December 1st, 2023
Vit Novacek is an Associate Professor of Computer Science at the Faculty of Informatics of Masaryk University. He still helps to lead the Unit for Biomedical Discovery Informatics at the Data Science Institute of National University of Ireland Galway. His research interests include AI and health informatics.
November 17th, 2023
Dr. Cui Tao’s background is in clinical informatics and computer science, and her research interests include ontologies, standard terminologies, semantic web, information extraction and integration, machine learning as well as applying these technologies to clinical and translational studies.
November 10th, 2023
Roberto Navigli is a full professor of Natural Language Processing in the Department of Computer, Control and Management Engineering at the Sapienza University of Rome and the head of the Sapienza NLP Group. In 2007 he received a Ph.D. in Computer Science from "La Sapienza" (winner of the Marco Cadoli 2007 AI*IA prize for the Best Ph.D. Thesis in AI). In 2013 he received the Marco Somalvico AI*IA prize, awarded to the best young Italian researcher in AI, who has provided a significant, highly-innovative personal contribution in AI.
November 3rd, 2023
Dr. Dawei Zhou is an Assistant Professor at the Computer Science Department of Virginia Tech and the director of the VirginiaTech Learning on Graphs (VLOG) Lab. Zhou’s prior research on rare category detection, graph mining, curriculum learning, and algorithmic fairness, with applications in financial fraud detection, cyber security, financial forecasting, social media analysis, and healthcare. He obtained his Ph.D. degree from the Computer Science Department of the University of Illinois Urbana-Champaign (UIUC).
October 27th, 2023
Axel-Cyrille Ngonga Ngomo is the Data Science (DICE) Chair at the Computer Science Department, University of Paderborn. He has (co)authored more than 200 reviewed publications and has developed several widely used frameworks. His research interests include areas around knowledge graphs and semantic web technologies, especially link discovery, federated queries, machine learning, and natural-language processing.
October 20th, 2023
Dr. Ying Ding is Bill & Lewis Suit Professor at School of Information, University of Texas at Austin. She has been involved in various NIH, NSF and European-Union funded projects. She is the co-founder of Data2Discovery company advancing cutting edge AI technologies in drug discovery and healthcare. Her current research interests include data-driven science of science, AI in healthcare, Semantic Web, knowledge graph, data science, scholarly communication, and the application of Web technologies.
October 13th, 2023
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, is the founder and Director of the Institute of Data Science at Maastricht University, and is a co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research aims to unlock the potential of data for scientific research. He is an expert in building and mining knowledge graphs for drug discovery and personalised medicine.
October 6th, 2023
Title: Decontamination of the scientific literature with the Problematic Paper Screener: Flagging suspect/erroneous/fraudulent papers to crowdsource post-publication reassessments (access)
Guillaume Cabanac is a Professor of Computer Science at the University of Toulouse, France. He holds a research chair at the Institut Universitaire de France titled “Decontamination of the scientific literature.” Cabanac develops the Problematic Paper Screener that contributes to the identification, reporting, and retraction of algorithmically generated and fraudulent papers. Cabanac’s research was profiled in the Nature’s 10: he was nicknamed ‘Deception sleuth’ in the journal’s annual list of ‘ten people who helped shape science in 2021.’
September 29th, 2023
Title: Knowledge (Graphs) in the Language Model Era (access)
Paul Groth is a Professor of Algorithmic Data Science at the University of Amsterdam where he leads the Intelligent Data Engineering Lab (INDElab). He holds a Ph.D. in Computer Science from the University of Southampton (2007) and has done research at the University of Southern California, the Vrije Universiteit Amsterdam and Elsevier Labs. His research focuses on intelligent systems for dealing with large amounts of diverse contextualized knowledge with a particular focus on web and science applications. This includes research in data provenance, data integration and knowledge sharing.
September 22nd, 2023 (recording)
Title: Fixing development with data: Responsible AI and state building in Africa (access)
Yousif Hassan is Illinois Distinguished Fellow and Postdoctoral Research Associate at the School of Information Sciences. He is a Faculty Affiliate with the Center for African Studies at the University of Illinois Urbana-Champaign. His research examines the relation between race, digital technology, and technoscientific capitalism. Dr. Hassan’s work is at the intersection of social and racial justice, and technology policy focusing on the social, economic, and political implications of emerging technologies including artificial intelligence (AI) and data. Prior to joining the iSchool, Dr. Hassan was a research fellow at the Harvard Kennedy School. His most recent project investigates the development of AI and its innovation ecosystem across multiple African countries focusing on data governance and the sociotechnical knowledge production practices of the state, scientists, and the tech industry.
