Heng Huang, Ph.D. - US grants

This is funding to support a Doctoral Consortium of approximately 12 promising doctoral students from U.S. institutions of higher learning along with 8 distinguished research faculty, to be held in conjunction with the 1st International Conference on PErvasive Technologies Related to Assistive Environments (PETRA08), which will take place July 15-19 in Athens, Greece. The goal of the PETRA08 conference is to bring together experts from diverse domains to address an important social and healthcare issue, namely that as the world's population ages there is an urgent need to develop solutions for in-home care of the elderly, as well as of people with Alzheimer's, Parkinson's, and other disabilities or traumas. PETRA08 will provide a unique venue that focuses on combining wireless computing, sensors, and other pervasive computing technologies to assistive environments; unlike traditional computer science conferences, it will create a channel for applying basic CS principles to the service of millions of humans in need. More information about PETRA08 may be found at www.petrae.org; the organizers hope the conference will become an annual event. The goals of the Doctoral Consortium are to increase the exposure and visibility of the participants' work within the community, to help establish a sense of community among this next generation of researchers, and to help foster their research efforts by providing substantive feedback and guidance from a group of senior researchers in a supportive and interactive environment. Student participants in the Doctoral Consortium will make formal presentations of their work and will receive feedback from a faculty panel; the feedback is geared to helping students understand and articulate how their work is positioned relative to other research, whether their topics are adequately focused for thesis research projects, whether their methods are correctly chosen and applied, and whether their results are appropriately analyzed and presented. Doctoral Consortium attendees will have short papers on their work included in the Conference Proceedings, and a summary report on the event will be posted on the conference website.

Broader Impacts: The PETRA08 Doctoral Consortium will bring together some of the best students, researchers and practitioners in relevant fields, and will thereby afford the younger participants a unique opportunity to gain wider exposure for their innovative ideas while also receiving reinforcement for the importance and value of conducting research with societal impact. The workshop will also allow the junior participants to create a social network both among themselves and with senior colleagues. Since the conference is expected to host a diverse group along several dimensions (such as nationality, scientific discipline, and research specialization), participants? horizons will be broadened and new collaborations will emerge, to the future benefit of the field.

EAGER: Collaborative Research: Cross-domain Knowledge Transformation via Matrix Decompositions

Traditional data mining algorithms discover knowledge in new domains starting from the scratch, ignoring knowledge learned in other domains. Knowledge transformation is a transformative paradigm that utilizes previously acquired knowledge in other domains to guide knowledge discovery process in a new domain and is especially useful for large data sets. In particular, utilizing applicable knowledge in other domains helps to stabilize the unsupervised learning and generate results that we may have preliminary understanding.

The goal of this project is to design and develop cross-domain knowledge transformation mechanisms for knowledge discovery. The transformation mechanisms are based on matrix decompositions where the knowledge been transferred are represented directly and explicitly ? making them easy to comprehend and be utilized in practice. The proposed mechanisms provide a versatile knowledge transformation framework with solid theoretical foundation and enable a new paradigm of unsupervised learning with domain knowledge.

The usefulness of these knowledge transformation mechanisms/systems will be demonstrated for effective information retrieval, consumer recommender systems, and product/online opinion sentiment analysis. The versatility of this transformative metholody will be verified across many domains.

This award provides NSF support to sponsor students and faculty mentors from US institutions to participate in a doctoral symposium held during the First Workshop on Privacy and Security in Pervasive e-Health and Assistive Environments (PSPAE?09 http://www.petrae.org/docs/PSPAE_ann_f.pdf ) in Corfu, Greece, June 9-13, 2009. This workshop is in conjunction with the Second International Conference on Pervasive Technologies Related to Assistive Environments (PETRA?09 http://www.petrae.org).

The doctoral symposium shares the aims of the PSPAE workshop, seeking new directions in security and privacy research and education. The focus of the PSPAE workshop is a multidisciplinary discussion aimed towards developing new insights into privacy and security issues for pervasive technologies that will be deployed for the benefit of society. The scientific community is expected to benefit from discussions on the crossover between security and privacy and on the issues arising in complex application areas such as healthcare. The doctoral consortium seeks to open new horizons for graduate students who may pursue privacy and security research in healthcare and other pervasive assistive environments. Another objective is to create an international forum that can expose graduate students to the differences in privacy policies around the world, especially regarding health privacy and security.

