Alec Stashevsky
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About Me
Technical leader with a track record of shipping machine learning / AI solutions at scale. I love building things people use, and I’m passionate about interdisciplinary research and application. Currently, I am leading teams working at the crux of computer vision, natural language understanding, and graph machine learning.
When I am not thinking about graphs and transformers you can find me snapping shots on my Yashica T4 Super D, hiking, and making pottery.
Interests
- Graph Machine Learning
- Search and Recommendation
- Inequality and Development
- Occult Economies
- Place-making, Migration, Mobility
- Spatial Politics
- Anthropology
- Decarbonization
Education
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B.A., Mathematics-Economics
Reed College
Recent Posts
Publications
Experience
Tech Lead Manager, Core Machine Learning
Tech Lead Manager for core Document AI. We build ML/AI to power the Fetch app. Our systems extract information from +10 million receipts in real-time every day, and process over $150 billion in gross merchandise volume annually, and equivalent to the third largest retailer in the United States.
We build and deploy solutions touching computer vision, natural language processing, graph machine learning, semantic search, and entity resolution.
Lead partnerships with open-source and academic communities including Stanford University, Hugging Face, PyTorch, PyTorch Geometric, AWS SageMaker, and Streamlit.
We build and deploy solutions touching computer vision, natural language processing, graph machine learning, semantic search, and entity resolution.
Lead partnerships with open-source and academic communities including Stanford University, Hugging Face, PyTorch, PyTorch Geometric, AWS SageMaker, and Streamlit.
Senior Machine Learning Scientist
Document AI and Graph ML
Financial Economist
Look inside the books of some of the largest financial institutions in the world to estimate damages and predict the performance of complex financial instruments responsible for high-profile banking crises, securities fraud, and market-manipulation cases.
Build probabilistic financial models using Markov chain Monte Carlo methods, random matrix theory, and time-series forecasting.
Identify, explain, and value litigation involving mortgage-backed securities (RMBS), collateralized debt obligations (CDOs), swaps, and other derivatives underpinning trillions of dollars in assets.
Provide evidence and expert economic testimony for Fortune 500 companies, SEC, DOJ, and FINRA.
Build probabilistic financial models using Markov chain Monte Carlo methods, random matrix theory, and time-series forecasting.
Identify, explain, and value litigation involving mortgage-backed securities (RMBS), collateralized debt obligations (CDOs), swaps, and other derivatives underpinning trillions of dollars in assets.
Provide evidence and expert economic testimony for Fortune 500 companies, SEC, DOJ, and FINRA.
Economist
Design and evaluation of demand-side management programs, including a $600k+ randomized control trial on smart thermostat direct load-control.
Forecast electric vehicle diffusion, demand elasticity, and electrification for budgeting hundreds of millions of dollars under diverse energy industry clients’ management.
Design difference-in-difference models, demand-elasticity programs, and causal inference mechanisms to provide gold-standard reporting to regulators and operators responsible for most of the United States energy supply.
Forecast electric vehicle diffusion, demand elasticity, and electrification for budgeting hundreds of millions of dollars under diverse energy industry clients’ management.
Design difference-in-difference models, demand-elasticity programs, and causal inference mechanisms to provide gold-standard reporting to regulators and operators responsible for most of the United States energy supply.
Data Scientist
Designed marketing mix models, audience creation, and targeted advertising solutions for a bootstrapped e-commerce startup.
Deployed recommendation systems and probabilistic engagement models (pCTR, pConversion) for targeted ads and notifications systems that led to 8% reduction in CAC.
Deployed recommendation systems and probabilistic engagement models (pCTR, pConversion) for targeted ads and notifications systems that led to 8% reduction in CAC.
Certifications
Generative Adversarial Networks (GANs)
Machine Learning Engineering for Production (MLOps)
Machine Learning
Foundations for Big Data Analysis with SQL (with Honors)
Analyzing Big Data with SQL (with Honors)
Introduction to Spark with sparklyr in R
Support Vector Machines in R
Introduction to Scala
Tools I Use
Python
R
Snowflake (SQL)
PyTorch
Hugging Face
PyTorch Geometric
Amazon Web Services
Docker
Git
Apache Kafka
Redis
Apache Airflow
MLflow
AWS SageMaker
Stata
Contact
- Los Angeles, CA
- Book an appointment