2016 - 2017 Projects

Working on text-to-speech conversion for one of Prof. Kahng’s classes. Their work involves synthesizing speech based on the text given, and the sound files of the speaker.

Students: Meiyi He, Siya Li, Lusha Li

Mentor: Prof. Andrew Kahng

Working on improving performance and efficiency in computing by using the principles of Approximate Computing. They are using Pin (a tool to insert code in a program to analyze its performance) to find values that remain unchanged, or have changed by an insignificant amount, in order to reduce redundant computations and increase efficiency.

Students: Zhiran Chen, Vivian Lam, Yuxuan Zhang

Mentor: Prof. Dean Tullsen,  Atieh Lotfi

Building a database that includes patients with different types of cancers that are related to brain metastasis. They are focusing only on the brain metastasis related data in order to identify patterns that may establish links between genes and brain metastasis.

Students: Yutong Qiu, Joyce Fang, James Jiang, Shijie Fan

Mentor: Prof. Debashis Sahoo

Working on integrating Kepler (an open-source application for workflows), on the XSEDE environment. Their work majorly involves running four basic workflows on the virtual machine, and give the documentation of all the problems faced, in order to facilitate the smooth use of Kepler on XSEDE resources and minimize the number of problems faced by scientists using Kepler.

Students: Kevin Khuong, Amanda Smith, Parth Shah, Robert Eaton

Mentor: 

Dr. Ilkay Altintas, Shweta

Their project involves recommending the type of workout a user should follow using machine learning techniques, based on the user’s data like health condition, etc. and other parameters such as speed, altitude, etc.

Students: Shuo Li, Zhizhen Qin, Shuai Huang

Mentor: Prof. Julian McAuley

Using different machine learning techniques to distinguish obese from lean individuals. The key feature they are using is gut microbiota, which has been shown to be an important factor in determining the success of bariatric surgery. They are working on replicating existing studies, and possibly enhancing the results of the studies.

Students: Shweta Kinger, Barbara He, Xiaobin Lin, Victoria Tom

Mentor: 

Prof. Rob Knight

They are working on using augmented reality for robotic minimally invasive surgery. Their work involves performing scans on pig liver, and writing code to find the overlap between the actual 3-D image and the reconstructed 3-D image.

Students: Eduardo Tapia, Brendon Chen, Xinyi Yang

Mentor: Prof. Ryan Kastner, Michael Barrow

Working on analyzing quadcopter failures by using different machine learning techniques and comparing them to get the optimal output. The classification of the failure of a quadcopter will depend on several features like the yaw, pitch and roll, altitude of the quadcopter, etc.

Students: Jiayin Wang, Purisa Jasmine Simmons, Lichen Zhang, Catherine Lin

Mentor: Prof. Sorin Lerner

Conducting a user study to test the effectiveness of Tazi, an interface to program robots. They are performing user studies with participants having different abilities and experience in coding (ranging from novice to expert programmers).

Students: Allison Reiss, Erick Soto, Jingxuan Wei, Isaiah Aponte

Mentor: Prof. Steven Swanson

They are working on mapping abnormal air quality changes, specifically on obstacle detection and modeling air quality. For this purpose, they are testing different sensors to measure accuracy and ensure robustness. They are also working on choosing subgroups of sensors that can accurately predict the values of the other sensors to model air quality efficiently.

Students: Jonathan Luck, Woojin Cheon, Robin Osekowsky, Joshua Ramos

Mentor: Prof. Tajana Rosing