Experience
Machine Learning Engineer Jan 2023 - Present
UNDO Carbon
Conducted a literature review and developed predictive models for flood detection and river-to-ocean chemistry changes. Utilized PyTorch and hydrology
ML packages to implement LSTM and GRU models to improve forecasting accuracy and efficiency. Additionally, led the development of a white paper on the
potential of ML for global implementation of enhanced rock weathering.
Particle Physics Research Analyst Sep 2020 – Sep 2022
Lippincott Lab - UCSB
Implemented deep neural network models using TensorFlow to classify Kr83m and Deuterium-Deuterium particle interactions with 98% accuracy.
Created an automated data quality monitoring tool for the skin detector that saved hundreds of hours of independent analysis from 300+
researcher members. This allowed for improved distinguishability of detector sensitivities and facilitated the dark matter search.
Projects:
Identifying and Outlining Breast Cancer Tumors
Developed a deep U-Net model for image segmentation to detect cancer in ultrasound images, resulting in a validation accuracy of ∼ 95%, Mean IoU of 0.735,
and F1 score of 0.793 on the test set. Visualized true masks and model masks to compare the predicted location of cancer with the ground truth annotation,
contributing to cancer recognition
AI Art Curator: Binary Classification
Built a binary classification model that can classify artworks in different art movements using image augmentation techniques and pre-trained ResNet50V2 model. Scraped the dataset using 'WikiArt Retriever' by Lucas David and uploaded to Kaggle for public use
Spotify Song Grouping with K-Means Clustering
Implemented data preprocessing techniques, including cleaning and normalizing values, on a dataset of Spotify songs to prepare
for clustering using K-Means algorithm. Evaluated the quality of the resulting clusters using silhouette score evaluation metrics
and visualized the clusters through 2D projections, coloring data points according to cluster assignment
Language Detection with Deep Learning
Built deep learning module for text-to-language detection, with 18 languages and 97.9% accuracy, using Language Detection and 4000+ Kurdish words dataset from Kaggle
Text From Image Recognition with Convolutional Recurrent Neural Network (CRNN)
Developed model to recognize text from images of common English words employeeing keras tuner for hyperparameter tuning. This model was trained on the combination of the Synthetic Word dataset on kaggle and a character level image dataset.
Boston Crime Mapping and Analysis
Created several mappings of crime geospatial data in Boston. Performed analysis of interesting features of data such as crime over time using time-series and most commited crimes.
Ongoing Projects:
Dynamic Object Tracking
Deep learning-based computer vision project for real-time object tracking in videos
Data-Driven Justice: Supreme Court Predictions
Developing predictive model using NLP techniques to determine the outcome of Supreme Court cases from a dataset of 3304 cases from 1955 to 2021
Cost of Living Insights: A Global Analysis of Urban Economies
Analysis of global urban economies using a dataset of over 4500 cities to provide valuable insights on cost of living trends,
utilizing data visualization and statistical techniques
Certifications:
Coursera Deep Learning Specialization
Relevant skills: Artifical Neural Networks, Hyperparameter Tuning, Recurrent Neural Networks (RNNs), Convultional Neural Networks (CNNs), LSTMs
DataCamp Associate Data Scientist
Relevant skills: Statistical Experimentation, Data Management in Python, Exploratory Analysis, Communication and Visualization
AWS Associate Solutions Architect Course
Relevant skills: Cloud Design and Deployment, EC2, S3, AWS Automation Tools, VPC, RDS, Disaster Recovery and Availability, Route 53
