Christopher Katins is a Researcher at the Institute of Computer Science within the Faculty of Mathematics and Natural Sciences at Humboldt University of Berlin. His work focuses on Human-Computer Interaction , particularly in Mixed Reality , Extended Reality , and applications to safety-critical environments such as aviation. Research Trends : Recent publications emphasize Large Language Models in UX design, XR user studies , and safety-critical interface challenges . Key areas include public perceptions of XR , spatial interaction techniques , and empathic interface development . Labs & Teams : Affiliated with the Human-Computer Interaction for Scientific Software group, driving innovation in immersive analysis and aviation-specific interfaces.
Yen-Chia Hsu is an Assistant Professor at the Informatics Institute, University of Amsterdam, where they teach courses in Information Visualization and Data Science. Previously, they served as a Postdoctoral Researcher at the Department of Sustainable Design Engineering, Faculty of Industrial Design Engineering, TU Delft, and as a Project Scientist in the CREATE Lab at Carnegie Mellon University (CMU). Their academic journey reflects a unique interdisciplinary background bridging computer science and architectural design. Dr. Hsu earned their Ph.D. degree in Robotics in 2018 from the Robotics Institute at CMU, where they conducted research on using technology to empower local citizens and communities. Prior to that, they received their Master's degree in tangible interaction design in 2012 from the School of Architecture at CMU, where they studied and built prototypes of interactive robots and wearable devices. Before CMU, they earned a dual Bachelor's degree in both architecture and computer science in 2010 at National Cheng Kung University, Taiwan. Dr. Hsu is a computer scientist with an architectural design background whose research focuses on Community-Empowered Artificial Intelligence (AI) , where they co-design, implement, deploy, and evaluate interactive AI systems that empower communities, especially in addressing environmental and social issues. Their work spans both social and technical aspects of community engagement with technology. On the social side, they have proposed an alternative framework called Community Citizen Science (CCS) , which extends traditional citizen science methods to a hyper-local scale, emphasizing continued community engagement after technology interventions. On the technical side, they investigate human feedback in AI pipelines and algorithms that enable machine learning models to incorporate different types of human input. Dr. Hsu's scholarly output demonstrates a consistent focus on applying computer vision, machine learning, and data science to environmental monitoring and community empowerment. Their recent work shows an evolution from developing specific tools for pollution monitoring toward more comprehensive frameworks for community engagement with AI systems. A notable trend is the increasing emphasis on empathy-centered design and policy implications of community-driven data collection systems. Their research bridges the gap between technical innovation and social impact, particularly in the domains of air quality monitoring and environmental justice. Outstanding Student Academic Achievement (2005, 2006, 2007) from Department of Architecture, National Cheng Kung University, Taiwan Third Prize, National Country House Design Competition (2008) from Ministry of the Interior, Taiwan Best New Artist, The National Golden Award for Architecture (2009), Taiwan Webby People's Voice Award, Best Use of Video or Moving Image (2014) Best Paper Honorable Mention Award (Top 5%) at ACM CHI Conference (2017) Best Paper Honorable Mention Award (Top 2.5%) at ACM IUI Conference (2019) Prize for Community Collaboration, The Constellation Prize (2020) Dr. Hsu has been actively involved in numerous research projects that bridge academia and community action. Their work on the Smell Pittsburgh platform, which allows citizens to report pollution odors to regulators, has been particularly influential in environmental advocacy. They have collaborated with organizations including ACCAN, PennEnvironment, GASP, Sierra Club, ROCIS, Blue Lens, LLC, PennFuture, Clean Water Action, and Clean Air Council. Their research has received support from the Heinz Endowments and has been featured in TIME, Pittsburgh Post-Gazette, PC Magazine, and other media outlets. Dr. Hsu also maintains an active open-source presence, with several tools and datasets released to support community-driven environmental monitoring. Dr. Hsu leads projects that focus on developing tools for community engagement at scale, including COCTEAU, an empathy-based tool for decision-making, and Project RISE, which recognizes industrial smoke emissions. Their work connects with the Multimedia Analytics Lab Amsterdam, where they contribute to data science education and research. Their approach emphasizes co-creation with communities rather than top-down technology deployment, positioning them at the forefront of human-centered AI research with real-world social impact.
