Prof. Dr. Nina Gantert is a distinguished Professor of Probability Theory at the Technical University of Munich (TUM) , affiliated with the TUM School of Computation, Information and Technology . She has held faculty positions at Karlsruhe Institute of Technology and the University of Münster prior to joining TUM in 2011. Her research focuses on probability theory , particularly stochastic processes , large deviations , and random media . She investigates random walks in random environments as models for transport in disordered systems and explores applications in physics and biology . Recent publications highlight her work on branching random walks , mixing times , biased random walks , and large deviation principles for complex stochastic systems. She has co-authored studies on random walks in dynamical percolation , interacting edge-reinforced processes , and extremal point processes in branching models. Scientific Awards: Elected fellow of the IMS (2016) Her academic career spans institutions including ETH Zürich, University of Bonn, Technical University of Berlin, and TUM. She has supervised numerous Bachelor’s and Master’s theses on topics ranging from mixing time analysis to percolation theory , often collaborating with international co-authors.
Max Planck Institute for Molecular GeneticsGermany
Professor Knut Reinert is a leading figure in algorithmic bioinformatics at the Free University of Berlin, where he holds a professorship in the Department of Mathematics and Computer Science. He also maintains a significant affiliation with the Max Planck Institute for Molecular Genetics in Berlin, where he leads the Efficient Algorithms for Omics Data group. His research spans both institutions through the Reinert Lab, which focuses on developing novel computational approaches for biological data analysis. Reinert's educational background includes a Diploma in Computer Science (1994) and a Doctorate (Dr. Ing./Ph.D., 1999, with honors) from the Max-Planck-Institut for Computer Science and Universität des Saarlandes in Saarbrücken. Prior to his professorship, he worked as a computer scientist under Prof. Gene Myers at Celera Genomics in Rockville, USA (1999-2002). His primary research interests center on algorithmic bioinformatics with specific focus on developing novel algorithms and data structures for biomedical mass data analysis. This includes creating mathematical models for genomic sequence analysis and algorithms for mass spectrometry data to detect differential protein expression between normal and diseased samples. His work bridges the gap between computational tool development and practical biological applications, with particular emphasis on NGS and proteomics data. The publications and projects led by Prof. Reinert demonstrate a consistent focus on advancing computational methods in bioinformatics. His research spans genomic sequence analysis, RNA research (particularly long non-coding RNAs), parallel computing applications, and GPU acceleration for biological data processing. The work shows increasing sophistication in handling large-scale biological datasets through innovative algorithmic approaches. Intel® Parallel Computing Center designation for his lab CUDA Research Center status DFG funding of 530 thousand Euros for RNA research de.NBI funding of 2 million Euros BMBF funded projects 'LIVE-DREAM' and 'EssBar' Prof. Reinert leads multiple significant research projects and has established strong collaborations with international partners including Texas A&M, Kings College London, Eberhardt-Karls Universität Tübingen, Robert-Koch-Institute, and various Turkish institutions. His lab receives funding from major organizations including DFG, BMBF, and Intel. The Reinert Lab maintains active teaching responsibilities at FU Berlin, offering courses at BSc, MSc, and PhD levels using both traditional and innovative learning concepts like e-learning and inverted classrooms. The Reinert Lab consists of two interconnected research groups that work closely with experimental biologists and medical researchers to develop practical computational solutions for real-world biological problems. The lab has established itself as a key player in the German and international bioinformatics community through its development of the widely-used SeqAn library and participation in national infrastructure initiatives.
