About Hankyu Jang

“First Prize” in 2017 Indiana Medicaid Data Challenge – Data Analysis


Hankyu is a first-year Ph.D. student in Computer Science at the University of Iowa specializing in Computational Epidemiology. He received his master’s degree in Data Science from Indiana University Bloomington in the year 2018.

During his master’s degree, he gained knowledge in Artificial Intelligence. He did several research projects that apply Deep Learning, Machine Learning, and Reinforcement Learning techniques to solve problems.

In the field of Computer Vision, he built a CNN-RNN in an encoder-decoder scheme for Image Captioning from scratch using Keras with two other colleagues. For the encoder (Convolutional Neural Networks) he used VGG16, VGG19, and ResNet50 models to generate bottleneck features from images, using pre-trained weights. He used LSTM network as a decoder (Recurrent Neural Networks) to generate sentences using word embedding as input. He trained the LSTM and word embeddings to learn a mapping between image features and training captions.

In the area of Signal Processing, he and two other collegues built a module that detects ambulance siren from traffic sounds and then locates the ambulance. He explored several Machine Learning and dimensionality reduction algorithms to solve this problem. He had an idea of giving ear to self-driving cars so that when the car hears an ambulance coming, the module would help the car to detect the ambulance and pull over.

He also has experience in Reinforcement Learning, a subfield in Artificial Intelligence. He explored different AI agents on pathfinding problems. As for a research project, he designed a novel environment “BusGridworld” that could be utilized to check the adaptiveness of the agents to the non-stationary world.

He enjoys solving real-world problems using AI approach. He applied Hierarchical Agglomerative Clustering to group 100 students into subgroups in a class based on their preferences. He loves playing chess; apart from playing chess, he built a chess agent using Minimax with Alpha-Beta pruning that could search through a few depths within seconds.

He is exploring various domains to apply the learning techniques in Artificial Intelligence: biomedical data, images, signal processing, social media, etc. He is dedicated to solving problems using his toolkits.