プロフィール詳細

プロジェクトを作成

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Pooja M.に依頼

India

PhD in Computer Science | RnD Developer | Healthcare and Clinical | Research Mentor | Thesis & Academic Writing

プロフィール概要

I am a Computer Science researcher with 7+ years of industry experience as an R&D Developer at Dassault Systèmes, working across Healthcare (Medidata) and Manufacturing domains. Alongside my industry experience, I have a strong academic research background with published papers in IEEE-sponsored conferences and international journals. My work has been recognized with “Best Research Paper” awards, and I have consistently maintained top academic performance throughout my education

専門分野

• ☆ Python
• ☆ SQL
• ☆ R
• ☆ Tableau
• ☆ Markup Languages

サービス

• Writing
• Research
• Consulting
• Data & AI
• Product Development

Writing Technical Writing, General Proofreading & Editing

Research Market Research, User Research, Feasibility Study, Technology Scouting, Fact Checking, Gap Analysis, Scientific and Technical Research

Consulting Scientific and Technical Consulting

Data & AI Predictive Modeling, Statistical Analysis, Data Visualization, Data Mining, Data Processing, Data Insights

Product Development Product Evaluation, Product Validation, Prototyping

職務経験

Scholar

Janardan Rai Nagar Rajasthan Vidyapeeth University

2月 2022 - 現在

RnD Developer

dassault system pvt ltd

1月 2018 - 8月 2024

学歴

M.Tech

Vellore Institute of Technology

6月 2015 - 7月 2017

M.Tech (Computer Science)

VIT

6月 2015 - 7月 2017

認定資格

• EXTENDED BANKER’S ALGORITHM FOR DEADLOCK AVOIDANCE WITH OPTIMAL SEQUENCE USING SRTF SCHEDULING ALGORITHM.

  TIJER - INTERNATIONAL RESEARCH JOURNAL

  https://tijer.org/tijer/certificatemanager.php?a_rid=159652

  9月 2025 - 現在

出版物

CONFERENCE PAPER

3.Javaid M, Haleem A, Singh RP. Health informatics to enhance the healthcare industry’s culture: an extensive analysis of its features, contributions, applications and limitations. Informatics and Health. 2024;1(1):123–48. doi: 10.1016/j.infoh.2024.05.001.4.Huhn S, Axt M, Gunga H-C, Maggioni MA, Munga S, Obor D, et al. The impact of wearable technologies in health research: scoping review. JMIR Mhealth Uhealth. 2022 Jan 25;10(1):e34384. doi: 10.2196/34384.Shah, P., Kendall, F., Khozin, S., et al.(2019). Artificial intelligence and machine learning in clinical development: A translational perspective.Nature Digital Medicine, 2, 69. https://doi.org/10.1038/s41746-019-0148-35.Liu J, Allen PJ, Benz L, Blickstein D, Okidi E, Shi X. A machine learning approach for recruitment prediction in clinical trial design. arXiv [Preprint]. 2021. doi:10.48550/arXiv.2111.07407.6.Teodoro D, Naderi N, Yazdani A, Zhang B, Bornet A, et al. A scoping review of artificial intelligence applications in clinical trial risk assessment. npj Digit Med. 2025;8:486. doi:10.1038/s41746-025-01886-7. 7.Hulstaert L, Twick I, Sarsour K, Verstraete H. Enhancing site selection strategies in clinical trial recruitment using real-world data modeling. PLoS One. 2024;19(3):e0300109. doi:10.1371/journal.pone.0300109. 8.Jin S, Chen L, Ding H, Wang M, Yu L. Can artificial intelligence predict clinical trial outcomes? arXiv [Preprint]. 2025. doi:10.48550/arXiv.2411.17595. 9.Qian L, Lu X, Haris P, Zhu J, Li S, Yang Y. A survey of artificial intelligence methods for clinical trial outcome prediction. ChemRxiv [Preprint]. 2024. [No DOI available] 10.Zhang X, Xiao C, Glass LM, Sun J. DeepEnroll: patient-trial matching with deep embedding and entailment prediction. arXiv [Preprint]. 2020. 11.Biswal S, Xiao C,Glass LM, Milkovits E, Sun J. Doctor2Vec: dynamic doctor representation learning for clinical trial recruitment. arXiv [Preprint]. 2019. 12.Tam A, Laurent C, Gauthier S, Dansereau C. Prediction of cognitive decline for enrichment of Alzheimer's disease clinical trials. arXiv [Preprint]. 2021.13.Kolluri S, Lin J, Liu R, et al. Machine Learning and Artificial Intelligence in Pharmaceutical Research and Development: a Review. AAPS J. 2022;24:19. doi:10.1208/s12248-021-00644-3. PubMed14.14. Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Molecular Diversity. 2021;25:1315–1360. doi:10.1007/s11030-021-10217-3. PubMedDigital Diagnostics15.15. Müller C, Rabal O, Díaz Gonzalez C. Artificial Intelligence, Machine Learning, and Deep Learning in Real-Life Drug Design Cases. In: Heifetz A, editor. Artificial Intelligence in Drug Design. Methods in Molecular Biology, vol 2390. New York, NY: Humana; 2022. p. 383–407. doi:10.1007/978-1-0716-1787-8_16. Europe PMCSpringer Nature Experiments16.Selvaraj C, Chandra I, Singh SK. Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries. Molecular Diversity. 2022;26:1893–1913. doi:10.1007/s11030-021-10326-z .

JOURNAL ARTICLE

Pooja Mehta, Sagar Ap., Anuradha J., B.K. Tripathy (2015). Genome Sequence Analysis in Distributed Computing using Spark . International Journal of Knowledge Discovery in Bioinformatics.

Pooja Mehta Mehta EXTENDED BANKER’S ALGORITHM FOR DEADLOCK AVOIDANCE WITH OPTIMAL SEQUENCE USING SRTF SCHEDULING ALGORITHM.