nimuh / nimuh.github.io

Experience

• Absci AI Lab, AI Scientist Intern (June 2022 - August 2022)
  • Worked on building deep learning models for drug discovery research.

Education

• Oregon State University, Ph.D. Artificial Intelligence (Present - 2026)

  • Research focused on deep learning and computational biology. Advised by Dr. Maude David.

• University of Hawaii at Manoa, M.Sc. Computer Science (2021)

  • Interpretable deep learning for genomic datasets. Advised by Dr. Mahdi Belcaid.

• University of California, San Diego, B.Sc. Cognitive Science (2018)

  • Minor in Computer Science

Current Interests

I am passionate about artificial intelligence and using it to solve hard problems. My interests are in deep learning and computational biology. I am currently a member of Dr. Maude David's Lab at Oregon State University where I am working on protein language models as well as graph neural networks for knowledge transfer from biological databases. Outside of working in this incredible field I enjoy surfing, playing chess, rock climbing, and hiking.

Selected Projects

Genome Deep Learning

This research focuses on predicting cancer risk across multiple cancer types only from the genotype of an individual. We were fortunate to get access to data from The Cancer Genome Atlas to perform this research. Since this was a group effort, my role was to try different neural architectures to find the best performing model. This required some creativity: a standard feed-forward network was not cutting it! We eventually found our best performing model to be an encoder-based architecture that attempts to learn a latent representation of the genomic data and a classifier at the same time. Ultimately, the performance of the model from the paper this project was inspired by was not replicable in our case. We performed further analysis on the data to find much overlap between different cancer types and concluded that the classifier was having difficulty separating these highly entangled data points. My capstone project expanded upon this by attempting to look at how different loci in the genomic data contributed to the classification and if we can observe which cancers are similar based on the representation learned from the genomic data.

Deep Learning Interpretability for Genomics

This research was my focus for my MSc. It expands upon the project described above by exploring the hypothesis that we can extract class similarity from the learned neural network. To be thorough, I worked with simulated data and real genomic data. This was interesting work because it involved analysis of the learned parameters of neural networks and studying if we can gain useful information about the problem being studied.

Image Segmentation for Monocular Visual Odometry

This survey was my my first step into computer vision. Some backstory to this project: I worked on a code challenge for a company that involved predicting the speed of a vehicle purely from the stream of frames captured by a dashboard camera. I completely failed the challenge (my solution did not generalize as well as I thought!) so this brought me to think about scene understanding, or image segmentation, for road scenes and how segmentation could help with estimating vehicle speed. This project involved surveying multiple classical methods for scene segmentation and a full implementation of SegNet. The survey of methods provided a fresh perspective on the power of neural networks and how to perform scene segmentation with deep learning. The implementation itself was done in PyTorch with use of Weights and Biases to monitor training performance. I also used a GTX1660 Super to speed up the training of the neural network.

Structural Balance in R

This project involved optimizing R code with C++ for speeding up large scale graph computations. We specifically created a library that can be used to compute the structural balance of signed graphs. To be clear, a graph is considered "balanced" if we can separated the nodes into groups that do not like each other. The relationship between nodes is represented by a + or -, where - means the connected nodes are enemies. This is computationally intensive. We created an algorithm from scratch and then implemented it so others can use it. My responsibility in this project was to write the C++ code for the graph computations required by our algorithm. The technical details are all provided in the title link.