Interfaces in Potsdam, Germany, and coordinates the International Max Planck Research School (IMPRS) on Multiscale Bio-Systems. His research bridges theoretical physics and biological applications with a focus on quantitative modeling of complex biological phenomena. Dr. Valleriani earned his PhD in High Energy Physics from SISSA in Trieste, Italy (1996), following a Laurea Degree in Theoretical Physics from the University of Bologna (1992) with full marks and honors. His academic journey includes Visiting Scientist positions at Max Planck Institutes in Golm and Dresden before becoming a Group Leader in November 2000. His research interests encompass: Stochastic modeling of biological processes RNA biology and translational control mechanisms mRNA and tRNA turnover dynamics Population genetics and evolutionary biology Biostatistical data analysis Dr. Valleriani's recent publications (2022-2024) reveal a strong emphasis on computational approaches to biological problems, particularly in ribosome dynamics, protein synthesis regulation, and cellular remodeling processes. His work demonstrates sophisticated integration of mathematical modeling with experimental biology. He maintains active collaborations with researchers from multiple institutions including the University of Potsdam (Silke Leimkühler, Carsten Beta, Stefanie Barbirz), University of Cambridge (Davide Chiarugi), Weizmann Institute (Ziv Reich, Ruti Kapon), DRFZ Berlin (Ria Baumgrass), and others. The research group actively recruits MSc students from physics, mathematics, engineering, and bioinformatics backgrounds for challenging thesis projects in computational biology and biophysics.

Shing-Chi CheungView profile

Professor

• Software Engineering
• Program Analysis
• Software Testing
• +7 more

Shing-Chi Cheung is a Professor of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST), School of Engineering. He founded the CASTLE research group and co-founded the International Workshop on Automation of Software Testing (AST) in 2006. His leadership includes serving as General Chair of FSE 2014 and chairing multiple APSEC conferences. His research focuses on software quality enhancement through program analysis, testing, debugging, and AI techniques, targeting Android apps, open-source software, deep learning systems, smart contracts, and spreadsheets. Current projects include metamorphic testing frameworks, binary analysis tools, and vulnerability detection systems for emerging technologies. His publication portfolio demonstrates consistent contributions to software engineering since 2016, with recent work emphasizing AI-integrated testing methodologies, smart contract security, and deep learning system reliability. Key trends show increasing focus on cross-language analysis, data visualization quality, and compiler-level verification for modern software stacks. Distinguished Member of the ACM Fellow of the British Computer Society Editorial board member: Science of Computer Programming (SCP), Journal of Computer Science and Technology (JCST) Former editorial board member: IEEE Transactions on Software Engineering (2006-2009), Information and Software Technology (2012-2015) Four patents in China and the United States Cheung actively mentors through the CASTLE research group and serves on program committees for major conferences including ICSE, ESEC/FSE, and ISSTA. His work bridges academic research with practical applications through industry collaborations and tool development. He has contributed to numerous workshops and symposia as steering committee member and program chair.

• Health Economics
• Applied Microeconomics
• Medical Innovation
• +4 more

Julian Reif is Associate Professor of Finance and Economics at Gies College of Business, University of Illinois. He maintains significant research affiliations with the National Bureau of Economic Research, J-PAL North America, the Wilson Sheehan Lab for Economic Opportunities, and the Institute of Government and Public Affairs. Professor Reif is an applied microeconomist specializing in health economics, with research focusing on the determinants and value of health. His work examines the health and medical spending effects of air pollution, Medicare Part D impacts on drug utilization, and the value of medical innovation. He serves as Principal Investigator of the Illinois Workplace Wellness Study and as coeditor at The American Journal of Health Economics . His research has been supported by major funding organizations including J-PAL North America, the National Institutes of Health, the National Science Foundation, and the Robert Wood Johnson Foundation. Professor Reif's work has received significant recognition including the Kenneth J. Arrow Award for the best published paper in health economics and the NIHCM Foundation Research Award. Kenneth J. Arrow Award for the best published paper in health economics NIHCM Foundation Research Award for outstanding published work in health care Professor Reif has been featured in major media outlets including The New York Times , The Wall Street Journal , and The Washington Post . His methodological contributions include developing statistical tools like the 'dobatch' command for Stata that enables parallel processing of dofiles. His educational background includes a PhD in Economics from the University of Chicago and a BA from Vanderbilt University, with prior professional experience as a Senior Consultant at Bates White.

Erhan GençView profile

Professor

• Neuroscience
• Differential Psychology
• Cognitive Functions
• +5 more

Prof. Dr. Erhan Genç serves as a University Professor (W2) for 'Neuroscientific Foundations of Differential Psychology' at TU Dortmund University since 2023, operating under the Jülich model for research activities at IfADo (Leibniz Research Centre for Working Environment and Human Factors). He leads the 'Neuroimaging and Interindividual Differences' research group at the Institute for Occupational Safety and Health of the German Social Accident Insurance in Dortmund, Germany. His primary research focuses on: Neural basis of interindividual differences in cognitive functions and personality Application of multimodal neuroimaging methods (7T, 3T EEG/fMRI, TMS/fMRI, DWI, neurogenetics) Genç's research program investigates how variations in brain structure and function correlate with differences in cognitive abilities and personality traits. His work employs advanced neuroimaging techniques to uncover neural mechanisms underlying human intelligence and cognitive control processes, with particular emphasis on structural and functional brain connectivity. His publication record demonstrates consistent contributions to understanding the relationship between brain microstructure (particularly white matter organization and neurite architecture), brain network properties, and cognitive abilities. His recent work has established important links between polygenic scores, brain network efficiency, and intelligence measures. Methodologically, his expertise spans diffusion MRI for assessing microstructural properties of brain tissue, functional MRI for investigating brain connectivity patterns, and multimodal integration of different neuroimaging techniques to provide comprehensive insights into brain-behavior relationships.

