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Ludovic JAUBERTView profile

Researcher

• Condensed Matter Physics
• Quantum Spin Liquids
• Frustrated Magnetism
• +5 more

Ludovic JAUBERT is a CNRS Researcher in the Condensed Matter Theory Group at the Laboratoire Ondes et Matière d’Aquitaine (LOMA) in Bordeaux. He focuses on quantum magnetism, particularly frustrated systems, spin liquids, and topological phases. His research explores emergent phenomena like monopoles, fractons, and quantum phase transitions in materials such as spin ices and pyrochlore lattices. Education: Ph.D. in Theoretical Physics from École Normale Supérieure de Lyon (2005), with prior work at institutions including Okinawa Institute of Science and Technology (OIST), Oxford, and Dresden. Research Highlights Quantum spin ice dynamics and coupled matter systems Topological spin liquids and their gauge field descriptions Machine learning applications in identifying quantum phases He has organized international conferences and workshops on frustrated magnetism and quantum matter. Collaborates with experimental groups globally and has supervised multiple PhD and master’s students in theoretical condensed matter physics.

• Artificial Intelligence
• Multi-Agent Systems
• Social Network Analysis
• +5 more

Gita Reese Sukthankar is a Professor at the University of Central Florida, specializing in artificial intelligence, multi-agent systems, and social network analysis. Her work integrates machine learning, human-robot interaction, and collaborative systems to address challenges in decentralized task allocation, online community dynamics, and human-machine teaming. She has contributed to projects involving agent-based modeling, reinforcement learning, and ethical AI applications in open-source software and policy discourse moderation. Affiliations: University of Central Florida Key Research Areas: Multi-Agent Systems, Social Network Analysis, Reinforcement Learning, Human-Computer Interaction Her research explores topics such as conflict prediction in team dialogues, bot impact on GitHub workflows, and chess mastery analysis using language models. She has co-authored over 185 publications in top venues like AAAI, AAMAS, and IROS, often collaborating with researchers like Rahul Sukthankar, Katia Sycara, and Kiran Lakkaraju. Her work bridges theoretical AI advancements with practical applications in robotics, software development, and policy analysis, emphasizing ethical considerations and scalable solutions.

• Functional Genomics
• Genetic Networks
• CRISPR Screening
• +3 more

Maximilian Billmann is Junior Professor at the University of Bonn's Institute of Human Genetics, developing computational methods for functional genomics and genetic network analysis. His research integrates CRISPR screens with machine learning to study cellular responses. Research Interests: Genetic interaction networks and pathway analysis CRISPR screen optimization and reproducibility Machine learning for functional genomics Mitochondrial function in disease Cancer signaling rewiring

• Neuroengineering
• Brain-Computer Interfaces (BCI)
• Human-Robot Interaction (HRI)
• +3 more

Stefan Ehrlich is a Researcher at the Institute for Cognitive Systems (ICS) at the Technical University of Munich (TUM). His work focuses on neuroengineering, particularly non-invasive brain-computer interfaces (BCI) for human-robot interaction (HRI), neuroadaptive systems, and EEG-based neurotechnology. He collaborates closely with Prof. Gordon Cheng and contributes to projects involving error-related potentials (ErrPs), affective neurofeedback, and neuroprosthetics. His research integrates cognitive neuroscience principles with robotics to enhance human-machine collaboration and adaptive systems. Key research areas include: Development of passive BCI systems for real-time robotic adaptation Design of co-adaptive human-agent interfaces using neural signals EEG-based assessment of exoskeleton and humanoid robot performance Neuroimaging techniques for music-brain interactions His publications emphasize interdisciplinary approaches, combining neuromorphic engineering, machine learning, and social robotics. Ongoing work explores low-power neuromorphic hardware for EEG decoding and neurophysiological mechanisms underlying human-robot trust dynamics. Collaborations involve institutions like the Bernstein Center for Computational Neuroscience and industry partners in wearable robotics. Labs/Teams: Active contributor to the EEG Laboratory and projects like the Soft Wearable Robotics initiative. Engages in international conferences (IROS, IEEE EMBC) and co-organizes workshops on HRI and neurotechnology.

