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Shih-Chieh Hsu (University of Washington Seattle (US))26/10/2022, 10:20
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Amy Walton (NSF)26/10/2022, 10:30
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Nate Quarderer (University of Colorado Boulder)26/10/2022, 11:00
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26/10/2022, 11:20
Panelist:
Michelle Holko @GoogleDivide audience into small groups, each of which discusses a scenario
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■ who controls the data?
■ how do you handle the data?
■ how do you communicate about the data?
■ where are the data stored? -
26/10/2022, 11:40
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26/10/2022, 12:00
Each poster speaker can deliver a 2 min highlight.
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In addition, a 10 min video is expected to be uploaded to the agenda by Oct 14. -
Juliana Freire (New York University)26/10/2022, 13:30
Data-driven exploration has revolutionized science and led to the establishment of Data Science as a new discipline that integrates approaches from computer science -- including data management, visualization, machine learning -- statistics, applied mathematics, and many application domains. I will give my perspective of how the field emerged and evolved over the past decade, and the virtuous...
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Prof. Adam Smith (Boston Univeristy)26/10/2022, 14:00
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Prof. Adam Smith (Boston Univeristy), Prof. Eric Torberer (Colorado School of Mines), Prof. Hamed Hassani (University of Pennsylvania), Prof. Tanya Berger-Wolf (Ohio State University)26/10/2022, 14:20
Chair: Prof. Adam Smith
- Prof. Eric Toberer, Colorado School of Mines, HDR Institute: Institute for Data Driven Dynamical Design
- Prof. Tanya Berger-Wolf, Ohio State University, HDR Institute: Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning
- Prof. Hamed Hassani, University of Pennsylvania, TRIPODS Phase II: EnCORE: Institute for...
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26/10/2022, 14:40
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Geoffrey Fox (Indiana University)26/10/2022, 15:30
MLCommons Research is described as a community to collaborate with and as a model for similar communities. Its working groups cover Algorithms, Datasets, Platforms, Storage and Science and Medical applications. MLCommons involves 62 companies, 6 DOE laboratories, 11 Universities with a flagship benchmark set MLPerf and the mission of “Accelerating machine learning innovation to benefit...
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Carol X. Song (Purdue University)26/10/2022, 15:40
The Core Cyberinfrastructure (CI) Capabilities and Services is one of the six focus areas in the I-GUIDE, an NSF HDR Institute for Geospatial Understanding through an Integrative Discovery Environment. Its primary mission is to bridge a wide range of distributed, heterogenous and rapidly increasing geospatial datasets with convergence research to achieve a greater society resilience and...
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Charles Stewart (Rensselaer Polytechnic Institute)26/10/2022, 15:50
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26/10/2022, 16:00
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Philip Coleman Harris (Massachusetts Inst. of Technology (US))26/10/2022, 16:30
A3D3 Institute, Accelerated Artificial Intelligence Algorithms for Data-Driven Discovery, aims to pursue next generation AI Algorithms combined with next generation processor technology to develop AI algorithms that can be run fast to solve real-time scientific problems with AI Domains: High Energy Physics, Multi-Messenger Astronomy, and Neuroscience. We will present Hardware-Algorithm...
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Dr Jennifer Drew (University of Florida), Dr Satyanarayan Dev (Florida Agricultural and Mechanical University)26/10/2022, 16:30
An interdisciplinary team from the University of Florida and Florida Agricultural and Mechanical University are leading a project to enhance diversity, access, impact of a strong AI curriculum. Artificial intelligence is poised to make unprecedented impacts across all aspects of our society. Developing technical expertise in AI or relegating AI education to the computer and data science...
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Alex Franks26/10/2022, 16:30
Our collaborative program establishes pathways for data science training through coursework and real-world projects, connecting three main public higher education institutions in California. Students learn the underlying principles of data science, including data-generating processes and the role of measurement, ethics and privacy, information-processing tools for harnessing the power of big...
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Patrick Flaherty (University of Massachusetts Amherst)26/10/2022, 16:30
We consider the problem of sequential multiple hypothesis testing with nontrivial data collection cost. This problem appears, for example, when conducting biological experiments to identify differentially expressed genes in a disease process. This work builds on the generalized $\alpha$-investing framework that enables control of the false discovery rate in a sequential testing setting. We...
