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Research Experience for Undergraduates (REU) Research Projects


Research Experience for Undergraduates (REU) Research Projects


The proposed research projects are subject to change, contingent on new mentors and/or the research interests of the students.

Research Together​


Network analysis of 3D genome organization

Chromosomes are organized into a nonrandom hierarchical structure that consists of two mega-base long compartments called compartments A and B. Compartments A are stretches of transcriptionally active regions which are punctuated by transcriptionally inactive regions called compartments B. These compartments are further segmented into modular units called topologically associated domains (TADs), which exhibit much higher levels of interactions inside the domain compared to interdomain interactions. Genome organization allows the necessary interactions and loops between different regulatory regions to establish the proper gene network for genome function.
This proposed project will use a network analysis approach to understand the topology of the chromosomal contacts that exist in genome organization. Students will perform exploratory network analysis on chromosomal contact network data obtained from Hi-C experiments. The analysis will involve calculating and interpreting network metrics such as the degree of a node, average path length, strong components, clustering coefficients, importance of a node using centrality metrics, etc. Students will also apply different community detection algorithms to detect communities in the network. Subsequent analyses such as association and gene ontology analysis will be performed to interpret these detected communities.


Prerequisites:

  • Basic programming skills (Python preferred)

 

Learning Outcomes:

Undergraduate researchers will:

  • Learn and apply different network analysis algorithms.

  • Learn how to use different network analysis software tools.

  • Exposed to applications of graph theory in biology.

Mentors:

  • Benjamin Soibam - Associate Professor, Department of Computer Science and Engineering Technology


Privacy-Preserving Sharing of Correlated Data

Local differential privacy (LDP) is a state-of-the-art definition to preserve the privacy of the individuals in data sharing with an untrusted data collector, and hence it is a promising technology for privacy-preserving sharing of different data types such as genomic data or location data. Perturbing data before sharing provides plausible deniability for the individuals. However, a very limited number of tasks, such as frequency estimation, heavy hitters, frequent itemset mining, marginal release, and range queries have been demonstrated under LDP. In addition, the original LDP definition does not consider the data correlations. Hence, applying existing LDP-based data sharing mechanisms directly on personal data having correlations (genomic data, location trajectories, etc.) makes perturbed data vulnerable to attacks utilizing correlations in the data.
The aim of this project is to study applicability of existing LDP techniques to correlated data to preserve privacy in data sharing. The students will have an opportunity to learn about privacy-enhancing technologies and privacy-preserving data analytics. They will also gain experience in programming by implementing state-of-the-art techniques.


Prerequisites:

  • Basic programming skills (C++ or Java preferred), Discrete Mathematics, and Probability

 

Learning Outcomes:

Undergraduate researchers will:

  • Be exposed to developing privacy-preserving data sharing techniques

  • Learn how to design and conduct experiments and analyze the collected data

  • Cross train in data analytics and cybersecurity

Mentors:

  • Emre Yilmaz - Assistant Professor, Department of Computer Science and Engineering Technology


Attentiveness Detection of Autonomous Vehicle Drivers

The mass production of autonomous vehicles has made autonomous vehicles affordable. They are likely to revolutionize the way we interact with vehicles. Although autonomous vehicles have made driving less stressful, they pose a different kind of challenge. They reduce the drivers' attentiveness when the vehicle is driven in autonomous driving mode. Multiple studies have reported a slow reaction time of unattentive drivers during the transition from autonomous to manual driving mode. Hence, early detection of drivers' unattentiveness is necessary to alert drivers of possible accidental situations. In this project, we plan to explore various techniques to monitor autonomous vehicle drivers' attention while the vehicle is in autonomous driving mode. We aim to study the driver's in-vehicle behavior while driving the vehicle in autonomous and manual modes. The study findings will allow us to design an AI-based audio-visual feedback mechanism to alert the driver during the onset of unattentive states.

Through this research project, students will have a flavor of computer science research, including studying collected data, developing computer algorithms, analyzing computer models, and presenting the results. In addition, the students will be able to enhance their soft skills, such as working in a team, problem-solving skills, and adaptability to different problem-solving approaches.


Prerequisites:

  • Basic programming skills (Python preferred)

  • Having a background in machine learning is preferred.

 

Learning Outcomes:

Undergraduate researchers will:

  • Be exposed to the human-centered computing research

  • Learn how to design and conduct experiments and analyze the collected data

  • Cross-train in computational sciences and experimental methods

  • Enhance soft skills, such as working in a team, problem-solving skills, and adaptability to different problem-solving approaches.

Mentors:

  • Dvijesh Shastri - Professor and Assistant Chair, Department of Computer Science and Engineering Technology


Process Automation – Design of Feedback Controllers

The objective of this project is to develop and experimentally evaluate the performance of adaptive proportional-integral-derivative (PID) controllers. The PID controller is the most widely used control algorithm in manufacturing industries. Indeed, several design/tuning methods for PID controllers have been proposed and are used in practice. Such tuning methods require knowledge of a process model which is typically developed offline from carefully conducted process tests. In practice, however, such models degrade over time because of process changes or equipment fatigue. Adaptive control attempts to address the model degradation and time varying nature of many processes by updating the process model. Some adaptive controllers can take the form of a simple PID algorithm. However, there are not many adaptive PID controllers widely used in industry.
Thus, this research project aims to develop a stable and robust adaptive PID controller and experimentally test its closed loop performance. The adaptive PID controller will update its tuning parameters subject to process constraints such as maximum and minimum limits on the controlled variable and maximum and minimum limits on the size and rate of change of the manipulated variable. Use of co-simulation concepts will be considered in this effort.
Through this research project, students will learn about on-line, closed loop process model identification, develop a new adaptive PID controller, program the controller and use computer control to run an experimental process. The hands-on experiences will significantly enhance student’s scientific problem-solving skills, appreciation of the role of the scientific method for solving technical problems, and the link between theory and practice.


