Prof. Dr. André Bardow is a Full Professor in Energy and Process Systems Engineering at ETH Zurich , leading research at the intersection of thermodynamics, machine learning, and sustainable energy systems. Previously, he held professorships at RWTH Aachen University (2010-2020) and TU Delft (2007-2010). He also served as part-time director at Forschungszentrum Jülich (2017-2022) and visiting professor at UC Santa Barbara (2015/16). His work focuses on energy systems optimization , computer-aided molecular design , and CO2 capture & utilization . PhD from RWTH Aachen University Current ETH Zurich affiliation Former roles at RWTH Aachen, TU Delft, Jülich Research Center His research integrates machine learning with thermodynamic modeling to optimize processes like crystallization and electrochemical cooling . Recent publications demonstrate advancements in solvent design, CO2 transport LCA, and ORC working fluid optimization. He chairs the VDI Technical Committee for Thermodynamics (2016-2024) and has received multiple awards including the Covestro Science Award and Arnold-Eucken-Award . Current projects address carbon circular economies , electrified chemical production , and AI-driven process optimization . His lab at ETH Zurich develops cutting-edge technologies like ML-CAMPD frameworks for sustainable separation processes and photoacid-based CO2 capture systems. Funding from the H2020 Systemic Expansion of Circular Ecosystems (grant 101036854) supports these initiatives. 2024 Clarivate Highly Cited Researcher 2022 Inaugural Lecture: "To sustainability and beyond: A computer-animated story on energy & chemicals" Recipient of multiple teaching and research excellence awards
Teruko Mitamura is a prominent researcher at Carnegie Mellon University with over three decades of contributions to natural language processing, computational linguistics, and artificial intelligence. Her work spans from foundational research in event representation to advanced applications in multimodal systems and question answering. Her research interests focus on event detection and understanding, question answering systems, information retrieval, and multimodal processing. She has made significant contributions to event coreference resolution, timeline construction, and cross-document event analysis, developing methodologies that have become standard in the field. Her work often bridges theoretical advances with practical applications, particularly in complex information environments requiring deep semantic understanding. Natural Language Processing : Specializing in event extraction, coreference resolution, and narrative understanding with over 179 publications Question Answering Systems : Developing advanced techniques for complex question answering, particularly through NTCIR QA Lab and PoliInfo tasks Multimodal Processing : Integrating textual, visual, and temporal information for richer understanding in systems like ProMQA Evaluation Methodologies : Creating robust frameworks for assessing NLP systems through TAC KBP Event Tracks Her recent publication trends show a strong focus on leveraging large language models for event understanding, multimodal question answering, and timeline construction. She has expanded her research into specialized domains including patent analysis and novelty examination, demonstrating the breadth of her research impact across academic and practical applications. Active participant in major NLP conferences including ACL, EMNLP, NAACL, and AAAI with consistent publications Long-standing collaborator with researchers at CMU's Language Technologies Institute including Eduard H. Hovy and Eric Nyberg Contributor to shared tasks that have shaped research directions in event processing and question answering Organizer of multiple NTCIR QA Lab tasks focused on political information question answering Dr. Mitamura has mentored numerous researchers who have gone on to make their own contributions to the field, as evidenced by her extensive co-authorship network and the progression of her former students and collaborators into faculty and research positions. Her work continues to evolve with the field while maintaining her focus on deep semantic understanding of events and narratives.
