Upcoming events
B²C² Initiative
Machine Learning Day 2024
ASU West Valley campus, Zoom
La Sala Ballroom
Friday, April 26 | 8:50 a.m. – 4 p.m. AZ MST
You are invited to Arizona State University’s West campus for the fifth annual Machine Learning Day — an interactive hybrid event!
Machine Learning Day is a unique opportunity for scholars across Arizona and beyond to come together around this year's core thematic focus on: Research in Generative AI, Health and Machine Learning, and The Future of Education and Machine Learning across disciplinary boundaries. Researcher insights on these topics will help us develop solutions to the critical social, technical, cultural, and environmental issues facing 21st-century Arizona, ensuring sustainability. Many talks will also touch on ASU’s newest design aspiration: principled innovation, an area that Machine Learning Day has been engaging with for years. This event aims to inspire new ideas and new collaborations in machine learning research that will further the progress of ML and AI-based science.
With Keynote Speaker
Laura Cassani
Deputy Director of the Intelligent Performance Analytics Division at Aptima – “Collaborative Frontiers: Multidisciplinary Insights in an Era of Novel Teaming”
Join us as we hear from the following speakers on our session topics:
Generative AI
Tom Hartvigsen
Assistant Professor, School of Data Science, University of Virginia – “Talk Title Forthcoming”
Peter Jansen
Associate Professor, School of Information Science, University of Arizona – “DiscoveryWorld: Using Games to Evaluate Automated Scientific Discovery”
Health and Machine Learning
Gina Dumkrieger
Clinical Research, Mayo Health – “Machine Learning in Neurology at Mayo Clinic Arizona”
Kathleen Pine
Associate Professor, College of Health Solutions, Arizona State University – “Batman and Robin in Healthcare Knowledge Work: Human-AI Collaboration by Clinical Documentation Integrity Specialists”
Kristin Swanson
Clinical Research in Brain Cancer, Mayo Health - “Building Models of the Brain Cancer Ecosystem: Tracking Spatial and Temporal Dynamics of Tumor-Immune Interactions in Patients”
Education and Machine Learning
Loretta Cheeks
Founder, DS Innovation – “Leveling Up Machine Learning Education for Innovation and Societal Impact or Leveling Up Machine Learning for Innovation and Societal Impact”
Jacob Greene
Assistant Professor, Department of English, Arizona State University – “Talk Title Forthcoming”
Sean Leahy
Research Scientist, School for the Future of Innovation in Society, Arizona State University – “Futures of Generative AI in Education: Aligned with UNESCO SDGs”
with afternoon Lightning Talks, including:
Bilal Aslam
School of Informatics, Computing, and Cyber Systems, Northern Arizona University - “Satellite-Driven Traffic Volume Estimation: Harnessing Hybrid Machine Learning for Sustainable Urban Planning and Pollution Control”
Maitreya Jitendra Patel
School of Computing and Augmented Intelligence, Arizona State University - “Improving Resource Efficiency of Text to Image Generative Models”
Rezwana Islam
Mary Lou Fulton Teachers College, Arizona State University - “Chatting with AI: Implication on education”
Ruoyao Wang,
chool of Information Science, University of Arizona - “ByteSized32: A Corpus and Challenge Task for Generating Task-Specific World Models Expressed as Text Games”
Nayoung Kim
School of Computing and Augmented Intelligence, Arizona State University - “Talk Title Forthcoming”
Michael Cohen
Human Systems Engineering, Arizona State University - “Talk Title Forthcoming”
and student poster presentations during the lunch hour.
*Registration must be completed by April 24 at 5 p.m. (AZ) to attend.
This event is free to attend! Complimentary breakfast and lunch will be provided for in-person attendees.
Questions about this event? Contact Nicholas Proferes at nicholas.proferes@asu.edu
Past events
Artificial Intelligence, Operations Research and the Homeland Security Enterprise
Thursday, Nov. 9
Ross Maciejewski, Arizona State University
Abstract: In this talk, ASU Professor Ross Maciejewski will provide an overview of the Center for Accelerating Operational Efficiency (CAOE), a Department of Homeland Security (DHS) Center of Excellence. A broad overview of the CAOE's goals and projects will be covered along with in-depth discussion on a variety of projects in our portfolio including how artificial intelligence and operations research techniques are being applied within the Transportation Security Administration, Customs and Border Protection, US Coast Guard, and more. Topics will include trust in AI, deep learning for predicting migration flows, risk analysis and red teaming for security. The talk will conclude with a discussion on funding opportunities from the center and how faculty can get involved.
