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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220915T000000
DTEND;TZID=America/New_York:20220915T235959
DTSTAMP:20260415T132040
CREATED:20220204T155956Z
LAST-MODIFIED:20220808T140411Z
UID:1110-1663200000-1663286399@njbda.org
SUMMARY:Journal of Big Data Theory and Practice (JBDTP) Call for Manuscripts
DESCRIPTION:It is our great pleasure to announce the call for papers for the Journal of Big Data: Theory and Practice (JBDTP). JBDTP is an open access peer-reviewed journal which is devoted to the publication of high-quality papers on the theoretical and practical aspects of big data\, AI\, applications and machine learning. \n\n\n\nAuthors are invited to submit novel\, high quality work that has neither appeared in\, nor is under consideration for publication elsewhere. \n\n\n\nAreas of interest include the following (but not restricted to):  \n\n\n\n• Theory and Foundational Issues• Data Mining Methods• Machine Learning Algorithms• Knowledge Discovery Processes• Application Issues and Case Studies• Ethical\, policy and economic aspects of big data\, machine learning and AI • Big data analytics and decision-making• Human interaction with AI \n\n\n\nSubmission Guidelines\n\n\n\nManuscripts (MS Word or PDF format)\, formatted in a single column\, with double spacing\, 12 pt. font and numbered pages. An abstract of 250 words or fewer should be included. Author names and identification information must be restricted to the initial Title page (Title\, author names\, affiliation\, contact details and brief 3 to 4 sentence bios.)\, to facilitate blind peer review. Other than these\, there are no style restrictions (e.g. APA\, IEEE etc.) for the initial submission. All papers will undergo the journal’s rigorous peer review process which can be found on the JBDTP journal website. Manuscripts must be submitted here. The editorial team aims to provide an initial decision within 3 months of acceptance for review. Final paper needs to be no more than 10 pages in length\, please refer to author guidelines. \n\n\n\nImportant Dates\n\n\n\nManuscript submission is on a rolling basis\, and will remain open perpetually. The submission window for the next issue will close on March 15\, 2022. \n\n\n\nMore information on the JBDTP site.
URL:https://njbda.org/event/jbdtp-call-for-manuscripts-deadline/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220807T235900
DTEND;TZID=America/New_York:20220807T235900
DTSTAMP:20260415T132040
CREATED:20220711T130424Z
LAST-MODIFIED:20220711T130427Z
UID:1599-1659916740-1659916740@njbda.org
SUMMARY:Deadline for NJBDA Externship applications
DESCRIPTION:Externships through this partner program are provided by the Rutgers Masters of Business and Science (MBS) Externship Exchange Program and the NJBDA via a grant from the US Economic Development Administration (US-EDA). Upon successful completion\, students are eligible for a $2\,000 fellowship. More details on the NJBDA Externship page.
URL:https://njbda.org/event/deadline-for-njbda-externship-applications/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220509T000000
DTEND;TZID=America/New_York:20220509T235959
DTSTAMP:20260415T132040
CREATED:20211220T185741Z
LAST-MODIFIED:20240412T143513Z
UID:1021-1652054400-1652140799@njbda.org
SUMMARY:2022 NJBDA Symposium
DESCRIPTION:Tickets are now available for the 9th Annual New Jersey Big Data Alliance Symposium\, hosted by the New Jersey Institute of Technology. \n\n\n\nThe theme for the 9th Annual Symposium is “Building the Workforce Pipeline for a Data-Driven Economy” and will include keynotes\, a panel discussion\, and workshops. This year’s symposium will be held in person at the New Jersey Institute of Technology. \n\n\n\nOur keynote speakers are Stephen Ezell\, Vice President\, Global Innovation Policy\, Information Technology and Innovation Foundation and Florence Hudson\, Executive Director\, Co-Principal Investigator\, Northeast Big Data Innovation Hub. Visit the 2022 Symposium page for more information. \n\n\n\nFor questions\, please contact David Bader. For sponsorship and exhibitor inquiries\, please contact Selenny Fabre at info.datascience@njit.edu. \n\n\n\nView the agenda and speaker bios at the 2022 Symposium page. \n\n\n\nBUY TICKETS NOW
URL:https://njbda.org/event/2022-njbda-symposium/
LOCATION:Campus Center\, New Jersey Institute of Technology\, Newark NJ\, 150 Bleeker St\, Newark\, New Jersey\, 07102\, United States
CATEGORIES:Annual Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20220425T113000
DTEND;TZID=UTC:20220425T133000
DTSTAMP:20260415T132040
CREATED:20220327T203850Z
LAST-MODIFIED:20220328T005717Z
UID:1245-1650886200-1650893400@njbda.org
SUMMARY:Grow Your Profits with Data from Digital Technology: A Seminar for Restaurant and Foodservice Professionals
DESCRIPTION:New Jersey Big Data Alliance Workshop Series \n\n\n\nIn collaboration with Montclair State University’s New Jersey Policy Think Tank for the Food Industry and Restaurants \n\n\n\n \n\n\n\n\n\nJoin our panel of technology and marketing experts\, restaurateurs and food service professionals to discuss digital technologies\, including payment tools\, customer reservation\, and social media marketing tools to create value and grow your profits! \n\n\n\nOutstanding Panelists!● Rutgers Food Innovation Center● Urvashi Ghosh – Software as a Service (SaaS) and strategist for food industry● Fred Klashman – Publisher of Total Food Service● Emily Apple – NJEDA Food Insecurity Program● Amy Russo – Toast City Diner\, Montclair● Lulu Safi and Sadat Safi – Maya Halal Taqueria\, Oakland\, Calif.● Raoul Momo – TerraMomo\, Princeton \n\n\n\nLearn more about the New Jersey Policy Think Tank at Montclair State University. \n\n\n\nDownload the event flyer (PDF). \n\n\n\nEvent Sponsored by the US Economic Development Administration \n\n\n\nThe New Jersey Big Data Alliance Advances Computing Innovation and Education in New Jersey
URL:https://njbda.org/event/grow-your-profits-with-data-from-digital-technology-a-seminar-for-restaurant-and-foodservice-professionals/
CATEGORIES:workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20220408T123000
DTEND;TZID=UTC:20220408T143000
DTSTAMP:20260415T132040
CREATED:20220315T203432Z
LAST-MODIFIED:20220315T203524Z
UID:1234-1649421000-1649428200@njbda.org
SUMMARY:Intelligent Informatics @ Bloustein Workshop and Seminar Series
DESCRIPTION:Data Visualization with Python Jupyter Notebooks – A Hands-on Introduction \n\n\n\nREGISTER NOW \n\n\n\nThe session will consist of brief presentations\, with around 100 minutes of interactive work-along demo of data visualization with Jupyter Notebooks\, and end with Q&A. Participants will learn to create data visualizations and can opt to either access Rutgers University’s high-performance computing (HPC) platform/cloud* or to run the code locally on their own computers. \n\n\n\nDr. Bala Desinghu\, Senior Scientist from OARC\, Rutgers University will provide an interactive introduction to data visualization with insights on the use of high-performance computing.Dr. Abhishek Tripathi\, Associate Professor\, School of Business\, TCNJ\, will introduce the use of Python libraries such as Seaborn\, Plotly and Cufflinks for creating advanced and interactive data visualizations.Dr. Jim Samuel\, Associate Professor\, Rutgers Bloustein School\, will conclude the session with insights on the use of Artificial Intelligence for visualizations and language data visualizations.\n\n\n\n*Note to participants: If you want to use Rutgers high-performance computing (HPC) platform you do not have an active computing account on the Amarel cluster\, please follow these instructions. \n\n\n\nRequest an account by filling out this form:   https://oarc.rutgers.edu/amarel-cluster-access-request/You will need to establish a VPN (Virtual Private Network) to access the Amarel cluster when connecting outside the campus network. You can find more information here https://soc.rutgers.edu/vpn/Registration deadline to establish Amaral HPC account is Thursday\, April 7\, 10 AM EST.\n\n\n\nThis workshop is hosted by the Rutgers Urban and Civic Informatics Lab (RUCI Lab) at the Bloustein School and the Rutgers Office of Advanced Research Computing and sponsored by the Bloustein School Master of Public Informatics program. It is organized by Dr. Bala Desinghu and Dr. Jim Samuel in collaboration with our partners\, the Eastern Regional Network (ERN)\, the Northeast Big Data Innovation Hub (NEBDI Hub)\, and the New Jersey Big Data Alliance (NJBDA). \n\n\n\n 
