azjacobs at umich.edu
CV | Google Scholar
Assistant Professor of Information, School of Information
Assistant Professor of Complex Systems, College of Literature, Science, and the Arts
University of Michigan
CV | Google Scholar
ABIGAIL Z. JACOBS
Assistant Professor of Information, School of Information
Assistant Professor of Complex Systems, College of Literature, Science, and the Arts
University of Michigan
I am an Assistant Professor of Information at the University of Michigan in the School of Information and an Assistant Professor of Complex Systems in the College of Literature, Science, and the Arts. I am also an affiliate of the Center for Ethics, Society, and Computing (ESC) and the Michigan Institute for Data Science (MIDAS).
I am also a 2024 Microsoft Research AI & Society Fellow.
My current research interests are around measurement; the hidden assumptions in machine learning, focusing on measurement and validity as a lens; structure, governance, and inequality in sociotechnical systems; and social networks. Find my academic work below, on my CV, or on Google Scholar.
Since 2019, I am an Assistant Professor at the University of Michigan School of Information and the Center for the Study of Complex Systems. Previously I was a postdoctoral fellow at the Haas School of Business at UC Berkeley and a member of the Algorithmic Fairness and Opacity Working Group. I received a PhD in Computer Science from the University of Colorado Boulder. During my PhD I was fortunate to spend time at Microsoft Research NYC (intern/consulting researcher, 2015-2017) and to have funding from an NSF Graduate Research Fellowship. In 2015, I served as an organizer for the Women in Machine Learning Workshop, a technical workshop co-located with NeurIPS, and from 2018-2019 I was on the Board of Directors for Women in Machine Learning, Inc. I previously received a BA in Mathematical Methods in the Social Sciences and Mathematics from Northwestern University.
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I am also a 2024 Microsoft Research AI & Society Fellow.
My current research interests are around measurement; the hidden assumptions in machine learning, focusing on measurement and validity as a lens; structure, governance, and inequality in sociotechnical systems; and social networks. Find my academic work below, on my CV, or on Google Scholar.
Since 2019, I am an Assistant Professor at the University of Michigan School of Information and the Center for the Study of Complex Systems. Previously I was a postdoctoral fellow at the Haas School of Business at UC Berkeley and a member of the Algorithmic Fairness and Opacity Working Group. I received a PhD in Computer Science from the University of Colorado Boulder. During my PhD I was fortunate to spend time at Microsoft Research NYC (intern/consulting researcher, 2015-2017) and to have funding from an NSF Graduate Research Fellowship. In 2015, I served as an organizer for the Women in Machine Learning Workshop, a technical workshop co-located with NeurIPS, and from 2018-2019 I was on the Board of Directors for Women in Machine Learning, Inc. I previously received a BA in Mathematical Methods in the Social Sciences and Mathematics from Northwestern University.
Selected talks & conferences
June & July: I am on the planning committee for the Human and Organizational Factors in AI Risk Management: Workshop series hosted by the National Academies
︎︎︎ Workshop on Human and Organizational Factors in AI Risk Management: Evaluation, Testing, and Oversight. Organizing Committee The National Academies of Sciences, Engineering, and Medicine, June 20, 2024
[ Event recording ]
︎︎︎Workshop on Human and Organizational Factors in AI Risk Management: Designing Paths Forward in AI Risk Management. The National Academies of Sciences, Engineering, and Medicine, July 2, 2024, Washington D.C.
[ Register for online or in person ]
International Conference on Computational Social Science, July 2024. Joint work with Meera Desai and Dallas Card
Privacy Law Scholars Conference, May 2024. Joint work with Amina Abdu
Annual Ethical AI Symposium, Michigan Institute for Data Science, University of Michigan, April 2024. Keynote speaker.
Carnegie Mellon, Human-Computer Interaction Institute seminar, March 2024
SoLaR 2023: Socially Responsible Language Modelling Research, NeurIPS Workshop, December 2023.
TADA 2023: New Directions in Analyzing Text as Data, November 2023.