Stefan WagnerView profile

Professor

• Software Quality
• AI-Supported Software Engineering
• Empirical Studies
• +3 more

Stefan Wagner is a full professor of software engineering at the Technical University of Munich (TUM), where he leads the Chair of Software Engineering within the Informatics Heilbronn 3 department. He earned his PhD in computer science from TUM and has established himself as a leading researcher in software engineering with a unique interdisciplinary background combining computer science and psychology. Dr. Wagner's research spans multiple areas of software engineering including software quality, human factors, AI-based software engineering, automotive software, and empirical studies. His work often bridges theoretical foundations with practical applications, particularly in the automotive domain and AI-assisted development. He has published over 130 peer-reviewed scientific articles and authored the book 'Software Product Quality Control.' His recent publications demonstrate a growing focus on AI integration in software engineering, with particular attention to large language models, empirical validation of AI-assisted development practices, and the human aspects of these emerging technologies. There's also a strong thread of research on automotive software systems, technical debt, and software quality measurement. 2022 Class of IEEE Computer Society Distinguished Contributors Senior member of IEEE and ACM Best Full Paper Award, ACM/IEEE International Symposium on Empirical Software Engineering and Measurement 2020 Best Paper Award, EAI International Conference on Cognitive Computing and Cyber Physical Systems 2020 Keynote at the International Conference on Software Quality 2020 Google Research Award 2009 Dr. Wagner actively mentors students and early-career researchers, with several frequent co-authors on his publications likely representing his advisees. His research has been supported by various funding sources enabling his work on automotive software, AI in software engineering, and empirical studies. He serves on the editorial boards of IEEE Software, Empirical Software Engineering, and the Journal of Systems and Software, contributing significantly to the academic community. His research group appears to focus on several key areas including AI-assisted software development, automotive software systems, and empirical software engineering methods. The group maintains active collaborations with industry partners, particularly in the automotive sector, and engages in interdisciplinary research bridging computer science with psychology and human factors.

Annabelle Kehl-BeckmannView profile

Professor

• Extreme value theory
• Risk control
• Risk management
• +5 more

Professor Annabelle Kehl-Beckmann serves as Head of University at Deutsche Bundesbank University of Applied Sciences, holding a full professorship in quantitative finance. Her academic leadership includes membership in the University Senate and oversight of academic operations. Her research focuses on extreme value theory applications in financial risk modeling , with specialized expertise in event risk quantification, market/credit risk parameterization, and jump-diffusion processes. Key contributions include developing generalized Pareto models with time-varying thresholds for tail risk estimation in equity markets and banking supervision contexts. Publications since 2009 demonstrate consistent specialization in extreme value modeling for financial risk , with recurring themes in equity event risk, credit derivatives valuation, and regulatory capital frameworks. Her work bridges theoretical statistics with practical banking supervision requirements. Teaching responsibilities encompass advanced quantitative finance topics including: Stochastics and financial mathematics Credit portfolio modeling Rating validation procedures Interest book present value management Market risk model development Professional development includes significant industry engagement through workshops with Deutsche Bundesbank, DekaBank, and HSH Nordbank, alongside management of third-party-funded research projects in extreme value modeling.

Michael MayleView profile

Research Professor

• Computational Physics
• Sensor Technologies
• Ultracold Quantum Gases
• +2 more

Michael Mayle is a Research Professor for Applied Computational Physics at Nuremberg University of Applied Sciences. He serves as a member of the Doctoral Center for Physical and Biomedical Engineering (CPaB) and holds a deputy position on the CPaB doctoral committee. His office is located at space KA.434, with contact details including phone +49 (0)911 5880-1309 and email michael.mayle@th-nuernberg.de. His research centers on Computational Physics with specialized focus in Sensor Technologies , Ultracold Quantum Gases , and Acoustics . He leads the Computational Physics for Sensor Technologies research group, developing advanced computational models for physical systems with direct applications in sensor design and analysis. His work bridges theoretical physics with practical engineering solutions. Analysis of his publication trajectory (2006-2025) reveals a distinct evolution from fundamental quantum research to applied sensor technology. Early work (2006-2013) established expertise in ultracold molecules and Rydberg atom physics, while recent publications (2019-2025) demonstrate a strategic pivot toward industrial applications including piezoelectric material analysis, ultrasonic flow metering, and water consumption pattern recognition. Professor Mayle actively contributes to doctoral education through the CPaB framework and teaches core courses including Simulation of Physical Systems (Continuum Mechanics) for M-AMP, Physics (Oscillations and Waves) for B-MED, and Building Physics for B-EGT/B-BI programs. His research group maintains strong industry connections through sensor technology development and applied computational modeling projects.