Isaac schaider

Stanford University Class of 2022, Math and Computer Science

prof photo - Isaac Schaider.jpeg

Isaac Schaider is a student in the class of 2022 at Stanford University who is studying Math and Computer Science. He’s very interested in utilizing techniques from Computer Science and Statistics to solve economic and public health issues. This summer (2020), he’s been working as a software engineering intern at Facebook. Last summer, he worked as a summer analyst at the hedge fund Canyon Partners. He’s worked on a diverse set of projects, ranging from building machine learning models for a cube satellite that predicts wildfires, to developing a novel treatment for the autoimmune disease Antiphospholipid Syndrome through a computational model that identifies optimized receptor protein mutations. In his free time, he performs in a singing group on campus, and he loves to mountain bike!

Accomplishments on the ASPT:

  • Conducted due diligence on biotechnology start-ups through business model analysis, DCF valuation review, market research, analysis of current industry dynamics and company operations

  • Collaborated with company executives to build a strategy for bringing a medical device startup to market in China

Accomplishments since the ASPT:

  • Employment:

    • Research assistant at the Brookings Institution (Fall 2020)

    • Software engineering intern at Facebook (Summer 2020)

    • Hedge fund summer analyst at Canyon Parters (Summer 2019)

  • Projects:

    • Stanford Student Space Initiative

      • Built software in Python for a cube satellite (backed by IBM) that captures high-quality satellite Earth imagery for forest fire detection and prediction by utilizing deep learning for computer vision

      • Developed a convolutional neural network for onboard image selection that saves critical power by only downlinking important image features; a novel approach for cube satellite image processing that avoids downlinking homogeneous content

    • Deep Fake Detection

      • Developed an Inception-v3 convolutional neural network that identified Deep Fake versus real videos with a 98% accuracy on the Kaggle Deep Fake Detection Challenge dataset and drafted a complete research paper detailing the results

    • Data Analysis of Development in Botswana

      • Built regression models in R characterizing the evolution of human development and social order in Botswana compared to other Sub-Saharan African countries using data on infant mortality, polity scores, tertiary education, and coup probability

      • Calculated Botswana’s ethno-linguistic fractionalization (ELF) score to analyze the degree of heterogeneity in Botswana and to illustrate the relationship between ELF and the percent change in per-capita GDP

      • Developed regression models demonstrating the relationship between the rate of tertiary education and life expectancy in SSA

Skills:

  • Accounting

  • Attention to Detail

  • Communication

  • C++, SQL Server, Java, CSS

  • Data Collection &Analysis

  • Nonprofit Fundraising

  • Project Management

  • Science Writing for Investor Audience

Sectors of Interest:

  • Biotech Startups

  • Brain-computer interface companies

  • Entrepreneurship

  • Finance

  • Healthcare and Public Health

  • Policy

  • Venture Capital

Current Location

San Francisco, CA