Illinois Tech students are invited to see the College of Computing student poster session on Friday, March 22, from 12:45–1:45 p.m. at the Stuart Building (in the atrium). Undergraduate, graduate, and Ph.D. students will compete for the best poster. The audience will have a chance to vote for the audience choice award so come support your fellow students.
Questions? Email Scott Pfeiffer at pfeiffer@iit.edu.
Student posters include the following ideas:
• We present a noise guided trajectory based system identification method for inferring the dynamical structure from observation generated by stochastic differential equations.
• Metric analysis for Dynamic provisioning of kubernetes cluster
• Our IPRO project innovatively addresses the challenge of seamlessly integrating data from diverse databases like PostgreSQL, Accumulo, and SciDB. By navigating the complexities of these systems, we aim to minimize temporal disparities, enhancing efficiency in cross-database data integration. Our research offers valuable insights for improved database management and streamlined data integration processes.
• There is a need for an educational platform for teaching network protocols and programming. The goal of the project is to create, implement and introduce a system using P4 that provides a comprehensive learning opportunity. This will allow students to fully enter the complex realm of network protocols and programming through various hands-on sessions.
• Our project focuses on the development of an artificial pancreas system.
• Creating a new feature in tcpdump to process packets before displaying or writing them with Cryptography-based Prefix-preserving Anonymization (Crypto-PAn) for IP address anonymization.
• My student research is based in deploy the pmacct tool on FABRIC and then implement a tutorial on how to use it
• This work introduces CHROME, a novel concurrency-aware cache management framework. CHROME takes a holistic approach by seamlessly integrating intelligent cache replacement and bypassing with pattern-based prefetching. By leveraging online reinforcement learning, CHROME dynamically adapts cache decisions based on multiple program features and applies a reward for each decision that considers the accuracy of the action and the system-level feedback information.
• This project is the first deployment of GraphBLAS on FABRIC, an international network testbed platform used for research and experimentation in networking. GraphBLAS uses linear algebra for network traffic analysis. It forms a matrix of network addresses, with rows and columns denoting source and destination addresses, and elements indicating communication volume. If applied more widely on FABRIC, GraphBLAS can aid with threat identification and potentially enable real-time threat response. By correlating network traffic with contextual data, there’s a possibility of gaining valuable insights into incidents, which could aid in promoting effective responses. Moreover, the utilization of GraphBLAS for efficient processing of large-scale matrices offers scalability for analyzing network traffic data, making it a potentially suitable option for managing high-speed networks and large testbeds.
• In my AI Language Understanding course, I developed a classifier to predict a person’s gender based on a statement. The classifier was trained on 55,510 Twitter Tweets and utilized a naive bayes model. The model was able to achieve about 59% accuracy and I created an interactive demo for more statements to be classified.
• This poster will address the challenge of hard-coding credentials (usernames and passwords) in applications during deployment. Vault emerges as a key tool, offering a reliable way to manage secrets across infrastructure, application, and code levels by accessing these credentials using keys securely transmitted over https.