Education
Ph.D. and M.S. in Computer Science
B.S. in Bioinformatics
Experience
Salesforce, AI Research - Lead Applied Scientist
Sep 2021 - Present
- Conducting cutting-edge research in AI for IT operations and availability, covering topics such as time-series forecasting, anomaly detection, log analysis, and safe deployment.
- Taking the lead in developing open-source libraries, such as LogAI, a log analytics tool powered by AI. (https://github.com/salesforce/logai).
- Designing and building streaming machine learning platforms for Salesforce Monitoring Cloud, which processes millions of logging streams and alerts.
Amazon, AWS - Applied Scientist, Science Lead
Jan 2020 - Sep 2021
- Pioneering AI/ML advancements within AWS CloudWatch and Observability as the sole AS in the team.
- Owning CloudWatch anomaly detection service, elevating the service to new heights and driving substantial year-over-year growth..
- Designing, developing, and successfully managing a highly scalable time-series machine learning infrastructure.
Microsoft, Cloud and AI - Senior Data and Applied Scientist, Tech Lead
Mar 2017 - Jan 2020
- Proud founding team member of AIOps initiatives, delivering innovative solutions that have been widely adopted across multiple Azure teams to prevent critical issues, enhance DevOps efficiency, and minimize costs.
- Playing a critical role in the development of Gandalf, a comprehensive end-to-end analytical service for secure deployment in large-scale cloud infrastructure.
- Making key contribution to DeepAD, a world-class anomaly detection and auto-diagnostics system for Azure control plane, leveraging cutting-edge AI/ML technologies.
Microsoft, Azure - Research SDE Intern
May 2016 - Aug 2016
- Productizing machine learning solutions to optimize Azure resource allocation, identify bugs, and assess potential risks, delivering measurable results and improvements.
Microsoft, Research - Research Intern
May 2015 - Aug 2016
- Pioneering the development of the industry's first Gaussian mixture model for Azure compute user retention and churn analysis, providing valuable insights and actionable data.
- Creating the initial prediction model for Azure VM interrupts and downtime, paving the way for more accurate and reliable predictions in the future.
