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Faculty Mentors

Each Fellow must have at least one U-M faculty member as the primary mentor at the time of the application (the list of participating faculty will be posted by Sept. 1, and will keep being updated). A second mentor with complementary expertise is required when the Fellow starts in the program. If needed, the primary mentor can help identify a suitable second mentor. Working together with MIDAS, the mentors will provide career guidance to the Fellows, meet with them regularly to assess research progress, help the Fellows seek opportunities for career advancement, and participate in activities that strengthen the Fellows community and the U-M data science and AI community.

While all faculty members in research domains within the scope of this program are eligible to be mentors, not all have funding or bandwidth to accept new postdocs. In order to help postdoc applicants connect with potential mentors, we maintain a list of faculty members who have opted in this program. If you would like to opt in, please sign up with this form. Please note that faculty members who are not on this list can still be mentors. Being on the list simply makes it easier for applicants to find you.

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

Expectations

  • Cost share: Faculty mentors and MIDAS will provide 50:50 cost share to cover the postdoc’s salary and benefits.
  • Salary:  Commensurate to norms in each research field, funded jointly by MIDAS and faculty mentors. Faculty mentors will work with MIDAS to determine the exact salary. 
  • Office space:  Fellows are expected to conduct their work on U-M’s Ann Arbor Campus. They will be expected to spend at least 50% of time at MIDAS so that they can be part of the data science and AI postdoc network and enhance the interdisciplinary reach of MIDAS. 
  • Professional service:  The Fellows will be expected to spend 10% of their effort on MIDAS programs that strengthen MIDAS’s core pillars. Based on their career plans, they can choose to be involved in activities such as developing tools for research, training and mentoring, and data science projects for positive societal impact. They will also be expected to interact with the U-M data science and AI community through research presentations and other events. 
  • Selection process:  A faculty review committee will select the top candidates. MIDAS will consult with their faculty mentors to determine the salary, then notify the candidates.

How to participate

  • MIDAS faculty members can sign up to participate in this program, with the understanding that, if their applicant is selected, their portion of the funding needs to be in place before the Fellows start at U-M. You do not need to tell us what the source of funding will be until it is time to make offers. The only constraint is that the funding should not require substantial non-research time commitment from the fellow, such as teaching a course.
    • Anyone who is currently not a MIDAS affiliate faculty member will need to join before they can be placed on the participating faculty list.
    • Any faculty member who is not on the participating faculty list may still submit an application with a candidate. Not being listed simply means that it is very unlikely you will receive inquiries from potential applicants. 
  • MIDAS will post the participating faculty list online by Aug. 30, and will update the list as we receive information from faculty. Candidates and potential mentors will need to first agree on research direction and mentor funding commitment before submitting applications to MIDAS. The deadline for application is 11:59 pm EST Nov. 30, 2023.

