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Health Data Science Research Lab

Welcome to the Health Data Science Research Lab, located at Collins College of Professional Studies (CCPS), St. John's University, New York. Our lab is focused on conducting research in the areas of Biomedical Informatics, Explainable Artificial Intelligence, Knowledge Graph, Data Science, and Medical Image Processing. We are passionate about developing cutting-edge technologies and methodologies to address the core problems in healthcare informatics and data science.

Vision Statement: Our vision is to be a leading research lab that develops innovative solutions to the challenges in healthcare informatics and data science. We strive to advance the field of healthcare by developing trustworthy AI systems that improve the quality of patient care and outcomes.

Mission Statement: Our mission is to conduct high-quality research in healthcare informatics and data science, which generates insights that benefit patients, healthcare providers, and society at large. We are committed to mentoring and educating future healthcare informatics and data science professionals to help develop the next generation of leaders in the field.

Research Focus: Our lab's research efforts aim to improve the quality of healthcare by developing trustworthy AI systems. We believe that the combination of data science and biomedical informatics can help in creating effective solutions to complex healthcare challenges. We utilize innovative approaches such as machine learning algorithms, statistical models, natural language processing, and deep learning to analyze vast amounts of medical data and generate insights that can aid in medical diagnosis, treatment, and patient care.

Value of Our Research: The research conducted in our lab has the potential to have a big impact on improving people's lives. Our work can lead to the development of more effective medical treatments, early disease detection, and personalized patient care. We believe that our research can help in bridging the gap between healthcare providers and patients by providing a deeper understanding of healthcare data, resulting in better decision-making and treatment outcomes.

Publications: Our lab has a strong record of publications in reputable journals and conferences. We pride ourselves on the quality of our work and the recognition we have received within the academic community. You can read more about our publications here.

Principal Investigator

Dr. Bukhari


Dr. Bukhari, the Principal Investigator of the Health Data Science Research Lab, is an Assistant Professor at St. John's University, New York. He is a well-respected researcher in the field of healthcare informatics, with a Ph.D. in Computer Science from the University of New Brunswick and a postdoctoral fellowship at Yale University. Dr. Bukhari has received multiple internal and external grants to support his research efforts and has collaborated with prestigious institutions such as CEDAR Metadata Center, Stanford University, and the National Center for Biotechnology Information (NCBI), Bethesda.

You are read more about Dr. Bukhari here.

About Bukhari Lab

Fazel Keshitkar

Fazel Keshtkar, PhD.

Dr. Fazel Keshtkar is an Assistant Professor at the Department of Computer Science, ST. JOHN’S University, NYC (SJU), since 2016. Before joining SJU, from 2013-2016, Dr. Keshtkar was an Assistant Professor at Dept. of Computer Science, Southeast Missouri State University Fazel received his PhD and Master degrees in Computer Science from University of Ottawa, Canada, in 2011 and 2007, respectively.


Nadeem Iqbal

Nadeem Iqbal, PhD.

Dr. Nadeem Iqbal is a Fulbright Scholar at St. John’s University, New York, USA. He received his Ph.D. in the field of Computer Science from the Korea Advanced Institute of Science and Technology and completed his postdoctoral fellowship at the University of Leeds UK. He’s also an Associate Professor at the Department of Computer Science AWKUM, Pakistan.  Dr. Iqbal has achieved the status of Associate Fellow (AFHEA) in recognition of attainment against the UK Professional Standards Framework for teaching and learning support in higher education.  He had been the recipient of various prestigious scholarships/fellowships. He was the recipient of the Fulbright Fellowship.

Asim Abbas

Asim Abbas

My name is Asim Abbas. Currently, I am working as a research scholar at St. Johns University, USA. I have completed my B.S. degree in computer science from Islamia University, Peshawar, Pakistan in 2015. I have completed my master’s at Kyung Hee University, South Korea in 2020. I have worked as a visiting researcher at Halmstad University, Sweden, for two months. My research interests include Deep Learning, Machine Learning, Natural Language Processing, Text Mining, and Health Informatics.

Steve Mbouadeu

Steve Mbouadeu

Hello. I am a student studying Computer Science at St. John’s University. I have research interest and experience in Semantic Web and Entity Linking.


Avinash Bisram

I am a fourth-year undergraduate student in the Computer Science/Data Science BS/MS program at St. John’s University.


Joining Our Lab: If you have a strong background in the tools and technologies we use and share similar research interests, we invite you to join our lab. We welcome individuals who are passionate about conducting research in healthcare informatics and data science, even if they are not located in New York. We encourage you to visit our lab's website to learn more about our ongoing projects and how you can become a part of our team.

Power-Aware Fog Supported IoT Network for Healthcare Infrastructure Using Swarm Intelligence-Based Algorithms

A Decentralized Environment for Biomedical Semantic Content Authoring and Publishing

Skin Lesion Analysis and Cancer Detection Based on Machine/Deep Learning Techniques: A Comprehensive Survey

Attention-Based Explainability Approaches in Healthcare Natural Language Processing

Personalized Semantic Annotation Recommendations on Biomedical Content Through an Expanded Socio-Technical Approach

An NLP-Enhanced Approach to Test Comorbidities Risk Scoring Based on Unstructured Health Data for Hospital Readmissions Prediction

Knowledge Graph Based Trustworthy Medical Code Recommendations

Proficient Annotation Recommendation in a Biomedical Content Authoring Environment

EEG Forecasting With Univariate and Multivariate Time Series Using Windowing and Baseline Method

Classification of Brain Tumor from Magnetic Resonance Imaging Using Vision Transformers Ensembling

Explainable Prediction of Medical Codes through Automated Knowledge Graph Curation Framework

Detection of Cardiovascular Disease Based on PPG Signals Using Machine Learning with Cloud Computing

Analysis of dimensionality reduction techniques on Internet of Things data using machine learning

Personalized Outsourced Privacy-preserving Database Updates for Crowd-sensed Dynamic Spectrum Access

Exploration of Black Boxes of Supervised Machine Learning Models: A Demonstration on Development of Predictive Heart Risk Score

Biomedical scholarly article editing and sharing using holistic semantic uplifting approach

A Sociotechnical Framework for Semantic Biomedical Content Authoring and Publishing

A hybrid query expansion framework for the optimal retrieval of the biomedical literature

Towards structured biomedical content authoring and publishing

Optimizing Semantic Enrichment of Biomedical Content through Knowledge Sharing

Biomedical Scholarly Article Editing and Sharing using Holistic Semantic Uplifting Approach

A Sociotechnical Framework for Semantic Biomedical Content Authoring and Publishing

A hybrid query expansion framework for the optimal retrieval of the biomedical literature

Towards structured biomedical content authoring and publishing

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Information to come

Our research is supported by: