Aakash Tripathi, Ph.D.

Machine Learning Engineer

Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute

LinkedIn ResearchGate GitHub Hugging Face Google Scholar ORCID

Education

🎓 Education

Degree	Institution	Dates
Ph.D. in Electrical Engineering	College of Engineering, University of South Florida	09.2022 – 08.2025
B.S. in Electrical and Computer Engineering	Henry M. Rowan College of Engineering, Rowan University	09.2018 – 06.2022

Experience

💼 Work Experience

Position	Organization	Dates
Machine Learning Engineer	Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute	09.2025 – Present
Graduate Research Fellow	H. Lee Moffitt Cancer Center and Research Institute	08.2022 – 08.2025

👨‍🏫 Teaching Experience

Teaching Assistant: Mayo AI Summit Workshop

Scalable Multimodal AI in Oncology Using HONeYBEE: From Embeddings to Clinical Impact

Mayo AI Summit, July 8, 2025

Workshop Details

Teaching Assistant: Building Transformer-based Natural Language Processing

NVIDIA Deep Learning Institute, Public Student Workshop

NVIDIA DLI, North America, Virtual

Workshop Details

Teaching Assistant: Building Transformer-based Natural Language Processing

NVIDIA Deep Learning Institute, Public Student Workshop

NVIDIA DLI, North America, Virtual

Workshop Details

Teaching Assistant: Fundamentals of Deep Learning for Computer Vision

NVIDIA Sponsored Workshop

Rutgers Business School, Rutgers University, NJ, USA

Workshop Details

Honors & Awards

🏆 Honors & Awards

Best Poster Award

2024 Dr. Robert Gillies Machine Learning Workshop in Cancer (2024)

HONeYBEE: Enabling Scalable Multimodal AI in Oncology Through Foundation Model–Driven Embeddings

Award: $1,000

2nd Position - Moffitt Cancer Center Annual Bio-Data Club Hackathon

Moffitt Cancer Center (2024)

Project: Patient Data Vectors – An Efficient Cancer Research Framework

December 12-13, 2024

Graduate Assistantship Award

University of South Florida (2022)

Dean's List

Rowan University (2018-2022)

Named to the Dean's List for academic excellence throughout undergraduate studies

🎓 Professional Memberships

IEEE Eta Kappa Nu (IEEE-HKN) Honors Society

Member • April 16, 2024 – Present

Institute of Electrical and Electronics Engineers (IEEE)

Member • January 2019 – Present

📊 Research Impact

Total Citations: 493 (2022-2025)

From last manual update

Publications

View complete list on Google Scholar | ORCID

🎤 Presentations

Accelerate Cancer Research With AI-Driven Multimodal Data Integration

NVIDIA GTC 2025 Presentation

Session Page Poster Page Poster PDF

📝 Journal Articles

HONeYBEE: Enabling Scalable Multimodal AI in Oncology Through Foundation Model-Driven Embeddings

npj Digital Medicine 8(1), 622 (2025).

Aakash Tripathi, Asim Waqas, Matthew B. Schabath, Yasin Yilmaz, and Ghulam Rasool

DOI: 10.1038/s41746-025-02003-4

Robust Multimodal Fusion for Survival Prediction in Cancer Patients

Cancer Informatics 24, 11769351251376192 (2025).

Dominic Flack, Aakash Tripathi, Asim Waqas, Ghulam Rasool, and Delil Dera

DOI: 10.1177/11769351251376192

Self-Normalizing Multi-Omics Neural Network for Pan-Cancer Prognostication

International Journal of Molecular Sciences 26(15), 7358 (2025).

Asim Waqas, Aakash Tripathi, Sabeen Ahmed, Ashwin Mukund, Hamza Farooq, Joseph O. Johnson, Paul Stewart, Mia Naeini, Matthew B. Schabath, and Ghulam Rasool

DOI: 10.3390/ijms26157358

Transformers in Time-Series Analysis: A Tutorial

Circuits, Systems, and Signal Processing 42(12), 7433–7466 (2023).

Sabeen Ahmed, Ian E. Nielsen, Aakash Tripathi, Shamoon Siddiqui, Ravi P. Ramachandran, and Ghulam Rasool

DOI: 10.1007/s00034-023-02454-8

Building Flexible, Scalable, and Machine Learning-Ready Multimodal Oncology Datasets

Sensors 24(5), 1634 (2024).