Bio: Ross Maciejewski (PhD, Purdue University) is the Ira A. Fulton Professor of Computer Science and Director of the School of Computing and Augmented Intelligence at Arizona State University. He also serves as the Director of the Center for Accelerating Operational Efficiency, A Department of Homeland Security Center of Excellence, which works at the intersection of data analytics, operations research, economic analysis and risk science to improve operations in the Homeland Security Enterprise. His current research interests include visualization, data science, explainable artificial intelligence, and disinformation. Professor Maciejewski’s work has been honored by the United States Coast Guard with a Meritorious Team Commendation as part of his work on the Port Resilience for Operational Tactical Enforcement to Combat Terrorism (PROTECT) Team, several IEEE Visual Analytics Contest Awards (2010, 2013, 2015), a best paper award at EuroVis 2017, and two ACM CHI Honorable Mention Awards (2018, 2022). He has served as the Vice Chair for IEEE VIS 2017 and currently serves as the co-chair of the Visualization Executive Committee and as an Associate Editor-in-Chief of IEEE Transactions on Visualization and Computer Graphics. He is a Fellow of the Global Security Initiative at ASU and the recipient of an NSF CAREER Award (2014). For more information on his current work visit vader.lab.asu.edu.
Inferring Human Knowledge in Human-AI Teaming
Thursday, Nov. 30
Lixiao Huang, Arizona State University
Abstract: Artificial social intelligence (ASI) agents have great potential to aid the success of individuals, human–human teams, and human–artificial intelligence teams. To develop helpful ASI agents, Huang's research team created an urban search and rescue task environment in Minecraft to evaluate ASI agents’ ability to infer participants’ knowledge training conditions and predict participants’ next victim type to be rescued. The research team evaluated ASI agents’ capabilities in three ways: (a) comparison to ground truth—the actual knowledge training condition and participant actions; (b) comparison among different ASI agents; and (c) comparison to a human observer criterion, whose accuracy served as a reference point. The human observers and the ASI agents used video data and timestamped event messages from the testbed, respectively, to make inferences about the same participants and topic (knowledge training condition) and the same instances of participant actions (rescue of victims). Overall, ASI agents performed better than human observers in inferring knowledge training conditions and predicting actions. Refining the human criterion can guide the design and evaluation of ASI agents for complex task environments and team composition.
Bio: Lixiao Huang is an Associate Research Scientist at the Center for Human, Artificial Intelligence, and Robot Teaming (CHART) within Global Security Initiative (GSI) at Arizona State University. She completed her PhD in Human Factors and Applied Cognition from North Carolina State University in 2016 and Postdoc in the Humans and Autonomy Lab (HAL) at Duke University in 2018. She is the founding chair of the Human–AI–Robot Teaming (HART) technical group at Human Factors and Ergonomics Society, advocating cutting-edge HART research, interdisciplinary collaboration, advanced testbeds and analytics. Huang's research interests include (a) Human–AI–Robot Teaming effectiveness; (b) Humans’ responses (i.e., emotional states, behavioral patterns, and cognitive processes) to robots and technologies, especially emotional attachment, intrinsic motivation, coordination, trust, and metacognition; (c) The design of human-robot systems using Human Factors methods to make AI and robots effective, safe, user-friendly, trustworthy, and engaging.
Machine Learning Day 2023
April 14, 2023 | 8:50 a.m. AZ MST
ASU West Valley campus and Zoom
Arizona State University’s fourth annual Machine Learning Day — an interactive hybrid event!
Attendees learned from cutting-edge researchers from top institutes as they shared innovative research on machine learning theory and methods in diverse themes, including Learning from Social Data, AI for Good, and Foundation Models in Cognitive, Behavioral, and Biological Science.
