Monthly Archives: March 2018

Hopkins faculty promote better climate in machine learning

Institutional Climate
Today, 122 Hopkins faculty, post-docs, and grad students make a call to promote welcoming environments for women at machine learning conferences. In recognition of egregious behavior at recent conferences, they urge the preeminent conference in the field–called NIPS–to create a stronger code of conduct and to change its name, symbolizing a new era of inclusiveness in the field. The  letter is copied below.
Screen Shot 2018-03-29 at 10.24.59 PM
For background, please see  “Statistics, we have a problem” and Guardian article “Google AI researcher accused of harassment, female data scientists speak of ‘broken system,'” both from December 2017.  Additionally, you can see faculty responses from U-Mass and Duke.

 

Dear NIPS board members,

As researchers in machine learning and data science at Johns Hop kins University, we write as advocates of a welcoming and diverse machine learning community. Recently, some disappointing behavior at NIPS 2017 has come to our attention, and has led one lab here to ban participation in NIPS 2018. We understand that steps are being taken to evaluate changes to the meeting in light  of this behavior. In addition, the acronym of the conference is prone to unwelcome puns, such as the perhaps subversively named pre-conference “TITS” event and juvenile t-shirts such as “my NIPS are NP-hard”, that add to the hostile environment that many ML researchers have unfortunately been experiencing.

These incidents have brought up uncomfortable memories for some of us whose career paths have been affected by unwelcoming or harassing behavior. NIPS is at a time of significant expansion and change. We believe that serious actions are essential for the continuing health of the community.

We are aware that the NIPS board has appointed Diversity and Inclusion chairs and i s working towards a strengthened code of conduct to set behavioral expectations. As you know, templates for such codes of conduct are available from other conferences. We support this move, and further hope that conference attendees will be actively encouraged to speak up when they observe social behavior that may make other attendees uncomfortable. We also fully expect that this code of conduct will specify consequences for harassment and are in keeping with recent policy changes from NSF that mandate reporting of harassment.

We also recommend a more radical reform: rebranding of the meeting, including a change to the name of the conference. Certainly the NIPS name has a long and distinguished history, and it is an unfortunate coincidence that it is vulnerable to sexual puns. Changing the name at this time could serve as a powerful symbolic move which, in conjunction with other changes, would signal the Board’s commitment to improving the culture of the field and making the conference a leader in inclusion.

We appreciate that NIPS has hosted and prominently featured the Women in Machine Learning and Black in AI workshops, and we look forward to further efforts to welcome contributors to the field from a range of backgrounds. Our own ML community at Johns Hopkins includes and is taking active measures to welcome people with diverse gender identity and expression, sexual orientation, age, disability, physical appearance, body size, race, ethnicity, nationality and national origin, and religion. Locally, Johns Hopkins just announced its progress report on Diversity and Inclusion; we are taking steps to ensure harassment-free and equal inclusion within our data science community at JHU. We hope to eliminate disincentives to participation in ML research at our own university, and would be glad to help with efforts to do the same in the field at large.

Thank you for your concern and quick action.

