Wei-Jen Tang is a Professor at the University of Chicago, affiliated with the Ben May Department of Cancer Research. His work integrates structural biology and biochemistry to study protein interactions critical to human health, particularly in Alzheimer's disease, diabetes, and bacterial pathogenesis. Education: B.S. in Zoology, National Taiwan University; Ph.D. in Biological Sciences, University of Texas, Austin; Postdoctoral training in Virology and Pharmacology at University of Texas Southwestern. His research focuses on: Amyloid Peptide-Degrading Proteases: IDE and PreP for Alzheimer's and diabetes. Chemokines: CCL5 and CCL3 in inflammation and HIV. Bacterial Toxins: Edema factor in anthrax and bio-defense. Recent publications highlight structural insights into IDE, PreP, and anthrax toxins, with keywords spanning Structural Biology , Biochemistry , and Therapeutics . Funding includes NIH and American Heart Association grants. Awards include AHA Established Investigator and Cancer Research Foundation Young Investigator. Lab updates note new members and a 2025 publication on PreP.
Kenneth Ross is a Professor in the Computer Science Department at Columbia University in New York City. His primary appointment is within the Department of Computer Science, with affiliations including the Foundations of Data Science Committee. His work bridges theoretical database research and practical system implementation. His research focuses on database systems with particular expertise in query processing, query language design, data warehousing, and architecture-sensitive database system design. Additional research spans computational biology, especially analysis of large genomic data sets. Current projects include Linear Algebra Operators in Databases for machine learning workloads and Repeats and Somatic Mutation analysis in genomics. His work consistently addresses the intersection of hardware capabilities and database system design. Ross leads the Database Research Lab at Columbia, which has produced significant work on query optimization, GPU database processing, and hardware-conscious database systems. His recent publications demonstrate strong focus on adapting database systems to modern hardware including GPUs, SIMD processors, and persistent memory. His scientific recognition includes: Packard Foundation Fellowship Sloan Foundation Fellowship NSF Young Investigator Award Distinguished Faculty Teaching Award (2008) Ross actively advises undergraduate engineering students (juniors with last names P-Z) and has taught foundational courses including Introduction to Databases and Programming and Problem Solving for over two decades. His teaching portfolio shows consistent engagement with both theoretical concepts and practical implementation challenges in computer science education.
Alexandra Papoutsaki is an Associate Professor of Computer Science at Pomona College since 2017. Her research focuses on Human-Computer Interaction , particularly in webcam-based eye tracking and shared gaze for remote collaboration . Ph.D. in Computer Science from Brown University M.Sc. in Computer Science from Brown University B.Sc. in Computer Science from Athens University of Economics and Business Her work spans personal informatics , crowdsourcing methodologies , and remote usability testing , with recent projects examining collaborative drawing interfaces, baby tracking reflection, and digital health communities. Earlier contributions include computational biology research in genome-wide survival analysis and pan-cancer mutation networks. Key trends in her publications include: Remote collaboration tools enhanced by eye tracking Personal data systems for health and creative domains Foundational work in crowdsourcing quality control Interdisciplinary approaches combining computer science with psychology and medicine Scientific recognition includes: NSF CRII Grant (2020-2022) Wig Distinguished Professor Award (2020) Best Paper at RECOMB (2013) She has developed multiple open-source platforms including WebGazer for scalable eye tracking and Remotion for mobile usability testing. Her research team has published extensively in top venues like CHI, IMWUT, and IJCAI.
