PhD Mentorship and
Gender Representation in
Academic Mathematics

NetSci
June 4th, 2026

Phil Chodrow
Department of Computer Science
Middlebury College

Hi everyone! I’m Phil Chodrow

Prof. of CS at Middlebury College, a small liberal arts college in Vermont.

Students/postdocs: ask me about #LiberalArtsLife if you’re curious.

I like aikido, tea, my cats, being outside, hypergraphs, and math models of social systems.

Gender Representation in Academic Math

Data from the 2020 AMS Departmental Profile 🤦🏻‍♂️

*Actually this one is from 2017-2018 (Report on New Doctorates) 🤦🏻‍♂️🤦🏻‍♂️🤦🏻‍♂️

Career advancement shapes the ecosystem

But so does mentorship!

The Team

	Heather Brooks Harvey Mudd		Harlin Lee UNC Chapel Hill
	Mason Porter UCLA		Juan G. Restrepo CU Boulder
	Anna Haensch Tufts		Phil Chodrow Middlebury

Our Data

Ben Brill, UCLA ’22

Total of 116,306 advisor-student pairs in the US since 1950, representing 21,781 distinct advisors.  We observe or estimate math subfields for 94% of these pairs (predictions based on thesis titles).  We estimate gender for 95% of PhD students and 97% of advisors. 

Modeling Gendered PhD Production by an Advisor

Men have estimated careers ~4 years longer (on average)

Attrition

Addressing disparities in career attrition for female faculty would help to close the gender gap.

Qualitative match to Barrett-Walker et al. (2023).

Longer careers \(\times\) more students per year = more students per career

Hypothesis: greater student production per year by male advisors reflects unequal access to research resources; cf. Zhang et al. (2022)

Logistic model for advisee gender

Estimate the odds that the next student produced by an advsior is female based on subfield, advisor gender, and representation of women in advisor group and subfield.

\[ \begin{aligned} \log (\text{odds F}) = & \beta_0 \;+ &\quad \beta_0 &= -2.14 \; (0.04)\\ & \rho \times (\text{advisor is F}) \;+ &\quad\rho &= \phantom{-}0.42 \;(0.02)\\ & \gamma \times (\text{proportion F advisees in group}) \;+ &\quad \gamma &= \phantom{-}1.16\; (0.06)\\ & \eta \times (\text{proportion F in subfield}) &\quad \eta &= \phantom{-}3.27 \; (0.12)\\ \end{aligned} \]

We tried a lot of more complex models; this one had best validation AUC.

Mentorship Female advisors are more effective in attracting or retaining female graduate students.

Belonging Greater representation in the grad student community attracts women to programs and subfields.

Homophily effects: advisor-student and student-student

Both the gender of a students’ specific advisor and the overall proportion of female advisors in the subfield’s population contribute to the likelihood that the student is female.

Modeling Interventions on Medium Timescales: Hiring

Equal hiring rates

Suggested in most recent AMS survey data.

Male-biased hiring

Men 20% more likely than women to be hired as faculty.

Female-biased hiring

Women 20% more likely than men to be hired as faculty.

Intervening on Attrition + PhD Production

No intervention

Attrition + PhD production as estimated by model.

Faculty Intervention

Gender parity among current faculty on attrition and PhD production.

Student Intervention

Gender parity among future students on attrition and PhD production.

Faculty + Student Intervention

Both interventions combined.

What We’ve Learned

Mentorship: Female advisors are more effective in attracting or retaining female graduate students.

Belonging: Representation in the grad student community attracts women to programs and subfields.

Attrition: Addressing disparities in career attrition for female faculty would help to close the gender gap.

Next?

Thanks everyone!

	Heather Brooks Harvey Mudd		Harlin Lee UNC Chapel Hill		Mason Porter UCLA
	Juan G. Restrepo CU Boulder		Anna Haensch Tufts		Ben Brill UCLA ’22

National Science Foundation

ICERM @Brown Two summer programs!

Preprint coming soon 😬😬😬