Micah Altman | |
---|---|
Born | |
Nationality | American |
Citizenship | American |
Alma mater | |
Scientific career | |
Fields | Social Science Informatics, Software Engineering, Social Science, Statistics, Political Philosophy |
Institutions | |
Thesis | Districting Principles and Democratic Representation (1998) |
Doctoral advisor | Joseph Morgan Kousser |
Website | micahaltman |
Micah Altman (born August 31, 1967) is an American social scientist who conducts research in social science informatics. Since 2012, he has worked as the head research scientist in the MIT Libraries, first as director of the Program on Information Science (2012-2018) and subsequently as director of research for the libraries' Center for Research on Equitable and Open Scholarship. Altman previously worked at Harvard University. He is known for his work on redistricting, scholarly communication, privacy and open science. Altman is a co-founder of Public Mapping Project, which develops DistrictBuilder, an open-source software.
Altman was born on August 31, 1967, in St. Louis, Missouri, United States. He studied computer science and political philosophy at Brown University, graduating in 1989. [1] He then went to the California Institute of Technology where he studied social science under Morgan Kousser and received a Ph.D. in 1998. [2] [3] He worked as a postdoctoral researcher in Gary King's research group at Harvard University. [4] [5]
From 1998 to 2012 Altman held a number of research positions at Harvard University, including senior research scientist at the Institute of Quantitative Social Science, archival director for the Murray Research Archive and associate director of the Harvard-MIT Data Center. [6] In 1998, Altman was awarded the "Leon Weaver Award" from the American Political Science Association. [7] In 2004, together with Jeff Gill and Michael P. McDonald, he co-authored Numerical Issues in Statistical Computing for the Social Scientist, a book in the field of computational statistics that had several re-editions. [8] [9]
In January 2011, Altman and McDonald presented their Public Mapping Project, which developed DistrictBuilder, an open-source software redistricting application designed to provide online mapping tools. [10] This was awarded Best policy innovations from Politico (2011), the Antonio Pizzigati Prize for Software in the Public Interest from the Tides (2013) and the Brown Democracy Medal from Pennsylvania State University (2018). [10] [11] [12]
In March 2012, Altman was appointed as director of research at Massachusetts Institute of Technology Libraries and Head Scientist for the Program for Information Science, and a non-resident senior fellow at the Brookings Institution in Washington, DC. [4] [13] Also in 2012, he received "The Best Research Software Award" from the American Political Science Association. [14]
Altman's contributions to electoral districting and redistricting have been both theoretical and implementational. He established that the computational complexity of the districting problem is NP-hard and hence optimal redistricting is likely to be intractable. [15] [16]
The undesirable implications of this result are that redistricting cannot be fully automated in practice and the choice of constraints and manual selection of the winning, "optimal" plan from a group of auto-generated plans, reintroduce value-laden and politically biased decision making back into the redistricting process (something that the use of "objective" computer programs was hoped to avoid), while potentially also legitimizing such undercover gerrymandering for the less knowledgeable public. [15]
Further, computational simulations that he performed showed also that even the constraints that have been traditionally considered politically non-preferential, such as the overall compactness of the district, are not necessarily non-preferential because compactness requirements have different effects on political groups if the groups are distributed in geographically different ways. [17] This result was referenced by the Supreme Court justices in the Vieth v. Jubelirer case. [18]
Altman and his colleagues later created the DistrictBuilder software (a successor to the BARD package), the first open-source system to enable the public to participate in redistricting directly through the creation of legal redistricting plans. [19] [20] [21] [22] This effort was awarded the Brown Democracy medal and Pizzigati award (see awards and recognition), after being used by the public to create thousands of legal districting plans—which increased previous levels of public participation in redistricting. [20]
Altman's research in data curation and replication began in a collaboration with the Harvard libraries and Harvard-MIT Data Center (which is now a part of the Institute of Quantitative Social Science). This work included development of an open source institutional repository for data, named the Virtual Data Center, co-led with Sidney Verba and Gary King. [23] The successor to the Virtual data center, the Dataverse Network, remains in broad use for data preservation and scientific replication.
Altman co-authored Numerical Issues in Statistical Computing for the Social Scientist with Jefferson Gill, and Michael P. McDonald in 2004, which demonstrated that the reproducibility of statistical analyses used in social science are threatened by errors and limitations in the statistical computations and software used to estimate them. [8] [9] Based on this analysis, Altman, McDonald and Gill developed methods to detect issues in social science statistical models and provide more replicable and reliable estimates. [8]
Altman's research was focused on preservation, scientific replication, and scholarly communication. It included the development of standards for data citation; [24] the creation of semantic fingerprint methods to verify data for scientific reuse, and long-term archiving; [25] [26] the analysis of technical and institutional approach to long-term preservation; [27] the creation of taxonomic standards for author attribution (working with Amy Brand and other); [28] and the characterization of grand-challenge problems in scholarly communications. [27]
Over the last decade, Altman has been a leader in the Harvard University Privacy Tools project, which conducts research and develops tools to improve data privacy. Altman has published several research articles with this group characterizing the mathematical underpinnings on information privacy threats, and developing new technical and legal approaches to privacy protection. [29] [30] [31]
Year | Recognition | Recognition type | Awarding body |
---|---|---|---|
1998 | Leon Weaver Award [7] | Award | American Political Science Association |
1999 | Best Dissertation Award [32] | Award | Western Political Science Association |
2011 | Best policy innovations [10] | Award | Politico |
2012 | Best Research Software Award [14] | Award | American Political Science Association |
2012 | Data Innovation Award for Social Impact [4] | Award | O’Reilly Strata Conference |
2013 | The Antonio Pizzigati Prize for Software in the Public Interest [11] [33] | Award | Tides |
2010-2016 | Non-Resident Senior Fellow [34] | Fellowship | Brookings Institution |
2018 | Brown Democracy Medal [12] | Award | Pennsylvania State University |
Computer science is the study of computation, information, and automation. Computer science spans theoretical disciplines to applied disciplines. Though more often considered an academic discipline, computer science is closely related to computer programming.
