Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship

Information for Mentors

Expectations

What is the role of faculty mentors?

Each fellow is expected to have two named mentors, a Science mentor and an AI mentor.  Together, they will take responsibility for defining the research challenge, ensuring that the Fellow has adequate domain knowledge to tackle this challenge, and ensuring that the Fellow has adequate AI skills to execute this approach. How the two mentors jointly fulfill their duties, for example, whether to designate a primary mentor, how exactly to divide responsibilities, and who covers which portion of the research costs, will be decided between the mentors. MIDAS will require letters of support from both mentors to expressly commit to their responsibilities. Additional mentors are allowed but not required.

Who may qualify to be a faculty mentor?

A faculty mentor can be any faculty member on the tenure track, clinical track, or research track with a full-time appointment at the University of Michigan in any of the Ann Arbor, Flint, or Dearborn campuses, who is a MIDAS affiliate or has applied to be one.

Faculty members at other universities may not be named mentors. However, they may be members of the Fellowship Committee if they are able to fulfill the obligations of the committee. They may also serve as additional mentors.

For the sake of the program’s scientific diversity, faculty mentors of the current Fellows are discouraged to be mentors of new applicants. 

Who should be a Science mentor and who should be an AI mentor?

What we really look for is strong mentoring for both the research direction (and the specific project) and AI skills training. Oftentimes one mentor has stronger AI expertise, and the other has stronger domain expertise. In such cases it’s easy to designate the two mentors’ roles. However, sometimes both mentors are strong in AI and in domain science. In these cases we let the mentors make the designations with the understanding that, with their combined expertise, they will provide sufficient mentoring in both research and AI training to the Fellow. In the application, this should be clearly explained; Fellow and mentor applicant teams will be reviewed more favorably if the plan for AI and science domain mentoring is clearly explained and justified.

What are the obligations for the faculty mentors?

Faculty Mentors are expected to devote sufficient time to provide research and career guidance to the Fellows, meet with them regularly, attend Fellowship Committee meetings every six months, and help Fellows seek opportunities for career advancement. They are also required to cover all the costs of the Fellows’ research, other than Fellow salary and benefits, as discussed below in “What do the mentors pay for?” and “What is the role of faculty mentors?” sections. The Science Mentor, by default, will be responsible to ensure the availability of all of the above; but the specific arrangements and shared responsibilities will be determined by all Mentors of each Fellow.

Faculty Mentors are also expected to actively participate in the Schmidt AI in Science program activities in MIDAS, including bootcamps, workshops, collaborative learning sessions, developing training curriculum, among others.

The Schmidt AI in Science program will organize research and networking activities and conferences that will bring together Fellows and some mentors from across all of their postdoctoral training sites. The Fellows and mentors are expected to fulfill the Schmidt program’s participation expectations.

A rough estimate of time commitment for some of the events is as follows:

  • Annual bootcamp: each mentor is expected to attend parts of this weeklong event.
  • AI Carpentries: each mentor is expected to join these group working sessions (working at MIDAS space) for 2-5 days a year.
  • Weekly research meetings: occasional attendance.
  • Fellow selection: Several hours of application review once a year.
  • Participating in the AI in Science curriculum committee: Several hours a year.
  • Any attendance requirement set up by the funder (for example, they may invite some faculty mentors to their annual conference).

What do the mentors pay for? 

The mentors cover all costs of the Fellows’ research, including research space, lab facilities, computers, computing resources, data access, travel to research conferences (at least two each year), publication costs, and visa fees, if necessary*. Office desk space will be provided in MIDAS, but the faculty mentor may wish to make desk space available in their research group as well. The Science mentor (see above), by default, will be responsible to ensure the availability of all of the above; but the specific arrangements and shared responsibilities will be determined by the two mentors of each Fellow.

How can faculty sign-up to be a mentor?

While all faculty members in the science and engineering domains within scope of this program are eligible to be Science Mentors; only a limited number of faculty members on campus have the AI expertise and the bandwidth to be AI Mentors for the Fellows. In order to help postdoc applicants connect with potential AI Mentors, we will publish a list on the website of faculty members who have expressed interest to be AI Mentors. If you would like to express such interest, please use this form to sign-up. Please note that faculty members who are not on this list can still be AI Mentors. Being on the list simply makes it easier for applicants to find you.

