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fix: fill example resume to full 2 pages

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- Add postdoc bullets: unfolding pathways, mentorship (6 total)
- Add PhD bullet: force field benchmarking (4 total)
- Add 2 more publications (7 total) and 1 more award (4 total)
- Add Selected Presentations section (5 entries)
- Fix skill line wrapping — all lines fit single rendered line
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Akhil Reddy Peeketi
2026-03-09 20:02:32 -06:00
parent 80fb7d6d5a
commit 712034decc
2 changed files with 33 additions and 13 deletions
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resume_builder/examples/output/e2e_whitfield_proteineng_resume.tex
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@@ -50,31 +50,31 @@ Computational biologist with 8+ years combining \textbf{protein language models}
\begin{rSection}{Technical Skills}
\begin{skillgroup}{Molecular Simulation \& Modeling}
\skilldash{\textbf{GROMACS}, OpenMM, AMBER -- metadynamics, replica exchange MD, free energy perturbation}
\skilldash{AlphaFold2, Rosetta, AutoDock Vina -- protein structure prediction and molecular docking}
\skilldash{CHARMM36m, AMBER ff19SB, OPLS-AA/M -- force field benchmarking for disordered proteins}
\skilldash{Collective variable design, enhanced sampling protocol development, convergence analysis}
\skilldash{\textbf{GROMACS}, OpenMM, AMBER -- metadynamics, replica exchange MD, free energy perturbation, umbrella sampling}
\skilldash{AlphaFold2, Rosetta, AutoDock Vina -- protein structure prediction, homology modeling, and molecular docking}
\skilldash{CHARMM36m, AMBER ff19SB, OPLS-AA/M -- force field selection and benchmarking for disordered proteins}
\skilldash{Collective variable design, enhanced sampling protocol development, convergence analysis, MM/PBSA free energy}
\end{skillgroup}
\begin{skillgroup}{Machine Learning \& Data Science}
\skilldash{\textbf{Protein language models} (ESM-2), graph neural networks, transfer learning, active learning}
\skilldash{\textbf{PyTorch}, scikit-learn, BioPython -- model fine-tuning, feature engineering, sequence analysis}
\skilldash{Regression, cross-validation, Spearman/RMSE benchmarking, dataset curation from public DBs}
\skilldash{\textbf{Protein language models} (ESM-2, 650M params), graph neural networks, transfer learning, active learning loops}
\skilldash{\textbf{PyTorch}, scikit-learn, BioPython -- model fine-tuning, embedding extraction, feature engineering, sequence analysis}
\skilldash{Regression, classification, cross-validation, Spearman/RMSE benchmarking, dataset curation from public databases}
\end{skillgroup}
\begin{skillgroup}{Programming \& HPC}
\skilldash{\textbf{Python}, Bash, SQL -- scientific computing, analysis pipelines, database management}
\skilldash{\textbf{SLURM}, Snakemake, Git, DVC -- HPC workflow automation and reproducible research}
\skilldash{\textbf{Python}, Bash, SQL -- scientific computing, data pipelines, automated analysis workflows, database management}
\skilldash{\textbf{SLURM}, Snakemake, Git, DVC -- HPC job scheduling, workflow automation, version control, reproducible research}
\end{skillgroup}
\begin{skillgroup}{Analysis \& Visualization}
\skilldash{MDAnalysis, ProDy, PyMOL, matplotlib, seaborn -- trajectory analysis, publication figures}
\skilldash{PostgreSQL, pandas -- curated stability databases with automated quality filters for ML}
\skilldash{MDAnalysis, ProDy, PyMOL, matplotlib, seaborn -- trajectory analysis, structural visualization, publication figures}
\skilldash{PostgreSQL, pandas, NumPy -- curated stability databases with automated quality filters for ML pipelines}
\end{skillgroup}
\begin{skillgroup}{Domain Expertise}
\skilldash{Protein engineering, enzyme thermostability, folding thermodynamics, drug discovery}
\skilldash{Intrinsically disordered proteins, ligand binding, biocatalysis, directed evolution}
\skilldash{Protein engineering, enzyme thermostability, folding thermodynamics, drug discovery, virtual screening workflows}
\skilldash{Intrinsically disordered proteins, ligand binding free energy, biocatalysis, directed evolution, rational design}
\end{skillgroup}
\end{rSection}
@@ -90,12 +90,15 @@ Computational biologist with 8+ years combining \textbf{protein language models}
\item Co-developed transfer learning framework from protein language models reducing labeled training data by 60\% across 5 enzyme families, released as open-source tool with 200+ GitHub stars.
