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A Quick Guide for Effective Prompting

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Guide written by Ken Reid & Jing Liu, compiled in part from the  “An Introduction to Generative AI Tools for Research” tutorial by James Boyko.

 

ChatGPT and similar Large Language Models (LLMs) are pre-prompted to respond in a specific way (e.g. ‘polite and informative’). We can customize how LLMs respond to us by crafting our instructions and prompts. Click here to open ChatGPT in your web browser. 

Define Scope and Boundaries

  • Define what information you want or don’t want in LLM responses 
    • “Analyze the impact of social media on political polarization, focusing on demographic factors and excluding technological aspects.
    • “Explain the process of photosynthesis in C4 plants, emphasizing biochemical pathways and excluding evolutionary history.”

Iterate and Refine

  • Ask follow-up questions for additional information and clarification.
    • E.g., after an initial analysis of bridge design principles, ask: ‘Can you elaborate on how different materials affect load-bearing capacity in suspension bridges?’
    • If the LLM’s initial summary of a cognitive bias study doesn’t mention sample size concerns, refine your prompt with: ‘Reanalyze the study, focusing on methodology and potential limitations related to participant selection and sample size.’

Provide Context

  • Set the Stage: Give the LLM relevant background information, including the research problem, hypothesis, or any crucial data, to enhance accuracy and focus.
    • “The research problem is to understand the impact of remote work on urban economic structures. Analyze this dataset containing information on commercial real estate prices, commuting patterns, and local business revenues in major US cities from 2019 to 2023.”
    • “We’re investigating the efficacy of a new treatment for Type 2 Diabetes. Analyze this clinical trial data, considering the hypothesis that the treatment might be more effective in patients with specific genetic markers.”

Request Justification and Sources

  • Support the Answer: Instruct the LLM to provide evidence alongside its response, including rationale, examples, or cited sources for stronger claims. Request the LLM to cite sources for any substantive claims it makes, ensuring transparency and supporting your research.
    • “Analyze the themes of alienation in modernist literature. Include specific examples from works by authors such as James Joyce, Virginia Woolf, and T.S. Eliot. Cite relevant scholarly articles or critical analyses to support your claims.”

Request Prompt Advice

  • Seek guidance from the LLM itself on how to phrase your prompts effectively to achieve your desired outcome.
    • “I would like to ask you to summarize the research on the impact of globalization in indigenous cultures. But this is a vast topic. Can you suggest more specific topics within this broad theme to begin your summarization with?” 

Solicit Feedback and Clarification

  • Promote Open Communication: Instruct the LLM to actively request clarification if any aspect of your prompt is unclear, ensuring a smooth interaction.
    • “Before analyzing this document, ask me if I can provide any additional information about the context to help your analysis.”
  • Confirmation for Accuracy: Request the LLM to confirm its understanding of your query and verify that its response aligns with your expectations.
    • “I asked you to analyze the effects of microplastics on marine ecosystems. Before you provide a detailed report, can you summarize your understanding of the key factors I’m interested in (e.g., bioaccumulation, impact on different trophic levels) and anything else you plan to include?”

Specify Output Format

  • Clearly specify the desired format for the LLM’s output, such as bullet points, markdown, or LaTeX.
    • “Provide a proof for the Fundamental Theorem of Calculus. Structure your response using LaTeX formatting for mathematical notation, and divide it into clear steps with explanations for each.”
  • Organize for Clarity: 
    • “Analyze the strengths and weaknesses of this research paper. Divide the response into sections with headings like ‘Methodology,’ ‘Results,’ and ‘Conclusion.’”

Use Academic Personas

  • Assign the LLM a specific role, such as a journal reviewer or a statistical consultant, to tailor its response to your needs.
    • “You are a reviewer for the Journal of the American Chemical Society. Please write a review of my manuscript on a novel catalytic process  focusing on the experimental design and the significance of the results in the field of green chemistry.”
    • “You are a law professor specializing in digital rights and cyber law. Please write an analysis of this new legislation on data privacy for the Wall Street Journal. Focus on potential constitutional challenges and comparisons with existing international data protection frameworks.”

 

MIDAS announces 2024 Propelling Original Data Science (PODS) awards

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MIDAS announces awardees for the 2024 Propelling Original Data Science (PODS) Grants

The Michigan Institute for Data and AI in Society (MIDAS) announced the awardees of the 2024 round of Propelling Original Data Science (PODS) grants in June. This year, MIDAS collaborated with Microsoft and the Michigan Institute for Healthcare Policy and Innovation to provide more than $700K in funding to fourteen teams across three focused funding tracks for the PODS grants: (1) Data science and AI methodology and application, (2) Accelerating responsible AI research ecosystems, and, (3) AI for Health Policy and Healthcare: Impact & Governance. Read the full press release in the University Record.

The 2024 awardees are: 

PODS Track 1: Data science and AI methodology and applications

WinAI: Propelling UM Soccer with Data-Driven AI

Albert Berahas (College of Engineering) and Raed Al Kontar (College of Engineering)

Human-in-the-loop multi-agent sequential decision-making based Optimal Operation of Power Distribution System

Srijita Das (College of Engineering and Computer Science, U-M Dearborn) and Van Hai Bui (College of Engineering and Computer Science, U-M Dearborn)

Extrapolating with Generative Models for Design of Organic Molecules as Energy Carriers

David Kwabi (College of Engineering), Bryan Goldsmith (College of Engineering), and Yixin Wang (College of Literature, Science and the Arts)

Multimodal Modeling of Cognitive Load at Individual and Team Levels in Acute Care Teams using VR Simulations

Vitaliy Popov (Michigan Medicine), Mohamed Abouelenien (College of Engineering and Computer Science, U-M Dearborn), Michael Cole (Michigan Medicine), and James Cooke (Michigan Medicine)

Combining ecological first principles and AI to better upscale and predict global carbon, nutrient and water cycles on a changing planet

Peter Reich (School for Environment and Sustainability) and Mohammed Ombadi (College of Engineering)

Machine Learning for Automated Fish Detection and Characterization

Katie Skinner (College of Engineering) and Jacob Allgeier (College of Literature, Science and the Arts)

Distributing Expert Attention in Complementary Systems

Sabina Tomkins (School of Information), Derek Van Berkel (School for Environment and Sustainability), Grant Schoenebeck (School of Information), Ariel Hasell (College of Literature, Science and the Arts), and John Ryan (College of Literature, Science and the Arts)