Nicoline van HarskampView profile

Professor

• Performance Art
• Linguistics
• Digital Art
• +3 more

Nicoline van Harskamp is a Professor of Performative Art at the Kunstakademie Münster. Her work explores language dynamics, globalization, and technology through performance, installation, and interdisciplinary collaboration. She holds degrees from the Royal Academy of Fine Arts (Den Haag), Chelsea College (London), and the Rijksakademie (Amsterdam). Research interests include the evolution of English as a global language, the intersection of performance art with activism, and the impact of digital technology on artistic expression. Notable projects include Yours in Solidarity (2010–2013), Englishes (2014–2016), and the ongoing Prosodia series investigating synthetic speech and AI. Her works have been exhibited globally, including at the Tate Modern, Performa 11, and the Sydney Biennale. Collaborations include projects with institutions like the Sandberg Institute and Loughborough University’s Discourse and Rhetoric Group. Prosodia (2024) merges AI-generated speech with live performance, while Englishes MOOC offers an online course on multilingual art practices. Awards and grants include support from the Creative Industries Fund and the Pauwhof Fund. Van Harskamp’s class at Kunstakademie emphasizes cross-disciplinary collaboration, with projects like Performance Room and Actors and Directors . Her work interrogates language’s role in identity, politics, and technology, blending academic rigor with artistic innovation.

Fabian ScheiplView profile

Researcher

• Functional Data Analysis
• Statistical Modeling
• Machine Learning
• +3 more

PD Dr. Fabian Scheipl is the Head of the Department of Statistics at Ludwig-Maximilians-Universität München (LMU). His work focuses on cutting-edge statistical methodologies, particularly in Functional Data Analysis, and he leads the Functional Data Analysis research group. He specializes in developing advanced statistical models and open-source software tools for data science applications. His research bridges theoretical statistics with practical implementations, emphasizing reproducible research and educational resources. Dr. Scheipl’s research interests include functional additive mixed models, cluster analysis, and high-dimensional data techniques. He has contributed significantly to R package development, including refund , gamm4 , and lme4 , which are widely used in statistical computing. His work spans diverse fields such as healthcare (e.g., analyzing critical care outcomes), environmental science (e.g., earthquake dynamics), and computational biology (e.g., epigenetic studies). His publications highlight innovations in statistical methodology, such as improving cluster validation metrics and advancing topological approaches to outlier detection. While no awards are explicitly listed, his impactful contributions to statistical software and theory are widely recognized in academic circles. He actively engages in advising doctoral candidates and contributes to teaching and mentorship in statistical education. Labs/Teams: Leads the Functional Data Analysis group within LMU’s Department of Statistics, fostering interdisciplinary collaborations across computational, biomedical, and environmental research domains.

Ping XiongView profile

Researcher

• Explainable AI
• Bayesian Learning
• Machine Learning
• +3 more

Ping Xiong is a Researcher at the Berlin Institute for the Foundations of Learning and Data (BIFOLD), Technical University of Berlin. His academic background includes a Ph.D. in Industrial Engineering (2022–present), and both M.Sc. and B.Sc. degrees in Industrial Engineering from TU Berlin (2017–2022). Research interests focus on Explainable AI (XAI), Bayesian Learning, and applications in Machine Learning and Graph Neural Networks. He explores symbolic explanations, logical feature relationships, and robust ensemble methods against adversarial perturbations in graph structures. His recent work emphasizes efficient computation techniques for subgraph attribution and interpretable GNNs. Notable contributions include studies on β-GNN robustness and walk-based explanation methods for GNNs. Affiliated with BIFOLD Graduate School, his work bridges theoretical advancements in machine learning with practical interpretability challenges. No awards or grants explicitly mentioned, but active in publishing cutting-edge research in XAI and graph learning domains.

Matthias SchefflerView profile

Professor

• Kinetic Monte Carlo simulation
• ab initio DFT
• electronic structure theory
• +5 more

Prof. Dr. Matthias Scheffler is the Director of the Theory Department at the Fritz-Haber-Institut der Max-Planck-Gesellschaft. His research focuses on predictive multiscale modeling in catalysis and energy conversion, integrating electronic structure theory, kinetic Monte Carlo simulations, and machine learning. He leads a diverse team (25+ nationalities) exploring processes in catalysts and energy devices. Research Interests: Computationally modeling materials properties, electrochemical interfaces, and energy conversion. Specializes in density-functional theory (DFT), ab initio methods, and data science for accelerating materials discovery. Current projects include understanding electron spillover effects in electrocatalysis and developing self-driving labs for catalysis research. Recent Highlights: Pioneered the Automatic Process Explorer (APE) for atomic dynamics analysis, revealed quantum mechanical electron spillover in water interfaces, and secured funding renewal for the e-conversion Cluster of Excellence. Collaborates with institutions like TU Berlin, FZ Jülich, and Brown University. Labs/Teams: Heads the Theory Department with subgroups like Light-Matter Interactions (Dr. Matthias Kick) and Machine Learning Interatomic Potentials (Dr. Hendrik Heenen). Hosts retreats and international collaborations, including EU-funded projects and Humboldt Professorships.

