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  • Contact Info

Kim, Geena

Asst Professor Adjunct

Positions

Research Areas research areas

Research

research overview

  • My research interest is in applications of artificial neural networks. Previously I worked on computer vision problems using neural networks for healthcare applications: 1) brain tumor substructure segmentation using neural network architectures on multimodal brain MRI data, and 2) prediction of blood pressure in the coronary arteries and STENT surgery recommendation using neural network and machine learning models on intravascular ultrasound images. I’m also interested in studying/emulating cognitive mechanism in brain. My recent projects include 1) learning association of vision and sound using deep unsupervised learning, 2) how a visual memory can be represented as a graph and be used in a navigation problem using deep reinforcement learning, and 3) applying computer vision to develop a cost-effective test methods for safety of concrete at the construction site.

keywords

  • machine learning, artificial intelligence, medical image

Teaching

courses taught

  • CSCA 5622 - Introduction to Machine Learning - Supervised Learning
    Primary Instructor - Fall 2023 / Spring 2024 / Summer 2024 / Fall 2024
    This course introduces various supervised ML algorithms and prediction tasks applied to different data. Specific topics include linear and logistic regression, KNN, Decision trees, ensemble methods such as Random Forest and Boosting, and kernel methods such as SVM. Formerly offered as a special topics course. Same as DTSA 5509.
  • CSCA 5632 - Unsupervised Algorithms in Machine Learning
    Primary Instructor - Fall 2023 / Spring 2024 / Summer 2024 / Fall 2024
    Students will learn selected unsupervised learning methods for dimensionality reduction, clustering, finding latent features, and application cases such as recommender systems with hands-on examples of product recommendation algorithms. Formerly offered as a special topics course. Same as DTSA 5510.
  • CSCA 5642 - Introduction to Deep Learning
    Primary Instructor - Fall 2023 / Spring 2024 / Summer 2024 / Fall 2024
    Course will cover the basics of deep learning, such as multilayer perceptron, convolutional neural network, recurrent neural network, how to build and train neural network models, optimization methods, and application examples. Formerly offered as a special topics course. Same as DTSA 5511.
  • CSCI 1300 - Computer Science 1: Starting Computing
    Primary Instructor - Spring 2019
    Teaches techniques for writing computer programs in higher level programming languages to solve problems of interest in a range of application domains. Appropriate for students with little to no experience in computing or programming. Degree credit not granted for this course and ECEN 1310. Same as CSPB 1300.
  • CSCI 3022 - Introduction to Data Science with Probability and Statistics
    Primary Instructor - Spring 2019
    Introduces students to the tools methods and theory behind extracting insights from data. Covers algorithms of cleaning and munging data, probability theory and common distributions, statistical simulation, drawing inferences from data, and basic statistical modeling. Same as CSPB 3022.
  • ... more

Background

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