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Benjamin Lange is a Research Professor and Junior Research Group Leader in the Ethics of AI and Machine Learning at Ludwig Maximilians University of Munich (LMU) and the Munich Center for Machine Learning (MCML). He is also an Associate Research Fellow at the Oxford Uehiro Institute for Practical Ethics and a member of LMU's Zentrum für Ethik und Philosophie in der Praxis (ZEPP). His research bridges foundational ethical theory with practical challenges in technology, business, and organizational ethics. Lange holds a BSc from the London School of Economics, and a B.Phil and D.Phil from the University of Oxford. He previously held visiting positions at Google’s Responsible Innovation team and the University of Hamburg. His research focuses on ethical frameworks for AI, digital ethics, and corporate responsibility. Key themes include the ethics of human-AI collaboration, moral partiality in relationships, and organizational ethics. He has advised the European Commission on AI governance and developed assurance audit methodologies for algorithmic systems, including work for NYC’s AI Bias Law. His consulting practice focuses on helping organizations build ethical cultures through capabilities like moral imagination and compliance frameworks. Lange’s work has been featured in media such as CBS News, Business Insider, and ZDF heute journal. He is completing a book on the ethics of interpersonal relationships and partiality, aiming to connect philosophical insights with practical applications. His recent publications explore moral imagination in engineering teams, bias mitigation in AI systems, and the ethical implications of digital duplication technologies. He advises Fortune 500 companies, startups, and public institutions across sectors including finance, technology, and education. His approach emphasizes aligning ethical practices with organizational growth and social impact through tailored ethical frameworks and stakeholder engagement strategies.

• Ethics of AI
• Technology Ethics
• Normative Ethics
• +3 more

Zeynep Akata is the Liesel Beckmann Distinguished Professor of Computer Science at the Technical University of Munich (TUM) and Director of the Institute for Explainable Machine Learning at Helmholtz Munich. Previously she was a W3 Professor at the University of Tübingen (2019-2023) and held faculty and post-doctoral positions at the University of Amsterdam, UC Berkeley and the Max Planck Institute for Informatics. Her research focuses on multimodal learning and explainable artificial intelligence . Education: PhD, University of Grenoble / INRIA Rhône-Alpes, 2014 MSc, RWTH Aachen University, 2010 BSc, Trakya University, Turkey, 2008 Research Interests: Professor Akata’s group develops algorithms that learn from vision, language and other modalities simultaneously, with a strong emphasis on zero-shot, few-shot and continual learning . A central theme is making decisions interpretable, leading to work on explainable AI, concept bottleneck models, multimodal reasoning and human-aligned representation learning . Recent projects investigate large-scale multimodal language models, dataset distillation, model merging and continual knowledge editing. Publication Trends: Her 2024-2025 publications reveal a shift toward foundational large-scale models (diffusion, LLMs, vision-language transformers) while retaining the core themes of interpretability and generalization under limited supervision . Topics span dataset distillation, model merging, continual learning, fairness auditing of generative models and novel evaluation protocols for zero-shot learning systems. Scientific Awards: Lise-Meitner Award for Excellent Women in Computer Science (2014) Young Scientist Honour, Werner-von-Siemens-Ring Foundation (2019) ERC Starting Grant, European Commission (2019) DAGM German Pattern Recognition Award (2021) ECVA Young Researcher Award (2022) Alfried Krupp Award (2023) Advising & Funding: Prof. Akata currently supervises or co-supervises 25+ PhD students across TUM and the University of Tübingen via ELLIS and IMPRS-IS doctoral programs. She holds major grants including an ERC Starting Grant and DARPA Explainable AI funding, and is a frequent program chair and area chair for premier conferences (CVPR 2024, ECCV 2026, NeurIPS, ICML, etc.). Labs & Teams: She leads the Institute for Explainable Machine Learning at Helmholtz Munich and heads the Multimodal Learning and Explainable AI group at TUM. The institute collaborates closely with the ELLIS Institute Tübingen and Cyber Valley ecosystem, and maintains close ties with the Max Planck Institute for Intelligent Systems and Informatics.

