Emi Jhang

Leadership & Change Analyst at Oliver Wyman
M.S. in Computer Science at University of Texas at Austin emijhang@gmail.com Resume

About Me

Hi! I'm Emi Jhang! Currently I work at Oliver Wyman as an analyst for their Leadership and Change group, and am completing my Master's in Computer Science with The University of Texas at Austin. I graduated in spring of 2025 from Northeastern University in Boston, MA with a concentration was in Artificial Intelligence. I am also well-versed in full-stack software development.

Work Experience

Oliver Wyman
Analyst, Leadership & Change

Griffiss Institute (AFRL)
AI/ML Engineer Co-op
The overall goal of my co-op project was to develop a way to automate the BDA process to aid analysts who do classification manually. However, with the limited data available for training and testing, we cannot develop a reliable and accurate model. The idea is to use generative image models to supplement the data with synthetic images in which we test for the best performing model.
Such models included Pix2Pix, StyleGAN3, and Stable Diffusion. We evaluated images using metrics such as CNNs, FID, and Siamese Networks to determine similarity to real images and whether addition of images can outperform the model based in only real images. After confirming that supplemental generated images have the capability to improve classification performance by over 10%, identified potential avenues of future research and improvement.
Slides available here

Sanrel LLC
Software Engineering Intern
Sanrel LLC is a materials science start-up company. Alongside a team of interns, I collaborated to design, develop, and test software for internal and client use. We set up a server and software to host an inventorying system for lab materials and shipments. Additionally we reviewed, documented, and updated code for clarity and ease of use for future engineers regarding projects to be taken up by Sanrel.

White Plains Hospital Center for Cancer Care
Lung Cancer Research Volunteer
At White Plains Hospital Center For Cancer Care, I helped a team of researchers by performing outreach and scheduling for research patients. I screened applicants for study eligibility and communicated study results thoughtfully. I organized materials for researchers and prepared documents, materials, and charts they would need to complete patient labs and scans.

Projects

NextSongRec
Language/Tools: Python, PyTorch, Tensorboard, pandas
Recommended songs to add to a playlist/queue given a user's listening history. Using the Spotify Million Playlist Dataset and the 8M+ Song Audio Feature Dataset, modeled playlists as user listening sessions to recommend songs based on both ID and content features. With SASRec architecture as a baseline, achieved a hit rate of .83 and NDCG of .66. Modified TSSR architecture for our dataset and needs to evaluate on sequences of IDs and features rather than just ID and reached a conclusion of needing more comprehensive data to outperform the baseline.

Slides available here
The Nexus
Language/Tools: Typescript, React, MongoDB, HTML, CSS, Jest, Javascript
Extended an existing codebase with a team to propose, develop and implement features encouraging community. Implemented a following and toast notification system for the existing “Stack Overflow” -esque application, as well as community spaces with real-time update polls using sockets and moderation capabilites. Additionally implemented private games and leaderboard that update in real-time as well.

Live deployment available here

Github Repo available here
SatelliteMaskNet
Language/Tools: Python, PyTorch, Tensorboard, pandas
Developed a singular generalized NN model that performs pixel-by-pixel classification to segment buildings from a satellite image for analysts to work with to help determine factors such as building density and efficient land usage. The model was built to generalize to cities with various building densities and architectures and extract non-city specific features. On an unseen city achieves an accuracy of over 80%.

Slides available here
NUConnect
Language/Tools: Python, MySQL, Flask, Docker
Prototyped a web app of a community forum for Northeastern students searching for co-op to connect with each other to reduce isolation during the searching process. Allows students and employers alike to post to a forum, apply to jobs, and see the status for applications submitted. Constructed a relational database maintaining 22 tables with 100+ attributes, with over 1k instances of sample data.

Github Repo available here
ASL Image Classification
Language/Tools: Python, PyTorch, scikit-learn, pandas
Developed an image classifier for the ASL alphabet and numbers using CNN and multinomial logistic regression. Conducted extensive hyperparameter fine-tuning and augmentation to improve model robustness up to 97% accuracy with Pytorch-defined CNN.

Report available here
Mushroom Classification
Language/Tools: Python, PyTorch, scikit-learn, pandas
Developed a machine learning program to classify mushrooms as poisonous or edible given a set of features. Trained and tested neural network, logistic regression, and k-NN classification models up to 99% accuracy. Accurately predicts edibility given either all 20 features, or only a few easily recognizable features.

Report available here