[AISWorld] University of Michigan Faculty Position Openings: Data Science

Lionel Robert lprobert at umich.edu
Fri Sep 7 16:39:55 EDT 2018


Faculty Position Openings: Data Science

The University of Michigan School of Information
Tenure Track Positions in Data Science

*Position description*

The School of Information at the University of Michigan (UMSI) seeks
tenure-track professors at the assistant, associate or full level in the
broad field of data science. Successful candidates are expected to be able
to teach in UMSI’s online and/or residential programs related to applied
data science, and contribute to our vibrant research culture. Applicants in
some of the areas listed below may also be considered for joint research
appointments with the Institute for Social Research. We anticipate making
several hires across the following overlapping areas of emphasis:

*Social media* – We seek candidates who will use analysis of large-scale
data to understand how social media interactions affect society and
institutions. Areas of focus include (but are not limited to): online
harassment; discussion norms and spaces; social support; misinformation and
disinformation in social media channels; online violence; online civility;
and content moderation.

*Computational social science* -- We seek candidates with a strong
computational background who are specifically interested in applications to
social science, broadly defined. Candidates should demonstrate excellence
in both computational methods and social science theory. Applications of
interest include but are not limited to: social network analysis; crisis
informatics; political communication; large-scale social experiments;
social science applications to health; and simulations of social systems.

*Machine learning and causal inference* -- We seek candidates whose
research combines machine learning, experiments, and econometric techniques
of causal inference in order to design, implement and analyze applications
at the intersection of social science and information science. We are open
to the specific domain of study. Examples include (but are not limited to):
network analysis; health and behavior change; ICT for development;
user-generated content and other public goods problems; crowdsourcing,
collective intelligence and other information aggregation problems.

*Learning analytics* – We seek candidates in the field of large-scale
learning and behavioral data analytics who will focus on understanding,
evaluating, and designing systems designed to support teaching and learning
in formal and informal educational settings. Successful candidates will
combine insights from the learning, cognitive, and computer sciences
approaches to working with data generated by teachers and learners
interacting in face-to-face, blended, and online contexts. Candidates with
experience in experimental and developmental research to build, validate,
and effectively deploy analytic technologies or techniques that make a
positive impact on learning and teaching will be well-suited to this
position. Examples include (but are not limited to) research on: Massive
Open Online Courses (MOOCs); behavioral interventions for learners;
visualizations of learners/learning to support teaching; the use and impact
of social media and other online tools in learning/teaching; feedback
systems for learners; or adaptive/personalized learning and tutoring
systems.

*Application-inspired data science techniques* – We seek candidates who
have strong interest and expertise in creating novel computational
techniques related to data science methodologies, which include but are not
limited to: data mining; machine learning and optimization; information
retrieval; natural language processing; network analysis; information
visualization; and multimedia analysis.  Candidates should also have
interest in application areas that include (but are not limited to): Web
mining; content analysis; behavioral data analysis; mobile and sensor data;
recommender systems; social networks; computational social sciences; data
for social good; health informatics; learning analytics; digital
humanities; transportation informatics; and crowdsourcing.

*Computational humanities* – We seek candidates who have strong interest
and expertise in creating novel computational techniques related to testing
and expanding theories in the humanities, which include but are not limited
to: cultural analytics; literary text analysis; natural language
processing; historical linguistics; museum informatics; and multimedia
analysis. Applicants whose work integrates theory or expertise from one or
more humanities fields with machine learning or natural language processing
and those with work on multilingual or multimodal data will be particularly
well suited for this position.

*Ethics of AI and data science* – We seek candidates who investigate the
ethical dimensions and consequences of artificial intelligence (AI) and/or
data science, broadly defined. Candidates should be grounded in a
discipline or with core expertise in philosophy, public policy, applied
ethics (including information ethics, computer ethics, media ethics, data
ethics), science and technology studies, law, or some other perspective
centrally related to ethics. Candidate research may be focused on the
ethics of big data, data science ethics, digital media ethics, critical
data science, data justice, the ethics of algorithmic systems, the ethics
of machine learning, the ethics of autonomous or semi-autonomous agents
(software, vehicles, robots), or another related topic. Any setting or
context for the ethical investigation of AI and/or data science will be
considered, and this could include media, information, science, medicine,
education, war, labor, transportation, and/or the computer industry.



*Job Expectations and Responsibilities:*

Each contributing member of the UMSI faculty is expected to have teaching
effort equivalent to three residential courses per year.  In addition to
formal classroom and/or online teaching, faculty are expected to work with
students by serving as advisors for independent studies, master’s projects
and theses, and doctoral dissertations.   Additional job responsibilities
include but are not limited to:

   - Conducting scholarly research resulting in publications in peer
   reviewed journals, book chapters, edited books, books, and conference papers
   - Obtaining outside funding to support their research
   - Providing service to the school, University, and the broader academic
   community by way of committee work, journal editing, and other various
   opportunities



*About UMSI and U-M*

The mission of the School of Information is to create and share knowledge
to help people use information -- with technology -- to build a better
world. A successful candidate will be committed to, and will directly
contribute to our goal of being the best research and teaching institution
for the understanding and design of information and its technologies in
service of people and society.