Emory, Quantitative Theory and Methods seminar, October 2023
University of Maryland, Values-Centered Artificial Intelligence seminar, October 2023
Co-organizer: “Operationalizing the Measure Function of the NIST AI Risk Management Framework,” co-organized with the Center for Advancing Safety of Machine Intelligence (CASMI); the NIST-National Science Foundation (NSF) Institute for Trustworthy AI in Law & Society (TRAILS); and the Federation of American Scientists (FAS). October 2023 [writeup]
Organizer: “Sociotechnical Approaches to Measurement and Validation for Safety in AI” Workshop, hosted by the Center for Advancing Safety of Machine Intelligence (CASMI). July 2023 [writeup]
First Workshop on Generative AI and the Law (GenLaw), July 2023. Invited participant. [Report]
Privacy Law Scholars Conference, June 2023. Joint work with Amina Abdu, Lauren Chambers, Deirdre K. Mulligan
University of Washington Center for an Informed Public. May 2023
Yale Law School, Information Society Project, Data (Re)Makes the World. March 2023. Joint work with Amina Abdu, Lauren Chambers, Deirdre K. Mulligan
Carnegie Mellon University, Statistics seminar. October 2022
Stanford Graduate School of Business, Organizational Behavior seminar. October 2022
Data & Society, The Social Life of Algorithmic Harms workshop, 2022.
Joint work with Andrea Thomer and Jeff Lockhart
NeurIPS Workshop on AI for Science: Mind the Gaps. [short paper: Scientific Argument with Supervised Learning with Jeff Lockhart] December 2021
KDD Workshop on Measures and Best Practices for Responsible AI. [short paper: Measurement as governance in and for responsible AI] August 2021
Networks 2021 [paper: A large-scale comparative study of informal social networks in firms] July 2021
Networked Justice symposium at Networks 2021. June 2021
Organizer: Statistical Inference for Network Models symposium at Networks 2021. June 2021
Machine learning and economic inequality conference. Invited speaker. [video: Measurement as governance, slides] April 2021
ACM Conference on Fairness, Accountability, and Transparency (FAccT) 2021 [paper: Measurement and Fairness] March 2021
NIST Workshop on Bias in AI. Panelist. August 2020
Michigan Institute for Data Science Workshop on Learning environments in the time of COVID-19: (Towards) Evidence-Driven Innovation and Resilience at the University of Michigan. Organizer. June 2020
WebConf Workshop on Innovative Ideas in Data Science. Nominated for best paper. [short paper: Internet-Human Infrastructures: Lessons from Havana’s StreetNet] April 2020
ACM Conference on Fairness, Accountability, and Transparency (FAccT) Translation Tutorial: The Meaning and Measurement of Bias: Lessons from NLP. with Su Lin Blodgett, Solon Barocas, Hal Daumé III, & Hanna Wallach. [slides, relevant paper, video] January 2020
International Conference on Computational Social Science (IC2S2) July 2019
Women in Tech: The Future of AI Symposium. Panelist: Accountability in AI. November 2018
International Conference on Computational Social Science (IC2S2) July 2018
International Conference on Web and Social Media (ICWSM) Beyond Online Data workshop. Nominated for best paper award. June 2018
Algorithmic Fairness & Opacity Summer Workshop Panelist: Bias in Algorithms [Summary] June 2018
︎︎︎ Workshop on Human and Organizational Factors in AI Risk Management: Evaluation, Testing, and Oversight. Organizing Committee The National Academies of Sciences, Engineering, and Medicine, June 20, 2024
[ Event recording ]
︎︎︎Workshop on Human and Organizational Factors in AI Risk Management: Designing Paths Forward in AI Risk Management. The National Academies of Sciences, Engineering, and Medicine, July 2, 2024, Washington D.C.
[ Register for online or in person ]
International Conference on Computational Social Science, July 2024. Joint work with Meera Desai and Dallas Card
Privacy Law Scholars Conference, May 2024. Joint work with Amina Abdu
Annual Ethical AI Symposium, Michigan Institute for Data Science, University of Michigan, April 2024. Keynote speaker.
Carnegie Mellon, Human-Computer Interaction Institute seminar, March 2024
SoLaR 2023: Socially Responsible Language Modelling Research, NeurIPS Workshop, December 2023.
TADA 2023: New Directions in Analyzing Text as Data, November 2023.
