Research & industry

Experience

Building machine-learning systems across neuroscience, healthcare, climate science, and software engineering.

June 2025 — Present

Machine Learning Research Student

Investigating brain resilience in Alzheimer’s disease and aging by integrating structural MRI, functional MRI, diffusion tensor imaging (DTI), positron emission tomography (PET), and multi-omic data.
Developing computational approaches to identify neuroimaging and multi-omic markers associated with preserved cognitive function despite age-related and neurodegenerative changes.
Conducting integrative analyses across imaging and molecular modalities to characterize the biological mechanisms underlying resilience.
Principal investigator: Dr. Jaeseung Jeong.

April 2024 — April 2025

Developed MRI classification models to predict response to neuromodulation treatment.
Worked on impulsiveness classification using intracranial EEG.
Created a closed-loop auditory stimulation system for children with epilepsy and designed cognitive testing games.
Principal investigator: Dr. George Ibrahim.

May 2023 — December 2023

Helped develop Python filtering techniques that digitally reproduce skin-tissue staining.
Built algorithms for tissue stain normalization and color deconvolution.
Principal investigator: Dr. Parsin Haji Reza.

May 2023 — August 2023

Led development of a chatbot interface using fine-tuned large language models including OpenAI, AWS Lex, and Cohere.
Created CNN-based image-processing tools for patient behavior classification and hygiene monitoring.

June 2021 — April 2023

September 2022 — December 2022

Automated monitoring of server errors and login failures for more than 10,000 clients, reducing weekly computation cost by 90%.
Built Python scripts, Jenkins pipelines, and Kubernetes orchestration.

May 2022 — August 2022

Led development of a LiDAR terrain-recognition system using PointNet and RangeNet++, achieving 80% accuracy.
Developed machine-learning and deep-learning approaches for freezing-of-gait prediction using patient and wearable-sensor data.
Built a multimodal sensor-fusion prototype for context-aware assessment of neurological conditions.
Developed encryption and privacy-preserving representation algorithms to protect sensitive patient data.

January 2022 — April 2022

Contributed to application development and developed computer-vision algorithms for human-behaviour detection and analysis.
Evaluated state-of-the-art few-shot meta-learning methods on imaging datasets, including class-incremental learning strategies designed to reduce catastrophic forgetting.
Built and optimized PyTorch models with CUDA for large-scale user-behaviour classification.

May 2021 — August 2021

Developed machine-learning classifiers in Python to detect weather conditions and predict precipitation type.
Built Xarray and pandas pipelines to preprocess and analyze large CSV and NetCDF meteorological datasets.
Communicated weather-analysis results through technical reports and presentations.