Prof. Dr. Mirko Hornung is a Professor of Aircraft Design at the TUM School of Engineering and Design, Technische Universität München. His research focuses on conceptual aircraft design, integration of propulsion systems, and evaluation of aviation technologies in operational contexts. Education and Career: PhD in Aeronautical Engineering from the University of the Bundeswehr (2003), awarded a research prize for work on reusable space transport systems. 2003–2009: Worked at Airbus Group (EADS) on military air systems, propulsion integration, and program management. Executive Director of Research & Technology at Bauhaus Luftfahrt, a think tank for long-term aviation developments. Research Interests: Aircraft design optimization, including hybrid energy systems, electric propulsion, and UAV technologies. Environmental sustainability in aviation, such as hydrogen-powered aircraft and lifecycle assessment. Aerodynamic and structural analysis, including flutter suppression and composite materials. Key Publications Highlight Trends: Focus on hybrid-electric and hydrogen propulsion for reducing environmental impact. Advancements in UAV design, including morphing wings and autonomous systems. Integration of AI-driven tools for propulsion optimization and lifecycle analysis. Scientific Awards: EADS Promotion Award (1995) Research Prize for Thesis on Reusable Space Transport Systems (2003) Advising & Grants: Directs research at Bauhaus Luftfahrt, collaborating on future aviation concepts. Engaged in interdisciplinary projects like FLEXOP UAV demonstrator and Ce-Liner eMobility studies. Labs/Teams: Active in the Aircraft Design Professorship at TUM and leadership roles at Bauhaus Luftfahrt, focusing on next-generation aviation technologies.
Prof. Dr. Mathias Christmann is a faculty member at the Institute of Chemistry and Biochemistry, Freie Universität Berlin , leading the research group in Organic Chemistry . His work focuses on strategic and methodological challenges in synthetic chemistry, particularly in total synthesis, organocatalysis, and renewable resource transformations. Position: Professor Contact: mathias.christmann@fu-berlin.de Location: Takustr. 3, Room 24.16, 14195 Berlin Research Interests include: Natural product-inspired small molecule synthesis for biological pathway modulation Minimizing C-C bond formations through selective functionalization of terpene building blocks Organocatalytic and metal-catalyzed reactions in multistep sequences Flow chemistry applications for scalable and sustainable synthesis Biological evaluation of TRPC channel agonists/antagonists for cancer therapy Publication Trends highlight expertise in total synthesis of complex terpenoids, organocatalysis for stereocontrolled reactions, flow chemistry for late-stage transformations, and TRPC4/5 channel modulation in renal cancer studies. His group pioneers asymmetric desymmetrization , photo-oxidation protocols , and electrosynthesis methods with minimal reagent waste. Advisees include PhD candidates Jan-Hendrik Dickoff , Mayar Elbendary , Nadine Kreidt , Tobias Olbrisch , Kamar Shakeri , and Zhen Wang , focusing on terpene-based drug discovery and catalytic reaction design.
Prof. Waldemar Kolanus leads the Molecular Immunology and Cell Biology department at the University of Bonn's Life & Medical Sciences Institute (LIMES) . His research bridges immunoregulation , stem cell dynamics , and metabolic stress responses in immune cells. Unit 2 member at LIMES Principal investigator in SFB 704 and ImmunoSensation Cluster Leads a multidisciplinary lab with postdocs, PhD students, and technical staff His work focuses on intracellular signaling pathways connecting immune activation to tissue homeostasis, particularly through: Cytohesin proteins in integrin-mediated adhesion and migration TRIM71 in stem cell regulation and congenital hydrocephalus High-salt environments affecting macrophage function Publication trends show expertise in immune cell migration , genetic models , and chemical inhibition , with frequent use of mice and zebrafish for in vivo studies. Key articles explore: TRIM71's dual role in auditory development and germ cell maintenance Cytohesin family's Golgi regulation and insulin signaling Ruxolitinib's off-target migration inhibition of dendritic cells Contact details: Address: LIMES Institute, Carl-Troll-Straße 31, Bonn Email: kolanus.sekretariat@uni-bonn.de Phone: +49 228 73-62788
