Sebastián A. Cajas Ordóñez

sebasmos [ad] mit.edu, scajasordonez [ad] gmail.com

Curriculum Vitae
April 2026

WORK EXPERIENCE

01/2026 - Present. Senior AI/ML Software Engineer at Workday, Dublin, Ireland. Building human-AI systems for the Growth Team.
01/2020 - Present. AI Researcher at Massachusetts Institute of Technology (MIT). Researcher at MIT Critical Data, a grassroots global consortium led by the Laboratory for Computational Physiology at the MIT Institute for Medical Engineering and Science. Contributing to global projects in generative AI, quantum machine learning, and Human-AI systems. Key contributions include:
- Lead research programs across three continents (Americas, Europe, Africa) with Dr. Leo Celi, designing human-AI systems and AI agents for global health.
- Driven 20+ open-source projects spanning clinical NLP, satellite-based disease surveillance, privacy-preserving de-identification, and multi-modal learning for low-resource settings.
- Creator of BODHI, an engineering framework for curiosity-driven AI in clinical decision support — achieving 97.3% rate of appropriate clarifying questions vs 7.8% baseline.
- Work published in The Lancet, Nature Scientific Data, PLOS Digital Health, BMJ Health & Care Informatics, and IEEE Access. [MIT Critical Data] [BODHI]
09/2022 - 09/2025. Senior AI Scientist at CeADAR - Ireland’s National Centre for Applied AI, University College Dublin. Co-creator of ICOS, an EU-funded ecosystem for Edge AI. Key contributions include:
- Architected LLM pipelines (LLaMA, DeepSeek, Mistral) for enterprise document classification and summarization, reducing manual processing time by over 60%.
- Designed and deployed multimodal, online, and federated learning systems for edge AI at continental scale across 8 EU partner organizations.
- Delivered production AI APIs and MLOps workflows on HPC infrastructure using Docker, Kubernetes, and SLURM/Singularity.
- Published 6 papers and co-authored 1 EU architecture white paper during this period.
01/2022 - 01/2024. Fellow in Computer Science at Harvard University. Key contributions include:
- Led development of computer vision and deep learning algorithms for single-cell imaging (CyCIF) using high-dimensional microscopy in the Visual Computing Group and Harvard Medical School’s Lab of Systems Pharmacology.
- Worked on instance segmentation, unsupervised learning, image translation models, and unbiased pixel-level feature extraction for analyzing lateral spillover in segmented tissue images.
- Supervised by Hanspeter Pfister and Siyu Huang at the Harvard John A. Paulson School of Engineering and Applied Sciences.
01/2022 - 08/2022. Machine Learning Intern at EcoVadis, Paris, France. Key contributions include:
- Developed a CLIP-based attribution strategy for logo identification and verification in production documents.
- Combined YOLO and transformers for multi-modal logo detection; built a self-supervised annotation pipeline curating 20,000+ images.
- Supervised by Sophia Katrenko.
05/2020 - 10/2020. ML/CV Engineer at AI India Innovation Centre, Valencia, Spain. Delivered an end-to-end land-use classification system (dataset creation, model training, evaluation) and optimized geospatial workflows with cloud compute and GPU tooling for large-scale satellite image analysis.
2019. Teacher at Camp Poyntelle, USA. Educated 400+ students in astronomy and physics through lectures and hands-on observing sessions.
2018. CS Teacher at Social Hackers Academy, Athens. Built web and mobile applications as part of a digital literacy curriculum for refugees.
2018. Full-Stack Developer at Althaia Psychiatric Centre, Athens. Designed and developed the institution’s official website.
01/2016 - 01/2019. IT Technician at University of Cauca, Colombia. Managed IT infrastructure and network systems across faculties; implemented security protocols to ensure reliability and institutional compliance.

EDUCATION

2020 - 2022. Joint Master in Image Processing & Computer Vision (IPCV) — Erasmus Mundus Joint Master Scholarship. [IPCV] [News]. Full scholarship (€48,000+). Collaborative program across the University of Bordeaux (France), Autonomous University of Madrid (Spain), and Pázmány Péter Catholic University (Hungary).
2020. Data Science for All (DS4A)/Colombia 4.0 — Correlation One. Intensive program in Data Science and AI, sponsored by the Ministry of Information and Communication Technologies of Colombia (MinTIC).
2014 - 2019. B.Eng. in Electronics and Telecommunications Engineering from the University of Cauca, Colombia. Ranking: Top 3%.