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Prof. Hridesh Rajan (Iowa State University)26/10/2022, 16:30
Data-driven discoveries are permeating critical fabrics of society. However, unreliable discoveries lead to decisions that can have far-reaching and catastrophic consequences on society, defense, and to individuals. This makes the dependability of data-science lifecycles producing discoveries and decisions a critical issue that requires a new holistic view and formal foundations. Furthermore,...
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Prof. Jing Gao (University of Delaware)26/10/2022, 16:30
The Delaware And MiD-Atlantic Data Science Corps (PI Bianco) is an NSF HRD-sponsored, regional partnership between the University of Delaware (UD), Lincoln University (LU), and Delaware State University (DSU) aimed at creating an equitable, accessible program for undergraduate data science education that: (1) is accessible to students of any background with a focus on STEM preparation level;...
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Valerie Barr26/10/2022, 16:30
While coursework provides undergraduate data science students
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with some relevant analytic skills, many are not given the rich experiences with data and computing they need to be successful in the workplace. Additionally, students often have limited exposure to team-based data science and the principles and tools of collaboration that are encountered outside of school. The DSC-WAV program is a... -
Venkata Gandikota (Syracuse University)26/10/2022, 16:30
Group testing is the study of pooling strategies that allow the identification of a small set of k defective items among a population of n using a small number of pooled tests. State-of-the-art testing schemes have shown that \Theta(k log n) schemes are both necessary and sufficient for the purpose which provides large gains when k is small (sublinear in n). However, these schemes are not...
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Dr Yu Liang (University of Tennessee at Chattanooga)26/10/2022, 16:30
Led by an interdisciplinary team from the University of Tennessee at Chattanooga, Howard University, and Chattanooga State Community College, the proposed Anthropocentric Data Analytics for Community Enrichment (ADACE) program will develop a sustainable education and research platform for human-centric data science, where humans are either considered as the research subjects or regarded as a...
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Dr Aryya Gangopadhyay (UMBC)26/10/2022, 16:30
The goal of this project is to develop a team-based data science corps program for undergraduate students from Computer Science, Information Systems, and Business integrating both academic training as well as hands-on experience through real-world data science projects. This project is a collaborative effort with the University of Maryland Baltimore County as the coordinating as well as an...
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Varun Gupta26/10/2022, 16:30
This IDEAL (Phase I) project involves the development of a multi-discipline and multi-institution collaborative institute in the Chicago area that focuses on key aspects of the theoretical foundations of data science. The institute leverages existing strengths across computer science, statistics, economics, electrical engineering and operations research across Northwestern University, Toyota...
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Dr Vandana Janeja (UMBC)26/10/2022, 16:30
The melting of the polar ice sheets contributes considerably to ongoing sea-level rise and changing ocean circulation, leading to coastal flooding and impacting tens of millions of people globally. However, we are yet unable to accurately predict how quickly the ice sheets will continue to shrink contributing to the sea level rise. In particular, we are still challenged by a limited...
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Tanya Berger-Wolf (Ohio State University)26/10/2022, 16:30
Introducing the new NSF HDR DIRSE Institute Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning. The institute aims to establish a new field of science, imageomics: from images to biological traits using biology-structured machine learning.
Images are the most abundant, readily available source for documenting life on the planet. Ranging in...
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Thomas Mustillo (University of Notre Dame), Kristin Kuter (Saint Mary's College)26/10/2022, 16:30
The iTREDS program trains undergraduate students in data science through a lens of social responsibility and community engagement, including rigor and responsibility, ethics, society, and policy. The students also develop superskills in the areas of teamwork, working with stakeholders,ethics,communication, and entrepreneurship. The goal of this 15-credit program is to develop scholars with an...
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Prof. Amanda Hering (Baylor University)26/10/2022, 16:30
The field of water and wastewater treatment (W/WWT) is brimming with data analysis opportunities, but many working in the field lack the skills needed to navigate and extract knowledge from this data. This project began in 2019 with the development of a prerequisite-free course in data science and a five-week summer undergraduate research program. Both were populated with real problems and...
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Jeffrey Errington26/10/2022, 16:30
There is significant demand for a workforce that is proficient in data science and analytics. Employers seek graduates with an ability to (1) understand, interpret, and analyze data, (2) effectively communicate results that stem from the analysis of data, (3) practice the ethical use of data, and (4) apply data science concepts to solve practical problems with real-world relevance. While the...
