TERT

term

The Illinois LSAMP STEM Pathway and Research Alliance (ILSPRA) project (2019 – 2024) integrates a new component to support its goals. This component is called the Technology and Engineering Research Toolkit (TERT). TERT provides remote research opportunities for ILSPRA students in the areas of Technology and Engineering. TERT offers students excellent professional development opportunities to work on interesting research projects with researchers across the nation. TERT’s research projects are supported by experienced faculty who are aware of what’s necessary to include in a reasonable undergraduate research project. Students will select their project from a list of projects available for qualified students from across the ILSPRA institutions. Each researcher will determine the deliverables of his/her project, the necessary skills, and the number of hours needed to do the project. 

Technology and Engineering Research Toolkit

  • Paid research opportunity for research students
  • Provides remote research opportunities for ILSPRA students in Technology and Engineering
  • Offers students excellent professional development opportunities

Areas of Research Include

  • Electrical Engineering
  • Mechanical Engineering
  • Industrial Engineering
  • Cybersecurity
  • Information Technology
  • Data Science
  • Artificial Intelligence

TERT Project Lead

Dr. Moussa Ayyash, Chicago State University

Email: mayyash@csu.edu

 

 

Apply to TERT

TERT Application Form
in atleast 700 characters, state relative experience if any
Choose 3 from above options
 

 

Active Research Projects

In this project, the student is expected to develop a MATLAB Graphical User Interface software toolkit that processes GPS data and outputs the position estimates on a Google Map (or similar) Throughout this process, the student will learn the basics of GPS positioning and converting the GPS raw measurements to position estimates, and utilize that in writing a user friendly interface and displays the results on a map. The student will also be exposed to processing Inertial Measurement Unit (IMU) data, and the theory behind it, which will be used to provide a robust localization output. The student is expected to run through the whole process from collecting the data to running the software and producing the mapped results.

Mentor: Dr. Samer Khanafseh

Institution: Illinois Institute of Technology

Necessary Skills: MATLAB, Basics of Basics of Linear Algebra (vector/matrix operations) 

Machine-learning based network intrusion detection systems (ML-NIDS) are increasingly popular in the fight against network attacks. In particular, promising detection results have been demonstrated in conjunction with Software-Defined Networks (SDN), in which the logically centralized control plane provides access to data from across the network. Hence, this project aims at building a novel intrusion detection and prevention system (IDPS) to thwart common SDN security issues (e.g., Denial of Service, Link Layer Discovery Protocol exploitation, etc.).

Mentor: Dr. Mohamed Rahouti

Institution: Fordham University

Necessary Skills: 

  • basics of computer networking
  • some knowledge in programming (e.g., Pyhton, C, or Java)
  • basics of machine learning

In this project, the student(s) will work on the development of an open source software toolkit for modeling optical wireless communication (OWC) systems. This includes the development of basic signal processing blocks for modulation/demodulation schemes and the optical channel model. The students will work remotely with students at UMass Boston who will validate the toolkit with existing OWC hardware. The toolkit will be made openly available to the research community in order to assist researchers who are new to the field and/or interested in working on higher layer OWC research problems.

Mentor: Dr. Michael Rahaim

Institution: University of Massachusetts, Boston

Necessary Skills: 

  • Basic programming experience (C++ and Python and/or Matlb/Simulink)
  • Some knowledge of Linux and/or signal processing is preferred
  • Prior use of source code management (e.g., Git) is also preferable

According to its website, GDELT “monitors the world's broadcast, print, and web news from nearly every corner of every country in over 100 languages and identifies the people, locations, organizations, themes, sources, emotions, counts, quotes, images and events driving our global society every second of every day.” Community resilience is defined as “a process linking a set of networked adaptive capacities to a positive trajectory of functioning and adaptation in constituent populations after a disturbance” (Norris et al, 2008). This process of adaptation by communities in response to stress has been modeled by Norris as a set of four networked resources or capacities – “Economic Development, Social Capital, Information and Communication, and Community Competence.” Kulig, Edge, Townshend, Lightfoot and Reimer (2013) also present a model of community resilience with the following three key factors: interactions as a collective unit, expressions of a sense of community and community action. Factors from both models can be developed and promoted in the context of library services to communities.

Mentor: Dr. Kimberly Black

Institution: Chicago State University

Necessary Skills: 

  • Experience with or understanding of inferential statistics
  • Experience with R using the RStudio environment (or possibly some other statistical analysis package) OR using GDELT Knowledge Graph
  • Experience extracting large datasets from publicly available sources such as Google Big Query

Come up with a model to predict the ability of Artificial intelligence to replace a certain Profession. The expected model will take as input the types of tasks performed in a given profession. The model will produce as output the ability of Artificial intelligence to replace that profession, where it could be fully replaceable by AI, not replaceable, or partially replaceable with a possible percentage.

Mentor: Dr. Sufyan Almajali

Institution: Vertex International

Necessary Skills: 

  • computer science/engineering or related background
  • AI general understanding

IoT applications are becoming common nowadays with over 30 billion IoT devices in the market in 2020, and the number is expected to reach 70 billion devices by 2025. IoT Applications over edge computing architectures (ECAs) are gaining attention as edge computing provides IoT applications with the new abilities such as applications with minimum delay in communication, processing, and storage options near the end user of IoT devices. Nevertheless, providing the right ECA for a given IoT application is a challenging task. This research projects attempts to build a framework that represents all the elements involved in ECA design according to IoT application requirements.