The School is home to vibrant research and teaching programs, with 50 FTE
professors, and over 800 students. We offer four degrees: a Ph.D.; a Master
of Science in Information; a Master of Health Informatics; and a Bachelor
of Science in Information.  In the fall of 2019 we expect to launch a new
online degree: Master of Applied Data Science.

Founded in 1817, the University of Michigan has a long and distinguished
history as one of the first public universities in the nation. It is one of
only two public institutions consistently ranked among the nation's top ten
universities. The University has one of the largest health care complexes
in the world and one of the best library systems in the United States. With
more than $1 billion in research expenditures annually, the University has
the second largest research expenditure among all universities in the
nation. The University has an annual general fund budget of more than $2.1
billion and an endowment valued at more than $10.9 billion. For more
information about UMSI or other job opportunities please visit
www.si.umich.edu

*Minimum Qualifications*

   - Ph.D. in an area such as information, computer science, the
   humanities, social sciences, or other relevant area
   - Demonstrated potential for successful teaching at the undergraduate
   and graduate level
   - Demonstrated potential for high scholarly impact
   - A strong commitment to teaching, interdisciplinary research, and
   cultural diversity


   - Ability to travel, both domestic and international, to attend or
   present at conferences and/or research meetings

*Desired Qualifications*

   - Experience teaching online courses preferred

*How to Apply:*

All applicants should submit a cover letter, a vita, three representative
publications, evidence of teaching excellence, a statement of teaching
philosophy and experience, a statement of current and future research
plans, contributions to diversity, and three letters of recommendation. All
application materials must be submitted electronically to:
https://apply.interfolio.com/54067.  Please direct enquiries about this
position to the chair of our faculty search committee, Dr. Kevyn
Collins-Thompson (kevynct at umich.edu).  This is a rolling search and we plan
to make offers as qualified candidates are identified and continue until
all positions are filled.  Consideration of applications will begin
immediately.

*Background Screening*

The University of Michigan conducts background checks on all job candidates
upon acceptance of a contingent offer and may use a third party
administrator to conduct background checks.  Background checks will be
performed in compliance with the Fair Credit Reporting Act.

*U-M EEO/AA Statement*

The University of Michigan is an equal opportunity/affirmative action
employer.



Best regards,

Lionel


*New Paper(s): Robots are Here...:) *

Robert, L. P. (2018). *Personality in the Human Robot Interaction
Literature: A Review and Brief Critique*, *Proceedings of the 24th Americas
Conference on Information Systems*, (*AMCIS 2018*), Aug 16-18, New Orleans,
LA. Link to the copy provided by the author:
http://hdl.handle.net/2027.42/143811.

Robert, L. P.  (2018). *Motivational Theory of Human Robot Teamwork*,
*International
Robotics & Automation Journal*, (*IRAJ*), 4(4), pp. 248-251, link to the
author's copy http://hdl.handle.net/2027.42/145157.

You, S. and Robert, L. P. (2018). *Human-Robot Similarity and Willingness
to Work with a Robotic Co-Worker,* *Proceedings of the 13th Annual ACM/IEEE
International Conference on Human Robot Interaction* (*HRI 2018*), March
5–8, 2018, Chicago, IL, USA. Link to copy provided by the author:
http://hdl.handle.net/2027.42/140719.

You, S. and Robert, L. P. (2018). *Emotional Attachment, Performance, and
Viability in Teams Collaborating with Embodied Physical Action (EPA) Robots*
, *Journal of the Association for Information Systems*, (*JAIS*),19(5), pp.
377-407. Link to copy provided by the author:
http://hdl.handle.net/2027.42/136918.

You, S., Ye, T., Robert, L. P. (2017). *Team Potency and Ethnic Diversity
in Robot-Supported Dyadic Teams*, *Proceedings of the 38th International
Conference on Information Systems* (*ICIS 2017*), Dec 10-13, Seoul, Korea.
Link to copy provided by the author: http://hdl.handle.net/2027.42/138124.

You, S. and Robert, L. P. (2017). *Teaming Up with Robots: An IMOI
(Inputs-Mediators-Outputs-Inputs) Framework of Human-Robot Teamwork*,*
International Journal of Robotic Engineering*, (*IJRE*), 2(3), Link to copy
provided by the author: http://hdl.handle.net/2027.42/138192.

*Robert, L. P.* (2017). *The Growing Problem of Humanizing Robots*,
*International
Robotics & Automation Journal,* (*IRAJ*), 3(1), Article 43. Link to copy
provided by the author: http://hdl.handle.net/2027.42/138018.


Lionel P. Robert Jr.
Associate Professor, School of Information
<https://www.si.umich.edu/people/lionel-robert>
Core Faculty, Michigan Robotics Institute
<https://robotics.umich.edu/core-faculty/>
Affiliate Faculty, National Center for Institutional Diversity
<https://lsa.umich.edu/ncid>
Affiliate Faculty, Michigan Interactive and Social Computing
<http://misc.si.umich.edu/>
Director of MAVRIC <https://mavric.si.umich.edu>
Co-Director of DOW Lab
University of Michigan
Email: lprobert at umich.edu
UMSI Website <https://www.si.umich.edu/people/lionel-robert> | Personal
Website  <https://sites.google.com/a/umich.edu/lionelrobert/home>
MAVRIC: https://mavric.si.umich.edu


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