Prof. Dr. Kim Frederic Albrecht is a Professor of Information Design at the Folkwang University of the Arts (since 2025), affiliated with metaLAB (at) Harvard, metaLAB (at) Berlin, and the Berkman Klein Center for Internet & Society at Harvard University. He holds a PhD in Media Theory (2021), an MA in Interface Design (2014), and a BA in Visual Communication (2012). Research & Teaching His work bridges data visualization, technology, and culture, focusing on critical approaches to algorithmic systems, data aesthetics, and AI ethics. He teaches information design, computational design, and data visualization at Folkwang. Previous roles include positions at Harvard, Northeastern University, and the Free University of Berlin. Key Projects - Artificial Worldviews : Maps ChatGPT’s knowledge universe through recursive API probing. - #MeToo Anti-Network : Analyzes social media activism dynamics. - Hypercam : Critiques video conferencing interfaces during the pandemic. - Tudor Networks : Visualizes 16th-century government communication. Awards & Recognition Recipient of multiple Information is Beautiful Awards (Bronze 2018, Gold 2017, Bronze 2016, Gold 2013). Work featured in ZKM, Cooper Hewitt Design Triennial, Ars Electronica, and Harvard Art Museums. Author of Insight by de-sign (2025). Grants & Collaborations Harvard Data Science Initiative grant for Black Lives Matter Street Mural Map (2022). Collaborates with institutions like IBM Research, the Robert Koch Institute, and the Network Science Institute. Labs & Teams Leads metaLAB initiatives exploring digital humanities, AI, and critical design. Part of Folkwang’s Design Faculty and Berlin Art Week collaborations.
Dr. Michael Reiss is a Research Fellow and Postdoc at the Leibniz Institute for Media Research | Hans Bredow Institute (HBI) since November 2023. He leads the BMBF-funded research project 'Generative Artificial Intelligence for Information Navigation,' investigating the societal implications of generative AI in political information contexts. His work is part of Research Program 1 on the 'Transformation of Public Communication.' Education: Bachelor's in Sociology and Economics, University of Heidelberg Master's in Socio-Ecological Economics and Policy (Vienna University of Economics and Business) Master's in Social Research Methods (London School of Economics and Political Science) Cumulative Dissertation (University of Zurich, Faculty of Arts): 'News Must Die for News to Live' (2023) His research focuses on political communication, news consumption patterns, computational social science methodologies (e.g., large language models), and the societal impact of digital media. Recent projects explore generative AI's role in information navigation and public opinion formation. Grants & Projects: Leading the BMBF-funded 'Generative AI for Information Navigation' project Contributions to the HBI's Research Program 1 Labs/Teams: Embedded within the Hans Bredow Institute's interdisciplinary research environment.
David Z. Pan is a Professor at the University of Texas at Austin, where he leads a prominent research group specializing in Electronic Design Automation (EDA) and Computer-Aided Design for Integrated Circuits. His extensive publication record spanning from 1997 to the present demonstrates his leadership in advancing the field of electronic design. Dr. Pan's research focuses on solving fundamental challenges in analog/mixed-signal circuit design automation, physical design methodologies, and the integration of machine learning techniques with traditional EDA problems. His work bridges theoretical advances with practical applications in semiconductor design, with particular emphasis on photonic computing, quantum circuit design, and FPGA optimization. His research has evolved from traditional layout and placement algorithms to incorporate cutting-edge AI and machine learning approaches for next-generation design automation. Analysis of his recent publications reveals a strong trend toward integrating artificial intelligence with EDA, including the use of large language models for circuit design automation, reinforcement learning for placement optimization, and deep learning for various aspects of the design flow. His work consistently addresses critical industry challenges while pushing the boundaries of what's possible in electronic design. Dr. Pan has advised numerous graduate students who have become significant contributors to the field, with many continuing their research careers in academia and industry. His research group has developed several influential tools and methodologies that have been adopted by both academic and industrial researchers. He actively contributes to major conferences in the field including ICCAD, DAC, ASP-DAC, and ISPD, often presenting invited talks that shape the future direction of EDA research. His work on open-source EDA tools has been particularly impactful, promoting accessibility and reproducibility in electronic design research.
Julian J. McAuley is a Professor in the Department of Computer Science and Engineering at the University of California, San Diego. His research focuses on advancing the fields of Artificial Intelligence, Machine Learning, and Natural Language Processing, with a particular emphasis on Recommender Systems and Large Language Models (LLMs). His work addresses challenges in ethical AI, generative models, and the integration of knowledge-driven approaches into recommendation systems. Key research interests include responsible AI, lifelong learning, and the development of agentic recommender systems. He explores techniques such as retrieval-augmented generation, causal reasoning, and multi-objective alignment in LLMs. His contributions span foundational theories and practical applications, including the design of scalable systems and frameworks for evaluating generative models. McAuley’s publications highlight advancements in areas like music generation, chain-of-thought reasoning, and knowledge transfer in recommendation systems. His work often bridges theory and practice, addressing real-world challenges in personalized systems and ethical AI deployment.