AI-driven Accelerated Optimization for the Design of Sustainable Aviation Fuels

Angela Violi (College of Engineering)

Neural Posterior Estimation (NPE) approaches for fitting high-dimensional stochastic epidemic models to real-world spatiotemporal disease data

Jon Zelner (School of Public Health) and Fan Bu (School of Public Health)

PODS Track 2: Accelerating responsible AI research ecosystems

Innovating, Applying, and Educating on Fairness and Bias Methods for Educational Predictive Models

Christopher Brooks (School of Information), Libby Hemphill (School of Information), and Allyson Flaster (Institute for Social Research)

Evaluating GenAI and Team-based Solutions to Reverse the Decline of Online Knowledge Communities

Yan Chen (School of Information) and Qiaozhu Mei (School of Information)

A Joint Human-AI Framework for Responsible AI

Rita Chin (College of Literature, Science and the Arts) and H.V. Jagadish (College of Engineering)

Advancing Responsible AI by Rethinking the Roles of Marginalized Communities in the Innovation Lifecycle: Developing the UBEC Approach

Shobita Parthasarathy (Ford School of Public Policy), Ben Green (School of Information) and Molly Kleinman (Ford School of Public Policy)

PODS Track 3: AI for Health Policy and Healthcare: Impact & Governance

Trust, Governance, and Humans in the Loop in Clinical AI

Kayte Spector-Bagdady (Michigan Medicine) and W. Nicholson Price (School of Law)

Since 2016, MIDAS has been offering funding to U-M faculty, and as of 2023 has already supported 75 teams with more than $12 million in funding to jumpstart their projects, form new collaborations, and secure external funding to expand upon their work. More than 116 projects with $125 million in external funding have resulted from these pilot grants. A list of past MIDAS funded projects can be found here.

For details about this year’s PODS projects, read below:

Albert Berahas (College of Engineering) and Raed Al Kontar (College of Engineering):

WinAI: Propelling UM Soccer with Data-Driven AI

The WinAI initiative seeks to help the University of Michigan (UM) soccer teams by tapping into the vast amounts of underutilized data in conjunction with cutting edge data science techniques. This proposal outlines a systematic plan to build, pilot and refine next generation innovative models for the UM Women’s and Men’s soccer teams, aiming to optimize tactics, refine team management, prevent injuries, and aid with scouting. WinAI constitutes a progressive, interdisciplinary venture aimed at harnessing untapped data to significantly enhance competitive strategy and player development, ultimately contributing to the UM data science, AI, and athletics ecosystems.

Christopher Brooks (School of Information), Libby Hemphill (Institute for Social Research and School of Information), and Allyson Flaster (Institute for Social Research):

Innovating, Applying, and Educating on Fairness and Bias Methods for Educational Predictive Models

Educational predictive models must be fair in order to ensure that all learners who need support are able to get it. Using 20 years of detailed student-level data from 19 universities and colleges, we demonstrate how institutions can train, share, and reuse predictive models in a way in which learner privacy is protected and the models perform well across learner identity groups. In addition to this large scale study, this project provides open educational materials to further the area of educational data science with the aim of upskilling and enabling educational researchers with state of the art techniques for achieving fair predictive models.

Yan Chen

Yan Chen (School of Information) and Qiaozhu Mei (School of Information)

Evaluating GenAI and Team-based Solutions to Reverse the Decline of Online Knowledge Communities

This project explores innovative ways to enhance online knowledge communities like Wikipedia by integrating generative AI tools with human editorial teams. We aim to develop new methods that make it easier for newcomers to join and contribute to these platforms, fostering a more inclusive and dynamic environment. Ultimately, our work seeks to sustain and enhance these essential communities by blending the strengths of both human creativity and artificial intelligence.

Rita Chin ( College of Literature, Science and the Arts) and H.V. Jagadish (College of Engineering):

A Joint Human-AI Framework for Responsible AI

Drawing on our cross-disciplinary expertise, our research team will identify the core values and concerns that scholars and developers should consider when building AI systems and engaging in AI research. These will be embodied in a framework of items and scales for responsible conduct of AI research, which will serve as the basis of an AI-specific training course modeled on the University of Michigan’s PEERRS program for responsible conduct of research. We also intend to produce a public-facing Code of AI Ethics akin to the “Code of Medical Ethics” and “Code of Anthropological Ethics.”

Srijita Das (College of Engineering and Computer Science, U-M Dearborn) and Van Hai Bui (College of Engineering and Computer Science, U-M Dearborn)

Human-in-the-loop multi-agent sequential decision-making based Optimal Operation of Power Distribution System

The proposal aims at effectively integrating distributed energy resources like batteries, solar panels, etc. into modern power systems using human-in-the-loop sequential decision-making to make these systems safe and reliable while minimizing their environmental impact. We propose to leverage different modalities of advice from humans, which would provide an additional layer of scrutiny and guidance to help these systems learn safe behavior and train faster. Finally, we would also make these systems scalable by developing suitable knowledge transfer mechanisms.

David Kwabi (College of Engineering), Bryan Goldsmith (College of Engineering), and Yixin Wang (College of Literature, Science and the Arts)

Extrapolating with Generative Models for Design of Organic Molecules as Energy Carriers

The redox-flow battery is a promising new technology for inexpensive storage of electricity on the grid and has the potential to significantly expand our use of renewable (e.g., solar and wind) power. This project will develop and use extrapolative equivariant flow matching (EEFM), a class of models that targets extrapolative generative modeling, for inverse design of organic molecules as energy carriers in these systems.

Shobita Parthasarathy (Ford School of Public Policy), Ben Green (School of Information) and Molly Kleinman (Ford School of Public Policy)

Advancing Responsible AI by Rethinking the Roles of Marginalized Communities in the Innovation Lifecycle: Developing the UBEC Approach

This project advances knowledge toward a responsible, and specifically more socially equitable and just, AI research ecosystem by developing and evaluating the novel UBEC approach for the innovation lifecycle, that centers the knowledge and needs of marginalized communities and includes expertise across academic disciplines. Collaborating with local community partners, we produce two kinds of deliverables: 1) technology that is collaboratively designed (e.g., generative AI to help formerly incarcerated people in the greater Detroit metropolitan area understand rules, regulations, and social services relevant to them); and 2) briefs and reports to build civic capacity for participating in AI-related public and policy discourse (e.g., policy briefs on the use of AI in the criminal legal system). This work will also produce best practices for researchers who seek to advance equitable and just AI, at UM and beyond.

