Robert M. Raddi
Personal Statement
Philadelphia, PA
(267) 312-0604

A passionate researcher, skilled in developing and applying machine/deep learning, Bayesian inference, and maximum entropy methods to a wide array of scientific problems. My current research effectively combines theory with experiment, enabling us to enhance the accuracy our molecular models. This interdisciplinary research has been recognized with numerous academic accolades and has fueled advancements in biophysics, structural biology, and drug discovery. With a strong commitment to scientific exploration and collaboration, I am eager to learn new methods, grow as a scientist, continue cutting-edge research, and hopefully influence other scientists along the way.

Education

2018 - 2024
Ph.D. in Theoretical Chemistry, Temple University, Philadelphia, Pennsylvania, USA

"A Bayesian Inference/Maximum Entropy Approach for Optimization and Validation of
Empirical Molecular Models"


Advisor: Dr. Vincent Voelz

2013 - 2017
B.S in Chemistry, Temple University, Philadelphia, Pennsylvania, USA
Minor in Mathematics

Experience

2024-2025
Adjunct Research Assistant Professor
Temple University, Philadelphia, PA
  • Submit co-first-author article to JCTC demonstrating high-resolution tuning of non-natural and cyclic peptide folding landscapes against NMR measurements.
  • Conducting experiments and obtaining promising preliminary results for a research proposal, which focuses on deep learning and generative modeling.
  • Addressing peer-reviewer comments for the publication of my preprints.
  • Writing and conducting experiments for a collaborative project: an optimization tool for free energy calculations.

2018-2024
Graduate Researcher/Fellow
Temple University, Philadelphia, PA
  • Awards granted by both the university and department for outstanding research contributions.
  • Conceptualized and designed numerous research projects, developed novel methodologies as needed, and established successful collaborations.
  • Published 11 high-quality papers in 6 years, advancing the fields of computational biophysics and computational structural biology.
  • Focused on Bayesian Inference and Maximum Entropy methods to enhance empirical models through ensemble refinement, force field optimization, and forward model improvement; specialized in Bayesian model selection and algorithms for machine-learned potentials and forward model predictors.
  • Conducted research on protein-peptide binding simulations and contributed predictions of logP, pKa, and absolute binding free energies to community-wide blind SAMPL challenges.
  • My published pKa prediction data and software are currently incorporated into the curriculum for Elements of Data Science, an undergraduate course at Temple University.

2018-2023
Teaching Assistant
Temple University, Philadelphia, PA
  • Received awards from both the university and department for exceptional teaching.
  • Instructed lectures & labs for Quantum Mechanics, Thermodynamics, Physical Chemistry of Biomolecules, Analytical Chemistry, and General Chemistry across various levels.

    • Certification: 2019 Teaching in Higher Education Certificate
    Outreach: 2018 Student Mentor for New Foundations Charter School

2019
Academic Intern
Temple University, Philadelphia, PA
  • Served as an Academic Intern for the Computing & Statistics Summer Workshop, facilitating a 2-week program that helped ~30 graduate students pursuing physical and social sciences.
  • Assisted with technical setups including package manager installations and provided support with Python, Bash, Jupyter Notebooks, and GitHub. Contributed to lecture materials and engaged actively in student learning.

Publications ($^{\large\boldsymbol{\dagger}}$these authors contributed equally)
  1. Robert M. Raddi, Tim Marshall and Vincent A. Voelz. "Automatic Forward Model Parameterization with Bayesian Inference of Conformational Populations." arxiv.org/abs/2405.18532
  2. Robert M. Raddi and Vincent A. Voelz. "Automated optimization of force field parameters against ensemble-averaged measurements with Bayesian Inference of Conformational Populations." arxiv.org/abs/2402.11169
  3. Dylan Novack, Robert M. Raddi, Si Zhang, Matthew F.D. Hurley and Vincent A. Voelz. "A simple method to optimize the spacing and number of alchemical intermediates in expanded ensemble free energy calculations."10.26434/chemrxiv-2025-7fdpf
  4. Robert M. Raddi, Tim Marshall, Yunhui Ge, and Vincent A. Voelz. "Model Selection using Bayesian Inference of Conformational Populations with Replica-Averaging." 10.26434/chemrxiv-2023-396mm;
  5. Thi Dung Nguyen$^{\large\boldsymbol{\dagger}}$, Robert M. Raddi$^{\large\boldsymbol{\dagger}}$, and Vincent A. Voelz. "High-resolution tuning of non-natural and cyclic peptide folding landscapes against NMR measurements using Markov models and Bayesian Inference of Conformational Populations." 10.26434/chemrxiv-2024-sz1xx;
  6. Matthew F.D. Hurley$^{\large\boldsymbol{\dagger}}$, Robert M. Raddi$^{\large\boldsymbol{\dagger}}$, Jason Pattis and Vincent A. Voelz. "Expanded Ensemble Predictions of Absolute Binding Free Energies in the SAMPL9 Host-Guest Challenge." Phys. Chem. Chem. Phys., 2023,25, 32393-32406. preprint; article
  7. Robert M. Raddi, and Vincent A. Voelz. "Stacking Gaussian Processes to Improve pKa Predictions in the SAMPL7 Challenge." Journal of Computer-Aided Molecular Design 35 (2021): 953-961. preprint; article
  8. Robert M. Raddi, Yunhui Ge, and Vincent A. Voelz. "BICePs v2.0: Software for Ensemble Reweighting using Bayesian Inference of Conformational Populations." J. Chem. Inf. Model. 2023, 63, 8, 2370–2381. preprint; article
  9. Robert M. Raddi and Vincent A. Voelz. "A Markov State Model of Solvent Features Reveals Water Dynamics in Protein-Peptide Binding." J. Phys. Chem. B 2023, 127, 50, 10682–10690. preprint; article
  10. Tim Marshall, Robert M. Raddi and Vincent A. Voelz. "An Evaluation of Force Field Accuracy for the Mini-Protein Chignolin using Markov State Models." preprint;
  11. Vincent A. Voelz, Yunhui Ge, and Robert M. Raddi. "Reconciling simulations and experiments with BICePs: a review." Frontiers in Molecular Biosciences 8 (2021): 325. article
  12. Steven Goold, Robert M. Raddi and Vincent A. Voelz. "Expanded Ensemble Predictions of Toluene‑Water Partition Coefficients in the SAMPL9 log$P$ Challenge."preprint; article
$^{\large\boldsymbol{\dagger}}$ these authors contributed equally
Fellowships and Awards

