“There are few experiences better suited to prepare a student for lifelong learning than an active participation in research early in his or her education. The only ‘prerequisites’ are curiosity, the willingness to learn something not contained in the standard curriculum, and to work on questions to which the answers are not known yet.”
UCSB Professor Herb Kroemer, winner of the Nobel Prize in Physics, 2000
Why Do Research as an Undergrad?
Your undergraduate education will benefit tremendously from outside research. Students that participate in research gain experiences and skills both academically and professionally that cannot be gained in a classroom setting. You'll also have the opportunity to network with professors, graduate students, and post-docs, and you'll get to contribute to the creation of new knowledge. Early research as an undergraduate will help you prepare for and clarify both academic and professional goals by challenging you to think critically and analytically, and to apply your classroom knowledge to real-world problems.
To provide research opportunities for FMS majors, the Center for Financial Mathematics and Actuarial Research (CFMAR) Undergraduate Lab is now open. To apply, please use this Google Form. Admission is competitive and not guaranteed. We expect to admit 8-12 students per year.
In the CFMAR Undergraduate Lab, students work on research projects mentored by a senior graduate student and supervised by a CFMAR Visiting Assistant Professor or PSTAT professor. Projects cover quantitative finance, risk management or stochastic process models. Examples include advanced option pricing with Monte Carlo methods, artificial Intelligence in finance, financial security and privacy, market dynamics and multi-agent economic systems, analysis of renewable energy generation and prices, network-based models for systemic risk, and reinforcement learning for stochastic games. Most but not all projects are computational and include work in Python or R. As part of the research experience, students also get training in scientific writing, presentation skills, and exposure to other projects in the Lab. Projects combine financial markets data with time series modeling, simulation, Markov chains, random graphs or deep learning. Some projects are for 2 students, others are for individual work.
Application requirements: Undergraduate students in their junior or senior years, with preference given to students who completed as many of PSTAT 160A-B, PSTAT 170 and PSTAT 176 with high grades as feasible. Students are not expected to have taken all these classes; a reasonable subset is sufficient. Students with GPA below 3.0, or low grades in multiple PSTAT classes are unlikely to be competitive. As part of the Application Form, please upload a recent transcript, and describe your interest in joining the Lab and any research topics you're especially interested in. All students who are interested and have met the application requirements are encouraged to apply.
Course structure: Students work on a project for six months -- a commitment for 2 quarters (Fall and Winter, or Winter/Spring) is expected. Students produce a written project report, and a short presentation to other Lab members. Students are also encouraged to present their results (in poster format) at UCSB’s Undergraduate Research Colloquium (URCA) in May. The CFMAR Lab structure includes independent work by the student on their own, a weekly meeting with the graduate mentor, and a mandatory bi-weekly all-Lab meeting. Students in CFMAR Undergraduate Lab will be concurrently enrolled in PSTAT 199 for 2 or 4 units per quarter.
The CFMAR Undergraduate Lab strongly supports undergraduate scholars from diverse backgrounds and perspectives on their path to developing careers as leaders in the field.
PSTAT 296A-B, Research Projects in Actuarial Science is also open to top undergraduates in Actuarial Science, FMS and Statistical Science majors. While the course primarily consists of students completing the five-year combined BS/MS in Actuarial Science program, we also invite top seniors from all PSTAT majors to apply.
In the PSTAT 296A-B course sequence, students work on team research projects under supervision of PSTAT faculty members and in coordination with industry sponsors. All projects are of interest to the insurance industry. Project areas include workers compensation analysis, telematics, Property & Casualty insurance and diabetes risk analysis. Previous PSTAT 296A-B projects employed such statistical methodologies as clustering, regression (including GLMs and logistic), time series, distribution fitting, simulation, etc. Students also get training in scientific writing, presentation skills, realistic data analysis, R programming, and group teamwork.
Application requirements: Undergraduate students in actuarial science, FMS and statistics are accepted on a competitive basis. PSTAT 296AB can count as an elective or as a substitute for one of the required PSTAT courses. As a graduate course, PSTAT 296AB automatically gives honors units. Students pursuing the MA in Applied Statistics should not apply. Undergraduate students should have a minimum overall GPA of 3.4, senior status, and A's in courses relevant to the project areas of interest, such as PSTAT 126, PSTAT 131, PSTAT 134, PSTAT 174 and PSTAT 183. Students are not expected to have taken all these classes; a reasonable subset is sufficient.
Course structure: Students work in teams of three for six months in the Fall and Winter quarters under supervision of a faculty member, and are required to produce a written project report and a poster for their project. Note that the commitment must be for 2 quarters (Fall and Winter). Students are also expected to present a poster at UCSB’s Undergraduate Research Colloquium (URCA) in May. Typically, students meet with their faculty supervisor once a week to discuss results and directions for future work, but most of the work is done by student teams on their own.
PSTAT 197A/-B-C is a year-long capstone sequence in the field of data science. In fall quarter, students study data science principles and practices, address a variety of ethical issues that arise in data science projects, and engage in project-based learning through a series of carefully selected and curated data science studies. One overarching goal of the fall quarter course is to prepare students to make a positive impact on the world with data-intensive methodologies through responsible data practice. In winter and spring quarters, students form teams and collaborate with academic and industry partners on real-world problems. They build skills in team work and communication while pursuing intensive research. Capstone groups are expected to produce a poster, report and/or presentation summarizing their work toward the end of the academic year.
Before applying for capstone, students should have a background in computing and statistics, demonstrated by a strong performance in such preparatory courses like PSTAT 100, PSTAT 131, PSTAT 126, and COMPSC 130A. Additionally, students must commit to the entire sequence and not graduate before Spring. Data Science Capstone is listed concurrently in the Computer Science Department as CMPSC 190DD-DE-DF. Examples of past projects can be found here: https://
You can also contact the Office of Research for help finding research opportunities, or for questions regarding undergraduate research.