Emergent Analogical Reasoning in Large Language Models
March 2, 2023 | 1 p.m. AZ MST
Taylor Webb, UCLA
Abstract: The recent advent of large language models — large neural networks trained on a simple predictive objective over a massive corpus of natural language — has reinvigorated debate over whether human cognitive capacities might emerge in such generic models given sufficient training data. Of particular interest is the ability of these models to reason about novel problems zero-shot, without any direct training on those problems. In human cognition, this capacity is closely tied to an ability to reason by analogy. Here, we performed a direct comparison between human reasoners and a large language model (GPT-3) on a range of analogical tasks, including a novel text-based matrix reasoning task closely modeled on Raven's Progressive Matrices. We found that GPT-3 displayed a surprisingly strong capacity for abstract pattern induction, matching or even surpassing human capabilities in most settings. Our results indicate that large language models such as GPT-3 have acquired an emergent ability to find zero-shot solutions to a broad range of analogy problems.
Bio: Taylor Webb is a postdoctoral scholar in the UCLA Department of Psychology, working with Keith Holyoak, Hongjing Lu, and Hakwan Lau. His research is situated at the interface between cognitive science and AI, with a particular emphasis on using neural network techniques to build cognitive models that are grounded in real-world perceptual inputs. He received his Ph.D. in cognitive psychology and neuroscience from Princeton University, where he studied with Jonathan Cohen and Michael Graziano.
Population-Level Balance in Signed Networks
Jan. 26, 2022 | 10:30–11:30 a.m. AZ MST
Professor Weijing Tang, Harvard University
Abstract: Statistical network models are useful for understanding the underlying formation mechanism and characteristics of complex networks. However, statistical models for signed networks have been largely unexplored. In signed networks, there exist both positive (e.g., like, trust) and negative (e.g., dislike, distrust) edges, which are commonly seen in real-world scenarios. The positive and negative edges in signed networks lead to unique structural patterns, which pose challenges for statistical modeling. We introduce a statistically principled latent space approach for modeling signed networks and accommodating the well-known balance theory, i.e., ``the enemy of my enemy is my friend'' and ``the friend of my friend is my friend''. This approach guides us towards building a class of balanced inner-product models, and towards developing scalable algorithms via projected gradient descent to estimate the latent variables. We also establish non-asymptotic error rates for the estimates. In addition, we apply the proposed approach to an international relation network, which provides an informative and interpretable model-based visualization of countries during World War II.
Bio: Professor Weijing Tang is currently a Postdoctoral Research Fellow in Biostatistics at Harvard University. She received her Ph.D. in Statistics from the University of Michigan in 2022, and in Fall 2023, she will start as an Assistant Professor in the Department of Statistics and Data Science at Carnegie Mellon University. Her research interests include statistical machine learning, survival analysis, and statistical network analysis. Weijing is also enthusiastic about interdisciplinary research on applying statistical machine learning to help solve healthcare problems.
Ensemble Dimensionality Reduction and Feature Gene Extraction for Single-Cell RNA-Seq Data
Jan. 26, 2022 | 10:30–1:30 a.m. AZ MST
Professor Xiaoxiao Sun, University of Arizona
Abstract: Single-cell RNA sequencing (scRNA-seq) technologies allow researchers to uncover the biological states of a single cell at high resolution. For computational efficiency and easy visualization, dimensionality reduction is necessary to capture gene expression patterns in low-dimensional space. Here we propose an ensemble method for simultaneous dimensionality reduction and feature gene extraction (EDGE) of scRNA-seq data. Different from existing dimensionality reduction techniques, the proposed method implements an ensemble learning scheme that utilizes massive weak learners for an accurate similarity search. Based on the similarity matrix constructed by those weak learners, the low-dimensional embedding of the data is estimated and optimized through spectral embedding and stochastic gradient descent. Comprehensive simulation and empirical studies show that EDGE is well suited for searching for meaningful organization of cells, detecting rare cell types, and identifying essential feature genes associated with certain cell types.
Bio: Xiaoxiao Sun, PhD, earned his Ph.D. in Statistics from the University of Georgia in 2018. His research focus is developing theoretically justifiable and computationally efficient methods for complex and big data arising in data-rich areas, such as genomics, social media, and neuroscience. His research interests include nonparametric modeling, computational biology, statistical computing, and big data analytics.
