Research Projects

This is a list of research projects that may have opportunities for undergraduate students. You can browse all the projects, or view only projects in the following categories:

Industrial Engineering

 

Atomistic Simulations of Gold-Silicon Interface

Research categories:  Aerospace Engineering, Chemical, Civil and Construction, Computational/Mathematical, Computer Engineering and Computer Science, Industrial Engineering, Material Science and Engineering, Mechanical Systems, Nanotechnology, Physical Science
School/Dept.: School of Aeronautics and Astronautics
Professor: Michael Sangid
Desired experience:   Junior standing and ability to develop computer codes.
Number of positions: 1

The size of electronic devices has been decreasing steadily over the years and it is expected to continue that trend, as there is significant interest in the development to microelectronics and nanoelectronics for applications in the biomedical, sensing, data storage and high-performance computing fields, among others. With the increasing miniaturization of electronics, it is important to consider any effects that might happen in the interfaces at the nanometer scale, as the behavior of materials at this length scales may differ markedly from the behavior at the macroscopic scale. This project studies the interactions occurring in the interface between gold and silicon, materials selected due to their excellent properties as conductor and semiconductor, respectively, and their popularity in electronic circuits. The behavior of gold and silicon is expected to differ from the properties observed in the bulk and at larger scales, so it is crucial to analyze and understand the mechanisms of this behavior for the design and manufacture of microelectronic devices utilizing these materials. The research will involve Molecular Dynamics modeling of the gold-silicon interface. Additionally, this project will be complemented by other research opportunities in our lab.

 

Characterizing fiber reinforced composite materials

Research categories:  Aerospace Engineering, Chemical, Civil and Construction, Industrial Engineering, Material Science and Engineering, Mechanical Systems
School/Dept.: School of Aeronautics and Astronautics
Professor: Michael Sangid
Preferred major(s): AAE, ME, or MSE
Desired experience:   Willingness to do hands-on work
Number of positions: 2

We are looking for a motivated, hard-working student interested in experimental composite materials research. This position is on a team investigating fiber orientation and length measurements in thermoplastic composites. These long fiber composites have a direct application to replace steel and aluminum structural alloys in the aerospace and automotive industries. Our team is comprised of Pacific Northwest National Lab, Autodesk, Plasticomp, Magna, Toyota, University of Illinois, and Purdue. Applicants will work under the mentorship of a graduate student and faculty member. The position includes hands on specimen preparation, in the form of extracting and polishing samples for fiber orientation measurements and melting samples and isolating the pertinent fibers for length measurements. Applicants should be undergraduate students interesting in composite materials.

 

Constructing Large Scale Representative Social Networks

Research categories:  Bioscience/Biomedical, Computational/Mathematical, Computer Engineering and Computer Science, Educational Research/Social Science, Industrial Engineering, Life Science, Physical Science
School/Dept.: Industrial Engineering
Professor: Mario Ventresca
Desired experience:   At least one student with object-oriented programming, data structures, algorithm analysis, as well as familiarity with at least basic discrete mathematics, statistics and probability (graph theory, distributions, hypothesis testing, regression, etc). Experience using MPI/parallel computation would be highly advantageous, but not necessary. Familiarity with Linux and R would also be useful. A student with a background in epidemiology or applied mathematics would also be very strongly considered.
Number of positions: 1-2

Facebook, Twitter, Orkut and LinkedIn are well known examples of cyber-social networks. However, social networks obviously exist outside of that domain and can represent the connections we make with the people around us. Unlike cyber-world social networks where the connections people make are fully known and observable, real-world networks must be inferred from limited statistical information as well as reasonable assumptions about human behavior. In both instances, a representative social network allows for the study of not only the network topological properties but also diffusive processes acting upon it, such as information spread, influence, disease, etc. There exists a gap in our ability to reconstruct real-world networks from partially observed, noisy and limited data.

This project will aid in developing efficient and scalable algorithms for constructing real-world social network representations at different scales and abstractions (up to global). An extensive literature review of existing capabilities and known social behaviors/mixing patterns will be conducted as part of the project, as will data acquisition and analysis.

 

Online Review Visualization

Research categories:  Industrial Engineering
School/Dept.: School of Industrial Engineering
Professor: Ji Soo Yi
Preferred major(s): Open to all majors
Desired experience:   Prior experiences in building anything just for fun will be required.
Number of positions: 1

Numerous online reviews, comments, and twits are currently being generated and consumed by online consumers. While searching for a good restaurant, a hair stylist, a doctor, or even a nursing home for a loved one, people want to know the truth from real people beyond deceiving advertisements. However, the (unfortunate) truth is that a consumer usually do not read more than 10 reviews per product. In other words, if a product has more than 10,000 reviews (not uncommon these days), more than 9,990 reviews are simply wasted. What a waste of human cognitive effort?

In this SURF project, we would like to come up with a way to help online review consumers make sense of a large chunk of reviews efficiently and effectively through information visualization and text mining techniques. I also hope that each individual review consumer's effort in reading reviews will be collected and recycled by other review consumers. Thus, the more review consumers read reviews, the easier to read reviews. After the system is implemented, we will conduct a series of human-subject studies to verify whether our technique is actually effective or not. This is an international collaborative project with researchers in University of Konstanz and Georgia Tech.

We will certainly implement some experimental websites using the cutting-edge web and visualization technologies (e.g., HTML/CSS, Ruby on Rails, and JavaScript/jQuery/d3.js) and data/text mining techniques. However, you don't need to know all of these. It is obviously good to know them, but we learned that you can learn very quickly if you are determined to do. Surely, we will help you.

Instead, we want you to have creativity, curiosity, and passion to tackle this interesting problem. We will work as a team, and it will be certainly fun to work together.