Vitaliy Popov

Vitaliy Popov (Michigan Medicine), Mohamed Abouelenien (College of Engineering and Computer Science, U-M Dearborn), Michael Cole (Michigan Medicine), and James Cooke (Michigan Medicine)

Multimodal Modeling of Cognitive Load at Individual and Team Levels in Acute Care Teams using VR Simulations

The emergency department (ED) is a complex environment that is highly susceptible to errors and resultant patient harm because cognitively dense decision-making occurs under information- and time-constrained circumstances. This cutting-edge project addresses this critical issue by combining immersive virtual reality simulations with advanced AI and biosensor technology to detect when acute care teams are approaching cognitive overload during crisis situations. By modeling the factors driving team overload, our research team aims to pioneer new personalized training methods that will enable acute care teams to provide optimal patient care, even in the most extreme circumstances.

Peter Reich (School for Environment and Sustainability) and Mohammed Ombadi (College of Engineering):

Combining ecological first principles and AI to better upscale and predict global carbon, nutrient and water cycles on a changing planet

We will use ecological intelligence combined with AI to improve our understanding of the global carbon cycle and our capacity to better model future feedbacks from terrestrial vegetation to the climate system.

Katie Skinner

Katie Skinner (College of Engineering) and Jacob Allgeier (College of Literature, Science and the Arts)

Machine Learning for Automated Fish Detection and Characterization

This project aims to develop new technology and machine learning methods to enable automated fish detection and characterization. This project will leverage data collected in real environments, including publicly available data, to enable development and evaluation of the proposed system. The future impact of this work will be to advance technology for environmental monitoring efforts in marine environments.

Kayte Spector-Bagdady (Medicine) and Nicholson Price (Law)

Trust, Governance, and Humans in the Loop in Clinical AI
A lack of research on the factors and characteristics that make AI trustworthy to patients is a significant problem for ensuring safe and effective AI. Using conjoint and vignette survey experimental designs, in this project we will (1) explore patient preferences and perceived acceptability of trade-offs regarding AI governance, performance, informational disclosure, and clinician oversight; and (2) measure patient preferences and perceived acceptability of trade-offs regarding characteristics of the overseeing clinician relative to AI governance, performance, and informational disclosure. Our overall goal is to inform the creation of trustworthy, patient-centered policies for AI governance.

Sabina Tomkins

Sabina Tomkins (School of Information), Derek Van Berkel (School for Environment and Sustainability), Grant Schoenebeck (School of Information), Ariel Hasell (College of Literature, Science and the Arts), and John Ryan (College of Literature, Science and the Arts)

Distributing Expert Attention in Complementary Systems

As AI becomes more ubiquitous there are open questions around how to design responsible and complementary human-AI systems. We propose a novel system for Distributing Expert Attention in Complementary Systems. Our system can be used to answer societally urgent questions from digital trace data and will be implemented here to address questions relevant to both biodiversity and climate change.

Angela Violi

Angela Violi (College of Engineering)

AI-driven Accelerated Optimization for the Design of Sustainable Aviation Fuels

This project seeks to create new designs for Sustainable Aviation Fuels (SAFs) that can be used in current aircraft engines while reducing environmental impact. By utilizing a machine learning framework, we aim to tailor the SAF formulations that uphold the necessary physical characteristics for compatibility with jet engines. Our method stands out by generating and analyzing an extensive range of potential SAF options, ensuring the best versions are both environmentally friendly and perform well, getting us closer to a future of cleaner skies.

Jon Zelner (School of Public Health) and Fan Bu (School of Public Health)

Neural Posterior Estimation (NPE) approaches for fitting high-dimensional stochastic epidemic models to real-world spatiotemporal disease data

Stochastic disease models have become a central tool for the practice of epidemiology but calibrating these models to analyze patterns in real-world data is a difficult task due to intractability of likelihood-based inference. We propose applying Neural Posterior Estimation (NPE), a state-of-the-art approach to simulation-based inference, to perform efficient and accurate parameter estimation for complex infectious disease models. We will then demonstrate NPE’s ability to synthesize multiple datasets to infer individual-level patterns of transmission for a hospital-associated infection in a long-term acute care facility.

MIDAS unites with Microsoft to forge the future of ethical AI

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At the intersection of technology and ethics, the Michigan Institute for Data Science (MIDAS) is forging new pathways in the responsible development and application of artificial intelligence (AI). Faced with AI’s potential to reshape the economy, human lives, advancements of the society, and global security, MIDAS researchers are committed to aligning technological growth with human rights, safety, societal values, and policy integrity.

Supporting MIDAS’s ambitious agenda, Microsoft is contributing over half a million dollars in resources which will fuel a diverse slate of research projects. “Core to our mission at Microsoft of advancing AI responsibly is to encourage innovative ideas, spur dialogue, and promote socio-technical approaches to AI development, policy, and governance. We are pleased to support MIDAS and the University of Michigan in this effort.” said Daniel Kluttz, Senior Director at Microsoft’s Office of Responsible AI.

These projects will focus on enhancing AI policy, developing technical solutions for regulatory compliance, and evaluating AI’s societal impact. As part of the MIDAS Propelling Original Data Science (PODS) program, this financial support endorses a comprehensive exploration of policies on current and emerging AI technologies.

“U-M and Microsoft are intensifying our combined efforts in research and workforce development,” said Ravi Pendse, vice president for information technology and chief information officer at U-M. 

“By integrating advanced AI systems like UMGPT and Maizey across our university, we are setting a benchmark for strategic academic-industry collaboration. This gift signifies the expansion of U-M collaboration with Microsoft in a highly strategic direction.”

This ambitious endeavor demonstrates how MIDAS, with the support of Microsoft, is charting the course for the responsible advancement of AI technology. It underscores a crucial dialogue across sectors that promises to drive advancements while ensuring AI remains a force for positive change. 

Elizabeth Bruce, Director, Microsoft Tech for Fundamental Rights adds, “Universities and research institutions are critical to advancing AI and innovation. This work is exemplary of our shared goal to increase access to emerging technologies to better understand potential harms and mitigations and build a more sustainable and equitable society.”  

