Statistical Machine Translation Professors and Supervisors in Germany

Dirk Reinelمشاهده پروفایل

استاد

Hof University of Applied SciencesGermany

Prof. Dr. Dirk Reinel serves as Professor at Hof University of Applied Sciences within the Faculty of Interdisciplinary and Innovative Sciences since the 2023/2024 winter semester. He leads the B.Sc. program in Innovative Health Care and acts as StartupLab Ambassador for Innovative Health Care, driving healthcare innovation initiatives. His academic journey includes Computer Science studies at Hof University followed by doctoral research at the University of Bamberg, where he earned his Dr. rer. nat. in 2018 through work on corpus-based lexical resource generation for opinion mining. Reinel's research bridges Health Informatics and Natural Language Processing , with dual specializations in health data management (particularly migraine epidemiology) and sentiment analysis . His publication record reveals two distinct trajectories: clinical studies analyzing migraine patterns relative to school schedules, weekly cycles, and temperature variations; and technical contributions to opinion mining including phrase generation algorithms and domain-specific applications for insurance and social media. His recent work demonstrates increasing focus on healthcare applications, culminating in the 2022 adolescent migraine study, while maintaining foundational NLP contributions from his 2018 dissertation. The integration of clinical research with advanced text analytics represents his distinctive interdisciplinary approach. Co-founding smartlytic GmbH in 2017 enabled practical implementation of his research, notably through the DMKG Kopfschmerzregister (Headache Register) developed with the German Migraine and Headache Society. His current StartupLab role extends this translational focus, mentoring healthcare innovation projects while directing the B.Sc. program in Innovative Health Care.

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Adrien Doerigمشاهده پروفایل

استاد مهمان

Free University of BerlinGermany

Adrien Doerig is a Visiting Professor in the Department of Education and Psychology at Freie Universität Berlin, where he leads research in the Cognitive Computational Neuroscience Lab. He is also affiliated with the Bernstein Center for Computational Neuroscience. His work bridges cognitive science, computational modeling, and neural mechanisms of perception. Doerig's research focuses on the intersection of computational neuroscience and artificial intelligence, with particular emphasis on visual perception, consciousness, and the development of biologically plausible neural network models. His work explores how the brain processes visual information, with special attention to phenomena like crowding, feature integration, and the temporal dynamics of perception. He has made significant contributions to understanding the limitations of convolutional neural networks for modeling human vision and has pioneered work on topographic neural networks that better capture cortical organization. A key trend in Doerig's recent publications is the exploration of connections between language models and visual processing in the brain. His groundbreaking 2025 Nature Machine Intelligence paper demonstrated that large language model representations align closely with visual representations in the human brain, opening new avenues for using language-based AI in modeling biological visual processing. His work consistently challenges existing paradigms while developing more biologically plausible computational frameworks. Doerig teaches a wide range of courses including Master's in Cognitive Neuroscience, Probability and statistical modeling (both theoretical and practical with focus on ANNs and neuroimaging), Introduction to programming, Applied computational cognitive neuroscience, and specialized courses on vision, language, affective & social neuroscience, and consciousness. He also supervises graduate students through the Cognitive Computational Neuroscience Lab, which includes postdocs, predocs, master's students, and interns working on cutting-edge research questions at the intersection of cognitive science and machine learning.

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Juri Rappsilberمشاهده پروفایل

استاد

Leibniz Institute for Crystal GrowthGermany

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.