URL:https://njbda.org/event/intelligent-informatics-bloustein-workshop-and-seminar-series/
CATEGORIES:workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220325T140000
DTEND;TZID=America/New_York:20220325T170000
DTSTAMP:20260415T132040
CREATED:20220208T144021Z
LAST-MODIFIED:20220327T230527Z
UID:1131-1648216800-1648227600@njbda.org
SUMMARY:Data Science Workshop: Deep Learning with Python
DESCRIPTION:Watch the recording by clicking on the image above\n\n\n\nDeep Learning (DL) outperforms Machine Learning (ML) in many of the applications related to the Computer Vision (CV) and Natural Language Processing (NLP). One of the biggest advantages of DL over ML is that they can automatically extract the important features from the data. With sufficient data and compute power\, DL methods\, in particular the supervised learning methods\, can achieve the prediction accuracies that were not seen before with any other statistical methods in applications related to CV and NLP. \n\n\n\nIn this workshop\, we will go through the basics of artificial neural networks (ANN)\, Convolutional Neural Networks (CNN)\, and Recurrent Neural Networks (RNN)\, and do hands-on training with these DL models to build predictive analytics for image and text data. \n\n\n\nObjective of the workshop \n\n\n\nUnderstand the basics of Artificial Neural Networks (ANN)Prepare image and text data suitable for the neural networksLearn how to apply various DL models such as ANN\, CNN\, and RNNImprove the accuracy of the model with Hyperparameter Optimization\n\n\n\nWhat is needed? Laptop/Desktop with Internet connection \n\n\n\nDuration: 3 hours \n\n\n\nLevel: Intermediate \n\n\n\nProgramming Platform: On-line resource or Laptop. Instructions for on-line resources will be given in the workshop. \n\n\n\nPrerequisite: Basic laptop usage. Basic knowledge of Python is helpful for doing the hands-on session. \n\n\n\nSlides and materials: Will be provided in the workshop \n\n\n\nThis workshop is hosted by the Office of Advanced Research Computing (OARC)\, Rutgers University\, organized by Bala Desinghu in collaboration with the Eastern Regional Network (ERN) and the New Jersey Big Data Alliance (NJBDA). \n\n\n\nParticipants are encouraged to attend with campus partners representing a variety of stakeholders for campus research and research computing (e.g.\, researchers\, research computing professionals\, students\, staff\, faculty\, and practitioners\, etc.). \n\n\n\nFor additional information\, feel free to contact bala.desinghu@rutgers.edu\, forough.ghahramani@njedge.net\, or gavirapp@kean.edu.
URL:https://njbda.org/event/data-science-workshop-deep-learning-with-python-and-keras/
CATEGORIES:workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220325T000000
DTEND;TZID=America/New_York:20220325T235959
DTSTAMP:20260415T132040
CREATED:20220119T195851Z
LAST-MODIFIED:20220204T164040Z
UID:1084-1648166400-1648252799@njbda.org
SUMMARY:2022 NJBDA Symposium call for abstracts
DESCRIPTION:The NJBDA Annual Symposium brings together academia\, government and industry from across the state and beyond\, to share information on the latest innovations\, research and future directions in Big Data. The concept for this year is: Education and Training of a Big Data Workforce: Building a Pipeline for a Data-Driven Economy. We invite faculty/researchers to submit abstracts of their research for presentation at the symposium. \n\n\n\nAbstracts should be a maximum of 650 words and submitted by March 25\, 2022. Indicate your name\, email\, department\, university affiliation\, title of presentation and track\, at the top of the abstract. This information does not count towards the 650-word limit. Notification of acceptance by April 15\, 2022.  \n\n\n\nSubmit Abstract to: https://easychair.org/conferences/?conf=9thannualnjbdasympos \n\n\n\nMore information: Call for Abstracts
URL:https://njbda.org/event/2022-njbda-symposium-abstract-submittal-deadline-march-25/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220311T140000
DTEND;TZID=America/New_York:20220311T170000
DTSTAMP:20260415T132040
CREATED:20220208T143910Z
LAST-MODIFIED:20220315T203001Z
UID:1129-1647007200-1647018000@njbda.org
SUMMARY:Data Science Workshop: Machine Learning with Python
DESCRIPTION:Watch the recording by clicking on the image above\n\n\n\nIn the last few years\, both industry and academia witnessed the rise of Machine Learning (ML) methods being applied in finance\, marketing\, retails\, science\, engineering\, healthcare\, and humanities. Learning how to apply ML methods to a domain specific application does not require a detailed knowledge about the inner machinery of these methods; however\, one needs to learn the best practices and recommendations followed by the community. \n\n\n\nIn this workshop\, after a brief overview on machine learning\, we will focus on doing the hands-on training in applying ML models on various data types including image\, text\, and time series. We will work through the use cases of classification and regression problems and discuss where to apply supervised or unsupervised methods. \n\n\n\nObjective of the workshop \n\n\n\nUnderstand supervised and unsupervised methodsChoose correct metrics and sampling methods for classification vs regression problemsFind out which features are important in a given datasetLearn to apply ML models such as Decision Trees\, Random Forest\, and Support Vector MachinesPerform clustering and dimensionality reductions (PCA\, t-SNE\, K-means\, etc.)Search the parameter space – hyperparameter optimization\n\n\n\nWhat is needed? Laptop/Desktop with Internet connection \n\n\n\nDuration: 3 hours \n\n\n\nLevel: Intermediate \n\n\n\nProgramming Platform: On-line resource or Laptop. Instructions for on-line resources will be given in the workshop. \n\n\n\nPrerequisite: Basic laptop usage. Basic knowledge of Python is helpful for doing the hands-on session. \n\n\n\nSlides and materials: Will be provided in the workshop \n\n\n\nThis workshop is hosted by the Office of Advanced Research Computing (OARC)\, Rutgers University\, organized by Bala Desinghu in collaboration with the Eastern Regional Network (ERN) and the New Jersey Big Data Alliance (NJBDA). \n\n\n\nParticipants are encouraged to attend with campus partners representing a variety of stakeholders for campus research and research computing (e.g.\, researchers\, research computing professionals\, students\, staff\, faculty\, and practitioners\, etc.). \n\n\n\nFor additional information\, feel free to contact bala.desinghu@rutgers.edu\, forough.ghahramani@njedge.net\, or gavirapp@kean.edu.