• Multimodal Learning
• Explainable Artificial Intelligence
• Zero-Shot Learning
• +2 more

Prof. Bryan Ford leads the Decentralized/Distributed Systems (DEDIS) lab at EPFL. He focuses on secure decentralized systems, including blockchain technology, privacy, and systems security. He earned his Ph.D. from MIT and held faculty positions at Yale University and EPFL. His work spans distributed consensus protocols, peer-to-peer networking, and privacy-preserving systems. Key projects include QuePaxa (timeout-free consensus), UIA (global connectivity for mobile devices), and MedCo (secure healthcare data sharing). He advises numerous PhD students and contributes to open-source projects like Bitcoin collective signing and privacy networks like Riffle. Education: Ph.D., MIT; Postdoctoral work at Yale Research interests include blockchain scalability, consensus algorithms, and cryptographic privacy. His lab develops systems like TRIP for coercion-resistant voting and F3B to mitigate blockchain front-running. His work on NAT traversal and peer-to-peer protocols (e.g., STUN/ICE) remains foundational in network architecture. He emphasizes practical, auditable security solutions such as CertiKOS and atomic cross-chain transactions (Atom). Notable contributions: CoSi (collective signing), OmniLedger (sharded blockchain), and privacy-preserving protocols like PURBs (Protected Unsealable Recursive Boxes). His lab collaborates with Swiss Post to audit e-voting systems and designs democratic cryptocurrencies like PoPCoin.

• Secure Decentralized Systems
• Blockchain Technology
• Systems Security
• +3 more

Lisa Soder serves as Senior Policy Researcher and Acting Head of Technical AI Governance at Interface, a leading European tech policy think tank, and is an incoming Visiting Research Fellow at Stanford University's Intelligent Systems Laboratory within the School of Engineering. She holds a Master's degree from the London School of Economics focusing on comparative transatlantic approaches to technology regulation and competition law, and brings prior experience from the Centre for the Governance of AI, Boston Consulting Group, and global health NGO work in Ethiopia. Her research centers on establishing AI accountability infrastructures with particular emphasis on developing third-party auditing ecosystems and bridging technical and regulatory aspects of AI governance. She has developed a taxonomy for Technical AI Governance organized along technical targets (Data, Compute, Algorithms and Models, Deployment) and governance capacities (Assessment, Access, Verification, Security, Operationalization, Ecosystem Monitoring). Her work examines open problems across these dimensions, highlighting the critical need for technical tools to support effective AI governance. Analysis of her publications reveals a strong focus on practical implementation challenges in AI regulation, particularly regarding the EU AI Act's provisions for general-purpose AI systems. Her research consistently addresses the gap between policy aspirations and technical capabilities, with particular attention to verification mechanisms, risk assessment frameworks, and the development of technical infrastructure necessary for oversight. She advocates for closer collaboration between technical experts and policymakers to ensure governance mechanisms are both feasible and effective. Lisa has been actively engaged in high-level policy discussions, participating in events such as the AI Action Summit in Paris, Sino-German Track 2 Dialogues on AI governance, and expert briefings on frontier AI systems. Her upcoming visiting research fellowship at Stanford University represents a formal academic affiliation that complements her policy-focused work at Interface.

• Artificial Intelligence Governance
• AI Policy
• AI Regulation
• +5 more

Dr. Koustuv Saha is an Assistant Professor of Computer Science at the University of Illinois Urbana-Champaign (UIUC), leading the OnCARE lab. He holds a PhD from Georgia Tech and a B.Tech from IIT Kharagpur. His research focuses on computational social science, social computing, and ethical AI applications in mental health and wellbeing. His work bridges computer science with psychology, sociology, and public policy to address societal challenges. Education: PhD in Computer Science (Georgia Tech, 2021), B.Tech in CSE (IIT Kharagpur, 2012). Previous roles include Senior Researcher at Microsoft Research Montreal (FATE group) and industry research experience in Silicon Valley. Research interests include wellbeing sensing technologies, algorithmic fairness, and large language models’ societal impacts. Recent work examines caregiver mental health, deceptive wellness apps, and AI ethics in content moderation. His studies combine causal inference, NLP, and multimodal data analysis. Publications span top venues like CHI, CSCW, ICWSM, and JMIR. Notable awards include Georgia Tech’s Outstanding Dissertation Award (2022) and Snap Research Fellowship (2020). He advises on AI governance and collaborates with policymakers, clinicians, and industry. OnCARE lab explores human-centered AI for societal good, with projects on mental health support systems, ethical tech design, and algorithmic transparency in health contexts. Current focus includes caregiver AI tools, LLM-based empathetic systems, and workplace wellbeing interventions.

