Diversity and Inclusion

This listserv provides a forum for discussion and tips for inclusive teaching and mentoring and topics at the intersections of the mathematical sciences and diversity, including announcements for the diversity and inclusion brown bag lunch group and the Statistics and Mathematics reading and discussion group on diversity. We invite everyone to subscribe, contact Seth Sullivant at smsulli2@ncsu.edu.

While graduate students receive high-quality research mentoring, professional norms are not generally taught. The Professional Strategies Working Group was established to provide students, particularly women and underrepresented minorities, with mentoring for career success. These groups may face different obstacles than students in the majority. The working group studies decision making and professional behavior, particularly for professional conferences. We also read books on professional development and leadership.

This is a joint group for graduate students, postdocs and faculty in the statistics and mathematics departments. The meetings are informal, with discussions around inclusive teaching and departmental climate. Meeting times and locations are announced through the Diversity-Mathstat Listserv.

English Corner is a place to practice communication skills by learning English idioms and having casual conversation. Everyone is welcome—whether you want to brush up on idioms or just want to socialize with other students. For the Fall 2021 semester, we are meeting virtually and in person (hybrid) on selected Thursdays at 4:30 pm in room 5104A. To confirm whether we’re meeting on a particular day, contact Alvin Sheng (asheng@ncsu.edu).

This program connects Statistics graduate students with Statistics program alumni from the time they are accepted into the program onward. The alumni share experiences about what to expect in graduate school and answer any questions that the student may have. For more information, contact Donald Martin (demarti4@ncsu.edu) or Marcia Gumpertz (gumpertz@ncsu.edu).

The SEAS traineeship program immerses graduate students in a unique interdisciplinary curricular and research environment in which the trainees are team-mentored by a diverse group of faculty and external industry and national laboratory scientists. This is a collaborative project with North Carolina Central University.

The Directed Research for Undergraduates in Mathematics and Statistics program at NC State is funded by the NSF and the NSA. DRUMS will recruit a talented and diverse group of students from across the country each year to engage in an intensive summer research project under the supervision of NCSU faculty. The overarching goals of the summer research program are to get more undergraduate students with diverse backgrounds interested in graduate degrees in mathematical and statistical sciences and to provide research opportunities that can generate high-quality outputs.

The Data Scientists Training (DST) program at North Carolina State University is a year-long exploration for high school students and educators. Led by Dr. Eric C. Chi and Dr. Ana-Maria Staicu, the program was designed specifically to introduce high school students to potential careers in data science, this exploration program will also feature activities led by Sustainable Horizons Institute to enable students to explore, plan, and navigate their own career pathways.

GRAD-Future is a 2-day all-expenses paid workshop for rising juniors and seniors who are interested in learning more about graduate study in Statistics or Data Science and who are committed to social justice, diversity, equity and inclusion. The workshop will cover topics such as Why go to graduate school? What types of programs are there? How to prepare? How to fund graduate studies? and How to find a good match? Participants will interact with current graduate students and alumni to hear first-hand about what to expect in graduate school.

2023 Workshop Dates: Thursday – Friday, June 8 – 9, 2023.

Application deadline: January 15, 2023.

Application instructions and form