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Janina Hesseمشاهده پروفایل

استادیار

Leibniz Institute for Resilience ResearchGermany

Janina Hesse serves as Junior Professor at the University Medical Center of Johannes Gutenberg University Mainz and leads the Computational Resilience Research group at the Leibniz Institute for Resilience Research (LIR) in Mainz. Her interdisciplinary work bridges mathematical modeling, machine learning, and clinical medicine to investigate resilience mechanisms across biological scales. Her educational background includes: PhD in Computational Neurosciences/Theoretical Biology (2011-2017), Humboldt-University Berlin Diploma in Physics & Cognitive Sciences, École Normale Supérieure Paris (2007-2011) Master of Science in Theoretical Physics of Complex Systems, University Pierre-et-Marie-Curie Paris (2007-2011) Bachelor of Science in Physics, École Normale Supérieure Paris (2007-2011) Vordiplom in Mathematics, Goethe University Frankfurt (2005-2007) Her research centers on computational resilience dynamics , employing dynamical systems theory to model phase transitions in neuronal networks, circadian impacts on cancer therapy, and stress-response mechanisms. The group develops integrative frameworks combining machine learning with experimental data to identify resilience biomarkers and optimize treatment timing, particularly in oncology and neuroscience contexts. Publication trends (2014-2023) reveal a consistent focus on nonlinear dynamics in biological systems , with increasing clinical translation. Early work established theoretical foundations in neural criticality and bifurcation theory, while recent studies apply these principles to colorectal cancer chronotherapy and temperature-dependent neural encoding, demonstrating strong interdisciplinary collaboration with medical institutions. Key recognitions include: Portuguese Pneumology Society First Prize (2020) Berlin Neuroscience Forum Poster Prize (2012) Michael-Loulakis Study Achievement Award (2007) She mentors four doctoral students and a Master's candidate in her LIR research group, with funding primarily from the Boehringer Ingelheim Foundation. Her advisory scope extends to co-initiating ScientistsForFuture (2019) and serving as neuroscience specialist for Germany's Federal Agency for Civic Education. Current collaborations span MSH Hamburg, Charité Berlin, and Oldenburg's Helmholtz Institute. The Computational Resilience Research group operates through six core projects examining neuronal phase transitions, mouse behavioral resilience, cancer gene expression dynamics, and seasonal stress variations. This work integrates theoretical modeling with experimental validation through close partnerships with clinical and basic science teams at Johannes Gutenberg University Mainz, emphasizing translational applications for stress-related disorders.

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Angelo Vallerianiمشاهده پروفایل

استاد پژوهشی

Max Planck Institute of Colloids and InterfacesGermany

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.

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Esther Kuehnمشاهده پروفایل

استاد

Helmholtz Association of German Research CentersGermany

Prof. Dr. Esther Kuehn serves as Group Leader at the Hertie Institute for Clinical Brain Research and the German Center for Neurodegenerative Diseases (DZNE) in Tübingen. Her translational research utilizes 3T, 7T, and 9.4T MRI to investigate cortical microstructure in health and disease across multiple neurological conditions. Her research focuses on cortical microstructure imaging using ultra-high field MRI to understand neural mechanisms in: Neurodegenerative diseases (ALS, Parkinson's) Neurological disorders (Multiple Sclerosis) Aging populations Sensorimotor system architecture Somatic pain pathways Her lab develops advanced imaging techniques to bridge structural and functional brain changes. Recent publications demonstrate expertise in layer-specific myelin mapping and computational modeling for fMRI data alignment. She actively organizes scientific events including the BrainInDepth2024 conference on AI applications in ultra-high field MRI research. Prof. Kuehn collaborates with researchers including Oliver Contier, Lisa Klemm, Elena Azanon, Stefanie Schreiber, Oliver Speck, and Christoph Reichert. Her lab currently recruits for positions including lab managers and MTAs to support ongoing translational neuroscience projects.