Mehran Sahami is the James and Ellenor Chesebrough Professor in the School of Engineering and Tencent Chair of the Computer Science Department at Stanford University. He holds the academic rank of Teaching Professor of Computer Science and is also a Senior Fellow by courtesy at the Freeman Spogli Institute for International Studies. As a Bass University Fellow in Undergraduate Education, he has made significant contributions to computer science education at Stanford. Dr. Sahami earned both his undergraduate and PhD degrees from Stanford University's Computer Science Department. After completing his PhD, he worked as a Senior Engineering Manager at Epiphany before joining Google as a Senior Research Scientist from 2002-2007, while also teaching as a Lecturer at Stanford. In 2007, he joined the Stanford faculty full-time, continuing to consult part-time at Google until 2010. Professor Sahami's primary research interests focus on computer science education, machine learning, and information retrieval on the Web. His work has significantly influenced how computer science is taught globally, particularly through his leadership in the ACM/IEEE-CS Joint Task Force on Computing Curricula 2013 (CS2013). He has pioneered approaches to teaching introductory programming and probability theory for computer scientists, with a particular emphasis on analyzing student performance trends as CS enrollments have grown dramatically. His recent publications demonstrate a strong shift toward educational research while maintaining connections to technical expertise in machine learning and data analysis. Bass University Fellow in Undergraduate Education Professor Sahami serves as the ACM Steering Committee Chair for the CS2013 effort to define international curricular guidelines for undergraduate computer science programs. He is also the founder and first Chair of the Symposium on Educational Advances in Artificial Intelligence (EAAI), an annual meeting for researchers and educators to discuss pedagogical issues in teaching AI. He has received significant grant funding through these initiatives and has been instrumental in shaping national and international computer science curriculum standards. At Stanford, he teaches CS106A: Programming Methodology and CS182: Ethics, Public Policy, and Technological Change, with his educational materials widely distributed through the Stanford Engineering Everywhere initiative. Professor Sahami maintains connections to the startup ecosystem through advisory board positions and has published a book on Text Mining with Ashok Srivastava. His career trajectory from industry researcher to academic educator gives him a unique perspective on practical applications of computer science education.
Professor Sir Nigel Shadbolt is Principal of Jesus College Oxford and Professor of Computing Science at the University of Oxford. He is Chairman and Co-founder of the Open Data Institute which he established with Sir Tim Berners-Lee. His career spans multiple prestigious institutions including the University of Southampton and University of Nottingham, where he held leadership roles in AI research. He has served in significant government advisory positions including as Information Advisor to the UK Prime Minister and member of the Public Sector Transparency Board. Nigel Shadbolt earned his undergraduate degree in Philosophy and Psychology from the University of Newcastle (1st Class Honours, 1978) followed by a PhD in Artificial Intelligence from the University of Edinburgh. His academic journey includes positions as the Allan Standen Professor of Intelligent Systems at Nottingham and leadership of the Web and Internet Science Group at Southampton before joining Oxford. Shadbolt's research spans the interdisciplinary field of Web Science, which he helped originate, focusing on human-centered AI, social machines, open data, and ethical considerations in technology. His work examines how humans and computers integrate at web scale to create novel applications, with recent emphasis on children's digital autonomy and privacy. He has published over 500 articles exploring topics from cognitive psychology to computational neuroscience, Artificial Intelligence to the Semantic Web. His notable contributions include the development of data.gov.uk and leadership in establishing Oxford's Institute of Ethics in AI. His 2018 book 'The Digital Ape: how to live (in peace) with smart machines' has been described as a 'landmark book' in the field. Knighted in 2013 for services to science and engineering Fellow of The Royal Society (FRS) Fellow of the Royal Academy of Engineering (FREng) Fellow of the British Computer Society (FBCS) Technology Pioneer status by the Davos World Economic Forum UK national BT Flagship IT Award As an advisor, Shadbolt has supervised numerous PhD students who have gone on to positions at Google DeepMind, the World Bank, and other prestigious institutions. His research has been supported by multiple grants including SOCIAM, KOALA, and ReTiPS projects. He co-founded Garlik Ltd, which was acquired by Experian, demonstrating his commitment to translating research into practical applications that benefit society. Shadbolt leads the Web and Internet Science research group at Oxford, focusing on social machines - applications that succeed at web scale by integrating humans and computers in novel ways. His work on the KOALA Hero Toolkit and CHAITok system addresses critical challenges in children's data autonomy on social media platforms, reflecting his commitment to ethical technology design.