Nonnegative matrix factorization (NMF) factorizes an input nonnegative matrix into two nonnegative matrices of lower rank. It was recently discovered that NMF has unique ability to solve challenging data mining and machine learning problems. The advantage of NMF over existing unsupervised learning methods are (1) NMF can model widely varying data distributions, (2) NMF performs both hard and soft clustering simultaneously. (3) Many other data mining problems such as semi-supervised clustering problems can be reformulated as NMF problem. Building upon these foundations, the investigators propose to establish a NMF-based comprehensive framework for data mining: (a) Provide deeper understanding of NMF's clustering capability;
(b) Extend data mining capability of NMF for solving various data mining and machine learning problems; (c) Develop fast numerical algorithms which incorporate the state-of-the-art developments from numerical optimization for various matrix factorization models; (d) Develop novel and rigorous proof strategies to prove the correctness and convergence properties of the numerical algorithms; (e) Apply and evaluate these new algorithms in real-world applications.


The proposed work creates a new paradigm of analyzing vast amount of data and discovering new knowledge from the data by transforming established matrix computational methodologies. This new technology can automatically group news articles into meaningful categories, discover protein modules in protein networks, extract weather patterns in climate data, segment pictures into distinct objects, detect communities on the Web, and enable many other scientific discoveries and new technologies creation. On a fundamental level, the proposed work establishes that a simple matrix factorization in fact solves challenging data mining problems. This research reinforces the importance of mathematics in today's data centric world and encourages students to learn mathematics.

This is funding to support a doctoral consortium (workshop) of approximately 10 promising graduate students from U.S. institutions of higher learning along with 3 distinguished research faculty, to be held in conjunction with the Third International Conference on Pervasive Technologies Related to Assistive Environments (PETRA 2010), which will take place June 23-25, 2010, on the island of Samos, Greece (http://www.petrae.org). PETRA is the leading annual interdisciplinary conference on assistive technologies. Its brings together experts in healthcare, sensors,, wireless communications, smart devices, intelligent software, privacy and security, to address an important social and healthcare issue, namely that as the world's population ages there is an urgent need to develop solutions for in-home care of the elderly, as well as of people with Alzheimer's, Parkinson's, and other disabilities or traumas. PETRA provides a unique venue that focuses on combining wireless computing, sensors, and other pervasive computing technologies into assistive environments. While PETRA 2010 will continue to bridge the continuum from data collection and processing to semantic understanding of human behavior, it will also evolve and incorporate new exciting aspects such as how to connect the genotype with phenotype, or the clinical/biomedical features with behavioral patterns and how these impact each other and thus refine research. PETRA 2010 will also study vital issues in privacy and security in monitoring ambient intelligent environments with the goal of identifying and predicting risks, intrusions, unauthorized access to information, or information leaking. PETRA projects assume a human is at the center of "cyberphysical systems" where the digital world merges with the physical.

The goals of the Doctoral Consortium are to increase the exposure and visibility of the participants' work within the community, to help establish a sense of community among this next generation of researchers, and to help foster their research efforts by providing substantive feedback and guidance in a supportive and interactive environment from a group of senior researchers. Student participants in the Doctoral Consortium will be drawn from diverse communities including computer science, engineering, psychology, social science, neuroscience, human-computer interaction, cognitive science and communication. They will make formal presentations of their work and will receive feedback from a faculty panel; the feedback is geared to helping students understand and articulate how their work is positioned relative to other research, whether their topics are adequately focused for thesis research projects, whether their methods are correctly chosen and applied, and whether their results are appropriately analyzed and presented. The workshop faculty members will bring a wide spectrum of expertise, and provide student mentoring and coordination. Doctoral Consortium attendees will have short papers on their work included in the Conference Proceedings, and a summary report on the event will be posted on the conference website.

Broader Impacts: The PETRA 2010 Doctoral Consortium will bring together some of the best students, researchers and practitioners in relevant fields, and will thereby afford the younger participants a unique opportunity to gain wider exposure for their innovative ideas while also receiving reinforcement for the importance and value of conducting research with societal impact. The workshop will allow the junior participants to create a social network both among themselves and with senior colleagues. Since the conference is expected to host a diverse group along several dimensions (such as nationality, scientific discipline, and research specialization), participants' horizons will be broadened and new collaborations will emerge, to the future benefit of the field. The organizing committee will make a concerted effort to attract participants who are women, members of under-represented minorities, and persons with disabilities.

