The Data Science group at Lacework works across many domain areas to provide value through data and modeling.- Security Data Science: Risk models for quantifying and reducing cloud security risk, detection of anomalies and active threats.- Product Data Science: Analysis and models to drive Lacework's product and business decisions, ensure their efficacy, and measure their success.- Infra Data Science: Use data to build scalable and efficient cloud infrastructure, while also improving the performance of Lacework's products.

Led cross-functional analysis and modeling efforts for cloud infrastructure optimization (Snowflake, AWS) and cost management.

Led a group of three data science teams focused on:-Time series forecasting, with applications in Facebook's capacity planning, elastic resource allocation, revenue operations, and others.- Efficiency, performance, and optimization of Facebook's core services.- Performance and reliability of Facebook's family of applications.

Infrastructure Data Science partners with engineering teams to develop data-driven solutions for significant infrastructure challenges such as app and site performance, systems efficiency and reliability, resource allocation and long-term capacity forecasts. Infra Data Scientists use a range of tools, from A/B testing to machine learning, to help Facebook make decisions about operations and system design. The team contributes to all parts of a project’s lifecycle, including scoping, data discovery, research, methodological design, code implementation, and reporting and interpreting final results. The teams’ work varies, in line with the complex and diverse challenges of maintaining one of the largest and most advanced enterprise infrastructures in the world. We look for candidates with a wide range of backgrounds to join our team and help with this work.

Knowledge LAB, Urban Center for Computation and Data

Assistant for the course Data Structures (CS 251).- Planning and teaching lab sessions.- Planning and grading written and programming assignments in C.

Collaborated with an interdisciplinary team analyzing large amounts of data regarding city services in Chicago. Involved in many aspects of the data analysis process: getting data, data cleaning and munging, exploratory analysis, statistical modeling and machine learning.

Researching in the field of artificial Intelligence.Specialties:* Stochastic planning* Machine learning* Data mining* Bayesian learning and inference* Multi-agent systems* Probability theory* Game theory* LogicsInterdisciplinary research:* Computational ecology (applied machine learning for data analysis)* Human-computer interaction (probabilistic reasoning for interactive museum exhibits)