About the Program
The Department of Industrial Engineering and Operations Research (IEOR) offers four graduate programs: a Master of Engineering (MEng), a Master of Science (MS), a Master of Analytics (MAnalytics), and a PhD. These programs have been developed to meet the needs of individuals with backgrounds in engineering or the mathematical sciences who wish to enhance their knowledge of the theory, development, and use of quantitative models for design, analysis, risk management, and decision-making. This knowledge applies to complex systems in the industrial, service, or public sectors, including energy systems, supply chains, healthcare systems, and financial systems. Students may concentrate on theoretical studies in preparation for doctoral-level research, or on applications of state-of-the-art techniques to real world problems.
Master of Engineering (MEng)
The MEng is a professional, full-time, accelerated professional master's degree program and is currently a lock-step, two-semester degree program. Students learn advanced techniques in IEOR and skills that prepare them to lead teams in developing new engineering solutions: skills in managing complex projects, motivating people, and directing financial and operational matters.
Master of Science (MS)
The MS is a full-time technical master's degree program. Students focus on both the theory of IEOR techniques and the application of those techniques. The MS is a terminal degree, meaning that students enrolled in the MS program do not typically continue further into the IEOR PhD program.
Master of Analytics (MAnalytics)
The 12-month in-person Master of Analytics program trains students in data-driven analytical methods and tools for optimization, statistics, simulation, and risk management with relevant industry context so that the graduates are not only highly skilled in the latest tools and fluent with working with large data sets, but also are able to raise the right questions to develop innovative models and find creative solutions to rapidly changing business and industry challenges, and communicate and implement their solutions.
Doctor of Philosophy (PhD)
The paramount requirement of a doctoral degree is the successful completion of a thesis on a subject within Industrial Engineering and Operations Research. Research areas may include but are not limited to the investigation of the mathematical foundations of and computational methods for optimization or stochastic models, including risk analysis. Research also may be undertaken to develop methodologies for the design, planning, and/or control of systems in a variety of application domains, including supply chains, energy systems, healthcare systems, and financial systems.
Admissions
Admission to the University
Applying for Graduate Admission
Thank you for considering UC Berkeley for graduate study! UC Berkeley offers more than 120 graduate programs representing the breadth and depth of interdisciplinary scholarship. The Graduate Division hosts a complete list of graduate academic programs, departments, degrees offered, and application deadlines can be found on the Graduate Division website.
Prospective students must submit an online application to be considered for admission, in addition to any supplemental materials specific to the program for which they are applying. The online application and steps to take to apply can be found on the Graduate Division website.
Admission Requirements
The minimum graduate admission requirements are:
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A bachelor’s degree or recognized equivalent from an accredited institution;
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A satisfactory scholastic average, usually a minimum grade-point average (GPA) of 3.0 (B) on a 4.0 scale; and
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Enough undergraduate training to do graduate work in your chosen field.
For a list of requirements to complete your graduate application, please see the Graduate Division’s Admissions Requirements page. It is also important to check with the program or department of interest, as they may have additional requirements specific to their program of study and degree. Department contact information can be found here.
Where to apply?
Visit the Berkeley Graduate Division application page.
Doctoral Degree Requirements
Please visit the IEOR Graduate Student Handbook for the most current information.
Normative Time Requirements
Normative Time to Advancement
Total normative time to advancement is two to two and a half years.
Step I: This process normally takes one year (to take the entrance exam).
Step II: After passing the preliminary or entrance exam, students prepare for their PhD oral qualifying examination. This step lasts one to one and a half years. With the successful passing of the orals, students are advanced to candidacy for the PhD degree.
Normative Time in Candidacy
Total normative time in candidacy is about three years.
Step III: Students undertake research for the PhD dissertation under a three-person committee. On completion of the research, workshops, and approval of the dissertation by the committee, the students are awarded the doctorate.
Total Normative Time
Total normative time is four to five years or eight to eleven semesters.
Before Advancement to Candidacy
Doctoral Entrance Exam
Every doctoral student is required to take the doctoral entrance (preliminary) examination at the end of their first year. Students entering without an MS degree are required to complete all MS degree requirements and may do so by completing the MS course requirements and passing the doctoral entrance exam. Doctoral students who do not pass the entrance exam are subject to dismissal from the IEOR PhD program.