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Prof. Lenore J Cowen (Tufts University)26/10/2022, 16:30
The Tufts University T-Tripods Phase I Tripods institute supports interdisciplinary research and learning in the foundations of data science, fostering collaboration among researchers in computer science, mathematics and electrical and computer engineering departments at Tufts, as well as connecting to scientists and scholars in a wide range of application domains.
The three focus areas...
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Mikyoung Jun26/10/2022, 16:30
This project (started in October 2021) plans to train next generation workforce in data science for energy industry, ranging from traditional oil and gas energy to renewable energy and energy transition. We are a team of five universities in the greater Houston region: University of Houston (UH) as the leading institution with UH-downtown, UH-Victoria, UH-clear lake, and Sam Houston State...
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26/10/2022, 19:30
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Sarah Stone (e-Science Institute, University of Washington)27/10/2022, 09:00
Experience of Data Science education and Ecosystem building as a center/institute leader within big university, and forward looking about advise to build a successful national-wise HDR Ecosystem.
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27/10/2022, 09:30
Each poster speaker can deliver a 2 min highlight.
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In addition, a 10 min video is expected to be uploaded to the agenda by Oct 14. -
Mark Neubauer (Univ. Illinois at Urbana Champaign (US)), Mark Stephen Neubauer (Univ. Illinois at Urbana-Champaign)27/10/2022, 10:10
A3D3 aims to be a nexus for exchanging new ideas, algorithms and tools between scientific domains, AI communities and industry partners for AI-Hardware co-design. In this presentation, we will show efforts based on strong foundation on the Fast Machine Learning (FastML) community efforts. Our on-going programs on Postbaccalaurate Fellowships, Training, Education, and strong connection with...
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Prof. Kathleen Prudic (University of Arizona)27/10/2022, 10:10
The workforce demand for data analysts and data scientists exceeds the current capacity for higher education to produce this skilled workforce. Our overall goal is to develop scalable, portable data science education that can be readily incorporated into existing programs concentrating on STEM with ecology, biodiversity, and conservation. We will do this by creating multiple curricular data...
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Dr Anand Padmanabhan (University of Illinois at Urbana Champaign)27/10/2022, 10:10
The Convergence Curriculum for Geospatial Data Science is an integrative curriculum to prepare students, scholars, and professionals to build the necessary knowledge, skills, and competencies to solve convergent problems without having to go through a series of multi-week regular courses. This multi-tiered curriculum starts with 5 Foundational Knowledge Threads to establish a common basis for...
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Ewerton Rocha Vieira (Rutgers)27/10/2022, 10:10
This work proposes an integration of surrogate modeling and topology to significantly reduce the amount of data required to describe the underlying global dynamics of robot controllers, including closed-box ones. A Gaussian Process (GP), trained with randomized short trajectories over the state-space, acts as a surrogate model for the underlying dynamical system. Then, a combinatorial...
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Paea LePendu (UC Riverside)27/10/2022, 10:10
The Data Science Career Pathways in the Inland Empire (DS-PATH) is a partnership that brings together 4-year and 2-year Universities and Colleges with a common goal of creating flexible pathways that will equip underrepresented students to become skilled and knowledgeable professionals in Data Science (DS). The partnership consists of six Hispanic Serving Institutions and covers all three...
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Babak Shahbaba27/10/2022, 10:10
We have developed a program comprising of curricular, training, and mentoring components to build a diverse community of learners. Our first cohort included 32 student fellows, including 28 (87%) women/underrepresented minority students, recruited from the three participating institutes: UC Irvine, CSU Fullerton, and Cypress College (representing the three-tiered structure of higher education...
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Dr Cameron Thieme (DIMACS, Rutgers University)27/10/2022, 10:10
We study a Weiner process that is conditioned to pass through a finite set of points and consider the dynamics generated by iterating a sample path from this process. Using topological techniques we are able to characterize the global dynamics and deduce the existence, structure and approximate location of invariant sets. Most importantly, we compute the probability that this...
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Prof. David Matteson (Cornell University)27/10/2022, 10:10
Distinguishing between global or macro patterns and local or micro fluctuations helps summarize the evolution of complex non-stationary dynamic systems. Herein, we focus on making distinctions between drift and shifts. Drift describes the micro-level evolution of a process. This may appear as variation about gradual trends. In contrast, shifts refer to discontinuities, rapid changes, or major...