Culture Change Toward More Open, Rigorous, and Reproducible Research
Feb. 9, 2022
Professor Brian Nosek, University of Virginia
Abstract: Improving openness, rigor, and reproducibility in research is less a technical challenge and more a social challenge. Current practice is sustained by dysfunctional incentives that prioritize publication over accuracy and privacy over transparency. The consequence is unnecessary inefficiency in research progress. Successful culture change requires coordinated policy, incentive, and normative changes across stakeholders to improve research credibility and accelerate progress. Some stakeholder groups and disciplines are making more progress than others. We can change the system, but if we do not act collectively we will fail. Let’s not fail.
Bio: Brian Nosek is a social-cognitive psychologist, professor of psychology at the University of Virginia, and the co-founder and director of the Center for Open Science. He also co-founded the Society for the Improvement of Psychological Science and Project Implicit. He has been on the faculty of the University of Virginia since 2002. In 2015, he was named one of "Nature's 10" by the scientific journal Nature.
Analyzing Likert-Scale Data Using Item Response Theory (IRT)
Feb. 23, 2022 | 10:30–11:30 a.m. AZ MST
Professor Yi Zheng, Arizona State University
Abstract: Likert-scale questionnaires are the most popular tool for social- or behavioral-scientists to measure latent traits of the human mind. A typical Likert-scale questionnaire consists of a number of similarly-formatted items, each including a statement and requiring the respondent to choose among a handful of options (e.g., strongly disagree, disagree, neutral, agree, strongly agree). The most common way of scoring a Likert-scale questionnaire is to give integer scores (e.g., 1 through 5) to each option, and then sum up all items. Albeit being simple and accessible, this scoring method has endured controversies and debates. The most compelling critique is perhaps that Likert-scale item scores are ordinal data and cannot be added. An alternative scoring method is by using item response theory (IRT). IRT is a measurement theory that uses statistical models (e.g., logistic models) and statistical estimation techniques to score ordinal data obtained from educational tests or psychological scales. In this talk Professor Zheng introduced the debates over Likert-scale questionnaires and how they can be scored using IRT.
Bio: Yi Zheng, Ph.D., associate professor jointly appointed with the School of Mathematical and Statistical Sciences and Mary Lou Fulton Teachers College at Arizona State University. Dr. Zheng's primary research area is psychometrics, a discipline that studies how to measure and quantify latent traits of the human mind. One of Dr. Zheng's specialties is designing computer adaptive tests, which automatically tailor to each individual test-taker, avoiding redundant items while maintaining a comparable level of measurement accuracy as the full-length tests. Recently, Dr. Zheng studied the application of machine learning techniques to building adaptive tests. Relatedly, one of her current projects is exploring paradigmatic relationships between psychometrics and machine learning. Dr. Zheng is an associate editor of Applied Psychological Measurement.
Understanding Brain Plasticity Through Neuroimaging and Modeling
March 30, 2022 | 10:30–11:30 a.m. AZ MST
Professor Yi-Yuan Tang, Arizona State University
Abstract: Experience always shapes brain and behavior. This talk demonstrated how long-term experience such as culture changes brain using functional connectivity modeling. This talk also explored how mental disorders such as addictions shape brain using dynamic causal modeling. Finally, the talk focused on how short-term experience such as mental training affects brain processing using multivariate pattern analysis.
Bio: Professor Tang is a professor in the College of Health Solutions. He studies neuroscience of cognitive, physical and mental health and behavior change over the lifespan using neuroimaging, data science, physiological, psychosocial, and genetic methods. His research has been supported by NIH, DoD and private foundations. He has published 9 books such as The Neuroscience of Mindfulness Meditation: How the Body and Mind Work Together to Change Our Behavior, Brain-Based Learning and Education: Principles and Practice, and over 300 peer-reviewed articles such as Nature Reviews Neuroscience, Proceedings of the National Academy of Sciences, Trends in Cognitive Sciences, and has received multiple awards including NIH Cutting-Edge Basic Research, NIH Phased Innovation and Complementary and Integrative Rehabilitation Medicine Research.