• Computational Social Science
• Social Computing
• Human-Centered Machine Learning
• +5 more

Julian Berger is a postdoctoral researcher at the Max Planck Institute for Human Development in the Center for Adaptive Rationality , where he explores how to enhance decision-making through hybrid human-AI systems. He is also a fellow of the Joachim Herz Foundation and has received funding from the Foundation of German Business and the Danish Data Science Academy. Education: M.A. Psychology in Business and Economics, Universidade Catolica Portuguesa (2021) B.A. Politics, Administration and International Relations, Zeppelin Universität (2018) His research spans human-AI collaboration , collective intelligence , and interpretable machine learning . A recurring theme in his work is developing methods to combine human expertise with AI capabilities for accuracy in domains like medical diagnostics , credit scoring , and football analytics . He has authored publications in high-impact venues such as PNAS , Nature Human Behavior , and Science and Medicine in Football . Scientific awards and funding include: Fellowship for interdisciplinary economics, Joachim Herz Foundation (2024) PhD funding from the Foundation of German Business (Stiftung der deutschen Wirtschaft) Research grant from the Danish Data Science Academy His recent article trends emphasize ensembling techniques that leverage complementary human and AI errors, algorithmic fairness, and practical heuristics like Hybrid Confirmation Trees. These works demonstrate significant improvements in diagnostic accuracy and decision cost-efficiency. Beyond academia, Berger works as a consultant and ML engineer with Simply Rational , focusing on interpretable models for financial and sports analytics. His work bridges theoretical research with real-world applications, prioritizing fairness, transparency, and human accountability in AI systems.

• Human-AI Interaction
• Collective Intelligence
• Machine Learning
• +3 more

Gerard Pons-Moll is a Professor at the University of Tübingen, endowed by the Carl Zeiss Foundation, and heads the Emmy Noether independent research group 'Real Virtual Humans'. He is a core faculty member at the Tübingen AI Center, a senior researcher at the Max Planck Institute for Informatics (MPII), and faculty at the International Max Planck Research School for Intelligent Systems (IMPRS-IS) and the Saarland Informatics Campus. His research focuses on computer vision, graphics, and machine learning, particularly in creating virtual human models and analyzing human motion from video and sensor data. Education: PhD (with distinction) in 2014 from Leibniz University of Hannover, Master's in Telecommunications Engineering (Northeastern University, 2008), and B.S./M.Sc. in Telecommunications Engineering from the Technical University of Catalonia (2002–2008). Research Interests: 3D human modeling, pose estimation, human-object interaction, and applications in industry and research. His work emphasizes real-world applications like virtual avatars and motion capture systems. Awards: Emmy Noether Grant (2018), German Pattern Recognition Award (2019), Google Faculty Research Award (2019), and multiple best paper awards at top conferences (BMVC’13, Eurographics’17, 3DV'18, CVPR'20). Advising & Grants: Served as program chair of 3DV 2021, area chair for ECCV, CVPR, and IJCAI. Active in reviewing for DFG, ANR, and ISF. Supervises research in areas like neural rendering frameworks (Blendify) and synthetic data generation (STAGE). Labs/Teams: Leads the Emmy Noether group and collaborates with MPII, Tübingen AI Center, and IMPRS-IS on projects like XNect (real-time 3D motion capture) and Human 3Diffusion (avatar creation).