Dr. David Goretzko is an Assistant Professor in the Department of Methodology and Statistics at Utrecht University's Faculty of Social and Behavioural Sciences. He leads the Measurement and Machine Learning Lab and specializes in the integration of data science techniques with psychometric theory. His academic journey includes: Ph.D. in Psychological Methods from LMU Munich (2020) M.Sc. in Statistics from LMU Munich (2018) M.Sc. in Psychology from LMU Munich (2016) B.Sc. in Physics from LMU Munich (2015) B.Sc. in Psychology from LMU Munich (2014) Dr. Goretzko's research primarily focuses on the intersection of machine learning and psychometrics. His work addresses critical challenges in factor analysis, measurement invariance, and model fit assessment. He develops innovative methods that combine traditional psychometric approaches with modern data science techniques, particularly in the areas of exploratory factor analysis trees, regularized factor analysis, and cost-sensitive machine learning applications in psychological assessment. His research has significant implications for improving the validity and reliability of psychological measurements across diverse populations. His recent publications reveal a strong trend toward integrating machine learning methodologies with traditional psychometric approaches. A significant portion of his work focuses on factor analysis techniques, particularly addressing the challenge of determining the appropriate number of factors. His research also extensively covers measurement invariance testing across multiple covariates using tree-based approaches, and he has made notable contributions to evaluating model fit in confirmatory factor analysis. The interdisciplinary nature of his work spans psychology, statistics, and computer science. Dr. Goretzko serves as an Associate Editor for the European Journal of Psychological Assessment and is an active reviewer for numerous prestigious journals including Psychological Methods, Behavior Research Methods, and Structural Equation Modeling. He also reviews grant proposals for major funding agencies such as the German Research Foundation (DFG), National Science Foundation (NSF), and Dutch Research Council (NWO). He currently holds a Project Grant from the German Research Foundation (DFG GO 3499/1-1) since 2021. His research program focuses on developing and validating new methodologies for psychological assessment that incorporate machine learning techniques while maintaining psychometric rigor. His work has practical applications in educational measurement, clinical psychology, and organizational assessment. Dr. Goretzko leads the Measurement and Machine Learning Lab at Utrecht University, which focuses on developing innovative methodologies that bridge the gap between traditional psychometrics and modern data science. The lab's research has particular relevance for improving measurement practices in cross-cultural research, educational assessment, and clinical psychology settings where measurement invariance and factor structure validation are critical concerns.
Professor Thomas Lukasiewicz is a Full Professor and Head of the Artificial Intelligence Techniques research group at the Faculty of Informatics, Vienna University of Technology (TU Wien). His research focuses on enabling machines to mimic human-like intelligence through techniques spanning deep learning, symbolic reasoning, and predictive coding. Key areas include explainable AI, hybrid neurosymbolic systems, and applications in healthcare and law. He teaches courses such as Deep Learning for Natural Language Processing, Scientific Research and Writing, and multiple seminars in artificial intelligence and knowledge representation. His research projects include Explainable AI in Healthcare (2023–2027) and foundational work on predictive coding networks. His publications (15+ recent articles) address medical image segmentation, neurosymbolic frameworks, and language model evaluation in mathematics. Notable work includes neurosymbolic hybrid models (CCN⁺), reinforcement learning for medical report generation, and theoretical foundations of predictive coding networks.
Dr. Liam O'Connor-Davis is a Senior Lecturer in the School of Computing at The Australian National University (ANU). He holds a PhD and BSc (Hons) in Computer Science from the University of New South Wales (UNSW), awarded in 2019 and 2013, respectively. His research interests include type systems, formal methods, and systems programming, as evidenced by his doctoral work on 'Type Systems for Systems Types' and his undergraduate project 'Formalising GHC's Type System'. He has held an external affiliation as a Lecturer at the University of Edinburgh until July 2024. His academic background emphasizes foundational aspects of programming languages and software engineering. Despite no explicit publications listed here, his educational focus suggests contributions to theoretical computer science and practical system implementations.
