College of Computing

SURE: Summer Undergraduate Research Experience

Spend the summer with a computer science or applied mathematics research team in a College of Computing Summer Undergraduate Research Experience (SURE) program.

SURE offers insight and learning in some of the hottest topics in applied mathematics and computer science through hands-on research experiences. Learn to work as a team member by interacting with graduate students and faculty, and gain an understanding as to what it takes to conduct real-world research.

A seven-week SURE program was hosted remotely in the summer of 2021, which involved six faculty mentors and seven undergraduate students.

Check back for information about summer 2022 research opportunities.

SURE Programs Summer 2021

Data Analysis and Applied Statistics

Adviser: Lulu Kang

Student: Kaylee Rosendahl

Project: Clustering and Segmented Approaches for Big Data Filtering


Adviser: Fred Hickernell

Student: Claude Hall 

Project: Bayesian Logistic Regression in QMCPy

Machine Learning and 360 Vision Analysis

Adviser: Yan Yan

Student: Sofia Martinez

Project: Understand Motions in 360-degree Videos

Student: Ryan Ciminski 

Project: An Elementary Study of Manipulating Variables within a Machine Learning Algorithm

Mathematical Modeling of Tumor Growth

Adviser: Shuwang Li

Student: Ginger Dragon 

Project: Nonlinear Simulation of Vascular Tumor Growth with Chemotaxis and Control of Necrosis

Responsible AI for Social Computing

Adviser: Kai Shu

Student: Diana Morales

Project: Responsible AI for Social Computing: Bias Identification in Disinformation

Secure and privacy-preserving AI

Adviser: Yuan Hong

Student: Truong Pham

Project: Differentially Private Probabilistic Database

Abstract: Hiding the database is not an effective privacy protection since attacks on privacy can be performed through the use of the database’s public APIs. Differential Privacy is a protection that is placed on top of every queries made to the database by adding noise to the results, which effectively creates deniability for all participants of the database. Probabilistic databases have another layer of natural noise injected into each record by virtue of how they are created. Our goal for this project is to devise a differentially private mechanism that can take into account the natural noise of the Probabilistic Database

For more information please email

College of Computing

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