Do interested AI mentors need to sign up annually?

No, faculty who have previously expressed an interest to be AI mentors do not need to sign up more than once. To see if you are already listed, please review the Mentors list.

Name Title Primary Department: Tagline:
Mark Ackerman George Herbert Mead Collegiate Professor School of Information; Electrical Engineering and Computer Science, College of Engineering; Learning Health Sciences, Medical School Computer-supported cooperative work, human-computer interaction, social computing
Fadhl Alakwaa Assistant Research Scientist Internal Medicine Machine learning, personalized medicine, predictive modeling
Karen Alofs Assistant Professor School for Environment and Sustainability Integrating data on environmental change and ecological communities
Camille Avestruz Assistant Professor Physics, LSA Simulations to model, predict, and interpret observed large-scale cosmic structures
Laura Balzano Associate Professor EECS, College of Engineering Statistical signal processing and optimization with large data.
Nikola Banovic Associate Professor Computer Science and Engineering, College of Engineering Behavior-aware User Interfaces, Computational Interaction, Computational models of human behavior
Kira Barton Associate Professor, Director of COE Research Mechanical Engineering and Robotics, College of Engineering Control theory and applications; iterative learning control; multi-agent systems; human/robot collaborations; smart manufacturing; manufacturing robotics; high-performance micro/nano-scale printing for electrical and biomedical applications
Erhan Bayraktar Professor Mathematics, LSA Mathematical finance and stochatic analysis
Dennis Bernstein James E Knott Professor Aerospace Engineering, College of Engineering Linear and nonlinear systems, identification, optimal, robust and adaptive control
Anthony Bloch Alexander Ziwet Collegiate Professor Mathematics, LSA Control, Hamiltonian and Lagrangian Systems, gradient flows
Elizabeth Bondi-Kelly Assistant Professor Computer Science and Engineering AI for social impact
Christopher Brooks Research Assistant Professor School of Information
Director, Learning Analytics and Research in the Office of Digital Education & Innovation
Visualizing the interaction between learners and learning technology
Ceren Budak Assistant Professor School of Information Computational social science
Dallas Card Assistant Professor School of Information Natural language processing, and computational social science, machine learning
Neil Carter Associate Professor Environment, School for Environment and Sustainability Addressing challenging local to global wildlife conservation issues in the Anthropocene
Timothy Cernak Associate Professor Medicinal Chemistry Computational design of chemical reactions and synthesis
Joyce Chai Professor Computer Science and Engineering, College of Engineering Artificial Intelligence, Natural language processing, situated communication
Yang Chen Assistant Professor Statistics, LSA Statistical inference and applied statistics in biology and astronomy
Cynthia Chestek Associate Professor and Associate Chair Biomedical Engineering, College of Engineering and Medical School, EECS, College of Engineering Brain machine interface (BMI) systems
Mosharaf Chowdhury Morris Wellman Faculty Development Assistant Professor EECS (Computer Science and Engineering Division), College of Engineering Large-scale systems for emerging machine learning and big data workloads
Kevyn Collins-Thompson Associate Professor School of Information,
EECS, College of Engineering
Intelligent information systems that help people learn and discover
William Currie Associate Dean of Research and Engagement, Professor of Environment and Sustainability, Professor of Environment Research and Engagement, School for Environment and Sustainability, LSA Ecosystem modeling, sustainability, water quality, knowledge representation
Matias del Campo Associate Professor Taubman College of Architecture and Urban Planning Research on Architecture Design and Artificial Intelligence
Michał Dereziński Assistant Professor EECS, College of Engineering Scalable randomized algorithms with robust statistical guarantees for machine learning, data science and optimization
Ivo D. Dinov Professor Computational Medicine and Bioinformatics Mathematical modeling, statistical analysis and data visualization
Karthik Duraisamy Associate Professor Aerospace Engineering, College of Engineering Data-driven modeling of computational physics problems
August (Gus) Evrard Professor Physics and Astronomy, LSA Computational cosmology