This is funding to support a doctoral consortium (workshop) of approximately 10 promising graduate students from U.S. institutions of higher learning along with 9 distinguished research faculty, to be held in conjunction with the Fourth International Conference on Pervasive Technologies Related to Assistive Environments (PETRA 2011), which will take place May 25-27, 2011, on the island of Crete, Greece (http://www.petrae.org). PETRA is the only annual conference that brings together theoreticians and practitioners from a wide variety of disciplines to focus on the application of pervasive technologies to assistive environments. What connects different disciplines in this highly interdisciplinary but topical conference is the ability to discover new alliances, share data and design new methods that have real-world social impact while also advancing each field scientifically. PETRA 2011 will address timely computational challenges including integration mechanisms of diverse sensor technologies, modeling heterogeneous data, synthesis and analysis of data streams, privacy and security of information, ranking, cleaning, storing and retrieving sensor data streams for pattern analysis and discovery, robust remote rehabilitation mechanisms, correlating brain imaging with behavioral imaging, handling intensive real time data in complex environments, and ways to interpret seemingly meaningless data in order to derive meaningful human behavioral patterns and/or to identify important "events" or changes. PETRA 2011 will also study vital issues in privacy and security in monitoring ambient intelligent environments with the goal of identifying and predicting risks, intrusions, unauthorized access to information, or information leaking. PETRA projects assume a human is at the center of "cyberphysical systems" where the digital world merges with the physical.

The goals of the Doctoral Consortium are to increase the exposure and visibility of the participants' work within the community, to help establish a sense of community among this next generation of researchers, and to help foster their research efforts by providing substantive feedback and guidance in a supportive and interactive environment from a group of senior researchers. Student participants in the Doctoral Consortium will be drawn from diverse communities including computer science, engineering, psychology, social science, neuroscience, human-computer interaction, cognitive science and communication. They will make formal presentations of their work and will receive feedback from a faculty panel; the feedback is geared to helping students understand and articulate how their work is positioned relative to other research, whether their topics are adequately focused for thesis research projects, whether their methods are correctly chosen and applied, and whether their results are appropriately analyzed and presented. The workshop faculty members will bring a wide spectrum of expertise, and provide student mentoring and coordination. Doctoral Consortium attendees will be asked to create and maintain "conference workbook" (webpage) where along with their work they include feedback on papers they attended and observations that they gathered (e.g., through meetings and discussions with conference speakers and other participants) that will apply to and impact their work. Short papers on the participants' work will be published in the Conference Proceedings (which are included in the ACM Digital Library), and a summary report on the event will be posted on the conference website.

Broader Impacts: The PETRA 2011 Doctoral Consortium will bring together some of the best students, researchers and practitioners in relevant fields, and will thereby afford the younger participants a unique opportunity to gain wider exposure for their innovative ideas while also receiving reinforcement for the importance and value of conducting research with societal impact. The workshop will allow the junior participants to create a social network both among themselves and with senior colleagues. The organizing committee will make a concerted effort to attract participants who are women, members of under-represented minorities, and persons with disabilities.

This is funding to support a doctoral consortium (workshop) of approximately 12 promising graduate students from U.S. institutions of higher learning along with 9 distinguished research faculty, to be held in conjunction with the Fifth International Conference on Pervasive Technologies Related to Assistive Environments (PETRA 2012), which will take place June 6-8 on the island of Crete, Greece (http://www.petrae.org). PETRA is the only annual conference that brings together theoreticians and practitioners from a wide variety of disciplines to focus on the application of pervasive technologies to assistive environments. What connects different disciplines in this highly interdisciplinary but topical conference is the ability to discover new alliances, share data and design new methods that have real-world social impact while also advancing each field scientifically. PETRA projects assume a human is at the center of "cyberphysical systems" where the digital world merges with the physical. PETRA 2012 will address timely computational challenges including integration mechanisms of diverse sensor technologies, modeling heterogeneous data, synthesis and analysis of data streams, privacy and security of information, ranking, cleaning, storing and retrieving sensor data streams for pattern analysis and discovery, robust remote rehabilitation mechanisms, correlating brain imaging with behavioral imaging, handling intensive real time data in complex environments, and ways to interpret seemingly meaningless data in order to derive meaningful human behavioral patterns and/or to identify important "events" or changes. PETRA 2012 will also incorporate new exciting aspects in improving the quality of human life and health, such as how clinical and biomedical indicators are connected with observed behavioral patterns over time to refine a particular treatment.