Carlos Cinelli is an Assistant Professor in the Department of Statistics at the University of Washington, where he conducts research at the intersection of causal inference, statistical methodology, machine learning, and artificial intelligence. He is also a data science fellow at the eScience Institute and affiliate faculty of the Center for Statistics and the Social Sciences, demonstrating his interdisciplinary approach to causal methodology. Dr. Cinelli received his Ph.D. in Statistics from the University of California, Los Angeles, advised by Chad Hazlett and Judea Pearl, two prominent figures in causal inference. His research focuses on developing new causal and statistical methods for transparent and robust causal claims in empirical sciences, with particular attention to challenges faced by social and health scientists. His work spans theoretical developments in causal identification, sensitivity analysis frameworks, and practical software implementations that enable researchers to assess the robustness of their causal conclusions. Cinelli's research program addresses fundamental questions about how unobserved confounding affects causal estimates and develops tools to quantify how sensitive findings are to potential violations of causal assumptions. His work on omitted variable bias frameworks has been particularly influential across multiple disciplines. Through his publications, Cinelli has established himself as a leading researcher in causal inference methodology, with papers appearing in top journals across statistics, machine learning, epidemiology, and social sciences. His work demonstrates both theoretical rigor and practical relevance, often accompanied by open-source software implementations that make his methods accessible to applied researchers. Best paper award at SBE 2024 in Econometrics Royalty Research Fund (RRF) Award recipient NSF/MMS research support As an advisor, Cinelli has successfully guided PhD students like Nick Irons to dissertation completion. He actively seeks new students with strong interests in causal inference. His research is supported by multiple funding sources including the National Science Foundation and the University of Washington's Royalty Research Fund. Cinelli contributes to the academic community through editorial work for the Journal of Causal Inference and by developing widely used software packages like sensemakr for sensitivity analysis.
Prof. Dr. Oliver Krüger is a behavioral ecologist and evolutionary biologist at Bielefeld University 's Faculty of Biology , where he leads the Department of Animal Behaviour since 2013. His research spans avian and marine mammal systems, focusing on life history strategies, parasite-host interactions, and environmental adaptation. Education: Biology studies at Bielefeld University (1994-1996) MSc in Oxford (1996-1997) PhD at Bielefeld University with Fritz Trillmich and Jan Lindström (1998-2000) Research Themes: Behavioral ecology, evolutionary biology, and population dynamics across tropical and temperate ecosystems. Key projects include NC³ (Niche Choice/Construction) and studies on Galápagos sea lions, common buzzards, and pinniped species. Scientific Leadership: Spokesperson, SFB TRR 212 "NC³" (2018-2025) Advisory Board member: German Ornithologists Union, IUCN SSC pinniped group, German Primate Centre Peer review roles: Humboldt Foundation, DFG, HFSP, NSF Awards: Leopoldina Prize (2001) Niko Tinbergen Award (2008) DFG Heisenberg Professorship (2010-2015)
Bertram Müller-Myhsok is a Research Professor and Research Group Leader at the Max Planck Institute of Psychiatry in Munich, Germany. His research focuses on statistical genetics and transcriptomic data analysis in psychiatric disorders, particularly major depression, PTSD, schizophrenia, and their treatment responses. He integrates machine learning with genetic and clinical data to develop predictive models and stratified treatment approaches. Professional activities include leadership roles in the International Max Planck Research School for Translational Psychiatry and collaborations with institutions like the Institut du Cerveau (Paris) and Bernhard Nocht Institute (Hamburg). His work spans genetic epidemiology, psychiatric genomics, and precision medicine, with over 400 publications in high-impact journals. Key research areas include identifying genetic risk factors for mental disorders, developing polygenic scores, and leveraging omics data to uncover disease mechanisms. He leads projects like Psych-STRATA, a Horizon Europe-funded initiative advancing personalized psychiatry through pharmacogenomics.
Prof. Heinz Koeppl is a Professor in the Department of Electrical Engineering and Information Technology at TU Darmstadt. His research focuses on self-organizing systems, systems biology, and control theory, with applications in synthetic biology, robotics, and stochastic processes. He explores interdisciplinary topics such as genetic circuit design, UAV swarm dynamics, and machine learning-driven modeling of biochemical systems. Key research areas include the development of deep learning frameworks for kinetic modeling, Bayesian optimization for riboswitch design, and mean field control theory for sparse networks. His work bridges theoretical foundations with practical engineering solutions, addressing challenges in molecular communication, gene regulation, and robotic swarm coordination. Publications from 2023–2025 highlight advancements in bio-inspired algorithms, swarm intelligence, and computational biology. Notable contributions include studies on RNA-based circuits, active matter dynamics, and optimization strategies for large-scale systems. His research emphasizes interdisciplinary collaboration, leveraging tools from electrical engineering, mathematics, and life sciences. No scientific awards are explicitly listed in the provided text. Advising and grants details are not available. Prof. Koeppl’s lab focuses on integrating systems biology approaches with engineering principles to solve complex problems in healthcare, environmental sustainability, and technological innovation.