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Hongyu Zhangمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Hongyu Zhang is a Professor and Dean of the School of Big Data and Software Engineering at Chongqing University, China, and an Honorary Professor at The University of Newcastle, Australia. Previously, he served as a Lead Researcher at Microsoft Research Asia and an Associate Professor at Tsinghua University, China. He received his PhD from the National University of Singapore in 2003. His academic journey spans prestigious institutions, combining industry research experience with academic leadership. Dr. Zhang's research interests focus on intelligent software engineering, software analytics, data-driven software engineering, software fault management, testing and debugging, and software maintenance and reuse. His work centers on improving software quality and productivity by mining and analyzing vast amounts of software data. Over the years, he has developed innovative methods that apply data mining, machine learning (including deep learning), and information retrieval techniques to extract knowledge from software data and solve complex software engineering problems. His research spans three major areas: intelligent programming (code search, code summarization, code generation), intelligent quality prediction (defect prediction, cloud failure prediction, performance prediction), and intelligent fault detection and diagnosis (log-based fault detection, crash-based fault localization, bug report analytics). His recent publications demonstrate a clear trend toward integrating large language models and deep learning techniques with traditional software engineering practices. The research spans intelligent programming assistance, code security, UI automation, distributed systems optimization, and performance analysis. His work increasingly focuses on practical applications of AI in software engineering, with emphasis on real-world impact in industrial settings, particularly in microservices, cloud systems, and large-scale software development environments. 8 ACM Distinguished Paper Awards Best Paper Award: How Long Will it Take to Mitigate this Incident for Online Service Systems? David Lorge Parnis Fellowship Senior Member of IEEE Distinguished Member of ACM Distinguished Member of CCF Fellow of Engineers Australia (FIEAust) Recognized in The Australian's Top Researchers special edition as leading researcher in Software Systems World's Top 2% Scientists (career-long) Dr. Zhang has successfully advised numerous PhD and Master's students who have gone on to prominent positions at leading technology companies and academic institutions worldwide. His research has been supported by significant grants including Australian Research Council Discovery Projects (as Lead CI) and multiple National Science Foundation of China projects. His work has made tangible impacts in industry, most notably through the Microsoft Developer Assistant project which received over 450K downloads in 2016. He leads research groups focused on intelligent software engineering and software analytics, with strong collaborations between Chongqing University, The University of Newcastle, and Microsoft Research. His teams develop practical tools for code intelligence, log analysis, and fault diagnosis that are deployed in real-world online service systems.

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Christian Fiebergمشاهده پروفایل

استاد

Bremen University of Applied SciencesGermany

Christian Fieberg serves as a Professor of Data Science at Hochschule Bremen (City University of Applied Sciences) in Bremen, Germany, and holds an Affiliate Professor position at Concordia University in Montreal, Canada. He is actively affiliated with the DTX research cluster within the Faculty of Business and Economics at Hochschule Bremen, where his work bridges theoretical modeling with practical financial applications. Professor Fieberg's research spans financial economics with particular expertise in risk and portfolio management, sustainable investments, and data-intensive financial analysis. His methodological approach integrates advanced statistical techniques, machine learning algorithms, and econometric modeling to address complex financial problems. He specializes in working with large datasets and developing practical software tools that translate research findings into actionable insights for financial practitioners. His publication record reveals a strong focus on market predictability, factor modeling, and cross-sectional analysis across various asset classes. Recent work demonstrates increasing attention to cryptocurrency markets and the application of machine learning techniques to international financial markets. His research consistently appears in top-tier finance journals including the Journal of Finance, Journal of Financial and Quantitative Analysis, and Review of Finance, reflecting both methodological rigor and practical relevance. Top 50 most research-intensive economists under 40 years of age in German-speaking countries (Wirtschaftswoche) Professor Fieberg maintains active collaborations with researchers across international institutions, as evidenced by his co-authored publications with scholars from Concordia University, Montpellier Business School, and the University of Bremen. His work with the DTX research cluster emphasizes innovative research approaches and strong academic-industry networking. His current projects include Bond Factor Prediction (2025-2026) and participation in international workshops such as the STARS EU workshop in Sweden. His laboratory work focuses on developing computational tools for financial analysis using multiple programming environments including Matlab/Octave, R, Stata, Python, and Excel/VBA. The research group maintains active data repositories on Harvard Dataverse for replication purposes, demonstrating commitment to research transparency and reproducibility.

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