Data science appeals to me as a way to solve difficult problems by creatively joining contextual expertise with statistical and computational methods. The highlight for me is that data science can be applied to so many domains, including genomics, consumer behavior, astrophysics, policy development, and much more. I am motivated to be a continual learner, and as a data scientist I can explore new subject matter and new methods, as the field is expanding at a breathtaking pace.

Harvard’s M.S. in Data Science degree is a highly selective, 3 semester program run by the Institute for Applied Computational Science (IACS) within the School of Engineering. In my first year of the program, I completed the following courses:

Semester 2:

• Generalized Linear Models (STAT 149): A statistics course on GLM’s, covering Bernoulli, Poisson, Gamma, and Negative Binomial regression. In addition, the class goes over Generalized Additive Models (GAM’s), as well as tree regression. 
• Critical Thinking in Data Science (AC 221): An ethics course with a computational component, primarily focused on data privacy and algorithmic bias.
• Data Science 2: Advanced Topics in Data Science (AC 209a): An advanced data science course, mainly focused on neural networks such as MLP’s, CNN’s, RNN’s, language models, clustering, variational autoencoders, and Generative Adversarial Networks (GAN’s).
• Special Topics in Applied Computation (AC 299r): An independent research project I did at Boston Children’s Hospital, building deep learning models to classify Congenital Heart Disease from ultrasound videos.

Semester 1:

• Linear Models (STAT 139): A statistical inference course covering parametric/non-parametric comparison tests, data transformations, and many flavors of linear modeling in R.
• Systems Development for Computational Science (CS 207): A computational methods course focusing on creating well-written, scalable software in Python.
• Data Science 1: Introduction to Data Science (AC 209a): A data science course detailing how to prepare and model data using regression techniques, decision tree algorithms, neural networks, and more.
• Artificial Intelligence (CS 182): An artificial intelligence course covering graph search algorithms, CSP’s, MDP’s, Bayesian networks, reinforcement learning, and game theory.

Currently, I am looking for full time positions at the intersection of data science and machine learning as I complete my third and final semester of the program. This semester I am taking a deep dive into machine learning and computer vision topics, as well as TA-ing for the Data Science 1 class. 

Before coming to Harvard I studied astrophysics at Brown University. Towards the second half of my B.S. degree I discovered a passion for blending computer science with natural science, allowing one to recreate natural processes using numerical models that follow the written laws of the universe. This was kickstarted when I joined the LUX dark matter research collaboration as an undergraduate researcher in the fall of 2015. At the time, graduate students were using Matlab to analyze unwanted electric current generated by partially ionized particles slamming into the cathode of a Photomultiplier Tube (PMT). I was spellbound by how neatly written blocks of computer code could immediately show me which elements had contaminated the inner vacuum of this device that was locked in a sealed chamber literally a mile from my dorm room. From that point on I decided to learn how to code.

Fast forward to the spring of 2017, and I was staring proudly at small blue dots dart and fizzle around one another on my computer screen. I had just finished coding an N-body gravity simulation, and each of these specks was a star dancing within my own little globular cluster. This computational physics project led to a summer internship offer from Lawrence Livermore National Lab (LLNL), writing python diagnostics for nuclear physics simulations. At LLNL I was immersed in a supercomputing research facility with fascinating projects all around me. There were scientists employing supercomputing resources to model nuclear fusion, network analysis to enhance cybersecurity, seismic data to monitor foreign nuclear proliferation, and even engineering special computer chips to model the electrical pathways in the human heart. This exposure to exciting initiatives led me to want to expand my horizons to tackle computational problems beyond physics, and I found data science as a pathway to achieve that goal.

Therefore, I applied to data science graduate programs to build upon my statistics and coding knowledge, and find directionality within the field. I chose to go to Harvard primarily for the flexibility in course options, and the program’s great balance between theory and application. I am absorbing so much new information, and am actively looking for summer internship opportunities to showcase my skills and grow further as a data scientist. 

1. Singing! I was a choir devotee throughout middle school and high school, and got really into a cappella at Brown. For all 4 years there I sang for the Bear Necessities, Brown’s ONLY all-suspendered a cappella group. Check out our 2015 album entitled Visions on Spotify!
2. Traveling! I grew up in an international family (half English), and took my first plane ride across the Atlantic at 8-weeks old. Since then I’ve been to 15 countries and about 25 states, learning about how others live their lives, and seeing some stunning sights. Most recently I went to Barcelona and saw La Sagrada Familia, an monstrously large, very eclectic cathedral that took the #1 spot as most interesting building I’ve ever witnessed.
3. Skiing! I am a warm weather fanatic, but hypocritically am also a big fan of skiing. My favorite mountain is the Alta/Snowbird duo in Utah, with the Swiss mountain, Klosters, coming in close second (they are both so magical). 
4. Comedy! While I am no comedian, I love watching comedy shows. I have a dry sense of humor, and therefore my favorite shows are the English panel programs like Mock the Week, QI, and Would I Lie to You? QI is especially fun because I end up learning just as much as I laugh.