Prerequisites:

  • Basic programming skills (MatLab preferred)

 

Learning Outcomes:

Undergraduate researchers will:

  • Develop research skills by working in an interdisciplinary environment with engineering and computer science professionals.

  • Be exposed to process dynamics and control concepts

  • Learn how to design and evaluate the performance of adaptive PID controllers

  • Cross train in computational sciences and experimental methods

Mentors:

  • Vassilios Tzouanas – Professor and Chair, Department of Computer Science and Engineering Technology


Human Engagement Analysis for Virtual Meetings

The COVID-19 situation has pushed us to adopt the online environment for meetings and discussions. Most universities now offer some form of online meetings for their courses. The online meetings pose a unique challenge in gauging participation attention in the discussions. Indeed, this is a major challenge for students who are believed to have a short engagement span. Specifically, how do we know if the participant in the online meeting is paying attention to the discussion and not engaged in other activities such as typing an email, watching a news channel, or chatting online? This research aims to understand participants' attention by monitoring their eye movements. We hypothesize that eye movements differ depending on the activity in which the participant is engaged in. The first phase of the research will focus on studying the associations between the various computer-bound activities (a few of them are mentioned above) and eye movements. In particular, computer vision algorithms from the OpenCV library will be employed to capture and examine eye movements. Next, machine learning algorithms will be developed to classify eye movement patterns.

Through this research project, students will have a flavor of computer science research, including studying collected data, developing computer algorithms, analyzing computer models, and presenting the results. In addition, the students will be able to enhance their soft skills, such as working in a team, problem-solving skills, and adaptability to different problem-solving approaches.


Prerequisites:

  • Basic programming skills (Python preferred)

  • Having a background in machine learning is preferred.

 

Learning Outcomes:

Undergraduate researchers will:

  • Be exposed to the human-centered computing research

  • Learn how to design and conduct experiments and analyze the collected data

  • Cross-train in computational sciences and experimental methods

  • Enhance soft skills, such as working in a team, problem-solving skills, and adaptability to different problem-solving approaches.

Mentors:

  • Dvijesh Shastri - Professor and Assistant Chair, Department of Computer Science and Engineering Technology


Design of Floating Barriers

The objective of this project is to expose students to Archimedes principle and the buoyancy of soil and bulky subjects such as concrete. Thus, this research project aims to develop a logical relationship between volume, weight, and buoyancy force considering the efficient size of the barriers. Material science and structural reliability of the artifact will be involved in developing a multi-functional formula that yields optimized used materials and optimum floating force. A program that can define barrier size and dimensions based on the water table would be created and limitations and input data defined the outcomes. Through this research, students will learn about fluid mechanics and materials science in depth with the practical application of theories in physics. The casted sample and hands-on experience, exposure to the concrete lab, and mix design in real problem solving can improve the gaps between theory and practice. Students' observations and learning will encourage them toward critical thinking practices and face the technical challenges of 3D printing, for the initial molding will expedite the concept of this research and expedite the learning and debugging process of molding.




Prerequisites:

  • Basic programming skills (Microsoft Office, high school physics)

 

Learning Outcomes:

Undergraduate researchers will:

  • Develop research skills by challenging themselves to find optimal solutions.

  • Be exposed to concrete technology.

  • Learn how to design and evaluate the performance of floating barriers.

  • Be multi-trained in mathematics and physics and material science.

Mentors:

  • Arash Rahmatian – Associate Professor, Department of Computer Science and Engineering Technology




Comparison of Industry Fate and Transport Models versus Customized Code

The objective of this project is to develop and simulate the chemical spills from storage tanks and other containments, which is a common problem in refineries. This modeling effort will be done at two scales and involve two types of modeling; the global scale effort, which provides a rough estimate of the contaminant when be done utilizing ALOHA, which is a U.S. Environmental Protection Agency model. The local scale effort, which provides a more precise estimate of contaminant will be done utilizing COMSOL, which is a computation fluid dynamic model.

Challenges that may result in errors associated with the global scale model include fluid turbulence, chemical nature, wind, temperature, relative humidity etc. conditions etc. of the spills make things harder to predict any reasonable data. The local scale effort will address some of the issues more systematically than was done in the global scale effort.

Thus, this research project aims to develop a local and global aspect of computer simulations for chemical spills. This will help track chemical spills and hazards in a reasonable fashion. Use of sharing data from local to global simulations and concepts will be considered in this effort.

Through this research project, students will learn about chemical spills, fate and transport in the environment, computer simulation of the spills, and hazards involved afterwards that can pollute the environment. The hands-on experiences will significantly enhance student's scientific problem-solving skills, appreciation of the role of the scientific method for solving technical problems, and the link between theory and practice.


Prerequisites:
Prerequisites are engineering fluid mechanics, thermodynamics, and an introductory computer science course.

Learning Outcomes:

Undergraduate researchers will:

  • Develop research skills by working in an interdisciplinary environment with engineering and computer science professionals.

  • Be exposed to research utilizing computers to simulate the fate and transport of contaminants released during an industrial accident.

  • Learn how to design and evaluate the hazards involved in chemical spills.

  • Cross train in computational sciences and experimental methods.

Mentors:

  • Mahmud Hasan - Assistant Professor, DEPARTMENT of Computer Science and Engineering Technology (CSET)

  • Henry Foust - Assistant Professor and Assistant Chair, department of Computer Science and Engineering Technology (CSET)