Aakash Tripathi, Asim Waqas, Kavya Venkatesan, Yasin Yilmaz, and Ghulam Rasool

DOI: 10.3390/s24051634

A Comparison of Feature Selection Techniques for First-Day Mortality Prediction in the ICU

2023 IEEE International Symposium on Circuits and Systems (ISCAS) , 1–5 (2023).

Jacob R. Epifano, Alison Silvestri, Alexander Yu, Ravi P. Ramachandran, Aakash Tripathi, and Ghulam Rasool

DOI: 10.1109/ISCAS46773.2023.10182228

Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Review

Frontiers in Artificial Intelligence 7, 1408843 (2024).

Asim Waqas, Aakash Tripathi, Ravi P. Ramachandran, Paul Stewart, and Ghulam Rasool

DOI: 10.3389/frai.2024.1408843

🧩 Abstracts

SeNMo: A Self-Normalizing Deep Learning Model for Enhanced Multi-Omics Data Analysis in Oncology

Cancer Research 84(6_Supplement), 908–908 (2024).

Asim Waqas, Aakash Tripathi, Sabeen Ahmed, Ashwin Mukund, Hamza Farooq, Paul Stewart, Mia Naeini, and Ghulam Rasool

DOI: 10.1158/1538-7445.AM2024-908

Multimodal Transformer Model Improves Survival Prediction in Lung Cancer Compared to Unimodal Approaches

Cancer Research 84(6_Supplement), 4905–4905 (2024).

Aakash Tripathi, Asim Waqas, Yasin Yilmaz, and Ghulam Rasool

DOI: 10.1158/1538-7445.AM2024-4905

🧪 Preprints

HoneyBee: A Scalable Modular Framework for Creating Multimodal Oncology Datasets with Foundational Embedding Models

arXiv preprint arXiv:2405.07460 (2024)

Aakash Tripathi, Asim Waqas, Yasin Yilmaz, and Ghulam Rasool

arXiv preprint arXiv:2405.07460

Self-Normalizing Foundation Model for Enhanced Multi-Omics Data Analysis in Oncology

arXiv preprint arXiv:2405.08226 (2024)

Asim Waqas, Aakash Tripathi, Sabeen Ahmed, Ashwin Mukund, Hamza Farooq, Matthew B. Schabath, and Ghulam Rasool

arXiv preprint arXiv:2405.08226

Embedding-Based Multimodal Learning on Pan-Squamous Cell Carcinomas for Improved Survival Outcomes

arXiv preprint arXiv:2406.08521 (2024)

Asim Waqas, Aakash Tripathi, Paul Stewart, Mia Naeini, Matthew B. Schabath, and Ghulam Rasool

arXiv preprint arXiv:2406.08521

TheBlueScrubs-v1: A Comprehensive Curated Medical Dataset Derived from the Internet

arXiv preprint arXiv:2504.02874 (2025)

Lucas Felipe, Carlos Garcia, Issam El Naqa, Michael Shotande, Aakash Tripathi, Vineel Rudrapatna, and others

arXiv preprint arXiv:2504.02874

EAGLE: Efficient Alignment of Generalized Latent Embeddings for Multimodal Survival Prediction with Interpretable Attribution Analysis

arXiv preprint arXiv:2506.22446 (2025)

Aakash Tripathi, Asim Waqas, Matthew B. Schabath, Yasin Yilmaz, and Ghulam Rasool

arXiv preprint arXiv:2506.22446

Trustworthy AI for Medicine: Continuous Hallucination Detection and Elimination with CHECK

arXiv preprint arXiv:2506.11129 (2025)

Carlos Garcia-Fernandez, Lucas Felipe, Michael Shotande, Marcus Zitu, Aakash Tripathi, Ghulam Rasool, and Gilmer Valdes

arXiv preprint arXiv:2506.11129

Explainable AI in Genomics: Transcription Factor Binding Site Prediction with Mixture of Experts

arXiv preprint arXiv:2507 (2025)

Aakash Tripathi, Ian E. Nielsen, Muhammad Umer, Ravi P. Ramachandran, and Ghulam Rasool

arXiv preprint arXiv:2507

Self-Normalizing Deep Learning for Enhanced Multi-Omics Data Analysis in Oncology

Preprints (2025)

Asim Waqas, Aakash Tripathi, Sabeen Ahmed, Ashwin Mukund, Hamza Farooq, Joseph Johnson, Paul Stewart, Mia Naeini, Matthew B. Schabath, and Ghulam Rasool

Preprint DOI:

Consensus-Based Reasoning with Locally Deployed LLMs for Structured Data Extraction from Surgical Pathology Reports

Preprint (2025)

Aakash Tripathi, Asim Waqas, Ehsan Ullah, Asma Khan, Farah Khalil, Zarifa Gahramanli Ozturk, Daryoush Saeed-Vafa, Wei-Shen Chen, Marilyn M. Bui, Matthew B. Schabath, and others

Preprint DOI:

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