Doctor of Philosophy in Computing and Information Sciences- Artificial Intelligence

罗切斯特理工学院

US

QS排名:

  • 学历文凭

    Ph.D.

  • 专业院系

    人工智能与机器人

  • 开学时间

  • 课程时长

  • 课程学费

    汇率提示

国际学生入学条件

Complete an online graduate application. Refer to Graduate Admission Deadlines and Requirements for information on application deadlines, entry terms, and more.
Submit copies of official transcript(s) (in English) of all previously completed undergraduate and graduate course work, including any transfer credit earned.
Hold a baccalaureate degree (or US equivalent) from an accredited university or college. Since the program encompasses a wide variety of disciplines, students with diverse backgrounds (e.g.: engineering, science, humanities, fine arts, business, and disciplines with sufficient computing backgrounds) are encouraged to apply.
Recommended minimum cumulative GPA of 3.0 (or equivalent).
Submit a current resume or curriculum vitae.
Two letters of recommendation are required. Refer to Application Instructions and Requirements for additional information.
Not all programs require the submission of scores from entrance exams (GMAT or GRE). Please refer to the Graduate Admission Deadlines and Requirements page for more information.
Submit a personal statement of educational objectives. Refer to Application Instructions and Requirements for additional information.
Submit professional or research paper sample(s), if available.
Have completed at least one full year of study in programming and computing concepts, strong mathematical background in subjects such as discrete mathematics, and probability and statistics, and aptitude, vision, and experience (if applicable) in computing and information sciences related research.

IELTS - 6.5
TOFEL IBT - 88
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雅思考试总分

6.5

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  • 雅思总分:6.5
  • 托福网考总分:88
  • 托福笔试总分:160
  • 其他语言考试:PTE Academics - 60
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课程简介

Our research efforts aim to understand the nature of visual perception so that we can create more visually intelligent machines. This is enabled via fundamental concepts in reasoning, prediction, supervised, semi-supervised and unsupervised learning, and stochastic optimization techniques. Our research involves (i) fundamental computer vision topics such as video analytics, detecting humans and their poses from images, object detection and tracking, etc, (ii) computer vision applications such as medical image analysis, understanding social dynamics from videos of human interactions, collision detection in self-driving cars, and other vision-related regression problems in videos, and (iii) the intersection of computer vision and graphics where we aim to model realistic avatars that interact more naturally with humans. We are constantly pushing the boundaries in applying computer vision techniques to a myriad of problems such as 3D reconstruction of the heart from MRI images, deception detection from visual cues, understanding group interactions such as in a volleyball game, improving STEM classroom learning through video analytics, and other such problems.
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