📄 My Resume as a PDF
Contact Info
Experience
- Building and deploying machine learning models to detect card testing fraud, helping protect merchants from fraudulent transactions.
- Developing pattern recognition systems to identify suspicious transaction patterns that indicate fraud or card testing attempts.
- Led the development and deployment of an ML model to evaluate posts and comments for potential community guideline violations. This model served as a critical metric for monitoring violative content on the platform.
- Proactively addressed data quality challenges; proposed and executed plan to use GPT to enrich the training dataset, significantly improving model performance (+0.12 PR-AUC).
- Led team of 3 developers to improve Abuse Detection model by incorporating media features, resulting in a 10% increase in model precision and my promotion to mid-level engineer.
- Conducted A/B online experiments, resulting in a 15% increase in remediation volume and addition of 2 tweet languages to our scope.
- Migrated tweet report ranking model to Google Cloud's Dataflow, leading to a 63% increase in report action rate and a decrease in model training time by approximately 90%.
- Independently created an internal web tool to run our abuse detection model with sample inputs.
- Simplified training data generation with BigQuery and refactored TensorFlow model training code to reduce engineering time required for training new models.
- Created novel deep learning architecture in PyTorch to reconstruct undersampled brain MRI acquisitions, achieving baseline performance with 50% fewer parameters.
- Adapting the U-net for Multi-coil MRI Reconstruction published as an extended abstract at the International Society for Magnetic Resonance in Medicine (ISMRM) 2021.
- Presented at Neuromatch 3.0, a neuroscience conference.
- Led development of instructional materials for 1000+ students per semester.
- Hosted exam review sessions, taught discussion sections, delivered lectures, and assisted students in office hours.
Skills
- Languages: Python, SQL, Scala, C++
- Technologies: Deep Learning Frameworks (TensorFlow/Keras, PyTorch), Stream and Batch Processing Systems (Spark, Beam), Query Engines (BigQuery, Databricks), ML Orchestration (Flyte, Kubeflow, Airflow)
Internships
- Designed and implemented AWS architecture so marketing campaigns could send push notifications into the web application. Used new browser technologies (Service Workers, Push API).
Education
- M.S. Computer Science, University of Michigan, 2021
- B.S.E. Computer Science, University of Michigan (Summa Cum Laude), 2020