Statistics Graduate Student Orientation

Michael W. Trosset, Director of Graduate Studies

Department of Statistics

Indiana University

This information is for Fall 2024. It is intended primarily for new graduate students in the MSSS (M.S. in Statistical Science) or Ph.D. degree programs.

Welcome!

Indiana University’s Department of Statistics was created in July 2006. It is currently located in Swain Hall East.

The department currently offers three graduate degree programs:

• M.S. in Applied Statistics (for students in other IU Ph.D. programs)
• M.S. in Statistical Science
• Ph.D. in Statistical Science

We currently have 17 core faculty and 23 graduate students in our MSSS and PhD degree programs.

Please welcome our 8 new graduate students!

Gehna Anand (Information Technology, India)

Mujia Chen (Mathematics, Franklin & Marshall College)

David Gerth (Mathematics, Indiana University)

Mason Griswold (Statistics, Indiana University)

Yijia Li (Accounting/Finance, China)

Karun Maganti (Mathematics, India)

Jinglong Wang (Mathematics, Ohio State University)

Fernanda Yepez-Lopez (Mathematics, Indiana University)

They join 8 returning PhD students (Derek Brown, Paul Hunt, Jongwook Kim, Jin
Lee, Haoran Liu, Nico Velasquez, Dingbo Wu, and Yue Yu), and 7 returning MSSS
students (Garrett Collier, Hanush Kumar, Abhirav Lande, Saranjeet Saluja, Kaiyi
Tan, Cem Tener, and Branden Neese).

Key Personnel

Kelly Hanna provides staff support for the department’s various degree programs. See her first for most of your needs!

Dana Fielding is the department’s manager and fiscal officer. See Kelly or Dana for help with course registration. See Dana about financial matters.

The Director of Graduate Studies (DGS) is Michael Trosset. Until such time as you have a Ph.D. dissertation advisor, the DGS is your academic advisor.

The Department Chair is Daniel Manrique-Vallier.

Hannah Bolte directs the Indiana Statistical Consulting Center (ISSC).

Core faculty:

Nathan Glatt-Holtz (University of Southern California)

Elizabeth Housworth (University of Virginia)

Matthew Pratola (Simon Fraser University)

Michael Trosset (UC Berkeley)

Chunfeng Huang (Texas A&M University)

Daniel Manrique-Vallier (Carnegie Mellon University)

Amanda Mejia - 621 (Johns Hopkins University)

Julia Fukuyama - 610 (Stanford University)

Fangzheng Xie (Johns Hopkins University)

Maoran Xu (University of Florida)

Yichi Zhang (North Carolina State University)

Brad Luen (UC Berkeley)

Arturo Valdivia - 631 (Arizona State University)

Jianyu Wang (Duke University)

Hyseun An (Indiana University)

Robert Granger (Indiana University)

Pulindu Ratnasekera (Simon Fraser University)

Advising and Mentoring

Advising is the formal process of ensuring that students are provided with the information and approvals they need to progress through their degree programs. Mentoring is a less formal and more nurturing attempt to ensure that students are supported in their efforts to negotiate the professional and personal demands placed upon them. To illustrate the distinction, it is the advisor who approves or disapproves a student’s request to substitute an inquiry methodology course for a required statistics course, whereas it is the mentor to whom the student complains that graduate coursework is too hard and that his/her advisor is unreasonable.

The DGS serves as academic advisor for all MSSS students and all PhD students who do not yet have a dissertation advisor. Once a PhD student has a dissertation advisor, the dissertation advisor becomes the academic advisor.

Here are the faculty mentors for new graduate students. . .

