Sebastián Castaño

Conception and development of a framework for knowledge management and discovery in machine learning teams. 该产品被定义为机器学习研究的发现引擎, tackling the problem of information overflow like more than 100 machine learning papers uploaded daily to arXiv.

Key Activities
• Implemented a PoC system consisting of a discovery engine for machine learning research using large language models.
•设计和部署服务于发现引擎的云基础设施.
•创建商业模式和市场策略.
• Conducted user discovery and development interviews with more than 50 interviewees.

Development, implementation, and deployment of a marketing mix model based on Bayesian statistical modeling for the company's global operations.

Key Activities
•实施基于最新研究论文的学习模式.
•根据可用数据的特定属性定制模型.
•将模型部署到云上，供MLOps团队使用.
•利用来自业务部门的反馈反复改进模型.
• Presented results to several non-technical stakeholders in the business unit.

The first machine learning-based adaptive deep brain stimulation system implemented in freely moving essential tremor patients. This project was executed in cooperation with colleagues at the University of Washington.

Key Activities
• Established the cooperation between our research lab at the University of Freiburg (brain state decoding lab) and the University of Washington (biorobotics lab).
•构思并开发底层机器学习, control, 以及数字信号处理方法.
• Deployed the algorithms on a host PC and the embedded system of the patients' neurostimulators.
•执行数据收集实验.
•对收集的数据进行离线分析.
•撰写并编辑了同行评审出版物的最终手稿.

Descriptive and prescriptive analysis of injury data for a Bundesliga soccer team. 在我分析了数据并将结果呈现给所有利益相关者之后, 包括非技术人员, the project became part of the team's research department to investigate injury prevention and talent development.

Key Activities
•准备和清理来自多个数据库的数据.
•对数据进行描述性和规范性分析.
• Presented the results to all stakeholders, 包括非技术人员.

Handover of a data management system that a large European bank used for the warehousing and analysis of credit risk data.

Key Activities
•配置和部署UAT和生产环境.
•为后端和前端实现了CI/CD管道.

A novel supervised ML approach for decoding the intensity of Parkinson's disease symptoms from brain signals. We collected data from seven patients undergoing deep brain stimulation therapy and showed that our novel ML approach improved the decoding performance of the symptoms.

Key Activities
•设计并执行数据收集实验.
•对数据进行预处理并进行探索性数据分析.
•构思并实现了新的ML方法.
• Validated the novel ML method with the collected data and a benchmark against state-of-the-art models, 包括深度卷积神经网络.
•应用AutoML对所有考虑的模型进行超参数优化.
• Wrote and edited the final manuscript published in a peer-reviewed journal.

The novel framework developed in this project allows for an objective evaluation and benchmarking of novel ML algorithms to analyze neurological data.

我们应对了以下挑战:
• Scarcity of data available when using data-driven methods in the analysis of brain signals.
•可用标签不可靠
•原始信号中的高水平噪声.

Key Activities
• Conceived the idea.
•执行数据分析.
•撰写了发表在同行评议期刊上的科学手稿.