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Suchitra Hari is a Principal Research Scientist at IBM Research AI, based at the Thomas J. Watson Research Center. Her primary research focus is on Trustworthy AI, encompassing areas such as explainability (XAI), fairness, robustness, and privacy in AI systems, particularly for Natural Language Processing (NLP) and machine learning models. She is dedicated to developing algorithms and frameworks that make AI systems more transparent, accountable, and aligned with human values. Suchitra's work aims to bridge the gap between cutting-edge AI capabilities and the practical need for reliable and ethical AI deployments in real-world applications. Before joining IBM, she was a Postdoctoral Associate at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). She earned her PhD in Computer Science from the University of Illinois Urbana-Champaign (UIUC), where her doctoral research explored topics in machine learning and information retrieval.
Suchitra Hari's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
Received IBM's most prestigious award, recognizing exceptional technical contributions and leadership that have had a significant impact on IBM's business and the field of AI.
Pioneered and contributed significantly to research in explainable AI, AI fairness, and model robustness, with numerous influential publications in top-tier AI conferences (e.g., NeurIPS, ICML, ACL, EMNLP) and journals.
Inventor on several U.S. patents focusing on novel machine learning algorithms, natural language understanding techniques, and methodologies for building more trustworthy AI systems.
Frequently invited to present her research and insights on Trustworthy AI at major international conferences, workshops, and industry events, contributing to the broader discourse on responsible AI development.
Contributed to the development and release of open-source toolkits and frameworks (e.g., related to AI Explainability 360 or AI Fairness 360) that enable practitioners to build more trustworthy AI models.
The University of Texas at Austin
UMIT
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