Salar Fattahi
Assistant Professor Industrial and Operations Engineering, College of Engineering Developing efficient and scalable computational methods for structured problems
Jeff Fessler Professor EECS, College of Engineering
Biomedical Engineering, College of Engineering
Radiology, Michigan Medicine
Large-scale inverse problems in image reconstruction
Danny Forger Professor Mathematics, LSA
Center for Computational Medicine and Bioinformatics
Center for Systems Biology
Computer simulation and mathematical modeling of biological clocks
Johann Gagnon-Bartsch Assistant Professor Statistics, LSA High-throughput and high-dimensional data analysis
Vikram Gavini Professor Mechanical Engineering Ab-initio calculations, DFT, electronic structure, exchange-correlation approximation, numerical methods
Alex Gorodetsky Associate Professor Department of Aerospace Engineering Bayesian Inference, Decision making under uncertainty, Tensor Networks, Uncertianty Quantification
Robert Gregg Associate Professor and Director of Locomotor Control Systems Laboratory Robotics, EECS (ECE Division), and Mechanical Engineering, College of Engineering Control mechanisms of bipedal locomotion with applications to wearable and autonomous robots
Yuanfeng Guan Professor Computational Medicine and Bioinformatics and Internal Medicine; Medical School Developing novel and high-accuracy algorithms that integrate these data for predicting gene functions and networks
Emanuel Gull Assistant Professor Physics, LSA Computational condensed matter physics
Yongqun Oliver He Professor Laboratory Animal Medicine and Veterinarian, Medical School AI Expertise: Knowledge representation and reasoning; Ontology; Semantic Web.
Libby Hemphill Research Associate Professor School of Information digital data, justice, social media
Alfred Hero Professor EECS, College of Engineering
Biomedical Engineering, College of Engineering
Statistics, LSA
Theory and algorithms for data collection, analysis and visualization
Wei Hu Assistant Professor Computer Science and Engineering, College of Engineering Deep Learning, Representation Learning, machine learning, optimization, theory
Xun Huan Associate Professor Mechanical Engineering, College of Engineering Uncertainty quantification, and numerical optimization, data-driven modeling
Edward Ionides Professor Statistics, LSA Time series analysis for ecology, epidemiology, health economics and biology
Abigail Jacobs Assistant Professor Information, School of Information
Complex Systems, College of Literature, Science, and the Arts
Computational social science, empirical design, governance, observational data, social networks, statistical inference
H. V. Jagadish Edgar F Codd Distinguished University Professor and Bernard A Galler Collegiate Professor; MIDAS Director EECS, College of Engineering Database systems, query models and analytics processes for reliable insight
David Jurgens Assistant Professor School of Information computational social science and natural language processing
Danai Koutra Assistant Professor Computer Science and Engineering, College of Engineering Mining and making sense of interconnected or graph data
Elizaveta Levina Vijay Nair Collegiate Professor Statistics, LSA Statistical inference on realistic models for network
Rada Mihalcea Professor EECS, College of Engineering Natural language processing, and applied machine learning, information retrieval
Emily Mower Provost Assistant Professor Computer Science and Engineering, College of Engineering Emotion modeling and assistive technology
Raj Rao Nadakuditi Associate Professor EECS, College of Engineering Statistical signal processing and random matrix theory
Long Nguyen Professor Statistics, LSA
EECS, College of Engineering
Statistical inference in richly structured data
Samet Oymak Assistant Professor EECS, College of Engineering Optimization methods and statistical learning theory to design efficient algorithms/architectures.
Necmiye Ozay Assistant Professor EECS, College of Engineering Heterogeneous data from sensor and information-rich networked dynamical systems
Atul Prakash Professor EECS, College of Engineering Design of operating systems and database mechanisms for sensitive data, Security, and adversarial machine learning., privacy
Qing Qu Assistant Professor EECS, College of Engineering Deep Learning, Inverse Problems, Nonconvex Optimization, Representation Learning
Majdi Radaideh Assistant Professor Nuclear Engineering and Radiological Sciences, College of Engineering Autonomous Control, Nuclear Reactor Design, Physics-informed Machine Learning, Uncertainty quantification, optimization
