プロフィール詳細

プロジェクトを作成

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Dr. Leonardo G.に依頼

United States

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

プロフィール概要

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.

専門分野

• ☆ 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

サービス

• 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

職務経験

AI Engineer

Atomic Machines

1月 2026 - 現在

Doctoral researcher/Research Assistant

University of Pennsylvania

5月 2021 - 5月 2026

Resident Assistant

University of Chicago Summer Session

1月 2026 - 1月 2026

AI intern

Atomic Machines

6月 2025 - 8月 2025

Resident Dean Assistant

University of Chicago

9月 2017 - 3月 2020

Student Director of International Pre-Orientation

University of Chicago - College Programming Office

3月 2019 - 9月 2019

Student Director

University of Chicago - College Programming Office

3月 2018 - 9月 2018

Teaching Assistant

Hyde Park Academy High School

11月 2017 - 3月 2018

学歴

Applied Mathematics & Computational Science

University of Pennsylvania

8月 2020 - 5月 2026

MS Computer Science

University of Chicago

8月 2019 - 5月 2020

Computational and Applied Mathematics

University of Chicago

8月 2016 - 5月 2020

認定資格

• Certificate in College and University Teaching

  University of Pennsylvania

  1月 2026 - 現在

出版物

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 .