Emory, Quantitative Theory and Methods seminar, October 2023
University of Maryland, Values-Centered Artificial Intelligence seminar, October 2023
Co-organizer: “Operationalizing the Measure Function of the NIST AI Risk Management Framework,” co-organized with the Center for Advancing Safety of Machine Intelligence (CASMI); the NIST-National Science Foundation (NSF) Institute for Trustworthy AI in Law & Society (TRAILS); and the Federation of American Scientists (FAS). October 2023 [writeup]
Organizer: “Sociotechnical Approaches to Measurement and Validation for Safety in AI” Workshop, hosted by the Center for Advancing Safety of Machine Intelligence (CASMI). July 2023 [writeup]
First Workshop on Generative AI and the Law (GenLaw), July 2023. Invited participant. [Report]
Privacy Law Scholars Conference, June 2023. Joint work with Amina Abdu, Lauren Chambers, Deirdre K. Mulligan
University of Washington Center for an Informed Public. May 2023
Yale Law School, Information Society Project, Data (Re)Makes the World. March 2023. Joint work with Amina Abdu, Lauren Chambers, Deirdre K. Mulligan
Carnegie Mellon University, Statistics seminar. October 2022
Stanford Graduate School of Business, Organizational Behavior seminar. October 2022
Data & Society, The Social Life of Algorithmic Harms workshop, 2022.
Joint work with Andrea Thomer and Jeff Lockhart
NeurIPS Workshop on AI for Science: Mind the Gaps. [short paper: Scientific Argument with Supervised Learning with Jeff Lockhart] December 2021
KDD Workshop on Measures and Best Practices for Responsible AI. [short paper: Measurement as governance in and for responsible AI] August 2021
Networks 2021 [paper: A large-scale comparative study of informal social networks in firms] July 2021
Networked Justice symposium at Networks 2021. June 2021
Organizer: Statistical Inference for Network Models symposium at Networks 2021. June 2021
Machine learning and economic inequality conference. Invited speaker. [video: Measurement as governance, slides] April 2021
ACM Conference on Fairness, Accountability, and Transparency (FAccT) 2021 [paper: Measurement and Fairness] March 2021
NIST Workshop on Bias in AI. Panelist. August 2020
Michigan Institute for Data Science Workshop on Learning environments in the time of COVID-19: (Towards) Evidence-Driven Innovation and Resilience at the University of Michigan. Organizer. June 2020
WebConf Workshop on Innovative Ideas in Data Science. Nominated for best paper. [short paper: Internet-Human Infrastructures: Lessons from Havana’s StreetNet] April 2020
ACM Conference on Fairness, Accountability, and Transparency (FAccT) Translation Tutorial: The Meaning and Measurement of Bias: Lessons from NLP. with Su Lin Blodgett, Solon Barocas, Hal Daumé III, & Hanna Wallach. [slides, relevant paper, video] January 2020
International Conference on Computational Social Science (IC2S2) July 2019
Women in Tech: The Future of AI Symposium. Panelist: Accountability in AI. November 2018
International Conference on Computational Social Science (IC2S2) July 2018
International Conference on Web and Social Media (ICWSM) Beyond Online Data workshop. Nominated for best paper award. June 2018
Algorithmic Fairness & Opacity Summer Workshop Panelist: Bias in Algorithms [Summary] June 2018
Selected ongoing projects
The Hidden Governance of AI. The Regulatory Review (2022). [link]
A. Z. Jacobs, Deirdre Mulligan.
Notre Dame-IBM Tech Ethics Lab grant. (2022-2023) “Expanding AI Audits To Include Instruments: Accountability, Measurements, and Data in Motion Capturing Technology.” Co-PI with Mona Sloane (New York University), Emanuel Moss (Intel Research).
Scientific argument with supervised learning. NeurIPS Workshop on AI for Science: Mind the Gaps. [working draft]
Jeff Lockhart, A. Z. Jacobs.
Measurement as governance in and for responsible AI. KDD Workshop on Responsible AI (2021) [working draft]
A. Z. Jacobs
Publications
Algorithmic Transparency and Participation through the Handoff Lens: Lessons Learned from the U.S. Census Bureau’s Adoption Differential Privacy.
ACM Conference on Fairness, Accountability and Transparency (FAccT), 2024. [pdf]
Amina Abdu, Lauren Chambers, Deirdre K. Mulligan, A. Z. Jacobs
An archival perspective on pretraining data.
Patterns, 2024. [pdf]
Meera A. Desai, Irene V. Pasquetto, A. Z. Jacobs, Dallas Card
The Cadaver in the Machine: The Social Practices of Measurement and Validation in Motion Capture Technology.
ACM Conference on Human Factors in Computing Systems (CHI), 2024, to appear. [pdf]
Emma Harvey, Hauke Sandhaus, A. Z. Jacobs*, Emanuel Moss*, Mona Sloane*
︎︎︎ ACM Best Paper Honorable Mention
︎︎︎ coverage by IEEE Spectrum: “AI Is Being Built on Dated, Flawed Motion-Capture Data“
The Role of Relevance in Fair Ranking.
ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2023. [pdf]
Aparna Balagopalan, A. Z. Jacobs, Asia J. Biega
︎︎︎ popular writeup for the Montreal AI Ethics Institute
An empirical analysis of racial categories in the algorithmic fairness literature.
ACM Conference on Fairness, Accountability and Transparency (FAccT), 2023. [pdf]
Amina Abdu, Irene V. Pasquetto, A. Z. Jacobs
︎︎︎ popular writeup for the Montreal AI Ethics Institute
Conceptualizing Algorithmic Stigmatization.
ACM Conference on Human Factors in Computing Systems (CHI), 2023. [pdf]
Nazanin Andalibi, Cassidy Pyle, Kristen Barta, Lu Xian, A. Z. Jacobs, Mark Ackerman.
A large-scale comparative study of informal social networks in firms.
Management Science. (2021). [publisher link, open access pdf]
A. Z. Jacobs, Duncan Watts.
Measurement and fairness.
ACM Conference on Fairness, Accountability and Transparency (FAccT). (2021). [arXiv, related tutorial, slides, tutorial video]
A. Z. Jacobs, Hanna Wallach.
Internet-human infrastructures: Lessons from Havana’s StreetNet.
WebConf Workshop on Innovative Ideas in Data Science, nominated for best paper. (2020). [preprint]
A. Z. Jacobs, Michaelanne Dye Thomas (joint work).
Translation tutorial: The meaning and measurement of bias: Lessons from natural language processing
ACM Conference on Fairness, Accountability and Transparency (FAccT). (2020). [ACM link, related tutorial, slides, tutorial video]
A. Z. Jacobs, Su Lin Blodgett, Solon Barocas, Hal Daumé III, Hanna Wallach.
Assembly in populations of social networks.
CSCW Workshop on Multi-Site Research. Short paper. (2018). [arXiv]
A. Z. Jacobs.
Assembling thefacebook: Using heterogeneity to understand online social network assembly.
ACM Web Science Conference (WebSci). (2015). [arXiv, data]
A. Z. Jacobs, Samuel F. Way, Johan Ugander & Aaron Clauset.
Learning latent block structure in weighted networks. Journal of Complex Networks 3(2), 221-248. (2015). [arXiv]
Christopher Aicher, A. Z. Jacobs & Aaron Clauset.
A unified view of generative models for networks: models, methods, opportunities, and challenges. NIPS Workshop on Networks: From Graphs to Rich Data. (2014). [arXiv]
A. Z. Jacobs & Aaron Clauset.
Efficiently inferring community structure in bipartite networks. Physical Review E 90, 012805. (2014). [arXiv, code]
Daniel B. Larremore, Aaron Clauset & A. Z. Jacobs.
Complex life cycles in a pond food web: effects of life stage structure and parasites on network properties, trophic positions and the fit of a probabilistic niche model. Oecologia 174 (3) 953-965. (2014). [publisher link]
Daniel L. Preston, A. Z. Jacobs, Sarah A. Orlofske & Pieter T.J. Johnson.
Adapting the stochastic block model to edge-weighted networks. ICML Workshop on Structured Learning (SLG). (2013). [arXiv, code]
Christopher Aicher, A. Z. Jacobs & Aaron Clauset.
Detecting friendship within dynamic online interaction networks. AAAI Conference on Weblogs and Social Media (ICWSM). (2013). [arXiv]
Sears Merritt, A. Z. Jacobs, Winter Mason & Aaron Clauset.
See also:
Report of the 1st Workshop on Generative AI and Law.
Cooper, A. Feder and Lee, Katherine and Grimmelmann, James and Grimmelmann, James and Ippolito, Daphne and Callison-Burch, Christopher and Choquette-Choo, Christopher A. and Mireshghallah, Niloofar and Brundage, Miles and Mimno, David and Choksi, Madiha Zahrah and Balkin, Jack M. and Carlini, Nicholas and De Sa, Christopher and Frankle, Jonathan and Ganguli, Deep and Gipson, Bryant and Guadamuz, Andres and Harris, Swee Leng and Jacobs, Abigail and Joh, Elizabeth E. and Kamath, Gautam and Lemley, Mark A. and Matthews, Cass and McLeavey, Christine and McSherry, Corynne and Nasr, Milad and Ohm, Paul and Roberts, Adam and Rubin, Tom and Samuelson, Pamela and Schubert, Ludwig and Vaccaro, Kristen and Villa, Luis and Wu, Felix T. and Zeide, Elana.
