UC Davis Mathematics

Quarterly Research Projects for Undergraduates

Please note that this is a dynamic list each quarter. Check back regularly to see if new research positions have been added. If you are interested in working on an area of research not represented on this list below, you are encouraged to contact faculty directly who are doing work in that area. A list of math faculty, including their research areas, is available here.

For more information about research units and the benefits of research, please visit the Math Undergraduate Research homepage

Apply for Spring 2019 Research: The application for Spring 2019 research opportunities is available online.  Please note that you will need the Application Code (from the section below) for the position(s) you are interested in. If you are interested in research opportunities with multiple faculty members, submit a separate application for each faculty member. 

Link to application: https://forms.gle/Tc7Bss83NFTaKSZh8

For full consideration, please apply before Sunday, May 12.

SPRING/SUMMER 2019 Research Projects 

RESILIENT PRODUCTION COST MODELS

Principal Investigator (PI): Prof. Woodruff

Description: Modify and extend the grid operations scenario maker (gosm) software to produce inputs for extreme events such as hurricanes. The current software deals primarily with energy scenarios; it will be extended to include line and load outages due to extreme events.

Requirements: Proficiency as a Python programmer. Coursework in probability theory and statistics. Optimization coursework is a plus, but not required.

Application Code: woodruff

 

SWIMMING MICRO-ORGANISMS: Multiple Projects

Principal Investigator (PI): Prof. Bob Guy
 
I am interested in mentoring students on a variety of projects related swimming micro-organisms. From single cells to complex multicellular organisms, locomotion requires coordination of different parts of the body. In cells such as sperm, this coordination emerges from mechanochemical coupling (i.e. chemical reactions influenced by mechanical feedback). In worms such as C. elegans, the coordination is thought to arise through neuromechanical coupling (i.e. neuron interactions influenced by mechanical feedback). In both systems, the gait emerges from coupled dynamics of the body, muscles/molecular motors, and the surrounding fluid. Below I list several possible projects related to these systems.

Requirements for projects below: Programming and ODEs (MAT 119A)

Project 1A: Models of active filaments: elastic fluids.
Simple models of so called "active filaments" have been used to study the emergence of the beating pattern of flagella. These systems have been studied only in viscous fluids (e.g. water), but many cells function in viscoelastic fluids. We are interested in investigating how fluid elasticity affects the emergence of the beat. This project involves performing numerical simulations of ODEs and PDEs as well as analyzing the bifurcation at which the system breaks symmetry and begins to beat. Application Code: guy1A

Project 1B: Models of active filaments: synchronization.
Using the same type of models as in project 1A, we will investigate whether nearby filaments synchronize their beats. Nearby filaments affect the beat of one another though the motion of the common surrounding fluids. Do interactions though the fluid cause the filaments to beat together or with some other rhythm? How do elastic forces in the fluid affect the rhythms that emerge? Application Code: guy1B

Project 2: Optimal gaits under constraints.
What body shape should a worm use given an energy budget in a given environment? The worm C. elegans swims with larger head movements compared to the tail, while sperm swim with larger tail movements compared to the head. The organisms swim in different environments and have different body design. Did these organisms evolve to optimize their swimming performance? Using computational models of the fluid and body mechanics, we can investigate how the optimal swimming shape depends on the external environment and body design. Application Code: guy2

 

DEVELOPING A LIQUID CRYSTAL MODEL FOR DNA INSIDE VIRUSES

Principal Investigator (PI): Prof. Javier Arsuaga
 
Description: In certain viruses DNA is highly condensed and it is believed that the DNA molecule is found in the form of a liquid crystal. Motivated by these experimental observations we are interested in developing a model of liquid crystal for DNA under confinement. The project is open to the computer implementation of continuous or discrete models of liquid crystalline phases.
 
Requirements: Some background in PDEs or Physics is desirable. Programming experience, preferably C++ and/or Python. Ability to conduct numerical simulations with care and interest in the underlying biological motivation.
 
Application Code: arsuaga

 

MATHEMATICAL MODELING PROJECTS IN NEUROBIOLOGY & CARDIAC ELECTROPHYSIOLOGY 
 
Principal Investigator (PI): Prof. Tim Lewis
 
Description: Topics include:
  - neural and mechanical mechanisms of locomotion in "model" systems
  - autonomic (neural) regulation of cardiac activity
  - effects of pharmacological drugs on electrical activity in the heart. 
 
Requirements: MAT 22AB necessary; MAT 119A and/or MAT 124 highly preferred. Some experience in computer programming is required, and a willingness to learn mathematical modeling and biology is essential.  
 
Application Code: lewis

 

WINTER 2019 Research Projects 

KNOT INVARIANTS AND NEURAL NETWORKS

Principal Investigator (PI): Prof. Allison Moore
 
Description: There is a lot of publicly available, numerical data about knots and knot invariants. The goal of this project is to predict the behavior of certain knot invariants using neural networks. More specifically, we will design and implement a feed-forward neural network to make predictions about three and four-dimensional knot invariants.
 
Requirements: Linear algebra and experience with programming in Python, Matlab or R. No knot theory or topology background is required. Student will be expected to implement a feed-forward neural network in Python and/or to write helper functions that manipulate data and "vectorize" knots. Prior experience with neural nets is helpful, but this is not a requirement either.
 