Fellowships:
  • 2024 Temple University College of Science and Technology Dissertation Grant
Research Awards:
  • 2023 Temple University College of Science and Technology Outstanding Research Award (one winner per dept.)
  • 2023 Francis H. Case Research Award (one winner)
  • 2022 Daniel Swern Research Award (two winners)
  • 2021 1st Place Poster Presentation at ACS EUS YCC Research Symposium
Teaching Awards:
  • 2022 Temple University College of Science and Technology Outstanding Teaching Assistant Award (one winner per dept.)
  • 2019 The Guy Allen Award for Outstanding Teaching (one winner)
Presentations

  • 2024 Biophysical Society 66th Annual Meeting in Philadelphia, PA
  • 2023 Temple University CAD talk (talk - 50 min)
  • 2023 Biophysical Society 64th Annual Meeting in San Diego, CA (poster)
  • 2021 Roivant (talk - 30 min)
  • 2021 ACS YCC in Philadelphia, PA (poster)
  • 2021 Folding@Home science talk (talk - 30 min)
  • 2021 Protein Folding Consortium (poster)
  • 2020 Biophysical Society 64th Annual Meeting in San Diego, CA (poster)
  • 2020 ACS YCC in Philadelphia, PA (poster)
  • 2018 Biophysical Society 62nd Annual Meeting in San Francisco, CA (poster)
  • 2018 ACS YCC in Philadelphia, PA (poster)
  • 2017 ACS YCC in Philadelphia, PA (poster)
Peer Reviewer
  • The Royal Society of Chemistry
Skills
  • Programming: Python, C/C++, Cython, Bash (~10 yrs of experience for each)
  • Machine Learning: PyTorch, scikit-learn
  • Bayesian Inference & Statistical Inference: Expertise in developing algorithms for data-driven and knowledge-driven predictive modeling.
  • Deep Learning: Experience in applying/developing deep learning techniques for pKa and logP prediction
  • Cheminformatics: QSAR calculations, RDKit, OpenEye scientific software, etc.
  • Computational Chemistry: Advanced molecular modeling, MD/MCMC, force field optimization, forward model optimization, kinetic network models, and much more.
Mentoring

I am dedicated to mentoring the next generation of scientists, with a particular focus on supporting younger researchers in their development. Over the course of my career, I have guided five graduate students and eight undergraduate students in the Voelz lab, helping them develop their skills and navigate their research. In addition, I extended my mentoring to high school students, participating in an outreach program in 2018 through New Foundations Charter School, a Philadelphia lottery-based high school. For their annual Career Shadow Day, I hosted three students at Temple University, where we explored classic demonstrations with liquid nitrogen and dry ice. When they expressed interest in computer science, I shared movies of molecular dynamics simulations from my own research. The students seemed to thoroughly enjoy the experience, and I hope to have inspired them to pursue their interests further.

References

[voelz@temple.edu]
(215) 204-1973
1. Vincent A. Voelz
Professor / Associate Department Chair of Chemistry, Temple University

[ronlevy@temple.edu]
(215) 204-0607
2. Ronald M. Levy
Laura H. Carnell Professor of Biophysics and Computational Biology, Temple University

[vincenzo.carnevale@temple.edu]
(215) 204-4214
3. Vincenzo Carnevale
Associate Professor of Biology, Temple University

[sharpk@mail.med.upenn.edu]
(215) 573-3506
4. Kim A. Sharp
Associate Professor of Biochemistry and Biophysics, University of Pennsylvania