Later this year, MIDAS will work with Microsoft to convene leading academic researchers as well as AI developers, users and policy makers to develop a roadmap for cross-sector collaboration to address key issues in AI policy and maximize AI’s contribution to scientific research and discovery. Through such activities, MIDAS and U-M will play a critical role in developing an informed, ethical approach to integrating AI technologies into our daily lives.

MIDAS announces 2023 Propelling Original Data Science (PODS) Grant awardees

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MICHIGAN INSTITUTE FOR DATA SCIENCE announces 2023 Propelling Original Data Science (PODS) Grant awardees

The Michigan Institute for Data Science (MIDAS) announced the awardees of the 2023 round of Propelling Original Data Science (PODS) Grants. Nine teams will receive funding support for a wide range of exciting projects with data science and Artificial Intelligence (AI) as the common thread, including topics such as multimodal learning for disease prediction, using video data to study political discourse, text analysis to detect and reduce bias in graduate admissions, and explainable AI for building trust in AI-aided decisions. The awarded projects are: 

From ground to air, and the traveler experiences in-between: Human-centered data-driven performance measures for multimodal transportation systems

Atiyya Shaw (Civil and Environmental Engineering) and Max Li (Aerospace Engineering and Industrial and Operations Engineering)

A Data Science Toolkit for Examining Local Governance

Justine Zhang (School of Information) and Yanna Krupnikov (Communications & Media)

Bayesian modeling of multi-source phenology to forecast airborne allergen concentration

Kai Zhu (School for Environment and Sustainability) and Kerby Shedden (Statistics)

Interpretable machine learning to identify tumor spatial features from longitudinal multi-modality images for personalized progression risk prediction of poor prognosis head and neck cancer

Lise Wei (Radiation Oncology) and Liyue Shen (Computer Science and Engineering)

MI-SPACE: Multiplex Imaging based Spatial Analysis for Discovery of Cellular Interactions in the Tumor Microenvironment

Maria Masotti (Biostatistics)

Detecting and Countering Untrustworthy Artificial Intelligence (AI) through AI Literacy

Nikola Banovic (Computer Science and Engineering)

Foundations of Sequence Models for Learning, Estimation, and Control of Dynamical Systems

Samet Oymak (Electrical and Computer Engineering) and Necmiye Ozay (Electrical Engineering and Computer Science & Robotics)

Neural Quantum States at Scale: Applications in Sciences and Engineering

Shravan Veerapaneni and James Stokes (Mathematics)

Machine-Processing of Graduate Student Applications for Diversity, Equity, and Inclusion

Wenhao Sun (Materials Science and Engineering) and Dallas Card (School of Information)

Since 2016, MIDAS has been offering funding to U-M faculty to enable groundbreaking disciplinary and interdisciplinary research through data science and AI, making it possible for research teams to form many new collaborations, formulate groundbreaking ideas, and secure external funding to expand their work. As of 2022, a total of $12M MIDAS funding has jump-started 63 research projects, which expanded into 112 follow-on projects with $114M of external funding. In addition, “year after year, the applicants propose to employ increasingly more sophisticated data science and AI methods to address increasingly more profound research questions,” says Dr. H. V. Jagadish, Director of MIDAS. “This reflects the rapid advancement of data science and AI and their transformation of science and society, and U-M researchers are at the forefront of it.”

The 2023 PODS teams will present their projects at the U-M Annual Data Science and AI Summit to be held November 13-14, 2023. Read more about their projects:

Atiyya Shaw (Civil and Environmental Engineering) and Max Li (Aerospace Engineering and Industrial and Operations Engineering)

From ground to air, and the traveler experiences in-between: Human-centered data-driven performance measures for multimodal transportation systems

Both ground and air transportation systems have traditionally been assessed using system-based metrics that discount human experiences. While there is growing consensus that the management of these systems should integrate human-centered performance metrics, the primary sources of data to obtain these metrics are difficult to obtain, and the challenges are only increasing. This project aims to examine the potential of applying AI-based approaches to integrate passively collected travel data with rich behavioral insights from smaller scale passenger survey datasets, with the goal of linking across transportation modes and advancing multimodal transportation networks to be more equitable, accessible, and efficient.

Justine Zhang (School of Information) and Yanna Krupnikov (Communications & Media)

A Data Science Toolkit for Examining Local Governance

We will collect a novel, large-scale dataset containing transcripts of city council meetings in Michigan. On top of this data, we will combine domain expertise and machine learning pipelines to generate a rich set of annotations that capture key political qualities of the meeting discourse. This dataset will lay the groundwork for new empirical research on local governance, political division, discourse and civic participation.

Kai Zhu (School for Environment and Sustainability) and Kerby Shedden (Statistics)

Bayesian modeling of multi-source phenology to forecast airborne allergen concentration
We aim to improve the short-term and long-term predictions of airborne allergens under climate change, an emerging public health concern. To achieve this, we propose to develop novel data science tools to effectively assimilate multiple data sources and integrate various data-driven and process-based models. Beyond innovative methodology, our project also advances the biological understanding of pollen and fungal spores, and ultimately, our work helps alleviate the impacts of airborne allergens on people’s health.

Lise Wei (Radiation Oncology) and Liyue Shen (Computer Science and Engineering)

Interpretable machine learning to identify tumor spatial features from longitudinal multi-modality images for personalized progression risk prediction of poor prognosis head and neck cancer
Our research project focuses on the development of an interpretable machine learning model designed to efficiently integrate multimodal data, including images and biological information. Our model also identifies crucial tumor changes over time, enabling personalized progression risk prediction for patients with poor prognosis head and neck cancer. This innovative approach aims to enhance the efficacy and precision of radiation therapy for high-risk patients, ultimately resulting in improved treatment outcomes and quality of life.

Maria Masotti (Biostatistics) 

MI-SPACE: Multiplex Imaging based Spatial Analysis for Discovery of Cellular Interactions in the Tumor Microenvironment
The tumor microenvironment is emerging as the next frontier in cancer research, where scientists are working to understand how the spatial interplay of multiple cell types surrounding the tumor affects immune response, tumor development, response to treatment, and more. Existing methods to quantify cellular interactions in the tumor microenvironment do not scale to the rapidly evolving technical landscape where researchers are now able to map over fifty cellular markers at the single cell resolution with thousands of cells per image. We will develop a statistically-oriented, scalable framework and software toolkit to help researchers discover novel associations between cellular cross-talk in the tumor microenvironment and patient-level outcomes such as response to treatment or survival.

