add MCQMC2024 proceedings preprint update

Author: alegresor

resume/sorokin_resume.pdf

@@ -100,7 +100,7 @@ \section{Experiences}
 
 \section{Projects}
 \cvitem{\textbf{Fast Gaussian Processes with Derivatives for Solving PDEs}}{The cost of Gaussian process regression can be reduced from $\mathcal{O}(n^3)$ to $\mathcal{O}(n \log n)$ when one has control over the design of experiments. This is achieved by pairing quasi-random sampling with matching kernels to induce structure in the kernel matrix. My PhD research studies generalizations for quickly incorporating gradient information into the ML model and using these efficient strategies to solve PDEs with either random or deterministic coefficients.}
-\cvitem{\textbf{QMCPy Software}}{I lead development of the open source project QMCPy, a Quasi-Monte Carlo Python Library. This package provides high quality quasi-random sequence generators, automatic variable transformations, adaptive stopping criteria algorithms, and diverse use cases. Over the past five years, this project has grown to dozens of collaborators and \cite{choi.challenges_great_qmc_software,choi.QMC_software,sorokin.MC_vector_functions_integrals,sorokin.QMC_IS_QMCPy}. See \itlink{qmcpy.org}{https://qmcpy.org} for more information.}
+\cvitem{\textbf{QMCPy Software}}{I lead development of the open source project QMCPy, a Quasi-Monte Carlo Python Library. This package provides high quality quasi-random sequence generators, automatic variable transformations, adaptive stopping criteria algorithms, and diverse use cases. Over the past five years, this project has grown to dozens of collaborators and \cite{choi.challenges_great_qmc_software,choi.QMC_software,sorokin.MC_vector_functions_integrals,sorokin.QMC_IS_QMCPy,hickernell.qmc_what_why_how}. See \itlink{qmcpy.org}{https://qmcpy.org}.}
 \cvitem{\textbf{Argonne: AI on Supercomputers}}{I studied  \emph{AI Driven Science on Supercomputers} during my time at \emph{Argonne National Laboratory}. Key topics included handling large scale data pipelines and parallel training for neural networks. %Coursework at \itlink{github.com/alegresor/ai-science-training-series}{https://github.com/alegresor/ai-science-training-series}.
 }