Computational Methods: High-Performance Computing (HPC), Machine Lear

Kameswari Gnana Asritha Varanasi

716-***-**** ï linkedin.com/in/asrithavaranasi # *****************@*****.*** Education

University at Buffalo, The State University of New York Aug 2023 – June 2025 Master’s, Chemical Engineering GPA – 3.7/4.0

Coursework : Chemical Kinetics, Chemical Engg Computation and Mathematics, Material Principles, Six Sigma, Molec- ular Modeling, Modeling Potential Energy Surfaces

Jawaharlal Nehru Technological University, Hyderabad Aug 2014 – June 2018 Bachelor’s, Chemical Engineering GPA – 7.9/10

Coursework : Fluid Mechanics, Heat Transfer, Mass Transfer, Chemical Reaction Engineering, Thermodynamics, Material Science, Process Modeling and Simulation

Research Experience

Graduate Student Researcher, University at Buffalo Dec 2023 – June 2025 Understanding the Behavior of Machine Learning Potentials for Simulation of Gas Adsorption

• Generated three datasets with different energy ranges using RASPA for training in Allegro – Machine Learning Po- tential, an E(3)-equivariant neural network potential (includes interaction energies, forces, and atomic coordinates).

• Utilized stratified sampling to train and deploy datasets in Allegro, analyzing model performance through parity plots to assess training accuracy.

• Performed Grand Canonical Monte Carlo (GCMC) simulations at 10+ pressures using deployed machine learning models in gRASPA and compared adsorption predictions with UFF classical force fields.

• Achieved agreement between Allegro and UFF models, then introduced noise (mimicking real-time DFT data contamination) into the dataset, retrained and deployed 15+ noisy models, conducted GCMC simulations, and analyzed the impact of noise on MLP performance for reliable adsorption predictions. Published on Proquest : https://www.proquest.com/docview/321-***-**** Undergraduate Student Researcher, IIT - Hyderabad Dec 2017 – May 2018 Magnetite Medium Segregation in Dense Medium Cyclone

• Conducted a comparative performance study between a 4-inch Dense Medium Cyclone (DMC) and a standard wooden DMC, focusing on operational parameters such as spigot diameter, slurry density (ranging from 1.3 to 1.5 g/cm

3

) and input pressure (1–2 bar) to evaluate separation efficiency.

• Analyzed the impact of varying coal particle sizes (0.5 mm to 13 mm) on the DMC’s cut-point and performance, identifying optimal particle ranges for enhanced separation accuracy.

• Collected and processed over 100 experimental data points using mass balance calculations to determine recovery, misplacement, and separation efficiency curves for both cyclone setups.

• Demonstrated strong research and analytical capabilities by synthesizing findings into actionable conclusions, im- proving understanding of DMC performance under varying conditions for academic and industrial applications. Work Experience

Clinical SAS Trainee, Covalent Technologies, Hyderabad, India July 2021 – Dec 2021

• Ensured 100% compliance with CDISC standards for 5+ anti-cancer therapy trials, optimizing data formatting and processing for regulatory submissions.

• Carried out e-CRF analysis using Clinical SAS, achieving 100% data accuracy and reducing validation errors by 40%, while organizing and structuring 500+ clinical trial datasets for consistency and compliance.

• Collaborated with 4+ cross-functional teams, streamlining data integrity processes and reducing inconsistencies by 30%, ensuring smooth regulatory workflows.

• Assisted in generating 10+ clinical data reports, expediting the regulatory submission process and enhancing overall trial efficiency.

Product Manager, Prolab Technologies, Hyderabad, India Jan 2019 – July 2021

• Created and updated 50+ product documents, ensuring accuracy and uploading them to the company website for customer access while developing and maintaining detailed equipment manuals for 10+ product lines to improve clarity and compliance.

• Addressed 100+ customer inquiries on installation, specifications, maintenance, pricing, and shipping, increasing customer satisfaction rates by 25% and enhancing overall client experience.

• Coordinated 200+ product shipments, ensuring smooth client interactions, on-time deliveries, and seamless order fulfillment.

• Provided technical support, reducing issue resolution time by 30% and optimizing customer experience through efficient troubleshooting and assistance.

Projects

Impact of Sterol Chemical Structure on Plant Cell Membranes Jan 2024 – May 2024

• Designed lipid bilayer models with campesterol and stigmasterol, performed 300 ns simulations using GROMACS, and set up systems with CHARMM-GUI.

• Assessment of area per lipid (packing efficiency), membrane thickness (structural integrity), radial distribution function (lipid sterol interactions) and lipid density maps (molecular organization).

• Used VMD for interpretation, providing insights into sterol-induced membrane modifications and lipid domain stability in plant membranes.

Process Optimization in Methanol Production Using DMAIC Methodology Aug 2023 – Dec 2023

• Implemented the DMAIC methodology to enhance quality and efficiency in methanol production, optimizing key process parameters and improving overall operational performance.

• Utilized UNISIM simulation software to analyze 100+ process data points under varying conditions, assessing the impact of kinetic dependencies on partial pressures in a catalyzed gas-phase reaction.

• Completed statistical analysis in MINITAB, identifying 5+ key factors influencing methanol yield and purity, and evaluated 2 catalysts through iterative analysis to determine the optimal selection for performance and efficiency. Study of Production Operations of Refinery at Tatipaka Complex Apr 2017 – May 2017

• Conducted an in-depth study of refinery operations at Tatipaka Complex, analyzing key production units including distillation, hydrocracking, and product blending processes, handling data from over 5 process streams.

• Gained hands-on exposure to refinery utilities, control systems, and safety protocols, and compiled findings into a technical report reviewed by a panel of senior process engineer and committee members. Skills

Computational Methods: High-Performance Computing (HPC), Machine Learning Potentials (MLPs), Grand Canon- ical Monte Carlo (GCMC) Simulations, Molecular Dynamics (MD), Classical Force Fields, Gas Adsorption Simulation & Modeling Tools: RASPA, gRASPA, LAMMPS, GROMACS, UNISIM, CP2K, Aspen Plus, Allegro, NequIP, PyTorch, Mathematica

Programming & Scripting Languages: Python, MATLAB, Bash/Shell Data Analysis & Productivity Tools: Minitab, Microsoft Office (Excel, PowerPoint, Word) Manufacturing & Quality Tools: Six Sigma (DMAIC), Root Cause Analysis, Process Optimization, Control Charts, Design of Experiments (DOE), Statistical Process Control (SPC) Professional & Interpersonal Skills: Strong work ethic, teamwork and collaboration, effective communication, time management, adaptability, attention to detail, problem-solving Additional Academic Engagements

• Presented a research poster on my Master’s thesis titled ”Machine Learning Potentials for Gas Adsorption Modeling” at the Graduate Research Symposium, University at Buffalo (October 2024), showcasing advancements in machine learning potentials for gas adsorption simulations.

• Worked as a Teaching Assistant for CE212 – Fundamentals Principles of Chemical Engineering, assisting with grading, student queries, and academic support for a class of 50+ students.

• Employed as a Student Administrative Assistant in the Department of Industrial and Systems Engineering, man- aging student databases, editing departmental webinars, coordinating faculty mail, and supporting front-desk op- erations.

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