Yale Law & Economics Research Paper, (November 16, 2023). [pdf]
Comparative, population-level analysis of social networks in organizations. Dissertation. 2017.
Untangling the roles of parasites in food webs with generative network models. Preprint. (2015). [arXiv]
A. Z. Jacobs, Jennifer A. Dunne, Cristopher Moore & Aaron Clauset.
Adapting to non-stationarity with growing expert ensembles. Preprint. (2011). [arXiv]
Cosma R. Shalizi, A. Z. Jacobs, Kristina L. Klinkner & Aaron Clauset.
The Hidden Governance of AI. The Regulatory Review (2022). [link]
A. Z. Jacobs, Deirdre Mulligan.
Notre Dame-IBM Tech Ethics Lab grant. (2022-2023) “Expanding AI Audits To Include Instruments: Accountability, Measurements, and Data in Motion Capturing Technology.” Co-PI with Mona Sloane (New York University), Emanuel Moss (Intel Research).
Scientific argument with supervised learning. NeurIPS Workshop on AI for Science: Mind the Gaps. [working draft]
Jeff Lockhart, A. Z. Jacobs.
Measurement as governance in and for responsible AI. KDD Workshop on Responsible AI (2021) [working draft]
A. Z. Jacobs
Publications
Algorithmic Transparency and Participation through the Handoff Lens: Lessons Learned from the U.S. Census Bureau’s Adoption Differential Privacy.
ACM Conference on Fairness, Accountability and Transparency (FAccT), 2024. [pdf]
Amina Abdu, Lauren Chambers, Deirdre K. Mulligan, A. Z. Jacobs
An archival perspective on pretraining data.
Patterns, 2024. [pdf]
Meera A. Desai, Irene V. Pasquetto, A. Z. Jacobs, Dallas Card
The Cadaver in the Machine: The Social Practices of Measurement and Validation in Motion Capture Technology.
ACM Conference on Human Factors in Computing Systems (CHI), 2024, to appear. [pdf]
Emma Harvey, Hauke Sandhaus, A. Z. Jacobs*, Emanuel Moss*, Mona Sloane*
︎︎︎ ACM Best Paper Honorable Mention
︎︎︎ coverage by IEEE Spectrum: “AI Is Being Built on Dated, Flawed Motion-Capture Data“
The Role of Relevance in Fair Ranking.
ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2023. [pdf]
Aparna Balagopalan, A. Z. Jacobs, Asia J. Biega
︎︎︎ popular writeup for the Montreal AI Ethics Institute
An empirical analysis of racial categories in the algorithmic fairness literature.
ACM Conference on Fairness, Accountability and Transparency (FAccT), 2023. [pdf]
Amina Abdu, Irene V. Pasquetto, A. Z. Jacobs
︎︎︎ popular writeup for the Montreal AI Ethics Institute
Conceptualizing Algorithmic Stigmatization.
ACM Conference on Human Factors in Computing Systems (CHI), 2023. [pdf]
Nazanin Andalibi, Cassidy Pyle, Kristen Barta, Lu Xian, A. Z. Jacobs, Mark Ackerman.
A large-scale comparative study of informal social networks in firms.
Management Science. (2021). [publisher link, open access pdf]
A. Z. Jacobs, Duncan Watts.
Measurement and fairness.
ACM Conference on Fairness, Accountability and Transparency (FAccT). (2021). [arXiv, related tutorial, slides, tutorial video]
A. Z. Jacobs, Hanna Wallach.
Internet-human infrastructures: Lessons from Havana’s StreetNet.
WebConf Workshop on Innovative Ideas in Data Science, nominated for best paper. (2020). [preprint]
A. Z. Jacobs, Michaelanne Dye Thomas (joint work).
Translation tutorial: The meaning and measurement of bias: Lessons from natural language processing
ACM Conference on Fairness, Accountability and Transparency (FAccT). (2020). [ACM link, related tutorial, slides, tutorial video]
A. Z. Jacobs, Su Lin Blodgett, Solon Barocas, Hal Daumé III, Hanna Wallach.
Assembly in populations of social networks.
CSCW Workshop on Multi-Site Research. Short paper. (2018). [arXiv]
A. Z. Jacobs.
Assembling thefacebook: Using heterogeneity to understand online social network assembly.
