My name is Junho Lee :)

• Undergraduate: Department of Computer Engineering, Yonsei University, Republic of Korea. 2019.3. ~ Present

• Intern, Applied Data Science lab (Yonsei Univ.) 2020.6. ~ 2021.3.
  • Analyzed and implemented papers about Olayan, Peska, PyDTI benchmarked the performance of each ML model
  • Defined custom AUC, AUPR metric for more accurate evaluation
  • Refined papers by pointing out the experimental error about dataset configuration
• Intern, Database lab (Yonsei Univ.) 2021.1. ~ 2021.3.
  • Developed real-time hospital data visualization program via socket programming
  • Built real-time vehicle tracking program for autonomous driving using Google map API
• Intern, Clinical pharmacology lab (Yonsei Univ.) 2021.2. ~ 2021.4.
  • Enhanced genealogy cell position visualization program by calculating weighted distance instead of single distance
• Co-Organizer, Center for Clinical Imaging Data Science (Yonsei Univ.) 2021.9. ~ Present
  • Analyzed Parathyroid carcinoma vs adenoma radiomics
  • Predict Nonfunctioning pituitary adenoma (DR, SSTR2, SSTR5)
  • Analyzed difference in fractal dimension, lacunarity according to hTERT mutation in Meningioma
• Intern, Medical Artificial Intelligence Laboratory (Yonsei Univ.) 2022.1. ~ Present

• Software Digitalhealthcare Academic Seminar Challenge Grand Prize 2021.9.
• Yonsei Data Visualization Contest with Intel Encouragement Award 2021.8.

• YAI (Yonsei Artificial Intelligence Conference) 2021.7. ~ Present
• Under the direction of Professor YongRae Cho 2021.5. ~ 2021.6.
  • Research topic: Foundation of Deep Learning with MRCNN, DCGAN
  • The purpose of this study was segmentation of 3D tooth model using MRCNN
• Research meeting Under the direction of Professor SungSoo Ahn (Sirano) 2021.9. ~ Present
  • Research topic: nnU-Net : a self-configuring method for deep learning based biomedical image segmentation

• Molecular Classification of Cancer by Gene Expression Monitoring 2020.1. ~ 2020.4.
  • Preprocessed the noisy molecular data with PCA reconstruction
  • Compared the noisy molecular data provided by the Kaggle using various ML model
• AI Competition to Predict Solar Power Generation 2020.12.
  • Analyzed by using statistical method of the correlation between solar power generation and weather
• Autonomous Project 2021.3. ~ 2021.5.
  • Predicted and Analyzed time series corona data by using LSTM
• Pneumonia Detection Challenge 2021.7. ~ 2021.9.
  • Regularized Dicom Data & Metadata Analysis
  • Detection Lung Opacity using Segmentation model
• Intel Data visualization Challenge (Yonsei Univ & Korea Univ) 2021.8. ~ 2021.9.
  • Analyzed EMR Data & Cohort Data
  • Visualized preprocessed data
• Software Digitalhealthcare Academic Seminar Challenge 2021.9. ~ 2021.10.
  • Regularized Dicom Data
  • Designed Faster R-CNN configured EfficientNet backbone network
• Cancer Instance Segmentation form Tissue 2021.10. ~ 2021.11.
  • Segmentation Tissue using RefineNet
  • Evaluated by BCE, Dice loss, IoU, confusion matrix
• AIDD Challenge (Artificial Intelligence Diabetes Datathon 2021) 2021.11. ~ 2021.11.
  • Preprocessed Tabular Data
  • Found optimal method using ML each case
• Autonomous Project 2021.11. ~ 2021.12.
  • Preprocessed Gene Ontology DNA Sequence Data & PPI data
  • Measured Semantic Similarity using annotation, node, and edge-based method

⭐️ From Dongha Kim