Nikola Banovic (Computer Science and Engineering)

Detecting and Countering Untrustworthy Artificial Intelligence (AI) through AI Literacy

Distinguishing trustworthy from untrustworthy Artificial Intelligence (AI) is of critical importance to broader societal adoption of AI, as AI gets deployed into high-stakes decision-making scenarios. However, end-users who are not computer-science savvy and who lack AI literacy fail to detect untrustworthy AI, despite existing approaches that attempt to promote AI trustworthiness by explaining and justifying AI decisions. Here, we propose to design and evaluate novel explanation mechanisms to help such end-users develop AI literacy they require to detect and counter untrustworthy AI, and in turn reduce their undue reliance on such AI.

Samet Oymak (Electrical and Computer Engineering) and Necmiye Ozay (Electrical Engineering and Computer Science & Robotics)

Foundations of Sequence Models for Learning, Estimation, and Control of Dynamical Systems

Powerful sequence models such as transformers have revolutionized natural language processing however their use in dynamic decision making remains unproven and unsafe. This project will unlock the potential of sequence models in data-driven control and enable their safe and robust use through innovative theory and algorithms.

Shravan Veerapaneni and James Stokes (Mathematics)

Neural Quantum States at Scale: Applications in Sciences and Engineering

Neural networks have achieved unparalleled performance on a diversity of tasks ranging from image processing to natural language generation. This project will leverage these successes to unravel the mysteries of quantum many-body physics. The project hinges on the idea that the quantum many-body problem can be posed as a machine learning problem for a quantum many-body wave function. By drawing upon state-of-art machine learning techniques, this project will make possible the application of neural-network techniques to quantum many-body problems of unprecedented scale, thereby unlocking a spectrum of applications in physics, chemistry and materials science.

Wenhao Sun (Materials Science and Engineering) and Dallas Card (School of Information)

Machine-Processing of Graduate Student Applications for Diversity, Equity, and Inclusion
Every year the UM College of Engineering receives tens of thousands of graduate applications, which faculty reviewers initially down-select using numerical indicators of merit such as GPA, test scores, and undergraduate school prestige. Unless an applicant meets a predefined numerical threshold, richer portions of an application—such as letters of recommendation and statement of purposes—may remain overlooked. This project aims to use Natural Language Processing methods to process graduate student applications and identify ‘hidden gem’ applicants, who are exceptional students from underrepresented or less-privileged backgrounds but have a strong propensity for PhD research.

Announcing the 2023 cohort of postdoctoral fellows

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Michigan Institute for Data Science announces 2023 fellows

Written by Jennifer Lewis

The Michigan Institute for Data Science (MIDAS) announces two new cohorts of postdoctoral fellows. Eleven new fellows will join the Eric and Wendy Schmidt AI in Science Fellowship program in the fall and two new fellows will join the Michigan Data Science Fellowship program.

The AI in Science Fellows will not only work on their individual research projects but will also collaborate on initiatives to support the adoption of AI methods in science and engineering research in the U-M research community. The Data Science Fellows will work at the boundaries of data science methods and domain sciences in an intellectually vibrant environment, and develop collaborative relationships with the U-M data science community. Both Fellowship programs are components of MIDAS’ effort to catalyze the transformative use of Data Science in a wide range of disciplines to achieve lasting societal impact, through research, training, outreach and partnership. The new Fellows will join a close-knit postdoc community with collocated work space at MIDAS and a variety of structured collaborative learning activities. 

“I am constantly amazed by their fantastic research, their enthusiasm to learn new skills together, and their effort to strengthen the data science and AI campus research community.” says Dr. H. V. Jagadish, Director of MIDAS. “In the past few years, our postdocs have collaborated with researchers from more than 30 U-M departments. They have also been developing research incubation activities and technical workshops for the campus community.”

The postdocs will offer an annual AI in Science and Engineering symposium in the spring. They will also offer summer academies on AI methods to enable science and engineering research. Researchers who would like to discuss their ideas with the postdocs at their regular meetings can contact Jen Lewis, postdoc program manager (jlolewis@umich.edu).

The 2023 postdoctoral fellows, with their discipline, affiliated department, faculty mentors, and their degree-granting institution are:

Kamal Abdulraheem

Kamal Abdulraheem

Ph.D., Nuclear Engineering
Schmidt AI in Science Fellow
AI Mentor: Majdi Radaideh, Alex Gorodetsky, Aerospace Engineering
Science Mentor: Brendan Kochunas, Nuclear Engineering and Radiological Science
Research Theme: AI management of nuclear reactors

Jacob Berv

Jacob Berv

PhD., Ecology and Evolutionary Biology
Schmidt AI in Science Fellow
AI Mentor: David Fouhey, Computer Science and Engineering
Science Mentor: Brian Weeks, Environment and Sustainability
Research Theme: ML models for avian evolution

Vital Fernández

Vital Fernández

Ph.D., Fluid Mechanics; Ph.D., Astrophysics
Schmidt AI in Science Fellow
AI Mentor: Xun Huan, Mechanical Engineering
Science Mentor: Sally Oey, Astronomy
Research Theme: Deep Learning for spectral analysis for distant galaxies

Matthew Andres Moreno

Dual Ph.D., Computer Science and Engineering and Ecology and Evolutionary Biology
Schmidt AI in Science Fellow
AI Mentor: Kevin Wood, Biophysics
Science Mentor: Luis Zaman, Complex Systems; Ecology and Evolutionary Biology
Research Theme: Digital Evolution

Amirhossein Moosavi

Ph.D., Management
Data Science Fellow
Science Mentor: Mariel Lavieri, Industrial and Operations Engineering
Research Theme: Using AI methods to improve optimization algorithms and incorporating personal and organizational constraints for healthcare management decision making

Kevin Napier

Kevin Napier

Ph.D., Physics
Schmidt AI in Science Fellow
AI Mentor: Camille Avestruz, Physics
Science Mentor: Hsing-Wen Lin, Physics
Research Theme: Computer Vision Detecting the faintest objects in the Solar System

Christin Salley

Christin Salley

Ph.D., Civil Engineering
Schmidt AI in Science Fellow
AI Mentor: Lu Wang, Computer Science and Engineering
Science Mentor: Sabine Loos, Civil and Environmental Engineering
Research Theme: Analysis of city planning and infrastructure. Impact of and recovery from natural hazards

Alyssa Schubert

Alyssa Schubert

Ph.D., Environmental Engineering
Schmidt AI in Science Fellow
AI Mentor: Bryan Goldsmith, Chemical Engineering
Science Mentor: Mark Burns, Chemical Engineering
Research Theme: AI for sensor data analysis for water quality monitoring

Jeremy Seeman

Ph.D., Statistics and Social Data Analytics
Data Science Fellow
Science Mentor: Yajuan Si, Institute for Social Research
Research Theme: Refining formal privacy methods and applying them to survey data.