Mentor: Dr. Sufyan Almajali

Institution: Vertex International

Necessary Skills: 

  • computer science/engineering or related background
  • AI general understanding

The purpose of this project is to expose students to the different aspects of smart homes security. Students will be introduced to the different components of smart homes networks and the vulnerabilities when it comes to network security. Students will work with TERT faculty on identifying the key vulnerabilities and what is takes to secure smart homes. This project involves Internet and library research as well as implementing proof-of-concept experiments. .

Mentor: Dr. Moussa Ayyash

Email: mayyash@csu.edu

Institution: Chicago State University

Necessary Skills: 

  • Access to internet

Data science is a growing research field. Student researcher will identify a data science field from a multitude of areas (environment, healthcare, sports, computing, etc.) Student will formulate a research question in his/her data science area.

Mentor: Dr. Moussa Ayyash

Institution: Chicago State University

Necessary Skills: 

  • Access to internet

Cognitive radio (CR) is an important field in wireless communications. CR offers an intelligent solution for spectrum scarcity. Student researcher will work on a study to compare the different tools available to investigate and do research in CR. Students will be introduced to the key concepts related to wireless technologies, characteristics of wireless signaling, CR, etc.

Mentor: Dr. Moussa Ayyash

Institution: Chicago State University

Necessary Skills: 

  • Access to internet

This project’s primary research goal is to identify critical privacy and security threats of machine learning (ML) deployments in future wireless networks through analyzing data from multiple sources that are related to ML privacy and security issues and to relate them to privacy and security threats in the context of future wireless networks and associated applications.

Mentor: Dr. Hany Elgala

Institution: University at Albany- State University of New York (SUNY)

Necessary Skills: 

  • Wireless communications and networks, cybersecurity
  • Pyhton

Machine learning and artificial intelligence is having a large impact on systems in Army Applications. As the corporate R&D laboratory for the Army, this project will focus on using open ARL datasets to train state of the art machine learning algorithms for future predictions. The datasets may include sensing & IOT, ballistic applications for Soldier protection, advanced materials & manufacturing, power & energy, autonomy & maneuver, or UAS propulsion. This project will start with common low-level ML algorithms like linear regression and/or logistic regression as a baseline, and then explore different algorithms, architectures, and hyperparameters to improve the performance on validation and test sets.

Mentor: Dr. Mark Tschopp

Institution: Army Research Laboratory

Necessary Skills: Knowledge of advanced math (linear algebra, linear regression) and some computer programming is desirable. MATLAB or Python is a plus.

Within artificial intelligence (AI) is the continued challenge to create intelligent systems that reason in similar ways that individuals reason as they make decisions and perform actions. An important area of AI falls under artificial reasoning. Though artificial reasoning we can generate computational models that enable intelligent systems to reason across various data types. There are two main aspects to explore applying mathematical and AI/ML techniques included in the research (1) using various methods for formulating equations that model the expressions and associated data, (2) using various methods to uncover trends and patterns for forecasting or making predictions dynamically as data is available, (3) create computational models for integrating reasoning which includes looking toward other mathematical techniques including setting a series of linear and non-linear equations for solving for the associated value. The possible solutions can be various ML methods, hybrid reward function methods, optimization methods, tensor algebra methods or others. This project involves both of these aspects utilizing heterogeneous data and human-in-the-loop experimentation.

Mentor: Dr. Adrienne Raglin

Institution: Army Research Laboratory

Researcher Name: Dr. Abdelmounaam Rezgui  
Researcher Institution: Illinois State University  
Skills:  Basic Computer Networking Knowledge, SDN (preferably), Basic Machine Learning, Good Programming Skills.
 
Description: 
In this project, we explore how SDN programmability and machine learning (ML) can combine to take SDNs to their next level where they become intelligent, self-configurable software-defined networks. Specifically, the student is expected to extend one of the existing open-source SDN controllers by adding ML modules that would proactively configure SDN switches based on OpenFlow rules derived through ML. The intelligent SDN controller would provide better alternatives by combining the available global view of the entire network and historical communication patterns as the input to a prediction machine learning algorithm. 
Project Description: Body vital fluid dynamics are key to understanding physiological processed and pharmacokinetic applications. The students will be first introduced to static, simplified body fluid compartments and the biochemical characteristics of the interfaces that separate them. Second, they will be introduced to parameters that govern the passage of vital substances or medications between these compartments. Third, the documented permeability of compartment interfaces to relevant vital fluid contents will be used as inputs for designing a computerized core bioengineering model. Fourth, dynamic interactive animation modules will be integrated based on reported applicable variables and therapeutic target sites.
Mentor Name: Dr. Walid M. Al-Ghoul
Institution: Chicago State University 
Skills: Access to computer and Internet. Desire to learn computer programming and animation.

Project Description: Argonne supports the Department of Energy, Advanced Manufacturing Office, on a multi-laboratory strategic analysis team. With this multi-laboratory team, we evaluate the feasibility and life cycle impacts of energy and sustainable manufacturing and energy conversion systems. For this project, we are interested in creating software models for analyzing and optimizing direct air capture (DAC) systems. DAC is a technology for directly removing CO2 from the atmosphere, with the possibility of converting the captured CO2 into some useful chemical. This technology can help achieve the goal of mitigating adverse climate change impacts.

Mentor Name: Chukwunwike Iloeje
Institution:  Argonne National Laboratory     
Skills: A background in chemical, mechanical or other related engineering would be an advantage. Competence or familiarity with any programming language (python, julia) is a plus, but not required, as long as the student is willing to learn.