Prof. Dr. Florian Jeltsch leads the Plant Ecology and Nature Conservation research group at the Institute of Biology and Biochemistry, University of Potsdam. His work spans multiple ecological domains with a focus on understanding biodiversity patterns and processes in changing environments. His research group investigates community ecology across various landscapes including grasslands, agricultural systems, and drylands. Dr. Jeltsch's research interests encompass Community Ecology of Grasslands and Agricultural Landscapes, Community Ecology of Drylands, Biodiversity in Heterogeneous Environments, Movement Ecology and Biodiversity Dynamics, and Applied Regional Conservation. His work integrates theoretical and empirical approaches to address pressing ecological questions related to global change. He has been instrumental in developing and applying individual-based models to understand complex ecological systems. Analysis of Dr. Jeltsch's recent publications reveals a strong focus on individual-based ecological modeling, movement ecology, and the impacts of landscape heterogeneity on biodiversity. His work spans from theoretical foundations to practical conservation applications, with particular emphasis on savanna ecosystems, habitat fragmentation effects, and climate change impacts. The research demonstrates integration of multiple scales from individual energetics to community dynamics. Dr. Jeltsch has mentored numerous doctoral students and postdoctoral researchers who have gone on to establish their own research careers. His group has been involved in multiple collaborative projects including BioHet, GrassYear, BioMove, and ORYCS, addressing biodiversity conservation in heterogeneous environments. The Plant Ecology and Nature Conservation group maintains active research stations including the Gülpe Research Station, providing field-based research opportunities. The group's work bridges theoretical ecology with practical conservation applications, particularly in the context of global environmental change.
Niels Dingemanse is a tenured Professor of Behavioural Ecology at Ludwig Maximilians University (LMU) in Munich, Germany, and leads the Evolutionary Ecology of Variation research group at the Max Planck Institute for Ornithology. His academic journey includes roles as a Postdoctoral Research Fellow at the University of Groningen and the University of Wales, Bangor. He holds a PhD in natural sciences from Utrecht University (2003) and an M.Sc. in Ecology from the University of Groningen (1997). His research focuses on evolutionary and behavioural ecology, particularly the ecological and evolutionary significance of individual variation in behaviour, physiology, and life-history traits. Key themes include personality evolution, indirect genetic effects, and the integration of genomic approaches with ecological field studies. Dingemanse has contributed to projects like the Great Tit HapMap initiative, exploring genomic variation across continental scales. Publications emphasize interdisciplinary methods, combining field experiments with genomic and statistical tools. His work addresses topics such as mate choice evolution, the role of environmental fluctuations in shaping social plasticity, and the genetic basis of vocal rhythms in birds. Grants and fellowships include the Veni Innovational Research Incentives Scheme (Netherlands Organisation for Scientific Research) and ALW open competition grants. He has advised numerous postdoctoral researchers and leads collaborative projects across Europe.
Kai Leonhard is an Adjunct Professor at the Chair of Technical Thermodynamics , RWTH Aachen University. His research focuses on computational chemistry, thermodynamics, and molecular modeling, particularly in solvent design and reactive chemical processes. Department: Chair of Technical Thermodynamics Email: kai.leonhard@ltt.rwth-aachen.de Prof. Leonhard's work integrates quantum chemistry with computer-aided molecular and process design (CAMD/CAPD), emphasizing solvation thermodynamics, reaction kinetics, and machine learning applications. His projects span biofuel combustion, microgel synthesis, and sustainable solvent development. Recent publications highlight advancements in COSMO-RS-based solvent screening, reaction network exploration via ChemTraYzer-TAD, and multi-fidelity modeling for partition coefficients. He employs machine learning to enhance predictive thermodynamic models and optimize chemical processes.
