fix a few typos

Author: alegresor

resume/sorokin_resume.pdf

@@ -71,8 +71,8 @@ \subsection{Experiences}
 \newentry{\normalfont{2022.09 - 2022.11}}{\textbf{Participant} in \textbf{Argonne National Laboratory's Course on AI Driven Science on Supercomputers}. Key topics included handling large scale data pipelines and parallel training for neural networks.} %\itlink{github.com/alegresor/ai-science-training-series}{https://github.com/alegresor/ai-science-training-series}.
 \subsection{Open-Source Software}
 
-\newentry{\texttt{QMCPy}}{\textbf{Quasi-Monte Carlo Python Software} (\href{https://qmcsoftware.github.io/QMCSoftware}{qmcsoftware.github.io/QMCSoftware}). I led dozens of collaborators across acadamia and industy to develop QMC sequence generators, automatic variable transformations, adaptive error estimation algorithms, and diverse use cases \cite{sorokin.thesis,sorokin.2025.ld_randomizations_ho_nets_fast_kernel_mats,choi.challenges_great_qmc_software,choi.QMC_software,sorokin.MC_vector_functions_integrals,sorokin.QMC_IS_QMCPy,hickernell.qmc_what_why_how,jain.bernstein_betting_confidence_intervals}.}
-\newentry{\texttt{FastGPs}}{\textbf{Scalable Gaussian Process Regression in Python} (\href{https://alegresor.github.io/fastgps}{alegresor.github.io/fastgps}). This supports GPU scaling, batched inference, robust hyperparameter optimization, multi-fidelity GPs, and efficient Bayesian cubature. \texttt{FastGPs} is the first package to implement GPs which require only $\mathcal{O}(n)$ storage and $\mathcal{O}(n \log n)$ computations compared to the tyipcal  $\mathcal{O}(n^2)$ storage and $\mathcal{O}(n^3)$ computations requirements.}
+\newentry{\texttt{QMCPy}}{\textbf{Quasi-Monte Carlo Python Software} (\href{https://qmcsoftware.github.io/QMCSoftware}{qmcsoftware.github.io/QMCSoftware}). I led dozens of collaborators across academia and industry to develop QMC sequence generators, automatic variable transformations, adaptive error estimation algorithms, and diverse use cases \cite{sorokin.thesis,sorokin.2025.ld_randomizations_ho_nets_fast_kernel_mats,choi.challenges_great_qmc_software,choi.QMC_software,sorokin.MC_vector_functions_integrals,sorokin.QMC_IS_QMCPy,hickernell.qmc_what_why_how,jain.bernstein_betting_confidence_intervals}.}
+\newentry{\texttt{FastGPs}}{\textbf{Scalable Gaussian Process Regression in Python} (\href{https://alegresor.github.io/fastgps}{alegresor.github.io/fastgps}). This supports GPU scaling, batched inference, robust hyperparameter optimization, multi-fidelity GPs, and efficient Bayesian cubature. \texttt{FastGPs} is the first package to implement GPs which require only $\mathcal{O}(n)$ storage and $\mathcal{O}(n \log n)$ computations compared to the typical  $\mathcal{O}(n^2)$ storage and $\mathcal{O}(n^3)$ computations requirements.}
 \newentry{\scalebox{.9}{\texttt{QMCGenerators.jl}}}{\textbf{Randomized Quasi-Monte Carlo Sequences in Julia} (\href{https://alegresor.github.io/QMCGenerators.jl}{alegresor.github.io/QMCGenerators.jl}).}
 \newentry{\texttt{QMCToolsCL}}{\textbf{Randomized Quasi-Monte Carlo Sequences in C / OpenCL} (\href{https://qmcsoftware.github.io/QMCToolsCL/}{qmcsoftware.github.io/QMCToolsCL/}).}
 \newentry{\scalebox{.95}{\texttt{TorchOrthoPolys}}}{\textbf{Orthogonal Polynomials in PyTorch} (\href{https://alegresor.github.io/TorchOrthoPolys/}{alegresor.github.io/TorchOrthoPolys/}) with GPU support.}