Philipp JungView profile

Researcher

• Machine Learning
• Data Cleaning
• Artificial Intelligence
• +4 more

Philipp Jung is a researcher affiliated with the Calgo Lab and the TU Berlin's Data Science +X Graduate School. He holds a physics degree from TU Berlin and an industrial engineering background from BHT Berlin. His work focuses on applied machine learning for data cleaning, with interdisciplinary research in AI-driven healthcare, industrial processes, biodiversity, and human-computer interaction. Key Research Projects: APPL-FM (DFG): AI in health and cell biology KIP-SDM (BMBF): Data science in medical applications HUMAN.VR.LAB (BMBF): VR interface development GCA (BMUV): Green AI for biodiversity He collaborates with tech startups like QualiFiction and contributes to initiatives like the Berlin Big Data Center. His work is funded by DFG, BMBF, and EU programs.

Alex MitrevskiView profile

Lecturer

• Cognitive Robotics
• Robot Learning
• Knowledge Representation and Reasoning
• +4 more

Dr. Alex Mitrevski is a Lecturer and postdoctoral researcher at the Institute for Artificial Intelligence and Autonomous Systems (A2S) at Hochschule Bonn-Rhein-Sieg (H-BRS). He completed his PhD in Knowledge-Based Systems at RWTH Aachen University, with a focus on robot action transparency and introspection. His research emphasizes cognitive robotics, lifelong learning, and robots' ability to adapt to human-centered environments. Education: PhD in Knowledge-Based Systems (RWTH Aachen University/H-BRS) Research Interests: His work spans knowledge representation, robot fault detection, simulation-based learning, and assistive robotics. He aims to develop service robots that enhance everyday human life through personalized interactions and robust failure analysis. Projects: KEROL : Developing simulation-based environments for robot skill evaluation. MigrAVE : Creating assistive technologies for children with Autism Spectrum Disorder. ROPOD : Innovating cost-effective autonomous logistics solutions. Advising & Grants: Supervised over 20 master's theses and R&D projects on topics like robot learning, fault diagnosis, and natural language processing. Actively involved in grant-funded initiatives for robotics deployment and therapy applications. Labs & Teams: Leads research at the A2S Institute, collaborating on interdisciplinary projects in cognitive robotics and autonomous systems.

• Geophysics
• Hydrogeochemistry
• Digital Rock Physics
• +4 more

Frieder Enzmann is an Associate Professor and Academic Director at the Institute of Geosciences, Johannes Gutenberg University Mainz. He leads the Computed Tomography Laboratory (currently out of service) and oversees projects like HyINTEGER, ReSKIN, ReSALT, ReSKIN_Move, BIOPORE, MABEIS I/II, and others. His research focuses on geophysical imaging, porous media dynamics, and subsurface processes using advanced computational and tomographic techniques. Research Interests: Computed tomography (CT) applications in geosciences Pore-scale modeling of fluid flow and mineral precipitation Rock physics and reservoir characterization Geomorphological hazards (e.g., landslides, debris flows) Climate-related studies involving ice dynamics and sediment flux Projects highlight interdisciplinary collaboration, such as analyzing rockfall risks in Rhineland-Palatinate and modeling geothermal dynamics in flooded mines. His work bridges geology, engineering, and computer science, with applications in energy storage and environmental risk assessment. Publications span over 20 years, with recent emphasis on AI-driven image analysis and pore-scale simulations. He collaborates internationally on projects like HyINTEGER (hydrogen storage) and ReSalt (reactive reservoir systems).

• Software Engineering
• Formal Methods
• Model-Driven Development
• +3 more

Alexander Raschke is a Professor at the University of Ulm, affiliated with the Institute of Software Engineering and Compiler Construction. His research focuses on formal methods, model-driven engineering, software safety and security, and abstract state machines. He has authored/co-authored over 50 publications, including works on collaborative modeling frameworks, security analysis techniques, and programming by demonstration. He holds a PhD in Computer Science from the University of Ulm (2009). As an academic, he has organized and chaired conferences such as ABZ (2020, 2021), demonstrating leadership in the formal methods community. His work bridges theoretical foundations with practical applications in software systems, emphasizing safety-critical and self-adaptive systems. Notable contributions include the development of EditQL—a textual query language for evolving models—and frameworks for attack-tree generation in cybersecurity.