Two Approaches to Analogy: Deep Learning versus Structural Models
Nov. 17, 2021 2:00 p.m. AZ MST
Professor Hongjing Lu, Departments of Psychology and Statistics, UCLA
Abstract: Human perception and reasoning goes beyond knowledge about individual entities, extending to inferences based on relations between entities. We recognize objects from visual input and understand meanings of entities from text input, and also see the relations among objects and entities in their context. It remains unclear what types of representations are deployed to achieve these feats. Can human perception and reasoning be best emulated by applying deep learning models to massive numbers of reasoning problems, or should learning instead focus on acquiring structural representations, coupled with the ability to compute similarities based on such representations? To address this question, Professor Lu presented two modeling projects, on visual analogy and on verbal analogy, comparing human performance to predictions derived from deep learning models and from models based on structural representations. Their approach demonstrates that human-like analogical mapping can emerge from comparison mechanisms applied to rich semantic representations of individual concepts and relations. Professor Lu argued that perception, semantics and reasoning are closely coupled, and that structural representations play an essential role in facilitating analogical reasoning.
Going Beyond the Here and Now: Counterfactual Simulation in Human Cognition
October 20, 2021 | 2 p.m. AZ MST
Assistant Professor of Psychology Tobias Gerstenberg, Stanford University
Abstract: As humans, we spend much of our time going beyond the here and now. We dwell on the past, long for the future, and ponder how things could have turned out differently. In this talk, Professor Gerstenberg argued that people's knowledge of the world is organized around causally structured mental models, and that much of human thought can be understood as cognitive operations over these mental models.
The Machine Learning Human Footprint Index
Oct. 6, 2021 | 2 p.m. AZ MST
Professors Elizabeth A Barnes and Patrick Keys
Abstract: The human footprint index (HFI) is an extensively used tool for interpreting the accelerating pressure of humanity on Earth. Up to now, the process of creating the HFI has required significant data and modeling, and updated versions of the index often lag the present day by many years. Here they introduced a near-present, global-scale machine learning-based HFI (ml-HFI) which is capable of routine update using satellite imagery alone. They presented the most up-to-date map of the HFI, and document changes in human pressure during the past 20 years (2000–2019). Moreover, they demonstrated its utility as a monitoring tool for the United Nations Sustainable Development Goal 15 (SDG15), 'Life on Land', which aims to foster sustainable development while conserving biodiversity. Moving forward, the ml-HFI may be used for ongoing monitoring and evaluation support toward the twin goals of fostering a thriving society and global Earth system.
Bios: Professor Barnes: Elizabeth (Libby) Barnes is an associate professor of Atmospheric Science at Colorado State University. She joined the CSU faculty in 2013 after obtaining dual B.S. degrees (Honors) in Physics and Mathematics from the University of Minnesota, obtaining her Ph.D. Dr. Barnes' research is largely focused on climate variability and change, and the data analysis tools used to understand it. She has received many prestigious awards including AGU Macelwane Medal, AGU Turco Lectureship and an NSF CAREER grant.
Professor Keys: Patrick Keys is a Research Scientist for SoGES. His research is focused on a broad range of global sustainability challenges, including climate change impacts, cross-scale risks, and social-ecological tele-connections. Prior to joining SoGES, Pat founded an environmental consultancy that worked with local and international partners.
Critical Digital Studies, DH, and Ethical Collaboration
Sep. 22, 2021 | 2–3 p.m. AZ MST
Professors Liz Grumbach & Sarah Florini, Arizona State University
Abstract: Critical digital studies and digital humanities share an investment in interrogating and disrupting systems of power and oppression. Yet, they are rarely in dialogue. In their talk, Liz Grumbach and Sarah Florini will discuss projects they are undertaking that put these disciplines in conversation. Undertaken in cooperation with the Lincoln Center for Applied Ethics under the broad umbrella of “Technologies of Domination,” these ongoing projects include a two-day design lab workshop on technology and oppression, a collaborative research endeavor with TikTok creators, and a de(con)structive approaches to whiteness and UX. Grumbach and Florini demonstrated how critical digital studies and digital humanities can form a generative partnership grounded in ethics of collaboration and community.
Bios: Liz Grumbach is the Program Manager for Digital Humanities and Research for the Lincoln Center for Applied Ethics, an organization committed to exploring co-creative and participatory strategies for ethical technological innovation, at Arizona State University. Her work has been published in scholarly publications such as Digital Humanities Quarterly, Scholarly Research and Communication, Digital Scholarship in the Humanities, and the Journal on Computing and Cultural Heritage.
Sarah Florini is an Associate Professor of Film and Media Studies in the Department of English at Arizona State University. Her work combines critical digital studies, critical race theory, and African American and Black Studies to explore the intersection of race and technology. Her book, Beyond Hashtags: Racial Politics and Black Digital Networks, is available on New York University Press.
