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Hire Dr. Leonardo G.

United States

USD 40 /hr

AI Engineer | Applied Mathematics PhD | Computer Science M.S. | Machine Learning, Scientific Computing & AI Research

Profile Summary

AI Engineer and scientific machine learning researcher with a PhD in Applied Mathematics & Computational Science from the University of Pennsylvania, an M.S. in Computer Science from the University of Chicago, and more than five years of research experience spanning machine learning, scientific computing, and computational modeling. I currently work at Atomic Machines within the Artificial Intelligence, Modeling & Simulation team, developing machine learning methods for physical and engineering systems. My doctoral research in Penn’s Predictive Intelligence Lab under Prof. Paris Perdikaris focused on deep learning for scientific computing, surrogate modeling, uncertainty quantification, operator learning, physics-informed neural networks, Bayesian and black-box optimization, active learning, autonomous experimentation, and data-driven simulation of PDEs and complex physical systems. My technical work includes Python, JAX, and Flax, together with numerical computing and research workflows for developing, testing, and evaluating machine learning models. My research record includes an ICLR 2025 Spotlight paper on ActNet, peer-reviewed work in Scientific Reports on Neural Epistemic Operator Networks for Bayesian optimization, and 2026 research on multimodal scientific learning and information-theoretic active learning. I have also received an NSF SoftAE NRT Fellowship and a Samsung Global Research Outreach grant. My core strengths include machine learning engineering, Python, JAX, Flax, scientific computing, applied mathematics, deep learning, model evaluation, optimization, uncertainty quantification, operator learning, physics-informed machine learning, research methodology, and technical problem solving.

Subject Matter Expertise

• ☆ Data Analysis
• ☆ Data Visualization
• ☆ Machine Learning
• ☆ Neural Networks
• ☆ Artificial Intelligence
• ☆ Deep Learning
• ☆ Computer Vision
• ☆ Data Security
• ☆ Applied Mechanics
• ☆ Quantum Mechanics
• ☆ Computational Physics
• ☆ Applied Physics
• ☆ Applied Mathematics
• ☆ Mathematical Optimization
• ☆ Computational Sciences
• ☆ Computational Mathematics
• ☆ Python
• ☆ Matlab
• ☆ SQL
• ☆ Excel
• ☆ Java
• ☆ LaTex

Services

• Writing
• Research
• Data & AI
• Product Development

Writing Technical Writing, General Proofreading & Editing

Research Meta-Research, Technology Scouting, Scientific and Technical Research

Data & AI Statistical Analysis, Image Processing, Image Analysis, Algorithm Design-Non ML, Algorithm Design-ML, Data Visualization, Data Cleaning, Data Processing

Product Development Quality Assurance & Control (QA/QC), Reverse Engineering

Work Experience

AI Engineer

Atomic Machines

January 2026 - Present

Doctoral researcher/Research Assistant

University of Pennsylvania

May 2021 - May 2026

Resident Assistant

University of Chicago Summer Session

January 2026 - January 2026

AI intern

Atomic Machines

June 2025 - August 2025

Resident Dean Assistant

University of Chicago

September 2017 - March 2020

Student Director of International Pre-Orientation

University of Chicago - College Programming Office

March 2019 - September 2019

Student Director

University of Chicago - College Programming Office

March 2018 - September 2018

Teaching Assistant

Hyde Park Academy High School

November 2017 - March 2018

Education

Applied Mathematics & Computational Science

University of Pennsylvania

August 2020 - May 2026

MS Computer Science

University of Chicago

August 2019 - May 2020

Computational and Applied Mathematics

University of Chicago

August 2016 - May 2020

Certifications

• Certificate in College and University Teaching

  University of Pennsylvania

  January 2026 - Present

Publications

OTHER

Guilhoto, Leonardo Ferreira, Kaushal, Akshat, Perdikaris, Paris(2026). A Mutual Information Lower Bound for Multimodal Regression Active Learning .

Guilhoto, Leonardo Ferreira, Kaushal, Akshat, Perdikaris, Paris(2026). Multimodal Scientific Learning Beyond Diffusions and Flows .

JOURNAL ARTICLE

Campbell, Gregory M., Muhaxheri, Gentian, Guilhoto, Leonardo Ferreira, Santangelo, Christian D., Perdikaris, Paris, Pikul, James H., Yim, Mark(2025). Active Learning Design: Modeling Force Output for Axisymmetric Soft Pneumatic Actuators . IEEE Robotics and Automation Letters. 10. (10). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 10838--10845.

Guilhoto, Leonardo Ferreira, Perdikaris, Paris(2024). Composite Bayesian Optimization in function spaces using NEON—Neural Epistemic Operator Networks . Scientific Reports. 14. (1). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 29199.

Kissas, Georgios, Seidman, Jacob H., Guilhoto, Leonardo Ferreira, Preciado, Victor M., Pappas, George J., Perdikaris, Paris(2022). Learning Operators with Coupled Attention . Journal of Machine Learning Research. 23. (215). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 1--63.

CONFERENCE PAPER

Guilhoto, Leonardo Ferreira, Perdikaris, Paris(2025). Deep Learning Alternatives of the Kolmogorov Superposition Theorem . International Conference on Learning Representations (ICLR 2025).

Sankaran, Shyam, Wang, Hanwen, Guilhoto, Leonardo Ferreira, Perdikaris, Paris(2022). On the Impact of Larger Batch Size in the Training of Physics Informed Neural Networks . The Symbiosis of Deep Learning and Differential Equations II. NeurIPS

REPORT

Guilhoto, Leonardo Ferreira(2019). Numerically Solving the Dirichlet Problem Using Stochastic Calculus .

Guilhoto, Leonardo Ferreira(2018). An Overview of Artificial Neural Networks for Mathematicians .

Guilhoto, Leonardo Ferreira(2017). Applying Markov Chains to Monte Carlo Integration .