Professor Tobias Cremer at the University of Sustainable Development Eberswalde (HNEE) holds the Professorship for Forestry Utilization and Wood Market within the Faculty of Forest and Environmental Sciences. His work focuses on agroforestry systems, sustainable forest resource management, and innovative digital forestry technologies including drone-based 3D modeling and LiDAR applications. Key research projects: ADAPT-Wald-Holz (2023–2028) for climate-resilient forestry in Brandenburg-Berlin, ReBuilt (2023–2025) for regenerative built environments, and Intelliway (2021–2024) for digital forest road monitoring International collaborations in Ghana’s Lake Bosomtwe Biosphere Reserve and with European institutions Leadership roles: Former Dean of Forest and Environmental Sciences (2019–2023), Senate Chair (2017–2019), and active participation in forestry policy committees His recent publications emphasize wood volume measurement innovations, bark biomass utilization, and agroforestry’s role in climate adaptation. Cremer actively contributes to sustainability discourse through conference presentations and media engagements, particularly in Northeast Germany and international contexts.
Prof. Dr. Dieter Horns is a Professor (W2) for Astroparticle Physics at the University of Hamburg, Faculty of Mathematics, Informatics and Natural Sciences, Institute of Experimental Physics. He has held this position since 2007. Previously, he was an Assistant at the University of Tübingen (2004-2007) and Research Assistant at Max-Planck Institute for Nuclear Physics (2001-2004). He obtained his Doctorate in Physics from Universität Hamburg in 2000. His research focuses on high-energy astrophysics (keV-TeV range), multi-wavelength investigations of gamma-ray sources, experimental techniques for ground-based air Cherenkov telescopes (HESS I&II, CTA), time-domain astrophysics, low-noise photon detection, and dark matter searches (WISPs). He leads significant projects including the HAFUN research building and coordinates dark matter research for the Quantum Universe Cluster of Excellence. Horns' publications demonstrate expertise in gamma-ray astronomy, dark matter phenomenology, cosmic ray physics, and telescope instrumentation. His recent work shows strong focus on CTA/LST-1 telescope operations, pulsar studies, dark matter constraints, and cosmic ray measurements using H.E.S.S. data. He has trained 32 B.Sc. students, 44 M.Sc. students, 22 PhD students, and 13 PostDocs since 2007. He coordinates the Quantum Universe Research School and leads multiple international collaborations including H.E.S.S. and CTA. As Principal Investigator for the HAFUN research building, he oversees major instrumentation projects.
Alexander Korotin is an Assistant Professor at the Skolkovo Institute of Science and Technology (Skoltech) where he heads the Generative AI research group. He is also a senior research scientist at the Artificial Intelligence Research Institute (AIRI), leading the "Foundations of Generative AI" group. His academic journey includes a PhD in Math & Physics from Skoltech (2023), an MSc in Computer Science from the Higher School of Economics (HSE), and a BSc in Mathematics also from HSE. Dr. Korotin's research focuses on generative modeling, with particular emphasis on developing novel algorithms based on Optimal Transport and Schrodinger Bridges. His work bridges theoretical mathematics with practical machine learning applications, contributing significantly to the field of generative artificial intelligence. He has pioneered approaches to make Schrodinger Bridge solvers more efficient and practical, most notably with his "Light Schrödinger Bridge" framework that simplifies complex computational procedures while maintaining theoretical rigor. His publication record shows a clear progression toward making advanced generative modeling techniques more accessible and computationally efficient. Recent work demonstrates increasing sophistication in handling complex distribution matching problems through physics-inspired approaches (like electrostatic field matching) and novel distillation techniques that accelerate inference. The research spans from theoretical foundations to practical applications in image processing, semi-supervised learning, and reinforcement learning. Dr. Korotin has received recognition for his contributions to neural optimal transport and Schrodinger Bridges, with his papers frequently appearing in premier machine learning venues. His work on efficient computational methods for optimal transport has established him as a rising expert in these specialized areas of machine learning. As an academic leader, Dr. Korotin advises research students and collaborates extensively with colleagues across institutions, contributing to the advancement of generative AI through both theoretical developments and practical implementations. His work continues to push the boundaries of what's possible in generative modeling, with recent publications focusing on making advanced mathematical approaches more computationally efficient for real-world AI applications.
