People

Dr. Hao Liu is an Assistant Professor in the School of Computing at Montclair State University. His research focuses on health informatics, with a particular emphasis on natural language processing, knowledge representation, and data mining in the biomedical domain. Dr. Liu’s work has been published in leading health informatics journals such as the Journal of the American Medical Informatics Association (JAMIA) and the Journal of Biomedical Informatics (JBI). His research aims to improve the quality and accessibility of clinical trial information, support evidence-based medicine, and enhance clinical decision-making through advanced informatics techniques.

Clinical trial informatics: Dr. Liu researched in the field of clinical trial informatics. His work includes the development of a knowledge base of clinical trial eligibility criteria (JBI, 2021), ontology-based categorization of clinical studies by their conditions (JBI, 2022), and a framework for assessing clinical trial population representativeness using electronic health records data (ACI, 2021). These studies aim to improve the efficiency and effectiveness of clinical trial design, recruitment, and evidence synthesis.

Natural language processing for biomedical ontologies: Dr. Liu has developed novel methods for using deep learning techniques, such as convolutional neural networks and transformer models, to support the maintenance and enrichment of biomedical ontologies. His work has focused on SNOMED CT (AMIA, 2018; AMIA, 2019; JBI, 2020) and the National Cancer Institute Thesaurus (ICBO, 2018), demonstrating the potential of machine learning to enhance the quality and coverage of these critical knowledge resources.

COVID-19 research: In response to the COVID-19 pandemic, Dr. Liu has collaborated on several projects aimed at leveraging informatics to support clinical and public health efforts. These include the development of the COVID-19 Trial Finder (JAMIA, 2021), a data-driven approach to optimizing clinical study eligibility criteria for COVID-19 trials (JAMIA, 2021), and an analysis of the misalignment between COVID-19 hotspots and clinical trial sites (JAMIA, 2021).

Dr. Aparna Varde is the Associate Director of Graduate Studies and Research, and an Associate Professor in the School of Computing at Montclair State University, NJ, USA. She is an Associate Director of the Clean Energy and Sustainability Analytics Research Center (CESAC) at Montclair.

Dr. Varde has been a visiting researcher at Max Planck Institute for Informatics, Saarbrucken, Germany. She obtained her PhD and MS in Computer Science from WPI, Massachusetts, and BE in Computer Engineering from University of Bombay, India. Her work spans Artificial Intelligence, Machine Learning, Data Mining, Databases, Environmental Computing, and Computational Linguistics. Her honors include 4 best paper awards at IEEE conferences. She is Doctoral Faculty in the PhD Program in Environmental Science and Management at Montclair. She has been a dissertation advisor, committee member, and mentor for around 10 PhD students at Montclair State University, and external committee member / mentor for around 8 PhD students worldwide.

Dr. Varde has over 150 publications (journals, conferences, book chapters, edited volumes) by IEEE, ACM, AAAI, Springer etc. She has been a panelist for NSF, PC member at conferences, and reviewer / editorial board member for journals by IEEE, ACM, Elsevier etc. Her research is funded by grants from organizations such as PSE&G, NOAA and NSF. Dr. Varde has been classified as an outstanding researcher by the Citizenship and Immigration Services, USA.

Dr. Jiayin Wang is an Associate Professor in the School of Computing at Montclair State University. She is also the Graduate Program Coordinator for M.S. in Data Science. She received her Ph.D. in Computer Science from the University of Massachusetts, Boston in 2016 and her Bachelor’s degree in Electrical Engineering from Xidian University, China in 2007.

Dr. Wang’s research interests lie in the areas of big data, cloud computing, mobile computing, and wireless networks. Her work focuses on resource management, scheduling, and performance evaluation in big data computing systems.

Some of her recent research outputs include developing new scheduling algorithms to improve performance and resource utilization in Hadoop YARN clusters, evaluating the performance of resource management schemes for cloud native platforms using computing containers, and proposing a hierarchical framework for sentiment analysis on Twitter data. Dr. Wang has published her research in leading IEEE journals and conference proceedings.

Dr. Jing Peng is a Professor in the School of Computing at Montclair State University. His research spans a broad spectrum of machine learning, ranging from subspace representation and classification to rapid query search in large databases.

He has authored or coauthored more than 100 peer-reviewed publications in the areas of Machine Learning, Natural Language Processing, reinforcement learning, classification, learning representations. Dr. Peng has also secured several research grants from the National Science Foundation (NSF) and other funding agencies to support his research. Dr. Peng, in collaboration with Anna Feldman, received the best paper award: Idioms: Hunting them all out , in ACM 14th International Conference on Intelligent Text Processing and Computational Linguistics 2013.

Dr. Katherine Grace Herbert-Berger is a Professor of Computer Science at Montclair State University. She received her Ph.D. in Computer Science from the New Jersey Institute of Technology in 2004, her M.S. in Computer Science from NJIT in 2001, and her B.S. in Computer Science & Mathematics from Saint Peter’s College in 1999.

Dr. Herbert’s research interests lie in the areas of big data, data mining, bioinformatics, scientific databases, and science informatics. She has authored numerous peer-reviewed journal articles, conference papers, book chapters, and a book on bioinformatics database systems. Dr. Herbert has also secured several grants from the National Science Foundation to support her research and educational initiatives.

In addition to her research, Dr. Herbert is actively involved in curriculum development, having authored and co-authored multiple degree programs and courses at both the undergraduate and graduate levels. She serves as an undergraduate advisor and has previously held roles as a Graduate Program Coordinator. Dr. Herbert is deeply committed to student mentoring and has supervised many undergraduate and graduate research projects. She is also engaged in various outreach activities to promote computer science education and inspire the next generation of scientists and engineers.

Shibbir Ahmed Arif is a graduate student in the Data Science program. While pursuing his masters, he is also working as a Graduate Teaching Assistant at the School of Computing. Besides, he is involved in faculty-led research as a research assistant within the school of computing, particularly in the realm of Natural Language Processing. His research interests include Artificial Intelligence, Natural Language Processing and Health Informatics. On top of that, he likes learning latest AI technologies and collaborative work.

Ernest Chianumba is currently enrolled in the Master of Data Science program at Montclair State University. He has over 8 years of experience in data analysis and operations management, specializing in healthcare analytics and e-commerce. Proficient in Python, SQL, and PowerBI, Ernest has contributed to research in healthcare predictive analytics, including currently working on a project funded by Bristol Myers Squibb, where he is utilizing NLP models to improve clinical predictions for underrepresented populations. As a Data Analyst Intern at the Port Authority of NY&NJ, he developed interactive dashboards and reports to support key decision-making processes. Ernest is committed to advancing AI and data science technologies through innovative research and practical applications.