Cam Dennis

Welcome to my personal website! This site serves as a hub for my research, publications, and academic journey. My work focuses on the physics of jammed systems, glassy materials, and complex energy landscapes.

Feel free to explore the sections above to learn more about my research, publications, and interests. Don’t hesitate to reach out if you have any questions or would like to collaborate!

R. Cameron Dennis, Ph.D.

Physicist | Quantitative Researcher | Data Scientist

University of Pennsylvania

Syracuse University

Biography

About

I am a physicist and computational modeler with expertise in quantitative modeling, machine learning, and financial risk analysis. My background spans statistical physics, stochastic processes, and complex systems, with extensive experience in high-performance computing (HPC), CUDA, and Python-based modeling.

I am actively seeking roles in quantitative finance, data science, or academia, where I can leverage my expertise in modeling, statistical analysis, and programming to drive impactful solutions.

Experience & Expertise

Quantitative Research: Developing models for financial risk, market behavior, and algorithmic trading.
Data Science & Machine Learning: Applying Bayesian inference, generative models, and predictive analytics.
Computational Physics: Designing simulations for disordered materials and large-scale dynamical systems.
Mentorship & Teaching: Experienced in guiding junior scientists and leading interdisciplinary collaborations.

Research & Publications

I have published in top-tier journals, including Physical Review Letters and Physical Review E, on topics ranging from energy landscapes in jammed systems to machine-learned softness in granular materials.

For my full list of publications, visit my Google Scholar profile.

Contact

Interested in working together? Let’s connect! Feel free to reach out via email or LinkedIn.

Interests

Quantitative Modeling & Simulations
Machine Learning & Data Science
Statistical Physics & Soft Matter
Financial Modeling & Risk Analysis

Education

PhD in Physics, 2016-2021
University of Oregon
BA in Physics & Mathematics, 2012-2016
Wabash College (Indiana)

Skills

Technical & Analytical Expertise

Python & Scientific Computing

Extensive experience with NumPy, SciPy, pandas, and optimization libraries.

C++ & High-Performance Computing

Proficient in performance-critical applications, including OpenMP and pybind11.

Fortran & Legacy Code Optimization

Skilled in maintaining and optimizing scientific codebases.

Statistical Mechanics & Complex Systems

Expertise in modeling disordered systems, phase transitions, and stochastic processes.

CUDA & Parallel Computing

Experience in GPU acceleration for large-scale simulations.

Machine Learning & Data Science

Experience with TensorFlow, PyTorch, Bayesian inference, and time-series analysis.

Quantitative Finance & Risk Analysis

Understanding of options pricing, Monte Carlo methods, and statistical arbitrage.

Teaching & Mentorship

Experienced in leading courses, mentoring students, and interdisciplinary research collaboration.

Experience

Research, Teaching, and Awards

Postdoctoral Researcher - Quantitative Modeling

University of Pennsylvania & Syracuse University

Jan 2022 – Dec 2024

Applied machine learning to study avalanche dynamics in granular matter.
Developed predictive models for mechanical failure in disordered systems.
Mentored graduate students and led interdisciplinary collaborations.

Teaching Experience

University of Oregon & University of Pennsylvania

Sep 2016 – Dec 2022

Co-instructor for Physical Models of Biological Systems (UPenn).
Led recitations and labs in Thermal Physics, Electricity and Magnetism, and Modern Physics (UO).
Mentored students in computational modeling and advanced physics.

Ph.D. Researcher - Soft Matter & Computational Physics

University of Oregon

Aug 2016 – Aug 2021

Designed and implemented high-performance simulations for glassy and jammed systems.
Developed numerical models for complex energy landscapes in disordered materials.
Published groundbreaking research on jamming transitions and soft matter physics.

Awards & Honors

Various Institutions

May 2015 – Dec 2024

Weiser Doctoral Thesis Award, University of Oregon (2022)
Research as Art Winner, ArtSci Oregon (2019)
Physics Department Writing Prize, Wabash College (2015)
Phi Beta Kappa Prize, Wabash College (2015)
Fuller Prize for Excellence in Physics, Wabash College (2015)

Projects

Dionysian Packing

Overview Dionysian Packing explores the fundamental limits of rigidity in low-density sphere packings. Traditional high-strength lightweight materials rely on rigid frameworks that balance compressive and tensile forces. However, purely compressive materials such as granular media typically lack a high strength-to-weight ratio.

Hyperuniform Jammed Sphere Packings Have Anomalous Material Properties

Abstract In this study, we investigate the material properties of hyperuniform jammed sphere packings. Our findings reveal that these packings exhibit anomalous characteristics, distinguishing them from typical disordered systems. Key Findings Anomalous Density Fluctuations: Hyperuniform packings show suppressed density fluctuations at large scales, leading to unique mechanical properties.

Emergence of Zero Modes in Disordered Solids Under Periodic Tiling

Abstract This research explores how periodic tiling influences the mechanical properties of disordered solids. We demonstrate that introducing periodic boundary conditions can lead to the emergence of zero-frequency modes, affecting the material’s rigidity and stability.

Hierarchical Jamming

Hierarchical Structure in Jamming Energy Landscapes Jammed systems, such as glasses and granular materials, exhibit complex energy landscapes that influence their physical properties and behaviors. Recent research has demonstrated that these landscapes possess a hierarchical and ultrametric organization, even in finite three-dimensional systems.

The Ideal Glass and the Ideal Disk Packing in Two Dimensions

Abstract We present a method for constructing ideal jammed packings of soft spheres in two dimensions, effectively realizing the zero-temperature ideal glass. These configurations exhibit high bulk and shear moduli, anomalously high density, and hyperuniformity, providing insights into the nature of two-dimensional jammed and glassy systems.

Finite-Size Effects in Jammed Configurations

Investigating Finite-Size Effects in Jammed Systems Jamming criticality encompasses a range of systems, from glasses and colloids to foams and neural networks. A notable characteristic of this phenomenon is the emergence of power-law distributions in small interparticle forces (f) and gaps (h).

Methods for Creation and Linear Elastic Response Analysis of Packings of Semi-flexible Soft Polymer Chains

Abstract This methods paper presents an extension of the soft sphere model to linked spheres, facilitating the study of packings of semi-flexible soft polymer chains. The approach enables analysis of the physical properties of materials ranging from flexible chains to rigid molecules.