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Dr David Schmale (Virginia Tech)27/10/2022, 10:10
The ultimate goal of our program is to provide interdisciplinary education and research opportunities in data and decisions science for undergraduate students who are experts in a core discipline of engineering or biology, but who are also proficient in the alternate discipline. We are training students with complementary disciplinary expertise that can address problems at the...
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Suzan van der Lee (Northwestern University)27/10/2022, 10:10
We established the Metropolitan Chicago Data science Corps (MCDC) in the Fall of 2021. MCDC is a partnership between five Chicago-area universities and local not-for-profit organizations. It serves data science needs of the organizations and provides real world data science questions, data sets and experience for data science students. Goals of MCDC are to advance data-driven decision making,...
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Rebecca Napolitano (Penn State University), Prof. Wesley Reinhart27/10/2022, 10:10
The goal of this project is to develop a curricular framework for data science education and workforce development that is transferable between diverse institutions, so STEM-related programs can “plug and play” data science lessons with existing curricula without much overhead. These lessons will be created in conjunction with community stakeholders and industry partners to ensure a focus on...
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Eric Toberer (Colorado School of Mines), Prof. Jane Greenberg (Drexel U.), Prof. Steven Lopez (Northeastern)27/10/2022, 10:10
The NSF Institute for Data-Driven Dynamical Design (ID4) aims to transform how scientists and engineers harness data when designing materials and structures. From chemistry to civil engineering, we seek to create platforms that accelerate the discovery of new mechanisms and dynamics through the complementary union of human and machine intelligence. Cross-cutting these efforts are efforts to...
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Prof. Shaowen Wang (University of Illinois Urbana-Champaign)27/10/2022, 10:10
In today’s interconnected world, disasters such as floods and droughts are rarely isolated events, and their cascading effects are often felt far beyond their locations of origin. The Institute for Geospatial Understanding through an Integrative Discovery Environment (I-GUIDE) creates an open platform for harnessing geospatial data to better understand interconnected interactions across...
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Naoki Saito (University of California, Davis)27/10/2022, 10:10
We have generalized the multiscale basis dictionaries (e.g., the Haar-Walsh wavelet packet dictionary and local cosine dictionary), which were developed for digital signals and images sampled on regular lattices and have a proven track record of success (e.g., audio/image compression, feature extraction, etc.), to for the graph setting. Our previous such basis dictionaries (e.g., Generalized...
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Dr Nathan Quarderer (CU Boulder/CIRES/Earth Lab/ESIIL)27/10/2022, 10:10
The Earth & Environmental Sciences (EES) produce vast amounts of data at a pace and on a scale that precipitate a need for EES researchers who are equipped with the technical data analytic skills required to work with large EES data sets. There are currently limited opportunities to learn these critical earth and environmental data science (EDS) skills leading to a gap between the demand for...
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X. Carol Song (Purdue University)27/10/2022, 10:10
The I-GUIDE platform is designed to harness the vast, diverse, and distributed geospatial data at different spatial and temporal scales and make such data broadly accessible and usable to convergence research and education enabled by cutting-edge cyberGIS and cyberinfrastructure. The platform comprises composable and interoperable tools and cyberinfrastructure capabilities integrated through...
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Dr Mark Daniel Ward (Purdue University)27/10/2022, 10:10
The National Data Mine Network launched in August 2022. Our students work on data-driven projects with our Corporate Partners and with faculty members. The Corporate Partners working with NDMN students this year include Bayer (2 projects), Convo, John Deere (2 projects), Indiana Family and Social Services Administration, Inogen, Lockheed Martin, Merck, Raytheon (2 projects), Sandia National...
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Lev Reyzin (University of Illinois at Chicago)27/10/2022, 10:10
Our institute is a multi-institution and transdisciplinary collaborative Phase II Institute for Data, Econometrics, Algorithms, and Learning (IDEAL), which focuses on key aspects of the foundations of data science. IDEAL will consolidate and amplify research devoted to the foundations of data science across all the major research-focused educational institutions in the greater Chicago area:...
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Philip Coleman Harris (Massachusetts Inst. of Technology (US))27/10/2022, 12:00
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Jeremias Sulam (Johns Hopkins University)27/10/2022, 12:10
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Valerie Barr27/10/2022, 12:20
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Vandana Janeja (UMBC)27/10/2022, 12:20
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Anand Padmanabhan, Jianwu Wang27/10/2022, 12:40
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27/10/2022, 13:00
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Shih-Chieh Hsu (University of Washington Seattle (US))27/10/2022, 13:30
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