The goals of the Doctoral Consortium are to increase the exposure and visibility of the participants' work within the community, to help establish a sense of community among this next generation of researchers, and to help foster their research efforts by providing substantive feedback and guidance in a supportive and interactive environment from a group of senior researchers. Student participants in the Doctoral Consortium will be drawn from diverse communities including computer science, engineering, psychology, social science, neuroscience, human-computer interaction, cognitive science and communication. They will make formal presentations of their work and will receive feedback from a faculty panel; the feedback is geared to helping students understand and articulate how their work is positioned relative to other research, whether their topics are adequately focused for thesis research projects, whether their methods are correctly chosen and applied, and whether their results are appropriately analyzed and presented. The workshop faculty members will bring a wide spectrum of expertise, and provide student mentoring and coordination. Doctoral Consortium attendees will be asked to create and maintain "conference workbook" (webpage) where along with their work they include feedback on papers they attended and observations that they gathered (e.g., through meetings and discussions with conference speakers and other participants) that will apply to and impact their work. Short papers on the participants' work will be published in the Conference Proceedings (which are included in the ACM Digital Library), and a summary report on the event will be posted on the conference website.

Broader Impacts: The PETRA 2012 Doctoral Consortium will bring together some of the best students, researchers and practitioners in relevant fields, and will thereby afford the younger participants a unique opportunity to gain wider exposure for their innovative ideas while also receiving reinforcement for the importance and value of conducting research with societal impact. The workshop will allow the junior participants to create a social network both among themselves and with senior colleagues. The organizing committee will make a concerted effort to attract participants who are women, members of under-represented minorities, and persons with disabilities. To further assure participant diversity, the organizers will endeavor not to accept more than 2 students from any one institution.

This is funding to support a doctoral consortium (workshop) of approximately 14 promising graduate students from U.S. institutions of higher learning, along with 6 distinguished research faculty (4 of whom are women), to be held in conjunction with the Sixth International Conference on Pervasive Technologies Related to Assistive Environments (PETRA 2013), which will take place May 29-31 on the island of Rhodes, Greece. The PETRA Conference mission is to promote interdisciplinary research that can improve the quality of life and empower people with greater capabilities using pervasive ambient intelligent environments. This is the only annual conference that brings together theoreticians and practitioners from a wide variety of disciplines to focus on the application of pervasive technologies to assistive environments. Innovations to be presented during this year's conference include intelligent human sensing, robotic devices to enable persons with disabilities, algorithms for data fusion, new computer aided rehabilitation methods, therapy game development that personalizes a game to an individual's needs, gesture recognition tools, medication management, remote secure communications and data sharing, remote health monitoring, and many other enabling technologies. More information about the conference may be found online at http://www.petrae.org.

The goals of the Doctoral Consortium are to increase the exposure and visibility of the participants' work within the community, to help establish a sense of community among this next generation of researchers, and to help foster their research efforts by providing substantive feedback and guidance in a supportive and interactive environment from a group of senior researchers. Student participants in the Doctoral Consortium will be drawn from diverse communities including computer science, engineering, psychology, social science, neuroscience, human-computer interaction, cognitive science and communication. They will make formal 20-minute presentations of their work and will receive feedback from a faculty panel; the feedback is geared to helping students understand and articulate how their work is positioned relative to other research, whether their topics are adequately focused for thesis research projects, whether their methods are correctly chosen and applied, and whether their results are appropriately analyzed and presented. The workshop faculty members will bring a wide spectrum of expertise, and provide student mentoring and coordination. Doctoral Consortium attendees will be asked to create and maintain a "digital workbook" where along with their work they include feedback on papers they attended and observations that they gathered (e.g., through meetings and discussions with conference speakers and other participants) that will apply to and impact their work. Short papers on the participants' work will be published in the Conference Proceedings (which are included in the ACM Digital Library), and a summary report on the event will be posted on the conference website.

