Kristofer G. Reyes

University at Buffalo, State University of New York — United States

Materials Science · Physical Sciences

21h-index2.0kcitations59works3.82yr avg
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Research Topics

Machine Learning in Materials Science(14), Advanced Multi-Objective Optimization Algorithms(11), Quantum Dots Synthesis And Properties(7), Perovskite Materials and Applications(6), Gaussian Processes and Bayesian Inference(6)

Publications59 total

A self-driving fluidic lab for data-driven synthesis of lead-free perovskite nanocrystals
Digital Discovery·2025· 13 citedOpen Access
Identifying Bayesian optimal experiments for uncertain biochemical pathway models
Scientific Reports·2024Open Access
Pessimistic asynchronous sampling in high-cost Bayesian optimization
arXiv (Cornell University)·2024Open Access
Smart Dope: A Self‐Driving Fluidic Lab for Accelerated Development of Doped Perovskite Quantum Dots (Adv. Energy Mater. 1/2024)
Advanced Energy Materials·2024· 2 citedOpen Access
Autonomous nanomanufacturing of lead-free metal halide perovskite nanocrystals using a self-driving fluidic lab
Nanoscale·2023· 24 citedOpen Access
Smart Dope: A Self‐Driving Fluidic Lab for Accelerated Development of Doped Perovskite Quantum Dots
Advanced Energy Materials·2023· 41 citedOpen Access
Identifying Bayesian Optimal Experiments for Uncertain Biochemical Pathway Models
arXiv (Cornell University)·2023Open Access
Two-dimensional van der Waals materials and their mixed low-dimensional hybrids for electrochemical energy applications
MRS Bulletin·2023· 4 cited
AI for Optimal Experimental Design and Decision-Making
WORLD SCIENTIFIC eBooks·2023
AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning
Nature Communications·2023· 162 citedOpen Access
Exact Gaussian processes for massive datasets via non-stationary sparsity-discovering kernels
Scientific Reports·2023· 20 citedOpen Access
Pharmacodynamic model of PARP1 inhibition and global sensitivity analyses can lead to cancer biomarker discovery
2023· 1 citedOpen Access
Mathematical nuances of Gaussian process-driven autonomous experimentation
MRS Bulletin·2023· 12 citedOpen Access
A Rigorous Uncertainty-Aware Quantification Framework Is Essential for Reproducible and Replicable Machine Learning Workflows
arXiv (Cornell University)·2023· 1 citedOpen Access
A rigorous uncertainty-aware quantification framework is essential for reproducible and replicable machine learning workflows
Digital Discovery·2023· 9 citedOpen Access
Designing lattices for impact protection using transfer learning
Matter·2022· 28 cited
Exact Gaussian Processes for Massive Datasets via Non-Stationary Sparsity-Discovering Kernels
Research Square (Research Square)·2022· 1 citedOpen Access
Decision-Making Under Uncertainty for Multi-stage Pipelines: Simulation Studies to Benchmark Screening Strategies
JOM·2022· 1 cited
Exact Gaussian Processes for Massive Datasets via Non-Stationary Sparsity-Discovering Kernels
arXiv (Cornell University)·2022· 1 citedOpen Access
Autonomous Nanocrystal Doping by Self‐Driving Fluidic Micro‐Processors
Advanced Intelligent Systems·2022· 14 citedOpen Access
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Frequent Co-authors

Milad Abolhasani(12), Warren B. Powell(9), Robert W. Epps(8), Fazel Bateni(7), Keith A. Brown(6), Amanda A. Volk(4), Kameel Abdel‐Latif(4), Suyong Han(4), Aldair E. Gongora(4), Elise F. Morgan(4), Sina Sadeghi(4), Negin Orouji(4), Jeffrey A. Bennett(4), Christine Stark(4), Marcus M. Noack(4), Francis J. Alexander(4), Byung-Jun Yoon(4), Patrick Riley(3), Felix N. Castellano(3), Peter Smereka(3)