WHY YOU CHOSE APMA:
- As I began to explore different career opportunities in a variety of fields (finance, data science, tech, etc), I realized that all these industries seek candidates who think quantitatively. In particular, if you plan on working in finance, it’s really important to be comfortable with different quantitative tools and strategies—at the end of the day, most of your work will involve extracting insights from a bunch of numerical data. Brown has a really strong APMA program and I feel that classes here prepare me for all the number-crunching work I’ll be doing in the future.
COURSES TAKEN WITHIN MAJOR:
- Advanced Placement Calculus (MATH 0170)
- Intermediate Calculus (MATH 0180)
- Linear Algebra (MATH 0520)
- Statistical Inference I (APMA 1655)
- Operations Research - Probabilistic Models (APMA 1200)
- Applied ODEs (APMA 0350)
- Intro to Numerical Optimization (APMA 1160)
- Accelerated Intro to CS (CSCI 0190)
- Discrete Structures and Probability (CSCI 0220)
- Intro to Software Engineering (CSCI 0320)
- Machine Learning (CSCI 1420)
- Computer Vision (CSCI 1430)
- Deep Learning (CSCI 1470)
- Design and Analysis of Algorithms (CSCI 1570)
HIDDEN GEM COURSES TAKEN AT BROWN:
- Writers on Writing Seminar (LITR 0710)
- Contemporary Architecture (HIAA 0860)
- Studio Foundations (VISA 0100)
FIELD(S) OF INTEREST:
- Quantitative Research in Finance: There are a lot of numbers in finance; quant researchers play around with these numbers until they extract insights that can bring value to the firm. They do this by developing mathematical models and algorithms, then running these models or algorithms on large sets of financial data. The actual goal of each model is different—different firms and asset classes have different quant-research needs—but at its crux, it comes down to using math and code to figure out what’s important. I'm drawn to the fast-paced and quantitative aspects of finance. A lot of data within the finance industry is publicly available, so it's just a matter of using the right mathematical tools to extract the most useful, predictive information. I find this really interesting and rewarding. There's also a lot of coding involved in quant research, which I enjoy (but not software engineering-type coding; it's more about writing scripts to run models at scale).
- Deep Learning Research: Deep learning is a really hot field right now. It’s at the cutting-edge of tech and a lot of researchers are working to develop a more accurate, reliable deep learning algorithms. The research I did focused on another aspect: application. Often, a certain model can be applied to a variety of tasks depending on what data you feed it. Of course, there’s also some preprocessing and tweaking involved. In my research group, most of our time was actually spent cleaning and preprocessing data using scripts we wrote in Python. I spent the past three semesters working in an interdisciplinary research group. Our group used residual networks to detect and classify strokes using patients' brain CT scans. Although I wasn't too familiar with the medical component of the project, I worked extensively on the CS side (the problem boils down to image-processing). I liked the strong statistical grounding of deep learning algorithms, and I think the methods I've learned in this lab can be applied to many other fields, including finance.
INVOLVEMENT AT BROWN:
- Brown Data Science, Women in Computer Science, Brown Political Review, Club Tennis, Canadian Club
OTHER PERSONAL TOPICS WILLING TO DISCUSS:
- Diversity in STEM, WiCS (Women in CS), being an international students / dealing with visas
INTERNSHIPS, RESEARCH, AND OTHER PROFESSIONAL EXPERIENCE:
- Summer after freshman year: I did an I-Team UTRA at Brown. The project was titled 'Deep Learning for Stroke Detection.' Details about that can be found above under my fields of interest.
- Summer after sophomore year: I was a Quant Research intern at Fidelity Investments in Boston. Fidelity is one of the world's largest asset managers. I worked in the Global Asset Allocation group (GAA), which manages over 60 different funds and portfolios. Quant researchers in GAA work directly with portfolio managers to develop investment strategies. My project had three main components. Part one involved using options data to predict moves in stock indices. Part two was analyzing different option strategies to determine the return of each strategy when there’s a shift in the underlying asset. And finally, in part three, I made a dashboard using Python to show my analysis in a user-friendly way.
Other professional TOPICS willing to discuss:
- Software Engineering vs Quant Research
- Different positions in finance (Investment Banking, Sales & Trading, Quant Research)
- Research in industry vs academia