aadit narayanaswamy
Quantitative Research Intern at Cerebellum Capital, Inc
quant researcher
work , find signal noisy background . free time , apply tool theoretical computer science statistic solve problem biotech . recently graduated uc berkeley . opportunity mind want chat , contact aadit.narayanaswamy @ berkeley.edu
Cerebellum Capital, Inc
https://www.linkedin.com/company/cerebellum-capital-inc
Quantitative Research Intern
Focused on signals from NBBO (National Best Bid and Offer).
San Francisco, California
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Teza Technologies
https://www.linkedin.com/company/teza-technologies
Quantitative Research Intern
Part of the Alternative Data Group, focusing on sentiment analysis.
San Francisco Bay Area
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Parade (Dray)
https://www.linkedin.com/company/paradeai
Business and Operations Intern
Co-wrote a case study on commoditization in the transportation industry. Was presented to several multimillion dollar brokerages as a method of capturing business.
Belmont, California
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Optimal Asset Management, Inc
https://www.linkedin.com/company/optimal-asset-management-inc
Finance Intern
Created higher performing portfolios for groups of under-served investors using Optimal's factor-based portfolio software, FactorAllocator.
Los Altos, California
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Stanford University School of Medicine
https://www.linkedin.com/company/stanford-university-school-of-medicine
Research Assistant, Wernig Lab
Researched transcription factor induced neuronal differentiation.
Stanford, California
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Mathematics and Data Science
University of California, Berkeley
https://www.linkedin.com/school/uc-berkeley/
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Palo Alto High School
https://www.linkedin.com/school/palo-alto-high-school/
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Google Case Competition - Finalist
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Kyocera Case Competition - Finalist
Kyocera
SOMAmetrics Case Competion - Finalist
SOMAmetrics
Infosys Case Competition - 1st Place
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National AP Scholar
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President's Volunteer Service Award - Gold
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Eagle Scout Award
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Applied Data Science and Machine Learning
IEOR 135
Computing with Data
Statistics 133
Concepts of Probability
Statistics 134
Decision Analysis
IEOR 166
Discrete Mathematics
Math 55
Healthcare Economics
Economics 157
Introduction to Financial Engineering
IEOR 221
Introduction to Machine Learning
CS 189
Linear Algebra
Math 110
Linear Programming and Network Flows
IEOR 162
Mathematical Theory for High Frequency Trading
IEOR 222
Microeconomic Analysis
Economics 100A
Portfolio Optimization
IEOR 224
Stochastic Processes
Statistics 150
Multifactor ETF Model
Built low-turnover multifactor model trading GICS ETFs. Sharpe of 1.84 out-of-sample, a statistically significant improvement from our control model (Sharpe 0.82).
Predicting CMS Star Rating
Built a model for Medicare-certified healthcare providers that predicts CMS Star Rating (for any of 54 treatment courses) with ~95% accuracy (on average) out-of-sample, with no given treatment having lower than ~80% accuracy OOS.
Predicting Winners of the 2018 Midterm Elections
Built model using scraped polling data and financial contribution data. Predicted winners of the 2018 Midterm Elections (OOS) with ~94% accuracy several weeks in advance.
Education
Buddies4Math
Activities Coordinator
Helped increase Mariano Castro Elementary's state test scores by 9% over three years.
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UC Berkeley Data Science BA Alumni
https://www.linkedin.com/groups/13726010
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UC Berkeley Economics
https://www.linkedin.com/groups/2043330
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UC Berkeley Statistics BA Alumni
https://www.linkedin.com/groups/4529144