URL:https://njbda.org/event/data-science-workshop-machine-learning-with-python-and-scikit-learn/
CATEGORIES:workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220218T140000
DTEND;TZID=America/New_York:20220218T170000
DTSTAMP:20260415T132040
CREATED:20220208T143724Z
LAST-MODIFIED:20220315T202710Z
UID:1127-1645192800-1645203600@njbda.org
SUMMARY:Data Science Workshop: Python for Big Data
DESCRIPTION:Watch the recording now by clicking on image above\n\n\n\n \n\n\n\nPython for Big Data Analytics\n\n\n\nIn recent years\, Python has become one of the top programming languages for doing data analysis due to its inherent advantages such as simplicity\, readability\, portability\, etc.\, However\, Python is slow compared to C or Fortran\, and it does not manage memory well. These limitations\, with speed and memory management\, may not be significant when analyzing small datasets\, but they become bottlenecks when analyzing big datasets. \n\n\n\nTo address the challenges associated with big data analytics\, the Python community developed and tested several techniques. In this workshop\, we will go through some of these techniques including vectorization\, parallelization\, just in time compilation\, and distributed task executions. We will do hands-on exercises to emphasize the following solutions. \n\n\n\nObjectives:\n\n\n\nHow to speed up the data analysis? \n\n\n\nWhat to do when the data set size exceeds the available physical memory? \n\n\n\nHow to distribute the workloads when doing machine learning for big data sets? \n\n\n\nWhat is needed? \n\n\n\nLaptop/Desktop with Internet connection \n\n\n\nDuration: \n\n\n\n3 hours \n\n\n\nProgramming Platform: \n\n\n\nOn-line resource or Laptop. Instructions for on-line resources will be given in the workshop. \n\n\n\nPrerequisite: \n\n\n\nBasic laptop usage. Basic knowledge of Python is helpful for doing the hands-on session. \n\n\n\nSlides and materials: \n\n\n\nWill be provided in the workshop \n\n\n\nRegistration: \n\n\n\nAfter registration\, the zoom link will appear on the registration page and as well as in your confirmation email. Register on Eventbrite now! \n\n\n\nThis workshop is hosted by the Office of Advanced Research Computing (OARC)\, Rutgers University\, organized by Bala Desinghu in collaboration with the Eastern Regional Network (ERN) and the New Jersey Big Data Alliance (NJBDA). \n\n\n\nParticipants are encouraged to attend with campus partners representing a variety of stakeholders for campus research and research computing (e.g.\, researchers\, research computing professionals\, students\, staff\, faculty\, and practitioners\, etc.). \n\n\n\nFor additional information\, feel free to contact bala.desinghu@rutgers.edu\, forough.ghahramani@njedge.net\, or gavirapp@kean.edu.
URL:https://njbda.org/event/data-science-workshop-python-for-big-data/
CATEGORIES:workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220204T140000
DTEND;TZID=America/New_York:20220204T170000
DTSTAMP:20260415T132040
CREATED:20220201T200822Z
LAST-MODIFIED:20220315T202906Z
UID:1090-1643983200-1643994000@njbda.org
SUMMARY:Data Science Workshop Series: Introduction to Python
DESCRIPTION:Watch the recording now by clicking on the image above\n\n\n\nAre you interested in learning data science and modern computational tools for your research?  If so\, come join our Data Science Workshop Series on topics including Python Programming\, Machine Learning\, Distributed Machine Learning for Big Data\, and Deep Learning. \n\n\n\nThe workshop offers practical experience in learning Python\, especially for beginners. By the end of the workshop\, the participants will be able to write simple scripts\, understand Python modules\, and manage packages. \n\n\n\nThis is a virtual event\, and it is free. Register on Eventbrite: https://www.eventbrite.com/e/introduction-to-python-tickets-255576445027 \n\n\n\nThis workshop is hosted by the Office of Advanced Research Computing (OARC)\, Rutgers University\, organized by Bala Desinghu in collaboration with the Eastern Regional Network (ERN) and the New Jersey Big Data Alliance (NJBDA).  \n\n\n\nParticipants are encouraged to attend with campus partners representing a variety of stakeholders for campus research and research computing (e.g.\, researchers\, research computing professionals\, students\, staff\, faculty\, and practitioners\, etc.). \n\n\n\nFor additional information\, feel free to contact bala.desinghu@rutgers.edu\, forough.ghahramani@njedge.net\, or gavirapp@kean.edu.
URL:https://njbda.org/event/data-science-workshop-series-introduction-to-python/
CATEGORIES:workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211210T090000
DTEND;TZID=America/New_York:20211210T120000
DTSTAMP:20260415T132040
CREATED:20211111T142424Z
LAST-MODIFIED:20220328T010209Z
UID:992-1639126800-1639137600@njbda.org
SUMMARY:The New Jersey Big Data Alliance Research Forum
DESCRIPTION:The New Jersey Big Data Alliance is convening the NJBDA Research Forum which is open to all research professionals\, faculty\, research computing professionals\, students\, and other campus partners interested in shaping the Big Data research collaboration landscape. Please feel free to share with your communities! \n\n\n\nAgenda\nThe New Jersey Big Data Alliance Research Collaboration Forum  \n\n\n\nDecember 10\, 2021\, 9:00 AM – 12:00 PM ET – Virtual Event \n\n\n\nAgenda \n\n\n\n09:00 AM Welcome and Opening Remarks \n\n\n\nForough Ghahramani\, EdD\, Associate Vice President for Research\, Innovation\, and Sponsored Programs\, Edge\, and Vice President Research Collaborations Committee\, NJBDA \n\n\n\n09:10 AM Resources and Communities for Advancing Research Collaborations \n\n\n\nIEEE DataPort  \n\n\n\nMelissa Handa\, Program Director Technical Activities\, IEEE DataPort™\, Senior Program Manager\, IEEE \n\n\n\nThe Eastern Regional Network  \n\n\n\nBarr von Oehsen\, PhD\, ERN Steering Committee and  Vice President for the Office of Research and Advanced Computing\, Rutgers University \n\n\n\nResearch with New Jersey \n\n\n\nJudith Sheft\, Executive Director \n\n\n\nNJ Commission on Science\, Innovation\, and Technology \n\n\n\n10:00 AM Multi-institutional  Collaborations   \n\n\n\nNew Jersey Build Back Better Regional Challenge Projects NJEDA Perspective \n\n\n\nPallavi Madakasira\, Director\, Clean Energy\, New Jersey Economic Development Authority (NJEDA) \n\n\n\nGreater Newark Smart Port Regional Growth Cluster \n\n\n\nAtam Dahwan\, PhD\,  Senior Vice Provost for Research\, Distinguished Professor of Electrical and Computer Engineering\, New Jersey Institute of Technology  \n\n\n\nSmart Aviation Growth Cluster  \n\n\n\nCarole M. Mattessich\, Esq.\, Director\, Smart Airports & Aviation Partnership\, National Institute of Aerospace \n\n\n\nClean Energy Resource\, Training\, and Innovation (CERTI) Cluster \n\n\n\nTabbetha Dobbins\, PdD\, Interim VP for Research and Dean of the Graduate School\, Dept. of Physics & Astronomy\, Rowan University \n\n\n\nCommunications\, Data\,and Intelligent Technologies (CoDIT) Regional Growth Cluster \n\n\n\nVictor Lawrence\, PhD\, Distinguished Research Professor and the Director of the Center for Intelligent Networked Systems\, Stevens Institute of Technology \n\n\n\nBuilding Inclusive Entrepreneurial Ecosystems in American Opportunity Zones \n\n\n\nAnne-Marie Maman\, Executive Director\, Princeton Entrepreneurship Council \n\n\n\n11:15 AM National Science Foundation Cyberinfrastructure Funding Opportunities  \n\n\n\nRick McMullen\, PhD\, Research Advisors Group \n\n\n\n11:45 AM Closing Remarks \n\n\n\n () if you have not yet registered. \n\n\n\n\nDownload agenda: PDF \n\n\n\nForum Highlights: \n\n\n\nBig Data Research CollaborationsCommunities and Resources for Advancing CollaborationsFunding OpportunitiesEstablish Contacts with the New Jersey Big Data Research Community \n\n\n\nJoin us for this unique opportunity to connect to the Big Data Research Community! \n\n\n\nFor additional information contact: research@njedge.net  \n\n\n\nRegister for the NJBDA Research Forum on December 10\, 2021 here:  \n\n\n\nDownload flyer: PDF | Word
URL:https://njbda.org/event/the-new-jersey-big-data-alliance-research-forum/
CATEGORIES:forum/panel discussion
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211209T183000
DTEND;TZID=America/New_York:20211209T193000
DTSTAMP:20260415T132040
CREATED:20210720T012050Z
LAST-MODIFIED:20211203T145923Z
UID:941-1639074600-1639078200@njbda.org
SUMMARY:Distinguished Lecture Series in Business Analytics\, Seton Hall University
DESCRIPTION:Jeffrey S. Noto\, M.S.\, SVP and CFO\, Global Network and Technology\, Verizon  \n\n\n\nAnalytics from the CFO’s PerspectiveThe presentation will discuss analytical applications focused on improving the financial outcomes in a corporate environment\, including how improved operational data leads to advancements in financial outcomes – business volume insights\, investment analytics\, profitability insights\, financial forecasting and overall improved financial results. \n\n\n\nREGISTER NOW
URL:https://njbda.org/event/distinguished-lecture-series-in-business-analytics-seton-hall-university-4/
LOCATION:Virtual
CATEGORIES:lectures/talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211209T130000
DTEND;TZID=America/New_York:20211209T170000
DTSTAMP:20260415T132040
CREATED:20211124T170618Z
LAST-MODIFIED:20220202T194709Z
UID:1002-1639054800-1639069200@njbda.org
SUMMARY:Enabling Protein Structure Prediction with AI
DESCRIPTION:Artificial Intelligence/Machine Learning (AI/ML) methods are being used for de novo protein structure prediction. In addition to expert lectures\, this crash course will provide hands-on training on AlphaFold2 and RosettaFold. These methods made a big impact in the field of protein prediction by solving a 50-year-old grant challenge. Please check the registration page (https://iqb.rutgers.edu/node/245)  for more details.
