
Cocoan lab (Computational Cognitive Affective Neuroscience Laboratory)
Introduction
The mission of our lab is to understand pain and emotions in the perspective of Computational, Cognitive, and Affective Neuroscience. We also aim to develop clinically useful neuroimaging models and tools that can be used and shared across different research groups and clinical settings.
Our main research tools include functional Magnetic Resonance Imaging (fMRI), psychophysiology measures (skin conductance, pupilometry, electrocardiogram, respiration), electroencephalogram (EEG), and other behavioral measures such as face recording camera, eye-tracker, etc. Most importantly, we use computational tools to model and understand our affective, cognitive, and behavioral responses.
Selected Recent Publication
Research experience
Education

Team
Bon-Kyoung Koo

Team
Ji-Hye Yun

Team
Heetak Lee
Team
Eunae Sandra Cho

Host-virus interactions are complex processes that span binding to cells, entry, dissemination, and finally lytic or latent infection. During infection, host innate immune system restricts viral replication. However, mechanisms underlying viral propagation out of the host surveillance remain unclear.
Our research goal is to understand the molecular basis of virus latency establishment and how it plays a critical role in cellular immune evasion and/or viral reactivation. In addition, we are interested in understanding the role of “the endogenous parasites”, retroelements, in host-virus interaction and innate immunity.

High-resolution mapping of RNA-protein interactions & ultrasensitive and super-resolution proximity labeling proteomics

Our laboratory is working on innovative solutions to combat the degenerative diseases of the musculoskeletal system that inevitably come with aging.

Hyeshik Chang develops and applies transcriptome profiling technologies to explore new RNA regulation mechanisms. His team has specialty in modifying high-throughput sequencing techniques and employing machine learning to identify patterns within large data sets.

Our lab combines molecular biology, biochemistry, high-throughput sequencing, quantitative imaging, and computational modeling for systems-level analysis of RNA networks. Emerging engineering tools make quantitative assessment of biological systems not only possible, but necessary. Moreover, recent advances in sequencing techniques provide plethora of data and bring more global picture to the microscopic system. Quantitative tools and mathematical models to interpret massive quantitative high-throughput data is becoming increasingly important in understanding complex biological systems and developing therapeutics for medical applications. We are interested in applying engineering principles to develop testable models that will provide both molecular and systems-level understanding of different regulatory networks that are centered around RNAs.