Research Summary


Research Interests



 1. How reproductive behaviors (or ecological facets) lead heterogeneous genital structures in male ants and how we could delimitate the boundaries of ant species especially using reproductive castes? - Taxonomy, Phylogeny, Morphology
2. How are genotypes related to phenotypes, ecology, or behaviors? Phylogeny
3. How are Aphaenogaster ants distributed in across the world, where they come from? - Paleoentomology, Phylo-biogeography
4. What are the potential ant species which can be spread to the world rapidly and what ecological features they have? - Ecology

5. Could taxonomy be enhanced by using deep learning and Computer vision? - Deep learning, Computer vision


1. 3D based ant morphology and anatomy

Taxonomy, Systematics, Morphology

 

 


3D BASED MORPHOLOGY

Figuring out the clear morphological characteristics are still considered highly important in the taxonomy even though molecular studies based on various markers are getting more important to understand the taxonomic status of each species. 3D modeling can be a very useful method when it can be properly provided with CT-scanned stl file or many 2D scientific photographs. It is also can be freely sent as a 3D file format and printed out. All the 3D files can be further animated by Autodesk MAYA and some of rendering programs to visualize the specific behaviors such as walking patterns or mating behaviors.

L: 3D model of Dcatria templaris

1. 3D model of Linguamyrmex sp.

2. 3D model of Neoponera sp. 

3. 3D model of Aphaenogaster sp,

4. 3D model of Aphaenogaster sp, (2)


‌ A photogrammetry is very helpful method in examining the particular characters which are might very important in taxonomy but hard to visible in 2D photos. It can bring the micro habitats of each species to virtual reality as well. It will be way more informative than traditional biological descriptions. ‌

 

2. Using deep learning and Computer vision in entomology

 Image segmentation, object detecting and motion tracking


Automatic ant ID:
Image segmentation, multiple object detecting and motion tracking... 
How these can be applied to entomology?

Deep learning and Computer vision are becoming very strong tools to solve the problems in many scientific fields. I can use some of libraries for constructing deep learning which are represented by Pytorch and Tensorflow. Image classification by R-CNN algorithm, real-time multiple objects detection,  and Image segmentation will be very attractive ways to do many entomological studies that we could not been approached.

L: Automatic ant ID program I made using Tensorflow. Please wait for a second to see how it works

1. Vehicle detecting program I have conducted using Pytorch and YOLO v5 (1)

2. Tensorboard to see how the program I have made works well 

3. Vehicle detecting program I have conducted using Pytorch and YOLO v5 (2)

4. Image crawling using Selenium Python to make my own dataset .

3. Ant Phylogeny & Evolutionary biology

Paleomyrmecology, Impression and amber fossils

 

 

1. Impression fossil excavated from Pohang

2. Impression fossil excavated from Jinju

3. Line drawing of impression fossil

4. Co-evolution: ants and other myrmecophile animals

The relationships between ants and other creatures