The entrance examination consists of three parts:
- Optimization exams: Students are required to take IND ENG 262A and at least one other course in Group A (see below) to be prepared for these exams.
- Stochastic processes exams: Students are required to take IND ENG 263A and at least one other course in Group B (see below) to be prepared for these exams.
- An exam on modeling and applied operations research: Students are required to take two courses in Group C (see below) to be prepared for this exam.
All required courses for the doctoral entrance examination must be taken for a letter grade.
The entrance examination will be offered near the end of every spring semester, during the week before finals. Passing the entrance examination is based on both superior performances on all parts of the exam, and on previous coursework. Students are required to take all parts of the exam at the end of their first year.
In order to take the exam, students are expected to perform sufficiently well in their first-year courses. During the middle of the spring semester, a faculty committee will review the performance of first-year doctoral students, and students who have performed sufficiently well in their coursework will be permitted to take the exam.
The entrance examination will consist of two written exams, in optimization and in stochastics, taken on separate days, and three oral examinations in the areas of optimization, stochastics, and modeling. Each student will be examined by two faculty members for the oral exams. In optimization and stochastics, each student will be examined on topics related to the two courses taken in the area (in case the student took three or more courses in optimization or stochastics, they should state a preference as to which course will be considered the second). For modeling, the students will be provided with a case study approximately three weeks prior to the exam. Each student is expected to address the case study using applied operations research techniques and present their analysis at the modeling oral exam.
Curriculum
Advanced undergraduate courses in linear algebra (equivalent to MATH 110) are prerequisites for the PhD program. Students who have not taken these courses prior to entering the graduate program are required to do so during their first year. Real analysis (equivalent to MATH 104) is also recommended.
Some students have specific research interests and goals when they enter a doctoral program; for others, these interests develop in the process of taking courses and preparing for the entrance examination. In either case, students are expected to start on a research project during the summer of their first year. Every student is required to complete at least one unit of independent study with a faculty member each semester after passing the entrance examination until finding a dissertation adviser.
A minimum of nine letter-graded graduate courses is required in the major, including those taken prior to the entrance examination. Usually, these are courses taken in the IEOR department, but to a very limited extent, courses taken in other departments may be counted as part of this requirement. These courses should provide depth in the student's probable research area.
In addition, course work is required in two minor areas. Each minor must consist of six units at the graduate level, at least three of which must be taken for a letter grade. A minor may serve either to strengthen theoretical foundations (e.g., measure-theoretic probability theory), or as an area of application (e.g., transportation). At most one course of one minor can be a course from within this department, as long as this course is distinct from the nine courses in the major. Both minors should be selected to strengthen the student's background in his or her research area, and minors are subject to the approval of the head graduate adviser.
The dissertation advisor, once known, should be consulted about all matters regarding the program of study.
Coursework is comprised of an approved study list based on the student’s research interest, which must include the following:
| Code | Title | Units |
|---|---|---|
| IND ENG 262A | Mathematical Programming I | 4 |
| IND ENG 263A | Applied Stochastic Process I | 4 |
| IND ENG 298 | Group Studies, Seminars, or Group Research | 1 |
| Group A: Optimization: Select a minimum of one of the following: | 3 | |
| Mathematical Programming II [3] | ||
| Computational Optimization [3] | ||
| Network Flows and Graphs [3] | ||
| Integer Programming and Combinatorial Optimization [3] | ||
| Group B : Stochastic Modeling: Select a minimum of one of the following: | 3 | |
| Financial Engineering Systems I [3] | ||
| Introduction to Data Modeling, Statistics, and System Simulation [3] | ||
| Experimenting with Simulated Systems [3] | ||
| Applied Stochastic Process II [3] | ||
| Queueing Theory [3] | ||
| Group C: Modeling and Applied Operations Research: Select a minimum of two of the following: 1 | 6 | |
| Economics and Dynamics of Production [3] | ||
| Introduction to Financial Engineering [3] | ||
| Financial Engineering Systems II [3] | ||
| Introduction to Production Planning and Logistics Models [3] | ||
| Facilities Design and Logistics [3] | ||
| Supply Chain Operation and Management [3] | ||
| Production and Inventory Systems [3] | ||
| Frontiers in Revenue Management [3] | ||
| Heathcare Analytics [3] | ||
- 1
In addition to the courses listed here, many occasionally-offered 290 series courses fit into this category; check with the head graduate advisor about specific courses which may be approved.
