Research Projects by Subject

Note: Each research project will involve background reading for the interns provided by their mentors. Each research project will involve a final presentation by the interns.

Interns are expected to work collaboratively on the same project and/or data set. This may preclude rising seniors from submitting papers based on such projects to the Intel Science Talent Search competition.

Computer Science/Computer Engineering

Code Research Project Descriptions
CSE-01 Title: Activity Analysis in the Developing Brain
Primary mentor: Sydney Weiser
Faculty advisor: Prof. James Ackman
Other mentors: Brian Mullen
Location: UCSC Main Campus
Number of interns: 2

Project description: The Ackman Lab studies brain activity in newborn mice to determine how neural activity patterns affect the maturation of the developing brain. The mentor analyzes brain activity to identify developing circuits within the brain, and classify different brain states in the developing mouse (i.e., dreaming vs. alert states). One focus of the mentor's research is how spontaneous activity in the eye assembles and fine-tunes circuits in the regions of the brain responsible for visual processing.

Tasks: Depending on the SIP interns' areas of interest and expertise, they will either help in developing activity analysis tools, build interactive graphical user interfaces for viewing data, aid in analysis of visual processing circuitry, or help streamline the processing pipeline. The existing code is predominantly in Python; previous experience with Python is preferred, but not necessary.

URL: https://ackmanlab.com/research.html
Required skills for interns prior to acceptance: None
Skills intern(s) will acquire/hone: Computer programming; statistical data analysis
Program Week Number: 1 2 3 4 5 6 7 8 9 10
Mentor's availability: ON ON ON REM ON ON ON ON ON ON


Code Research Project Descriptions
CSE-02 Title: Computer Vision to Track Mouse Behaviors
Primary mentor: Brian Mullen
Faculty advisor: Prof. James Ackman
Other mentors: Sydney Weiser
Location: UCSC Main Campus
Number of interns: 2

Project description: Development of functional brain regions has been shown to be associated with spontaneous and sensory signals throughout the nervous system. The mentor's research group is attempting to map brain regions throughout development. One facet of mapping involves understanding how an animal is behaving. This project will use computer vision (openCV) packages available to Python to track and identify mouse movements. Ultimately, the SIP mentors and the mentor will use their results to correlate with brain activity at various stages of development. This will give insight into how experience influences brain function. 

Tasks: The SIP interns will learn how to use Python to write scripts accessing openCV packages in order to identify when motion is occurring and record characteristics from all motion. In addition, the SIP interns will help build graphic user interfaces (GUIs) to better interact with the data. Finally, if time permits, the SIP interns will use machine learning to classify each behavior based on the characteristics of the motion. Previous experience with programming is preferred, but not required.

URL: https://ackmanlab.com/research.html
Required skills for interns prior to acceptance: None
Skills intern(s) will acquire/hone: Computer programming; statistical data analysis
Program Week Number: 1 2 3 4 5 6 7 8 9 10
Mentor's availability: ON ON ON REM REM ON ON ON ON ON


Code Research Project Descriptions
CSE-03 Title: Studying Internet Round-Trip Times
Primary mentor: Daniel Alves
Faculty advisor: Prof. Katia Obraczka
Location: UCSC Main Campus
Number of interns: 3

Project description: An important concept in networking is the Round-Trip Time (RTT), the time required to receive a reply (e.g., confirmation of receipt) to a message sent from one computer to another. RTTs are often used as indication of the load on the network, whether data has been lost, etc. Therefore, being able to estimate RTTs based on past ones can provide important insight into how the network will perform. One of the main challenges in studying Internet RTTs is related to the size of the Internet, the heterogeneity of the devices that connect to it, and the heterogeneity of the underlying networks that connect those devices. Consequently, observed behavior can vary greatly which makes it hard to predict the future. Another significant challenge is testing new RTT prediction methods: it is hard to test them in real network scenarios due to the difficulty in controlling the environment and ensuring a fair comparison with other methods. A more manageable approach would be to use network simulations to model a particular network. However,  this method also suffers from the scalability and heterogeneity problems mentioned above. This project aims to understand how to better model realistic RTT behavior in simulations using the ns-3 network simulation platform. The ns-3 simulator is a packet-level network simulator that models individual components of a network to model what happens in real scenarios as packets are transmitted. The work will consist of testing different network topologies on ns-3, collecting measurements, comparing those to real data, as well as testing and evaluating different RTT prediction methods.

Tasks: The SIP interns will take part mostly in the manipulation and analysis of data. This will involve creating programs to organize data for study as well as creating programs that will analyze the data, primarily through calculation of statistics and distribution fitting. Interns on this project will derive the maximum benefit from this project if they have prior (i.e., pre-SIP) exposure to computer programming.