Application Code: moore
 
 
CELLULAR AUTOMATA

Principal Investigator (PI): Prof. Janko Gravner
 
Description: The students would find interestting structures in cellular automata evolutions, and investigate the role of randomness in such dynamics.
 
Requirements: Some knowledge of probability, and willingness to do computer programming.
 
Application Code: gravner
 

IS THERE A PROJECTION OF A KNOT WHOSE NUMBER OF UNKNOTTED DIAGRAMS IS EXPONENTIAL ON THE NUMBER OF CROSSINGS?

Principal Investigator (PI): Prof. Mariel Vazquez and Carolina Medina

Description: 

It is known that for every regular knot diagram D with n crossings, the number of unknot diagrams that can be obtained from D by crossing exchanges is at least 2^{\sqrt[3]{n}}.  

(Umin(n)>= 2^\sqrt[3]{n})

We are interested in estimating an upper bound. As our fist approach we would like to investigate the Gauss Codes arising from canonical projections of toroidal knots T(p,q) when p and q are small integers. This will help us understand the behavior of these projections for larger p and q. The interested student will be involved in the implementation of an efficient algorithm for the automated calculation of certain knot invariants.

Requirements: Programming background, preferably C++ and/or Python. Ability to conduct numerical simulations with care, learn existing tool suites, and interest in the underlying biological motivation.

Application Code: vazquez
 
 
DEVELOPING A LIQUID CRYSTAL MODEL FOR DNA INSIDE VIRUSES

Principal Investigator (PI): Prof. Javier Arsuaga
 
Description: In certain viruses DNA is highly condensed and it is believed that the DNA molecule is found in the form of a liquid crystal. Motivated by these experimental observations we are interested in developing a model of liquid crystal for DNA under confinement. The project is open to the computer implementation of continuous or discrete models of liquid crystalline phases.
 
Requirements: Some background in PDEs or Physics is desirable. Programming experience, preferably C++ and/or Python. Ability to conduct numerical simulations with care and interest in the underlying biological motivation.
 
Application Code: arsuaga
 

IMPROVING ALGORITHMS FOR FIXED SMALL PATTERNS 

Principal Investigator (PI): Prof. Chaim Even Zohar
 
Description: 
A permutation of size n is an ordering of {1,2,…,n}, for example 38261457, of size 8. This particular permutation contains 824 as a sub-sequence. We say that this is an occurrence of the pattern 312, since the map from 8,2,4 to 3,1,2 respectively preserves the order relations. Indeed, it maps the largest to the largest, the second largest to the second largest, and so on.
 
Think of a large permutation of size n. How can we tell if it contains an occurrence of the pattern 312 or not? A naive approach would have to exhaust all (n choose 3) sub-sequences. This would have a running time of O(n^3), and in general O(n^k) for a fixed pattern of size k. However, it was shown by Guillemot and Marx (2014) that this can actually be done in linear time, O(n), where only the implicit constant coefficient depends on the fixed parameter k.
 
What about the more general problem of counting the number of occurrences of a fixed pattern in a large permutation of size n. Again, the naive approach requires time O(n^k), for a fixed pattern of size k. One can somewhat improve on that in some simple special cases, such as the patterns 12, 123, or 321.
 
The goal of the proposed project is to explore this problem, and see how much we can improve our algorithms for various fixed small patterns. We will hope that looking at several small special cases of this problem would help us to reason about the general case. From there, the project may proceed in a number of different directions.
 
Requirements: Background in discrete math and algorithms, basic programming skills.
 
Application Code: evenzohar
 
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FALL 2018 Research Projects

 
DETECTING PERCOLATION IN RANDOM ENVIRONMENTS
 
Principal Investigator (PI): Prof. Javier Arsuaga
 
Description: Certain morphogenesis processes in molecular biology seem to be subject to percolation. In previous work we have developed algorithms to detect percolation in planar lattices and have done some preliminary work on detecting percolation in random environments (see reference below). In this project, one or two, student(s) will work together to develop algorithms to detect percolation in random environments in the plane and in spheres. The applications of these models include kinetoplast DNA and the formation of some viral capsids.
 
Reference: 
Percolation phenomena in disordered topological networks
V Rodriguez, Y Diao, J Arsuaga - Journal of Physics: Conference Series, 2013

Requirements: GPA at least 3.6, MAT128A and B or C. Interested in applications of mathematical physics to molecular biology. Excellent programming skills, excitement for interdisciplinary work. At least 10 hour/week commitment.
 

TAU FUNCTIONS OF DRINFELD-SOKOLOV HIERARCHIES
 
Principal Investigator (PI): Prof. Martin Luu
 
Description: Drinfeld and Sokolov constructed in the 1980’s interesting dynamical systems attached to suitable Lie algebras (such as square matrices with vanishing trace). For each point in the phase space one expects to have a special function, the so-called tau function, encoding important and subtle information. Only recently a general definition of this function has emerged. In this project the student will write computer code that can (with suitable initial data) actually calculate these functions.
 
Requirements: Most important is a good grasp of linear algebra as covered in MAT 67 as well as a strong background in programming.
 

RENEWABLE ENERGY STOCHASTICS
 
Principal Investigator (PI): Prof. David L. Woodruff (Graduate School of Management)
 
Description: Paid internship: Help develop, test, document and experiment with software for characterizing uncertain energy production from renewable energy sources.
 
Requirements: Coursework in probably and statistics, demonstrated skill as a Python programmer.