Elena Shresta

Ph.D., Aerospace Engineering
Schmidt AI in Science Fellow
AI Mentor: Katie Skinner, Robotics
Science Mentor: Dimitra Panagou, Aerospace Engineering
Research Theme: Intelligent visual and flow-based navigation for autonomous underwater vehicles

Yiluan Song

Ph.D., Environmental Studies
Shmidt AI in Science Fellow
AI Mentor: Yang Chen, Statistics
Science Mentor: Kai Zhu, Environment and Sustainability
Research Theme: Projecting nature’s calendar under climate change

Nanata Sophonrat

Ph.D., Materials Science and Engineering
Schmidt AI in Science Fellow
AI Mentor: Ambuj Tewari, Statistics
Science Mentor: Anne McNeil, Chemistry
Research Theme: Chemist in the loop ML for plastics recycling

Weichi Yao

Ph.D., Statistics
Shmidt AI in Science Fellow
AI Mentor: Yixin Wang, Statistics
Science Mentor: Bryan Goldsmith, Chemical Engineering
Research Theme: Causal reasoning in materials science

AI in Science Fellows appointed in 2022 who will continue fellowships include: James Boyko, Computational Methods, Microevolutionary Biology; Yossi Cohen, Responsible AI, Industrial Decision-Making; Nathan Fox, Crowdsourced data ML, Human Nature Interactions; Jennifer Li, AI, Transient Sky; Andreas Rauch, Data-Driven Modeling, Computational Fluid Dynamics; Soumi Tribedi, AI methods, Electronic Structure Issues in Chemistry; Anastasia Visheratina, AI, Advanced Functional Materials & Devices; Yutong Wang, Developing Machine Learning Theory, Scientific Engineering Applications;  Xin Xie, AI in Topological Photonics. 

Data Science Fellows appointed in 2022 who will continue fellowships are Bernardo Modenesi, Data Science and Elyse Thulin, Computational Methods to Better Understand Human Behaviors.

Fellowships are made possible by generous gifts from Schmidt Futures and the Rocket Companies. The call for applications for the 2024 cohort will be published in August, 2023.

For more information about the AI in Science Fellowship, please visit our program page

For more information about the Data Science Fellowship, please visit our training page.

Notes on the Road – Michigan Road Scholars 2023

By | News

Notes on the Road

Michigan Road Scholars 2023

Written by MIDAS Executive Director Jing Liu. Viewpoints and opinions expressed are her own.

The Michigan Road Scholars tour is a one-week road trip organized by the University of Michigan Vice President for Government Relations Office. 30 scholars, mostly faculty, visit many parts of the state to get to know our communities and people, their passions and challenges, and our natural environment.  Given that I work in a data science and Artificial Intelligence (AI) institute, I wanted to see how data and AI are impacting our state and explore the opportunities they present. I was fortunate enough to be selected among many applicants to participate in the program this year, and was on the road May 1-5.

Working is beautiful

This was a quote from a book that I read as a kid. I don’t even remember the name of the book, but this quote stayed with me for all these years. Being at work, especially doing something you love or are good at, gives so much meaning to our lives: our self worth, fulfillment, and contribution to society.

On the road, I met so many people who demonstrate that working is beautiful:


  • Ryan, the manager in the Detroit Diesel engine plant (upper left), whose pride in his plant and in his work was obvious in everything he said.
  • The incarcerated individuals in the Vocational Village at Handlon Correctional Facility (upper right), who are taking classes in carpentry, welding, auto repair and more, getting high school diplomas and taking college classes.
  • The many passionate women who started their microbusinesses through the help of Build Institute (bottom left).
  • In Detroit, we heard about how Lafayette Green (bottom right), a community garden, employs teenagers to both plant trees for Detroit and to help them build work ethics.

What struck me the most is that every one of them is so passionate and knowledgeable about their work.

Resources, labor and the American privilege

The other side of the story is the lack of resources prevalent across the organizations and individuals whom we met, including schools, public universities, and community colleges from Detroit to all the way up north; rural healthcare systems; Native American tribal nations; and a housing shortage spanning the entire state.

By the end of the first day, I had already learned one of the biggest lessons of this trip: I am always proud that I work really hard, but so many people out there work just as hard with far fewer resources.

Up north, we also heard from a few groups of people about the lack of labor. Hotels are hiring workers on temporary visas from other countries to run their day-to-day operations. We looked around in the houses that Habitat for Humanity is building. By American standards, these are pretty basic starter homes with three bedrooms and all the necessary amenities. But here is when I remembered how my family of four lived in one 160-square foot room for years in China when I was a child, and some of my friends lived in even smaller homes. One of my fellow Road Scholars said that, when he was a child in India, he and his three siblings slept on the floor of his grandma’s room. As our communities face so many challenges because of the lack of resources, how do we keep our effort in the context that the US has 4.25% of the world population but 31% of the wealth?

We – the US – certainly don’t lack resources. How can we ensure that these resources reach the people who really need them? How can data scientists help to both raise awareness of the resource inequalities and their impact on the entire society, and support data-informed policies?

Will AI take over?

While we marvel at so many people’s passion about their work, we are also concerned about how robots, GPTs and other forms of AI are reshaping the nature of work.


  • At the Detroit Diesel engine plant, what struck us was the extent of automation (pic 1). Few workers were around, the vast 3 million square feet of the factory floor were mostly run by automated processes, machines and robots.
  • At the LaFarge cement factory in Alpena (pic 2) – the largest cement plant in the world by some records – with an annual output of 2.4M tons of cement and with the plant running 24/7, there are a total of only 280 employees.
  • At the dinner with the Old Town Playhouse and their senior performance group Aged to Perfection, the seniors talked with me about whether they’d trust AI when it replaces experienced doctors to make diagnosis and treatment plans.
  • At the Enbridge oil pipeline (pic 3), workers talked about using AI to predict water traffic safety breeches.

