Photo of Ron Volkovinsky

Ron Volkovinsky

Device Engineer at Intel

Semiconductor manufacturing professional with a broad background in chemical engineering, materials science, and data-driven modeling.

Education

Georgia Institute of Technology

BS Chemical Engineering

Minor: Materials Science & Engineering

May 2020 – December 2023

Research

Grover Lab

  • Worked on the Polymer Informatics project to facilitate data-driven elucidation of key parameters in organic semiconducting devices
  • Applied Pymongo to create a robust data pipeline from the lab bench to the cloud via MongoDB
  • Fabricated organic field-effect transistors with P3HT and DPPDTT solutions using blade and spin coating techniques
Lab Website

Data Driven Process Systems Engineering Lab

  • Predicted parameters of mechanistic kinetic models from sparse datasets using Neural Ordinary Differential Equations
  • Explored the effect of different numerical differentiation techniques, surrogate models, and model selection criteria on the accuracy of model parameters
  • Trained in Python packages such as pandas, SciPy, NumPy, and scikit-learn to analyze cheminformatic datasets
Lab Website

Franck Lab — Cornell Center for Materials Research REU

  • Studied cell growth behavior using hemocytometer assays and automated cell counting
  • Used MATLAB signal processing to analyze and visualize data collected from a novel proliferation assay based on laser light detection
  • Cloned cell lines from the Northwestern dictyostelium stock center
  • Presented results at the 2021 CCMR research symposium
Lab Website

Paul Russo Materials Science Lab

  • Trained in optical microscopy, DSC, and TGA, with results used in published research
  • Created detailed engineering models of custom lab-scale testing apparatus with SolidWorks
  • Presented findings at the SPN Research Symposium Poster Session
Lab Website

Publications

Enabling Global Interpolation of Sparse, Multi-Experiment Data via Neural ODEs

William Bradley, Ron Volkovinsky, Fani Boukouvala

Engineering Applications of Artificial Intelligence

Conjugated Polymer Process Ontology and Experimental Data Repository for Organic Field-Effect Transistors

Aaron L. Liu, Myeongyeon Lee, Rahul Venkatesh, Jessica A. Bonsu, Ron Volkovinsky, J. Carson Meredith, Elsa Reichmanis, Martha A. Grover

Chemistry of Materials

Physical Properties of Sodium Poly(styrene sulfonate): Comparison to Incompletely Sulfonated Polystyrene

Paul Balding, Rachel Borrelli, Ron Volkovinsky, Paul S. Russo

Macromolecules

Cellulose Nanocrystal–Polyelectrolyte Hybrids for Bentonite Water-Based Drilling Fluids

Paul Balding, Mei-Chun Li, Qinglin Wu, Ron Volkovinsky, Paul S. Russo

ACS Applied Bio Materials

Experience

Device Engineer

Intel Corporation

January 2026 – Present

Portland, OR

  • Supporting end-of-line semiconductor device performance testing for key Foundry products.
  • Providing statistical analysis for pilot experiments given end-of-line electrical test data.

Wet Etch Equipment Engineer

Intel Corporation

March 2024 – January 2026

Hillsboro, OR

  • Drove continuous improvement of first-of-kind high volume wet etch tool with model-based problem solving, supplier engagement, and hands-on troubleshooting.
  • Isolated root cause of robot failures, corrected with new maintenance schedule and procedures
  • Engaged tool supplier for systemic fluid-in-exhaust issue, initiated redesign after investigation
  • Implemented new SPC methodology and automation flow, 2x reduction in technician hours
  • Created automated machine availability report for at-a-glance updates of fleet status

R&D Intern

Dow Chemical

May 2023 – August 2023

Lake Jackson, TX

  • Fingerprinted novel GPCxHTLC characterization instrument to collect polymer composition data
  • Found back pressure buildup in the instrument, initiated design change with new pump
  • Developed automated data analysis Python script, faster than manual-input vendor software

Process Engineering Intern

Ereztech

May 2022 – August 2022

Sheboygan, WI

  • Converted small-batch organometallic precursor process to continuous pilot, 5x faster production
  • Worked with analytical department to develop GC-MS baseline and procedure for key products
  • Designed and ran DOE to find precursor/solvent contamination, improving product purity by 15%
  • Trained in and completed an ISO 9001/2015 Design & Development internal audit

Founding President — OPALL

Open Polymer Active Learning Lab

August 2021 – May 2023

Atlanta, GA

  • Managed over $100,000 in funding to grow a polymer chemistry and biomaterials makerspace
  • Led hands-on events and demos of industry-standard polymer characterization instruments
  • Reviewed procedural, engineering, and administrative safety controls for all experiments
  • Acquired by the MILL

Skills

Engineering & Analysis

Electromechanical Troubleshooting Root Cause Analysis SPC DOE Semiconductor Device Testing

Software & Data

Python pandas scikit-learn PyTorch NumPy SciPy SQL JMP MongoDB

Characterization

GPC DSC TGA GC-MS Optical Microscopy

Other

Clean Room Glovebox ISO 9001 Auditing