Sareeta Amrute

Sareeta Amrute is an anthropologist whose work investigates the social meanings of data-centric technologies. She is the author of the award-winning ethnography, Encoding Race Encoding Class: Indian IT Workers in Berlin (Duke University Press). Sareeta is currently at work on a new book, on cybersecurity and anticaste thought titled Staying Safer. She was the inaugural Director of Research at the Data & Society Research Institute, where she co-authored A Primer on AI in/from the Majority World and founded the Trustworthy Infrastructures Initiative, which re-imagines AI and related technologies from the perspectives of global majority epistemologies. Amrute is Associate Professor of Strategic Design and Management at Parsons, The New School.

Fernando Pereira

Fernando Pereira is Vice President, Research at Google DeepMind, where he focuses on putting language AI into practice. Before that, he led language AI and ML teams at Google Research from 2008 until 2022.  Before coming to Google, he was chair of the Computer and Information Science department of the University of Pennsylvania, head of the Machine Learning and Information Retrieval department at AT&T Labs, and held research and management positions at SRI International. He received a PhD in Artificial Intelligence from the University of Edinburgh in 1982, and has over 120 research publications on computational linguistics, machine learning, bioinformatics, speech recognition, and logic programming, and several patents. He is a fellow of AAAI, ACM, and ACL; member of the American Philosophical Society, of the American Academy of Arts and Sciences, and of the National Academy of Engineering; and past president of the Association for Computational Linguistics.

Mitra Taheri

Mitra Taheri is a professor in the Department of Materials Science and Engineering, director of the Materials Characterization and Processing facility, and a member of the Hopkins Extreme Materials Institute. Her research focuses mainly on electron microscopy and specifically in-situ microscopy. Taheri’s team designs and builds platforms to study materials in different, often extreme, environments including high temperatures, stress, radiation, oxidation, and more. Her team also develops new ways of detecting what is being seen through the microscope faster and in more efficient ways through integrating artificial intelligence. Taheri hopes this integration will improve not only researchers’ understanding of materials processes, but how new materials can be built for the next generation of applications. She earned her BS, MSE, and PhD in materials science and engineering from Carnegie Mellon University.

Alexis Battle

Alexis Battle, Johns Hopkins AI-X Foundry Deputy Executive Director, is also the director of the Malone Center for Engineering in Healthcare and an associate professor in biomedical engineering with secondary appointments in computer science as well as the department of genetic medicine at Johns Hopkins School of Medicine. She was a 2016 Searle Scholar and a 2019 Microsoft Investigator Fellow. Her research focuses on machine learning and developing statistical methods to examine how genetic differences between individuals contribute to differences in health, from cellular-level changes to disease outcomes. She specializes in unlocking the secrets and function of the human genome by analyzing large-scale genomic sequencing data.

Stephanie Hicks

Stephanie Hicks is an Associate Professor in the Departments of Biostatistics and Biomedical Engineering at Johns Hopkins with affiliations with the Malone Center for Engineering in Healthcare, the Center for Computational Biology, and the Department of Genetic Medicine. She is an expert in developing scalable computational methods and open-source software for biomedical data science, in particular single-cell and spatial transcriptomics genomics data, leading to an improved understanding of human health and disease.

Jeremias Sulam

Jeremias Sulam received his bioengineering degree from Universidad Nacional de Entre Ríos, Argentina, in 2013, and his PhD in Computer Science from the Technion—Israel Institute of Technology, in 2018. He joined the Department of Biomedical Engineering at Johns Hopkins University in 2018 as an assistant professor, and he is also a core faculty at the Mathematical Institute for Data Science (MINDS) and the Center for Imaging Science at JHU. He is the recipient of the Best Graduates Award of the Argentinean National Academy of Engineering, and the Early CAREER award of the National Science Foundation. His research interests include machine learning, sparse representation modeling, inverse problems, and biomedical imaging.

Daniel Khashabi

Daniel Khashabi is an assistant professor in computer science at Johns Hopkins University and the Center for Language and Speech Processing (CLSP) member. He is interested in building reasoning-driven modular natural language processing systems that are robust, transparent, and communicative, particularly those that use natural language as the communication medium. Khashabi has published over 40 papers on natural language processing and AI in top-tier venues. His research has won several awards, including the ACL 2023 Outstanding Paper Award, NAACL 2022 Best Paper Award, research gifts from the Allen Institute for AI, and an Amazon Research Award 2023. Before joining Johns Hopkins, he was a postdoctoral fellow at the Allen Institute for AI (2019-2022) and obtained a PhD from the University of Pennsylvania in 2019.