Ki-Woong Park is a tenure-track full Professor in the Department of Computer and Information Security at Sejong University. He leads the System Security and Computer Engineering Research (SysCore) Lab, which focuses on system security research with numerous ongoing projects funded by major Korean research institutions including IITP, NRF, and KRIT. Sejong University, Department of Computer and Information Security System Security and Computer Engineering Research (SysCore) Lab Leader Member of IEEE, IEEE Computer Society, and ACM Education: Ph.D. in Electrical Engineering & Computer Science, KAIST (Advisor: Prof. Kyu-Ho Park) M.S. in Electrical Engineering & Computer Science, KAIST (Advisor: Prof. Kyu-Ho Park) B.S. in Computer Science, Yonsei University (Summa Cum Laude) Exchange Student at University of California, Los Angeles (UCLA) Professor Park's research focuses on designing, building, and analyzing secure systems, particularly for cloud computing, networked systems, and embedded systems. His work often involves reevaluating existing security mechanisms and actual system implementations with subsequent evaluation in real computing environments. He has made significant contributions to areas including cloud security, IoT security, ransomware detection, moving target defense, and metaverse security. His research approach emphasizes both theoretical foundations and practical implementation, with numerous publications in top-tier security and systems venues. His recent publications (2023-2024) demonstrate a strong focus on emerging security challenges in modern computing environments, particularly in metaverse platforms, UAV systems, and edge computing. These works span both theoretical security frameworks and practical implementations, with an emphasis on visualization techniques, hardware-based security mechanisms, and AI-enhanced security analysis. His research shows a clear progression from traditional cloud and network security toward next-generation security challenges in immersive virtual environments and cyber-physical systems. Scientific Awards: Microsoft Research Fellowship (2009-2010) Best Poster Gold Award at WISA 2020 Best Paper Award at MobiSec'18 Professor Park actively mentors numerous graduate and undergraduate students through the SysCore Lab, with current members including Ph.D. students, MS students, and undergraduate researchers. His research is supported by multiple significant grants, including the NRF Outstanding Researcher-Mid-career Researcher project, IITP Information Security Core Source Technology Development, and Defense Technology Advancement Research Institute projects. These grants total tens of billions of Korean won and address critical national security challenges in cyber defense, cloud security, and metaverse technologies. The SysCore Lab, under Professor Park's leadership, maintains a strong industry and government collaboration network, with part-time researchers from organizations including Hyundai Duty Free, Astron Security, Korea University, and various military cyber commands. This unique structure enables the lab to address both theoretical security challenges and practical implementation issues in real-world systems.
Ping He is a Professor in the Department of Molecular, Cellular, and Developmental Biology (MCDB) at the University of Michigan in Ann Arbor. His research focuses on plant immunity mechanisms, particularly using Arabidopsis as a model system to study pathogen defense activation, signaling pathways, and the interplay between immunity and environmental stress responses. He also leads the Molecular, Plant-Microbe Interaction Laboratory, applying interdisciplinary approaches (genetics, biochemistry, cellular biology) to enhance crop resilience through foundational plant science discoveries. His work bridges plant biology and computational biology, with recent contributions to AI-driven medical imaging applications such as bladder cancer treatment response assessment, lung cancer early detection, and breast tomosynthesis denoising. These efforts emphasize integrating machine learning into clinical workflows and establishing best practices for AI in healthcare. Research Highlights: Plant immunity signaling and environmental stress crosstalk Radiomics and deep learning for cancer diagnosis/prognosis AI model validation and multi-institutional clinical trials Medical imaging artifact correction (e.g., motion blur, noise) Publications emphasize AI applications in oncology imaging, radiologist decision support systems, and multimodal data fusion. He has contributed to AAPM task group guidelines for AI in computer-aided diagnosis and advocates for rigorous quality assurance frameworks in medical AI deployment.