September 15th, 2023 (recording)
Title: Applying FAIR in the LUMC hospital (access)
Núria Queralt Rosinach, born in Reus (Catalonia, Spain), is a biomedical informatics researcher who joined the Biosemantics Group in April 2020. She obtained a MSc in Bioinformatics from Pompeu Fabra University (UB/UPF) in 2008 and a PhD in Computational Chemistry from Rovira i Virgili University (URV) in 2010. She has expertise in Semantic Web technologies, Artificial Intelligence (AI) approaches over knowledge graphs, formal logic and interpretable machine learning for hypothesis generation and drug repurposing. She is currently investigating how to exploit FAIR data for explainable AI and hypothesis generation to improve rare, common and infectious disease discovery, how to integrate FAIR research and patient data for modelling, prediction, rationalization and analysis, and how to make FAIR data clinically actionable for bench to bedside translation.
September 8th, 2023 (recording)
Title: Geometric Deep Learning for Drug Discovery (access)
Jian Tang is currently an associate professor at Mila - Quebec AI Institute, a leading AI Institute in Canada founded by A.M. Turing Award laureate Yoshua Bengio. He is also a Canada CIFAR AI Research Chair and the founder and CEO of BioGeometry, an AI startup focusing on generative AI for antibody discovery. His main research interests are deep generative models, graph machine learning and their applications to drug discovery. He is an international leader in graph machine learning, and his representative work LINE on node representation learning has been widely recognized and cited more than 5,000 times. He has also done many pionnering work on AI for drug discovery, including the first open-source machine learning framework for drug discovery, TorchDrug and TorchProtein.
September 1st, 2023 (recording)
Title: Explaining hidden neuron activations using Semantic Web methods (access)
Pascal Hitzler is Professor and endowed Lloyd T. Smith Creativity in Engineering Chair and Director of the Center for Artificial Intelligence and Data Science (CAIDS) at the Department of Computer Science at Kansas State University. He is director of the Data Semantics (DaSe) Lab. In 2001 he obtained a PhD in Mathematics from the National University of Ireland, University College Cork, and in 1998 a Diplom (Master equivalent) in Mathematics from the University of Tübingen in Germany. For more information about him, please visit this link.
August 25th, 2023 (recording)
Title: A Knowledge First World: a knowledge revolution is coming (...or at least there should be one!) (access)
Juan Sequeda is the Principal Scientist and Head of the AI Lab at data.world. He holds a PhD in Computer Science from The University of Texas at Austin. Juan’s research and industry work has been on the intersection of data and AI, with the goal to reliably create knowledge from inscrutable data, specifically designing and building Knowledge Graph for enterprise data and metadata management. Juan is the co-author of the book “Designing and Building Enterprise Knowledge Graph” and the co-host of Catalog and Cocktails, an honest, no-bs, non-salesy data podcast.
April 28th, 2023 (recording)
Title: Differences in Network Perceptions within and across Groups (access)
Keith Hunter is an associate professor at the University of San Francisco’s School of Management. His primary research interests revolve around organizational networks, culture, and leadership. He is particularly interested in how social networks both influence and reflect the active mental models and power dynamics within organizations. His investigations of the patterns of interaction among people and their implications for human behavior and organizational outcomes are of critical significance to tomorrow's business leaders.
April 14th, 2023
Title: Knowledge Graphs for a Deeper Understanding of Science (access)
Jay Pujara is a research assistant professor of Computer Science at the University of Southern California as well as research team lead and director of the Center on Knowledge Graphs at USC's Information Sciences Institute. His research focuses on artificial intelligence, specifically knowledge graphs and statistical relational learning. Jay is the author of over fifty peer-reviewed publications, has received four best paper awards, and been featured in AI Magazine.
April 7th, 2023
Title: Biomedical evidence extraction and synthesis (access)
Lucy Lu Wang is an Assistant Professor at the University of Washington Information School. Her research uses natural language processing and data science techniques to make sense of scientific and research output. Specifically, she investigates NLP methods for understanding biomedical text, systems to help us make better and more data-driven care decisions, as well as how text analysis techniques can be used to identify trends in research communication.
March 31st, 2023 (recording)
Title: Learning Deep Translational Patient Representations for Rare Disease Classification and Subphenotyping (access)
Dr. Callahan received her Ph.D. in Computational Biology from the University of Colorado Anschutz Medical Campus in 2021 and is currently a Postdoctoral Research Fellow in the Department of Biomedical Informatics at Columbia University Irving Medical Center. Dr. Callahan’s PhD thesis leveraged graph representation learning and neural-symbolic reasoning of large-scale biological knowledge graphs in order to develop realistic estimates of human disease mechanisms. She is currently applying these methods to improve the detection, prediction, and understanding of complex phenomena like phenotyping of pediatric rare disease and adverse event detection.
March 24th, 2023 (recording)
Title: Evidence Graphs and AI/ML Explainability (access)
Tim Clark is an Associate Professor of Public Health Sciences, Neurology, and Data Science at the University of Virginia (UVA). He has played a major role in the development and application of FAIR (Findable, Accessible, Interoperable, Reusable) principles and design patterns to scientific publications and repositories. His current research focuses on the development of provenance-aware research data commons environments. He is a co-PI of the NIH Bridge2AI: Cell Maps for Artificial Intelligence project, and Technical Director of UVA’s Center for Advanced Medical Analytics. He holds a Ph.D. in Computer Science from the University of Manchester.
March 10th, 2023 (recording)
Title: Towards Neural Graph Databases (access)
Michael is a Research Scientist at Intel AI Labs working on Graph Machine Learning and Geometric Deep Learning. Previously, Michael was a postdoc at Mila - Quebec AI Institute working with Will Hamilton, Jian Tang, and Reihaneh Rabbany on various graph learning tasks ranging from reasoning and knowledge graphs to molecular representation learning.
March 3rd, 2023 (recording)
Title: Machine Learning with Biomedical Ontologies (access)
Robert Hoehndorf is an Associate Professor of Computer Science at King Abdullah University of Science and Technology (KAUST), where he is the Principal Investigator of the Bio-Ontology Research Group (BORG). His main academic interests are knowledge representation and symbolic approaches to artificial intelligence and using them to gain novel biological insights. They develop knowledge-based methods for the analysis of large, complex and heterogeneous datasets in biology, and apply them to understanding genotype-phenotype relations.
February 24th, 2023 (recording)
Title: Knowledge Graphs in Action: a Tour of Extensions and Real-World Applications of the Vadalog System (access)
Emanuel Sallinger is Assistant Professor of Computer Science at Vienna University of Technology (TU Wien) and Lecturer for Knowledge Graphs and Database Design at Oxford University. His main research focus is on Knowledge Graphs, including all theoretical and practical aspects. In particular, he is interested in reasoning in such systems, including all of the AI methodologies for that. Within such systems, his interest is in achieving scalable solutions, making sure that theory translates into practice.
February 17th, 2023 (recording)
Title: Papers and Patents are Becoming Less Disruptive over Time (access)
Erin Leahey is Professor and Director of Sociology at the University of Arizona and an elected member of the Sociological Research Association. She is known largely for her work on science, scientific careers, and inequality therein. Recently she has focused on studying the costs, benefits, and precursors of interdisciplinary research at both the individual and organization levels.
February 10th, 2023 (recording)
Title: Semantic Publishing of Scientific Contributions in the Open Research Knowledge Graph (access)
Jennifer D’Souza is a Postdoctoral Researcher at the TIB Leibniz Information Centre for Science and Technology in the R&D Department. Her research interests mainly include developing supervised machine learning techniques for natural language processing to facilitate text mining and automated information extraction. Her current primary research theme is knowledge graph construction from scientific text by NLP methods. Aside from this, she is also interested in scientometrics.
February 3rd, 2023 (recording)
Title: Can Research Resource Identifiers (RRIDs) be Used to Better Understand Scientific Literature? (access)
Dr. Bandrowski is a researcher in the department of neuroscience at UCSD, a visiting professor at the QUEST center at the Berlin Institute of Health, and the CEO of a technology startup called SciCrunch Inc. She started and runs the Research Resource Identification Initiative, asking authors to put persistent unique identifiers, RRIDs for tools they used in their paper, into their publications as a way to improve parts of reproducibility in science. She is passionate about fixing bits of the reproducibility crisis in science that can be engineered out of existence!
January 27th, 2023 (recording)
Title: The mediKanren Biomedical Reasoner and the Precision Medicine Case Review Process (access)
Will Byrd is a scientist at the Hugh Kaul Precision Medicine Institute at the University of Alabama at Birmingham. He leads the Precision Medicine Institute's effort to develop mediKanren, biomedical reasoning software funded under the NIH NCATS Biomedical Data Translator Project. Will is one of the creators of the miniKanren family of constraint logic programming languages, and co-author of 'The Reasoned Schemer' (MIT Press, 2018).
