Principal Investigators

William Ratcliff

Uses neutron scattering and AI to explore superconducting materials that might be useful for quantum computers.
Email: wratclif@umd.edu
Web: www.nist.gov/people/william-d-ratcliff
Affiliation: National Institute of Standards and Technology

Julie Butler

Theoretical nuclear physicist using machine learning for many-body calculations. Former DSECOP Fellow (2022 and 2023 cohorts).
Email: butlerju@mountunion.edu
Web: juliebutler.blog
Affiliation: University of Mount Union

Alexis Knaub

Consultant, evaluator, and researcher in postsecondary STEM education.
Email: avknaub@gmail.com
Web: www.alexis.science
Affiliation: American Association of Physics Teachers

Editor-in-Chief

Mohammad Soltaniehha

Computational condensed matter physics, machine learning in cancer research and forecasting, and embodied AI. Co-founder of GDS (APS’s Data Science Unit).
Email: msoltani@bu.edu
Web: soltaniehha.com
Affiliation: Boston University

2026 Fellows

We are accepting applications for the 2026 DSECOP Fellowship cohort. View the full announcement and apply here.

2023 Fellows

Julie Butler

Using machine learning to extend the range of theoretical many-body calculations in regards to infinite matter
Email: butle222@msu.edu
Web: https://juliebutler.blog
Title: PhD Student until August 1; Assistant Professor of Physics from August 1.
Affiliation: Michigan State University until August 1; University of Mount Union from August 1.

Ashley S. Dale

Spin crossover materials for novel low-power memory devices; latent feature extraction for trusted and explainable AI.
Email: daleas@iu.edu
Web: https://daleas0120.github.io
Title: PhD Student
Affiliation: Indiana University–Purdue University Indianapolis

Richard Harry

Developing low/high-frequency sensor devices from multiferroic materials with target applications such as smart-grid power systems, wearable electronics, and tactile interference systems.
Email: rharry3999@tuskegee.edu
Web: https://www.linkedin.com/in/richard-harry-b74a04100/
Title: PhD Student
Affiliation: Tuskegee University

Joseph Dominicus Lap

Using hep-th techniques to understand hot nuclear phenomena
Email: Joseph.Dominicus.Lap@yale.edu
Web: DSECOP Fellows
Title: PhD Student
Affiliation: Yale University

Connor Robertson

Modeling for active nematic fluids and bacterial growth directly from experimental observations via data-driven and machine learning approaches
Email: cjr59@njit.edu
Web: https://cnrrobertson.github.io
Title: PhD Student
Affiliation: New Jersey Institute of Technology

Karan Shah

Machine learning accelerated electronic structure simulations for matter under extreme conditions.
Email: k.shah@hzdr.de
Web: https://karan.sh
Title: PhD Student
Affiliation: Center for Advanced Systems Understanding, Helmholtz-Zentrum Dresden-Rossendorf, Görlitz, Germany

Olivia Young

Real-time FPGA and GPU Algorithm Development for Transient Hunting on the Long Wavelength Array
Email: ory3002@rit.edu
Web: https://livsguidetothegalaxy.github.io/
Title: PhD Student
Affiliation: Rochester Institute of Technology

2022 Fellows

Sebastian Atalla

Investigating the use of deep learning in denoising and reconstructing hyperpolarized xenon-129 MRI and xenon-enhanced CT
Email: atalla@unc.edu
Web: https://github.com/swatalla
Title: PhD Student
Affiliation: The University of North Carolina at Chapel Hill

Fatemeh Bagheri

Observations of exoplanets orbiting source stars in microlensing events and the direct detection of light reflection from exoplanets.
Email: bagheri.fateme@gmail.com
Web: DSECOP Fellows
Title: NSF Postdoc
Affiliation: The University of Texas at Arlington (UTA)

Julie Butler

Machine learning in many-body studies of the nucleus and related nuclear systems
Email: butle222@msu.edu
Web: DSECOP Fellows
Title: PhD Student
Affiliation: Michigan State University

Cunwei Fan

Deep learning methods to analyze leptons from data produced by hadron collider and Monte Carlo simulation from CERN
Email: cfan11@illinois.edu
Web: https://fancunwei95.github.io/
Title: PhD Student
Affiliation: University of Illinois at Urbana Champaign

Radha Mastandrea

Trains neural networks to recognize the physical symmetries of particle collision events and use these symmetries for classification tasks.
Email: rmastand@berkeley.edu
Web: https://rmastand.github.io/
Title: PhD Student
Affiliation: UC Berkeley, Lawrence Berkeley National Laboratory

Karan Shah

Former Project Contributors

We gratefully acknowledge the contributions of the following individuals to DSECOP:

Wolfgang Losert — Principal Investigator, IPST and Physics at the University of Maryland
Maria (Marilena) Longobardi — Principal Investigator, University of Basel
Jacob Hale — Reviewer, DePauw University
Anıl Zenginoğlu — Community Manager, IPST at the University of Maryland