Ludwig Lautenbacher is a researcher at the Chair for Computational Mass Spectrometry at Technische Universität München (TUM). He contributes to projects democratizing machine learning (ML) applications in proteomics research. University: Technische Universität München Academic Rank: Researcher Email: ludwig.lautenbacher@tum.de Research Interests: Ludwig focuses on integrating machine learning with proteomics, emphasizing accessibility, interoperability, and reproducibility of ML models. His work addresses computational challenges in predicting peptide properties, fragment intensities, and retention times, while developing open-source resources like Koina and ProteomicsDB . Recent Publications (2021–2024) demonstrate expertise in: Temporal proteomics for drug mechanism analysis Deep learning for TMT-labeled peptide identification Fragment ion intensity prediction Open-source platform development Mass spectrometry data interpretation Collaborative software integration (Skyline, EncyclopeDIA, FragPipe) Laboratory Affiliation: Works in the research group of Prof. Dr. Mathias Wilhelm at TUM, contributing to computational mass spectrometry advancements.
Kaan Aykurt is a Researcher at the Department of Communication Networks within the College of Engineering at Technical University of Munich (TUM) , where he works under the supervision of Prof. Wolfgang Kellerer. He joined TUM in May 2022 after completing his M.Sc. and holds dual B.Sc. degrees in Electrical and Electronics Engineering and Economics from Koç University, Istanbul. B.Sc. Electrical & Electronics Engineering (Koç University, 2019) B.Arts Economics (Koç University, 2019) M.Sc. Technical University of Munich (2022) Kaan's research focuses on data center networks , multi-domain autonomous network management , and machine learning applications in communication networks . His work explores graph neural networks (GNNs) for network optimization, large language model benchmarks for network configuration, and TCP behavior in reconfigurable environments. Key projects include netLLMBench, HyPA autoscaling, and digital twin systems. Recent publications span GNN-based management for 6G networks (2023), TCP performance in dynamic data centers (2023), and ML framework traffic analysis (2021). His work often intersects AI-driven networking , automated resource allocation , and network protocol evaluation . Collaborations include researchers from Northeastern University and University of Augsburg. As a research associate, Kaan contributes to the Chair of Communication Networks 's initiatives in autonomous systems and network intelligence. His work aligns with broader projects like 6G Future Lab Bavaria and DFG-funded research at TUM.
Itay Hen is an Associate Professor of Research in the Department of Physics and Astronomy and Principal Scientist at the Information Sciences Institute (ISI), University of Southern California. He has held research faculty positions at USC since 2013, progressing from Assistant Professor (2016-2020) to his current Associate Professor role since 2020, while leading ISI's quantum computing initiatives. His educational background includes dual bachelor's degrees in Physics and Psychology from Tel Aviv University, followed by a Ph.D. in Physics from the same institution in 2009. Postdoctoral training included theoretical condensed matter research at Georgetown University and UC Santa Cruz, plus a senior scientist role at NASA Ames Research Center within the Quantum Artificial Intelligence Laboratory—a NASA/Google/USRA collaboration. Dr. Hen's research centers on Quantum Computing and Computational Physics , with specific expertise in gate-based quantum simulation algorithms, quantum annealer limitations, and methods for studying equilibrium/non-equilibrium properties of strongly correlated quantum systems. His work bridges theoretical frameworks with practical quantum hardware applications. Analysis of his 15 most recent publications (2017-2025) reveals consistent focus on quantum algorithms and Monte Carlo techniques, with accelerating output in 2024. Key themes include Feynman path integrals, spin/Bose-Hubbard model simulations, and quantum spectrum estimation, primarily published in Physical Review journals and Quantum. He leads the Hen Lab at USC's ISI, which operates within the Quantum Artificial Intelligence Laboratory framework. His research has been supported through NASA/Google/USRA collaborations focused on quantum optimization for complex computational problems, though specific grant details and student mentorship records aren't provided in the source material.
Jörn Hees is a Professor at the German Research Center for Artificial Intelligence (DFKI), affiliated with the Smart Data & Knowledge Services department. His work bridges deep learning, linked data, and knowledge graphs, with projects like TreeSatAI (AI for environmental monitoring) and DeFuseNN (deep network fusion). Research Interests : Deep learning, machine learning, data mining, knowledge graphs, semantic web, linked data, and association modeling. Projects : TreeSatAI (remote sensing AI), MInD (machine intelligence for digital transformation), DeFuseNN (neural network fusion), MOM (multimedia opinion mining). Recent publications focus on outlier detection for tabular data (Fin-Fed-OD, RECol), super-resolution techniques, and transformer-based models. He actively develops open-source tools like the RDFLib Python library and Graph Pattern Learner for SPARQL query generation. No scientific awards are explicitly mentioned in the available data.