Broader Impacts: The PETRA 2013 Doctoral Consortium will bring together some of the best students, researchers and practitioners in relevant fields, and will thereby afford the younger participants a unique opportunity to gain wider exposure for their innovative ideas while also receiving reinforcement for the importance and value of conducting research with societal impact. The workshop will allow the junior participants to create a social network both among themselves and with senior colleagues. The organizing committee will make a concerted effort to attract participants who are women, members of under-represented minorities, and persons with disabilities. To further assure participant diversity, the organizers will endeavor not to accept more than 2 students from any one institution, and in addition the PI plans to ask her dean for separate funds to support 2-3 exceptional undergraduates to participate in the conference.

The increasingly large amounts of Electronic Medical Record (EMR) data offer unprecedented opportunities for EMR data mining to enhance health care experiences for personalized intervention, improve different diseases risk stratifications, and facilitate understanding about disease and appropriate treatment. To solve the key and challenging problems in mining such large-scale heterogeneous EMRs, the investigators aim to develop: (i) new computational tools to automate the EMRs processing, including new techniques for filling in missing values using a new robust rank-k matrix completion method; (ii) annotation of unstructured free-text EMRs using multi-label multi-instance learning; (iii) a new sparse multi-view learning model to integrate heterogeneous EMRs to predict the readmission risk of Heart Failure (HF) patients and to support personalized intervention; (iv) novel methods for identifying the longitudinal patterns using high-order multi-task learning; (v) a nonparametric Bayesian model for predicting the event time outcomes of the HF patients readmission.

The sparse multi-view feature learning and robust multi-task longitudinal pattern finding algorithms have a broad range of applications beyond EMR data mining. Free dissemination of source implementations of the algorithms enable other researchers to further develop and apply the resulting techniques. In particular, the methods and tools are expected to impact other EMR and public health research. This project offers enhanced opportunities for research-based advanced training of students (including members of minorities and under-served populations) and integration of research results into curricula at the University of Texas at Arlington, the University of Texas Southwestern Medical Center at Dallas, and Southern Methodist University. For further information see the web site at: http://ranger.uta.edu/~heng/NSF-III-1302675.html

Accurate human motion tracking and activity recognition are important in supporting numerous areas of computer science and engineering research, ranging from user modeling and human robot interaction to graphics and animation. A good resource of annotated datasets and annotation tools is particularly important in research involving physical human limitations, persons with chronic disabilities, such as post-stroke, ALS, Rheumatoid Arthritis, Cerebral Palsy, and others. Currently, a comprehensive community CRI with human activity data and analysis tools is not in place. The development of new methods and algorithms that will improve and enable real-world motion tracking applications is hampered by an inherent difficulty: the lack of large sets of training and testing data. This planning activity brings together experts in computer vision, machine learning, data mining, user interface design, assistive environments, human robot interaction, databases research, big data involving real time human activity, therapists, clinicians, device makers and sensor developers in order to identify the specific human activity data that should be collected and processed when building a repository of automatically and accurately annotated video data of human motion.

Specifically, a repository of automatically and accurately annotated video data of human motion has the potential to impact the research of several disciplines using human motion tracking and recognition by significantly improving the ability to recognize behavioral markers for assessing the confluence of environment, drugs, human psychology and thus extend clinical and psychological areas. However, due to big data issues (volume, velocity, variety, veracity, arising from multisensory visual input of human behavior), the tasks of automating data collection and especially annotation are non-trivial. Traditionally, video data of human motion are collected and annotated manually. Due to the large amount of data generated by video sensors, this task is very tedious and error prone. For this reason, most existing datasets are very small and specialized to the problem for which they were collected. This project will gather input from the research user communities on how to best develop such a CRI repository of human activity. A workshop will be organized with invited experts who will help identify the specific human activity data that should be collected and processed. The input solicited during the workshop will help define the features and facilities of the proposed Smart-Move repository, a spatiotemporal annotated human motion repository by focusing on the following: (1) determining the specific activities to be captured and the methods of multisensory data collection; (2) tools for accurate real-time annotation and storage of the collected data; (3) search facilities to enable researchers to contribute and provide feedback; (4) event recognition and data summarization tools to enable researchers to analyze their own new big data automatically; (5) feasibility of the proposed data collection and analysis using a strategically chosen set of multi-stream data to keep the overall project cost low; (6) limitations when modeling heterogeneous types of human activity data collected by different sensors or of different sample frequencies.

