个人信息

姓　　名: 郎非 性　　別: 男 导师类型: 硕士生导师
技术职称: 副教授 电子邮箱: langf@njupt.edu.cn
学术型硕士招生学科: (081002)信号与信息处理
专业型硕士招生类别(领域): (085400)电子信息

个人简介:郎非，男，工学博士，副教授。2004年入通信与信息工程学院任职至今。研究领域：大数据与机器学习。已发表论文二十余篇。讲授《模式识别》课程已有十余年，近期还开设有《Web数据挖掘》课程。从2015年初投身工业界，聚焦在数据科学和人工智能产品研发上。Lang Fei received his Ph.D. degree in Information and Signal Processing from Nanjing University of Posts and Communications (NUPT). In 2004, he joined NUPT, where he is currently an Associate Professor in the School of Communications and Information Engineering, with a joint appointment at Institute of Big Data Research at Yancheng. His research interests are in Big Data and Machine Learning. In recent years, he is focusing on the industry-grade development of data science products and the applications of web data mining. He teaches the courses: Pattern Recognition and Web Data Mining, etc.
研究领域:1、基于Web的数据科学产品研发：Web数据爬取、数据清洗、非结构化文本解析、核心业务算法建模（数据挖掘、聚类分析、智能推荐等）、mySQL & MongoDB数据库操作、大数据云平台上的部署与优化等。2、人工智能算法研究：机器学习、数据挖掘和自然语言处理（NLP）。欢迎对落地产品研发感兴趣的同学们加入我们数据科学与人工智能小组，也欢迎对算法建模研究感兴趣的同学加入我们小组。同时需要同学们随时做好丰富自身知识系统和技术生态的准备，并在数学、编程、英语和EQ等方面有基础有信心做到优秀。这样我们就可以一起玩转大数据啦！1. Developing industrial data science products which involved the technologies of data crawling, data cleaning, unstructured data analysis, modeling（data mining, clustering, intelligent recommendation）, operations on mySQL and MongoDB, performance optimization on high concurrency system, data deployment and optimization of big data apps on the cloud, etc.2. Developing innovative deep and width learning methodologies and algorithms in Natural Language Processing (NLP).3. Developing high dimensional data analysis, parallel and distributed computing.Welcome all the students join our team if you are interested in the development of industry-grade products of data science and AI, or the research of algorithms and modeling. Moreover, you need to be accustom to update your knowledge system and rich your technology ecology at any time. You also need to have a bit of intelligence in math, a bit of enthusiasm in coding, a bit of patience in reading English resources, and a bit of EQ in the communications with your team. And I believe that you will find it great fun doing data science in Dig Data time!