Maria Wirzberger is an Assistant Professor at the University of Stuttgart, specializing in Teaching and Learning with Intelligent Systems (LLiS). She serves as Spokesperson of the Stuttgart Research Focus IRIS and Co-Director of the AI Software Academy, focusing on adaptive teaching systems, cognitive modeling, and human-AI interaction. Her work bridges cognitive psychology, educational technology, and human-computer interaction, with a strong emphasis on self-regulation and attention control. Research Interests: Modeling human cognition, statistical analysis of behavioral data, distraction/interruption handling in digital environments, development of AI-driven educational tools, multimodal cognitive load assessment, and trust in AI systems. Scientific Contributions: 15 recent articles explore topics like AI feedback mechanisms for focus, emotion-performance dynamics in tutoring systems, sustainable behavior cognition, and neuroergonomic approaches to workload analysis. Grants & Collaborations: Involved in projects like the AI Software Academy and interdisciplinary studies on sustainability personas and cognitive pathways. Labs & Teams: Affiliated with the Stuttgart Research Focus IRIS and University of Stuttgart's LLiS department.
Julian Berberich is a Lecturer (Akademischer Rat) at the Institute for Systems Theory and Automatic Control, University of Stuttgart, Germany. His research bridges systems and control theory with quantum computing, focusing on theoretical properties of quantum computing elements and practical methods to enhance their reliability. Key areas include robustness of quantum algorithms against hardware errors, modularity, feedback mechanisms, and data-driven control of nonlinear systems. Academic Appointments Lecturer, University of Stuttgart (2022–present) Visiting Researcher, ETH Zürich (2022) Research/Teaching Assistant, University of Stuttgart (2018–2022) Research Themes Koopman Operator in Control Model Predictive Control (MPC) Quantum Computing & Control Robustness Analysis Data-Driven Methods Nonlinear System Stability His recent publications emphasize data-driven control strategies, particularly Koopman-based methods, bilinear systems, and min-max optimization for robust stability. Collaborations span institutions like ETH Zürich, KTH Stockholm, and WITTENSTEIN SE. Julian holds an M.Sc. in Engineering Cybernetics and a B.Eng. in Electrical Engineering. He has contributed to journals such as IEEE Transactions on Automatic Control , Physical Review A , and conferences like the IEEE Conference on Decision and Control .
Ana Rita Grosso is an Assistant Professor at the Department of Life Sciences , NOVA School of Science and Technology (FCT-NOVA) , NOVA University Lisbon , and heads the Computational Multi-Omics Group . Since 2018 she has built an independent research program focused on leveraging multi-omics to decode cancer and other pathologies. Education PhD in Biomedical Sciences, Faculty of Medicine, University of Lisbon (iMM/FMUL, 2010) MSc in Bioinformatics, Faculty of Sciences, University of Lisbon (FCUL-IGC, 2006) Licenciatura (BSc) in Biology, Faculty of Sciences, University of Lisbon (FCUL, 2002) Research Interests Grosso’s work sits at the intersection of computational biology , bioinformatics , and biomedicine . She integrates large-scale genomics, transcriptomics, and epigenomics datasets to uncover molecular events underlying tumor evolution, metastasis, and tissue-specific splicing regulation, aiming to translate findings into biomarkers and therapeutic targets. Scientific Awards & Grants 3 × Pfizer Awards for Basic Research 10 competitive project grants (4 as PI/Co-PI, 6 as team member) 4 salary/stipend grants (PhD, Post-Doc, and 2 research contracts) EMBO Workshop Grant Advising & Training She has supervised 4 PostDocs , 3 PhD students , 3 MSc fellows , 10 MSc students , 7 BSc students , and numerous short-term trainees, while teaching Genomics and Computational Biology courses at FCT-NOVA. Laboratory & Teams She founded and leads the Computational Multi-Omics Lab at FCT-NOVA, an interdisciplinary team applying systems-biology approaches to cancer and biomedical questions.
