UKC Data Science Workshop 2023

Data Science Workshop

“Machine Learning on Biomedical Data”

Open to all non-biomedical and biomedical backgrounds with some programming experience.

August 5, 2023, Saturday 1:30-6:00pm CT

Workshop Registration and Fee Required (see below)
Location: Innovation Room

Last Workshop (DSW at UKC2022 Washington D.C.)


This year’s Data Science Workshop (DSW) at UKC aims:

  • To provide a deeper hands-on experience with a crash course on data analysis, machine learning, and deep learning,
  • For those with some prior programming but no data science experience to those looking to expand their knowledge, and
  • For those without any biomedical background, as the techniques learned are domain and data agnostic.


The data science workshop will be a team-based mini-project from start to finish of a data science problem using real-world biomedical data.  Participants will begin with data cleaning, build a machine learning model, and end with presenting their own trained model.  Instructors will assist teams of 2-3 participants on each mini-task to achieve the final goal of training a machine learning model and presenting it.  Participants should have some programming experience with Python preferred. The computing environment will be Google Colab and the dataset will be announced at the workshop. The program schedule tentatively includes:

1) Data Analysis of Tabular Biomedical Data (1:30-3:45pm)

  • Introduction to data science basics and machine learning concepts (e.g. definition of AI vs machine learning vs deep learning, supervised vs unsupervised, accuracy vs interpretability)
  • Hands-on review of data handling and machine learning models (e.g. logistic regression, random forests, gradient boosting, artificial neural networks) on real-world biomedical tabular data (such as breast cancer histopathology or cardiac ultrasound measurement data)

2) Machine Learning Modeling and Project Presentations (4:00-6:00pm)

  • Hands-on model building with code from templates and scratch using Python
  • Team presentations of model building and performance results
  • Bonus demonstration of deep learning models such as biomedical image classification using convolutional neural networks


The data science workshop registration is available within the UKC registration and requires an additional $70 fee for regular members and $35 fee for students. Late onsite registration is $80 for regular and $40 for students. Participants are required to bring a laptop.

Update: Seats are limited to 30 participants and then registration sign-up will close. Onsite registration is only available based on open seats and then a waitlist. If interested please email us below.

Any questions may be addressed to the organizers at

Participant Reviews

Past Workshop (DSW at UKC2021 Los Angeles)
  • As a student in DS, it was a great opportunity to try the code and meet awesome professors and engineers. I love the fact that I was able to attend this in-person at Washington D.C. fly all the way from Washington State. It is definitely a valuable opportunity.” -Jiyeon Song (Student, UC San Diego), 8/2022
  • The workshop is very good for someone who is starting data science-related research. Thanks a lot for this to the organizers!” -Yong-Rak Kim (Professor, Texas A&M University), 12/2020.
  • What I liked about this workshop was that it appealed to those who either have a beginner or intermediate skill level with these topics. The important parts were explained thoroughly, and the instructors were clear in their direction. I have never worked with Convolutional Neural Networks, but after a brief 45-minute tutorial and walkthrough, it set the stage for learning more in the future. ” -Isaiah Kim (Technical Rotational Associate, USC) 12/2020.

Organizers & Instructors

Benjamin Lee (Chair) – Senior Research Associate at Weill Cornell Medicine: Benjamin Lee is a researcher developing machine learning algorithms for cardiovascular medical imaging in CT, Echo, ECG, and histopathology data for heart failure, heart transplantation, and coronary plaque characterization. Ben received his Ph.D. at the University of Michigan in Electrical Engineering specializing in image processing and image reconstruction and his B.S. from Cornell University. He is currently based in New York City.

June Park (Co-Chair) – Data Engineer at Daugherty Business Solutions: June Park is a Data Engineer at a solutions-based consulting company, Daugherty Business Solutions, building and maintaining data pipeline solutions for clients. She was previously a backend software engineer at Groundspeed Analytics, an insurtech startup. June received M.S. in Information at the University of Michigan and B.S. in Computational Media at Georgia Tech. She is currently based in Dallas, Texas.

Karl Kwon (Organizer) – Engineering Lead at MITRE: Karl Kwon has worked on various projects, including the development of data visualization, the implementation of machine learning models, and the design of software systems. He holds a Ph.D. in Computer Science from the University of Houston, where he invented and developed a powerful data visualization called ScholarPlot. He earned his MS in computer science and his BS in software engineering. Karl Kwon is currently located in the NYC area.

DK Kim (Organizer) – Project Lead & Senior Data Analytics Consultant at Zurich North America: DK Kim worked as a data analyst in the healthcare sector prior to completing a Data Science coding bootcamp. Since then, he has worked in marketing, energy, and finance, and is currently working for an insurance company. DK received his BS in Industrial Engineering with minors in Statistics and Mechanical Engineering from Texas Tech. He is currently pursuing his MS in CS part-time from Georgia Tech. DK is currently located in Chicago, IL.