AWARDS

2024. Meta Grant Award Phase 2 Finalist. [Link]
2023 - 2026. Google GCP Grant Winner for quantum embedding ML image classification. [Code]
2022. Hack2Hack First Place Biodiversity Winner. [Link]
2021. OpenCV 2021 Competition Phase 1 Winner: Finalist in the world’s largest spatial AI competition, sponsored by Microsoft Azure and Intel, chosen from over 1,400 submissions, earning 6 OAK-D devices.
2021. Best President, IEEE AESS Colombia: Recognized as AESS Best President 2020; chapter awarded best student chapter in Colombia. [Link]
2021. €5,000 ESA-Funded Project for Dengue Forecasting: Towards a Smart Eco-epidemiological Model of Dengue in Colombia using Satellite Imagery. In collaboration with MIT Critical Data. Supported by ESA Network of Resources Initiative. [Data] [Slides] [HuggingFace]
2020. Erasmus Mundus Scholarship for MAIA & IPCV programs (€48,000+). [Link]
2020. Embry-Riddle MSc in Aerospace Engineering Scholarship: Graduate Teaching Assistantship in the Advanced Dynamics and Control Lab (projects sponsored by NASA & SBIR/STTR Technologies).
2020. Hult Prize Regional Finalist in Popayán and Monterrey, Mexico.
2020. First Place, IEEE R9 Humanitarian Activities: Ozone Purifier Project (US $5,200).
2020. First Prize, IEEE SIGHTS COVID-19: Mechanical ventilator development (US $3,914). [Link]
2020. Data Science for All (DS4A) Winner, joining the AI task force of the Colombian government, organized by MinTIC and Correlation One.
2020. Best Impact Global Award for COVID-19 at AAPM Hackathon. [Link]
2019. Excellence Award, Regional Huawei ICT Competition, Bogotá, Colombia.

THESIS

Logo verification and characterization with machine learning for document analysis. EcoVadis, Paris, France. [Online]
Mechanism for the characterization of motion artifacts in photoplethysmography signals under low-intensity movements for tachycardia and bradycardia detection. University of Cauca, Popayán, Colombia. [Online]

PUBLICATIONS

2026

S. A. Cajas Ordóñez, R. Castro, L. A. Celi, R. Delos Reyes, J. Engelmann, et al. “Beyond Overconfidence: Embedding Curiosity and Humility for Ethical Medical AI.” PLOS Digital Health, 5(1), e0001013, 2026. [Paper] [Code]
J. Arslan, K. Benke, S. A. Cajas Ordóñez, R. Castro, L. A. Celi, G. A. Cruz-Suarez, et al. “Engineering Framework for Curiosity-Driven and Humble AI in Clinical Decision Support.” BMJ Health & Care Informatics, 2026. [Paper] [Pre-print] [MIT News]