• Computer Vision
• Computer Graphics
• Machine Learning
• +3 more

Prof. Dr. Matthias Weidlich is a faculty member at Humboldt University of Berlin within the Institute of Computer Science under the Faculty of Mathematics and Natural Sciences . His research focuses on Process Mining , Complex Event Processing , and Data Privacy with applications in Business Process Management and Scientific Workflows . Research Interests: Business Process Management and Process Mining Complex Event Processing and Stream Data Analysis Data Privacy and Security in Process Systems Scientific Workflow Systems and User Behavior Heterogeneous Network Embeddings Algorithm Design and Optimization Recent Publications (2023-2025) demonstrate expertise in: Efficient stream processing techniques Privacy-preserving process mining frameworks Scientific workflow analysis tools Graph neural network applications Multi-modal data integration Adaptive querying systems Contact: Office: Unter den Linden 6, 10099 Berlin Phone: 030 2093-41277 Email: matthias.weidlich@hu-berlin.de Web: hu.berlin/data

• Business Process Management
• Process Mining
• Complex Event Processing
• +5 more

Kevin Baum is a computer scientist currently serving as the deputy head of the Neuro-Mechanistic Modelling (NMM) department at the German Research Center for Artificial Intelligence (DFKI) since January 2023, and head of the Centre for European Research in Trusted AI (CERTAIN) at DFKI since December 2023. Based at the Saarland Informatics Campus in Saarbrücken, Germany, he completed his doctorate in philosophy in March 2024, combining technical expertise with philosophical depth. His work bridges computer science with ethics, focusing on making AI systems transparent and accountable to human users. Dr. Baum's research program centers on interdisciplinary questions concerning the explainability and transparency of AI systems. His work spans multiple significant projects including the Explainable Intelligent System (EIS) initiative and project E7 of the Transregional Collaborative Research Centre 248 "Foundations of Perspicuous Software Systems" (CPEC). He has developed frameworks for understanding stakeholder perspectives on explainable AI and investigated how different information types about automated systems affect user perceptions of fairness and justice. His approach consistently combines theoretical foundations with practical implementations across diverse contexts. Analysis of his publication trends reveals a clear trajectory from theoretical foundations in machine ethics toward practical implementations of explainability requirements in real-world contexts. His work demonstrates increasing focus on human oversight effectiveness, fairness monitoring, and ethical considerations across various AI applications. He has made significant contributions to both academic discourse and practical AI development guidelines, with publications spanning computer science, philosophy, psychology, and human-computer interaction venues. Award for Ethics for Nerds lecture series As a research leader, Dr. Baum contributes to shaping AI development practices through his departmental leadership and interdisciplinary collaborations. His current work with CERTAIN focuses on establishing European research standards for trusted AI development and deployment, emphasizing the practical implementation of ethical requirements in AI systems. He maintains active collaborations across multiple institutions and disciplines, reflecting his commitment to bridging technical and philosophical considerations in AI development. At DFKI, he leads research that combines neuroscientific insights with AI development to create more interpretable systems. The NMM department focuses on both theoretical research on explainable AI foundations and practical applications in various domains, with particular attention to how different stakeholders understand and require explanations from AI systems.