Prof. Dr. Michael Ulbrich is a full professor and Chair of Mathematical Optimization at the Technical University of Munich (TUM), within the School of Computation, Information and Technology. He has held this position since 2006 and previously served as Dean of Studies (2007–2010) and Vice Dean of the Faculty of Mathematics (2012–2015). His research focuses on nonlinear optimization, optimal control, and numerical analysis, with applications in fluid dynamics, shape optimization, and PDE-constrained systems. He leads projects in the DFG SPP 1962 and IGDK 1754, and has received prestigious awards including the Howard Rosenbrock Prize (2015) and the Doctoral Award from the TUM Association of Friends (1996). Ulbrich is Editor-in-Chief of Optimization and Engineering and contributes to multiple journals. His work bridges theoretical foundations and practical applications, including CO2 sequestration, fluid-structure interaction, and distributed optimization algorithms. Education: PhD (1996), Habilitation (2002) in Mathematics at TUM. Research stays at Rice University (USA) under DFG funding. Research Areas: Semismooth Newton methods, PDE-constrained optimization, optimal control of Navier-Stokes equations, and distributed parameter systems. Awards: Rosenbrock Prize, Teaching Excellence Awards, and recognition for doctoral work. Leadership Roles: Department Head of Mathematics (2022–), Member of TUM Senate (2019–2022), and Co-Chair of GAMM 2018. Ulbrich has authored influential textbooks like Semismooth Newton Methods for Variational Inequalities and Nichtlineare Optimierung . His recent projects include OptiGeoS (2024–2026) and collaborations on nonsmooth optimization and stochastic algorithms. His academic contributions span over 100 publications, emphasizing both algorithmic innovation and rigorous mathematical analysis.
Martin Haspelmath is a Senior Research Scientist at the Department of Linguistic and Cultural Evolution at the Max Planck Institute for Evolutionary Anthropology (MPI-EVA), Leipzig, and an honorary (adjunct) professor at Leipzig University. His research focuses on comparative linguistics, language universals, and typology, particularly in syntactic and morphological systems. He co-edited the World Atlas of Language Structures (2005/2013) and co-founded Language Science Press . Education: PhD in Linguistics, Freie Universität Berlin (1990–1993) MA in Linguistics, University of Cologne and University at Buffalo (1985–1989) Undergraduate studies in Linguistics, University of Vienna (1983–1985) Research Interests: Haspelmath investigates global grammatical diversity, form-function correspondences, and universals in syntax, morphology, and lexical systems. His work emphasizes comparative concepts for cross-linguistic analysis and critiques traditional grammatical categories. Grants & Projects: ERC project leader at Leipzig University (2015–2020) Co-creator of Grambank , an open-access database of grammatical structures Leading initiatives for open-access publication and linguistic data sharing Labs/Teams: Active in the MPI's Department of Linguistic and Cultural Evolution, collaborating on projects like APICS (Atlas of Pidgin and Creole Language Structures) and ValPal (Valency Patterns Leipzig).
Prof. Dr. Andrea Stocco is a Professor at the Technische Universität München (TUM), affiliated with the TUM School of Computation, Information and Technology. His research focuses on the intersection of software engineering and deep learning, particularly addressing the robustness and reliability of data-intensive systems. Key areas include autonomous vehicles, web application testing, and automated functional oracles for deep learning systems. He leads initiatives such as the Lehrstuhl für Software und Systems Engineering , collaborating on projects like CrESt and SUPPRA – Algorand Center of Excellence . Research interests encompass monitoring techniques for AI-driven systems, test suite maintainability, and scenario-based testing of cyber-physical systems (CPS). His work emphasizes practical applications, such as improving testing frameworks for evolving web applications and enhancing interoperability in autonomous driving systems (ADS). Recent efforts include leveraging large language models (LLMs) for secure code assessment and benchmarking generative AI for test input generation. No scientific awards are explicitly mentioned in the provided texts. His publications reflect a strong focus on testing methodologies, with over 40 articles since 2013, covering domains like web test automation, dependency-aware testing, and safety-critical failure prediction in autonomous systems. Advising and grants details are not detailed in the current data, but his lab contributes to TUM's broader efforts in software engineering and systems reliability.