2025

S. A. Cajas Ordóñez, L. F. Torres Torres, M. J. Meni, C. A. D. Paredes, E. Arazo, C. Bosch, et al. “Uncertainty Makes It Stable: Curiosity-Driven Quantized Mixture-of-Experts.” arXiv preprint arXiv:2511.11743, 2025. [Paper]
S. A. Cajas Ordóñez, L. F. Torres Torres, M. Bifulco, C. A. Duran, C. Bosch, and R. S. Carbajo. “Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learning.” CEUR Workshop Proceedings, Vol. 4153, 2025. [Paper] [arXiv]
S. A. Cajas Ordóñez, M. Lange, T. M. Lunde, M. J. Meni, and A. E. Premo. “Humility and Curiosity in Human–AI Systems for Health Care.” The Lancet, 406(10505), 804–805, 2025. [Paper]
S. A. Cajas Ordóñez, J. Samanta, A. L. Suárez-Cetrulo, and R. S. Carbajo. “Intelligent Edge Computing and Machine Learning: A Survey of Optimization and Applications.” Future Internet, 17(9), 417, 2025. [Paper]
S. A. Cajas Ordóñez, J. Samanta, A. L. Suárez-Cetrulo, R. Ghosh, et al. “An Open-Source AI-as-a-Service Framework for Federated, Efficient, and Drift-Robust Learning in the Continuum Edge–Cloud.” IEEE Access, 14, 26265–26277, 2025. [Paper]
M. Aspis, S. A. Cajas Ordóñez, A. L. Suárez-Cetrulo, and R. S. Carbajo. “DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts.” arXiv preprint arXiv:2507.18464, 2025. [Paper]
G. J. Baker, E. Novikov, S. Coy, Y.-A. Chen, C. B. Hug, Z. Ahmed, S. A. Cajas Ordóñez, et al. “Morphology-Aware Profiling of Highly Multiplexed Tissue Images Using Variational Autoencoders.” bioRxiv, 2025. [Paper]
J. Garcia, X. Masip-Bruin, A. Giannopoulos, P. Trakadas, S. A. Cajas Ordóñez, et al. “ICOS: An Intelligent MetaOS for the Continuum.” Proceedings of the 2nd International Workshop on MetaOS (MECC 2025), ACM EuroSys, 2025. [Paper]
A. Barceló, S. A. Cajas Ordóñez, J. Samanta, A. L. Suárez-Cetrulo, R. Ghosh, et al. “Offloading Artificial Intelligence Workloads Across the Computing Continuum by Means of Active Storage Systems.” Future Generation Computer Systems, 108271, 2025. [Paper] [Code]
C. Y. Chen, R. Abulibdeh, A. Asgari, S. A. Cajas Ordóñez, L. A. Celi, D. Goode, et al. “Algorithms Trained on Normal Chest X-rays Can Predict Health Insurance Types.” arXiv preprint arXiv:2511.11030, 2025. [Paper]
G. D. Agreda, C. A. D. Paredes, M. B. Samboni, J. A. Andrade, and S. A. Cajas Ordóñez. “Bridging Theory and Practice in Quantum Game Theory: Optimized Implementation of the Battle of the Sexes with Error Mitigation on NISQ Hardware.” arXiv preprint arXiv:2508.09050, 2025. [Paper]
C. A. D. Paredes, J. A. G. Obando, S. A. Cajas Ordóñez, and C. Sanchez. “Urbanphony-3-CNN: A Convolutional Neural Network for Identifying the Urban Soundscape Taxonomy in Spectrograms Generated from Audios of Historic Cities.” Ingeniería e Investigación, 45(2), 12, 2025. [Paper]
V. Aher, E. S. Villa, L. V. G. Mosquera, L. F. T. Torres, V. K. Verma, and S. A. Cajas Ordóñez. “Intensity-Based Prompt Generation for Multi-Modality 3D Medical Image Segmentation.” CVPR 2025 Workshop: Foundation Models for 3D Biomedical Image Segmentation, 2025. [Paper]
I. R. Alberto, L. A. Celi, C. Corti, J. Ellen, A. Fiske, M. Kebede, G. H. Kwak, S. A. Cajas Ordóñez, et al. “The Rise of ‘Vibe Science’: How AI Threatens Scientific Rigor.” TechRxiv preprint, 2025. [Pre-print]

2024

D. Moukheiber, D. Restrepo, S. A. Cajas Ordóñez, M. P. A. Montoya, L. A. Celi, K. T. Kuo, et al. “A Multimodal Framework for Extraction and Fusion of Satellite Images and Public Health Data.” Scientific Data (Nature), 11(1), 634, 2024. [Paper]
S. A. Cajas Ordóñez, D. Restrepo, D. Moukheiber, K. T. Kuo, C. Wu, et al. “A Multi-Modal Satellite Imagery Dataset for Public Health Analysis in Colombia” (version 1.0.0). PhysioNet, 2024. [Paper] [Code]
S. A. Cajas Ordóñez, J. Samanta, A. L. Suárez-Cetrulo, and R. S. Carbajo. “Adaptive Machine Learning for Resource-Constrained Environments.” DELTA 2024, LNCS, Springer, 2024. [Paper] [Pre-print] [Code]
K. T. Kuo, D. Moukheiber, S. A. Cajas Ordóñez, D. Restrepo, et al. “DengueNet: Dengue Prediction Using Spatiotemporal Satellite Imagery for Resource-Limited Countries.” arXiv preprint arXiv:2401.11114, 2024. [Paper]
K. Xie, S. Huang, S. A. Cajas Ordóñez, H. Pfister, and D. Wei. “S3-TTA: Scale-Style Selection for Test-Time Augmentation in Biomedical Image Segmentation.” IEEE International Symposium on Biomedical Imaging (ISBI), 1–5, 2024. [Paper]
D. Restrepo, C. Wu, S. A. Cajas Ordóñez, L. F. Nakayama, L. A. Celi, and D. M. López. “Multimodal Deep Learning for Low-Resource Settings: A Vector Embedding Alignment Approach for Healthcare Applications.” arXiv preprint arXiv:2406.02601, 2024. [Paper]

2023

A. L. Suárez-Cetrulo, S. A. Cajas Ordóñez, and J. Samanta. “Intelligence Layer — ICOS Project Architecture White Paper.” 2023. [Paper]

2021

S. A. Cajas Ordóñez, P. Astaiza, D. S. Garcia-Chicangana, C. Segura, and D. M. López. “ECG Arrhythmia Classification Using Non-Linear Features and Convolutional Neural Networks.” Computing in Cardiology, 1–4, 2021. [Paper] [Code]
J. S. Osorio-Valencia, D. Restrepo, S. A. Cajas Ordóñez, D. Moukheiber, et al. “Towards the Implementation of Eco-epidemiological Models for Dengue in Colombia Using Machine Learning and Satellite Images: Policy Advocacy and Open Data Repositories.” Biomédica: Revista del Instituto Nacional de Salud, 41, 2021.