What will the workplace look like in two, five, or ten years? When the incarcerated individuals in the vocational village receive certificates in carpentry or auto repair, when the next generation of factory floor workers are ready to seek jobs, what jobs will await them? How are we preparing for the age when humans and AI co-exist?

Academia, government, community, industry and the need to collaborate

On the road, we also heard communities and school districts talk about how difficult it is to influence government policy; industry people talk about challenges in their communication with local communities; and sometimes how difficult the different groups in the communities can get on the same page. We also heard many groups of people say to us, “We are so happy you are here!” by which they meant University of Michigan. Every time I heard this, I was actually a bit embarrassed, because many of us actually know so little about these communities.

welcome sign at grand traverse MI

We are embracing a fast-changing world. New technologies such as AI are rapidly changing our definition of work, human relationships, and even what is human. Data and the capability of using data to guide policy are enriching the already rich communities and leaving others behind. Climate change is happening in front of our eyes. How we preserve and seek livelihood, fulfillment and dignity in this new world is something too big for anyone to tackle alone. This is the time when we ALL need to work together.

Celebrating people, community, and our natural habitats

Thunder Bay Diving Center. Credit: NOAA

  • At Archangel Ancient Tree Archive in Copemish, we saw how David Milarch’s family uses modern biotechnology to clone ancient trees that could help reverse climate change (upper left).
  • At the Little Traverse Bay Bands of Odawa Indians, we learned about how they respect the animals that they hunt (upper right).
  • At 9 Bean Rows Farmstead, we learned how the farmers supported each other during the pandemic (lower left).
  • At Thunder Bay, we heard about how local students learn technical skills and help preserve the Great Lakes through working with the National Oceanic and Atmospheric Administration (lower right).
  • (bottom) In Traverse City, we had dinner with community theater members and watched plays performed by Aged to Perfection – seniors from 55 to 93, who performed with energy, humor, wisdom, and charm. One woman in her 80s told me about how hard it was for her in her 20s to pursue a job that was neither a teacher nor a nurse. I asked her, if she could go back in time, what she would say to her 25-year-old self. Her answer: “Don’t let anyone tell you you can’t.”

They are the reasons why we work hard in the first place.

MIDAS Collaborates with Campus Units to Navigate the Fast-Changing AI Landscape and Ethical Implications

By | News

During the last holiday season, millions of people worldwide experienced what it was like to let ChatGPT write an essay, debug code, or dispense sage advice. They also witnessed firsthand just how smoothly ChatGPT could make up information that it didn’t know.

Artificial Intelligence (AI) is rapidly claiming its place in everything we do: it drives cars and fighter planes; it detects cancer in radiological images and black holes in telescope images; it helps us choose which songs to listen to; it helps government offices decide who gets welfare; it even designs and runs experiments, even faster than scientists are able to.

As a campus-wide organization to support data science and AI research, one focus area of the Michigan Institute for Data Science (MIDAS) is to enable the use of data science and AI to accelerate scientific discovery. During the Winter 2023 semester, MIDAS faculty affiliates, postdocs, and campus collaborators organized colloquia on “Automated Research Workflows”, “Data Justice, AI and Design” (jointly with Taubman College and the Center for Ethics, Society and Computing), and “AI in Science and Engineering.” The fourth and final one of this semester, “Implementing AI in Health,” will take place on April 17 as a joint event of MIDAS and the Department of Learning Health Sciences. These events brought together experts from around the country and U-M faculty to stimulate research ideas and collaboration.

Yet, this rapid advancement of AI is also challenging some of our fundamental beliefs. What is unique about being human, when machines can write novels, compose music, and beat the chess world champion? To address these complicated questions, MIDAS, LSA and the AI Lab organized a panel discussion in February on how AI tools challenge traditional thinking about classroom learning. On April 19, the second installment, hosted jointly with the School of Music, Theatre and Dance, focused on generative AI, music composition, the meaning of creativity, and the challenges AI poses to intellectual property.

The fascination about AI also coexists with uneasiness. The increasingly complex AI tools, sometimes in combination with flawed data, can give rise to harm in many ways, including the magnification of disinformation or surveillance capitalism, that will perpetuate or amplify existing biases and injustice. The concerns about the ethical and responsible use of AI are also not limited to human research. Ecologists need to consider whether their datasets are inclusive of endangered species. Jet engine designers need to know that their AI-engineered solutions are safe for humans. How can academic researchers address these concerns as AI developers and users? Another focus area of MIDAS is exactly to promote responsible data science and AI.

The 2023 Future Leaders Summit recently took place, with the theme of “Responsible data science and AI.” This MIDAS annual event brings together outstanding PhD students and postdocs from major research universities, midwest universities, and minority-serving universities, with the majority of the attendees being women and under-represented minorities. Trainees listened to vision talks from mentors, gave research presentations, attended career mentoring sessions, and networked with peers. The focus on responsible data science and AI helps to prepare these trainees, who will be the next generation of academic leaders in data science and AI, to maximize the positive impact of data and AI, while preserving the ideals and values of our society.

The Summit included a mini-symposium with the following speakers:

  • H. V. Jagadish, University of Michigan, “Equity in data science.”
  • Ellie Sakhaee, Microsoft, “Building a culture of Responsible AI (and what it means for researchers)”
  • Tanya Berger-Wolf, the Ohio State University, “Human-machine partnership for conservation: AI and humans combatting extinction together”
  • Andrew J. Connolly, University of Washington, “From interstellar rocks to dark energy: building data science across research communities.”

On May 16, 2023, MIDAS hosted From Theory to Practice: Building Ethical and Trustworthy AI, a forum jointly organized with Rocket Companies featuring speakers from academia, industry and government presenting on the ethical use of AI and its regulation.

“Humans constantly grapple with how new technological advances fit into our moral and ethical framework. But the massive amount of data in the world today and how quickly AI is getting more powerful makes this the critical moment for ethical data science and AI.” Says Dr. H. V. Jagadish, MIDAS Director. “We look forward to working with all researchers to tackle this challenge together.”