Required skills for interns prior to acceptance: Computer programming
Skills intern(s) will acquire/hone: Computer programming; statistical data analysis
Program Week Number: 1 2 3 4 5 6 7 8 9 10
Mentor's availability: ON ON ON ON ON ON ON ON ON ON


Code Research Project Descriptions
CSE-04 Title: Collision-Detection and Obstacle Avoidance for Unmanned Aerial Vehicles
Primary mentor: Yegeta Zeleke
Faculty advisor: Prof. Ricardo Sanfelice
Location: UCSC Main Campus
Number of interns: 3

Project description: The focus of this project is to develop a system that consists of a mathematical model and numerical algorithms for a smart drone. The system serves as a testbed for an obstacle avoidance algorithm that uses Model Predictive Control (MPC) to plan linear trajectories for a small sized quad-rotor in the event of an incoming projectile. The predictive aspect of this system is to detect the event of a collision between the projectile and quad-rotor and react accordingly to avoid the projectile. The goal for the quad-rotor is to maximize its distance from the projectile while remaining within the bounds of our system. We will also explore the problem of uniting local and global controllers in which we will assess a method to overcome the problem of designing a continuous-time feedback controller that performs globally and robustly while avoiding obstacles. Experimental validation will be carried out in a motion capture system composed of eight medium range motion capture cameras. A Crazyflie 2.0 brand quad-rotor will be used along with the Motive Optical motion capture software. To prove the correctness of the mathematical models and system design, the SIP interns and mentor will experiment with the pursuit-evasion problem. The problem is comprised of two players: an evader, who wins if never caught by the pursuer, and a pursuer, who wins if it catches the evader. 

Tasks: Although this project is listed in the area of computer science/computer engineering, it is highly interdisciplinary with other fields such as mathematics and electrical engineering. There are numerous ways in which the SIP interns can assist with this project. On the programming side, the SIP interns can gain experience both from designing system algorithms for vehicle decision making and explicitly implementing a motion planning algorithm. In terms of mathematics and electrical engineering, the position will have an abundance of opportunities for the interns to witness electrical engineering design and components that are used in the mentor's lab. This project will provide excellent opportunities for the SIP interns: exposure to elegance, and the creativity and fun of mathematics that is rarely seen in the high school setting. Moreover, the interns will gain insight into higher level mathematical concepts and notation, as well as their importance and usefulness in the control systems that the mentor's research group designs and applies. In general, the mentor would like to see a degree of versatility in the work of the SIP interns.

URL: https://hybrid.soe.ucsc.edu/home
Required skills for interns prior to acceptance: None
Skills intern(s) will acquire/hone: Computer programming
Program Week Number: 1 2 3 4 5 6 7 8 9 10
Mentor's availability: ON ON ON ON REM ON ON ON ON ON

Note: This project may not be eligible for science competitions; interns should check the competition guidelines.
Code Research Project Descriptions
CSE-06 Title: Motivation Measurement using Physiological Signals
Primary mentor: Fatemeh Mirzaei
Faculty advisor: Prof. Sri Kurniawan
Location: UCSC Main Campus
Number of interns: 2

Project description: Motivation is drive and desire to fulfill our goals and needs, and to solve problems. Our daily life is full of times when we are motivated versus when we are not. Sometimes we are actively working toward achieving our goals and sometimes we feel exhausted and have no passion for our goals. By learning how to regulate our motivations, we will be able to achieve our goal and have a life full of happiness and achievements. The first step to regulate motivation is to understand motivation and its important components. We then need to learn about motivation measurement methods. Special measurements for each particular domain (such as health, education, social, and decision making) is required. Each domain has its own factors that differentiate it from the others. Hence, data gathering and user study approaches may vary. Based on flow theory, the state of motivation happens when a task or goal is challenging enough and an individual has enough skill to fulfill a task or goal. In this project, flow experience theory along with autonomous physiological body responses are leveraged to measure motivation. These responses are collected while the subject performs a particular designed task such as playing a game. The experiments are done at different challenge and skill levels and the motivation state of the subject are explored.

Tasks: The SIP interns will participate in a practical scientific study and will become familiar with dealing with real word interesting challenges in this project. The interns will learn how to collect data using wearable sensors and become familiar with coding, visualizing, and analyzing the data using some basic statistical analysis methods.

Required skills for interns prior to acceptance: None
Skills intern(s) will acquire/hone: Computer programming; statistical data analysis
Program Week Number: 1 2 3 4 5 6 7 8 9 10
Mentor's availability: ON ON ON ON OFF ON ON OFF ON ON