Current Data Science Mentors

Name Title Primary Department: Tagline:
Arun Agrawal Professor School for Environment and Sustainability The politics of international development, and environmental conservation, institutional change
Raed Al Kontar Assistant Professor Industrial and Operations Engineering, College of Engineering Smart and connected products and systems
Fadhl Alakwaa Assistant Research Scientist Internal Medicine, Medical School Machine learning, personalized medicine, predictive modeling
Jacob Allgeier Associate Professor Ecology and Evolutionary Biology, LSA The mechanisms by which behavioral, population, and community dynamics mediate nutrient and energy pathways
Camille Avestruz Assistant Professor Physics, LSA Evolution of galaxy clusters, machine learning in cosmology
Veera Baladandayuthapani Professor Biostatistics, School of Public Health High-dimensional modeling and Bayesian inference
Elizabeth Bondi-Kelly
Assistant Professor Computer Science and Engineering AI for social impact
David Brang Associate Professor Psychology Multisensory, electrophysiology, machine learning, neuro-oncology, neuroscience
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
Bilal Butt Associate Professor School for Environment and Sustainability Agritech, Big Data, Environmental Social Sciences
Dallas Card Assistant Professor School of Information Natural language processing, and computational social science, machine learning
Neil Carter Associate Professor School for Environment and Sustainability Challenging local to global wildlife conservation issues in the Anthropocene
Tim Cernak Assistant Professor Medicinal Chemistry, College of Pharmacy
Chemistry, LSA
Graduate Program in Chemical Biology
Computational design of chemical reactions and synthesis
Joyce Chai Professor Computer Science and Engineering Artificial Intelligence, Natural language processing, situated communication
Sriram Chandrasekaran Assistant Professor Biomedical Engineering, College of Engineering Computer models of protein networks
Yang Chen Assistant Professor Statistics, LSA Statistical inference and applied statistics in biology and astronomy
Thomas L. Chenevert Professor Radiology, Michigan Medicine Longitudinal Image Registration, Oncologic MR Imaging, Quantitative Imaging Biomarkers, Tumor Volume Segmentation
Lia Corrales Assistant Professor Astronomy Statistical techniques for studying astrophysical aerosols in the X-ray and UV
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
Walter Dempsey Assistant Professor of Biostatistics, Assistant Research Professor at the Institute of Social Research Department of Biostatistics Mobile Health, Network Science, Statistical Methods
Paramveer Dhillon Assistant Professor School of Information Computational social science, Natural language processing, Statistical Machine Learning, and Field/Digital Experiments
Ivo D. Dinov Professor Computational Medicine and Bioinformatics
Human Behavior and Biological Sciences
Michigan Institute for Data Science (MIDAS)
Mathematical modeling, statistical analysis and data visualization
Anne Draelos Assistant Professor Biomedical Engineering, College of Engineering, Medical School Understanding computation in large-scale neural circuits
Marisa Eisenberg Assistant Professor School of Public Health Mathematical modeling of disease mechanisms, and intervention design, forecasting
August (Gus) Evrard Professor Physics and Astronomy, LSA Computational cosmology
Jeff Fessler Professor EECS, College of Engineering
Biomedical Engineering, College of Engineering
Radiology, Michigan Medicine
Large-scale inverse problems in image reconstruction
Nancy Fleischer
Associate Professor Epidemiology Survey methodology, epidemiology, health equity, policy, public health
Lana Garmire Associate Professor Computational medicine and bioinformatics, Medical School Data science for actionable transformation of human health from the bench to bedside
Andrew Gronewold Associate Professor School for Environment and Sustainability Quantifying and communicating uncertainties arising within long-term hydrological monitoring networks and data
Anhong Guo Assistant Professor EECS, College of Engineering, School of Information
Hybrid human-AI intelligent interactive systems to provide access to visual information in the real world
Yongqun Oliver He Associate Professor Laboratory Animal Medicine and Veterinarian, Medical School Vaccine Informatics, Kidney Precision Medicine, Ontology and Semantic Web
Libby Hemphill Research Associate Professor School of Information Digital data, justice, social media
Wei Hu Assistant Professor Computer Science and Engineering Deep Learning, Representation Learning, machine learning, optimization, theory
Xun Huan Assistant Professor Mechanical Engineering, College of Engineering Uncertainty quantification, and numerical optimization, data-driven modeling
Xianglei Huang Professor Department of Climate and Space Sciences and Engineering Climate diagnostics, data modeling, optimization, remote sensing, time-series analysis
Meha Jain Assistant Professor School for Environment and Sustainability Agriculture, machine learning, remote sensing, sustainability
David Jurgens Assistant Professor School of Information Computational social science and natural language processing
Jian Kang Associate Professor Biostatistics Bayesian methods, composite likelihood approach and missing data problems, efficient statistical computation algorithms, graphical models, latent source separation methods, network inference, ultrahigh-dimensional feature selection
Evan Keller Professor of Urology and Pathology Urology Single cell and spatiotemporal analyses of healthy and diseased tissues
Nicholas Kotov Professor Chemical Sciences and Engineering Biomimetic Nanostructures
Arpan Kusari Assistant Research Scientist Assistant Research Scientist, UMTRI LiDAR, Reinforcement Learning, autonomous vehicles, realistic simulation, uncertainty estimation
Elizaveta Levina Vijay Nair Collegiate Professor Statistics, LSA Statistical inference on realistic models for network
Gen Li Assistant Professor Department of Biostatistics Data Integration, Dimension Reduction, Microbiome Research, Parsimonious Models, graphical models, predictive modeling
