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Kanika Chopra
cv
I have recently graduated from a Master's in Science, Statistics at the University of Toronto. I completed my Bachelor's of Mathematics in Statistics at the University of Waterloo.
During my Master's, I researched bias and fairness in machine learning in health applications in collaboration with Dr. Jessica Gronsbell.
I have previously worked as a Data Scientist Intern at Uber on the Safety & Insurance team,
as a Data Analyst/Scientist Intern at Wish on the Data & Relevancy team,
interned as a Data Scientist at Intact Data Lab, and Goldspot Discoveries Corporation .
Mentorship and advocating for diversity, equity and inclusion are really important to me. Most recently, I worked as a Program Assistant for Shad UBC which is a month-long enrichment camp for high-achieving high school students.
I have also volunteered as a teen mentor with Big Brother Big Sisters Canada for 5+ years and mentored students interested in breaking into data science through Tech+ UW and UW Math Society.
During my undergrad, I was also the External Co-Director for Tech+ UW, which is a club at that advocated for DEI within the tech industry.
In my free time, I like to embroider, watercolor paint and cook. If you want to chat, feel free to email me at kanikadatt [at] gmail [dot] com !
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Research
- Research Assistant (Sept 2022 - May 2023)
Under the supervision of Dr. Jessica Gronsbell to develop a tutorial
on bias and fairness in healthcare applications for clinicians.
- Research Assistant (Dec 2022 - July 2023)
Collaborating with Dr. Nathan Taback, Dr. David Liu and Dr. Nathalie Moon to develop a teaching tool to randomize data sets for students and autograde quantitative assessments.
- Research Assistant (Jan 2022 - July 2022)
Collaborated with Dr. Martin Lysy to develop and document a Python package, projplot , that will provide users with additional plots to confirm optimality when building optimizers. [code] [docs]
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Research
- Time-to-Exoneration Model Analysis
Investigated the effects of demographic, geographic and crime factors on time-to-exoneration in the U.S with a hierarchical Bayesian model [code] [paper]
- Highlighting Ethnic Biases in COVID-19
Leveraged word embeddings to quantify the biases towards Asians from 140K global articles surrounding COVID-19 [code] [paper]
- Predicting Parkinson's Disease
Built a logistic regression model to predict PD using auditory speech signals with 85% accuracy [code] [report]
- Netflix Browsing Time Experiment
Conducted an experiment to determine the optimal combination of preview length, match score and tile size to minimize average browsing time spent on the homepage [code] [report]
- Forecasting Hourly Air Temperature
Build Holt-Winters additive, smoothing and regression models to determine best fit for forecasting hourly air temperature [code] [slides]
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Teaching
Teaching and mentorship are integral to my life and I aspire to teach at the university-level in the future. Thus far, I have been a teaching assistant for the following courses:
- STA238 - Probability, Statistics and Data Analysis II
Hosted weekly tutorials to teach students how to code and complete statistical analyses in R. Graded assignments for roughly 300 students.
- STA457 - Time Series Analysis (Fall 2022)
Hosting office hours to assist students with any questions they have regarding course content. Grading assignments for roughly 175 fourth-year students.
- CFM101 - Introduction to Financial Markets and Data Analytics (Spring - Fall 2021)
I had the opportunity to work with Dr. James R. Thompson to develop a new FinTech course introduced in Fall 2021. I was responsible for creating assignments, solutions and tutorials for the first iteration of students.
- STAT231 - Statistics (Fall 2020)
Graded assignments for over 400 students covering statistical analyses, confidence intervals and hypotheses tests.
In addition to teaching assistantships, I have tutored students/hosted workshops for the following:
- GGPlot Workshop (Oct 2022)
Co-hosted a Halloweek-themed workshop for undergraduate students on plotting with GGPlot, covering topics such as aesthetics and facetting.
- Introduction to Data Science (July 2022)
Provided high school students at Shad UBC an introduction to data science and taught them how to use pandas and NumPy for data analysis
- Python for Data Analysis (Mar 2022)
A series of tutoring sessions covering an introduction to pandas, NumPy, Matplotlib and applying K-Means clustering on an Uber dataset. [notebooks]
- Clustering for Image Analysis (Feb 2021)
A workshop covering the basics of K-Means Clustering applied on the MNIST dataset. This covered a brief introduction to data science, Python, NumPy and K-Means Clustering. This workshop was co-hosted with Nicholas Vadivelu. [slides] [notebook ]
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Speaker Events
- March 2023 AMA with Waterloo Alumni>, UW DSC
- February 2023 Florence Nightingale Event, CANSSI Ontario
- June 2021: My Pride in Tech, Tech+
- Mar. 2021: Career Stories of Women in Tech, Laurier Data Science Club
- Feb. 2021: Lightning Talk, Tech+
- Oct. 2020: Working Through a Pandemic: Data Science Edition, UW Stats Club
- Sept. 2020: Introduction to Data Science, UW DSC
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