Effective Fall 2022, the department requires one of the Group C courses be one of INDENG 250, 251, 253, 254, 255, or 256.
Foreign Language(s)
In addition to English, the program does not require another language.
Qualifying Examination (QE)
The Qualifying Examination is an oral examination with a written component administered by four faculty members. Three of these faculty members are required to be IEOR faculty members and the fourth faculty member must be a Faculty Senate Member from another department with expertise in one of the student’s minor areas of study. Students are expected to take the Qualifying Examination within three semesters after passing the Doctoral Entrance Exam. Priority in department funding (especially NRTs) will be given to students who have passed their Doctoral Entrance Exams and are in their 3rd, 4th, and 5th semesters. Although it is necessary for a student to identify a potential research area and some potential dissertation topics in order to complete this exam, it is not necessary for the student to do a substantial amount of research in the area of the examination.
By the end of the first-year spring semester (May), every first year PhD student is to submit for approval a preliminary course plan for two minors (as stated above). At the time of application to take the Qualifying Examination, the student is required to submit an updated plan (Program of Study) for completing the major and minor courses within two semesters.
Prior to the exam, the student is required to identify a research area (broadly defined) in which he or she will be able to demonstrate expertise during the oral part of the examination. In addition, the student must be prepared to present preliminary research in that area, including a literature review and ideas for future research. The student must also demonstrate expertise in one minor field. The objective of the exam is to assess the student’s ability to demonstrate knowledge in a broad research area, do preliminary research, and to identify potential research topics within this area.
At least six weeks prior to the approximate date of the Qualifying Examination, the student must meet with the Head Graduate Advisor and provide the following: a list of topics which will form the basis of the exam (also known as a “syllabus”), a Program of Study that includes all major and minor courses taken or planned (whether or not they are included in the syllabus), a list of faculty members who have agreed to serve on the exam committee, a preliminary draft of the Qualifying Exam Report for the exam committee (also known as a “prospectus” or “technical report”), and approval from the student's advisor of the intended date and topics. The syllabus should include topics from major subject areas, equivalent to several courses, together with topics from one of the minor areas. The Program of Study must be approved by the Head Graduate Advisor before submitting the "Application for Qualifying Examination" via CalCentral to the Graduate Division.
The student needs to begin arranging the Qualifying Examination logistics with their Qualifying Exam Committee by determining a date and time when all committee members can attend. The student must also request a room in which the exam can be held. This exam is to be scheduled for three hours, at a time when all Committee members can attend.
At least four weeks prior to the exam and upon approval from the Head Graduate Advisor, the student must submit the "Application for Qualifying Examination" to the Graduate Division. To do so, the student must log in to CalCentral > Student Resources > Higher Degree Committees Form > select Qualifying Exam from the dropdown and enter all information. Students must remember to upload the syllabus and Qualifying Exam Report.
At least two weeks prior to the exam, the student must submit his or her Qualifying Exam Report to the qualifying exam committee. This report should be in the form of a research proposal, and should include both a substantial survey and critical evaluation of the literature in the likely area of the dissertation, and a potential research agenda in this area. If the student has completed preliminary research in this area, it is also appropriate to include a report of this research in this document.
The Qualifying Exam document will be reviewed by the student’s Qualifying Examination Committee to determine adequacy of preparation for the research area. For students who follow these guidelines and the recommendations of the Graduate Adviser and Dissertation Adviser, this usually results in quick approval. However, if preparation is judged to be inadequate, they may recommend additional course work and postponement of this Examination.
The oral portion of the Qualifying Examination has two parts. In the first part, the student presents a 45-minute talk based on his or her Qualifying Examination Report. The Committee will ask questions pertaining to the report and presentation at this time. During the second part of the oral examination, the committee will ask more general questions to determine the student’s level of expertise in the broadly defined research area specified by the student (and described in the syllabus). During this time, the outside committee member will also ask questions about one of the student’s minor areas.
If the student's performance is judged to be unsatisfactory, the Committee may recommend reexamination, possibly after additional preparation has been completed. If the reasons for the unsatisfactory performance are judged to be major and fundamental, the Committee may recommend that a second attempt be denied.
Effective Fall 2022, students who do not pass the Qualifying Examination by their third semester after passing the Doctoral Entrance Examination are subject to progress probation through the Graduate Division and are responsible for payment of their own non-resident tuition.