ACM Web Science Conference (WebSci). (2015). [arXiv, data]
A. Z. Jacobs, Samuel F. Way, Johan Ugander & Aaron Clauset.
Learning latent block structure in weighted networks. Journal of Complex Networks 3(2), 221-248. (2015). [arXiv]
Christopher Aicher, A. Z. Jacobs & Aaron Clauset.
A unified view of generative models for networks: models, methods, opportunities, and challenges. NIPS Workshop on Networks: From Graphs to Rich Data. (2014). [arXiv]
A. Z. Jacobs & Aaron Clauset.
Efficiently inferring community structure in bipartite networks. Physical Review E 90, 012805. (2014). [arXiv, code]
Daniel B. Larremore, Aaron Clauset & A. Z. Jacobs.
Complex life cycles in a pond food web: effects of life stage structure and parasites on network properties, trophic positions and the fit of a probabilistic niche model. Oecologia 174 (3) 953-965. (2014). [publisher link]
Daniel L. Preston, A. Z. Jacobs, Sarah A. Orlofske & Pieter T.J. Johnson.
Adapting the stochastic block model to edge-weighted networks. ICML Workshop on Structured Learning (SLG). (2013). [arXiv, code]
Christopher Aicher, A. Z. Jacobs & Aaron Clauset.
Detecting friendship within dynamic online interaction networks. AAAI Conference on Weblogs and Social Media (ICWSM). (2013). [arXiv]
Sears Merritt, A. Z. Jacobs, Winter Mason & Aaron Clauset.
See also:
Report of the 1st Workshop on Generative AI and Law.
Cooper, A. Feder and Lee, Katherine and Grimmelmann, James and Grimmelmann, James and Ippolito, Daphne and Callison-Burch, Christopher and Choquette-Choo, Christopher A. and Mireshghallah, Niloofar and Brundage, Miles and Mimno, David and Choksi, Madiha Zahrah and Balkin, Jack M. and Carlini, Nicholas and De Sa, Christopher and Frankle, Jonathan and Ganguli, Deep and Gipson, Bryant and Guadamuz, Andres and Harris, Swee Leng and Jacobs, Abigail and Joh, Elizabeth E. and Kamath, Gautam and Lemley, Mark A. and Matthews, Cass and McLeavey, Christine and McSherry, Corynne and Nasr, Milad and Ohm, Paul and Roberts, Adam and Rubin, Tom and Samuelson, Pamela and Schubert, Ludwig and Vaccaro, Kristen and Villa, Luis and Wu, Felix T. and Zeide, Elana.
Yale Law & Economics Research Paper, (November 16, 2023). [pdf]
Comparative, population-level analysis of social networks in organizations. Dissertation. 2017.
Untangling the roles of parasites in food webs with generative network models. Preprint. (2015). [arXiv]
A. Z. Jacobs, Jennifer A. Dunne, Cristopher Moore & Aaron Clauset.
Adapting to non-stationarity with growing expert ensembles. Preprint. (2011). [arXiv]
Cosma R. Shalizi, A. Z. Jacobs, Kristina L. Klinkner & Aaron Clauset.
Advising
Ph.D. students, UMich School of Information
Working with me:
Candidates interested in pursuing a PhD should apply and be admitted to the UMSI PhD program (deadline: Dec 1). If you are a University of Michigan student and are interested in doing research with me, you are welcome to email me and include your interests, major, degree, and resume/CV.
Working with me:
Candidates interested in pursuing a PhD should apply and be admitted to the UMSI PhD program (deadline: Dec 1). If you are a University of Michigan student and are interested in doing research with me, you are welcome to email me and include your interests, major, degree, and resume/CV.
Teaching
Winter 2020-2023. SI 485 Information Analytics Projects course
Fall 2019-2022. CMPLXSYS 501 Foundations of Complex Systems. [syllabus]
Fall 2019-2022. CMPLXSYS 501 Foundations of Complex Systems. [syllabus]
Contact
Online:
Find me on Google Scholar, or as azjacobs + umich.edu. I almost never tweet as @az_jacobs or verb at mastodon as @az_jacobs, or now on bluesky at @azjacobs.bsky.social
Find me on Google Scholar, or as azjacobs + umich.edu. I almost never tweet as @az_jacobs or verb at mastodon as @az_jacobs, or now on bluesky at @azjacobs.bsky.social
Offline:
I often go by Abbie and as a consequence will respond to Abby, Abi, Aby, and other creative variations of Abigail.
I often go by Abbie and as a consequence will respond to Abby, Abi, Aby, and other creative variations of Abigail.