Justin Dainer-Best is an Associate Professor of Psychology at Bard College, where he leads the Affective Science Lab. His clinical work as a licensed psychologist in New York's Hudson Valley focuses on anxiety, depression, trauma, and gender/sexuality-related issues. He employs evidence-based therapies like CBT and ACT in his practice. PhD in Clinical Psychology, 2018, The University of Texas at Austin Pre-Doctoral Clinical Internship, 2018, The University of Vermont BA in Psychology and English, 2009, Haverford College His research explores how positive and negative emotions shape self-perception, particularly in mood disorders. The Affective Science Lab investigates self-referent cognition, positive imagery training, and brief internet interventions, using online and in-person samples. He has contributed to symptom-level analyses in depression and trauma research. Recent publications focus on self-referent cognition in depression (2025), positive self-schema changes (2025), comparative ACT vs. trauma-focused CBT for Afghan adolescents (2024), and Bayesian network analyses of depression symptoms (2021). His work spans clinical trials, cognitive theory, and methodological advances in affective science. Students in his lab assist with participant recruitment, data collection, and study development. He teaches advanced methodology courses, statistics, psychopathology, and trauma-focused seminars, emphasizing inclusive pedagogy. The lab prioritizes diversity in research participation and welcomes underrepresented groups in psychology.
Marie-Paule Cani is a Professor of Computer Science at Ecole Polytechnique (IP Paris), where she serves as Dean of the Master of Science and Technology program. She is a member of the Académie des sciences and leads research in the LIX laboratory (CNRS/Ecole Polytechnique, IP Paris). Her career spans several prestigious institutions including Grenoble INP and Collège de France. Dr. Cani earned her M.Sc. in Computer Science from Ecole Normale Supérieure & University Paris XI in 1987, followed by a Ph.D. in Computer Graphics from University Paris XI in 1990 under advisor Claude Puech. She completed her Habilitation in Computer Science from Institut National Polytechnique de Grenoble in 1995. Professor Cani's research focuses on advancing user-centered, creative 3D modeling and animation systems. Her work aims to develop intelligent systems that help users express shapes in motion they have in mind, whether they are computer artists, engineers, or scientists from other disciplines. She pioneered methodologies that provide expressive, gesture-based control (such as sketching, deformation, copy-pasting) while augmenting graphical models with knowledge from priors or statistics learned from examples. Her research spans multiple areas including implicit surfaces, developable surfaces, procedural models, physically-based animation, and sketch-based modeling. Her recent publications demonstrate a continued focus on innovative approaches to 3D modeling, animation, and simulation. Her work bridges computer graphics with applications in geology, biology, and virtual environments. Recent trends show increasing integration of machine learning techniques with traditional graphics approaches, particularly in procedural modeling and animation control. ACM Siggraph Steven A. Coons Award (2023) Member of the ACM Siggraph Academy (2019) Member of Academia Europaea (2013) Silver medal from CNRS (2012) EUROGRAPHICS award for Outstanding Technical Contributions (2011) Irène Joliot Curie national award, mentorship category (2007) Junior member of the Institut Universitaire de France (1999) Professor Cani has supervised numerous students throughout her career and has led multiple significant research projects funded by prestigious organizations. Her ERC Advanced Grant EXPRESSIVE (2012-2017) focused on EXPloring RESponsive Shapes for the creation of Interactive Virtual Environnements. She created and led the STREAM Team at LIX from 2016-2019. More recently, she has been Principal Investigator in the chair between Google and Ecole Polytechnique on Artificial Intelligence & Visual Computing (2018-2021), and in the International Training Network CLIPE (2020-2024), funded by the European Research Council. She currently leads the interdisciplinary project Paleomob-3D (2023-2026) between LIX and HNHP, funded by CNRS 80-Prime. Professor Cani has led several research teams throughout her career, including the Imagine joint team (Inria/LJK lab, 2011-2016) and the Evasion joint team (Inria/GRAVIR lab, 2003-2010). She currently leads research in the LIX laboratory at Ecole Polytechnique, where she was the leader of the "Modeling Simulation and Learning" pole from 2019 to 2023.
Dr. Renata Wacker is a Researcher at the Clinical Psychology of Social Interaction Group within the Institute of Psychology at Humboldt University of Berlin . She serves as a Psychological Psychotherapist (in training) at the Institute for Psychoanalysis, Psychotherapy and Psychosomatics Berlin since 2019, and previously worked as a Researcher and Clinical Psychologist at the German Heart Center Berlin (2018-2020). Education: M.Sc. in Psychology from Ruhr University Bochum (2011) Doctoral research funded by German National Academic Foundation (2013-2018) Research Focus: Dr. Wacker specializes in social cognition and mental state communication , examining their impact on social functioning. Her work integrates evidence-based psychodynamic perspectives with transdiagnostic interventions for social interaction difficulties. Notable contributions include studies on AI's social-cognitive capabilities and empathy training applications in healthcare settings. Scientific Contributions: Her research portfolio spans interdisciplinary investigations into mindreading, gender differences in social perception, and self-management strategies for medical patients. Key publications demonstrate methodological rigor across dynamic audiovisual stimuli , clinical interventions , and occupational stress prevention . 2025: Pioneering study on generative AI's social-cognitive assessment 2021: National multicenter research on ventricular assist device patients 2017: Gender-specific mindreading capabilities analysis 2016: Nonviolent communication training effectiveness in healthcare 2015: Cross-disorder mental state inference study Recognition: Awarded a German National Academic Foundation scholarship during her doctoral studies, reflecting her research excellence potential.