The Massachusetts Institute of Technology (MIT) is a private land-grant research university in Cambridge, Massachusetts. Established in 1861, MIT has played a significant role in the development of many areas of modern technology and science.
Privacy is the ability of an individual or group to seclude themselves or information about themselves, and thereby express themselves selectively.
Microsoft Research (MSR) is the research subsidiary of Microsoft. It was created in 1991 by Richard Rashid, Bill Gates and Nathan Myhrvold with the intent to advance state-of-the-art computing and solve difficult world problems through technological innovation in collaboration with academic, government, and industry researchers. The Microsoft Research team has more than 1,000 computer scientists, physicists, engineers, and mathematicians, including Turing Award winners, Fields Medal winners, MacArthur Fellows, and Dijkstra Prize winners.
Simson L. Garfinkel is a Program Scientist at AI2050, part of Schmidt Futures. He has held several roles across government, including a Senior Data Scientist at the Department of Homeland Security (DHS), the US Census Bureau's Senior Computer Scientist for Confidentiality and Data Access. and a computer scientist at the National Institute of Standards and Technology (2015-2017). Prior to that, he was an associate professor at the Naval Postgraduate School in Monterey, California (2006-2015). In addition to his research, Garfinkel is a journalist, an entrepreneur, and an inventor; his work is generally concerned with computer security, privacy, and information technology.
Digital humanities (DH) is an area of scholarly activity at the intersection of computing or digital technologies and the disciplines of the humanities. It includes the systematic use of digital resources in the humanities, as well as the analysis of their application. DH can be defined as new ways of doing scholarship that involve collaborative, transdisciplinary, and computationally engaged research, teaching, and publishing. It brings digital tools and methods to the study of the humanities with the recognition that the printed word is no longer the main medium for knowledge production and distribution.
Alex Paul "Sandy" Pentland is an American computer scientist, the Toshiba Professor of Media Arts and Sciences at MIT, and serial entrepreneur.
The Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) is the engineering school within Harvard University's Faculty of Arts and Sciences, offering degrees in engineering and applied sciences to graduate students admitted directly to SEAS, and to undergraduates admitted first to Harvard College. Previously the Lawrence Scientific School and then the Division of Engineering and Applied Sciences, the Paulson School assumed its current structure in 2007. Francis J. Doyle III has been its dean since 2015.
Data sharing is the practice of making data used for scholarly research available to other investigators. Many funding agencies, institutions, and publication venues have policies regarding data sharing because transparency and openness are considered by many to be part of the scientific method.
The Dataverse is an open source web application to share, preserve, cite, explore and analyze research data. Researchers, data authors, publishers, data distributors, and affiliated institutions all receive appropriate credit via a data citation with a persistent identifier.
Jefferson Morris Gill is Distinguished Professor of Government, and of Mathematics & Statistics, the Director of the Center for Data Science, the Editor of Political Analysis, and a member of the Center for Behavioral Neuroscience at American University as of the Fall of 2017.
Differential privacy (DP) is a system for publicly sharing information about a dataset by describing the patterns of groups within the dataset while withholding information about individuals in the dataset. The idea behind differential privacy is that if the effect of making an arbitrary single substitution in the database is small enough, the query result cannot be used to infer much about any single individual, and therefore provides privacy.
The social data revolution is the shift in human communication patterns towards increased personal information sharing and its related implications, made possible by the rise of social networks in the early 2000s. This phenomenon has resulted in the accumulation of unprecedented amounts of public data.
Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data.
Catherine Tucker is the Sloan Distinguished Professor of Management at MIT Sloan, where she is also chair of the PhD program. She is known for her research into the consequences of digital data for electronic privacy, algorithmic bias, digital health, social media and online advertising. She is also a research associate at the NBER, cofounder of the Cryptoeconomics lab at MIT with Christian Catalini and coeditor at Quantitative Marketing Economics.
Yaniv Altshuler, is an Israeli computer scientist and entrepreneur. He is a researcher at the MIT Media Lab, at the Human Dynamics group headed by professor Alex Pentland.
Samer Hassan is a computer scientist, social scientist, activist and researcher, focused on the use of decentralized technologies to support commons-based collaboration. He is Associate Professor at Universidad Complutense de Madrid (Spain) and Faculty Associate at the Berkman Klein Center for Internet & Society at Harvard University. He is the recipient of an ERC Grant of 1.5M€ with the P2P Models project, to research blockchain-based decentralized autonomous organizations for the collaborative economy.
The Center for Research on Computation and Society is a research center at Harvard University that focuses on interdisciplinary research combining computer science with social sciences. It is based in Harvard John A. Paulson School of Engineering and Applied Sciences. It is currently directed by Milind Tambe.
Michael P. McDonald is an American political scientist. He is a Professor of Political Science at the University of Florida where he focuses on the United States elections.
Automated decision-making (ADM) involves the use of data, machines and algorithms to make decisions in a range of contexts, including public administration, business, health, education, law, employment, transport, media and entertainment, with varying degrees of human oversight or intervention. ADM involves large-scale data from a range of sources, such as databases, text, social media, sensors, images or speech, that is processed using various technologies including computer software, algorithms, machine learning, natural language processing, artificial intelligence, augmented intelligence and robotics. The increasing use of automated decision-making systems (ADMS) across a range of contexts presents many benefits and challenges to human society requiring consideration of the technical, legal, ethical, societal, educational, economic and health consequences.