Created an auto-tuning program for the company Redshift database. Tuned all the tables (compression, sort/dist keys, etc) – saving 10% of space and improving the average runtime of all workflows by 30-40%. Previously workflows touching on tables > 1TB would often fail to complete at all, after the tuning – some workflows runtimes went from 1h to 10 min or less. Greatly helped the product analytics, bizops teams and product team in the company.• Started implementing materialized views on Redshift and Snowflake where appropriate – this greatly improved certain targeted workflows.• Implemented data lake concept – using Redshift Spectrum and airflow to create external daily updated tables on s3.• Created a daily scheduled program (on ec2 then ported into airflow) to resort/vacuum tables on daily basis in Redshift.• Set up and managed Airflow Scheduler (on Vagrant, then self hosted on ec2, then Google Composer) to schedule and run workflows/pipelines. Created numerous data pipelines to incrementally pull data from 3rd party systems into our databases (Snowflake, Redshift, RDS, Treasure Data) to run on Airflow. Created data pipelines to do complex transforms using python.• Worked on migration from Redshift to Snowflake, created scheduled pipelines to transform data using DBT and/or airflow. For large event tables ( > 1TB) created external tables on snowflake – s3 bucket accepts streaming data then a scheduled snowpipe updates external tables on snowflake, tables sorted by date for faster querying.• Consolidated financial transaction flows into a single schema, architecting the structure for efficient analytics (group effort). About 60m indiv. customer accounts, 16k merchant accounts – bringing data from 3rd party systems, joining and enriching pay data with customer/merchant data, consolidating into final curated tables – all automated, daily. Curated tables are used by bizops/product analytics to create tableau dashboards to calculate company metrics.

Studied various tools for large data manipulation and analysis. In three weeks developed a data pipeline using on-demand AWS Lambda as part of my consulting project with a company called Fivestars.

Investigated the twinning interfaces of LaCoO3. Numerical simulations of those interfaces achieved the first explanation of the origin of ferromagnetism in pure LaCoO3. Investigated non Fermi liquid physics in quasi-one-dimensional spin-leg ladder compounds. Results of ARPES spectroscopy done on SrX-14CaXCu24O41 (0 < x < 11) I compared favorably to the simulations.

Taught intermediate physics-major laboratory course; Physics 133 Advanced lab: planned the course, managed TAs, wrote and later graded homework, graded lab reports, prepared/gave lectures on mean/variance calculations, data fitting, propagation of errors, weighing data values by propagated uncertainty, long-form technical writing. The labs included an electronics component, a radiation component, and aspectroscopy segment, each requiring a different approach and a separate set of skills to complete.

Lower division physics labs: Mechanics (3 quarters), Wave/Optics(2 quarters), Electricity/Magnetism (3 quarters)Upper Division Lecture courses (physics majors only): Electrodynamics (1 quarter), Thermodynamics(2 quarters)Advanced Physics lab (physics majors only): Physics 134 (2 quarters), Practical Electronics: Physics 160 (2 quarters)Duties: Ran and graded physics lab sections weekly, graded exams and led discussions sections weekly for the lecture course, managed independent projects - extensions to the main experiments. The description of each course is available upon request.

Created an all-optical fingerprint identification system using a Michelson interferometer. I set up a table top interferometer using Fourier corrected optics and He-Ne laser. Matching fingerprints would generate an interference pattern upon recombining after the beam splitter.