Professor Ralf Stanewsky leads the Stanewsky Group at the Institute of Neuro- and Behavioral Biology, University of Münster. His research focuses on the molecular mechanisms of circadian rhythms in Drosophila melanogaster , particularly how environmental cues like light and temperature reset the circadian clock. The group employs genetic, molecular, histological, and behavioral approaches to study sensory pathways and their integration in central clock neurons. Member of the Multiscale Imaging Centre (MIC) and Imaging Network – Microscopy Current lab members: Ph.D. students Anna Katharina Eick, Angelica Coculla, Maia Zabel Barroso Technical assistants Regina Hube and Ume Aiman Research Themes Light and temperature synchronization of circadian clocks Temperature compensation mechanisms in biological timing Neuronal integration of environmental signals Evolutionary aspects of circadian regulation Professor Stanewsky’s work spans molecular clock components (e.g., cryptochromes, timeless gene variants) to broader ecological implications of temporal niche choice. His lab investigates how clock gene expression responds to seasonal changes and environmental stressors, while also exploring novel synchronization pathways beyond classical photoreceptors. Publication Trends Recent articles emphasize temperature-dependent clock regulation , evolutionary capacitance via Hsp90 , and non-canonical phototransduction in circadian systems. Key subfields include nuclear transport dynamics, kinase evolution, and computational modeling of periodic patterns across species. Contact Information Institute of Neuro- and Behavioral Biology, University of Münster MIC | Röntgenstraße 16, D-48149 Münster, Germany Email: stanewsky@uni-muenster.de Phone: +49 251 8321029
Elena Anatolyevna Babushkina is a Professor at the Department of Construction and Economics of Siberian Federal University. She serves as director and scientific consultant of the Scientific and Educational Laboratory 'Dendroecology and Environmental Monitoring' . Her work spans dendrochronology, climate change impacts on tree growth, wood anatomy, and environmental monitoring in Siberian ecosystems. Doctor of Biological Sciences (2020) Corresponding Member of the Russian Academy of Sciences Extensive collaborations with international institutions like University of Arizona, University of Cambridge, and Swiss Federal Institute for Forest, Snow and Landscape Research Her research focuses on climatic reconstruction through tree rings , moisture-limited forest ecosystems , and environmental drivers of xylogenesis . Recent studies analyze earlywood/latewood dynamics, drought sensitivity, and cross-species growth patterns in Siberian larch, spruce, and Scots pine populations. Elena’s publications (100+ scientific, 10+ methodological) include 15 recent articles on tree-ring-based climate proxies , crop yield modeling , and seasonal growth regulation . Key journals include Forests , Dendrochronologia , and Scientific Reports . Notable scientific awards include the 2021 Honorary Worker of Education of the Russian Federation title and multiple Presidential and Ministerial Certificates of Appreciation . She leads national grants (RFBR, RSF) on climate-crop interactions and genetic adaptation to environmental stress .
Paul J. Kennedy is a Professor at the University of Technology Sydney's Centre for Artificial Intelligence. He holds a PhD from the same institution (1999). His research focuses on machine learning applications in healthcare, bioinformatics, medical imaging, and data mining. Key areas include developing algorithms for genomic data analysis, healthcare pathway modeling, and edge-cloud frameworks for omics data. Education: PhD in Artificial Intelligence (1999, UTS). Research interests span machine learning, health informatics, and data compression. Notable work includes studies on administrative health records, lung nodule detection, and virtual reality-based cancer cohort analysis. He has co-authored over 100 publications across journals like BMC Bioinformatics, IEEE Transactions, and Artificial Intelligence in Medicine. Advising: Collaborates extensively with students/researchers but no explicit student list provided. Grants and labs: Active in interdisciplinary projects involving medical and computational teams, though specific grants are not detailed here.
