On the nonnegative least squares. - Cláudio P. Santiago (2009)

Physics and optimal routing for urban radiation source search. - Richard Wheeler; Daniel M. Faissol; Claudio Santiago; Thomas Baginski; Karl Nelson (MFI, 2016)

Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis. - Brenden K. Petersen; Jiachen Yang; Will S. Grathwohl; Chase Cockrell; Claudio Santiago; Gary An; Daniel M. Faissol (CoRR, 2018)

A new algorithm for the small-field astrometric point-pattern matching problem. - Cláudio P. Santiago; Carlile Lavor; Sérgio Assunção Monteiro; Alberto Krone-Martins (J. Glob. Optim., 2018)

An efficient algorithm for the projection of a point on the intersection of two hyperplanes and a box in ( mathbb {R}^n ). - Cláudio P. Santiago; Sérgio Assunção Monteiro; Helder Inácio; Nelson Maculan; Maria Helena Jardim (EURO J. Comput. Optim., 2019)

Deep Reinforcement Learning and Simulation as a Path Toward Precision Medicine. - Brenden K. Petersen; Jiachen Yang; Will S. Grathwohl; Chase Cockrell; Claudio Santiago; Gary An; Daniel M. Faissol (J. Comput. Biol., 2019)

Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients. - Brenden K. Petersen; Mikel Landajuela; T. Nathan Mundhenk; Cláudio Prata Santiago; Sookyung Kim; Joanne Taery Kim (ICLR, 2021)

Discovering symbolic policies with deep reinforcement learning. - Mikel Landajuela; Brenden K. Petersen; Sookyung Kim; Cláudio P. Santiago; Ruben Glatt; T. Nathan Mundhenk; Jacob F. Pettit; Daniel M. Faissol (ICML, 2021)

Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding. - T. Nathan Mundhenk; Mikel Landajuela; Ruben Glatt; Cláudio P. Santiago; Daniel M. Faissol; Brenden K. Petersen (NeurIPS, 2021)

Improving exploration in policy gradient search: Application to symbolic optimization. - Mikel Landajuela; Brenden K. Petersen; Soo K. Kim; Cláudio P. Santiago; Ruben Glatt; T. Nathan Mundhenk; Jacob F. Pettit; Daniel M. Faissol (CoRR, 2021)

Incorporating domain knowledge into neural-guided search. - Brenden K. Petersen; Cláudio P. Santiago; Mikel Landajuela (CoRR, 2021)

Symbolic Regression via Neural-Guided Genetic Programming Population Seeding. - T. Nathan Mundhenk; Mikel Landajuela; Ruben Glatt; Cláudio P. Santiago; Daniel M. Faissol; Brenden K. Petersen (CoRR, 2021)

A Unified Framework for Deep Symbolic Regression. - Mikel Landajuela; Chak Shing Lee; Jiachen Yang; Ruben Glatt; Cláudio P. Santiago; Ignacio Aravena; Terrell Nathan Mundhenk; Garrett Mulcahy; Brenden K. Petersen (NeurIPS, 2022)

Deep Symbolic Optimization: Reinforcement Learning for Symbolic Mathematics. - Conor F. Hayes; Felipe Leno da Silva; Jiachen Yang; T. Nathan Mundhenk; Chak Shing Lee; Jacob F. Pettit; Claudio Santiago; Sookyung Kim; Joanne Taery Kim; Ignacio Aravena Solis; Ruben Glatt; Andre R. Goncalves; et al. (CoRR, 2025)