Network Inference from Grouped Observations
Sep. 8, 2021 | 2–3 p.m. AZ MST
Professor Yunpeng Zhao, School of Mathematical and Natural Sciences
Abstract: Statistical network analysis typically deals with inference concerning various parameters of an observed network. In several applications, especially those from social sciences, behavioral information concerning groups of subjects are observed. Over the past century a number of descriptive statistics have been developed to infer network structure from such data. However, these measures lack a generating mechanism that links the inferred network structure to the observed groups. In this talk, they presented a model-based approach called the hub model, which belongs to a family of Bernoulli mixture models. They further presented theoretical results on model identifiability, a notoriously difficult problem in Bernoulli mixture models, and estimation consistency.
Bio: Yunpeng Zhao is an associate professor in the School of Mathematical and Natural Sciences in New College of Interdisciplinary Arts and Sciences at Arizona State University. His primary research interest includes machine learning methodology and theory in network analysis with applications in biology and the social sciences. He is also working on high dimensional data analysis with applications in genomics.
Download flyer: b2c2-flyer.pdf
B²C² Data Initiative Seminar | Developing An App To Address Police Officer Stress & Decision-Making: Goals, Challenges, and Cross-Disciplinary Opportunities
April 14, 2021 | 2-3 p.m. AZ MST
Abstract: Our interdisciplinary team provided a presentation and panel/audience discussion of developing research on police officer decision-making under stress. Wearable technologies have the potential to monitor officers’ physiology and alert them to contexts where decision-making might be compromised. We discussed issues related to collecting continuous psychophysiological (e.g., heart rate) and vocal data; mapping physiology to stress and situational contexts; developing and deploying predictive algorithms and creating personalized machine-learning models; facilitating transmission, storage, and processing of intensive, high volume data; and integrating user feedback into all aspects of the process.
Speakers:
Professor Nicole A. Roberts, School of Social & Behavioral Sciences
Professor Nicholas Duran, School of Social & Behavioral Sciences
Professor Yasin Silva, School of Mathematical & Natural Sciences
Professor Ming Zhao, School of Computing, Informatics & Decision Systems Engineering
Download PDF Flyer: Seminar, April 14, 2021
Machine Learning Day
Apr 9, 2021 | 8:50 a.m. AZ MST
We learned from cutting-edge researchers as they shared innovative research on machine learning theory and methods in diverse domains, including applied statistics, biology, psychology, social science and ethics.
Graduate students had the option to present research in a virtual poster session hosted through gather.town!
Download (PDF) flyer: Machine Learning Day Program flyer
Leveraging Auxiliary Information for Detecting Differentially Expressed Gene Pathways
March 3, 2021 | 2 p.m. AZ MST
Assistant Professor of Biostatistics and Bioinformatics Yue Wang, School of Mathematical and Natural Sciences
Abstract: Recent genetic studies have identified genes related to specific human diseases or traits. Besides marginal analysis of individual genes, analyzing biologically meaningful gene pathways, i.e., networks with nodes being genes and edges characterizing the presence/absence of the gene-gene interactions, may yield valuable insights. Identifying gene pathways that differ between conditions can be formulated as a multivariate hypothesis testing problem, but existing approaches handle the gene-gene correlations in inefficient ways, leading to inflated type I error rate and/or compromised power. In this paper, we propose a Hotelling's T2-type statistic, named the T2-DAG test, which efficiently leverages the edge information in the gene pathway through a linear structural equation model. The presenters investigated asymptotic properties of the T2-DAG test under pertinent assumptions and compare the T2-DAG test with six existing methods under various simulation settings. They also applied the T2-DAG test to a lung cancer gene expression data set, and identity several interesting gene pathways that are relevant to different stages of lung cancer.
Bio: Dr. Wang is an Assistant Professor of Statistics in the School of Mathematical and Natural Sciences at Arizona State University. His research focuses on developing statistical methods for analyzing massive biological data to address cutting edge biological, clinical and public health related problems.
