Summary
Nandini Ramanan is a Principal Data Scientist with 11 years of experience building interpretable, probabilistic ML models that bridge research and production, currently driving anomaly detection for firewall service metrics at Palo Alto Networks. She combines a strong academic foundation—a PhD candidate and prior research roles at UTD and Indiana University—with hands-on industry impact, moving models from prototype to customer-facing systems. Her specialty is efficient algorithms for relational, large-scale data and statistical relational learning, informed by a publication record and patented work. Early work includes a high-accuracy ML approach to early cyber threat prediction during a Palo Alto Networks internship, and research contributions span causal Bayesian networks, functional gradient boosting, and human-in-the-loop concept learning. Based in Mountain View, she blends deep probabilistic modeling with practical feature engineering and domain expert knowledge to improve operational efficiency. Colleagues describe her as a researcher-practitioner who consistently translates complex probabilistic methods into scalable, interpretable solutions.
11 years of coding experience
11 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at The University of Texas at Dallas
Bachelor’s Degree Computer science and Engineering, Bachelor’s Degree Computer science and Engineering at Amrita Vishwa Vidyapeetham, Coimbatore
Master’s Degree Informatics, Master’s Degree Informatics at Indiana University Bloomington