1. Research
  2. Interdisciplinary Programs
  3. Academic Collaboration
Students walking down Fairfield Way in front of the Homer Babbidge Library and the Information Technologies Engineering Building. Oct. 19, 2021. (Sean Flynn/UConn Photo)

Academic Collaboration

The School of Computing has a strong focus on interdisciplinary collaboration. It would be difficult to highlight all of our department’s interdiscplinary projects, so we consider only several efforts below.

A flowchart illustrating a data analysis process involving smartphone data

Drs. Jinbo Bi, Alexander Russell, and Bing Wang collaborate with researchers at the UConn Health Center on an exciting project that uses smartphone sensor data to predict depression and support diagnosis. This work involves smart and connected health systems employing a combination of techniques from networks, machine learning, algorithms, and health & medicine. Dr. Bi also works with physicians (particularly psychiatrists, psychologists and oncologists) and biologists to understand disease and design personalized medicine using machine learning.

Dr. Shin is currently collaborating with bone biologists at UCONN Heath Center, Drs. David Rowe and Peter Maye, on a project entitled Skeletal Phenotyping of Heterozygotes from IMPC Embryonic Lethal Lines supported by NIH/NICHD (Grant No.: R01 HD098636), where he is leading the project’s bioinformatics component. This project is part of an internationally coordinated effort, called Knockout Mouse Project (KOMP), which aims to turn off every protein-coding gene in mouse genome to infer its normal function. This recent NICHD funded KOMP project is a continuation of this group’s previous KOMP project supported by NIH/NIAMS (Grant No.: R01 AR063702). The data obtained from the previous phase is publicly available on its web portal. New data from the current phase of this KOMP will include publishing the knockout gene’s inferred functions in terms of user-friendly pathway diagrams using TOPAS that are overlaid with published GEO gene expression data in support for the inferred gene function.

Dr. Mukul Bansal has a long-standing collaboration with Drs. J. Peter Gogarten and Joerg Graf (Molecular and Cell Biology at UConn) to improve understanding of horizontal gene transfer in microbes. Their work has resulted in a number of publications systematically studying horizontal gene transfer in prokaryotes and detecting high frequency transfers. He is also collaborating with Dr. Gregory Fournier (Department of Earth, Atmospheric and Planetary Sciences at MIT) to better understand the chronology of life on Earth.

Through collaboration with radiologists from the Radiology department at the UConn Health Center (UCHC), Dr. Sheida Nabavi and co-investigators are working on developing deep learning models for cancer classification, detection and segmentation. She has further collaborations with biologists at the Jackson Laboratory For Genomic Medicine (JAX) to develop software tools for analyzing raw DNA and RNA sequencing data.

A sample of other faculty with interdisciplinary collaborations include

  • Dr. Fei Miao who collaborates with the electrical engineers, chemical engineers, and social scientists to design theory and algorithm foundations to improve the efficiency and cybersecurity of renewable energy based microgrid networks.
  • Dr. Derek Aguiar who has extensive collaborations with researchers in statistics, molecular biology, clinicians, and law researchers on various projects that include algorithmic and  machine learning methods development.
  • Dr. Qian Yang collaborates extensively with colleagues in Chemical and Biomolecular Engineering, Materials Science, and Mechanical Engineering on machine learning for physical science applications.