Theobald LohmuellerView profile

Senior Lecturer

• Bioplasmonics
• Photonics on Membranes
• Nanophotonics
• +3 more

PD Dr. Theobald Lohmueller is a Senior Lecturer at the Chair of Photonics and Optoelectronics (Photonik und Optoelektronik) within the Faculty of Physics at Ludwig-Maximilians-Universität München (LMU). His research focuses on Bioplasmonics , Photonics on Membranes , and Optical Manipulation , with applications in biophysics and biomedical engineering. He leads a research group investigating light-matter interactions in biological systems, including optical control of lipid membranes and nanoparticle-based sensing technologies. Education & Career: 2017: Habilitation and venia legendi in Physics, LMU Munich 2011: Group leader at the Chair for Photonics and Optoelectronics, LMU 2009–2011: Postdoctoral Research Fellow, UC Berkeley (Department of Chemistry & Lawrence Berkeley National Laboratory) 2004–2008: PhD student, Max Planck Institute for Metals Research (Stuttgart) & Heidelberg University Research Highlights: Developed photoswitchable phospholipids for optical control of membrane processes and drug delivery. Pioneered optical trapping and manipulation of plasmonic nanoparticles for biosensing and cell engineering. Advanced surface-enhanced Raman spectroscopy (SERS) using DNA-origami nanoantennas. Awards & Grants: 2016 and 2014 CeNS Publication Awards (Best junior scientist) Klaus Tschira Award (2009) for public science communication German Research Foundation (DFG) Post-Doc Fellowship (2009) Labs & Collaborations: His lab integrates nanophotonics, biophysics, and materials science, collaborating with institutions like the Nanosystems Initiative Munich (NIM) and the Center for NanoScience (CeNS). Key projects include developing plasmonic sensors for real-time biological monitoring and AI-driven analysis of membrane dynamics.

Mathias KlierView profile

Professor

• Big Data Analytics
• Data Quality
• Artificial Intelligence
• +3 more

Prof. Mathias Klier holds the Péter Horváth Endowed Professorship for Business Administration with a Focus on Business Information Management at the University of Ulm's Department of Business Analytics. His research focuses on big data analytics, data quality, AI explainability, and social impact of information systems. Klier has held previous academic roles at the Universities of Augsburg, Innsbruck, and Regensburg, where he contributed to data quality metrics and enterprise systems research. He has authored over 100 peer-reviewed articles in journals like MIS Quarterly and Decision Support Systems, and serves as a track chair for ICIS and ECIS conferences. Education: PhD in Business Administration (2008), University of Augsburg (Dissertation: 'Designing Customer-Centric Information Systems') MSc in Business Mathematics (2005), University of Augsburg Research Interests: Klier’s work bridges technical data quality challenges with societal impacts. Key themes include probabilistic metrics for data currency/consistency, ethical AI design, and leveraging peer networks for unemployment support. His recent projects apply explainable AI to public services and workforce development. Key Achievements: Recipient of Research Prize of the Swabian Economy (2008) Winner of Vodafone Foundation Award (2005) Awarded Ciborra Award (2018) for innovative research on event-driven duplicate detection Advising & Grants: Klier’s team has secured funding for projects on future skills forecasting and refugee integration via digital peer groups. He advises on AI literacy initiatives in K-12 education and collaborates with industry partners like German car manufacturers. Labs/Teams: Leads the Business Analytics Institute at Ulm, focusing on data-driven decision making and AI ethics. Active in interdisciplinary research networks including the German Information Quality Management Initiative.

• Cell Biology
• Structural Biology
• Biophysics
• +3 more

Dr. Martin Beck is a Research Professor and Director at the Max Planck Institute of Biophysics, focusing on the structural and functional analysis of large macromolecular complexes such as nuclear pore complexes (NPCs). His research employs advanced techniques including cryo-electron tomography, live-cell imaging, and computational modeling to study NPC dynamics, HIV capsid transport, and cellular membrane integrity. Beck has received three prestigious ERC grants (Starting, Consolidator, and Advanced) for his groundbreaking work. His interdisciplinary approach bridges structural biology, cell biology, and biophysics, with applications in virology and drug discovery. Education highlights include a PhD from Technical University of Munich (2006) and postdoctoral research at ETH Zurich and EMBL Heidelberg. He leads a lab investigating nuclear transport mechanisms, membrane interactions, and the mechanical properties of cellular organelles. Key achievements include uncovering NPC dilation dynamics and elucidating HIV capsid-nuclear pore interactions. Scientific awards include ERC grants and contributions to structural biology methodologies. Ongoing projects explore nuclear pore safeguarding mechanisms, drug action at the cellular level, and AI-driven structural analysis. Beck collaborates extensively with institutes worldwide, advancing both fundamental science and translational applications.