​Introducing NICA lab

This video briefly introduces our lab's research direction (4 min, Language: Korean).

Video Seminar

A few research topics in our lab are introduced in this seminar (12 min, Language: English).

Acquiring Big Data from Brain 

Functional Imaging

​With genetic engineering, neurons can be modified to change their brightness as a function of the their activity (i.e., neurons "blink" as they fire) which literally makes the brain activity visible. The main challenge is to record the optical signals at a high spatiotemporal resolution and we develop optical imaging techniques to tackle this.

Computational Imaging

The performance of imaging systems is impacted by a range of factors, including physics, biology, information theory, and the sampling theorem. To mitigate these limitations, we're utilizing computational imaging methods that leverage machine learning to predict more information from limited data.

Multiplexed & Super-resolution Imaging

Fluorescence microscopy is limited to imaging only four proteins simultaneously due to the broad emission spectra of fluorescent molecules. To surpass this limitation and visualize a larger number of proteins, we are developing multiplexed imaging technologies that use machine learning algorithms for blind signal separation.

Analyzing Big Data from Brain 

Neuro-image Processing

State-of-the-art functional imaging methods generate more than a gigabyte of data per second, necessitating the development of automated analysis algorithms. We develop fast and scalable machine learning algorithms capable of processing such brain images without the need for labeled data.

Neuro-data Mining

Neural activity underlies many functions in our brain, but our understanding of the fundamental principles of neural signal processing remains limited. To gain greater insight, we apply computational methods to analyze brain activity data and quantify information flow, uncovering the functional connections between neurons. Our aim is to identify repeating patterns, discover local circuits that operate together, and extract synaptic strength information from brain activity, leading to a deeper understanding of the brain.

Artificial Intelligence & Biomedical Engineering 

Bio-inspired AI

A key objective in the field of artificial intelligence is to create a machine intelligence that truly mirrors biological intelligence. To pursue this goal, we aim to incorporate characteristics of biological intelligence in terms of both architecture and learning processes into artificial intelligence.

Medical AI

Medical diagnosis often relies on patient medical images that can be formulated as image classification problems which AI excels in. However, the variation in the imaging condition and equipment poses a a significant challenge for bringing AI into real-world diagnosis. Furthermore, limited labeled data is often available in most cases. To overcome these limitations, we are combining AI techniques and an understanding of the data's characteristics.