Download flyer: b2c2-wang.pdf
NSF National AI Institute for Student-AI Teaming
Professor Sidney D'Mello, Institute of Cognitive Science and Department of Computer Science at the University of Colorado Boulder
Feb. 17, 2021 | 2–3 p.m. AZ MST
Abstract: Professor D'Mello discussed the vision for the NSF National AI Institute for Student-AI Teaming. The Institute will: (1) develop the theories, Artificial Intelligence (AI) technologies, and know-how for creating next-generation collaborative learning environments composed of diverse students, teachers, and AI; (2) grow a diverse workforce of future AI researchers and practitioners by engaging 5,000 middle/high school students in innovative AI education through AI-enabled pedagogies; (3) serve as a national nexus point for empowering diverse stakeholders to engage in responsible co-design of student-AI collaborative technologies. This national institute brings together a geographically distributed team of researchers from nine Universities with partners from academia, K-12 school districts, and industry to address the central challenge of how to promote deep conceptual learning via rich socio-collaborative learning experiences for all students. To meet this challenge, the Institu te will reframe the role of AI in education, moving towards a future where AI is viewed as a social, collaborative partner that help students work and learn more effectively, engagingly, and equitably, while helping educators focus on what they do best: inspiring and teaching students. The Institute will adopt responsible innovation and polycultural approaches for developing ethical AI technologies by integrating foundational and use-inspired AI research across more than 12 interdisciplinary research areas spanning the computing, learning, cognitive and affective sciences. The Institute aims to lead the nation towards a future where all students routinely participate in rich and rewarding AI-enabled collaborative learning experiences that scale across a large number of classrooms, resulting in deeper student engagement and persistence in STEM, more inclusive classroom cultures, and significant improvements in learning outcomes.
Bio: Sidney D’Mello (PhD in Computer Science) is an Associate Professor in the Institute of Cognitive Science and Department of Computer Science at the University of Colorado Boulder. He is interested in the dynamic interplay between cognition and emotion while individuals and groups engage in complex real-world tasks. He applies insights gleaned from this basic research program to develop intelligent technologies that help people achieve to their fullest potential by coordinating what they think and feel with what they know and do. D’Mello has co-edited seven books and published more than 300 journal papers, book chapters, and conference proceedings (16 of which received awards at international conferences). His work has been funded by numerous grants and he currently serves as associate editor for Discourse Processes. D'Mello is the principal investigator of the AI Institute for Student-AI Teaming, which is one of the inaugural NSF National Artificial Intel ligence (AI) Research Institutes.
Download flyer: b2c2_dmello_flyer.pdf
Pattern Recognition: Best Practices for (Interdisciplinary) Data Science
Professor Michael Simeone, Director of Data Science and Analytics for ASU Libraries
Nov. 18, 2020 | 2–3 p.m. AZ MST
Abstract: In this presentation, Michael Simeone coverrf best research practices when it comes to observing and interpreting patterns, from using data visualization to designing experiments and workflows.
Bio: Michael Simeone is a researcher interested in multidisciplinary data science. He currently serves as the director of Data Science and Analytics for ASU Libraries at Arizona State University. His research includes multidisciplinary data science, post-cybernetic culture and technology, analysis of human-technology networks, data visualization, and data-driven collaborations that bridge environmental sciences and humanities. Currently, he serves as a Domain Champion for Humanities for the Extreme Science and Engineering Discovery Environment. He received his doctorate in English from the University of Illinois at Urbana-Champaign.
Download flyer: b2c2_simeone.pdf
Rescorla-Wagner Models with Dynamic Attention and Ensembles
Professor Joel Nishimura, School of Mathematical and Natural Sciences, ASU
Oct. 28, 2020 | 2–3 p.m. AZ MST
Abstract: The Rescorla-Wagner (R-W) model is a discrete time stochastic process frequently used to model human learning, wherein an agent learns associations between cues and subsequent responses by dynamically updating cue associative strengths proportionally to a prediction error. Professor Nishimura characterized the 'curse of dimensionality' for the R-W model and propose a method to overcome this limitation that uses dynamic attention to learn sparse signals. Given the difficulty in selecting features for prediction, dynamic attention faces challenges beyond those faced by the R-W model. The new framework not only satisfies a constraint on the number of attended cues, it also performs better than the R-W model on a number of natural learning tasks, can correctly infers associative strengths, and focuses attention on predictive cues while ignoring uninformative cues.