Prof. Dr. agr. Kaspar Bienefeld is an Honorary Professor at the Humboldt University of Berlin within the Albrecht Daniel Thaer-Institute of Agricultural and Horticultural Sciences. His research focuses on honeybee genetics, breeding optimization, and disease resistance mechanisms. Research Interests: Honeybee population genetics, Varroa mite resistance, genomic selection, and behavioral ethology. Key Contributions: Development of SNP assays for resistance traits, simulation studies for breeding schemes, and conservation strategies like Europe’s first honeybee gene bank. Publications: Over 40 peer-reviewed articles spanning honeybee breeding, genetic parameter estimation, and stress biomarker analysis.
Michael Nothnagel is a Professor at the University of Cologne, where he leads the Department of Statistical Genetics and Bioinformatics within the Cologne Center for Genomics (CCG). His work spans statistical genetics, genetic epidemiology, and forensic genetics, focusing on methodological development and large-scale genomic data analysis. His research interests encompass theoretical and applied statistical genetics, with emphasis on human genetic diversity, disease etiology, and forensic applications. Key areas include Y-chromosomal phylogeography, genome-wide association studies for complex diseases, development of statistical methods for variant interpretation, and forensic marker optimization. His group leverages next-generation sequencing data and specialized forensic markers to address questions in population history, disease mechanisms, and identification systems. Recent publications reveal a strong focus on computational approaches to genetic analysis, including spatial frequency interpolation for haplogroup mapping, polygenic risk score applications for behavioral traits, and advanced methods for variant classification. His work demonstrates consistent integration of statistical theory with practical applications in medical and forensic genetics, often through international collaborations like the VISAGE Consortium. Nothnagel maintains active involvement in the Cologne Center for Genomics, contributing to seminars and collaborative projects including the upcoming 34th International Genetic Epidemiology Society meeting. His research group operates at the intersection of computational biology and medicine, with particular strengths in handling complex genomic datasets and developing novel analytical frameworks for genetic epidemiology.
Professor Stefan Siebert is Head of the Department of Crop Sciences at the Agricultural Faculty of the University of Göttingen, a position he has held since October 2017. His academic career spans multiple prestigious institutions including the University of Bonn, University of Frankfurt, and University of Kassel, where he completed his doctoral studies. University of Göttingen (2017-present): Professor and Head of Crop Science/Agronomy University of Bonn (2016-2017): Temporary Head of Chair Crop Science University of Bonn (2009-2015): Habilitation in Crop Science and Resource Conservation University of Frankfurt (2004-2009): Postdoctoral Scientist University of Kassel (2002-2005): Ph.D. Student and Scientist Professor Siebert's research focuses on sustainable resource use in crop production, climate change impacts on plant growth, plant growth modeling, and drought risk assessment. His work integrates large-scale data analysis with modeling approaches to understand the complex interactions between resource use, crop management, and productivity. He has developed comprehensive datasets on water, soil, and nutrient use in agriculture and pioneered methods for analyzing irrigation systems globally. His recent publication trends reveal a strong emphasis on irrigation systems, climate change impacts on agriculture, and advanced crop modeling techniques. The analysis of his 15 most recent articles shows consistent focus on water resource management, spatial analysis of agricultural systems, and the effects of climate variability on crop production. His work frequently combines remote sensing data with ground-based measurements to create high-resolution datasets for agricultural decision-making. Professor Siebert leads several significant research projects including ZERN (Future of Nutrition in Lower Saxony, 2024-2029), Collaborative Research Center 1502 on Regional Climate Change (2022-2025), and OUTLAST (Development of a global drought hazard forecasting system, 2022-2025). He has been continuously developing and maintaining a global dataset on irrigated areas since 1997, establishing himself as a leading authority in agricultural water management. His teaching portfolio is extensive, covering scientific writing, field production exercises, general crop production, methodical work, plant biology, crop production and breeding, sustainability of production systems, and applications of data analysis to agronomy. Professor Siebert's work bridges theoretical research with practical applications in sustainable agriculture, making significant contributions to our understanding of how to maintain food security in the face of climate change.
