Welcome to

eXplainable

Deep

Intelligence

Lab

News!

Partnership and Collaborative Hub

Research Projects

Our research lab is focused on developing cutting-edge technologies in the fields of Computer Vision (CV) and Natural Language Processing (NLP) for Computer Aided Detection (CAD) on local and Cloud computing clusters. Specifically, we are working on applied Deep Learning (DL) for localization and segmentation, sentiment analysis and response generation with the focus on explainability (XAI).

  • All
  • CV
  • NLP
  • XAI
  • Cloud

Question Answering from Electronic Health Records (EHR)

Pretrained language models can be utlizied to address clinical questions from unstructure free form text in EHR. Providing potential to improve clinical decision making by accessing critical information in EHR notes.

Fairness in Segmentation

Automatic segmentation offers the potential to help surgeons in preoperative planning. However, analyzing the fairness and any probable racial and gender bias in such models has been very limited. .

Sentiment Analysis

The utilization of sentiment analysis techniques for depression detection can assist in identifying expressed emotions and opinions, enabling the identification of potential threats or opportunities.

Medication Effectiveness

ML models can be trained to predict an outcome of interest from a set of input features. XAI techniques and algorithms can be utilized to shed a light on feature importance to make better clinical decisions based on evaluated treatment efficacy.

Few-Shot Learning

Few-shot learning for segmentation provides the potential to significantly reduce the need for large amounts of annotated data, which is often costly and time-consuming to acquire.

Cloud Performance Modeling

Many critical applications are running on the cloud, ewhere scaleable infinite processing power is provided. However, due to collocation of Virtual Machine (VM) and performance interfierences, perfomamnce aware scheduling of critical and time sentsitive jobs to the cloud is needed.

Knee Localization

Enabling more precise and efficient analysis of issues and injuries in medical imaging can be facilitated by localizing a region of interest within an image.

Transfer Learning for Retinal Disease Classfication

To accurately predict the most significant abnormality among multiple categories of retinal disease, with limited data in each category, state-of-the-art pretrained multi-class models are required.

Multimodal Pain Prediction

Multimodal models can be designed to integrate and process multiple sources of data as input to identify potential sources of bias and increase model fairness.

Hamidreza Moradi, PhD

Director North Carolina A&T State University

Our Research Team

Our research team is highly diverse and consists of individuals with a range of educational backgrounds and experience, including graduate and PhD students, doctors, and faculties, all of whom bring unique perspectives and expertise to the research projects. This diversity in the team ensures that there is a broad range of skills, knowledge, and experience that can be drawn upon to effectively tackle the research questions at hand. The team's diversity also promotes creativity, innovation, and inclusivity, and helps to ensure that the research outcomes are rigorous and well-rounded.

Shashank Shekhar, MD

Clinical Advisory Board Duke University

Matthew Morris, PhD

Clinical Advisory Board Vanderbilt University Medical Center

Hamidreza Arabnia, PhD

Academic Advisory Board University of Georgia

Sally Hodder, MD

Clinical Advisory Board University of West Virginia

William B. Hillegass, MD, PhD

Clinical and Biostatistical Advisory Board University of Mississippi Medical Center

Ehsan Alam

Graduate Research Assistant North Carolina A&T State University

Stefan Green

Graduate Research Assistant North Carolina A&T State University

Everett-Alan Hood

Undergraduate Research Assistant North Carolina A&T State University

David M. Lee

Graduate Research Assistant University of Mississippi Medical Center

Tran T. Le

Graduate Research Assistant University of Mississippi Medical Center

Ahmad P. Tafti, PhD

Extramural Collaborator University of Pittsburgh

Soheyla Amirian, PhD

Extramural Collaborator University of Georgia

Nickolas Littlefield

Extramural Collaborator University of Pittsburgh

Candace Howard-Claudio, MD

Extramural Collaborator University of Mississippi Medical Center

News

Join Our Team

We are looking for talented and motivated graduate and undergraduate students to join our lab at North Carolina A&T State University (NCAT).

Opportunities for PhD Students

Skills We Are Looking For:
  • Strong understanding of deep learning fundamentals (e.g., CNN, RNN, LLM) and frameworks (e.g., TensorFlow, PyTorch).
  • Proficiency in Python.
  • Excellent analytical and problem-solving abilities.
Qualifications:
  • Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field.
How to Apply:

Interested candidates should email hmoradi-at-ncat-dot-edu with the following:

  • Your CV.
  • A brief statement of your research interests, including your experience in deep learning and its applications.
  • Relevant publications or project descriptions.
  • WES evaluation report of your degrees.
  • IELTS or TOEFL scores.
  • GRE scores.

Opportunities for Current Undergraduates or Master Students

We are also looking for motivated undergraduate and master students to join our lab as research assistants. This is a great opportunity to gain hands-on experience in AI and deep learning projects while contributing to impactful research.

Skills We Are Looking For:
  • Knowledge of Python programming.
  • Interest in AI, Machine Learning, or Data Science.
  • Willingness to learn and work collaboratively.
How to Apply:

Email me or drop by my office in MERIC!

Contact Us

Contact Us

Connect with Us and Reach Out to Our Team

Our Address

North Carolina A&T State University

Department of Computer Science

MERIC 331, 1101 E Market St, Greensboro, NC 27401

Email Us

hmoradi {\a/} ncat {dot} edu

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