国际学生入学条件
The IEOR PhD program is designed for students with an interest in pursuing advanced studies in Industrial Engineering and Operations Research. Prospective applicants come from a diverse set of backgrounds including Math, Applied Math, Statistics, Computer Science, Electrical Engineering and Operations Research.
Students entering the doctoral program must demonstrate proficiency in the following areas: Real Analysis, Linear Algebra, Probability and Statistics.
The basic requirement for admission as a graduate student is the bachelor's degree received from an institution of acceptable standing. Ordinarily, the applicant will have majored in the field in which graduate study is intended, but in certain programs preparation in a related field of engineering or science is acceptable. The applicant will be admitted only if the undergraduate record shows promise of productive and effective graduate work. The Master of Science (MS) degree is required for admission into the PhD and EngScD degree programs. A student who holds an appropriate bachelor's degree in engineering may apply for admission to either the MS only or MS leading to PhD program.
International applicants or applicants whose undergraduate degree was received in a country in which English is not the official and widely spoken language must submit the following additional requirements with the application for admission:
1. Test of English as a Foreign Language (TOEFL), International English Language Testing System (IELTS) or Pearson Test of English (PTE Academic)
2. Translation of the official transcript(s) and degree/diploma certificate(s) if the institution(s) attended does not issue transcripts in English. The translation must be conducted by a reputable service provider.
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雅思考试总分
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雅思考试指南
- 雅思总分:6
- 托福网考总分:60
- 托福笔试总分:160
- 其他语言考试:NA
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课程简介
Optimization theory and algorithms are foundational building blocks of Operations Research and Data Science. This is a major research area in the IEOR department, with significant emphasis on both discrete optimization (including integer and combinatorial optimization) and continuous optimization. Here, the main goal is to design efficient near-optimal algorithms for large-scale problems with provable performance bounds and advance the theoretical frontiers for such problems. Another important aspect especially from the perspective of practical applications is handling uncertainty in input data. This is a very active research area in IEOR with focus on various approaches including stochastic, robust, online and dynamic optimization. Optimization algorithms are central to most computational problems in data science including statistical estimation, machine learning, and business analytics, and find applications in most problems in practice. The research in IEOR in this area spans a broad spectrum of application areas including scheduling, matching markets, energy, health and financial and business analytics. Our faculty and students collaborate regularly with colleagues in Computer Science and other engineering departments and play an active role in the Data Science Institute.
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