Ke Li, PhD
Machine Learning Scientist at G42- Claim this Profile
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French Professional working proficiency
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English Professional working proficiency
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Chinese Native or bilingual proficiency
Topline Score
Bio
Credentials
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Advanced Algorithms and Complexity
CourseraNov, 2022- Nov, 2024 -
Algorithms on Graphs
CourseraSep, 2022- Nov, 2024 -
Data Structures
CourseraSep, 2022- Nov, 2024 -
Algorithmic Toolbox
CourseraAug, 2022- Nov, 2024 -
Convolutional Neural Networks
CourseraJul, 2022- Nov, 2024 -
Getting Started with Redis and RediSearch
Coursera | GoogleJul, 2022- Nov, 2024 -
Sequence Models
CourseraFeb, 2022- Nov, 2024 -
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
CourseraDec, 2021- Nov, 2024 -
Neural Networks and Deep Learning
CourseraDec, 2021- Nov, 2024 -
Structuring Machine Learning Projects
CourseraDec, 2021- Nov, 2024
Experience
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G42
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United Arab Emirates
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IT Services and IT Consulting
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500 - 600 Employee
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Machine Learning Scientist
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Jun 2021 - Present
Sports Analytics and Consumer-Oriented Applications 1. Participated in building a recommender system for the most popular daily active used instant messaging application in UAE, which involves up to 100M daily requests and 8M daily active users’ data processing, and advertisement recommendation. Improved the CTR (Click-Through Rate) by more than 200% compared with the history. 2. Constructed the chatbot ecosystem for the instant messaging application which provides users with various convenient services, such as mobile recharge, online shopping, etc. 3. Worked on the UAE Cycling project and built the MMP (Mean Maximal Power) prediction models for UAE Team Emirates, which allows assisting athletes in training.
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Sorbonne Université
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France
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Higher Education
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700 & Above Employee
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PHD
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Mar 2018 - Jun 2021
French National Research Agency Project: Exploring Topic Evolution in Large Scientific Archives with Pivot Graphs 1. Proposed a generic topic evolution workflow for the extraction of meaningful evolution patterns from very large document archives. 2. Defined higher-level quality conditions about some properties of the extracted topics and the topic evolution graphs. 3. Proposed a diversity-controlled topic extraction method as well as a method to characterize topics that meet given structural and quantitative conditions about their evolution. 4. Built new analytic tools for exploring interactively topic evolution maps. 5. Implemented our data model on top of Apache Spark using real-world scientific corpus containing millions of documents.
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Atos
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France
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IT Services and IT Consulting
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700 & Above Employee
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Software Engineer in Big Data
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Apr 2017 - Sep 2017
WAVES project: Real-time water resources management in an IoT context 1. Studied RDF stream processing in a distributed context and some existing RSP engines. 2. Realized an efficient RDF encoding scheme (LiteMat) which allows to encode the RDF stream in a distributed context and which possesses the high scalability and the reasoning. 3. Implemented the LiteMat in an RDF stream processing engine which bases on Spark Streaming. WAVES project: Real-time water resources management in an IoT context 1. Studied RDF stream processing in a distributed context and some existing RSP engines. 2. Realized an efficient RDF encoding scheme (LiteMat) which allows to encode the RDF stream in a distributed context and which possesses the high scalability and the reasoning. 3. Implemented the LiteMat in an RDF stream processing engine which bases on Spark Streaming.
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BESSYSTEM
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Beijing City, China
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Software Engineer in Big Data
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Jul 2016 - Aug 2016
Title: System for real-time analysis of logs of user traces Built a modular of a distributed stream processing system on top of Apache Spark Title: System for real-time analysis of logs of user traces Built a modular of a distributed stream processing system on top of Apache Spark
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Pierre and Marie Curie University
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France
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Research
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700 & Above Employee
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Academic trainee in Data Science
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Feb 2016 - Apr 2016
Title: Traffic analysis of users in public transport networksDiscovered features of metro stationsAnalyzed user data in transport networks, studied traces of travelers by using the Clustering learning algorithmInvolved technologies: Web Scraping, Clustering, Python
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Academic trainee in Big Data
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Feb 2016 - Apr 2016
Title: Analysis of books in the libraries of ParisI. IntroductionWe were interested in books in the libraries of Paris.Since many libraries are public services, we found data related to the documents that were present in Open Data.II. Organization 1. Web capture through the extraction of HTML pages We have added to each book in the database a price and a score up to 5 (for 13,000 lines) using a web scraping script that changes IP (to avoid being banned) and by consulting FNAC website. 2. Construction of 3 schemes and 3 associated OLAP cubesIII. Tools Python, Pentaho, Google ChartsIV. Technologies ETL, Data Warehouse, Cube OLAP, Data Mining, Data Visualization
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Education
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2018 - 2021
Sorbonne Université
Doctor of Philosophy - PhD, Data Science -
2015 - 2017
Université Pierre et Marie Curie (Paris VI)
Master, Data Science -
2014 - 2015
Université Pierre et Marie Curie (Paris VI)
3rd year of Bachelor, Computer Science -
2012 - 2014
Université de Cergy-Pontoise
1st year, 2nd year of Bachelor, Computer Science -
2011 - 2012
Northwest A&F University
1st year of Bachelor, Software engineering
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