I am a member of Credo AI's data science team. I strategize and develop AI-powered technologies for digital risk management and design tools that ensure responsible AI development and deployment.
In my prior roles, I strategized and developed advanced data analytics and machine learning solutions to support intelligent infrastructure operations and smart cities management. I have authored many high-impact scientific papers and hold a patent in the smart water networks domain from the U.S. Patent and Trademark Office.
I have played key roles in cross-functional teams consisting of water professionals, social scientists, cybersecurity experts, and software developers. I have secured $800,000 funding for Smart Water research and development initiatives. I have served as the Chair of the ASCE Task Committee on Cyber-physical Security of Water Distribution Systems and have been the receptionist of the 2013 ASCE Journal of WRPM Best Paper Award and the NSF-CMMI Graduate Fellowship Award. I have given invited talks at institutions like Rice University and the University of Toronto.
Methodological: risk and statistical analysis (uncertainty propagation, Bayesian probability); optimization (mathematical and evolutionary); machine learning (dimensionality reduction, classification, NLP); hydraulic and quality modeling (EPANET, HEC packages); object-oriented programming (Java, Python); database (MongoDB, PostgreSQL); Big Data processing (Spark); deep learning (TensorFlow, Keras); cloud computing (AWS EMR); geospatial analysis (QGIS); software project management (Jira, BitBucket).
Today, we are excited to unveil a set of Large Language Model (LLM)-powered features to help speed up the process of AI governance, risk management, and compliance.
The California EO on AI is not just a set of instructions for state agencies, but a blueprint for how governments and enterprises alike can navigate the evolving landscape of AI, ensuring that AI tools are adopted responsibly.