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
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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
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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
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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
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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 CSCI 1310 and CSCI 1320 and ECEN 1310. Same as CSPB 1300.
CSCI 3022 - Introduction to Data Science with Probability and Statistics
Primary Instructor
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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.
CSCI 4622 - Machine Learning
Primary Instructor
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Spring 2020
Introduces students to tools, methods, and theory to construct predictive and inferential models that learn from data. Focuses on supervised machine learning technique including practical and theoretical understanding of the most widely used algorithms (decision trees, support vector machines, ensemble methods, and neural networks). Emphasizes both efficient implementation of algorithms and understanding of mathematical foundations. Same as CSPB 4622.
CSPB 1300 - Computer Science 1: Starting Computing
Primary Instructor
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Summer 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 CSCI 1310 and CSCI 1320 and ECEN 1310. Same as CSCI 1300.
CSPB 3022 - Introduction to Data Science with Probability and Statistics
Primary Instructor
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Summer 2019 / Fall 2019 / Summer 2020 / Fall 2020 / Spring 2021 / Fall 2021 / Spring 2022 / Summer 2022
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 CSCI 3022.
CSPB 3202 - Introduction to Artificial Intelligence
Primary Instructor
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Spring 2020 / Summer 2020 / Fall 2020 / Spring 2021 / Summer 2021 / Fall 2021 / Summer 2022
Surveys artificial intelligence techniques of search, knowledge representation and reasoning, probabilistic inference, machine learning, and natural language. Knowledge of Python strongly recommended. Same as CSCI 3202.
CSPB 3287 - Design and Analysis of Database Systems
Primary Instructor
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Fall 2019 / Summer 2020 / Fall 2020
Introduces the fundamental concepts of database requirements analysis, database design, and database implementation with emphasis on the relational model and the SQL programming language. Introduces the concepts of Big Data and NoSQL systems. Same as CSCI 3287.
CSPB 4830 - Special Topics in Applied Computer Science
Primary Instructor
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Spring 2020 / Summer 2022
Covers topics of interest in applied computer science at the undergraduate level. Content varies from semester to semester.
DTSA 5509 - Introduction to Machine Learning - Supervised Learning
Primary Instructor
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Spring 2022 / Summer 2022 / Fall 2022 / Spring 2023 / Summer 2023 / 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 CSCA 5622.
DTSA 5510 - Unsupervised Algorithms in Machine Learning
Primary Instructor
-
Summer 2022 / Fall 2022 / Spring 2023 / Summer 2023 / 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 CSCA 5632.
DTSA 5511 - Introduction to Deep Learning
Primary Instructor
-
Summer 2022 / Fall 2022 / Spring 2023 / Summer 2023 / 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 CSCA 5642.
