As a data enthusiast with strong technical background in programming and statistics, I am driven by a curiosity for using advanced techniques to solve complex problems. I love to dive deep in to data to garner insights that others may have missed. Python is my tool of choice, and I have used it almost daily for about four years. My passions include the universe, the environment, and using analytical tools for good. I recently graduated from the University of Michigan - Ann Arbor, earning a B.S. in Astronomy & Astrophysics and Interdisciplinary Physics with a minor in Statistics and I currently work as a Research Assistant with the NASA Goddard Space Flight Center studying planets around distant stars. I will begin pursuing a Master's in Analytics at Georgia Tech this fall.

My Resume


The Orbital Separation Distribution and Binary Fraction of M-Dwarfs

Fall 2016 - Present

We present a new estimate of the binary fraction of M-Dwarfs using a log-normal fit to the orbital distribution. We use point estimates of the binary fraction from four M-Dwarf surveys sampling distinct orbital radii to fit a log-normal function to the orbital distribution of companions. This model, alongside the companion mass ratio distribution given by Reggiani and Meyer (2013), is used to calculate the frequency of companions over the ranges of mass ratio (q) and semi-major axis (a) over which the referenced surveys were collectively sensitive - [0.60 < q < 1.00] and [0.00 < a < 10,000 AU]. This method was then extrapolated to calculate a binary fraction which encompasses the broader ranges of [0.10 < q < 1.00] and [0.00 < a < 1 AU]. Finally, the results of these calculations were compared to the binary fractions of other spectral types of stars. The binary fraction over the constrained regions of [0.60 < q < 1.00] and [0.00 < a < 10,000 AU] was found to be 0.230 +/- 0.040. The extrapolated binary fraction over the broader ranges of q (0.10 - 1.00) and a (0.00 - 1 AU) was calculated as 0.461 +/- 0.078. Lastly, the binary fraction of M-Dwarfs is similar to that of FGK and A stars over the constrained regions of mass ratio and semi-major axis.
The poster I presented on this subject.

Constraining the Origins of Massive Stars Away from the Galactic Plane

May 2019, in collaboration with Johnny Dorigo Jones

We discuss the formation and dynamical evolution of high-mass stars in the Milky Way through studying their kinematics and multiplicity. We have selected 15 O- and B-type stars located away from the galactic plane, which helps ensure that these are stars which exist in the field rather than in a cluster. To study the kinematics of these stars, our goal is to determine their statuses as runaways, field stars with high 3-D velocities. The 3-D velocity of each star is computed from two components: the transverse velocity and the radial velocity. Furthermore, we will perform direct imaging of the target stars to help determine the presence of a visual binary. Due to issues with accurately determining the Doppler shifts of spectral lines, we were not able to obtain radial velocities, and thus we cannot determine runaway statuses with our own observations. However, using radial velocities quoted in SIMBAD, we found that 7 of the 15 sampled OB stars can confidently be considered runaways. Our photometric analysis and distance calculations allowed us to conclude 2 of the 14 OB stars that were observed have a nearby stellar companion. Constraining both the fraction of high galactic latitude OB stars which are runaways and the fraction which have a binary companion will help us learn more about massive star formation and dynamical evolution in the Milky Way (Chini et. al. 2012; de Wit et. al. 2005; Hoogerwerf et. al. 2000).
The work for this project, including all observations, was done entirely on-site at Kitt Peak National Observatory by me and my partner.

Kaggle Competitions

I participate in Kaggle competitions to hone my data science and machine learning skills. Here are some of the projects I have worked on:

  • Titanic: Machine Learning from Disaster (June 2019)
    • This challenge tasks participants with determining which passengers of the Titanic would survive the catastrophe based on their gender, socioeconomic status, and other factors. To make these predictions, I first processed the training data to impute missing values and convert categorical variables into numerical ones via one-hot encoding. Next, I built a gradient boosted decision tree model using XGBoost and trained the model using the pre-processed training data and a subset of validation data. After that, I processed the test data in the same manner as before and fit my model to make predictions based off of this. I was able to predict 79% of the passengers' fates correctly, which placed my submission in the 75th percentile of the leaderboard.
    • Skills Developed: Supervised Machine Learning, Binary Classification, Python Pandas
  • Digit Recognizer (July 2019)
    • The goal of this competition is to correctly identify numerical digits from the set of hand drawn images of the MNIST (Modified National Institute of Standards and Technology) database. To do this, I developed a sequential computer vision model with multiple convolution and dense layers. I tested different combinations and parameter values of the model using a validation subset of the training data and eventually found an implementation which minimized the validation loss. After fitting the model on the training data and then making predictions based of the test data, I was able to identify 99% of the hand drawn images correctly.
    • Skills Developed: Computer Vision, Deep Learning, Unsupervised Machine Learning

All code relating to each of these projects is available on my personal GitHub



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