Jackie Giang Vo

I am an arising graduate from Carnegie Mellon University with a Master's degree in Computational Biology. I have background molecular biology and biochemistry with 3 years work experience. I am enthusiastic about Machine Learning/AI in biomedical fields, especially in drug discovery and medical diagnosis and treatment.

Beyond my academic pursuits, I am an avid athlete who find immense joy and balance in leading an active lifestyle, continuously striving to challenge and improve myself physically. I am currently challenging myself in upcoming full marathon and triathlalon.

Education

Carnegie Mellon University, Pittsburgh PA

Master in Computation Biology

2022-2024

St. John’s University, Jamaica, NY

BS in Biology, Minor Chemistry

2016-2019

Experience

Computational Biologist Intern

Predictive Oncology Inc.

Pittsburgh, PA

May 2023 - Aug 2023

During my 12-week internship, I delved extensively into bioinformatics, software development, and machine learning, collaborating on projects that honed my skills in teamwork, communication, and efficient work management. This comprehensive internship experience, coupled with invaluable mentorship, propelled me beyond my comfort zone both personally and professionally, equipping me with an array of skills beyond my expectations.

  • Led Variant Calling project using the Illumina DRAGEN pipeline and conducted analysis for tumor-related patients, contributing to the enhancement of PEDAL—an ML, AI-based drug response prediction platform.

  • Contributed to the development of the Drug Combination Pipeline, leveraging experimental data encompassing cell lines, drug combinations, doses, and empirical responses to predict cell line responses to specific drug combinations.

  • Supported the analysis of tumor stained histopathology images utilizing AWS.

  • Utilized Confluence for effective task logging and team communication, streamlining collaborative efforts.

Research Assistant

University of Pittsburgh

Pittsburgh, PA

Jan 2023 - May 2023

Dr. David Koes’ Lab focuses on developing computational machine learning method on drug discovery, involving in protein structures and docking mechanism.

  • Developed Next Generation CrossDock pipeline between proteins and ligands using PySpark and bioinformatics packages such as DeeplyTough.

  • Enhanced and debugged bioinformatics packages, ensuring optimal performance and reliability in research workflows.

Research Assistant

Memorial Sloan Kettering Cancer Center

New York, NY

Mar 2020 - July 2022

The Center of Molecular Oncology (CMO) Innovation Laboratory and Michael Berger’s Laboratory focus on novel computational and experimental techniques to characterize the spectrum of genetic mutations in human tumors in order to identify biomarkers of cancer progression and drug response. Additionally, the Innovation Laboratory develops assays and platforms for the molecular profiling of tumors and associated computational pipelines and evaluates emerging technologies for potential clinical use.

  • Devised and fine-tuned an innovative, automation-compatible dual DNA and RNA extraction method from FFPE samples, streamlining processes effectively.

  • Formulated comprehensive Standard Operating Procedures (SOPs) and collaborated closely with the clinical research department, facilitating the transition from manual methodologies to automated platforms.

  • Engaged in collaborative efforts with a team of three and computational biologists to meticulously analyze DNA and RNA sequencing data, ensuring thorough and accurate insights.

  • Conducted extensive research and experiments aimed at refining miRNA extraction methodologies from serum and plasma, contributing to ongoing advancements in the field.

SURF Fellow

SUNY Upstate Medical University

Syracuse, NY

June 2019 - Aug 2019

I worked at Dr. Bruce Knutson Laboratory for my summer fellowship. Knutson Lab focuses on molecular and biochemistry of RNA polymerase I transcription.

  • Employed computational tools like Patch-dock, PyMOL, and MS-XL data to create an in silico model of the RNA Polymerase I PIC (Pre-Initiation Complex).

  • Successfully expressed Core Factors and TATA Binding Protein (TBP) utilizing different epitope tags. The proteins were purified via a meticulous series of column purification methods, including affinity chromatography, size exclusion chromatography, and AKTA purification.

  • Refined and executed in vitro Fe-BABE protein hydroxyl radical cleavage assays to pinpoint the precise location of TBP within the Pre-Initiation Complex of RNA Polymerase I.

Technical Skills

Python, R, Golang, Bash

Programing Languages

MySQL

Database Management

AWS, Spark

Cloud-based Technologies

Confluence, Notion

Task Management

PyTorch

Machine Learning Tools

Relevant Experience

02604 - Bioinformatics Algorithms, Spring 24, Carnegie Mellon University

Teaching Assistant

Dr. David Koe’s Lab, University of Pittsburgh

Research Assistant

Projects

To impute or not to impute

Carnegie Mellon University

02750 - Automation Science Research

(This is an ongoing collaborative project)

This project intends to implement the neural network architecture Multiple Input Multiple Output (MIMO) which will be trained on protein mutation and corresponding expression signal data in order to predict the expression signal.

Finding MIMO

Carnegie Mellon University

09616 - Neural Networks and Deep Learning in Science

(This is an ongoing collaborative project)

The project focuses on tackling and comparing of several method of data imputation methods on active learning and their disadvantages in handling missing data in active learning.

Leadership

The Graduate Student Assembly (GSA) is the branch of student government that represents all graduate students at Carnegie Mellon University, whose mission is to advocate for and support the diverse needs of all CMU graduate students in their personal, professional, and public lives.

As a GSA Rep, I was responsible for organizing monthly social events, strengthen the community between all cohorts of Master students in three majors including Computational Biology (CB), Quantitative Biology and Bioinformatics (QBB), and Biotechnology and Pharmaceutical Engineering (BTPE).

MSCB GSA Representatives

I served as the captain for two intramural sport teams (volleyball and soccer) for the department in Fall 2023 and Spring 2024, playing the CMU intramural tournament.

Departmental Intramural Team Leader