Preethi R. Karpoor
San Diego,CA
Summary
Astrophysicist turned data scientist, skilled in Python, SQL, and machine learning. Expert in time-series analysis, deep learning, and large-scale data processing. Passionate about extracting insights from complex datasets and building AI-driven solutions. Known for reliability and adaptability, with swift capacity to learn and apply new skills. Committed to leveraging these qualities to drive team success and contribute to organizational growth.
Overview
4
4
years of professional experience
Work History
Research Assistant
University of California, San Diego
09.2022 - 12.2024
- Designed and implemented automated ETL pipelines, processing and cleaning terabytes of photometric data, optimizing workflows, and reducing processing time by 30%.
- Developed EXOSCAPER, an automated pipeline for detecting and vetting extrasolar planet candidates, increasing Earth-like planet detection accuracy by 20% through statistical modeling and machine learning techniques.
- Conducted exploratory data analysis (EDA) and applied statistical models, quantifying exoplanet occurrence rates and uncovering key population trends in planet formation and migration.
Research Assistant
California Institute Of Technology
03.2021 - 04.2022
- Developed Transformer-based deep learning models (Time-Series & Vision Transformers) to enhance detection of subtle extrasolar planetary signals, improving low-signal planet characterization accuracy by 25%
- Designed and automated data pipelines, processing large-scale photometric datasets from TESS satellite, reducing manual data processing time by 40%.
- Applied advanced statistical modeling and machine learning techniques to analyze photopolarimetric data, identifying correlations in polarization trends of quasars.
Research Assistant
Indian Space Research Organization - IISc
01.2021 - 04.2022
- Developed an autoencoder based machine learning framework to calibrate X-ray Fluorescence (XRF) Spectroscopic data from Chandrayaan-2 Lunar exploration mission, optimizing for noise reduction and high-resolution feature extraction.
- Enhanced the accuracy of lunar surface compositional analysis by 40% by implementing advanced computational and statistical techniques, showcasing the integration of advanced computational techniques with planetary science for efficient, data-intensive interpretations
Education
Master of Science - Astronomy
University of California San Diego
San Diego, CA12-2024
B.E - Mechanical Engineering
Visvesvaraya Technological University
Bangalore, India08-2020
Skills
- Programming & Data Processing: Python, SQL, MATLAB, C
- Data Management & Querying: MySQL, PostgreSQL, ADQL, Astroquery
- Data Visualization: Tableau, Power BI, Matplotlib, Seaborn
- Machine Learning & AI: TensorFlow, PyTorch, Scikit-learn, Deep Learning, Time-Series Analysis
- Tools & Version Control: Git, LaTeX, MS Office
- Operating Systems: Windows, Mac OS, Linux
Accomplishments
- Awarded the Astronomy Excellence Award by University of California San Diego in recognition of outstanding track record
- Awarded Valedictorian for Ay 2016-2020 at the CMR Institute of Technology, Bangalore, India
- Global Ambassador for the Society of Women Engineers from 2019-2022
Languages
English
Native or Bilingual
Kannada
Native or Bilingual
Hindi
Native or Bilingual
Spanish
Elementary
French
Elementary
Timeline
Research Assistant
University of California, San Diego
09.2022 - 12.2024
Research Assistant
California Institute Of Technology
03.2021 - 04.2022
Research Assistant
Indian Space Research Organization - IISc
01.2021 - 04.2022
Master of Science - Astronomy
University of California San Diego
B.E - Mechanical Engineering
Visvesvaraya Technological University
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