Time in Candidacy
Advancement
After passing the qualifying examination, the student should file an application for advancement to candidacy in CalCentral, which sets up a three-person guidance committee for the dissertation. Once this is approved, the student is eligible for reduced fees. After advancing to candidacy, the student is expected to spend full time doing research on his or her dissertation and on related teaching tasks.
Required Professional Development
Teaching Opportunities
The Department of IEOR strives to provide every student with an opportunity to gain teaching experience. Students work as teaching assistants (Graduate Student Instructors, or GSIs), responsible for discussion or laboratory sections, and serve as readers assisting with grading but not conducting independent teaching.
Workshops
At least once a year after passing the qualifying examination, the student is required to hold a dissertation workshop. Each dissertation workshop has two primary objectives:
- It provides the department an opportunity to review the progress of students who have passed the qualifying examination, toward completion of their doctoral dissertation.
- It facilitates interaction between the student and the dissertation committee and provides the basis for useful and consistent guidance. While the dissertation committee is primarily responsible for providing guidance, feedback from other faculty members and from students is sought as well.
During the workshop, the candidate is expected to present a prospective of, and results from, the dissertation research. Dissertation advisers should advise students about the appropriate time for the workshops. However, initiation of the workshops is the student's responsibility. The student needs to notify the department at least one month in advance of the desired workshop date, and coordinate this date with the dissertation committee. At least two weeks prior to each Workshop, the student shall distribute to the Dissertation Committee a report called the “Dissertation Prospectus.” All Workshops, including the Final Workshop, will be open to, and announced to, the IEOR faculty and PhD and MS students.
Each workshop is divided into two parts. The first part is devoted to a public presentation by the student and subsequent discussion. This part is conducted as a seminar and is open to all faculty and students. Graduate students and faculty who have research interests that relate to the workshop are encouraged to attend; this may be their best opportunity to provide constructive feedback to the candidate. (Graduate students who have not yet reached this stage in their own program often find that participating in workshops is a valuable educational experience.) The dissertation committee moderates the presentation and discussion, controls the asking of questions by the audience, and calls an end to the first part of the workshop.
In the second part of the workshop, which immediately follows the public presentation, the dissertation committee and other interested faculty members will reconvene in private with the candidate for the purpose of giving more feedback and specific guidelines for continuing research. At this time, the committee may decide that the candidate's progress is unsatisfactory. Should the committee reach this conclusion, it will be reported in writing, with proper justification, to the candidate and the department chair. The committee may require an additional workshop sooner than one year after the unsatisfactory one. Recurrent failure to present a satisfactory prospectus workshop may result in the disqualification of the student and termination of doctoral candidacy.
Final Workshop
Once the candidate has completed his or her research and written the dissertation, a final workshop must be scheduled and held. The dissertation must be distributed to the committee at least two weeks before this final workshop. This workshop will follow the same format as other workshops. The committee will inform the candidate about any remaining problems or issues with the dissertation. If the committee has serious issues with the dissertation, it may require an additional final workshop.
Master's Degree Requirements (MS)
Please visit the IEOR Graduate Student Handbook for the most current information.
Unit Requirements
Students are required to complete 24 semester units of upper division and graduate coursework, 12 units of which must be graduate courses in the major taken for a letter grade. IND ENG 298 units do not count towards this 12-unit requirement.
Curriculum
All students are required to take 1 unit of IND ENG 298; at least one course each from the following categories: Optimization, Stochastic Models, and Modeling (see below); and additional courses.
Beyond these requirements, the program is quite flexible. No more than two units of IND ENG 299 may be counted toward the degree. The remainder of the program can include electives outside the department.
All students must take at least one course from each of the following three categories (optimization, stochastics, modeling), as listed below.
