Nicholas Ho

Welcome!

Thank you for checking my website out! My name is Nicholas Ho and I am a first year Computational Biology PhD student at Carnegie Mellon University. I am incredibly grateful to be advised by Professor Jian Ma and Professor Eric Xing. I am also very fortunate to be supported by the NSF Graduate Research Fellowship. My research interests are broadly in using machine learning to study epigenomics and gene regulation.

Previously, I worked with Professor Marinka Zitnik to develop multimodal models to learn rich representations for biological systems for novel drug development. I also worked with Professor Ross Maciejewski and Professor Abhishek Singharoy to merge statistical mechanics and machine learning to solve problems in biophysics and protein dynamics.

Feel free to reach out to me! I'm always looking for interesting problems to talk about!

Publications

Most recent publications on Google Scholar.
^‡ indicates equal contribution.

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Projects

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Learning Free Energy Pathways through Reinforcement Learning of Adaptive Steered Molecular Dynamics
A paper accepted to NeurIPS Workshop Machine Learning for Structural Biology 2022, First Author

Towards Conditional Generation of Minimal Action Potential Pathways for Molecular Dynamics
A paper accepted to ELLIS ML4Molecules Workshop 2021.

Particle System Dynamics for Compromised Social Networks
This is the code and additional results for my APM541 final project on Particle System Dynamics for Compromised Social Networks.

An Interpretable Method of Learning Stochastic Game Dynamics from CMSAC
Developed a new physics-inspired framework for analyzing soccer ball dynamics by modeling underlying potential landscapes.

CyanoPATH - a knowledgebase of genome-scale functional repertoire for toxic cyanobacterial blooms
CyanoPATH is a database that curates and analyzes the common genomic functional repertoire for cyanobacteria harmful algal blooms (CyanoHABs) in eutrophic waters.

Bayesian Information Criterion from Scratch
The Bayesian Information Criterion implemented from scratch in python to predict change points from noisy data.

Learning Free Energy Pathways through Reinforcement Learning of Adaptive Steered Molecular Dynamics
A paper accepted to NeurIPS Workshop Machine Learning for Structural Biology 2022, First Author

Towards Conditional Generation of Minimal Action Potential Pathways for Molecular Dynamics
A paper accepted to ELLIS ML4Molecules Workshop 2021.

Particle System Dynamics for Compromised Social Networks
This is the code and additional results for my APM541 final project on Particle System Dynamics for Compromised Social Networks.

An Interpretable Method of Learning Stochastic Game Dynamics from CMSAC
Developed a new physics-inspired framework for analyzing soccer ball dynamics by modeling underlying potential landscapes.

CyanoPATH - a knowledgebase of genome-scale functional repertoire for toxic cyanobacterial blooms
CyanoPATH is a database that curates and analyzes the common genomic functional repertoire for cyanobacteria harmful algal blooms (CyanoHABs) in eutrophic waters.

Bayesian Information Criterion from Scratch
The Bayesian Information Criterion implemented from scratch in python to predict change points from noisy data.

HonorCode Tutoring Website
A System that organizes service and provides tutoring for members. Link for the website is tutors.concordiashanghai.org.

Gibbs Sampling of a Gaussian Mixture Model from Scratch Using Python
Using the Gibbs Sampling Scheme and Metropolis-Within-Gibbs Sampling Scheme to learn parameters of Gaussian Mixture Models.

Concordia International IOT Environmental Sensors and Data Analysis
Soldered, built and programmed 30 microcontroller sensors that streamed data to an SQL database. Built a data analysis dashboard from scratch for school admins to view air quality dynamically.

Gaussian Process from Scratch with Python for simple interpolation
Just a quick and dirty implementation of GP from scratch using python

Viterbi Algorithm for Hidden Markov Models
Coding up the viterbi algorithm for solving hidden markov models from scratch using python.

Interactive Assistant Winter
Project Winter is a proof of concept where several APIs are connected through Flask and VueJS and activated via parsed intents from DialogFlow.

Vitæ

Please check out my full resume too! PDF.

• Carnegie Mellon University August 2023 - Now

  PhD Student
  Advised by Prof. Jian Ma and Prof. Eric Xing.
• Harvard Medical School June 2022 - May 2023

  Visiting Research Scholar
  • Accepted as a visiting research fellow at Harvard DBMI through a highly competitive application process.
  • Currently developing a novel machine learning architecture to deal with missing and imbalanced modalities.
• Struct. Sys. Bio at Biodesign Institute at ASU Feb 2020 - May 2023

  Research Associate
  Worked as a Research Associate in the Singharoy Lab. Lead research for my honors thesis to utilize deep ML methods with statistical mechanical methodologies.
  
  • Lead and wrote a project on discovering free energy pathways with reinforcement learning
  • Developed a molecular differentiable simulator for Jax based on TorchMD
  • Helped develop novel conditional generative models for deriving free energy pathways
  • Implemented and tested several physics-informed models.
  
• Carnegie Mellon University June 2021 - August 2021

  Visiting Research Scholar
  Accepted as a visiting research fellow in CMU Statistics through a highly competitive summer research program.
  • Created and a novel methodology for using stochastic simulators for soccer game predictions. Our method had comparable results to models trained directly on scores.
• Pichel Lab Biodesign Inst. May 2020 - May 2021

  Assistant Researcher under Ferran Garcia Pichel at Biodesign Institute
  Developed a Python Plugin for Qiime2 for the relationship between ribosomal gene copy number and size.
• Cao Lab Biodesign Inst. August 2019 – May 2021

  Assistant Researcher
  • Conducted data engineering and analysis on genomes assembled from a metagenome in order to study the strain level variance within microbiome communities.
  • Implemented a web system that highlighted which genes present in a pathway for particular cyanobacteria species using PFAM’s Hidden Markov Model package, JavaScript, SQL databases, shell and Python.
• Western Tool & Supply June 2020 - July 2020

  Software Engineer, Intern
  • Implemented and trained an LSTM Recurrent Neural Network to predict customer purchase likelihoods. • Integrating and using Bluetooth LE between microcontrollers and Google Chrome into their IOT system.
• TGEN May 2020 - August 2020

  Virtual Helios Scholar
  Due to covid, the Helios Scholars Program was moved to a virtual format.
• Arizona State University August 2019 - May 2023

  B.Sc. Honors Student
  Double Major in Mathematics (4.0) and Computer Science (4.0)

Hobbies