Mohammad Alodadi

Computational Scientist

Highly analytical and results-oriented data scientist with experience in managing technical teams and meeting objectives within strict timelines. His ability to handle ambiguity and coordinate with cross-organizational teams underlines his practical approach. He can simplify and present complex ideas effectively, and has a strong grasp of data collection and analysis from varied resources. He has a keen interest in Data Science, Machine Learning, Deep Learning, Data Mining, Statistical Data Analysis, and Information Retrieval, with notable work in Natural Language Processing and Large Language Models. I am currently a Computational Scientist at The Frederick National Laboratory for Cancer Research (FNLCR)

In addition to my scientific research, I serve as an AI consultant to several US-based companies, where I apply my expertise to address complex business challenges through the lens of artificial intelligence. In this capacity, I collaborate with interdisciplinary teams to design and implement AI-driven solutions that optimize operations, drive innovation, and create value. My consultancy role allows me to translate cutting-edge research into practical applications, ensuring that businesses remain at the forefront of technological advancements and maintain a competitive edge in their respective industries.

Scholar

@MAlodadi

@MAlodadi

@MAlodadi

CV.pdf

Education

Ph.D. in Information Systems

University of Maryland Baltimore County (UMBC), Baltimore, MD

Advisor: Vandana Janeja

Dissertation: Explaining Knowledge Discovery through Linking Multiple Heterogeneous, Unstructured Data Streams: A Case of Clinical Notes Mining

M.S. in Information Systems

University of Maryland Baltimore County (UMBC), Baltimore, MD

B.S. in Computer Science in Education

King Khalid University, Abha, Saudi Arabia

Experience

Computational Scientist

Frederick National Laboratory for Cancer Research (FNLCR). , National Cancer Institute(NCI).

Work as a scientific researcher in the Advanced Bioinformatics and Computational Science (ABCS) group. My research focuses on developing and applying machine learning and deep learning algorithms to analyze and interpret large-scale biological data, including genomics, transcriptomics, proteomics, and metabolomics data. I also work on developing and applying natural language processing (NLP) and large language models (LLM) to analyze and extract genes, diseases and their co-occurrences from biomedical literature for rare genetic diseases.

Bioinformatics Data Scientist

Frederick National Laboratory for Cancer Research (FNLCR). , National Cancer Institute(NCI).

Developed and implemented AI algorithms for Rare-SOURCE™ to efficiently search published literature, emphasizing the identification of rare diseases and associated genes. I oversaw database design and backend operations to facilitate seamless information flow. Additionally, I provided guidance on search strategies, emphasizing the utility of the 'Diseases' and 'Genes' for a comprehensive approach.

Graduate Research Associate/ General Assistant

University of Maryland Baltimore County (UMBC), Baltimore, MD

Engaged in multifaceted roles within the academic setting, involving research, support in writing and publishing academic papers, overseeing lab resources, and handling administrative tasks. Additionally, I provide teaching assistance, contributing not only to course instruction but also participating in curriculum development. Responsibilities include grading, managing student projects to ensure both academic progress and project completion. I guide undergraduate students in their research tasks and project development. Furthermore, I actively participate in professional meetings and research-related events as panelist, speaker, and participant.

Graduate School Dissertation Fellow

University of Maryland Baltimore County (UMBC), Baltimore, MD

As a Graduate School Dissertation Fellow, my responsibilities include conducting focused research, actively contributing to dissertation writing and publication, managing resources, and handling administrative tasks. I provide teaching assistance, contribute to curriculum development, grade assignments, and oversee student projects to ensure academic progress. Additionally, I guide graduate students in their research tasks and actively participate in professional meetings and research events.

Graduate Teaching Assistant

University of Maryland Baltimore County (UMBC), Baltimore, MD

As a Graduate Teaching Assistant, I played a pivotal role in instructing various courses, including IS410 (Introduction to Database Design) and IS420 (Database Applications Development) for undergraduates, as well as IS676 (Information Integration), IS733 (Data Mining), and IS734 (Data Analytics for Cybersecurity) for graduate students. Beyond teaching, I actively contributed to curriculum development and provided support in grading assignments, ensuring the academic growth and success of students.

Publications

Radiology Clinical Notes Mining Using Weighted Association Rules

Mohammad Alodadi

2017 IEEE International Conference on Healthcare Informatics (ICHI), 2017

Similarity in Patient Support Forums Using TF-IDF and Cosine Similarity Metrics

Mohammad Alodadi, Vandana P. Janeja

2015 International Conference on Healthcare Informatics, 2015

Linking Knowledge Discovery in Clinical Notes and Massive Biomedical Literature Repositories

Mohammad S. Alodadi, Vandana P. Janeja

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2019

Clinical Entities Association Rules (CLEAR): Untangling Clinical Notes in Electronic Health Records

Mohammad S. Alodadi, Vandana P. Janeja

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2019

Knowledge Discovery through Linking Multiple Heterogeneous, Unstructured Data Streams: A Case of Clinical Notes Mining

Mohammad S. Alodadi

PhD Thesis, University of Maryland, Baltimore County, 2020

Opportunities for Ethical Decision Making: A Case Study in K-NN

A Goldstein, M Alodadi, V Janeja

INTED2023 Proceedings, 2023

Rare Disease Variant Curation from Literature: Assessing Gaps with Creatine Transport Deficiency in Focus

Erica L Lyons, Daniel Watson, Mohammad S Alodadi, et al.

BMC Genomics, Volume 24, Number 1, 2023

Awards and Recognitions

2023 National Center for Advancing Translational Sciences (NCATS) Director’s Award

For the conception and development of “RARe-SOURCE™”, an innovative bioinformatics data platform to facilitate therapeutic discovery and advance translational science more quickly for rare diseases.

2023 Frederick National Laboratory for Cancer Research Outstanding Achievement Award – Scientific

For the team's commitment to improving the diagnosis and treatment of rare diseases by developing an integrated bioinformatics resource for rare disease research, showcasing originality, impact, and dedication beyond normal job expectations.

NCI-Frederick 2023 Spring Research Festival Outstanding Poster

In the category of Informatics with a poster titled, "Rare Diseases Phenotype-Genotype Associations from Biomedical Literature: Language Model Workflow", Apr 2023.

Travel Grants and Fellowships

• Frederick National Laboratory for Cancer Research travel grant to attend SIG KDD2022-DC
• Frederick National Laboratory for Cancer Research travel grant to attend IEEE BIBM 2021
• UMBC Summer 2020 Dissertation Fellowship, Jun 2020 – Aug 2020
• UMBC Graduate Students Association Travel grant, Nov 2019 - For presenting two papers at IEEE BIBM 2019
• UMBC Department of Information Systems Travel grant, Nov 2019 - For presenting two papers at IEEE BIBM 2019
• NSF Workshop travel grant, Jun 2019 - For attending as a student volunteer organizer for an Invitation-only workshop on Including Ethics in Data Science Pedagogy, EDSP 2019- Alexandria, VA
• UMBC Graduate School Doctoral Candidacy Awarded, May 2019
• IEEE ICHI Travel award, Aug 2017 - For attending and presenting my work on clinical notes mining
• UMBC Department of Information Systems Travel grant, Aug 2017 - For presenting my research and attending Doctoral Students Consortium at IEEE ICHI 2017
• IEEE ICHI Analytics challenge Finalist, Oct 2015 - For presenting my paper and attending Doctoral Students Consortium at IEEE ICHI 2015
• Saudi Arabia Scholarship for PhD Degree, Aug 2014 - Full-tuition scholarship with a stipend for PhD studies. One of 300 awardees in 2014