Briana B. Morrison is a prominent academic affiliated with the University of Virginia, specializing in computer science education and pedagogical innovation. She actively contributes to academic conferences such as SIGCSE and ICER, serving roles like organizing the ICER 2024 Call for Participation. Her research focuses on subgoal labeling in programming instruction, broadening participation in computing, and teacher training programs. She collaborates extensively with institutions and researchers globally to advance CS education practices and policy. Her work emphasizes evidence-based teaching methods, including studies on subgoal labeling efficacy and the integration of large language models in educational tools. Morrison also advocates for K-12 CS curriculum development and has led initiatives to enhance diversity and inclusion in computing fields. Her recent publications highlight her commitment to ethical curriculum design and the transition of educational resources like EngageCSEdu. Morrison’s contributions extend to advising and collaborative research, evidenced by her numerous co-authored papers on topics ranging from cognitive load theory to outreach strategies. While specific student advisees are not listed, her collaborative projects suggest a strong mentorship role in academic communities.
Praneeth Vepakomma is an active researcher in the field of machine learning with a strong publication record spanning from 2015 to the present. His work primarily focuses on privacy-preserving distributed learning systems, with particular emphasis on federated learning, split learning, and techniques for maintaining data privacy while enabling collaborative AI development. His research interests center around developing efficient and privacy-preserving machine learning frameworks. Vepakomma has made significant contributions to federated learning optimization, split learning architectures, and differential privacy techniques for deep learning systems. His recent work has increasingly focused on adapting these techniques for large language models, addressing communication efficiency challenges and privacy concerns in distributed fine-tuning scenarios. Analysis of his publication trends reveals a clear trajectory from foundational work in privacy-preserving machine learning (2015-2019) to increasingly sophisticated approaches for distributed learning systems (2020-2025). His recent publications demonstrate a strong focus on solving practical challenges in federated and split learning for large language models, with particular attention to communication efficiency, privacy guarantees, and architectural innovations for heterogeneous environments. Vepakomma has established a robust collaborative network, most notably with Ramesh Raskar (50+ co-authored papers), indicating a long-standing research partnership. His work bridges theoretical foundations with practical implementations, making significant contributions to the advancement of privacy-preserving distributed machine learning.
Michael S. Horn is a Professor in the Department of Computer Science and the Learning Sciences at Northwestern University's School of Education and Social Policy, with additional affiliation at Tufts University's Computer Science department. His interdisciplinary work bridges Human-Computer Interaction, Learning Sciences, and Educational Technology, focusing on creating innovative learning experiences across formal classrooms, museums, and informal settings. His research encompasses: Design of tangible and embodied learning interfaces Computational thinking and programming education Museum and informal learning environments Integration of music and coding in educational contexts Application of artificial intelligence in educational settings Children's understanding of complex scientific concepts Horn's recent work demonstrates a significant shift toward exploring the intersection of artificial intelligence and education, particularly how Large Language Models can support learning processes and research methodologies. His publications reveal consistent attention to making abstract computational concepts accessible through concrete, embodied interactions that connect learners with meaningful content. His notable scientific contributions include: Development of the NetLogo programming environment for agent-based modeling in education Creation of museum exhibits like DeepTree and FloTree for visualizing evolutionary concepts Innovation in music+code learning environments such as TunePad Research on children's understanding of electricity through augmented circuit exhibits Pioneering work on room-scale augmented reality for computational modeling Horn has mentored numerous graduate students who have become productive researchers in learning sciences and educational technology. His collaborative network is extensive, with particularly strong ties to Uri Wilensky (31 joint publications), Chia Shen, and other leaders in the field. His work consistently demonstrates how well-designed technological interventions can transform how learners engage with complex concepts across multiple domains.
Constantin Eichenberg is a researcher with a focus on interdisciplinary areas spanning nonlinear dynamics, machine learning, and computational science. His work bridges theoretical mathematics and applied artificial intelligence, with contributions to model optimization, generative systems, and neural network architectures. Notably, he has explored parameter update methods in neural networks (e.g., u-μP framework) and developed techniques for fusing pre-trained models (MultiFusion) to enhance multi-lingual and multi-modal capabilities. Eichenberg collaborates frequently with institutions and researchers in the field of deep learning, focusing on robust language models and efficient system design. His research often addresses challenges in model pruning, quantization, and hierarchical processing, with applications to transformers and adaptive systems. Recent work emphasizes improving the accessibility and efficiency of large language models through novel parameterization and token metric analysis.
PD Dr. phil. Valia Kordoni is a Senior Lecturer in the Department of English and American Studies at Humboldt-Universität zu Berlin, part of the Faculty of Philology and Literature. She specializes in computational linguistics, syntax-semantics interface, and grammar engineering, with a focus on multiword expressions, treebanking, and language technology applications. Her work bridges theoretical linguistics and applied NLP, emphasizing register analysis and cross-linguistic methodologies. Research Interests: Her research spans computational linguistics, syntax-semantics interface, grammar engineering (e.g., DeepBank project), and register analysis. She has contributed to projects like GeRMaN (German register marking) and collaborated on international initiatives such as the CRC 1412 on language register. Her work often integrates corpus linguistics, discourse analysis, and formal grammar frameworks. Articles Trends: Recent work focuses on register variation across languages, metaphor annotation, and ethical implications of AI. Earlier contributions include studies on Germanic syntax, Greek valence alternations, and treebank construction for spoken language processing. Her publications reflect a blend of theoretical innovation and practical applications in computational linguistics. Grants and Labs: She has been involved in collaborative research centers and EU-funded projects, though specific grants are not detailed here. Her work aligns with interdisciplinary teams exploring language use in situated contexts and technological applications. Labs/Teams: Affiliated with the Department’s research initiatives on language technology and register studies, contributing to projects like the TraMOOC consortium for machine translation of educational content.
Wray L. Buntine is a faculty member at Monash University in Melbourne, Australia, specializing in machine learning, natural language processing, and Bayesian methods. His research bridges theoretical foundations and practical applications, with a strong focus on NLP innovations, topic modeling, and efficient learning techniques. His research interests span: Machine Learning : Bayesian neural networks, active learning, and low-resource model optimization. Natural Language Processing : Topic modeling, machine translation, LLM evaluation, and dialogue systems. AI Applications : Healthcare (e.g., medication recommendation), education (e.g., dialogue classification), and graph-based anomaly detection. Recent publications (2023–2025) demonstrate a shift toward LLM-centric research, including automatic evaluation of topic models, uncertainty-aware language agents, and logical verification frameworks. His work frequently integrates Bayesian approaches with deep learning, emphasizing model interpretability and data efficiency. While no awards or grants are detailed in the source text, Buntine leads collaborative projects across NLP and ML, often co-authoring with researchers at Monash University and international institutions. Labs or teams are not explicitly referenced.
Prof. Dr. Mirco Schönfeld is a Junior Professor for Data Modeling and Interdisciplinary Knowledge Generation at the University of Bayreuth since 2019. He holds a PhD in Computer Science from LMU Munich (2016) and previously worked as a postdoc at TU Munich's School of Politics, focusing on Computational Social Science. His research bridges computer science, digital humanities, and social sciences, emphasizing algorithmic challenges in analyzing large-scale social, political, and economic systems. Key interests include network analysis, knowledge graphs, and contextual data interpretation. His work often explores interdisciplinary contexts, such as UN Security Council debates, social media discourse, and global research collaboration dynamics. Recent projects include the Cluster of Excellence Africa Multiple, emphasizing contextualized research data management. He has published extensively on topics like knowledge graph embeddings, network centrality metrics, and semantic anchors in text analysis. His contributions highlight the intersection of computational methods and societal challenges, fostering innovative approaches to data-driven decision-making.
Gerrit Großmann is a researcher and educator at the Universität des Saarlandes, focusing on numerical methods for stochastic dynamical processes on complex networks. His work bridges computational epidemiology, network science, and machine learning, with applications to epidemics, drug discovery, and AI-driven scientific discovery. He has developed tools like the Network Epidemic Playground and contributed to open-source platforms such as TeachOpenCADD. Education includes a PhD in Stochastic Spreading on Complex Networks, a Master's in Lumping the Approximate Master Equation, and a Bachelor's in Markov Model Likelihoods. His research emphasizes the limitations of traditional ODE models in epidemiology and advocates for more nuanced stochastic approaches. Teaching spans advanced topics in diffusion modeling, AI for drug design, and complex network dynamics. Current interests include LLM4Science, exploring how large language models can transform scientific reasoning and collaboration. Key contributions include analyzing the role of network structure in epidemic spread, developing efficient simulation techniques, and critiquing predictive modeling practices in public health. His tools and frameworks are widely used in academic and applied research settings.
Prof. Dr. Christian Hänig is a Professor at the Department of Computer Science and Languages and a Temporary Lecturer at the Department of Electrical Engineering, Mechanical Engineering and Industrial Engineering at Anhalt University of Applied Sciences. He advises the Data Science (Full-Time Program) Master of Science degree and teaches courses such as Artificial Intelligence, Data Mining, and Deep Learning. His research focuses on Machine Learning, Deep Learning, Natural Language Processing (NLP), Computer Vision, Medical Imaging, Multimodal Document Processing, and Data Science. Recent work emphasizes applications in financial domains (e.g., German financial language models and corpus development) and agricultural sector benchmarking. Earlier research includes VR education analytics, unsupervised NLP techniques, and knowledge extraction from unstructured data. Key publications (2024) include developing benchmarks for Ukrainian language models, evaluating agricultural LLMs, and creating financial domain corpora. His contributions span over 20 years, addressing challenges in domain-specific NLP, clinical text mining, and industrial quality analysis. Prof. Hänig’s academic service includes roles as a degree program advisor and committee member. Office hours are Thursdays 4:30–6:00 PM (by appointment) at the Ratke Building, Room 23-114, Köthen campus.