URL:https://njbda.org/event/enabling-protein-structure-prediction-with-ai/
CATEGORIES:lectures/talks,workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211112T120000
DTEND;TZID=America/New_York:20211112T130000
DTSTAMP:20260415T132040
CREATED:20211001T154242Z
LAST-MODIFIED:20220202T194826Z
UID:977-1636718400-1636722000@njbda.org
SUMMARY:Analytics\, Data Science & Artificial Intelligence Workshop & Seminar Series @ Rutgers University: Intelligent Informatics @ Bloustein
DESCRIPTION:Analytics\, Data Science & Artificial Intelligence Workshop & Seminar Series @ Rutgers University: Intelligent Informatics @ Bloustein    \n\n\n\n11/12/2021: Public Informatics for a Better Future: Textual Analytics with R and Social Media Data by Dr. Jim Samuel\, Executive Director- Informatics\, EJ Bloustein School of Planning & Public Policy\, Rutgers\, The State University of New Jersey.   \n\n\n\nOpen/ free\, registration link:    https://rutgers.zoom.us/webinar/register/WN_9mvOSO4uRgyiwWvZ2nd0bg
URL:https://njbda.org/event/public-informatics-for-a-better-future-textual-analytics-with-r-and-social-media-data/
CATEGORIES:workshops
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DTSTART;TZID=America/New_York:20211111T183000
DTEND;TZID=America/New_York:20211111T193000
DTSTAMP:20260415T132040
CREATED:20210720T011856Z
LAST-MODIFIED:20210720T011903Z
UID:939-1636655400-1636659000@njbda.org
SUMMARY:Distinguished Lecture Series in Business Analytics\, Seton Hall University
DESCRIPTION:Kimya Lee\, Ph.D.\, Executive Director\, Strategic Workforce Planning & Analysis\, Office of the Chief Human Capital Officer\, U.S. Department of Homeland Security \n\n\n\nThe People Part of Analytics (or When Data Isn’t Enough)Exploring the intersections among data\, analytics\, personnel & HR policy. What to do when your data model isn’t enough. The discussion will focus on the complexities of data analytics\, how it affects policymaking\, and the importance of storytelling with data. \n\n\n\nREGISTER NOW
URL:https://njbda.org/event/distinguished-lecture-series-in-business-analytics-seton-hall-university-3/
LOCATION:Virtual
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DTSTART;TZID=America/New_York:20211021T183000
DTEND;TZID=America/New_York:20211021T193000
DTSTAMP:20260415T132040
CREATED:20210720T011626Z
LAST-MODIFIED:20210720T011631Z
UID:937-1634841000-1634844600@njbda.org
SUMMARY:Distinguished Lecture Series in Business Analytics\, Seton Hall University
DESCRIPTION:Patrick McCreesh\, Ph.D.\, Managing Partner\, Simatree \n\n\n\nWhen People Feel Stuck: How to Get People to Use Your Analytical ToolsMany people struggle to adopt new analytical tools compared to using the experience-based approach they have always used.  This session will explain why people get Stuck on their old ways and how we can help them change their ways to use new tools and techniques.  When we get people to include data-driven tools in their decision-making process\, everybody wins! \n\n\n\nREGISTER NOW
URL:https://njbda.org/event/distinguished-lecture-series-in-business-analytics-seton-hall-university-2/
LOCATION:Virtual
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DTSTART;TZID=America/New_York:20211015T090000
DTEND;TZID=America/New_York:20211015T150000
DTSTAMP:20260415T132040
CREATED:20211007T113507Z
LAST-MODIFIED:20211007T113751Z
UID:985-1634288400-1634310000@njbda.org
SUMMARY:NJ Cybersecurity Conference 2021: Connect-Collaborate-Careers
DESCRIPTION:For every college student who dreams of a career in cybersecurity and connecting with cybersecurity business leaders\, Seton Hall University is proud to announce the 2021 New Jersey Cybersecurity Conference. The second annual New Jersey Cybersecurityconference will connect students and university faculty and administrators with cybersecurity business leaders. Registration is now open for the free virtual conference\, scheduled for October 15\, from 9 a.m. to 3 p.m. ET. \n\n\n\nCelebrating National Cybersecurity Awareness Month\, the event is sponsored by Seton Hall University\, PSEG and the New Jersey Economic Development Authority (NJEDA)\, in partnership with UPITCHNJ\, a statewide collegiate entrepreneurship organization and Edge\, the region’s nonprofit technology partner. \n\n\n\nThis conference is designed to raise awareness of cybersecurity career opportunities for higher education students and new college graduates\, conveying the appeal of cybersecurity careers among students and business leaders of university cybersecurity programs and potential candidates for these positions. Open to New Jersey’s higher education communities and cybersecurity industries\, the conference is free\, but registration is required. \n\n\n\nConference highlights include a keynote address by Gurdeep Kaur\, Chief Information Security Officer\, PSEG as well as welcome remarks from Brian Bridges\, Ph.D.\, Education Secretary\, NJ Department of Higher Education\, and Katia Passerini\, Seton Hall University Provost. \n\n\n\nGurdeep Kaur\, PSEG Chief Information Security Officer and keynote speaker\, noted that the conference provides a forum to raise student awareness about the array of well-paying entry-level cybersecurity jobs\, which are interesting\, challenging and vitally important to our national security. “Cybersecurity is a profession with purpose\, impact and coolness factor\, and money as well\,” she said. \n\n\n\nWorkshops and Topics \n\n\n\nAttendees can virtually register and participate in four workshops featuring more than 20 cybersecurity experts from industry\, government\, and academia\, including: \n\n\n\nTraining the Next Generation of Cybersecurity ResearchersThe Cybersecurity Job Market: Cybersecurity Employers Speak About the Array of Jobs Available and the Qualities They Seek in New HiresKeeping New Jersey Safe from Cybersecurity ThreatsDiversity Employment in Cybersecurity: Opportunities for Women and MinoritiesRead more about the agenda.\n\n\n\nNew this year\, the conference will include three afternoon workshops that will teach students widely used skills and technology for various background levels (introductory Python coding\, application of Splunk software suite\, computational privacy). \n\n\n\nConference Speakers and Cybersecurity Experts \n\n\n\nSeton Hall UniversityPSEGNew Jersey Economic Development AuthorityFederal Bureau of InvestigationState of New JerseyNew Jersey State PoliceCybereasonLeapYear TechnologiesNew Jersey Institute of TechnologyStevens Institute of TechnologyRutgers UniversitySHI International CorpOld Mutual InsuranceiQ4Take-Two Interactive\n\n\n\nQ&A Session \n\n\n\nA key feature of the program is a question and answer networking session\, “Day in a Life\, What’s it like to work in cybersecurity?” from 12:30 to 1:20 p.m.\, providing actual guidance to students and recent graduates who want to enter this field. They will be able to question their peers who now work in cybersecurity about their positions\, their companies\, and how they got their start in cybersecurity. \n\n\n\nJoin the conversation: #njcybersecurity2021
URL:https://njbda.org/event/nj-cybersecurity-conference-2021-connect-collaborate-careers/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210923T183000
DTEND;TZID=America/New_York:20210923T193000
DTSTAMP:20260415T132040
CREATED:20210720T004947Z
LAST-MODIFIED:20210720T004949Z
UID:929-1632421800-1632425400@njbda.org
SUMMARY:Distinguished Lecture Series in Business Analytics\, Seton Hall University
DESCRIPTION:Jeffrey J. Headd\, Ph.D.\, Senior Director of Commercial Data Sciences\, The Janssen Pharmaceutical Companies of Johnson & Johnson \n\n\n\nDriving Measurable Business Impact Through Data Science at ScaleSuccessful leverage of data science requires a strategy that balances talent\, use case selection\, organizational change management\, impact measurement\, and technology. Through this presentation\, you will learn how Janssen Pharmaceuticals has matured its data science capabilities from early proof of concept projects to a cornerstone capability enabling critical business initiatives. \n\n\n\nREGISTER NOW
URL:https://njbda.org/event/distinguished-lecture-series-in-business-analytics-seton-hall-university/
LOCATION:Virtual
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20210429
DTEND;VALUE=DATE:20210501
DTSTAMP:20260415T132040
CREATED:20210202T185504Z
LAST-MODIFIED:20210429T124008Z
UID:626-1619654400-1619827199@njbda.org
SUMMARY:8th Annual NJBDA Symposium