\item Extended protein language model to predict enzyme solvent tolerance across 8 organic co-solvent systems, validating against 50-ns explicit-solvent MD for 80 enzyme variants and identifying 4 candidates for green chemistry.
\item Automated sequence-to-simulation pipeline using Snakemake workflow manager, reducing per-variant setup from 4 hours to 10 minutes and supporting 6 researchers across 3 active projects.
\item Revealed sequence-dependent enzyme unfolding pathway divergence at 340 K through 200-ns replica exchange MD simulations, identifying stabilizing salt bridge networks that informed rational design criteria.
\item Mentored 3 graduate students on protein ML pipelines and MD simulation workflows, with 1 student co-authoring a peer-reviewed publication within 8 months of joining.
\end{rSubsection}
\begin{rSubsection}{Enhanced Sampling Methods for Protein Folding and Ligand Binding}{\textcolor{black!60}{Aug 2018 -- Jul 2023}}{Ph.D.\ Researcher, Westfield Institute of Technology}{}
\item Developed metadynamics-based enhanced sampling protocol for protein folding free energy landscapes, predicting folding temperatures within 8 K of experiment across 6 small proteins.
\item Calculated relative binding free energies for 40 congeneric ligand pairs via free energy perturbation, achieving 0.9 kcal/mol RMSE against experimental IC50 data across 3 drug target families.
\item Built curated protein thermostability database integrating 12,000 experimental melting temperatures from 3 public sources, with automated quality filters adopted by 8 lab members for ML training set construction.
\item Benchmarked 4 protein force fields on 15 intrinsically disordered protein sequences, establishing CHARMM36m as the optimal choice for IDP conformational ensemble prediction with 40\% better agreement with SAXS data.
\end{rSubsection}
\begin{rSubsection}{Computational Biophysics and Structural Analysis}{\textcolor{black!60}{May 2016 -- Jul 2018}}{Undergraduate Research Assistant, Eastgate University}{}
@@ -133,6 +136,10 @@ Computational biologist with 8+ years combining \textbf{protein language models}
\item \textbf{J.\ Chen}, P.\ Kowalski, L.\ Alvarez. ``Force Field Benchmarking for Intrinsically Disordered Protein Ensembles.'' \textit{J.\ Chem.\ Theory Comput.}, 2021.
\item \textbf{J.\ Chen}, P.\ Kowalski, L.\ Alvarez. ``Curated Thermostability Database for ML-Ready Protein Engineering Benchmarks.'' \textit{Bioinformatics}, 2021.
\item \textbf{J.\ Chen}, T.\ Yamamoto, K.\ Holmberg. ``Predicting Enzyme Solvent Tolerance with Fine-Tuned Protein Language Models.'' \textit{Proteins: Struct., Funct., Bioinf.}, 2025 (under review).
\end{rSection2}
\vspace{-0.15cm}
@@ -143,6 +150,19 @@ Computational biologist with 8+ years combining \textbf{protein language models}
\item \textbf{NSF Graduate Research Fellowship}, National Science Foundation (2019). Three-year fellowship supporting doctoral research in computational protein engineering.
\item \textbf{Best Oral Presentation}, Westfield Biophysics Symposium (2022). Enhanced sampling methods for protein folding thermodynamics.
\item \textbf{Dean's Teaching Award}, Westfield Institute of Technology (2021). Outstanding TA in computational biology.
\item \textbf{Outstanding Poster Award}, Gordon Research Conference on Proteins (2023). ML-guided enzyme thermostability screening.
\end{rSection2}
\vspace{-0.15cm}
%----------------------------------------------------------------------------------------
% SELECTED PRESENTATIONS
%----------------------------------------------------------------------------------------
\begin{rSection2}{Selected Presentations}
\item ``ML-Guided Screening of Thermostable Enzyme Variants.'' \textit{Gordon Research Conference on Proteins}, Ventura, CA (2023). Poster.
\item ``Transfer Learning from Protein Language Models for Low-Data Enzyme Property Prediction.'' \textit{ACS National Meeting}, San Francisco, CA (2024). Oral.
\item ``Enhanced Sampling Protocols for Protein Folding Free Energy Landscapes.'' \textit{Biophysical Society Annual Meeting}, San Diego, CA (2022). Oral.
\item ``Force Field Benchmarking for Intrinsically Disordered Protein Ensembles.'' \textit{AIChE Annual Meeting}, Boston, MA (2021). Poster.
\item ``Automated Screening Pipelines for ML-Guided Enzyme Engineering.'' \textit{Lakewood University Computational Biology Seminar Series} (2024). Invited talk.
\end{rSection2}
\vspace{-0.1cm}