Data Science Boot Camp
May 9, 2023
Jun 8, 2023
Mar 16, 2023
Academics from Member Institutions/Departments
Mar 16, 2023
Academics from Non-Member Institutions paying the $500 membership fee
Jan 16, 2023
Academics from Non-Member Institutions applying for Corporate Sponsored Fellowships
You are registered for this program.
The Erdős Institute's signature Data Science Boot Camp has been running since May 2018 thanks to the generous support of our sponsors, members, and partners. Due to its popularity, we now offer our boot camp online twice per year in two different formats: a 1-month long intensive boot camp each May and a semester long version each Fall.
Matthew Osborne, PhD
Head of Boot Camps
Fridays 11 AM - 12 PM ET, 3 - 4 PM ET
Don't hesitate to contact me with any questions or concerns, I'm looking forward to this May's boot camp!
Alec Clott, PhD
Head of Data Science Projects
Participants are welcome to reach out to me via slack or email. I normally work standard EST hours (9am-5pm), but can always find time to meet folks via Zoom too. Let me know how I can help!
The goal of our Data Science Boot Camp is to provide you with the skills and mentorship necessary to produce a portfolio worthy data science/machine learning project while also providing you with valuable career development support and connecting you with potential employers.
Those who successfully complete a team project will receive a digital certificate of completion with a sharable URL.
David Wen, Preston Pozderac, Wendson Barbosa
Root Insurance Bidding Strategy Challenge: We want to propose a bidding strategy for online ad placements based on customer demographics to increase sales of our car insurance policies while minimizing cost and obtaining at least 400 policies sold per 10,000 customers.
Correcting Racial Bias in Measurement of Blood Oxygen Saturation
Rohan Myers, Saad Khalid, woojeong kim, Brooks Miner, Jaychandran Padayasi
Fingertip pulse oximeters are the current standard for estimating blood oxygen saturation without a blood draw, both at home and in healthcare settings. However, pulse oximeters overestimate oxygen saturation, often resulting in ‘hidden hypoxemia’: a patient has hypoxemia (dangerously low oxygen saturation), but the oximeter returns a healthy oxygen value. Unfortunately, oximeter overestimation of oxygen saturation is exacerbated for patients with darker skin tones due to light-based oximeter technology. This results in Black patients experiencing hidden hypoxemia at twice the rate of white patients. By combining pulse oximeter readings (SpO2) with additional patient data, we develop improved methods for estimating arterial blood oxygen saturation (SaO2) and identifying Hidden Hypoxemia. The predictions of our models are more accurate than pulse-oximeter readings alone, and remove the systematic racial inequity inherent in the current medical practice of using oximeter readings alone.
Frank Hidalgo, Joseph Szabo, Christopher Zhang, Sean Perez, Kun Jin
Acronym/Abbreviation (short form) disambiguation is one of main challenges when using NLP methods to uderstand medical records. While this topic has long been studied, it is still a work in progress. Current strategies often involve having manually curated datasets of abbreviations and train classifiers. The main problem of that approach is that curated datasets are sparse and don't include all the short forms. In Dec 2020, a paper came out where they created a large dataset of short forms as one of their steps in their pipeline to pre-train models. The goal of our project would be to build upon their short form disambiguation piece and create a tool to disambiguate a medical short form using its context. Example of the usage of our tool: original_sentence = "The patient states that she has had dizziness, nausea, some heartburn, and some change in her vision. She is gravida 6, para 4, AB 2. She has no history of adverse reaction to anesthesia." AB could stand for "abortion", "ankle-brachial", "blood group in ABO system", "A, B lines in Kerley lines". disambiguated_sentence = "The patient states that she has had dizziness, nausea, some heartburn, and some change in her vision. She is gravida 6, para 4, abortion 2. She has no history of adverse reaction to anesthesia."
Supermassive Black Hole
Anna Brosowsky, Sayantan Khan, Nancy Wang, Ethan Zell, Yili Zhang
We built a movie finder app that allows a user to enter some details they remember about a movie (along with some optional filter info on the genre and release year) and then predicts what movie the user is thinking of. To solve this NLP problem, our tool uses an embed-and-rerank model. We have precomputed vectorizations of movie plot information for the approximately 34,000 movies in our dataset.
Our model’s first step is to vectorize the user’s query and do a fast comparison to find the 100 closest plot vectors. Then it reranks these top 100 closest plots, performing a more thorough comparison using a neural network that semantically compares the plot fragments with the original query. Finally, we output the 10 movies which show up at the top of this new ranking.