Apprentice in privacy protection, machine unlearning, federated learning, fairness and blockchain, from Earth with Love <3
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
federated-learning-seminar
October 27, 2020 – March 10, 2022
Apprentices in Federated Learning from ECNU <3
View ProjectCCFrank4dblp
August 28, 2019 – Present
Displays the China Computer Federation (CCF) recommended rank of international conferences and journals in the dblp, Google Scholar, Connected Papers and and Web of Science search results.
View Projectapprenticeship
November 16, 2018 – May 19, 2022
A notebook of awesome privacy protection,federated learning, fairness and blockchain research materials.
View Projectblockchain-group
March 29, 2018 – October 19, 2018
Apprentices in Blockchain from ECNU <3
View ProjectWitCampus
February 10, 2016 – August 6, 2017
A web app for campus ID card data analyzation and visualization
View ProjectPresentica
January 26, 2016 – November 17, 2018
@Deprecated 高效课堂移动考勤信息管理系统的研究 for 上海大学生创新活动计划
View ProjectiOSApprentice
January 24, 2016 – November 17, 2018
iOS Development Engineer, following 极客学院
View ProjectMCM-ICM-Paper
January 22, 2016 – November 17, 2018
2016 Interdisciplinary Contest In Modeling COMAP45538 Was Designated As Honorable Mention
View ProjectCultural Fit Analysis
The candidate's projects are primarily personal and academic, showing initiative and a research-oriented mindset. However, the projects lack direct alignment with typical industry-level Data Scientist roles, focusing more on general programming, web development, and specific academic topics like federated learning and blockchain. The diversity of technologies used (JavaScript, CSS, Java, Objective C, Swift, C++, Python, TeX, Matlab) indicates a broad interest but also a potential lack of deep specialization in data science specific tools and methodologies. The 'apprenticeship' and 'federated-learning-seminar' projects suggest an interest in advanced topics relevant to data science research, but their descriptions are too brief to ascertain depth.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. Psychometric test scores are 0, and there are no interview notes or detailed project descriptions to infer these aspects.