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.

SURE participants are eligible for a $500 per week stipend.

Enhance interdisciplinary communication skills through collaboration with Illinois Tech's SoReMo initiative.

Each program runs 6 -8 weeks from June until August. See program descriptions and registration* below. Registration deadline is June 25, 2021 with the exception of BigDataX, which has a May 24, 2021 registration deadline. For more information please email

SURE Programs

Big Data Computing (Start on 5/24)

Advisers: Ioan Raicu, Kyle Chard, Kyle Hale, and Aaron Elmore

Conduct research in big data computing systems by joining the REU BigDataX Summer 2021 Program. Or join a smaller group of three fellow students working as part of the Mystic project this summer.  A sample projects include XSearch exploring popular search data structures (Hashmaps,  Tries, Trees, Skip Lists), information retrieval libraries (Apache Lucene, Xapian), and cloud search platforms (Apache Solr, ElasticSearch) and their performance on modern hardware found in the Mystic system. Gain deep insight into inout/output and data-intensive programming,  database, and search engine design, advanced multithreaded synchronization techniques (lock-free synchronization, atomic operations), as well as experience with various parallel and distributed systems (Lustre, Ceph, HDFS, FusionFS).

Apply for Big Data X (May 17 deadline) 

Apply for Mystic (Big DAta Computing)

Secure and Privacy-Preserving AI

Adviser: Yuan Hong

Join Yuan Hong's lab on cybersecurity and privacy to conduct research in one of these example projects:

(1) privacy-preserving machine learning: privately and accurately analyzing datasets with randomization or cryptographic schemes.

 (2) adversarial attacks to AI systems and effective defenses


Data Analysis and Applied Statistics

Advisers: Lulu Kang, Fred Hickernell, and Yuhan Ding

Conduct research in Faster Monte Carlo Simulation. Monte Carlo methods solve problems involving many variables. This project will use QMCPy, a Python quasi-Monte Carlo library, to explore how to speed up Monte Carlo computations in finance, statistical inference, and uncertainty quantification. Gain some understanding of quasi-Monte Carlo methods, develop use cases for QMCPy, and join a research team of faculty and students.


Mathematical Modeling of Tumor Growth

Advisers: Shuwang Li and Chun Liu

Dive into the tumor growth problem and fundamental ideas of mathematical modeling. The first half of the project focuses on tumor biology ranging from the cell level to tissue level., Become introduced to mathematical equations to describe the tumor biology, in particular its size and morphology in the second part of the project. If time permits, we will talk about numerical computations and gain a sense into high performance computing.


Machine Learning in Financial Markets

Advisers: Matthew Dixon and Igor Cialenco 

One project is Statistical Analysis of Stochastic Evolution Equations. The goal is to explore some recent advances in statistical inference for high dimensional stochastic systems. In particular, we aim to understand the asymptotic properties, such as rate of convergence, of several key estimators, initially using numerical experiments and subsequently proving the obtained conjecture using theoretical tools.


Graph Coloring Problems Modeling Conflict-Free Allocation of Limited Resources

Adviser: Hemanshu Kaul

Study graph coloring problems modeling conflict-free allocation of limited resources by examining list coloring and DP-coloring of graphs, and how the optimal colorings are related to their corresponding counting functions.


Responsible AI for Social Computing

Adviser: Kai Shu 

Dive into advanced artificial intelligence algorithms to tackle emerging issues in social computing including disinformation/misinformation, data and algorithmic bias, cyberbullying, and more. The first half of the project will focus on leveraging social media mining to detect and mitigate disinformation in online communities. The second half of the project will include bias identification in disinformation and building fair machine learning models to counter disinformation. If time permits, we will look into the transparent and robust machine learning models for broad social computing applications.


*a Google account is needed to apply using the above links, or apply by sending an email to

College of Computing

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