MIDAS Welcomes its Inaugural Cohort of Eric and Wendy Schmidt AI in Science Postdoctoral Fellows

By | News

The Michigan Institute for Data Science (MIDAS) has welcomed its inaugural cohort of postdoctoral fellows as part of the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship program. Made possible by a generous gift of more than $10 million from Schmidt Futures, the program aims to catalyze AI-enabled research breakthroughs in science and engineering and cultivate the next generation of research leaders. Over the course of six years, MIDAS plans to hire a total of 60 fellows for the program.

“The fellowship program is a valuable opportunity for postdoctoral researchers to advance knowledge in the era of big data and AI,” said H.V. Jagadish, Director of MIDAS and the Edgar F. Codd Distinguished University Professor of Electrical Engineering and Computer Science.

The 2022 postdoctoral fellows:

The fellows will not only work on their individual research projects but will also collaborate on initiatives to support the adoption of AI methods in science and engineering research in the U-M research community. This includes the upcoming AI in Science and Engineering Day to showcase AI-enabled science and engineering research and encourage cross-disciplinary collaboration.

Having a centralized “program home” at MIDAS offers the possibility of building a close-knit postdoc community. The fellows will engage in collaborative learning activities such as an AI Boot Camp, a three-day session that includes tutorials on AI skills and mentoring from faculty. The postdocs also formed AI Carpentries, small groups that focus on learning and collaboration around specific themes, such as: Hands-on Machine Learning in Python; Deep Learning; Causal Inference and explainable AI; Uncertainties quantification and Bayesian statistics. Attendees of the AI in Science and Engineering Day will learn about the Carpentries and how they facilitate collaboration.

MIDAS is a leader among academic data science institutes to promote ethical data and AI, which offers the postdoc program a unique feature. Dr. Jagadish notes, “Our program’s emphasis on ethical and responsible data and AI science is essential for ensuring that AI is used to advance scientific discovery in a way that benefits society as a whole.”

For more information, please visit our program page.

MIDAS Data Science Fellow Elyse Thulin Awarded Best Poster by a Trainee at the UCSF Promoting Research in Social Media and Health Symposium 

By | Feature, News, Research, research papers
Elyse Thulin

At the 2022 UCSF Promoting Research in Social Media and Health Symposium, Elyse Thulin, a Postdoctoral Fellow at MIDAS and at the Addiction Center in the department of Psychiatry at Michigan Medicine, was awarded Best Poster by a Trainee.

Using traditional epidemiologic, mixed qualitative and quantitative, and computational machine learning methods, Elyse’s broad program of research focuses on how people use online and virtual spaces to interact in ways that both hinder and support wellbeing, mental health, and changes in substance use behaviors. More specifically, Elyse’s areas of research include cyber dating violence, online substance use recovery support groups, and online support groups for traumatic change/loss. Computational skills greatly enhance her work as it enables her to scrape data from online sources, utilize natural language processing to identify top terms, themes, and sentiment from text, and efficiently expand traditional qualitative methods to efficiently code thousands of posts. Elyse’s long term goal is to become a faculty member who teaches, mentors students, and conducts research around expanded applications of computational social science for health and wellbeing.

Online public support group for recovery from problematic cannabis use: trends of use and topics of discussion Elyse J. Thulin, PhD, Anne Fernandez, PhD, Erin Bonar, PhD, Maureen Walton, PhD

Download a PDF version of the poster here.

Elyse provided the following statement about her research:

“Over the past two decades, there have been significant increases in cannabis consumption in the U.S., tied to greater state legalization of recreational (21 states) and medical (37 states) cannabis use, new routes of administration (e.g., vaping, dabbing, edibles), and increased potency of THC. This is worrying given increases in emergency-room injuries related to cannabis use and the increased prevalence of cannabis use disorders (CUD). Despite increased risk of injury related to cannabis and growing prevalence of cannabis use disorder, admission rates for clinical treatment are down, and more than 85% of who would qualify as having CUD do not receive clinical forms of treatment. In contrast, in recent years there has been an uptick in the use of online nonclinical services for those looking to change their cannabis use behaviors. Despite this uptick, very little is known at this time about the functionality, content and interactions occurring within non-clinical, online spaces. In this poster presentation, I aimed to begin to fill this gap by identifying the major themes of conversation, contextualizing information of those themes, and overlap in the present themes with 4 domains of recovery proposed by the US Substance Abuse and Mental Health Services Administration (SAMHSA) in a publicly available online community of individuals who are aiming to cease using cannabis.

I used a data-driven approach to inform the methods of this study. I scraped data from 10 years from a popular Reddit forum on cannabis cessation. I then evaluated the growth of the community across the 10 year period. I next used pre-processing NLP methods (e.g., case uniformity, stemming, etc.) to ready the data for analysis, then identified the top words and terms present in posts. Finally, I extracted a subset of posts to analyze by hand using qualitative methods, to determine the context around top words and phrases. The growth of the community and top words can be found on the poster. Most importantly, we found five major themes in the present study present in posts to the online cannabis cessation community: 1.) individual identify & cannabis use; consequences of cannabis use; reasons for change; cessation strategies; and consequences of change. While examples within these five themes overlapped with the three SAMHSA domains of health, community and purpose, the domain of home was less common and may be less pertinent to this community. Simultaneously, many posts referenced individual identity and cannabis use in posts. Examples were “I smoked daily for ten years” and “I took my first tok at 14, and by 16 I was using in the morning, afternoon and night”. In the context of a common (but incorrect) public narrative that cannabis is not harmful or addictive, individuals in this community may find it important to share the frequency or longevity of their experiences to help emphasize the significant role that cannabis had in their day to day lives. It may be that increased public awareness of that cannabis can be addictive and harmful, particularly when use begins in adolescence or early adulthood or is heavy and frequent, would create greater opportunities for individuals who have experienced dependence or are wanting to change their cannabis use behaviors.”

MIDAS Challenge Award lead Dr. Stephanie Teasley and collaborator Dr. Kelly discuss learning analytics, higher education and employment

By | News, Research

Based on an NSF-funded workshop, Drs. Teasley (School of Information) and Kelly discuss learning analytics goals and research priorities for the coming decade.  They report on a research agenda that would strengthen the connection between learning analytics and recognition of learner competencies. This could have a transformative impact on the relationship between higher education and employment.  The agenda will also generate significant new research questions leading to insights about learning and the data science techniques for analyzing learning.

Read more: https://repository.isls.org/bitstream/1/6848/1/Teasley%20et%20al%20RCR%20Oct2020.pdf