? DESCRIPTION (provided by applicant): Mild cognitive impairment (MCI) is a prodromal stage of Alzheimer Disease (AD), and the existing studies have suggested that the individuals with amnestic MCI tend to progress to probable AD at a rate of approximately 10% to 15% per year. Early prediction of MCI patients with high risk of conversion to AD is of great importance for timely therapy and possible delay of the disease. Recent advances in acquiring multi-dimensional and longitudinal brain imaging and genome-wide array data provide exciting new opportunities to study the influence of genetic variation on brain structure and function. Integrating such multi-dimensional and longitudinal imaging genomic data holds great promise for a system biology of the brain to better understand the complex neurobiological mechanism of conversion of MCI to AD. However, the unprecedented scale and complexity of these neuroimaging genomic data sets have presented critical computational challenges for achieving the full transformative potential from comprehensive joint analysis of these heterogeneous and longitudinal data sets. This project aims to address these emerging challenges for early prediction of MCI-to-AD conversion with four aims. Aim 1 is to develop a novel sparse bi-multivariate learning model based system biology framework for analysis of genome-wide association results across a large number of the structural and functional phenotypes derived from neuroimaging scans of the whole brain. Our new methods are designed for bi-multivariate analysis of high-throughput genomic data and complex Quantitative Traits (QTs) related to MCI-to-AD conversion by utilizing the system biology knowledge. Aim 2 is to further extend the learning approaches in Aim 1 with the new structured sparse models to the multi-dimensional data integration methods to identify the heterogeneous biomarkers from multiscale interrelated imaging genomic data for outcome prediction. Meanwhile, we will utilize the joint multi-task learning scheme to identify the stable genetic and phenotypic biomarkers that are associated with cognitive functions decline and MCI-to-AD conversion simultaneously. Based on the studies in Aims 1 and 2, Aim 3 is focused on revealing the longitudinal biomarkers of the changes of MCI progression or cognitive impairment by a new structured low-rank multi-task regression model. These biomarkers can fully differentiate longitudinal profiles of relevant QTs and better capture genetic associations with QT changes over time. Aim 4 is to evaluate and validate our proposed machine learning and bioinformatics algorithms on both synthetic data and real imaging genomic data. The results of this project will be able to efficiently improve our understanding of the complex neurobiological mechanism underlying the MCI-to-AD conversion. The identified biomarkers will finally enhance the early and accurate prediction of MCI-to-AD conversion such that the clinical treatment can be provided in time.

The research objective of this proposal is to address the computational challenges in an innovative BIGDATA application on imaging-omics based precision medicine. Recent advances in high-throughput imaging (such as histopathology image) and multi-omics (such as DNA sequence, RNA expression, methylation, etc.) technologies created new opportunities for exploring relationships between histology, molecular events, and clinical outcomes using quantitative methods. However, the unprecedented scale and complexity of these imaging-omic data have presented critical computational bottlenecks requiring new concepts and enabling tools. This project builds a new computational framework to integrate novel big data mining algorithms with cloud and high-performance computing strategies for revealing complex relationships between histopathology images, multi-omics, and phenotypic outcomes. This project is innovative and crucial not only to facilitating the development of new big data mining techniques, but also to addressing emerging scientific questions in imaging-omics and many other biomedical applications. The developed methods and tools are expected to impact other cancer genomics research and enable investigators working on cancer medicine to effectively test their scientific hypothesis. This project facilitates the development of novel educational tools to enhance several current courses. University of Texas at Arlington is a minority-serving institution and has large population of Hispanic and Black Americans. This project engages the minority students and under-served populations in research activities to give them a better exposure to cutting-edge science research.

