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Steven Goldenberg

Postdoctoral Fellow at Jefferson Lab

Data Science Department

sgolden @ jlab.org

sgoldenCS

About Me:

Steven Goldenberg joined Jefferson Lab in September 2022 as a postdoctoral fellow in the Data Science department. His current work focuses on the development of uncertainty quantification methods for machine learning to improve accelerator operations and to predict flooding in Norfolk, Virginia. His PhD thesis primarily explored iterative methods for the singular value decomposition and numerical linear algebra with Professor Andreas Stathopoulos at William & Mary. Prior to graduate school, he studied music performance at the Peabody Conservatory and mathematics at Johns Hopkins University.

As a graduate student, Steven took courses focusing on Algorithms, Complexity Theory, Numerical Analysis, Computer Architecture, Distributed Systems, and Big Data among others.

Coding Experience:

MATLAB
C
C++
Python
Java
R

Research

Uncertainty Quantified Machine Learning for Street Level Flooding Predictions in Norfolk, Virginia
Steven Goldenberg, Diana McSpadden, Binata Roy, Malachi Schram, Jonathan L. Goodall, Heather Richter

NeurIPS 2023 Workshop: Tackling Climate Change with Machine Learning

https://www.climatechange.ai/papers/neurips2023/33
Distance Preserving Machine Learning for Uncertainty Aware Accelerator Capacitance Predictions
Steven Goldenberg, Malachi Schram, Kishansingh Rajput, Thomas Britton, Chris Pappas, Dan Lu, Jared Walden, Majdi I. Radaideh, Sarah Cousineau, Sudarshan Harave

Arxiv: Submitted to Machine Learning with Applications

https://doi.org/10.48550/arXiv.2307.02367
Forecasting Multi-Step-Ahead Street-Scale Nuisance Flooding Using Lstm and Seq2seq Lstm Surrogate Model for Real-Time Application: A Case Study for Norfolk, Virginia
Binata Roy, Jonathan L. Goodall, Diana McSpadden, Steven Goldenberg, Malachi Schram

SSRN

http://dx.doi.org/10.2139/ssrn.4602671
A Comparison of Machine Learning Surrogate Models of Street-scale Flooding in Norfolk, Virginia
Diana McSpadden, Steven Goldenberg, Binata Roy, Malachi Schram, Jonathan L. Goodall, Heather Richter

Machine Learning with Applications

https://doi.org/10.1016/j.mlwa.2023.100518
Multi-module-based CVAE to predict HVCM faults in the SNS accelerator
Yasir Alanazi, Malachi Schram, Kishansingh Rajput, Steven Goldenberg, Lasitha Vidyaratne, Chris Pappas, Majdi I. Radaideh, Dan Lu, Pradeep Ramuhalli, Sarah Cousineau

Machine Learning with Applications

https://doi.org/10.1016/j.mlwa.2023.100484
A Golub--Kahan Davidson Method for Accurately Computing a Few Singular Triplets of Large Sparse Matrices
Steven Goldenberg, Andreas Stathopoulos, Eloy Romero

SIAM Journal on Scientific Computing

https://doi.org/10.1137/18M1222004
Correlations between Panoramic Imagery and Gamma-Ray Background in an Urban Area
Mark S. Bandstra, Brian J. Quiter, Marco Salathe, Kyle J. Bilton, Joseph C. Curtis, Steven Goldenberg, Tenzing H.Y. Joshi

IEEE Transactions on Nuclear Science (TNS)

https://doi.org/10.1109/TNS.2021.3128472

Software

GKD: SVD Code

Work Experience

Postdoctoral Fellow

Sep 2022 - Current

Working at Jefferson Lab with the Data Science Department

  • Researching Uncertainty Quantified Machine Learning for Accelerator Systems and Flood Forecasting

Research Assistant

Aug 2017 - Current

Worked with Professor Andreas Stathopoulos on new iterative SVD algorithms

Teaching Fellow

Jan 2020 - May 2020

Taught CSCI 141 -- Computational Problem Solving at William & Mary

Intern

Jun 2018 - Aug 2018

Worked at Lawrence Berkeley National Lab with the Scientific Data Management Group

  • Collaborated with Mark Bandstra on panoramic image segmentation
  • "Correlations between Panoramic Imagery and Gamma-Ray Background in an Urban Area" accepted for oral presentation at 2019 IEEE NSS-MIC

Technical Support

May 2016 - Dec 2017

Working for the Computer Science Department at William & Mary

Graduate Assistant

Aug 2015 - May 2016

Grading for CSCI 303 -- Algorithms at William & Mary


Education

Doctor of Philosophy: Computer Science

William & Mary -- Williamsburg, VA
2015 - Current

Master of Science: Computer Science

William & Mary -- Williamsburg, VA
2015 - 2017

  • Zable Fellow
  • 4.0 GPA
  • Coursework focused on Algorithms, Complexity Theory, and Numerical Analysis

Non-Degree: Computer Science

George Mason University -- Fairfax, VA
2014 - 2015

  • 4.0 GPA
  • Coursework in Databases, Distributed Computing and Computer Organization

Bachelor of Arts: Mathematics

Johns Hopkins University -- Baltimore, MD
2010 - 2014

  • Graduated with General Honors from the Double Degree Program with the Peabody Institute
  • 3.79 Combined GPA
  • Dean's List

Bachelor of Music: Viola Performance

The Peabody Institute -- Baltimore, MD
2010 - 2014

  • Recipient of the Azalia H. Thomas Award for Excellence in Music Theory
  • Performed as Principal Violist in numerous ensembles including: