NATHANIEL JAMES LINDEN-SANTANGELI
natejlinden at gmail dot com | natelinden.com | linkedin.com/in/nate-linden
EDUCATION
PhD Engineering Sciences with a Specialization in Multiscale Biology
University of California San Diego | June 2025
- Advisors: Prof. Boris Kramer & Prof. Padmini Rangamani
- Thesis: Uncertainty quantification methods improve mechanistic predictability in systems biology
- Alfred P. Sloan Foundation Scholar \(\cdot\) NIH Ruth Kirschstein National Service T32 Awardee
MS Mechanical and Aerospace Engineering
University of California San Diego | 2022 | GPA 3.73/4.0
B.S. Bioengineering, minor in Applied Mathematics
University of Washington (Seattle, WA) | 2020 | GPA 3.75/4.0
- Graduated with departmental Honors
- Undergraduate research thesis:
EXPERIENCE
Pre-sales Data Scientist
FICO | July 2025 – Present | San Diego, CA
Systems Biology Researcher
UC San Diego | Sep. 2020 – June 2025 | San Diego, CA
- Developed a machine learning workflow to calibrate systems biology models to wet-lab data to predict intracellular signaling phenomena with quantified uncertainty. Enabled calibration for dozens of new models with applications in cancer and metabolism.
- Quantitatively evaluated models and algorithms using Bayesian model selection and multimodel inference in Python, Jax, and PyMC to identify the best models and infer biological mechanisms.
- Designed and conducted computational experiments to benchmark models and demonstrate the benefits of developed methods to non-technical stakeholders.
- Trains models on distributed HPC systems to accelerate model training and prototyping by 10–20x.
- Leads technical research projects on cross-functional teams with both technical and nontechnical stakeholders to strategically develop data-driven solutions.
- Analyzed biological data using mathematical models, Bayesian statistics, and machine learning.
Quantitative Systems Pharmacology Intern
Johnson and Johnson Innovative Medicine | May 2024 – Aug 2024 | Spring House, PA
- Developed machine learning-based approaches to discover biomarkers and risk factors of adverse events during treatment administration.
- Discovered novel predictors of rare adverse events using feature selection methods to inform adverse event prediction during clinical studies.
- Developed logistic regression and machine learning (decision tree and neural network) models to predict risk as a function of discovered features to optimize drug dosing.
Computational Neuroscience & Protein Engineering Researcher
University of Washington | Jan 2017 – Sep 2020 | Seattle, WA
- Led an independent project to analyze spatiotemporal patterns in wide-field calcium imaging data using a novel image analysis method (published in Journal of the Royal Society Interface).
- Optimized fluorescent biosensor sensitivity and performance by conducting proof-of-concept studies and analyzing fluorescent microscopy data.
- Performed laboratory experiments to engineer genetically encoded biosensors and develop a high-throughput protein engineering pipeline.
Research and Development Intern
Gauss Surgical Inc. (Now a part of Medtronic) | 2016, 2017 | CA
- Worked as a member of the R&D team to optimize the performance of an FDA-approved medical device.
- Research efforts directly expanded the number of customers successfully using the device.
PUBLICATIONS
- Linden-Santangeli NJ, Kramer B, Rangamani P. Systems modeling of AMP-activated protein kinase reveals distinct sub-cellular response during exercise-related signaling. 2025. NPJ Systems Biology and Applications.
- Linden-Santangeli NJ, Kramer B, Rangamani P. Increasing certainty in systems biology models using Bayesian multimodel inference. 2025. Nature Communications.
- Kobayashi E, Linden-Santangeli NJ, Chan N, Toomey CB, Mudaliar S, Temprosa M, Edelstein S, Goyal R, Rangamani P, Majithia AR, Diabetes Prevention Program Research Group. Longitudinal metabolic trajectories in Diabetes Prevention Program participants reveal subgroups with varying micro and macrovascular complication risk. 2025. Diabetes Care.
- Lingxia Qiao, Ali Khalilimeybodi, Linden-Santangeli NJ, Rangamani P. The evolution of systems biology and medicine: From mechanistic models to uncertainty quantification. 2025. Annual Reviews Biomedical Engineering.
- Reiner J, Linden NJ,** Vaziri R, Zobeiry N, Kramer B. Bayesian Parameter Estimation for the Inclusion of Uncertainty in Progressive Damage Simulation of Composites. 2023. Composite Structures.