Max Li Assistant Professor COE (AERO, IOE, CEE) Design, Management, and Optimization of Air Transportation Systems
Yi Li Professor Biostatistics, School of Public Health
Kidney Epidemiology and Cost Center
Statistical methods for kidney epidemiology
Zhongming Liu Associate Professor Biomedical Engineering Computational and cognitive neuroscience, deep neural networks
Jie Liu Assistant Professor Computational Medicine and Bioinformatics Machine learning and computational genomics
Sabine Loos Assistant Professor Civil and Environmental Engineering Data and user-driven approaches on disasters and equity
Wei Lu Professor Mechanical Engineering Integrating human knowledge into machine learning
Qiaozhu Mei Associate Professor School of Information
EECS, College of Engineering
Information retrieval and text mining
Briana Mezuk Associate Chair and Associate Professor, Epidemiology Department of Epidemiology Depression, epidemiology, integrative lifespan, suicide aging
Emily Mower Provost Professor Computer Science and Engineering Provost Emotion modeling and assistive technology
Nambi Nallasamy Assistant Professor of Ophthalmology and Visual Sciences and Assistant Professor of Computational Medicine and Bioinformatics Ophthalmology and Visual Sciences Machine learning algorithms for the enhancement of outcomes in cataract surgery
Joshua P Newell Associate Professor School for Environment and Sustainability Geographic information systems, gis, remote sensing, resource flow analysis, sustainability, urban systems
Mohammed Ombadi Assistant Professor Climate and Space Sciences and Engineering Impact of climate change on the hydrologic cycle using advanced data analytics
Samet Oymak Assistant Professor Electrical Engineering and Computer Science Decision-making, efficient and robust ML, learning and optimization theory
Bruce Allen Palfey Associate Professor & Associate Director of Academic Programs, Chemical Biology Program, Life Sciences Institute Biological Chemistry, Medical School Mechanisms of flavin-containing enzymes of pyrimidine metabolism, dihydroorotate dehydrogenases, dihydrouridyl-tRNA synthases, thymidylate synthases
Yulin Pan Assistant Professor Department of Naval Architecture and Marine Engineering Data assimilation, fluid mechanics, physical oceanography
Vitaliy Popov Assistant Professor Medical School, Department of Learning Health Sciences Clinical simulations, learning analytics, multimodal
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 Autonomous Control, Nuclear Reactor Design, Physics-informed Machine Learning, Uncertainty quantification, optimization
Arvind Rao Associate Professor Computational Medicine and Bioinformatics, Michigan Medicine
Radiation Oncology, Michigan Medicine
Biomedical Engineering, College of Engineering
Multi-modal decision algorithms that integrate clinical measurements
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
Perry Samson Professor Climate and Space Science, College of Engineering
School of Information
Relationships between student performance and behaviors
Maureen Sartor Professor Computational Medicine and Bioinformatics Bioinformatics, biomarkers, cancer, classification, epigenomics, integrative omics, prediction, regulatory genomics
Xu Shi Assistant Professor Department of Biostatistics EHR data, causal inference
Yajuan Si Research Assistant Professor Survey Research Center, Institute for Social Research Bayesian, confidentiality protection, missing data, statistics, survey
Peter Song Professor Biostatistics, School of Public Health Data Integration, distributed inference, machine learning
Wencong Su Assistant Professor EECS, College of Engineering Efficient management of a large number of devices through distributed intelligence
Vijay Subramanian Associate Professor EECS, College of Engineering Stochastic modeling, decision and control theory and applications to networks
Wenhao Sun
Assistant Professor
Materials Science and Engineering, College of Engineering
Materials Discovery and Design, Materials Informatics
Misha Teplitskiy Assistant Professor School of Information Computational methods, experimental methods, science of science
Ivy F. Tso Associate Professor of Psychiatry, Adjunct Associate Professor of Psychology Psychiatry Psychiatric disorders, brain network, computational and predictive models
V. G. Vinod Vydiswaran Assistant Professor Learning Health Sciences, Michigan Medicine
School of Information
Text mining, NLP, and ML for extracting relevant information from health-related text corpora
Venkat Viswanathan Associate Professor Aerospace Engineering, College of Engineering Programming languages, formal methods, software engineering, and artificial intelligence
Runzi Wang Assistant Professor School for Environment and Sustainability Influence of land cover change and urban development  pattern toward stream water quality and stormwater quality
Yixin Wang Assistant Professor Statistics, LSA Large-scale probabilistic machine learning, causal inference, machine learning for science
Joshua Welch Assistant Professor Computational Medicine and Bioinformatics Machine learning for representing molecular cell states
Zhenke Wu Assistant Professor Biostatistics, School of Public Health Statistical methods for precision medicine
Ming Xu Professor School for Environment and Sustainability
Civil and Environmental Engineering, College of Engineering
Computational and data-enabled methodology for sustainability
Qiong Yang
Associate Professor Biophysics, and affiliated with Bioinformatics, CDB and CMB Biophysics of Living Systems, Quantitative Systems/Synthetic Biology
Lei Ying Professor EECS, College of Engineering The interplay of complex stochastic systems and big-data
Donglin Zeng
Professor
Biostatistics, School of Public Health
Precision medicine, dynamic treatment regimes, machine learning, high-dimensional inference, semiparametric models, survival analysis, electronic health records and clinical trials.
Yang Zhang Professor Department of Nuclear Engineering and Radiological Sciences, Department of Materials Science and Engineering, Department of Robotics, Applied Physics Program 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
Paul Zimmerman Assistant Professor Chemistry, LSA Computational chemical reaction discovery