Alec Stashevsky

Machine Learning Leader // Applied Scientist // Open Source Developer

About Me

I am an applied scientist and leader building large scale machine learning applications. I love building things people use. I am passionate about interdisciplinary research and currently 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.

  • Graph Machine Learning
  • Search and Recommendation
  • Inequality and Development
  • Occult Economies
  • Place-making, Migration, Mobility
  • Spatial Politics
  • Anthropology
  • Decarbonization
  • B.A., Mathematics-Economics

    Reed College


Domestic Remittance in China: Rural—Urban Migration’s Trail of Inequality
Domestic Remittance in China: Rural—Urban Migration’s Trail of Inequality

This work examines the flows of rural-to-urban migration in China with a focus on the trail of remitted cash and its role in rural village income inequality. It is the first to decompose rural household income inequality by income factor component using all 5 waves of rural household data available from CHIP (and RUMiC) surveys. Repeated cross section data is used to examine trends of the rural income profile and income inequality. Rural household income inequality is decomposed by factor component through the Gini index, the constant of variation, and the half-squared coefficient of variation. This thesis focuses the lowest and most vulnerable strata of the rural household income distribution with attention to different income sources’ capacity to alleviate poverty, situated within the broader context of Chinese liberalization. Migrant worker remittance flows are framed as spatial links whose proliferation is a co-production of an increasingly liberalized and competitive setting both in rural villages and urban centers of China. Remittance share regressions, analogous to Engel Curve regressions, are run to examine the differential impact of remittances across the rural income gradient. The statistical dispersion of remitted income is used as a proxy to illuminate the links between migration and a shifting gradient of rural mobility. Remittance income is found to have significant mitigatory effects on rural income inequality. Households in the 10th-50th percentile of the income distribution are found to have a significant dependence on remittances.


Lead Scientist, Core Machine Learning
Oct 2022 – Present Los Angeles
Lead a team of 15+ scientists and engineers building world-class ML/AI technology powering the core of 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.

Core contributor and technical leader for Fetch’s largest product launch to date with over 200M in annual revenue attributable to our core document AI technology.

Research, build, and productionalize deep neural networks touching computer vision, natural language processing, and graph machine learning.

Pre-training and fine-tuning of large language models (LLMs), vision encoder-decoders, and graph neural networks.

Build out data labeling and collection efforts from the ground-up to support computer vision and large language modelling efforts.

Lead partnerships with open-source and academic communities including Stanford University, Hugging Face, PyTorch, PyTorch Geometric, AWS SageMaker, and Streamlit.
Data Scientist
Oct 2021 – Oct 2022 Los Angeles
Document AI and Graph ML
NERA Economic Consulting
Associate Analyst
Sep 2020 – Sep 2021 New York City
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 leading to the largest and most severe banking crises, securities fraud, and market-meltdowns humans have witnessed.

Build probabilistic financial models using advanced techniques including Markov chain Monte Carlo methods, random matrix theory applications, and stationary 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 economic investigation for Fortune 500 companies, SEC, DOJ, and FINRA.
The Cadmus Group
Research Analyst
Nov 2019 – Sep 2020 Portland, OR
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.
Data Scientist
Oct 2019 – Jul 2020
Lead a project investigating optimal advertising strategies for an online retailer by supplementing web analytics and operations resources to perform novel Geo-spatial analysis, market research, and ultimately lead generation.
KWM Wealth Advisory
Data Scientist Intern
Jul 2019 – Oct 2020 Pasadena, CA
Build internal dashboards for a boutique wealth advisory firm using R and Shiny to help advisors aggregate and contextualize regulatory stances during bear-markets.


Generative Adversarial Networks (GANs)
See certificate
Machine Learning Engineering for Production (MLOps)
See certificate
Machine Learning
See certificate
Foundations for Big Data Analysis with SQL (with Honors)
See certificate
Analyzing Big Data with SQL (with Honors)
See certificate
Introduction to Spark with sparklyr in R
See certificate
Support Vector Machines in R
See certificate
Introduction to Scala
See certificate

Tools I Use

Snowflake (SQL)
Hugging Face
PyTorch Geometric
Amazon Web Services
Apache Kafka
Apache Airflow
AWS SageMaker