Welcome to My Page !

Hi! I'm Shunsuke Kikuchi, a 2nd-year transfer student at UCLA. I'm interested in AI, Biology and Networks. Originally from Tokyo-metoropolitan area, I have been studying in the US since 2021. Aiming to be a researcher in the field of AI and Biology/Medicine. In my free time, I enjoy jogging, manga/anime, and traveling. Also as a Kaggle enthusiast, I have participated in various competitions and challenges. Usually choose to work on biology or medicine related competition, but I'm also curious about other fields, including NLP, GNNs and reinforcement learning.
Explore my work, research, and achievements below!

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News

Research

scGRNdb

Dr. Xia Yang's Lab @ UCLA

SCING is a machine learning model that predicts gene regulatory networks from single-cell & spatial transcriptomics data. Using GRNs from SCING, we are developing scGRNdb, an analysis pipeline with a database of gene regulatory networks for single-cell and spatial transcriptomics data. I am involved in the part of designing community detection algorithm, pipeline development, and Evaluations.

23 Winter - Current. Working as an Undergraduate Research Assistant.

Clinical Trial of Potential Migraine Drug on Rodent Model

Dr. Jennifer Xie's Lab @ Arkansas State University

This research proposes to investigate the efficacy of RgIA4, a selective α9α10 nicotinic acetylcholine receptor (nAChR) antagonist, in alleviating migraine-related pain using rodent models. The study will evaluate RgIA4's dose- and time-dependent effects in rat and mouse models by inducing migraines through environmental stress and inflammatory mediators. The research aims to determine whether blocking α9α10 nAChR can reduce pain and inflammatory responses, potentially offering a new therapeutic approach for migraines with fewer side effects and a reduced risk of medication overuse headaches.

21 Fall - 23 Summer. Working as an Undergraduate Research Scholor.

Kaggle


NeurIPS 2024 - Predict New Medicines with BELKA

Silver Medal (27/1950) (Leaderboard)

Compete with machine learning model which predict small molecule-protein interaction using the Big Encoded Library for Chemical Assessment (BELKA)

See more details of our solution here.

HMS - Harmful Brain Activity Classification

Bronze Medal (245/2767) (Leaderboard)

Compete with machine learning mode which classify seizures and other partterns of harmful brain activity in critically ill patients.

Conference Challenges


MICCAI Endoscopic Vision Challenge 2024: SegCol

Task 1 & 2: Results pending

4-class segmentation challenge for colorectal cancer screening using colonoscopy frames. Developed models to segment anatomy edges and instrument masks.

  • Task 1: Semantic segmentation for Surgical Tools and Colon Fold Edges
  • Task 2: Selecting set of images from pool to decide which images to annotate (Active Learning)

MICCAI Endoscopic Vision Challenge 2024: Open Suturing Skills Challenge

Task 1 & 2: Results pending

Classification of surgical suturing skills using simulated environment videos.

  • Task 1: Predicting total Global Rating Score (GRS)
  • Task 2: Predicting full OSATS scoring table

For the bost tasks, perform surgical tools segmentation and detect tips of then, then put the movements of them into 1DCNN+GRU model to extract features. Final prediction is performed by GBDT.

MICCAI Endoscopic Vision Challenge 2024: STIR Challenge

Task 1 & 2: Results pending

Predicting trajectories in surgical tatoos on tissues in robotic surgery video using the STIR (Surgical Tattoos in Infrared) Dataset.

  • 2D point tracking
  • 3D point tracking using stereo-manner left and right videos

6th National Medical AI Contest in Japan

4th prize (10000 JPY) (Leaderboard)

The task was Semantic segmentation of Mutli-Organ, using one of the largest dataset, TotalSegmentator. Segmented organs include gallbladder, liver, pancreas, spleen, kidneys, adrenal glands, aorta, stomach, and duodenum.

For the best model, we employed 3D-UNet approached with large backbone architecutre SeresNext50x4d, with UNet++ and scse block. Achieved >0.89 in mean DSC. Experimental Source Codes and solutions are here

Experience


Internship / ML Engineer

Jmees Inc. | June - October 2024

  • Involved in deveoping AI for surgical support systems
  • Partcipated in 3 MICCAI Challenges to give insights for our models

Curriculum Vitae

You can download my CV in PDF format below:

Download CV