Venkat Raman Associate Professor Aerospace Engineering, College of Engineering Simulation of large scale combustion systems
Jeffrey Regier Assistant Professor Statistics, LSA Bayesian models and deep learning for scientific applications
Alexander Rodriguez Assistant Professor EECS, College of Engineering The intersection of ML, time series, multi-agent systems, uncertainty quantification, and scientific modeling.
Daniel Romero Associate Professor Information, School of Information,
Complex Systems, LSA,
Electrical and Computer Engineering, College of Engineering
Empirical and theoretical analysis of social and information networks
Sarita Schoenebeck Professor School of Information Human-Computer Interaction, issues in digital society, social computing, social media
Clayton Scott Associate Professor EECS, College of Engineering
Statistics, LSA
Quantitative predictions and inferences about large, complex data
Kerby Shedden Professor Statistics, LSA
Biostatistics, School of Public Health
Director of Consulting for Statistics, Computing, and Analytics Research (CSCAR)
Applied statistics, data science and computing with data
Katie Skinner Assistant Professor Robotics, College of Engineering Robot vision and perception, field robotics
Vijay Subramanian Professor EECS, College of Engineering Stochastic modeling, decision and control theory and applications to networks
Misha Teplitskiy Assistant Professor School of Information computational methods, experimental methods, science of science
Jonathan Terhorst Associate Professor Statistics, LSA Computational population genetics
Ambuj Tewari Assistant Professor Statistics, LSA
EECS, College of Engineering
Statistical methods for sequential decision making in personalized health
Dawn Tilbury Professor and Chair, Robotics Robotics Modeling and control for smart manufacturing, human-robot teaming
Sabina Tomkins Assistant Professor School of Information Computational social science, Political science, education, sustainability
Shravan Veerapaneni Associate Professor Mathematics, LSA Fast and scalable algorithms for differential and integral equations on complex moving geometries
Venkat Viswanathan Associate Professor Aerospace Engineering, College of Engineering Programming languages, formal methods, software engineering, and artificial intelligence
Lu Wang Assistant Professor Computer Science and Engineering, EECS Natural language processing
Xinyu Wang Assistant Professor EECS, College of Engineering Programming languages, formal methods, software engineering, and artificial intelligence
Yixin Wang Assistant Professor Statistics, LSA Large-scale probabilistic machine learning, causal inference, machine learning for science
Michael P. Wellman Richard H Orenstein Division Chair of Computer Science and Engineering, Lynn A Conway Collegiate Professor EECS (Computer Science and Engineering division), College of Engineering Artificial Intelligence, electronic commerce
Jenna Wiens Associate Professor Computer Science and Engineering, College of Engineering Machine learning and data mining for healthcare data
Kevin Wood Associate Professor Biophysics and Physics; LSA Spatiotemporal dynamics in microbial communities
Zhenke Wu Associate Professor Biostatistics, School of Public Health The design and application of statistical methods that inform health decisions made by individuals
Gongjun Xu Assistant Professor Statistics, LSA cognitive diagnosis modeling, high-dimensional statistics, latent variable models, psychometrics, semiparametric statistics
Lei Ying Professor EECS, College of Engineering Complex stochastic systems and big-data, and large-scale graph mining, private data marketplaces
Y Z (Yang Zhang) Professor Nuclear Engineering and Radiological Sciences, College of Engineering Far-from-equilibrium physics, extreme robots, liquid state physics, long timescale phenomena, neutron scattering, rare events, soft robots, statistical mechanics, swarm robots
Xiang Zhou Professor Biostatistics, School of Public Health Statistical and computational methods for genetic and genomic studies
Ji Zhu Professor Statistics, LSA
EECS, College of Engineering
analysis of high-dimensional data, and their applications in health sciences, biology, finance and marketing., machine learning, statistical network analysis
Kai Zhu Associate Professor School for Environment and Sustainability Global change biology, ecological modeling, and environmental data science

*For questions or concerns regarding the financial support of visa fees, mentors should contact the Program Team, schmidt-aim@umich.edu.