DESCRIPTION:Smart State: Big Data for Community Impact\nHost: Princeton University\n\n\n\nThe New Jersey Big Data Alliance (NJBDA) is an alliance of 17 higher education institutions\, as well as industry and government members\, that catalyzes collaboration in advanced computing and data analytics research\, education and technology. \nThe NJBDA Annual Symposium brings together academia\, government and industry from across the state and beyond\, to share information on the latest innovations\, research and future directions in Big Data. \nOur 2021 event will showcase how our state\, cities\, and communities use big data to improve equity\, sustainability\, and prosperity for community members.​ \n\n\n\n\n\n\n\nThe 2021 Symposium will include academic research sessions with presentations on current research in Smart Cities\, AI\, Machine Learning and Big Data. \nAny current undergraduate or graduate students attending an institution of higher education may submit a project proposal to be featured at the symposium. More information can be found on the Student Research page and the communication server on Discord. Project proposals are due on April 5. \nFor questions\, please contact Spencer Reynolds\, Princeton University\, spencerr [at] princeton.edu. \nREGISTER NOW | DOWNLOAD AGENDA (PDF) | READ SPEAKER BIOS \nAgenda:\nDay 1\, Thursday\, April 29 \nZoom link:  \n9:00 am: Welcome\nMargaret Brennan-Tonetta\, President\, New Jersey Big Data AllianceAndrea Goldsmith\, Dean\, School of Engineering and Applied Science\, Princeton UniversityBeth Noveck\, Chief Innovation Officer\, State of New Jersey \n9:15 am: Keynote – “New Tools and New Frontiers for Community Impact through Data”\n \nStephen Goldsmith\, Derek Bok Professor of the Practice of Urban Policy\, Harvard Kennedy School \nWhile the past year has presented many challenges for cities and communities\, new technologies and data innovations have emerged in response to the multiple crises.  While challenges remain ahead into 2021\, opportunities abound for smarter cities in a post-Covid country. \n10:00 am: Panel discussion – “Smart Data for Communities: Vision and Implementation”\nModerator: E. Steven Emanuel\, CGI Consulting\, Former CIO\, State of New Jersey\, Former CIO\, City of Newark\, NJ. Panelists: Bernadette Kucharczuk\, Jersey City; Tim Moreland\, Chattanooga; Ruthbea Yesner\, IDC \nMunicipalities and states are leveraging data for community impact in myriad ways\, often within a vision or framework providing context and priorities. This session will explore several municipal cases\, and how the vision of data-enabled government co-evolves with the implementation\, to address opportunities and challenges. \n11:00am: Workshops (two concurrent tracks)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSmart Cities practitioners track\n\n\n\nStay in original Zoom link for Track 1:  \n\n\n\nBig Data Workshop:  Using COVID Data Intelligence Programs to make critical decisions in NJ cities and counties \n\n\n\nThis workshop will exemplify academic-industry partnerships. It will focus on the use of data intelligence in providing states\, counties\, and cities with critical tools during disasters and will be run roughly in two parts. First\, demonstrate the use of data intelligence in helping counties and municipalities to model morbidity\, mortality\, hospitalization and ICU bed rates\, transience\, vaccinations and school openings/ closings. Second we will look at socioeconomic\, structural\, and environmental factors that explain the varying impact of COVID-19 to different communities separated by location. We will inspect how these factors have common threads that run across communities and make some more vulnerable than others to various natural disasters including COVID-19. \n\n\n\nWorkshop leaders: George Avirappattu\, Kean University; Navin Vembar\, Camber; Margaret Piliere\, Madhu Chandran Sreekumuran Nair and E. Steven Emanuel\, CGI. \n\n\n\n\n\nStudent Projects track\n\n\n\nZoom link for Track 2:  \n\n\n\nExperiential Learning thru Capstones:  Opportunities and Challenges \n\n\n\nThe workshop will highlight the partnership between universities and industry necessary to provide experiential opportunities to the students in the form of Capstone courses.   Capstone courses are an integral part of the culminating experience of academic programs. These are immensely helpful opportunities for the students to prepare for careers in the industry. Experiential learning is learning by doing. Doing high-impact learning practices requires substantial student participation and effective faculty advising. Employers and hiring managers value college candidates with experiential learning across individual disciplines in real or very like real-world settings. An approach that has worked effectively is to involve the industry stakeholders in defining the problem scope and working closely with them for the accomplishment of the students’ deliverables. \n\n\n\nThe workshop will cover research on experiential learning and how its unique characteristics lends itself for capstone courses in business.   We will share experiences of successful partnerships\, what works and what does not\, issues and challenges\, and lessons learned. The role and responsibilities of the students in deriving the best out of such experiences will also be covered. \n\n\n\nWorkshop leaders: Rashmi Jain\, Montclair State University; Adam Spunberg\, AB InBev \n\n\n\n\n\nStudent and Faculty Poster presentations (all day)\n\n\n\nMore information can be found on the Student Research page and the communication server on Discord. These presentations will be available asynchronously.  \n\n\n\n“Rebalancing Shared Mobility Systems by User Incentivization via Reinforcement Learning: Framework Design and Analysis”\nMatthew Schofield (presenter)\, Shen-Shyang Ho and Ning Wang\, Rowan University \n\n\n\nIn shared mobility systems research\, there is increasing interest in the use of reinforcement learning (RL) techniques for improving the resource supply balance and service level of systems. The goal of these techniques is to effectively produce a user incentivization policy scheme to encourage users of a shared mobility system (e.g. bikeshare systems) to slightly alter their travel behavior in exchange for a small monetary incentive. These slight changes in user behavior are intended to over time increase the service level of the shared mobility system and improve its profit margin. Reinforcement learning techniques are gaining popularity as an approach to solve this problem\, as they can utilize deep learning for tasks that require many actions and produce a cumulative noisy reward signal. A reinforcement learning policy can be used to provide many incentives to users and then receive the service level of the target mobility system over time as a reward signal. We present an analysis and results of our extensive study on the effects of different frameworks for representing a shared mobility system on reinforcement learning performance for user incentivization\, in terms of service level. We utilize bikeshare trip-data from Washington D.C.’s Capital Bikeshare system between 2015 and 2019 in our experiments to produce data-driven simulations for experimentation. In analysis\, we show the relationship and effects on service level of user volume / mobility needs\, resource supply availability\, and incentivization budget. Further\, we analyze the effectiveness of various reinforcement algorithms and various framework approaches. \n\n\n\n\n“An Improved Weighted Support Vector Data Description”\nMehmet Turkoz (presenter) and Rajiv Kashyap\, William Paterson University \n\n\n\nSupport Vector Data Description (SVDD) is a support vector-based learning algorithm used to detect anomalies. SVDD obtains a spherically shaped boundary around the target data by transforming the target data into high-dimensional space. SVDD identifies the boundary by checking whether the data point is placed inside or outside of the boundary in the transformed space by using kernel distance. Although using kernel distance improves the performance of SVDD\, usage of only kernel distance does not improve the power of SVDD especially with the complex data. To overcome this situation\, considering density distribution is one of the well-known techniques. In most of the real-life applications\, each data point has different importance based on the density of the data. The data points placed in dense area are considered more important than the data points placed in less dense area. Thus\, utilizing each data point equivalently without considering density\, obtained boundary will be limited to describe the data. Therefore\, to increase the efficiency of traditional SVDD\, we propose a new SVDD procedure which considers density of a data set by assigning a weight for each data point. The effectiveness of the proposed procedure is demonstrated with various simulation studies and real-life datasets. \n\n\n\n\nResearch Proposals: \n\n\n\n“The Changing Association of Social Vulnerability to COVID Case and Mortality Rates over Time”\nSamantha Nievas\, Kean University; Nick Marshall\, Joseph Kajon\, Seton Hall University  \n\n\n\nThe impact of COVID-19\, measured in terms of positive case and mortality rates per 100\,000\, is known to be greater in communities with greater social vulnerability. Our research uses GIS mapping and statistical analysis to examine this relationship in Chicago\, IL. The results suggest that while there may be a strong association in earlier stages of epidemics\, the association is weaker as the pandemic continues\, indicating that even those with relatively low social vulnerability are eventually impacted by mass pandemics.  \n\n\n\n\n“Using Support Vector Machine to Predict Bitcoin Price”\nTrevor Carr\, Xiang Li\, Jennifer Simon\, Rutgers University \n\n\n\nSince the first block was mined in the year 2009\, Bitcoin has emerged as the\n world’s standard Cryptocurrency\, trading at prices fluctuating in the mere\n hundreds to prices over sixty thousand USD (Hicks\, 2020). This tremendous\n growth in valuation has also spurred many questions surrounding the\n predictability and “decentralized” nature of Bitcoin. While advertised as a\n decentralized alternative to traditional monetary policy\, the exact extent of\n this detachment with global monetary policy appears to be quite flimsy.\n Previous research has identified associations between both global monetary\n policy and business cycle fluctuations on the price volatility Bitcoin has seen\n in its short trading life (Corbet et al.\, 2017). Specifically\, Bitcoin’s\n valuation is heavily impacted by global quantitative easing (QE) measures\,\n centralized interest rate policy\, business cycle fluctuations\, price stability\,\n supply and demand\, and other market driving cryptocurrencies. Despite having\n these identifiable indicators\, many investors remain skeptical about the\n stability this asset has to offer. This skepticism is primarily rooted in the\n lack of research and verifiable information surrounding this incredibly young\n groundbreaking asset.\n \n\n\n\n Through the use of a Support Vector Regression supervised learning algorithm\,  the previously stated economic features will be quantifiably related and  analyzed to perform advanced Bitcoin regression forecasting. As Bitcoin  continues to become a more widely accepted and traded commodity\, greater  information regarding its volatility and decentralized nature is imperative to  supporting its growth as an investment opportunity and currency alternative.  Additional information regarding the impact of modern monetary policy on  fostering novel markets\, such as the cryptocurrency market\, may also be  uncovered in this study. It is the intention of this research to forecast Bitcoin’s pricing trajectory and uncover the macroeconomic links that influence this exciting new commodity.  \n\n\n\n\n“Urban Plastic Waste Disposal and Recycling”\nYeonho Choi\, Stevens Institute of Technology \n\n\n\nThe study is focused on the development of big data platform. It particularly\nstudied the platform for urban plastic waste disposal and recycling. There are\nlots of plastic usage and disposal in the most big cities in the world.\nHowever\, there are less study about its disposal and recycling. This study\ninvestigates the current status of plastic management and study the way to\ndevelop its platform.\n \n\n\n\n\n12:30 pm: End of Day 1\n\n\n\n\n\n\n\nREGISTER NOW | DOWNLOAD AGENDA (PDF) | READ SPEAKER BIOS \n\n\n\nDay 2\, Friday\, April 30\n\n\n\n9:00 am: Opening and recap of Day 1\n\n\n\nMargaret Brennan-Tonetta\, Executive Director\, New Jersey Big Data AlliancePiyushimita (Vonu) Thakuriah\, Dean\, Bloustein School of Planning and Public Policy\, Rutgers University \n\n\n\n9:15am: Keynote – “Power to the Public: The Promise of Public Interest Technology”\n\n\n\n\n\n\n\nTara Dawson McGuinness\, Fellow and Senior Adviser of the New Practice Lab\, New America \n\n\n\nThe events of the past year have demonstrated the important role that data and technology play in everything from understanding the spread of a global pandemic to tracking how well governments are doing at reaching people with services from unemployment insurance and stimulus checks to vaccine appointments.  This presentation will build on the ideas in Tara McGuinness and Hana Schank’s new book: Power to the Public: the Promise of Public Interest Technology making the case that governments and nonprofits need new ways and data tools to tackle the complexities of our time and really deliver for the public in an equitable way. \n\n\n\n10:00 am: Panel discussion – “Smart Data to Illuminate Community Grand Challenges”\n\n\n\nModerator: Piyushimita (Vonu) Thakuriah\, Dean\, Bloustein School of Planning and Public Policy\, Rutgers University. Panelists: Will Payne\, Rutgers University; Radha Jagannathan\, Rutgers University; Carl Gershenson\, Princeton University \n\n\n\nWhat are the grand challenges of urban communities today\, and what are the key dynamics within these challenges? This session will explore the innovative ways that university research faculty are using big data to understand these challenges\, and point the way to effective and efficient solutions. \n\n\n\n11:00 am: Academic Research Tracks (two concurrent tracks)\n\n\n\n\n\nSmart Cities (Room 1)\n\n\n\nModerated by Forough Gharamani\, Associate Vice President\, Edge \n\n\n\n“Big Data-Enabled AI-Powered Space Weather Analytics with a Community-Driven Infrastructure”\nHaodi Jiang (presenter)\, Jason T. L. Wang\, Ohad Ben-Shahar\, Jihad El-Sana and Haimin Wang; New Jersey Institute of Technology \n\n\n\nSpace weather is a term used to describe changing environmental conditions in the solar system caused by eruptions on the Sun’s surface such as solar flares. Understanding and forecasting of solar eruptions is critically important for national security and for the economy since they are known to have adverse effects on critical technology infrastructure such as satellites and power distribution networks. Space weather analytics is an emerging interdisciplinary field\, which aims to (i) understand the onset of solar eruptions and assess space weather effects on Earth through big solar and space data analysis\, and (ii) perform near real-time long-range predictions of extreme space weather events including solar flares\, coronal mass ejections (CMEs) and solar energetic particles (SEPs) as well as solar wind and geomagnetic storms by using advanced artificial intelligence (AI) techniques. \n\n\n\nHere we present a big data-enabled\, AI-powered\, community-driven cyberinfrastructure for performing space weather analytics. There are three interrelated tasks: (i) identifying\, detecting\, tracking\, and extracting patterns in solar and space data; (ii) synthesizing artificial solar images for studying solar activity in multiple solar cycles; and (iii) predicting solar eruptions and space weather events. We describe a database and tools we are developing for accomplishing these tasks. \n\n\n\n\n\n\n\n\n“Federated Energy Demand Prediction Design for Electric Vehicle Charging Station Networks”\nDylan Perry (presenter)\, Ning Wang and Shen-Shyang Ho\, Rowan University \n\n\n\nAccording to Statista’s prediction\, the number of Internet of Things (IoT) devices by the year 2025 will be more than 75 billion.  A huge amount of sensory data is generated by these devices every day. If this data can be used effectively\, existing systems performance can be improved. However\, it is non-trivial to achieve such a goal due to limited data on a single IoT device and non-independent\, identically distributed data generated from multiple IoT devices. To study this issue\, several federated energy demand prediction methods were tested on an Electric Vehicle (EV) charging station network and compared with baselines. \n\n\n\nPerformances across methods for machine learning models were compared in order to showcase the increase in accuracy that the proposed method provides. The results that were gathered show a reduction in prediction error over other state of the art model structures\, i.e.