• Explainable AI
• AI Ethics
• AI Fairness
• +4 more

Holger Dette is a Professor and Chair Holder of Stochastics (specializing in Statistics) at the Faculty of Mathematics, Ruhr University Bochum. He leads the prominent Group Dette within the Institute of Statistics, overseeing a team of researchers, doctoral students, and administrative staff including Birgit Tormöhlen as team assistant. His research group is deeply integrated within the university's mathematical ecosystem, collaborating with other research groups across algebra, analysis, numerics, and topology. Dette's research spans mathematical statistics with strong applications in real-world problems. His primary interests include optimal experimental design, time series analysis, functional data, change point problems, nonparametric regression, biostatistics, special functions, goodness-of-fit tests, and random matrices . His work bridges theoretical statistics with practical applications, particularly evident in his collaborations with pharmaceutical giants Novartis and Bayer AG in biostatistics, as well as Quasol, a spin-off company from his statistics institute. His recent publications (2024-2025) reveal a research program increasingly focused on high-dimensional and functional data analysis, privacy-preserving statistics, and novel methodological approaches to longstanding statistical problems. Dette's work shows strong interdisciplinary connections, particularly with biomechanics (analyzing joint angles during fatigue phases) and data science (addressing challenges in the era of big data). His research group is actively involved in multiple DFG-funded projects including the newly established 'Small Data' collaborative research center (Sonderforschungsbereich 1597) and the Spatio-temporal Statistics for the Transition of Energy and Transport (Transregio 391). Dette has received significant recognition including the prestigious Humboldt Research Award . His paper 'With Great Power Come Great Side Channels: Statistical Timing Side-Channel Analyses with Bounded Type-1 Errors' achieved second place at the CSAW'24 Applied Research Competition MENA. His research group has also secured multiple significant funding awards from the German Research Foundation (DFG). As an advisor, Dette supervises numerous doctoral and master's students including Pascal Quanz, Marius Kroll, and Carina Graw. His group offers statistical consulting services for scientists and students across bachelor's, master's, and doctoral phases. The group maintains strong industrial partnerships, particularly in biostatistics applications, demonstrating Dette's commitment to translating theoretical statistics into practical solutions for real-world challenges.

• Mathematical Statistics
• Applied Statistics
• Probability Theory
• +6 more

Alan Mislove is a Professor of Computer Science at Northeastern University and serves as Senior Associate Dean for Academic Affairs and Interim Executive Director of the Institute of Experiential Artificial Intelligence (EAI). He is also a core faculty member of the Cybersecurity and Privacy Institute and EAI at Northeastern. His research focuses on algorithmic auditing, fairness, and privacy in online systems. Notably, he has testified before Congress on algorithmic discrimination, challenged the CFAA in ACLU litigation, and contributed to landmark FTC and DOJ cases against Meta. His work has been funded by the NSF, Army Research Office, Google, and Amazon Web Services. Mislove’s academic leadership includes roles in Khoury College, where he supports over 2,000 students and faculty. He has advised numerous PhD students, including current research scientist Piotr Sapieżyński. His lab explores algorithmic transparency, PKI security, and middlebox traffic differentiation. Key awards include the 2017 ACM SIGCOMM Test of Time Award and recognition for work on QUIC protocol evaluation. He has authored over 150 publications, including seminal studies on Facebook ad discrimination and Uber’s surge pricing. His teaching spans systems, networks, and social computing courses, emphasizing experiential learning. Mislove actively serves on program committees for IMC, PETS, and other top conferences, and contributed to policy as Deputy U.S. CTO for Privacy (2023–2024).

• Algorithmic Auditing
• Fairness
• Privacy
• +4 more

Prof. Dr. Markus Strohmaier holds the Chair of Data Science in Economics and Social Sciences at the University of Mannheim's Business School. His research focuses on applying machine learning and data science to understand socio-economic systems and human behavior, leveraging text, relational, and emerging data types. He teaches graduate courses and supervises theses in these areas. Research interests include algorithmic fairness, behavioral analysis via computational methods, and the societal implications of AI. Notable work addresses demographic representativeness in large language models, bias mitigation in rankings, and gender gaps in blockchain adoption. His team is located at L15, 3rd floor – Room 307 in Mannheim, adjacent to the central station. Collaborations span interdisciplinary topics like social media analysis, network dynamics, and computational social science. Key contributions include developing benchmarks for neural network similarity (Resi) and frameworks for measuring algorithmic fairness perceptions (FairCeptron). Research often bridges technical innovation with societal impact, addressing ethical challenges in AI and data-driven decision-making.