Dr. Vincent Fortuin is a tenure-track Assistant Professor at the Technical University of Munich (TUM) and a research group leader at Helmholtz AI in Munich. He leads the Efficient Learning and Probabilistic Inference for Science (ELPIS) group and holds multiple prestigious fellowships including the Branco Weiss Fellowship. His academic affiliations include the TUM School of Computation, Information and Technology, the Konrad Zuse School of Excellence in Reliable AI, and the Munich Center for Machine Learning. Dr. Fortuin earned his BSc in Molecular Life Sciences from the University of Hamburg (2012-2015), followed by an MSc in Computational Biology and Bioinformatics from ETH Zürich (2015-2017), where he received the ETH Excellence Scholarship and the Willi Studer Prize. He completed his PhD in Machine Learning at ETH Zürich (2017-2021) under the supervision of Gunnar Rätsch and Andreas Krause, supported by a Swiss Data Science Center PhD Fellowship. Prior to joining TUM, he was a Research Fellow at St. John's College, University of Cambridge (2022-2023). His research focuses on the intersection of Bayesian statistics and deep learning, specifically developing methods for more robust, data-efficient AI systems with reliable uncertainty estimates. His work addresses critical limitations in standard deep learning approaches, particularly their tendency to be overconfident in predictions and require large datasets for training. He investigates better priors and more efficient inference techniques for Bayesian deep learning, deep generative modeling, meta-learning, and PAC-Bayesian theory, with applications in scientific and biomedical domains. Dr. Fortuin's recent publications demonstrate a consistent focus on improving uncertainty quantification in deep learning systems, with increasing emphasis on practical applications in scientific contexts. His work spans from theoretical foundations of Bayesian deep learning to practical implementations in protein design, materials science, and medical applications. A notable trend is his exploration of how to make Bayesian methods more scalable and applicable to modern large-scale AI systems while maintaining theoretical guarantees. Branco Weiss Fellowship (2023) St John's College Research Fellowship (2022) Swiss National Science Foundation Postdoc.Mobility Fellowship (2022) Swiss Data Science Center PhD Fellowship (2018) ETH Excellence Scholarship (2015) Willi Studer Award (2018) Dr. Fortuin actively supervises PhD and Master's students through his ELPIS research group at Helmholtz AI. He serves as a regular reviewer and area chair for major machine learning conferences and is an action editor for TMLR. He co-organizes the Symposium on Advances in Approximate Bayesian Inference (AABI) and the ICBINB initiative, demonstrating his commitment to advancing the field through community building. His research group receives funding from multiple sources including Helmholtz AI, the Branco Weiss Fellowship, and collaborations with international institutions. Dr. Fortuin leads the Efficient Learning and Probabilistic Inference for Science (ELPIS) group at Helmholtz AI, which focuses on fundamental machine learning research motivated by real-world scientific problems. The group collaborates extensively with researchers across Helmholtz centers and international institutions, particularly in biomedical applications where reliable uncertainty estimates are crucial.
Omer Bayraktar is a Group Leader at the Wellcome Sanger Institute , leading research in the Cellular Genomics Programme. His work focuses on decoding human brain cellular diversity using spatial transcriptomics , imaging , and functional screening to study neural complexity in health and disease. Bayraktar's educational background includes a PhD from HHMI under Chris Doe, investigating neural diversity development in Drosophila , followed by postdoctoral work at University of California, San Francisco and University of Cambridge as a Life Sciences Research Foundation Fellow. He developed a spatial transcriptomic pipeline during his postdoc to analyze astrocyte heterogeneity in the cerebral cortex. His research explores neural cell type mapping , glial-neuronal interactions , and cellular pathways in neurodevelopmental disorders . Recent publications emphasize 3D tissue mapping , multi-omic integration , and computational tools like Cell2fate and WebAtlas. His work bridges neurogenetics and computational biology to advance understanding of human tissue ecosystems. Bayraktar's lab collaborates with the Human Cell Atlas initiative and develops technologies such as automated histology pipelines and highly-multiplexed smFISH for molecular cell typing. His team also investigates glia-based therapies and astrocyte functional heterogeneity in neurodevelopmental contexts. Key scientific contributions include: Discovering astrocyte layer patterns independent of neuronal laminae Developing cell2location for spatial cell mapping Characterizing Drosophila neural stem cell models with human relevance Notable awards include the Life Sciences Research Foundation Fellowship during his postdoctoral training. His current group includes a PhD student , Senior Data Scientists , and Bioinformaticians .