To solve the key and challenge problems in big imaging-omics data mining, this project explores the following research tasks. First, the large-scale non-convex sparse learning models are developed for identifying outcome-relevant phenotypic traits from big histopathology images. Second, the biological domain knowledge is utilized to guide the sparse learning models to uncover the molecular bases of complex traits. Third, the data integration models are designed to integrate imaging-omics data from multiple sources and discover the heterogeneous biomarkers. Fourth, the Baysian learning model is explored to predict longitudinal cancer outcomes. Fifth, the cloud computing and high-performance computing strategies are developed to support the big imaging-omics data mining, such as optimizations for various data mining workloads on heterogeneous hardware (e.g. GPU and NUMA multicore processors) to fully unlock the potential of data center hardware. It is innovative to integrate big data mining algorithms with cloud and high-performance computing to imaging-omics that hold great promise for a systems biology of the precision medicine.

Recent advances in multimodal brain imaging and high throughput genotyping and sequencing techniques provide exciting new opportunities to ultimately improve our understanding of brain structure and neural dynamics, their genetic architecture, and their influences on cognition and behavior. However, data privacy and security issues have inhibited data sharing across institutes. Emerging multi-site collaborative data analysis can address these issues and facilitate data and computing resource sharing. In collaborative data analysis, the participating institutes keep their own data, which are analyzed and computed locally, and only share the computed results by communicating with a server. The server communicates with all institutes and updates the local models such that the trained machine learning models indirectly use all data and are shared with all institutes. Although some distributed/parallel computation techniques were recently proposed to address big data mining problems, most of them are synchronous models. Asynchronous distributed learning methods are much more efficient, because they allow the server to update the model with information from only one worker node without waiting for slow worker nodes in each round. However, the convergence analysis for the asynchronous distributed algorithms is much more difficult due to the inconsistent variables update across nodes. Thus, it is challenging to design efficient distributed machine learning algorithms for collaborative big data analysis. The research objective of this project is to address the computational challenges in the emerging multi-site collaborative data mining for brain big data.

This project seeks to harness the opportunities of designing new efficient asynchronous distributed machine learning algorithms with rigorous theoretical foundations for multi-site collaborative brain big data mining, creating large-scale computational strategies and effective software tools to reveal sophisticated relationships among heterogeneous brain data. This project designs the asynchronous distributed machine learning and principled big data mining models to conduct the comprehensive study of brain imaging genomics and connectomics. Specifically, the principal investigators investigate: 1) collaborative genotype and phenotype association study using new asynchronous doubly stochastic proximal gradient algorithms; 2) communication-efficient multi-site collaborative data integration models to integrate imaging genomics data for predicting outcomes of interest; 3) collaborative deep learning algorithm speedup by the asynchronous distributed algorithms with applications in temporal cognitive change prediction; and 4) new graph convolutional deep learning models for brain network mining. It is innovative to integrate new distributed machine learning and data-intensive computing with brain imaging genomics and connectomics that hold great promise for a systems biology of the brain. The developed methods and tools impact other neuroimaging, genomics, and neuroscience research, and enable investigators working on brain science to effectively test their scientific hypotheses. This project will also facilitate the development of novel educational tools.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

ABSTRACT In response to PAR-19-269 ?Cognitive Systems Analysis of Alzheimer's Disease Genetic and Phenotypic Data (U01 Clinical Trial Not Allowed)?, our project unites experts in AD genomics, machine learning and AI (including deep learning), large-scale data integration, and international data harmonization to work in a carefully-designed Consortium Structure in close partnership with the NIH, ADSP, and NIAGADS. We will develop a suite of complementary big data analytic approaches for ultra-scale analysis of Alzheimer?s Disease (AD) genomic and phenotypic data. The vast data volumes now generated by the Alzheimer?s Disease Sequencing Project (ADSP), National Alzheimer?s Coordinating Center (NACC), Alzheimer?s Disease Neuroimaging Initiative (ADNI), Accelerating Medications Partnership AD (AMP-AD), and UK Biobank (UKBB), far exceed the capacity of all current analytic methods, which have not kept pace with the scale and speed of data collection. This vast amount of genetic and phenotypic data mandates new and more powerful algorithms to: (1) store, manage, and manipulate whole-genome sequences and associated data on an ever-growing scale; (2) discover novel AD risk and protective loci by merging informatics and AD genomics databases; (3) relate whole-genome changes to the ATN(v) biomarkers that now define biological AD. Our Ultrascale Machine Learning Initiative, or ?ULTRA? - will offer new AI and deep learning tools to discover features in massive scale genomics data - relating whole genome data to biomarker features by merging all relevant data sources. Our team of experienced PIs will coordinate efforts across the U.S. to create these large-scale data analytic tools. Our MPI team and 6 Core Leads have decades of experience working together and with the AD community in pioneering machine learning methods for AD genetics and neuroimaging, including leadership of international neuroimaging consortia across the world. Dedicated Cores focus on Genomic, Imaging, and Cognitive Data Harmonization. Curated data will then be efficiently imported into AI approaches and informatics pipelines that will allow the AD research community to leverage ultra-scale, multidimensional genomic and phenotypic data from the ADSP, NACC, ADNI, AMP-AD, and others. Our work is organized by a carefully-designed and coordinated Consortium guided by all stake-holders, clinical leaders, and pioneering analysts in AD genomics and neuroimaging. Our ultrascale AI tools will advance AD genomics research and will include efforts in training, and a dedicated Drug Repurposing Core. This team effort will accelerate understanding of the genetic, molecular and neurobiological mechanisms of AD, yielding significant translational impact on disease and drug development.