• Machine Learning
• Data Science
• Social Sciences
• +7 more

Florian Leiser is a Professor at the Chair of Information Infrastructures (led by Prof. Dr. Ali Sunyaev) at Technical University of Munich's Heilbronn campus. His research focuses on human-AI collaboration, privacy-preserving algorithms, and explainability in machine learning systems. Current research areas include Hybrid Intelligence, Human-centered Generative AI (LLMs), Federated Learning, and Health Information Systems Recent publications demonstrate expertise in Explainable AI for medical imaging LLM hallucination detection Federated learning architectures Human-in-the-loop systems Healthcare data applications He contributes to teaching through Human-Centered Artifact Design courses Collaborative teaching roles in machine learning Supervising student projects

• Hybrid Intelligence
• Human-centered Generative AI
• Federated Learning
• +1 more

Rainer Böhme is a Professor at the University of Münster, Germany. His research focuses on cyber security, cryptography, steganography, digital forensics, and blockchain technology. He has made significant contributions to understanding cyber risk quantification, central bank digital currencies (CBDCs), and the socio-technical challenges in cryptocurrency systems. Key areas of research include steganalysis (analysis of hidden data in digital media), forensic techniques for neural network inference pipelines, and the economic implications of cyber insurance. He actively contributes to conferences like the Financial Cryptography Workshops (FC) and the Workshop on Information Hiding and Multimedia Security (IH). Böhme's work bridges theory and practice, addressing real-world issues such as privacy in CBDCs, adversarial attacks on AI systems, and the role of law enforcement in cryptocurrency markets. His recent studies explore the security implications of image orientation in JPEG files and the detection of privileged parties in blockchain transactions. He has collaborated with institutions like the University of Vienna and ETH Zurich, and his research has been published in top-tier venues such as IEEE Transactions on Information Forensics and Security and the ACM Conference on Computer and Communications Security (CCS).

• Cyber Security
• Cryptography
• Steganography
• +6 more

Stefan Decker is a full University Professor (Universitätsprofessor) at RWTH Aachen University, Germany, where he heads the Chair of Information Systems and Databases (Informatik 5) within the Faculty of Mathematics, Computer Science and Natural Sciences. He is actively involved in teaching, research, and the supervision of numerous ongoing and completed doctoral, master’s, and bachelor theses. Education & Academic Background Doctorate (Dr. rer. pol.) – field of Information Systems or related (exact institution/year not stated in text). Appointed as University Professor and Chair of Information Systems and Databases at RWTH Aachen University. Research Interests Prof. Decker’s work lies at the intersection of databases, knowledge graphs, semantic web technologies, data science, and cybersecurity . He investigates architectures and algorithms for large-scale, privacy-preserving, decentralized data analytics , develops ontology-driven information systems , and explores the use of large language models (LLMs) for educational technology, anomaly detection, and incident-response playbooks. Additional focal areas include smart energy systems, mixed-reality learning environments, FAIR data principles, and federated machine learning . Scientific Contributions & Trends His recent publications (2022-2025) demonstrate a clear trend toward explainable AI, LLM-enhanced systems, secure data spaces, and semantic interoperability . Key contributions include novel anomaly-detection frameworks for encrypted power-grid communications, knowledge-graph-driven chatbots for higher-education support, and methodological advances in decentralized analytics and FAIR data sharing. These works are disseminated in top-tier venues such as AAAI, IEEE ISGT Europe, ESWC, IDEAL, and various Springer LNCS and IEEE Transactions. Supervision & Grants Doctoral Theses Advised: A. T. Neumann – “Chatbots as professional companions in large-scale community information systems” (2024) S. M. Welten – “Methods for practical data sharing and decentralized analytics” (2025) Master’s Theses Co-Advised: A. R. Küsters – “Object-centric process constraints using variable bindings” (2025) Additionally supervising more than 30 ongoing bachelor, master, and doctoral projects covering topics such as LLM-driven cybersecurity playbooks, knowledge-graph construction for German law, privacy-preserving analytics in smart grids, and mixed-reality learning agents. Principal investigator or senior researcher in large collaborative projects including NFDI4DS, WestAI, champI4.0ns and several EU/national initiatives on sovereign data spaces and AI services. Labs & Teams Prof. Decker leads the Information Systems & Databases (DBIS) Research Group . The group operates well-equipped laboratories for knowledge-graph engineering, mixed-reality applications, privacy-enhancing technologies, and secure distributed analytics . Current team size exceeds 30 researchers including PhD candidates, postdocs, and scientific programmers, supported by national and EU funding streams.

• Information Systems
• Databases
• Knowledge Graphs
• +7 more