Michael Muehlebach leads the independent Learning and Dynamical Systems research group at the Max Planck Institute for Intelligent Systems in Tuebingen, Germany. His interdisciplinary work bridges machine learning, dynamical systems theory, and control engineering to develop algorithms for cyber-physical systems with theoretical guarantees and practical implementations. Dr. Muehlebach received his B.Sc. and M.Sc. in Mechanical Engineering from ETH Zurich in 2010 and 2013, specializing in robotics and control systems. He completed his Ph.D. at ETH's Institute for Dynamic Systems and Control under Prof. R. D'Andrea in 2018, followed by postdoctoral research with Prof. Michael I. Jordan at UC Berkeley. His research focuses on constrained optimization, reinforcement learning, and control theory with applications in robotics. He pioneered approaches that express constraints in terms of velocities rather than positions, enabling more efficient optimization algorithms. His work spans theoretical foundations to physical implementations, including the One-Wheel Cubli balancing robot and electromagnetic navigation systems. Recent publications reveal a strong trend toward physics-informed machine learning, particularly for robotics applications requiring real-time performance and safety guarantees. Dr. Muehlebach has received numerous prestigious awards: Outstanding D-MAVT Bachelor Award Willi-Studer prize for best Master's degree ETH Medal and HILTI prize for doctoral thesis Branco Weiss Fellowship (2018) Emmy Noether Fellowship (2020) Amazon Fellowship (2024) He actively mentors doctoral researchers including Hao Ma, Melis Ilayda Bal, and Onno Eberhard, with research supported by multiple grants. His group maintains strong collaborations with Bernhard Schölkopf's Empirical Inference group at the Max Planck Institute. The Learning and Dynamical Systems group develops innovative hardware and software platforms, including Floaty (a wind-harnessing flying robot), advanced electromagnetic navigation systems, and data-efficient learning methods for robotic table tennis. Their approach combines rigorous theoretical analysis with practical validation on physical systems, emphasizing the integration of known physical structure into machine learning algorithms to improve sample efficiency and ensure generalization.
Prof. Alexander Pretschner is a Professor of Software & Systems Engineering at the Technical University of Munich (TUM) and Founding Director of the Bavarian Research Institute for Digital Transformation (bidt). He also serves as Scientific Director of fortiss, a Bavarian research institute for software-intensive systems. His research focuses on software engineering, testing, information security, and ethical software development. Pretschner holds a PhD from TUM and has held academic positions at Karlsruhe Institute of Technology (KIT) and TU Kaiserslautern. He is a co-editor of several prestigious journals, including IEEE Transactions on Reliability and the Journal of Software Testing, Verification and Reliability. Education: PhD in Computer Science, Technical University of Munich MSc in Computer Science, University of Kansas (on Fulbright Scholarship) Diplom in Computer Science, RWTH Aachen University Research Interests: His work spans testing methodologies, secure software design, and ethical considerations in agile development. Notable contributions include frameworks for metamorphic testing, distributed data usage control, and accountability mechanisms for cyber-physical systems. Awards: IBM Faculty Award (2012, 2013) Google Focused Research Award (2011, 2012) EARTO Innovation Prize (2014) 2nd Platz Supervisory Award (2020) Advising & Grants: Pretschner has supervised numerous PhD and Master’s students, contributing to over 200 publications. He leads projects like EDAP (Ethical Deliberation in Agile Processes) and collaborates with industry partners on cybersecurity and AI ethics initiatives. Labs & Teams: His work is anchored in bidt, fortiss, and TUM’s Chair of Software & Systems Engineering, focusing on societal impacts of digitalization and trustworthy AI systems.