\, LSTM. Then\, a time-domain clustering algorithm is applied to break up the region of charging stations into subsections and group them through usage pattern similarity. The charging stations are divided into sections that allow for the greatest increase in accuracy over being trained individually. Furthermore\, the proposed model aggregation method allows for models being trained on local station groups to be combined for an even greater result compared with dataset aggregation. \n\n\n\nThe results that will be presented go on to imply that with the proposed machine learning model structure\, charging stations can supply the energy needed for the vehicles to use ahead of time with less error. \n\n\n\n\n\n\n\n\n“Load Management with Schedule Cooperation between Smart Buildings and E-Buses”\nNing Wang (presenter) and Jie Li\, Rowan University \n\n\n\nPublic transportation contributes to a big component of transportation sector emissions. Replacing existing buses with electric buses (E-Buses) is regarded as one of the major contributors in reducing petroleum use\, meeting air quality standards\, improving public health\, and achieving greenhouse gas emissions reduction goals. Although promising\, the charging of E-Buses has an impact on the electric distribution system because they consume a large amount of electrical energy\, and this demand of electrical power can lead to extra large and undesirable peaks in the electrical consumption.  To address the impact of the reliable operation of the electric distribution system introduced by E-Buses\, E-Buses charging regulation has to be implemented. The objective of this abstract is to explore a Machine Learning based scheduling scheme – to coordinately manage the electric distribution system demand by flattening the energy consumption curve and minimizing the electric energy cost. In the meantime\, the E-Bus’s charging needs and the building occupant’s energy needs and comfort levels will be guaranteed. The coordinated scheduling scheme will leverage the grid-interactive efficient buildings (GEBs)’ energy management and E-Bus charging flexibilities. Particularly\, realistic E-Bus charging requirements such as E-Bus departure state-of-charge (SoC)\, GEB’s scheduling flexibility\, utility electricity rate\, etc. \n\n\n\n\n\n\n\n\n“The Role of Inclusivity in the Evolution of Law and Policy to Accommodate Smart Sustainable Cities”\nMichael Bell\, New Jersey City University \n\n\n\nIn contrast to “smart cities” or “sustainable cities”\, only recently has attention focused on scholarship relevant to smart sustainable cities\, and even less so on the evolution of law and policy within that framework.  Smart sustainable cities are inclusive cities.  The goal of inclusivity is not only critical to the concerns of equity and fairness\, but is key to the development of resilient law and policy.  Viewing the city as a socio-ecological system\, the smart sustainable city is seen as an urban planning strategy within that system. Ultimately\, the goal is to achieve a balanced socio-ecological system.  Greater urbanization can lead to imbalance\, caused by environment harms\, social injustice\, social hazards\, etc.  In classic planning theory\, public action is justified by identification of public norms\, which are frequently rearticulated\, giving content and legitimacy to law and policy.  The ubiquitous use of new information and communications technology (ICT) and urban computing innovations is a public action which also should be justified – based on contribution to environmental and socio-economic needs and concerns as perceived by citizens.  Yet\, the smart city agenda masks a possible bias:  local governments outsource policy by having to rely on various third parties to deploy data-analytics. Utilization of these algorithms may not reflect the actual priorities of citizens. However\, when local government is inclusive and engages with diverse populations\, greater contextualization of law and policy can accommodate sustainable urban development. \n\n\n\n\n\n\n\n\n“Evaluating Intersection Safety Using Surrogate Safety Measures and Non-Compliance Behaviors”\nDeep Patel (presenter)\, Mohammad Jalayer\, Abdelkader Souissi\, Ghulam Rasool and Nidhal Carla Bouaynaya; Rowan University \n\n\n\nIn recent years\, identifying road users’ behavior and conflicts at intersections has become an essential data source for evaluating traffic safety. This study developed an innovative artificial intelligence(AI)-based video analytic tool to assess intersection safety using surrogate safety measures. Surrogate safety measures (e.g.\, Post-encroachment Time and Time to Collision) are extensively used to identify future threats\, such as rear-end collision due to vehicle and road users’ interactions. To extract the trajectory data\, the proposed work integrates a real-time AI detection algorithm\, YOLO-V5\, with tracking using Deep SORT algorithm. 30-minutes of high-resolution video data were collected from a busy signalized intersection in Morristown\, New Jersey. Non-compliance behaviors\, such as red-light running and pedestrian jaywalking\, are captured to better understand the risky behaviors at intersections. The proposed approach achieved an accuracy between 92% and 97% in detecting and tracking the road users’ trajectories. Also\, results demonstrated that the developed tool provides valuable information for engineers and policymakers to develop and implement effective countermeasures to enhance intersection safety. \n\n\n\n\n\n\nGeneral AI/ML (Room 2)\n\n\n\nModerated by Hieu Nguyen\, Professor\, Rowan University \n\n\n\n“Large-Scale Graph Analytics in Arkouda”\nOliver Alvarado Rodriguez (presenter)\, Zhihui Du and David Bader; New Jersey Institute of Technology \n\n\n\nExploratory graph analytics is a much sought out approach to help extract useful information from graphs. One of its main challenges arises when the size of the graph expands outside of the memory capacity that a typical computer can handle. Solutions must then be developed to allow data scientists to efficiently handle and analyze large graphs in a short period of time\, using machines that have the capacity to handle massive file sizes. Arkouda is a software package under early development created with the intent to bridge the gap between massive parallel computations and data scientists wishing to perform exploratory data analysis (EDA). The communication system between the Chapel back-end and the Python front-end helps to create an easy-to-use interface for data scientists that does not require knowledge of the underlying Chapel code and instead allows them to utilize the simple Python front-end to carry out all their large file and graph EDA needs. In this work\, a graph data structure is designed and implemented into the Arkouda framework at both the Chapel back-end and the Python front-end. The main attraction of this data structure is its ability to occupy less memory space and perform efficient adjacency edge searching. A parallel breadth-first search (BFS) algorithm is also presented to help demonstrate how easily one can implement parallel algorithms in Arkouda to increase EDA productivity with graphs. Lastly\, real-world graphs from different domains\, such as biology and social networks\, are utilized to evaluate the efficiency of the graph data structure and the BFS algorithm. The results obtained from this benchmarking help show that the Arkouda overhead is almost negligible\, and data scientists can utilize Arkouda for large scale graph analytics. This work can help further bridge the gap between high-performance computing (HPC) software and data science to create a framework that is straightforward for all data scientists to use. All of the code in this project and in Arkouda is open source and can be found on GitHub. This is joint work with Mike Merrill and William Reus. We acknowledge the support of National Science Foundation grant award CCF- 2109988. \n\n\n\n\n\n\n\n\n“The Case Against Sentiment Analysis for Natural Text”\nShamoon Siddiqui (presenter)\, Ghulam Rasool and Ravi Ramachandran\, Rowan University \n\n\n\nNatural language processing is a broad field that encompasses several sub-tasks. One problem that has gained visibility over the past several years is that of Sentiment Analysis. This is the process of determining the attitude of an author towards some subject across some spectrum\, typically “positive” or “negative\,” by analyzing the textual information. Whereas the field started with simple counting of words with certain characteristics\, it has grown in complexity with the advent of deep learning and neural network based language models. Typically\, datasets used to train and evaluate these models consist of text with appropriate labels\, such as movie reviews with an accompanied star rating. However\, the applicability of those results to other scenarios\, such as unstructured or natural text has not been clear. In this paper\, we demonstrate a clear and simple case that shows that the problem of sentiment analysis is fundamentally unsuitable for natural text. We consider state-of-the-art black box models developed and hosted by 3 of the largest companies in this field: Amazon\, Google and IBM. \n\n\n\n\n\n\n\n\n“A Study of Nonprofit Fundraising Strategies: What Drives Nonprofit Fundraising?”