Curriculum Vitae
University of Hawaiʻi Cancer Center, Department of Epidemiology Department of Information and Computer Sciences, University of Hawaiʻi at Mānoa
Education and Certifications
Ph.D., Computer Science, University of Hawaiʻi at Mānoa. In progress, expected 12/2028 Co-advisors: John A. Shepherd, Ph.D. and Huaijin Chen, Ph.D.
M.S., Computer Science, University of Hawaiʻi at Mānoa. 12/2024
Certificate, Full-Stack Web Development, Coding Dojo, Honolulu, HI. 05/2022
CompTIA Network+ and CompTIA Security+. 11/2015
B.S., Exercise Science, Utah Valley University, Orem, UT. 05/2015
Research Positions
Graduate Research Assistant, University of Hawaiʻi Cancer Center, Department of Epidemiology, and Department of Information and Computer Sciences, University of Hawaiʻi at Mānoa August 2024 – present · PI: John A. Shepherd, Ph.D.
Breast cancer risk modeling and federated learning in the Hawaiʻi and Pacific Islands Mammography Registry (HIPIMR). Developed the first personalized federated learning evaluation for mammographic cancer detection in Native Hawaiian and Pacific Islander populations. Work on deep embedding–derived imaging phenotypes, mammographic risk heterogeneity, and AI equity in skin cancer screening.
Graduate Research Assistant, Department of Information and Computer Sciences, University of Hawaiʻi at Mānoa January – August 2024 · PI: Peter Washington, Ph.D.
Machine learning for digital health screening. First author on a scoping review of machine learning for Parkinson’s disease using consumer digital technologies, and on a study of machine learning for autism screening across three international cohorts. Authored and managed IRB applications through approval for two phases of a web-based screening study; contributed to web platform development and led participant recruitment for the Parkinson’s disease web study.
Publications
First-Author Publications, Peer-Reviewed
Sollis LJ, Young PM, Bunnell A, Quon B, Hernandez BY, Wolfgruber TK, Shepherd JA. Personalized federated learning for equitable breast cancer detection in underrepresented Pacific Islander populations. 7th MICCAI Workshop on Distributed, Collaborative and Federated Learning (DeCaF 2026), Strasbourg, France. Springer LNCS vol. 17252 (MICCAI 2026 Satellite Events). Oral presentation. DOI and page numbers pending publication. Open access: papers.miccai.org/miccai-2026-sat/DeCaF_006.html
Software: GMIC-FL, the federated training and evaluation system developed for this work.
Sollis LJ, Shvetsov Y, Quon B, Wolfgruber TK, Bunnell A, Hernandez BY, Shepherd JA. Do deep embedding–derived imaging phenotypes reveal breast cancer risk heterogeneity beyond scalar risk stratification? In: Sechopoulos I, ed. Proceedings of the 18th International Workshop on Breast Imaging (IWBI 2026); June 28–July 1, 2026; Thessaloniki, Greece. Zenodo; 2026:423–432. Zenodo: zenodo.org/records/22122618
Sollis LJ, Wall DP, Washington PY. Compact subsets of autism screening items predict clinical diagnoses with a machine learning analysis of the QCHAT-10. Scientific Reports. 2025;15:39091. doi:10.1038/s41598-025-26131-9 · PMID: 41203747
First-Author Manuscripts Under Review
Both passed initial peer review with a major-revision decision; revised manuscripts are submitted and under review.
Sollis LJ, Bunnell A, Cho E, Willingham ML Jr, Cruz-Mattos G, Arii A, Lum C, Cassel K, Shepherd JA. Artificial intelligence across the skin cancer screening pathway: an umbrella review. npj Digital Medicine: major revision submitted, under review. Preprint: doi:10.21203/rs.3.rs-9069373/v1
Sollis LJ, Jaiswal A, Lim K, Raza F, Kim D, Banerjee A, Zhou AM, Brown E, Bove R, Abbasi-Asl R, Washington P. Machine learning for Parkinson’s disease diagnosis using digital technologies: a scoping review. PLOS Digital Health: major revision submitted, under review.