January 20th, 2023 (recording)
Title: Happimetrics - Leveraging AI to Untangle the Surprising Link Between Ethics, Happiness and Business Success (access)
Peter A. Gloor is a Research Scientist at the Center for Collective Intelligence at MIT’s Sloan School of Management where he leads a project exploring Collaborative Innovation Networks (COIN). His research focus is on the analysis of temporal communication patterns of virtual teams to increase knowledge worker innovation and productivity by discovering and optimizing Collaborative Innovation Networks and Collaborative Knowledge Networks.
November 18th, 2022 (recording)
Title: Data Science Ethics in Practice and for Practice: An Ethnographic Perspective (access)
Anissa Tanweer is a research scientist at the eScience Institute. She conducts ethnographic research on the practice and culture of data science, and brings a sociotechnical lens to bear on the design and implementation of data science training programs. She is passionate about leveraging action research to foster reflexive, ethical data science practices.
November 11th, 2022 (recording)
Title: Minorities in networks and algorithms (access)
Fariba Karimi is a team leader in computational social science at the Complexity Science Hub Vienna and an assistant professor at the Department of Network and Data Science at Central European University since March 2021. Her research focuses on computational social science, the emergence of biases and inequality in networks and algorithms, and modeling human behavior.
November 4th, 2022 (recording)
Title: The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization (access)
Manish Raghavan is the Drew Houston (2005) Career Development Professor at the MIT Sloan School of Management (in the Information Technology group) and department of Electrical Engineering and Computer Science. His primary interests lie in the application of computational techniques to domains of social concern, including online platforms, algorithmic fairness, and behavioral economics, with a particular focus on the use of algorithmic tools in the hiring pipeline.
October 28th, 2022 (recording)
Title: Science on the Web: How networks bias academic communication online (access)
Ágnes Horvát is an Assistant Professor in the Department of Communication Studies, (by courtesy) the Department of Computer Science, and (also by courtesy) the Kellogg School of Management at Northwestern University. Her research seeks to investigate how networks induce biased information production, sharing, and processing on digital platforms.
October 21st, 2022 （recording）
Title: Human-Centered Explainable AI (XAI): from Algorithms to User Experiences (access)
Vera Liao is a Principal Researcher at Microsoft Research Montreal where she is part of the FATE group. She is a human-computer-interaction (HCI) scientist by training and have broad interests in human-AI interaction. Most recently she hs been working on explainable AI and responsible AI.
Faculty host: Yun Huang
October 14th, 2022 (recording)
Title: Building Things that Matter: The Ambivalence of Tech for Good Initiatives (access)
Karina Rider is a sociologist studying how technologists try to build ‘tech for good.’ She is especially interested in how technologists think about the relationship between their careers and the types of technologies–and technological futures–they want to build. Her current project investigates civic technology nonprofits in the San Francisco Bay Area, and she is in the planning phase of a new project exploring grassroots support and opposition for tech campus construction projects.
October 7th, 2022 (recording)
Title: Towards Explainable and Accountable Fact-Checking (access)
Pepa Atanasova is a last-year Ph.D. student at the Natural Language Processing Section at the University of Copenhagen, supervised by Isabelle Augenstein. Her main research interests lie in the area of explainable machine learning with applications in complex reasoning tasks such as fact checking and question answering.
September 30th, 2022 (recording)
Title: The Loop: How Technology is Creating a World without Choices and How to Fight Back (access)
Jacob is a television correspondent and producer. Since 2018 he has been a correspondent for NBC News, reporting on the unanticipated consequences of science and technology in our lives. He was also a former fellow at Stanford University’s Center for Advanced Study in the Behavioral Sciences, a television series host on the science and implications of bias, and the editor-in-chief of Popular Science.
September 16th, 2022 (recording)
Title: Inequality and fairness with heterogeneous endowments (access)
Milena Tsvetkova is an Assistant Professor in the Department of Methodology at the London School of Economics and Political Science and completed her PhD in Sociology at Cornell University in 2015. Her research interests lie in the fields of computational and experimental social science. She employs online experiments, network analysis, and agent-based models to study fundamental social phenomena such as cooperation, contagion, and inequality.
September 9th, 2022
Title: Taking on Big Tech: New Paradigms for New Possibilities (access)
Safiya U. Noble is an internet studies scholar and Professor of Gender Studies and African American Studies at the University of California, Los Angeles (UCLA). In 2021, she was recognized as a MacArthur Foundation Fellow (also known as the “Genius Award”) for her ground-breaking work on algorithmic discrimination.
April 22nd, 2022
Title: Methods to Aid Model Debugging: From Rationales to Influence (access)
Byron Wallace is an associate professor in the Khoury College of Computer Sciences at Northeastern University. His research is primarily in natural language processing (NLP) methods, with an emphasis on their application in health informatics.
Faculty host: Halil Kilicoglu