| Code | Title | Units |
|---|---|---|
| Optimization courses | ||
| IND ENG 160 | Nonlinear and Discrete Optimization | 3 |
| IND ENG 162 | Linear Programming and Network Flows | 3 |
| IND ENG 169 | Integer Optimization | 3 |
| IND ENG 262A | Mathematical Programming I | 4 |
| IND ENG 262B | Mathematical Programming II | 3 |
| IND ENG 264 | Computational Optimization | 3 |
| IND ENG 266 | Network Flows and Graphs | 3 |
| IND ENG 268 | Applied Dynamic Programming | 3 |
| IND ENG 269 | Integer Programming and Combinatorial Optimization | 3 |
| Code | Title | Units |
|---|---|---|
| Stochastic Models courses | ||
| IND ENG 165 | Engineering Statistics, Quality Control, and Forecasting | 4 |
| IND ENG 166 | Decision Analytics | 3 |
| IND ENG 173 | Introduction to Stochastic Processes | 3 |
| IND ENG 174 | Simulation for Enterprise-Scale Systems | 3 |
| IND ENG 222 | Financial Engineering Systems I | 3 |
| IND ENG 231 | Introduction to Data Modeling, Statistics, and System Simulation | 3 |
| IND ENG 263A | Applied Stochastic Process I | 4 |
| IND ENG 263B | Applied Stochastic Process II | 3 |
| IND ENG 265 | Learning and Optimization | 3 |
| IND ENG 267 | Queueing Theory | 3 |
| Code | Title | Units |
|---|---|---|
| Modeling courses | ||
| IND ENG 145 | Fundamentals of Revenue Management | 3 |
| IND ENG 150 | Production Systems Analysis | 3 |
| IND ENG 153 | Logistics Network Design and Supply Chain Management | 3 |
| IND ENG 156 | Healthcare Analytics | 3 |
| IND ENG 215 | Analysis and Design of Databases | 3 |
| IND ENG 220 | Economics and Dynamics of Production | 3 |
| IND ENG 221 | Introduction to Financial Engineering | 3 |
| IND ENG 223 | Financial Engineering Systems II | 3 |
| IND ENG 230 | Economics of Supply Chains | 3 |
| IND ENG 245 | Fundamentals of Revenue Management | 3 |
| IND ENG 250 | Introduction to Production Planning and Logistics Models | 3 |
| IND ENG 251 | Facilities Design and Logistics | 3 |
| IND ENG 252 | Service Operations Management | 3 |
| IND ENG 253 | Supply Chain Operation and Management | 3 |
| IND ENG 254 | Production and Inventory Systems | 3 |
| IND ENG 255 | Frontiers in Revenue Management | 3 |
| IND ENG 256 | Heathcare Analytics | 3 |
| IND ENG 290 | Special Topics in Industrial Engineering and Operation Research (With approval of advisor) | 2-3 |
In addition to course requirements, students are required to complete one of two options: a comprehensive exam or a master's project and oral presentation of this project. The structure of the comprehensive exam may vary from year to year but is designed so that students whose curriculum includes 12 units of graduate courses in the major and satisfies the group distribution listed above should be prepared to take the exam. At the current time, the comprehensive exam consists of a short oral presentation to a panel of two faculty members of a solution to a case study, for which the students will be given at least three weeks to prepare, followed by relevant questions from the faculty panel.
All MS requirements are to be completed within two semesters.
Relation to Doctoral Requirements
In general, the first year doctoral requirements meet the requirements of the MS degree, but the reverse is not necessarily true. Students who are interested in earning a PhD should apply to enter the PhD even if they do not yet have an MS degree. More detailed information on the entrance exam may be found on the Doctoral Degree Requirements tab.
Master's Degree Requirements (MEng)
Please visit the IEOR Graduate Student Handbook for the most current information.
Unit Requirements
Minimum number of units to complete degree: 25 semester units
Curriculum
Technical Electives (must be taken for a letter grade):
- Core Courses: All students are required to take IND ENG 240 and IND ENG 241.
- Additional Technical Electives by Concentration:
- Management Science & Engineering students must complete two additional IND ENG 200-level technical elective courses from an approved list.
- FinTech students must select two of the following: IND ENG 221, IND ENG 222, IND ENG 223, and IND ENG 224. Additionally, FinTech students must take IND ENG 290: Applications of Machine Learning to Electronic Markets.
- IP & Entrepreneurship Strategy students must take IND ENG 242A and one additional INDENG 200-level technical elective course from an approved list.
Leadership Courses (must be taken for a letter grade):
- All students must complete 8 semester units of core leadership courses from an approved list.
Capstone Project Courses (must be taken for a letter grade):
- Students must take the capstone integration course each semester.
- All students must complete 5 units of capstone courses: 2 units in the fall semester and 3 units in the spring semester.