Bio: Joel Nishimura is an Assistant Professor of Applied Mathematics at the West Valley campus of ASU. He conducts research in several areas inside of and in the overlap between network science, dynamical systems and mathematical biology. A unifying feature of these systems is that simple rules can create complex behaviors and/or structures. He received his doctorate from Cornell University in 2013.
Download Flyer: b2c2-nishimura.pdf
Whom We Include and How They Succeed: A Role for Inclusion and Equity in Data-Informed Learning Engineering
Oct. 14, 2020 | 2-3 p.m. AZ MST
Professor Tom Fikes, Senior Researcher and Director of Research for the Action Lab at Arizona State University
Abstract: In this talk, Professor Fikes bagan with some definitions of “data science” and “learning engineering” – less for the purpose of arguing for the adoption of particular definitions, and more to begin a conversation about what the Action Lab is and does, with a goal of beginning a mutually beneficial research collaboration with B2C2 members around these topics in order to create even better learning environments for our ASU students (and particularly of our SSBS and New College students). He argued that data science, learning engineering, and other similar useful professional identifications are structurally defined, but are always (if implicitly) embedded within functional contexts; and that social transformation requires a deep awareness on the part of data scientists, learning engineers, and allied agents and actors as to the nature of those functional contexts. Indeed, given the inevitable competition between functional contexts, we must go beyond merely being aware of them to cultivating and sustaining them. The second portion of the talk focused around several data visualizations that highlight student learning, persistence, and retention at ASU. These visualizations allowed for the argument for a functional context that extends beyond “success for the average learner” (or for the institution) to one of equity, inclusion, and social justice. Not surprisingly, given the title, Professor Fikes situated this within the context of the ASU charter and its language of inclusion and success.
Bio: Tom Fikes is Senior Researcher and Director of Research for the Action Lab at Arizona State University. He completed his Ph.D. at U.C. Santa Barbara where he studied cognitive psychology and cognitive neuroscience. Following postdoctoral work in mathematical modeling and cognitive science at Indiana University, he served teaching, research, and faculty administrative roles as professor of psychology and neuroscience at the University of Puget Sound in Tacoma, WA and Westmont College in Santa Barbara, CA. His current work centers on plying data science, modeling, visualization, and design thinking toward increased inclusivity, equity, and excellence in student learning.
Fun with Visualization in the Data Deluge
Sep. 16, 2020 | 1-2 p.m. AZ MST
Professor Ross Maciejewski
Abstract: From smart phones to fitness trackers to sensor enabled buildings, data is currently being collected at an unprecedented rate. In this talk, Professor Maciejewski explored how visualization can be leveraged to help us entertain fun and unique questions in the data deluge. By thinking about fun questions for datasets, Professor Maciejewski demonstrated how visual computing can help build cross-domain collaborations, paving the way to discover new insights and challenges.
Bio: Ross Maciejewski is an Associate Professor at Arizona State University in the School of Computing, Informatics & Decision Systems Engineering and Director of the CAOE. His primary research interests are in the areas of geographical visualization and visual analytics. Professor Maciejewski is a recipient of an NSF CAREER Award (2014) and was named a Fulton Faculty Exemplar (2017) and Global Security Fellow at Arizona State. His work has been recognized through a variety of awards (2010, 2013, 2015, 2017, 2018). He currently serves as an Associate Editor for IEEE Transactions on Visualization and Computer Graphics.
Using Mathematical Models with Google Mobility Data to Predict COVID-19 in Arizona
Sep. 2, 2020 |1-2 p.m. AZ MST
Professor Haiyan Wang
Abstract: In June 2020, Arizona emerged as one of the country’s newest COVID-19 hot spots.In this talk, Professor Wang discussed a spatio-temporal forecasting model for COVID-19 cases with the help of human activity data from the Google Community Mobility Reports. The proposed model described the combined effects of transboundary spread among county clusters and human activities on the spread of COVID-19.
Bio: Haiyan Wang is a Professor of Applied Mathematics at the School of Mathematical and Natural Sciences at Arizona State University. He obtained his doctorate in mathematics and master in computer science at Michigan State University in 1997. Before coming to the West Valley campus of ASU in 2005, Dr. Wang had worked in industry as a software engineer for many years. Dr. Wang’s research interests include applied mathematics, differential equations, mathematical biology and data science.