Michael Mühlebach is a Research Group Leader at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, leading the independent Learning and Dynamical Systems group. His academic journey began at ETH Zurich where he earned his B.Sc. (2010) and M.Sc. (2013) in mechanical engineering, specializing in robotics, systems, and control. He completed his Ph.D. at ETH Zurich in 2018 under Prof. R. D'Andrea, followed by postdoctoral research at UC Berkeley with Prof. Michael I. Jordan. Dr. Mühlebach's research spans machine learning, dynamical systems, control theory, and optimization . His work bridges theoretical foundations with practical applications in robotics, developing methods that incorporate physical constraints and system dynamics into learning frameworks. His group focuses on online learning, physics-informed machine learning, and large-scale optimization for cyber-physical systems, with applications in electromagnetic navigation, robotic table tennis, and energy-efficient flight systems like the shape-changing robot Floaty . His publication record shows a strong focus on constrained optimization, with recent work exploring decision-dependent stochastic optimization, nonlinear feedback, and the theoretical foundations of reinforcement learning. His research integrates perspectives from control theory, dynamical systems, and optimization to develop algorithms with strong theoretical guarantees and practical performance. Outstanding D-MAVT Bachelor Award Willi-Studer prize for best Master's degree ETH Medal and HILTI prize for doctoral thesis Branco Weiss Fellow (2018) Emmy Noether Fellowship (2020) Amazon Fellowship (2024) Dr. Mühlebach actively mentors doctoral researchers and is seeking talented students for PhD and Master's projects. His research group has received funding from multiple prestigious fellowships and maintains collaborations across institutions including ETH Zurich, UC Berkeley, and various Max Planck research units. The group's work spans theoretical developments to practical implementations on robotic systems, demonstrating strong connections between mathematical theory and physical realization.
Prof. Dr. Dominik Schwarz is a faculty member at the Faculty of Physics , Bielefeld University. His research focuses on Cosmology and Particle Physics , particularly in the areas of Dark Energy , Dark Matter , Cosmological Inflation , and Large-Scale Structure Formation . He contributes to projects like the International LOFAR Telescope Consortium and the SFB-TRR 211 on strongly interacting matter. APART Fellow of Austrian Academy of Sciences Humboldt Fellow CERN Fellow His recent work explores the cosmic dipole anisotropy , axion density perturbations , and multi-wavelength cosmic web mapping . He also advances data science infrastructure through the PUNCH4NFDI consortium.
Anna Levina is an Assistant Professor for Computational Neuroscience at the University of Tübingen , affiliated with the Department of Computer Science under the Faculty of Science. Her research focuses on the self-organization of neuronal activity, critical dynamics in neural networks, and the excitation/inhibition balance in cortical circuits. Current positions: Assistant Professor (since 2018), Group Leader (2017-2018), Equality Officer (Computer Science) Previous roles: IST Fellow (2015-2017), Associated Researcher (2011-2015), Postdoc/PI (2011-2015), Postdoc (2008-2011) Her research integrates mathematical modeling , statistical physics , and computational neuroscience to study criticality phenomena, neural avalanches, and adaptive network dynamics. Key interests include: Self-organized criticality in neural systems Excitation/Inhibition balance mechanisms Network topology and dynamics Timescale analysis in neural processing Stochastic modeling of neural activity Recent publications reveal trends in understanding critical dynamics across biological and artificial networks, with applications to memory systems, sensorimotor integration, and disease modeling. She has received recognition as an IST Fellow .