Co-Author Publications, Peer-Reviewed
Tumpa ZN, Shahriar Zawad MR, Sollis L, Parab S, Azizian P, Honarmand M, Kargarandehkordi A, Tarrit K, Abbasi-Asl R, Bove R, Wall DP, Washington P. RobustPDx: a structured web-based Parkinson’s assessment dataset and benchmark for AI robustness to device type and handedness. Scientific Data. 2026. In press. doi:10.1038/s41597-026-08118-x
Shahriar Zawad MR, Tumpa ZN, Sollis L, Parab S, Washington P. Remote assessment of Parkinson disease using deep learning on structured mouse-trace data from suspected cases: machine-learning pilot feasibility study. JMIR Formative Research. 2026;10:e81368. PMID: 42361348
Tumpa ZN, Shahriar Zawad MR, Sollis L, Parab S, Chen IY, Washington P. Quantifying device type and handedness biases in a remote Parkinson’s disease AI-powered assessment. npj Digital Medicine. 2025;8(1):550. PMID: 40866555
Presentations
Oral
Personalized federated learning for equitable breast cancer detection in underrepresented Pacific Islander populations. 7th MICCAI Workshop on Distributed, Collaborative and Federated Learning (DeCaF), Strasbourg, France. October 1, 2026.
Posters
Do deep embedding–derived imaging phenotypes reveal breast cancer risk heterogeneity beyond scalar risk stratification? 18th International Workshop on Breast Imaging (IWBI 2026), Thessaloniki, Greece. June 28 – July 1, 2026. Accompanying proceedings paper: see publication #2. Sollis LJ, Shvetsov Y, Quon B, Wolfgruber TK, Bunnell A, Hernandez BY, Shepherd JA.
Can clustering of deep image representations reveal latent breast cancer risk subtypes in a multiethnic population? Deep-Brea3th 2025: 2nd Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care, MICCAI 2025. Daejeon, Republic of Korea. September 23, 2025. Sollis L, Bunnell A, Wolfgruber TK, Quon B, Kane C, Valdez D, Hernandez BY, Sadowski P, Shepherd JA.
Can clustering of deep image representations reveal latent breast cancer risk subtypes in a multiethnic population? 11th International Breast Density Workshop, Līhuʻe, Kauaʻi, HI. June 4–6, 2025. Sollis L, Wolfgruber T, Quon B, Kane C, Valdez D, Hernandez BY, Kerlikowske K, Sadowski P, Shepherd JA.
Professional Experience
Eight years of technical leadership in military and university information technology prior to beginning doctoral research, including team management, infrastructure operations, and data engineering.
Data Analyst and Assistant Database Manager, University of Hawaiʻi at Mānoa. July 2023 – August 2024 Energy data infrastructure for UH campus buildings: PostgreSQL schema normalization and key relationships, standardized ingest procedures, SQL analytics for Energy Use Intensity metrics, and Tableau reporting used by energy management leadership.
Information Technology Manager, U.S. Army, Schofield Barracks, HI. 2019 – 2022 Led a tier-1 helpdesk supporting a 600-person organization; account and equipment lifecycle management; oversaw maintenance and operation of networking and radio equipment valued over $10M.
Helpdesk Manager, U.S. Army, Bagram, Afghanistan. May – November 2017 Resolved 200+ local tickets and coordinated 600+ escalated tickets in a deployed environment; migrated a finance unit from on-site to virtualized remote servers; upgraded 100+ workstations.
Networking Team Manager, U.S. Army, El Paso, TX. 2016 – 2018 Planned and supervised operation of a network node and three radio nodes providing tactical internet and voice communications for a 500-person unit; directed technical training for a 30-person team.
Technical Skills
Languages: Python, R, SQL, Java, JavaScript, HTML/CSS, Perl, PHP, Dart ML / Scientific: PyTorch, Scikit-learn, Keras, Pandas, NumPy Federated learning: NVIDIA FLARE, FedAvg, FedProx, FedBN, Ditto; multi-site GPU federations Medical imaging: DICOM processing, mammographic preprocessing, GMIC, Mirai, Mammo-CLIP Databases: PostgreSQL, MySQL, MongoDB, Cloud Firestore Web / Application: React, Flask, Express, Spring Boot, Meteor, Bootstrap, Tailwind CSS, Flutter Tools: Git/GitHub, Docker, LaTeX, Jupyter, Google Colab, RStudio, Tableau, Figma Systems: Linux (Ubuntu), macOS, Windows, Minix, seL4