Oliver Grau is a Chair Professor for Image Science and a leading figure in Media Art research, affiliated with Hong Kong Baptist University (Academy of Visual Arts, Distinguished Fellow since 2023) and Danube University Krems (Chair Professor for Image Science from 2005–2022). He founded the Archive for Digital Art (ADA) and the international MediaArtHistories Conference Series , with key roles at institutions across Germany, Austria, and Australia. His work bridges art, science, and technology, focusing on immersive images, digital heritage, and emotion research. Key Positions: Director of ADA (since 2000), Founding Director of MediaArtHistories Conference Series (since 2005), PI for high-resolution digitization of the Goettweig Graphic Print Collection (2005–2017). Research Grants: Funded by DFG, Austrian Science Fund, Australian Research Council, VW Foundation (total 8.3 Mio EUR), and Erasmus+ Joint Master program (5.4 Mio EUR). Research Interests: Grau’s scholarship spans the history of media art, telepresence, artificial life, emotion research, and digital humanities. His work emphasizes the evolution of immersive visual experiences from historical art forms to digital media, integrating theoretical and practical approaches. Article Trends: His recent publications focus on digital art’s sociopolitical dimensions, archiving challenges in the digital era, and cross-disciplinary methodologies. Themes include media art conservation, web-based tools for digital humanities, and the intersection of artistic expression with contemporary global issues like climate change and surveillance. Scientific Awards: Distinguished Fellow at Hong Kong Baptist University (2023) Science Award of Lower Austria (2019) Honorary Doctorate from University of Oradea (2014) Invitations to G-20 summit, Olympic Games, and international symposia Labs & Teams: He leads the Archive for Digital Art, managing a global team of 28 staff, and co-founded the Erasmus+ Joint Master in Media Arts Cultures. His projects emphasize collaborative archiving tools (e.g., Web 2.0/3.0 archives) and large-scale digitization initiatives.
Leslie Valiant is the T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics in Harvard University's School of Engineering and Applied Sciences, where he has held a faculty position since 1982. A foundational figure in theoretical computer science, his work bridges artificial and natural computational phenomena across multiple disciplines. His academic background includes education at: King's College, Cambridge Imperial College, London Ph.D. in Computer Science from Warwick University (1974) Valiant's research spans computational complexity , machine learning theory , parallel systems , and computational neuroscience . He pioneered the PAC (Probably Approximately Correct) learning framework that established computational learning theory as a rigorous field. His holographic algorithms work revealed deep connections between computational complexity and statistical physics, while his neuroidal model and evolvability theory provide computational explanations for cognitive processes and biological evolution. Current investigations focus on cortical computation primitives and knowledge infusion architectures. His publication trends show increasing integration of neuroscience with computational theory since 2010, with dominant themes in holographic computation (2006-2018), cortical modeling (2012-2018), and evolvability (2009-2017). The work consistently applies computational complexity analysis to biological and cognitive systems. Major recognitions include: Nevanlinna Prize (1986) for mathematical aspects of computer science Knuth Award (1997) for foundational algorithms contributions EATCS Award (2008) for theoretical computer science impact Turing Award (2010) for computational learning theory and complexity Fellowship in the Royal Society and National Academy of Sciences Valiant's research has been supported by NSF and international grants enabling cross-disciplinary work in computational neuroscience and evolutionary algorithms. While specific advisees aren't documented in source materials, his theoretical frameworks have shaped generations of researchers in machine learning and complexity theory. His current research group explores neuroidal architectures for cognitive computation, investigating how cortical circuits achieve robust information processing through in-circuit testing methodologies. Ongoing projects aim to identify fundamental computational primitives in neural systems and develop biologically inspired AI frameworks.