Sincerely,
1. Joel S. Bader, Professor, Department of Biomedical Engineering
2. Brian Caffo, Professor, Department of Biostatistics
3. Jason Eisner, Professor, Computer Science
4. Michael L. Falk, Vice Dean for Undergraduate Education, Professor, Departments of
Materials Science and Engineering, Mechanical Engineering, and Physics
5. Donald Geman, Professor, Applied Mathematics and Statistics
6. Jeffrey Gray, Professor, Chemical & Biomolecular Engineering
7. Gregory D. Hager, Mandell Bellmore Professor of Computer Science and Director of the
Malone Center for Engineering in Healthcare
8. Philipp Koehn, Professor, Department of Computer Science
9. Jeffrey Leek, Professor, Departments of Biostatistics and Oncology
10. Daniel Q. Naiman, Professor, Department of Applied Mathematics and Statistics
11. Gary L. Rosner, Professor, Departments of Oncology and Biostatistics, Chair of the
Division of Biostatistics and Bioinformatics and Director of the Research Program of
Quantitative Sciences of the Sidney Kimmel Comprehensive Cancer Center
12. Alexander Szalay, Bloomberg Distinguished Professor, Physics and Computer Science
13. René Vidal, Professor of Biomedical Engineering, Director of Johns Hopkins
Mathematical Institute for Data Science (MINDS)
14. Laurent Younes, Professor and Chair, Department of Applied Mathematics and Statistics
15. Alan Yuille, Bloomberg Professor, Cognitive Science and Computer Science
16. Mark Dredze, John C Malone Associate Professor, Department of Computer Science
17. Peng Huang, Associate Professor, Departments of Oncology and Biostatistics
18. Rachel Karchin, The William R. Brody Faculty Scholar, Associate Professor, Departments
of Biomedical Engineering and Oncology
19. Feilim Mac Gabhann, Associate Professor, Department of Biomedical Engineering
20. Luigi Marchionni, Associate Professor, Department of Oncology
21. Michael C. Schatz, Bloomberg Distinguished Associate Professor of Computer Science
and Biology
22. Cristian Tomasetti, Associate Professor, Departments of Oncology and Biostatistics
23. Ravi Varadhan, Associate Professor of Biostatistics
24. Hao Wang, Associate Professor, Department of Oncology
25. Sarah J. Wheelan, Associate Professor, Department of Oncology
26. Raman Arora, Assistant Professor, Department of Computer Science
27. Alexis Battle, Assistant Professor of Biomedical Engineering
28. Najim Dehak, Assistant Professor, Department of Electrical and Computer Engineering
29. Elana J. Fertig, Assistant Professor of Oncology, Associate Director of the Research
Program in Quantitative Sciences of the Sidney Kimmel Comprehensive Cancer Center
30. Loyal A. Goff, Assistant Professor of Neuroscience & Genetic Medicine
31. Stephanie Hicks, Assistant Professor, Department of Biostatistics
32. Andrew Jaffe, Assistant Professor, Department of Mental Health
33. Kai Kammers, Assistant Professor, Department of Oncology
34. Emily Riehl, Assistant Professor, Department of Mathematics
35. Daniel P. Robinson, Assistant Professor, Department of Applied Mathematics and
Statistics
36. Suchi Saria, Assistant Professor of Computer Science, Statistics and Health Policy
37. Robert B. Scharpf, Assistant Professor, Departments of Oncology and Biostatistics
38. Ilya Shpitser, John C Malone Assistant Professor of Computer Science and Biostatistics
39. Benjamin Van Durme, Assistant Professor, Department of Computer Science
40. Zheyu Wang, Assistant Professor, Departments of Oncology and Biostatistics
41. Yanxun Xu, Assistant Professor, Department of Applied Mathematics and Statistics
42. William Gray-Roncal, Assistant Research Professor, Department of Computer Science
43. Bahman Afsari, Instructor, Department of Oncology
44. Narges Ahmidi, Assistant Research Scientist, Malone Center for Engineering in
Healthcare
45. Richard Brown, Associate Teaching Professor, Department of Mathematics
46. Benjamín Béjar, Associate Research Scientist, Department of Biomedical Engineering
47. Helen Cromwell, Administrative Manager, Department of Oncology, Biostatistics
48. Ludmila Danilova, Research Associate, Departments of Oncology and Biostatistics
49. Wei Fu, Senior Biostatistician, Department of Oncology and Biostatistics
50. Rumen Kostadinov, Research Associate, Department of Pediatric Oncology
51. Sean Kross, Associate Faculty, Department of Biostatistics
52. Anand Malpani, Assistant Research Scientist, Malone Center for Engineering in
Healthcare
53. Alisa Moore, Administrative Coordinator, Departments of Oncology and Biostatistics
54. Sara More, Associate Teaching Professor, Department of Computer Science
55. Bongsoo Park, Research Associate, Department of Environmental Health and
Engineering
56. Christine Piatko, Assistant Research Professor, Department of Computer Science
57. Thomas Sherman, Biostatistician, Department of Oncology