Recent developments of single-cell omics technologies enable multi-modality measurements at genome, transcriptome, epigenome, or proteome scale, which will lead to unprecedented insight and resolution to fundamental biological processes. The project will construct a novel bioinformatics framework with advanced machine learning models, efficient computational tools, and user-friendly software for single-cell multi-omics data analysis. The outputs will be available online to the public and are expected to impact biological research community and empower scientists working on single-cell data to effectively test biological hypothesis, especially knowledge extraction from massive high-dimensional and complex datasets. The project will facilitate the development of novel educational tools to enhance curriculum design. Minority students and under-served populations will be engaged in cutting-edge research activities.<br/> <br/>The project focuses on designing principled machine learning and bioinformatics algorithms for analyzing large-scale single-cell multi-omics data to create toolkits to facilitate biological research. Specially, the research team will investigate 1) new cross-modal deep canonical correlation self-supervised autoencoder for multi-modal single-cell data integration, 2) new computational methods to study the associations of single-cell RNA-seq data and protein markers via semi-supervised deep neural networks, 3) interpretation algorithms to enhance predictive model via utilizing structure semantic information and identified biomarkers, 4) statistical inference framework for identifying and inferring conditional dependence from single-cell data, 5) novel transformer based variational autoencoder model for super-resolution spatial transcriptomics, 6) tool portal development for single-cell data analysis to advance biology research, and 7) validations of the proposed methods and system using real large-scale single-cell data. The project is innovative in integrating large-scale machine learning and data-intensive computing for single-cell bioinformatics and will hold great promise for biological mechanism understanding and biomedicine development. The results of the project can be found at: https://sites.pitt.edu/~heh45/NSF2225775.html<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Pulmonary diseases constitute a major public health challenge, and pulmonary function testing (PFT) is the main method of evaluating the changes in human airway mechanics, which are the key symptoms of pulmonary diseases. Due to the possibility of frequent exacerbations in pulmonary diseases, it is important that PFT is accessible to patients anytime and anywhere out of clinic, but most of current in-clinic PFT techniques are too cumbersome and expensive to be used out of clinic. This project addresses these challenges to enable PFT anytime and anywhere, by developing new and integrated artificial intelligence (AI) and sensing systems on commodity smartphones. As pulmonary diseases widely affect the human society and result in billions of annual healthcare related costs, this project has great potential to benefit society by enabling efficient disease monitoring, diagnosis and management out of clinic. This project is also contributing to society by producing datasets that help understand the disease mechanisms, as well as developing new curricula, disseminating research for education and training, and engaging underrepresented students in research.<br/><br/>The primary goal of this project is to enable highly accurate, adaptable and generic PFT out of clinic using commodity smartphones. The project consists of four research tasks: (1) designing new acoustic sensing systems on commodity smartphones that measure the humans’ airway lumen dimensions and characteristics; (2) extracting appropriate biomarker profiles from acoustic sensory data for disease evaluation; (3) developing generalizable machine learning (ML) models that can be applied to evaluating different pulmonary diseases; (4) exploring distributed and asynchronous methods of training the ML models with new federated learning techniques.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.