2020

S. A. Cajas Ordóñez, M. A. Landínez, and D. M. López. “Modeling of Motion Artifacts on PPG Signals for Heart-Monitoring Using Wearable Devices.” 15th International Symposium on Medical Information Processing and Analysis, SPIE, 2020. [Paper] [Code]

SEMINARS & BLOGS

2025. “Towards a Functional Continuum Operating System (ICOS)”. [ICOS-Project]
2024. Poster. “Adaptive Machine Learning for Resource-Constrained Environments.” DELTA 2024 Workshop at ACM SIGKDD, Barcelona. [Online]
2023. “Unlocking the Potential of Machine Learning at the Edge: Challenges and Future Trends.” CeADAR / ICOS Project. [Online]
2021. “Satellite Imagery for NASA Space Apps Challenge 2021.” Conference with AESS Unicauca. [Video]

VOLUNTEERING AND OPEN-SOURCE PROJECTS

2025–Present: Co-lead, ShortKit-ML — open-source toolkit for detecting and mitigating shortcuts and biases in ML embedding spaces. 20+ detection methods, 6 mitigation strategies, unified API, interactive dashboard, and MCP server integration. Published on PyPI. [Code] [Docs]
2025–Present: Co-lead, latent-sr — Domain-specific latent representations for diffusion-based medical image super-resolution. [Code]
2025–Present: Creator, AutoResearch Dojo — automated research workflow tools. [Code]
2023-2026: Google-funded project lead on quantum-aware vector embeddings for ML image classification, supported by a GCP Research Grant. [Code]
2024: Co-Lead, Quantum-Based Initiative for Classification and Optimization. [Qubico]
2024: (Book) Machine Learning for Drifts and Shifts.
2020–Present: NASA SpaceApps Leader at University of Cauca and AESS-Unicauca, Colombia. [Website]
2020–Present: Open-source Research Data Scientist, MIT Critical Data Mentor. PI: Leo Celi. [Website]
2021–Present: Advisory Board Member, Ex-President, Founder of AESS Unicauca, University of Cauca. [Website]
2020: Satellite Extractor: Dockerized API for downloading satellite imagery, developed with MIT Critical Data Colombia, sponsored by SentinelHub. [Code] [Datasets] [Tutorials]
2020: Computer Vision Researcher (Open-source): Smart indoor positioning system for the visually impaired.
2020: IEEE Human Sights: Mechanical ventilator project, sponsored by IEEE Humanitarian Sights. [Website] [Code] [Mobile App]
2020: COVID Mutations Risk Dashboard. [Website] [Code]
2019: Telecommunications Engineer and Team Lead for satellite communications system in UAM challenge.
2016: Italian Teacher Assistant for Refugees in a European Union-supported project, AIESEC in Tortona, Italy.

TECHNICAL SKILLS & EXPERTISE

Programming: Python, C++ (8+ yrs); SQL, TypeScript, Shell (5+ yrs). OOP, API development.
Machine Learning & AI: PyTorch, TensorFlow, Hugging Face, scikit-learn, XGBoost, ONNX; model training, optimization, distillation, quantization. Federated learning, online learning, concept drift.
LLM & NLP: Fine-tuning (LoRA, PEFT, QLoRA), RAG, prompt engineering, in-context learning; LLaMA, GPT, Mistral, DeepSeek; LangChain, vLLM, spaCy.
Computer Vision: Instance segmentation, image translation, multi-modal fusion (CLIP, BLIP, ViLT), satellite imagery analysis.
Quantum Machine Learning: Quantum-classical hybrid models, variational quantum circuits, Qiskit, PennyLane.
Data Engineering: PostgreSQL, MySQL, MongoDB; ETL pipelines, large-scale dataset design and curation.
Cloud & DevOps: AWS, GCP, Azure, Oracle; Docker, Kubernetes, CI/CD, Terraform; scalable AI deployments.
High-Performance Computing: HPC clusters (Harvard FASRC, UCD Sonic), SLURM/Singularity, parallel and distributed training.