Magnus Bakke Botnan is an Assistant Professor at the Department of Mathematics, Vrije Universiteit Amsterdam, holding a VIDI career grant (€850,000) since 2018. His research bridges pure and applied mathematics within topological data analysis (TDA), focusing on multiparameter persistence, computational topology, and applications to sciences. PhD in Mathematics, Norwegian University of Science and Technology (NTNU), 2015 Postdoc at TU Munich, 2016-2018 His research group includes postdocs Hannah Rocio Santa Cruz Baur and Rui Dong, and PhD student Enes Devecioğlu. Recent work involves signed barcodes, rank decompositions, and stability of persistence modules. He co-authored the first comprehensive tutorial on multiparameter persistence with Mike Lesnick. Notable contributions include proving the NP-hardness of computing interleaving distance, establishing universality of bottleneck distance for extended persistence diagrams, and developing computational methods for non-branching complexes. Publications span journals like Foundations of Computational Mathematics , Discrete & Computational Geometry , and conferences SoCG, NeurIPS, and ICRA. Scientific Awards: VIDI Career Grant (€850,000) He has taught courses including Complex Analysis, Calculus, Topological Data Analysis, and seminars on analysis and dynamical systems. Actively organizes Applied Topology Days and collaborates on projects integrating TDA with physics, computer science, and statistics.
Daniel Kifer is a Professor in the Computer Science and Engineering department at Pennsylvania State University, with affiliations to the Huck Institutes of the Life Sciences. His work bridges computer science, privacy-preserving machine learning, and geoscience applications. With over 10,000 citations and a high h-index, he focuses on methods to unify theoretical and applied research. Research Interests: Differential Privacy, Privacy-Preserving Machine Learning, Physics-Informed Neural Networks, Landslide Prediction, and Formal Verification of Privacy Systems. Recent projects include grants from the National Science Foundation: SaTC: CORE: Small (2024): privacy-preserving user data embedding in machine learning pipelines. SaTC: CORE: Medium (2017-2023): formal methods for differential privacy and accuracy optimization. His research outputs span domains like geoscience, database systems, and policy analysis, emphasizing precision and scalability of privacy-preserving algorithms.
Thomas Riechert is a Professor at the Leipzig University of Applied Sciences (HTWK Leipzig), specifically within the Faculty of Computer Science and Media. He serves as Dean of Media Informatics, Chairman of the Media Informatics Study Commission, and Academic Advisor for Media Informatics. Additionally, he is a Member of the Faculty Council, Faculty representative in the IT committee, and Library Commission. He also acts as Spokesperson of the Institute of Computer Science at the HTWK. Appointed in 2014 in the field of information systems and data management, Riechert holds a doctorate from the University of Leipzig (2012, magna cum laude) and graduated as a computer scientist from TU Dresden in 2000. Professor Riechert's research focuses on information systems, data management, Semantic Web technologies, knowledge engineering, and Linked Data. His work bridges computer science with digital humanities, particularly in creating knowledge graphs for historical research. He has made significant contributions to RDF vocabulary management, ontology editing tools, and academic history databases. His research demonstrates a strong emphasis on practical applications of semantic technologies in information management systems. Analysis of his recent publications reveals a consistent trajectory in semantic web technologies applied to knowledge representation and historical data. His work shows progression from foundational semantic technologies toward increasingly sophisticated applications in digital humanities, particularly in academic history research. The Heloise project appears to be a major focus, developing common research models for collaborative historical research using Linked Open Data. His publications span both technical aspects of semantic technologies and their application in humanities contexts. Professor Riechert actively contributes to academic community building through conference organization and student engagement. His leadership roles in the Faculty of Computer Science and Media demonstrate his commitment to shaping computer science education. His research integrates practical software development with theoretical advancements in knowledge representation. As Dean of Media Informatics, Riechert oversees academic programs and research initiatives in this field. His work with the Heloise network demonstrates leadership in interdisciplinary research connecting computer science with historical scholarship. His office hours are held on Mondays from 10:00 to 11:00 in room ZU 407 at HTWK Leipzig.