58. James C. Spall, Research Professor, Department of Applied Mathematics and Statistics
59. Tamas Budavari, Department of Applied Mathematics and Statistics
60. Wikum Dinalankara, Postdoctoral Fellow, Department of Oncology
61. Shannon E. Ellis, Postdoctoral Fellow, Department of Biostatistics
62. Guilherme Starvaggi Franca, Postdoctoral Fellow, Center for Imaging Science
63. Peter F. Hickey, Postdoctoral Fellow, Department of Biostatistics
64. Jonathan P. Ling, Postdoctoral Fellow, Department of Neuroscience
65. Daniel Malinsky, Postdoctoral Fellow, Department of Computer Science
66. Daniel Mendat, Postdoctoral Fellow, Department of Electrical and Computer
Engineering
67. Garrett Nicolai, Postdoctoral fellow, Department of Computer Science
68. Genevieve Stein-O’Brien, Postdoctoral Fellow, Department of Oncology Biostatistics and
Institute of Genomic Medicine
69. Luciane Tsukamoto Kagohara, Department of Oncology
70. Zhihui Zhu, Postdoctoral fellow, the Center for Imaging Science
71. Jonathan Andersen, Research Assistant & Student, Undergraduate Program in
Neuroscience
72. Jonathan Augustin, PhD Candidate, Biochemistry, Cellular and Molecular Biology
Program; Neuroscience Department
73. Daniel N. Baker, Graduate Student, Department of Computer Science
74. Leandros Boukas, PhD Candidate, Institute of Genetic Medicine
75. Vikram Chandrashekhar, PhD Student, Department of Biomedical Engineering
76. Nanxin Chen, PhD Student, Department of Electrical and Computer Engineering
77. Emily Davis, PhD Student, Institute of Genetic Medicine
78. Kipper Fletez-Brant, PhD Candidate, Institute of Genetic Medicine
79. Yixin Gao, PhD Candidate, Department of Computer Science
80. Yuan He, PhD candidate, Department of Biomedical Engineering
81. Rachel Hegeman, Graduate Student, Department of Computer Science
82. Katharine Henry, PhD Candidate, Department of Computer Science
83. Jonathan D. Jones, Graduate Student, Department of Electrical and Computer
Engineering
84. Gaurav Kumar, Graduate Student, Department of Computer Science
85. Connor Lane, Graduate Student, Department of Computer Science
86. Natalie Larsen, Graduate Student, Department of Computer Science
87. Adam Li, PhD Student, Department of Biomedical Engineering
88. Rebecca Marvin, Graduate Student, Department of Computer Science
89. Effrosyni Mavroudi, PhD Candidate, Department of Biomedical Engineering
90. Molly O’Brien, Graduate Student, Department of Computer Science
91. Princy Parsana, PhD candidate, Department of Computer Science
92. Nathan Roach, Graduate Student, Department of Biology
93. Ashis Saha, Graduate Student, Department of Computer Science
94. Evan Schwab, PhD Candidate, Department of Electrical and Computer Engineering
95. Benjamin D. Shapiro, Graduate Student, Department of Computer Science
96. Eli Sherman, Graduate Student, Department of Computer Science
97. Rachel Sherman, PhD Student, Department of Computer Science
98. Heather C. Wick, PhD Candidate, Institute of Genetic Medicine
99. Siddharth Mahendran, PhD Candidate, Department of Electrical and Computer
Engineering
100. Sebastian J. Mielke, PhD Student, Department of Computer Science
101. Anirbit, Department of Applied Mathematics and Statistics
102. Leslie Myint, PhD Candidate, Department of Biostatistics
103. Razieh Nabi, Graduate Student, Department of Computer Science
104. Arun Asokan Nair, Graduate Student, Department of Electrical and Computer
Engineering
105. Carolina Pacheco, Graduate Student, Department of Biomedical Engineering
106. Srivathsa Pasumarthi, Graduate Student, Department of Computer Science
107. Chris Paxton, PhD Candidate, Department of Computer Science
108. Adam Poliak, PhD Candidate, Department of Computer Science
109. Sachi Sanghavi, Graduate Student, Department of Cognitive Science
110. Kayode Sanni, PhD Candidate, Department of Electrical and Computer
Engineering
111. Peter Schulam, PhD Candidate, Department of Computer Science
112. Ayushi Sinha, PhD Candidate, Department of Computer Science
113. David Snyder, PhD Candidate, Department of Computer Science
114. Aditya Upadhyayula, Graduate Student, Department of Psychological and Brain
Sciences
115. Long Wang, PhD Candidate, Department of Applied Mathematics and Statistics
116. Zach Wood-Doughty, Graduate Student, Department of Computer Science
117. Shijie Wu, Graduate Student, Department of Computer Science
118. Xiang Xiang, PhD Candidate, Department of Computer Science
119. Fangzheng Xie, PhD Candidate, Department of Applied Mathematics and
Statistics
120. Florence Yellin, PhD Candidate, Department of Mechanical Engineering
121. Corby Rosset, Alumnus ‘17, M.S.E & B.S. Computer Science
122. Xiaoge Julia Zhang, Alumnus ‘17, PhD & MHS, Department of International
Health, Department of Biostatistics

