卡内基·梅隆大学(Carnegie Mellon University)

是一所位于美国宾夕法尼亚州匹兹堡的研究型私立大学。 在2018年泰晤士高等教育世界大学排行榜中,卡内基梅隆大学排名世界第20位,在同一机构的学科排名中,学校的计算机科学排名世界第六位,工程和技术排名第十二位,商学和经济学排名第十五位。在USNews发布的排行榜中,学校排名全美第25位,其中计算机科学排名全美第一位。学校拥有世界顶尖的机器人学和戏剧学项目,以及全世界建立最早的计算机学院之一。 卡内基梅隆大学拥有来自全世界114个国家的13,650名学生,超过5,000名教职人员和超过100,000名的校友。历史上,学校的教员和校友中共有20人获得诺贝尔奖,12人获得图灵奖,22人获评美国艺术与科学院院士,19人进入美国科学促进会,72人入选美国国家学院,7人获得奥斯卡金像奖,44人获得托尼奖,114人获得艾美奖。

卡耐基梅隆大学-人机交互科研

一、课题方向

可用的隐私和安全

Privacy and security

移动计算和普适计算

Mobile computing and pervasive computing

机器学习智能手机编程操作系统

Machine learning intelligent programming system

数值分析

Numerical analysis

 

二、科研内容参考

Research Overview

This research consists of four distinct modules that are independently­structured building blocks of the system. These four distinct modules are equally critical to the final prototype system. Depending on applicants strength of research and development, each applicant can select up to two modules to work on. These modules are described below:

Module 1: User Study

User study is a dominant method in the domain of Human­Computer Interaction to evaluate system performance on real­world users. In this study, before development of the actual system, we perform user study using the online crowd to validate user needs of personal assistant for instant messaging. The research involves brainstorming user scenarios, generating storyboards, launching online study, and statistical analysis. Basic experience in web development and graphic design is needed. It would be a plus if

the applicant has rich experience in modern web development framework (e.g., jQuery, Ruby, Javascript, etc.)

Module 2: Mobile System

As a core component of this research, the mobile system module consists of two parts: 1) collecting personal data, and 2) design for personal assistant. In the first part, we will develop the virtual sensors to various types of personal data (e.g., event, physical activity, documents, emails, and location, etc. ). In the second part, we will design the software architecture as well as the user interface of the personal assistant so that it can deliver relevant answers to users in an efficient and effective way.

Module 3: Building Knowledge Graph

Knowledge graph is a powerful tool to surface complex relationships between personal data points. In this research, we leverage graph database to link rich types of personal data entities on smartphones. The applicant will need to transcribe the crowd­sourced user scenarios into knowledge graph. This knowledge graph can be considered to cover most popular user scenarios that are related to information needs in instant messages. Our working personal assistant can therefore easily query for subgraphs of this knowledge graph in each scenario.

Module 4: Natural Language Processing

In order to be qualified to work on this module, the applicant should be familiar with common tools and algorithms of natural language processing. For each incoming message, we should be able to transcribe it into structural data and map it to a query on the constructed knowledge graph.

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