- Linden NJ, Kramer B and Rangamani P. Bayesian Parameter Estimation for Dynamical Models in Systems Biology. 2022. PLoS Computational Biology.
- Linden NJ, Tabuena DR, Steinmetz NA, Moody WJ, Brunton SL and Brunton BW. Go with the FLOW: Visualizing spatiotemporal dynamics in optical widefield calcium imaging. 2021. Journal of the Royal Society Interface.
CONFERENCE PRESENTATIONS
- Mar 2025: Talk: “Bayesian Multimodel Inference to Account for Model Uncertainty in Systems Biology.” \(\cdot\) SIAM Computational Science and Engineering (Fort Worth, TX)
- Sep 2024: Talk: “Bayesian mull model inference to account for model uncertainty and increase predictive uncertainty in systems biology.” \(\cdot\) International data assimilation summer school (Boltenhagen, Germany)
- Feb 2024: Talk: “Multimodel inference to account for model uncertainty in systems biology.” \(\cdot\) SIAM Uncertainty Quantification 2024 (Trieste, Italy)
- July 2023: Talk: “Multimodel modeling for blood glucose and insulin measurements in diabetes” \(\cdot\) Society for Mathematical Biology Annual Meeting (Columbus, OH)
- Apr 2023: Talk: “Multimodel modeling: Accounting for model uncertainty in biology with multiple models” \(\cdot\) Southern California Applied Mathematics Symposium 2023 (Irvine, CA)
- Sept 2022: Talk: “A Framework for Bayesian Parameter Estimation and Uncertainty Quantification in Systems Biology.” \(\cdot\) SIAM Mathematics of Data Science 2022 (San Diego, CA)
- Mar 2022: Talk: UCSD Interfaces Research Symposium \(\cdot\) Awarded best student presentation.
WORKSHOPS AND PROFESSIONAL DEVELOPMENT
- 2023: UCSD Rady School of Business Micro MBA (San Diego, CA)
- 2023: Uncertainty Quantification Summer School (Los Angeles, CA)
- 2023: UCSD Responsible Conduct of Research (quarter-long course on research ethics)
VOLUNTEER AND OUTREACH ACTIVITIES
- 2023-2024: Tutor, The Preuss School UC San Diego \(\cdot\) Volunteer tutor in \(7^{th}\) grade mathematics and science classrooms.
- 2022-2024: Chair, UCSD MAE Peer-2-Peer Committee \(\cdot\) Represented students at the departmental level and improved peer mentoring.
- 2023-2024: Graduate Student Representative \(\cdot\) UCSD Faculty Senate Committee on Budget and Planning & Graduate and Professional Students Association.
- 2021: Judge, Greater San Diego Science and Engineering Fair
- 2019-2020: Vice President of Professional Development / Outreach Educator \(\cdot\) Biomedical Engineering Society, UW Student Chapter.
PROFESSIONAL SOCIETIES
- Society for Mathematical Biology (Student Member; 2023-2025)
- Society for Industrial and Applied Mathematics (Student Member; 2020-2025)
- Biophysical Society (Student Member; 2023-2024)
TEACHING EXPERIENCE
- 2024 Fall: UCSD MAE 208, Mathematics for Engineering \(\cdot\) Teaching Assistant
- 2022 Spring: UCSD BENG 276, Numerical Analysis for Multi-Scale Biology \(\cdot\) Guest Lecturer on VCell
- 2019 Fall: UW BIOEN 325, Biotransport I \(\cdot\) Teaching Assistant
- 2018 Winter: UW BIOEN 217, Matlab Fundamentals \(\cdot\) Teaching Assistant
AWARDS AND HONORS
- 2021-2023: NIH Ruth Kirschstein National Service T32 Awardee (Training in Multi-scale Analysis of Biological Structure and Function)
- 2020-2024: UCSD Alfred P. Sloan Foundation Minority PhD Program Scholar
- 2022: SIAM Student Travel Award (SIAM MDS 2022)
- 2022: SIAM/NSF Student Travel Award (SIAM UQ 2022)
- 2022: Best Student Presentation Award (UCSD Interfaces Research Symposium)
- 2019: Washington Research Foundation Innovation Undergraduate Fellow in Neuroengineering
- 2016-2020: UW Dean’s List Scholar (4x)