View at Medium.com

Here is a link: Hopkins letter to NIPS

Image credit: https://nips.cc/Conferences/2017

more on teaching & tenure

teaching

Leah Wasburn-Moses, after a long faculty meeting, went home and posted this on social media: apple-3256487_1920

“Friends on the Tenure Track: I feel as though our futures hinge on: (1) the amount of research we produce that nobody will ever read, (2) the extent to which our students like us, and (3) the number of committees we chair that will never do anything.”

In the Chronicle today, Wasburn-Moses discusses the race/gender biases of teaching evaluations as well as their meaninglessness, citing the 2017 study “Meta-analysis of faculty’s teaching effectiveness: Student evaluation of teaching ratings and student learning are not related.” 

Read the full article, with Wasburn-Moses’ recommendations: “We Make Tenure Decisions Unfairly. Here’s a Better Way.”

 

Nobel Laureate Carl Wieman has a lot to say about teaching effectiveness, as you likely know. Here’s an excerpt from a 2016 article on his work:

“For Wieman, the fact that most colleges and universities don’t even bother to systemically measure teaching quality is the bigger problem festering in higher education. Administrators, he argues, are instead obsessed with publishing and research funding, which remain the bedrock of tenure and promotion.

“‘The quality of teaching is not something that university administrators are rewarded for, and correspondingly know or care about,’ Wieman says. ‘If they improved the quality of teaching by 100 percent and in the process reduced the amount of research funding and publications by 1 percent, they would be penalized, since the latter is carefully measured and compared across institutions, while the former is never measured.'”

Fostering an Inclusive Classroom

teaching

Karen Fleming & I were paired for a Lunch & Learn: Faculty Conversations about Teaching session last month for the Center for Educational Resources. Karen’s talk focused on research on unconscious bias (drawing on research published in PNASMale-Female-Stereotypes in 2012 as well as earlier work). Particularly compelling  is research showing that identical candidates for a (hypothetical) lab manager position fared quite differently, in the eyes of professors of all genders and backgrounds, depending on the apparent gender of the candidate.

My presentation focused on classroom approaches that can help students of many different backgrounds (visibly and less so) find their footing–in the course, and in the academy more broadly. Macie Hall, editor of the Innovative Instructor blog, wrote a nice summary of our talks.

I also wrote a followup post, called Texts of Engagement, in which I offer some resources that seem to me versatile enough to use in different kinds of classrooms, and toward different ends.

I divided these sources into four categories (listed below); the sources in the first three sections, in particular, can nearly all live quite comfortably in most disciplines.

  1. What is college for?
  2. Why bother with stories
  3. How textual analysis works
  4. Ideas for writing assignments

I hope you’ll take a look.

—Anne-Elizabeth Brodsky

Student evaluations & potential employment discrimination

equitable policies & practices, teaching

The other the day the Chronicle posted resclassroom-1699745_1920earch by Kristina M. Mitchell and Jonathan Martin, “Gender Bias in Student Evaluations,” published in PS: Political Science & Politics last week. Here is the abstract:

“Many universities use student evaluations of teachers (SETs) as part of consideration for tenure, compensation, and other employment decisions. However, in doing so, they may be engaging in discriminatory practices against female academics. This study further explores the relationship between gender and SETs described by MacNell, Driscoll, and Hunt (2015) by using both content analysis in student-evaluation comments and quantitative analysis of students’ ordinal scoring of their instructors. The authors show that the language students use in evaluations regarding male professors is significantly different than language used in evaluating female professors. They also show that a male instructor administering an identical online course as a female instructor receives higher ordinal scores in teaching evaluations, even when questions are not instructor-specific. Findings suggest that the relationship between gender and teaching evaluations may indicate that the use of evaluations in employment decisions is discriminatory against women.”

Mitchell published another version of this research in Slate: “Student Evaluations Can’t Be Used to Assess Professors” (March 19, 2018)

P.S.: In 2015, Ben Schmidt created a webpage called Gendered Language in Teaching Evaluations where you can search for terms (“brilliant,” “friendly”) used in ratemyprofessor.com reviews and see how they correlate with gender and discipline.

Also, many thanks to CSW member Yi-Ping Ong for sharing “Is Gender Bias an Intended Feature of Teaching Evaluations?” from February in Insider Higher Ed.

Student Government Association visit

Institutional Climate

On Tuesdhopkins sgaay evening I had the opportunity, at the invitation of long-time Where We Stand participant and Executive VP AJ Tsang, to talk to the SGA about the 2017 Report Card on Vision 2020. What a fabulous group of people, with excellent questions.

After my part of the agenda, things got really interesting.  Moses Davis, Associate Dean for Diversity and Inclusion, came to discuss the Guidelines for Students in Support of Free Expression through Protests and Demonstrations at the Homewood Campus. I really encourage you to take a look at the guidelines. You can read about the SGA’s concerns about them here.