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BIOINFORMATICIAN

AI/ML Research Scientist, specializing in applying computational methods to analyze, model, and predict in the domains of medical research and protein structures. Experienced in developing and deploying bioinformatics pipelines for large-scale datasets including next-generation sequencing data. Proficient in Python and Linux/Unix, with expertise in experimental design, data interpretation, and machine learning applications.

TECHNICAL SKILLSProgramming

Python, Bash, Go, Java

Packages

Hail, Pymol, Pandas, Numpy, Pytorch
Matplotlib, Scikit-learn

Tools

Git, GCP, Linux (Ubuntu), Conda, Amber
Molecular Dynamics package

EDUCATION

Master of Science in Computational Biology

Carnegie Mellon University
2021 - 2023

Bachelor of Arts in Biology

Grinnell College
2012 - 2018

RESEARCH

Substance Abuse Prediction in Depression

Patients using DeepLearning
Mar 2024 - Ongoing

Project Advisor: Dr.LiRong Wang
University of Pittsburgh Pittsburgh, PA

Designed and implemented Python pipeline for processing Electronic Health Record (EHR) data, adapting BERT transformer to use EHR codes for single disease prediction task on NIH All of Us platform.

Predicted risk of alcohol and substance abuse in at-risk patients with ROAUC of 0.95 and accuracy of 92%, surpassing the baseline logistic regression model by 0.13 in ROAUC and 5% in accuracy.

Hidden Markov Model Guided Predictive
Enzyme Mutagenesis
Oct 2023 - Jan 2024

Project Advisor: Dr.Peng Liu
University of Pittsburgh Pittsburgh, PA

Utilized Amber package to run protein-ligand Molecular Dynamics (MD) simulation and extract time series atomic distance data from MD trajectory data.

Implemented Python pipeline identifying potential key positions at active site for mutations using Hidden- Markov Model (HMM) to improve enzyme-substrate binding specificity based on atomic-distance data.

Preterm Infant Growth Trajectory Prediction
using Microbiome
May 2022 - Aug 2022

Project Advisor:
Ziv Bar-Joseph, Jose Lugo-Martinez
CMU Pittsburgh, PA

Developed a HMM classifier in Python from scratch to predict growth trajectories (normal vs. faltering) in preterm infants using gut microbiome data, demonstrating proficiency in Python and machine learning.

Despite achieving a moderate AUC-ROC of 0.67, provided insights into limitations of HMM in sparse microbiome datasets, guiding research towards more suitable model, Input-Output HMM, to improve prediction.

COURSEWORK PROJECTS

Small Molecule Anti-Bacterial Potency
Screening using Active Learning
Feb 2023 - May 2023

Implemented Expected Model Change (EMC) active learning method using logistic regression as base model in Python script, which can be applied to general binary classification tasks with tabular datasets.

Using EMC method, achieved accuracy of 71% in predicting antibacterial effect of small molecules with 40% fewer samples than random sampling to achieve same accuracy, demonstrating efficiency of in reducing sample size during model training while effectively identifying informative samples.

Motif Search in Glioblastoma

Protein-Protein Interaction Network

Feb 2023 - May 2023

Developed a Python pipeline for identifying motifs in glioblastoma protein-protein interaction networks, employing NetworkX package for network generation and sub-graph isomorphism motif searching algorithm.

Designed a comparative analysis of PPI motifs between random and glioblastoma networks, revealing significantly different motif distributions and predicting motif functions through gene/protein ontology analysis.

DANIEL LEE

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