Jared Lee Katzman
My objective is to use artificial intelligence to create a safer, more equitable and enlightened society.
Led research for multiple projects, including statistical methods for polling and election forecasting, interactive data journalism to improve comprehension of politically-relevant data, and survey design for measuring impact of news media. Investigate a taxonomy for fairness and inclusiveness in image tagging and captioning AI services. Member of Fairness, Accountability, Transparency, and Ethics (FATE) reading group. Teaching Assistant for Microsoft Research Data Science Summer School, Summer 2021, an intensive, four-week hands-on introduction to data science for college students in the New York City area.
Out in Tech
Started Out in Tech U’s Digital Mentorship Program, where LGBTQ+ students work on projects with professional mentors over the course of 8 week semesters. Managed 4 volunteer coordinators to design and facilitate all digital programming for participants across the United States. Grew the program from an initial 30 participants to over 400 members annually. Participated in the Spring 2018 cohort as a mentor; Worked with a mentee, with no previous experience in data analysis, on how to use Python, Jupyter Notebooks, Pandas, NumPy, and Plotly to visualize the impact of America’s Opioid Crises.
Amazon Web Services
Founding engineer on team developing Machine Learning Bias and Explainability products and research across ML platform. Designed and implemented data explainability feature for SageMaker Autopilot within 1 month of joining team pre-launch. Researched technological and scientific vision and strategy for 5 new product proposals reviewed by senior leadership of AWS’s AI labs. Developed Entity Linking models on top of BERT which improved precision by 10% compared to baseline methods.
Researched, developed, and released IP Insights, a deep learning algorithm that learns the history of users' IP addresses to identify anomalous behavior such as fraudulent logins or account takeovers. Discovered and removed bottlenecks in algorithm's performance by tuning MXNet's distributed training, leading to a 50% reduction in training time for 75% of the comparable cost of scaling. Presented a talk on scalable, distributed machine learning on MXNet at Amazon's internal developer conference to 110 attendees. Designed and facilitated workshops on machine learning and security.
Built a real-time analytics platform with AWS services to transform security controls and operations from a perimeter, defensive model to a data-centric, automated-reasoning framework. Redesigned a batch processing architecture as a real-time, serverless, streaming framework which decreased a security control’s effect time from 24 hours to sub minutes and reduced monthly costs by a factor of 10.