Required Technical Elective Courses
| Code | Title | Units |
|---|---|---|
| IND ENG 240 | Optimization Analytics | 3 |
| IND ENG 241 | Risk Modeling, Simulation, and Data Analysis | 3 |
Required Leadership Courses
| Code | Title | Units |
|---|---|---|
| ENGIN 270A | Organizational Behavior for Engineers | 1 |
| ENGIN 270B | R&D Technology Management & Ethics | 1 |
| ENGIN 270C | Teaming & Project Management | 1 |
| ENGIN 295 | Communications for Engineering Leaders (Fall & Spring) | 1 |
| ENGIN 270K | Coaching for High Performance Teams | 1 |
Required Capstone Courses
| Code | Title | Units |
|---|---|---|
| ENGIN 296MA | Master of Engineering Capstone Project (Fall) | 2 |
| ENGIN 296MB | Master of Engineering Capstone Project (Spring) | 3 |
Comprehensive Technical Exam
Passing the departmental Comprehensive Technical Examination is a required milestone for all IEOR MEng students. The three-hour written exam is administered every year during RRR week of the Fall semester (the last instruction week of the semester). The exam is composed of two parts as follows:
- Optimization (90 minutes): Based on INDENG 240 material, the exam questions focus on fundamental principles of linear programming as well as formulation (modeling) of optimization problems, covering linear, integer, network, and nonlinear programming problems.
- Stochastic Modeling (90 minutes): Based on INDENG 241 material, the exam questions focus on basics of probability theory and stochastic processes, including random variables, conditional expectation, variance and covariance, and Poisson processes.
Comprehensive Leadership Exam
The Fung Institute will administer the MEng Comprehensive Leadership Exam, which will be held early in the spring semester. The format will be an individual, oral exam related to the student’s capstone leadership experience (e.g., teaming, stakeholder management, conflict resolution, and project scoping). The exam details and pass/fail assessment criteria will be shared on bCourses. Please contact Fung Institute staff for more details.
Capstone Project
Students are required to complete a capstone project. The project enables the student to integrate the core leadership curriculum with the concentration and gain hands-on industry experience.
Master's Degree Requirements (MAnalytics)
Please visit the IEOR Graduate Student Handbook for the most current information.
Unit Requirements
Minimum number of units to complete degree: 29 semester units
Curriculum
Python Boot camp
All students are encouraged to take a Python boot camp (50 hrs) in preparation for the technical coursework. The Python boot camp is a 0-unit course offered in July or August leading up to the program's start.
Technical Course work (must be taken for a letter grade)
| Code | Title | Units |
|---|---|---|
| Core Analytical Methods Courses | ||
| IND ENG 215 | Analysis and Design of Databases | 3 |
| IND ENG 240 | Optimization Analytics | 3 |
| IND ENG 241 | Risk Modeling, Simulation, and Data Analysis | 3 |
| IND ENG 242A | Machine Learning and Data Analytics | 4 |
| IND ENG 243 | Analytics Lab | 4 |
Elective Specialty Courses (MUST BE TAKEN FOR A LETTER GRADE)
Students must complete a minimum of 9 units of letter-graded IND ENG 200-level technical electives from an approved list.
SUMMER INTERNSHIP
All students must complete four units of the summer internship (individual study) course (200 hours, typically 20 hr/week over 10 weeks in May-August) during the final summer semester.
| Code | Title | Units |
|---|---|---|
| IND ENG 299 | Individual Study or Research | 1-12 |
Comprehensive Exam
Passing the departmental Comprehensive Examination is a required milestone for all IEOR MAnalytics students. The three-hour written exam is administered annually during the RRR week of the Fall semester (the last instruction week of the semester). The exam is composed of two parts as follows:
Optimization (90 minutes): Based on INDENG 240 material, the exam questions focus on fundamental principles of linear programming as well as formulation (modeling) of optimization problems, covering linear, integer, network, and nonlinear programming problems.
Stochastic Modeling (90 minutes): Based on INDENG 241 material, the exam questions focus on the basics of probability theory and stochastic processes, including random variables, conditional expectation, variance and covariance, and Poisson processes.
Contact Information
Department of Industrial Engineering and Operations Research
4141 Etcheverry Hall
Phone: 510-642-5484
Department Chair, Program Director of the Master of Analytics
Alper Atamturk
4143 Etcheverry Hall
IEOR Graduate Student Services
Heather Iwata and Erica Diffenderfer