Alexandre Kaiser

Lead Machine Learning Engineer & Head of Insights at GatherGov • MSCS at NYU Courant • BSAM at Northwestern

GatherGov Machine Learning Applied Mathematics Engineer NYU Northwestern French Canadian
Alexandre Kaiser

About Me

Hello! I'm Alexandre Kaiser, Lead Machine Learning Engineer & Head of Insights at GatherGov. I design and build AI systems that make local government data across the United States more transparent and actionable.

In 2024, I earned my Master's in Computer Science at NYU Courant, where I conducted research on deep learning theory under Arthur Jacot. Prior to that, I graudated with a Bachelor's in Applied Mathematics from Northwestern University—driven by a curiosity in how mathematical principles impact each domain.

Raised in a business-minded family, but drawn to philosophical questions, I thrive at the intersection of theory and real-world impact.

My work focuses on building scalable ML systems that bridge theoretical understanding with real-world impact. At GatherGov, I lead the full lifecycle of ML-powered insights, from strategic planning to hands-on development, delivering actionable intelligence through state-of-the-art models and agentic workflows.

My outward goal is to forge a dynamic balance between innovation and wastefulness: to relentlessly generate new solutions for problems others find worth solving, marshaling every resource to its fullest—wringing water from stones in the pursuit of value creation.

My inward goal is to cultivate the capacity to find happiness in everything; believing that beauty lies in the eye of the beholder and that any taste can be acquired, I see every moment of aversion as a missed opportunity to discover something truly beautiful.

French and Canadian Friendship Flag
Northwestern and NYU Universities
Analytical and Creative Mind

Professional Experience

Dec 2024 - Present

Lead Machine Learning Engineer & Head of Insights

GatherGov

  • Lead the full lifecycle of ML-powered insights, from strategic planning to hands-on development
  • Architect and maintain AI tools processing 30,000+ hours of transcripts and 20,000+ PDFs monthly
  • Define ML strategy through prototyping, feature engineering, model benchmarking, and scalable pipeline development
  • Design custom analytics pipelines to uncover high-value trends and market opportunities
  • Extract and publish marketable insights on social media, driving brand engagement
Jun 2023 - Jun 2024

Researcher

NYU Courant - Arthur Jacot Lab

  • Second authored "Hamiltonian Mechanics of Feature Learning: Bottleneck Structure in Leaky ResNets"
  • Proved low dimensional bias of regularized neural networks
  • Paper accepted for oral presentation at CPAL 2025
Jun - Aug 2023

Data Science Consultant

Neuron7

  • Developed library for benchmarking hybrid RAG solutions, improving search accuracy by up to 20%
  • Enhanced multilingual text retrieval performance by up to 30%
  • Led quality assurance for client search results and proprietary record labeling
Jun - Aug 2022

Assistant Modeling Engineer

Prophesee

  • Designed interactive model replicating experimental noise profile of proprietary vision system
  • Enabled hardware team optimization of 7 component parameters
  • Analyzed signal noise impact across 100+ conditions for product research direction

Research & Publications

Deep Learning Theory Feature Learning Online Learning Continual Learning Optimization

My research focuses on the theoretical foundations of deep learning, particularly in understanding how optimization affects feature learning and neural network dynamics. I investigate the implicit biases that emerge during training and their implications for model behavior and generalization.