プロフィール詳細

プロジェクトを作成

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Dr. Jaime D.に依頼

Switzerland

PhD AI Architect & Full-Stack Developer | Bridging Complex Biomedical Research with Deployable Software

プロフィール概要

I am an AI Scientist and Software Architect with a PhD and extensive postdoctoral experience specializing in the intersection of machine learning, biomedical signal processing, and medical imaging. My core expertise lies in transforming complex, noisy biological data into robust, deployable AI models and software applications. Through my technical consulting agency, Scifork, I have successfully developed end-to-end projects ranging from machine learning pipelines to complete backend/frontend software architectures and blockchain systems. I bridge the gap between rigorous academic research and market-ready technological solutions. Current & Previous Research Focus - Digital Pathology & Cancer Diagnostics (Current): I am currently developing state-of-the-art AI architectures to analyze and classify H&E (Hematoxylin and Eosin) slides. This involves advanced computer vision and deep learning techniques to assist pathologists in highly accurate cancer diagnostics. - Biomedical Signal Processing (Postdoctoral Research): During my postdoctoral tenure, I focused on applying advanced AI and statistical modeling to process complex biomedical signals, extracting clinical insights and biomarkers from raw physiological data. Core Competencies & Skills: - Artificial Intelligence & Machine Learning: Deep Learning, Computer Vision (specifically medical imaging), Predictive Modeling, and Algorithm Optimization. - Biomedical Data Analysis: Signal processing, handling complex healthcare datasets, and building diagnostic support tools. - Full-Stack Software Development: Designing and deploying scalable backend infrastructures and intuitive frontend interfaces to make AI models accessible to end-users. - Blockchain Architecture: Designing secure, decentralized systems and smart contracts (highly applicable for healthcare data security and Web3 projects). Whether you are a research lab needing to process large sets of biomedical data, a MedTech startup requiring a custom machine learning pipeline, or a company needing a full-stack architect to build a platform from scratch, I bring both the academic depth and the engineering pragmatism to deliver high-quality results. I am highly adaptable and accustomed to solving complex, multidisciplinary problems.

専門分野

• ☆ Python
• ☆ Tensorflow
• ☆ Scikit-learn
• ☆ Peer Review

サービス

• Research
• Consulting
• Data & AI

Research Scientific and Technical Research

Consulting Scientific and Technical Consulting

Data & AI Predictive Modeling, Statistical Analysis, Image Processing, Image Analysis, Algorithm Design-ML, Data Visualization, Big Data Analytics, Data Mining

職務経験

Founder, Director

Tsebix SA

5月 2025 - 現在

Director and Technical lead

SciFork

6月 2022 - 現在

Postdoctoral Reseracher

Universite de Geneve

6月 2016 - 現在

Chief Technical Officer

Liti Capital

6月 2021 - 3月 2022

Postdoctoral Researcher

University of Geneva

5月 2016 - 1月 2020

Assistant Professor

Universidad del Norte

7月 2014 - 7月 2019

学歴

Ph.D in Electronics Engineering (Faculty of Engineering and Natural Sciences)

Sabanci Universitesi

8月 2009 - 7月 2014

MSc in Electronics Engineering (Electrical and Electronics Engineering)

Universidad del Norte

1月 2008 - 7月 2009

Specialist in Computer Networks

Universidad del Norte

8月 2002 - 9月 2003

Ingeneer (Electrical and Electronics Engineering)

Universidad del Norte

1月 1998 - 10月 2002

認定資格

• 認定資格の詳細は未入力です。

出版物

JOURNAL ARTICLE

Delgado Saa, J.F., De Pesters, A., Cetin, M.(2016). Asynchronous decoding of finger movements from ECoG signals using long-range dependencies conditional random fields . Journal of Neural Engineering. 13. (3).

Saa, J.F.D., Pesters, A.D., McFarland, D., Çetin, M.(2015). Word-level language modeling for P300 spellers based on discriminative graphical models . Journal of Neural Engineering. 12. (2).

Delgado Saa, J.F., Cetin, M.(2013). Discriminative methods for classification of asynchronous imaginary motor tasks from EEG data . IEEE Transactions on Neural Systems and Rehabilitation Engineering. 21. (5). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 716-724.

Saa, J.F.D., Çetin, M.(2012). A latent discriminative model-based approach for classification of imaginary motor tasks from EEG data . Journal of Neural Engineering. 9. (2).

CONFERENCE PAPER

Delgado Saa, J.F., Çetin, M.(2011). Hidden conditional random fields for classification of imaginary motor tasks from EEG data . European Signal Processing Conference. Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 1377-1381.