Franz Jetzinger is a researcher at the Professorship for Computer Science Education (Lehrstuhl für Informatikdidaktik) at the Technical University of Munich (TUM), working under the direction of Prof. Dr. Tilman Michaeli. He is based at Marsstr. 20-22(2907)/IV, 80335 Munich, with his office in room 2907.04.446. Jetzinger teaches courses in the Computer Science Education program, including "digi4all - Kompetenzen für das Unterrichten in einer digitalen Welt" and "Übung zur Didaktik der Informatik" for both winter and summer semesters of 2024/25 and 2025. His research focuses on several key areas within computer science education, with particular emphasis on Artificial Intelligence and Machine Learning education for K-12 settings. He has developed and delivered professional development programs for computer science teachers, especially regarding AI integration into compulsory computer science classrooms. His work spans teacher training, curriculum development, and practical implementation strategies for bringing AI education to secondary school levels. Jetzinger's publications reveal a strong focus on AI education in K-12 settings, with multiple publications in 2023-2025 addressing scalable professional development for teachers and evidence-based approaches to teaching AI. His work also includes substantial contributions to German computer science textbooks for Bavarian high schools, covering topics from object-oriented modeling to AI education. Developed professional development programs for AI education Contributed to multiple computer science textbooks for Bavarian high schools Conducted research on debugging processes in learning environments Participated in the TRR419 SHARP research project Worked on the "Von Bits zu QuBits" quantum computing education initiative Through his work with the Bavarian Ministry of Education and various workshops, Jetzinger has been instrumental in implementing AI education across Bavarian schools, delivering numerous training sessions throughout 2024 and 2025 on both basic and advanced AI topics for teachers.
Dr. Annika Dix is a research associate at the Chair of Automobile Engineering, TU Dresden, where since August 2023 she has been establishing the new research group for Automotive Human Factors. She previously held research positions at TU Dresden and the interdisciplinary Centre for Tactile Internet with Human-in-the-Loop (CeTI). Her expertise spans cognitive psychology, neuroscience, and human–machine interaction. Education & Training Diplom-Psychologin (equivalent to MSc Psychology), Humboldt-Universität zu Berlin, specializing in Cognitive and Neuropsychology Doctorate (Dr. rer. nat.), 2015, Humboldt-Universität zu Berlin & Berlin School of Mind and Brain, dissertation: “Count on the Brain: Using EEG Oscillations and Eye Movements to Disentangle Intelligent Problem-Solving in Math” Research Interests Dr. Dix investigates how human cognition—especially problem solving and decision making—interacts with complex technological environments. Her core themes include: Automotive Human Factors: driver behavior, autonomous vehicle acceptance, and technical supervision roles. Human-Machine Interaction: effects of feedback latency, multisensory augmentation, and interface design on motor skill learning. Cognitive & Neural Mechanisms: utilizing EEG, fNIRS, pupillometry, and drift-diffusion modeling to understand mathematical cognition, incentive motivation, and aging. Scientific Contributions & Trends Across 20 peer-reviewed works (2012-2025), Dr. Dix has progressively widened her focus from fundamental mathematical cognition toward applied challenges in cyber-physical and autonomous systems. Recent outputs emphasize ethical and social dimensions of robotics (robots giving moral advice, acceptance of robo-advisors) and the impact of system latency on fine motor performance in augmented reality and tactile internet environments. Teaching & Mentoring She currently coordinates teaching and study-program development for the Chair of Automobile Engineering. Earlier instructional activities at TU Dresden and Humboldt-Universität covered Cognitive Psychology, Embodied Cognition, Embodied AI, and teacher-training courses on child development. Labs & Teams Dr. Dix leads the nascent Automotive Human Factors research group at TU Dresden and actively collaborates with CeTI’s interdisciplinary consortium, integrating psychologists, engineers, and computer scientists to design human-centered tactile internet technologies.