Gehna Anand: Prof Amanda Mejia

Mujia Chen: Prof Chunfeng Huang

David Gerth, Mason Griswold: Prof Matthew Pratola

Yijia Li: Prof Maoran Xu

Karun Maganti: Prof Yichi Zhang

Jinglong Wang: Prof Fangzheng Xie

Fernanda Yepez-Lopez: Prof Nathan Glatt-Holtz

Typical MSSS Sequence Map

Fall of Year 1:
Intro Statistical Computing
Statistical Theory I
Applied Linear Models I
 
Spring of Year 1:
Applied Statistical Computing
Statistical Theory II
Applied Linear Models II
 
Fall of Year 2:
Statistical Consulting
Elective
(Elective)
 
Spring of Year 2:
Consulting Internship or Research Project
Elective
(Elective)

An important component of the MSSS program is experience working in the Indiana Statistical Consulting Center (ISCC). Effective consulting requires (a) knowledge of statistical methodology, (b) practice analyzing data, and (c) good computing skills and work habits. The applied linear models (STAT-S 631–632) and statistical computing (STAT-S 610–611) sequences are designed to prepare students for supervised consulting with curated problems in their 3rd semester, followed by a one-semester internship at ISCC in their 4th semester.

Transitions and Resources

From undergraduate to graduate study.  Less breadth, greater depth.  Take 2–3 courses per semester, but learn everything you can.

From foreign universities to U.S. universities.  You are not alone!  Many of our faculty completed their undergraduate studies in other countries.

Communicate your concerns to our faculty, especially our DGS, your mentor, and your instructors. If you are struggling, don’t keep it to yourself—let us help!

Note the Resources for graduate students tab on the department website, including: a welcome letter from Kelly, advice from a former student, information about summer internships and jobs, etc.

Participate in the Statistics Club!

Academic Integrity at Indiana University

Our primary concern (and the primary concern of prospective employers) is that students who receive the MSSS degree achieve certain levels of knowledge about and proficiency in statistical science. One learns by doing one’s own work. Therefore. . .

As a student at IU, you are expected to adhere to the standards and policies detailed in the Code of Student Rights, Responsibilities, and Conduct (Code). When you submit a test or exam paper with your name on it, you are signifying that the work contained therein is entirely your own, unless otherwise cited or referenced. Any ideas or materials taken from another source for either written or oral use must be fully acknowledged. If you are unsure about the expectations for completing an assignment or taking a test or exam, then seek clarification with your instructor beforehand. All suspected violations of the Code will be handled according to University policies. Sanctions for academic misconduct may include a failing grade on the assignment, reduction in your final grade, a failing grade in the course, among other possibilities, and must include a report to the Dean of Students.

Some Practical Guidelines

Each instructor will establish specific policies and expectations for his/her course. Typically these policies will be stated in the course syllabus. If you are confused about the rules for completing an assignment, then ask the instructor for clarification. Ignorance (e.g., “I didn’t know that I wasn’t allowed to copy X’s final exam.”) is not a valid excuse.

Virtually all instructors prohibit collaboration with other students on tests and exams. Some instructors permit collaboration on homework assignments, others do not.

Individual projects should be completed without collaboration; team projects require each member to contribute.

Acknowledge Your Sources

Any resources you use must be cited. For example, writing

     “Essentially, all models are wrong, but some are useful.” (Box & Draper, Empirical Model-Building and Response Surfaces, 1987, p. 424.)

is fine, but writing

     Essentially, all models are wrong, but some are useful.

is plagiarism.

Opportunities for Financial Support

PhD students are offered 5-year Student Academic Appointments (SAAs), which provide a stipend, fee remission, and health insurance, typically in exchange for working 20 hours per week as a Teaching Assistant (TA). When resources are available, faculty sometimes hire PhD students as Research Assistants (RAs).

Depending on department needs, MSSS students are often hired as TAs, for which they receive hourly compensation and fee remission. Some MSSS students have Department Teaching Assistantships (DTAs), 2-semester commitments to work as a TA for 20 hours per week.

Currently, the department has many more TA positions than it does graduate students, obliging us to hire hourly TAs from outside the department.

We would prefer to fill TA positions with our own graduate students. Please be aware, however, that TA positions are of vital importance to the department’s mission and involve substantial obligations, e.g., to be responsive to instructor requests, to perform assigned tasks competently and efficiently, to be available for the entire semester, etc. Only those TAs who take their responsibilities seriously will be re-hired.