\nBahar Ashnai (presenter) and Saeed Shekari\, William Paterson University \n\n\n\nWe investigate the key fundraising strategies and competencies that enable the resources of nonprofit organizations to utilize their full potential and achieve desirable fundraising outcomes. We explore the fundraising strategies that empower the critical resources to achieve higher performance within each strategy. We acknowledge the parity of sales and fundraising\, in line with the anecdotal evidence attesting such a resemblance\, “ending the stigma: fundraising is sales.” We draw upon the science of business-to-business sales and business-to-consumer marketing to develop a strategic nonprofit fundraising framework. Key fundraising resources include dedicated fundraising staff\, brand equity\, and cultivated external relationships. Staff and resource scarcity is a major challenge for nonprofit organizations. We propose a nonprofit can follow two major fundraising strategies to promote fundraising performance. These two grand strategies are increasing the number of donors or increasing the donation dollar amount. We argue that at any point of time\, given limited resources\, the focal nonprofit had better opt for one of these strategies. Choosing both strategies at the same time spreads fundraising resources too thin. We use empirical data to test our suggested framework and the underlying hypotheses. There are over one million entities registered as nonprofit organizations in the US. Nonprofit tax returns\, and they constitute the Big Data that we are interested in. The subsample includes longitudinal data of 10\,000 nonprofit organizations over ten years (2009-2019)\, resulting in 100\,000 nonprofit-year data lines. Each nonprofit-year reports 350 data fields crating a database of 35\,000\,000 nonprofit-year-field. \n\n\n\n\n\n\n\n\n“Fostering Trustworthy Data Sharing: Establishing Data Foundations in Practice”\nAlexsis Wintour (presenter)\, Sophie Stalla-Bourdillon and Laura Carmichael; Lapin Limited \n\n\n\nIndependent data stewardship remains a core component of good data governance practice. Yet\, there is a need for more robust independent data stewardship models that are able to oversee data-driven\, multi-party data sharing\, usage and re-usage\, which can better incorporate citizen representation\, especially in relation to personal data.  \n\n\n\nWe propose that data foundations – inspired by Channel Islands’ foundations laws – provide a workable model for good data governance not only in the Channel Islands but also elsewhere. These offer a robust workable model for data governance in practice\, as they provide: a comprehensive rulebook; a strong\, independent governance body; an inclusive decision-making body; a flexible membership; a trust-enhancing technical and organisational infrastructure; and a well-regulated structure.  \n\n\n\nWe outline eight universal design principles to unite all data foundations: (a) all data are relevant\, (b) data stewards are independent\, (c) expected standards of good practice for data governance specified by a code of conduct\, (d) self-regulation\, (e) monitoring is the heartbeat\, (f) sustainability\, (g) accreditation stimulates market growth\, and (h) stakeholder approvals need to be maintained.   \n\n\n\nThere is an opportunity to advance the wider data institution movement through a legal structure that is ready for use and well-suited to the needs of data sharing initiatives\, in particular\, since data foundations incorporate the vital element of independent data steward through the statutory role of the guardian. \n\n\n\nThe principal purpose for this paper is to demonstrate why data foundations are well suited to the needs of data sharing initiatives and examine how they could be established in practice. \n\n\n\n\n\n\n\n\n“Assessing Community Preparedness to Disasters through GIS-Quantification of the Community Intrinsic Resilience Index”\nFiras Gerges (presenter)\, Michel Boufadel\, New Jersey Institute of Technology; Hani Nassif\, Rutgers University \n\n\n\nEnvironmental impacts of climate change are more observable today\, with the increased rate and severity of hurricanes and droughts\, and the continuous rise of sea level. To face these challenges\, resilience concepts have emerged as a way to enhance communities’ preparedness and capacity to absorb disasters. There are indices for community resilience in general\, but they are only relative\, based on comparison between entities\, and they do not account for the stress level on resilience. In this work\, we developed a new approach to quantify the absolute level of resilience for each of the critical community sectors\, and subsequently the community overall. Our approach aims to leverage the growth of big data by using records compiled from public sources (datasets\, GIS layers\, etc.) to capture the Community Intrinsic Resilience Index (CIRI) in a GIS-based web platform. This platform would advance the efforts to fill the gap between resilience research and applications and would enable practitioners to integrate resilience within the planning and design phases of disaster management. We applied the approach to New Jersey counties\, and we found that CIRI ranged from 63% to 80%. A post-disaster CIRI (following a scenario of flooding) revealed that two coastal counties would have low resilience due to the reduction of the road area and/or the reduction of the GDP (local economy shut down) to below minimum values. \n\n\n\n\n\n\n\n\nStudent Poster presentations (all day)\n\n\n\nSee Abstracts above in Day 1. More information can be found on the Student Research page and the communication server on Discord. These presentations will be available asynchronously. \n\n\n\n12:30 pm: End of Day 2
URL:https://njbda.org/event/8th-annual-njbda-symposium/
LOCATION:Online
CATEGORIES:symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210129T170000
DTEND;TZID=America/New_York:20210129T170000
DTSTAMP:20260415T132040
CREATED:20210112T204900Z
LAST-MODIFIED:20210112T205630Z
UID:609-1611939600-1611939600@njbda.org
SUMMARY:Rutgers MBS Externship deadline
DESCRIPTION:Externships are opportunities for students to gain real-world experience in their field. Externships through this partner program are provided by the Rutgers Masters of Business and Science (MBS) Externship Exchange Program and the NJBDA via a grant from the US Economic Development Administration (US-EDA). Upon successful completion students are eligible for a $2\,000 fellowship. \nApplication deadline is January 29\, 2021 at 5:00pm EST. \nLearn more at the Rutgers MBS-NJBDA Joint Externship Exchange Program page. \n 
URL:https://njbda.org/event/rutgers-mbs-externship-deadline-jan-29/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201120T090000
DTEND;TZID=America/New_York:20201120T120000
DTSTAMP:20260415T132040
CREATED:20201014T182614Z
LAST-MODIFIED:20201115T203747Z
UID:538-1605862800-1605873600@njbda.org
SUMMARY:Big Data: The Future of Aviation
DESCRIPTION:Virtual Workshop\nNovember 20\, 2020\n9:00 a.m. – 12:00 p.m. (EST) \nLearn how big data is transforming the aviation industry \nConnect with aviation companies in South Jersey \nOutstanding Speakers!\nJeff Cammerata\, Ph.D.\,\nRadar Systems Engineer\, Lockheed Martin\nAI/ML for RF and IR sensor systems \nDave Krause\nCo-Founder\, Influential Drones\nThe Future of Flight: How Data will Guide the Unmanned Industry\n(participants can earn WINGS credit (1 point Basic Knowledge) towards FAA’s Pilot Proficiency Program)\n \nRegister Now\nFor inquiries\, please contact \nHieu Nguyen\, Rowan University\nNguyen@rowan.edu \nDownload PDF flyer \nEvent Sponsored by the US Economic Development Administration \nAgenda: \n9am-9:10am\nWelcome/opening remarks\nDr. Hieu Nguyen\, Rowan University (Moderator)\nDr. Peggy Brennan-Tonetta\, President\, NJBDA; Rutgers University \n9:10-10am\nPresentation: AI/ML for RF and IR sensor system\, Jeff Cammerata\, Lockheed Martin \n10-10:10am\nBreak \n10:10-11am\nPresentation: The future of flight\, how data will guide the unmanned industry\, Dave Krause\, Influential Drones \n11-11:10am\nBreak \n11:10-11:50am\nQ/A Session \n11:50am-12pm\nWrap-up/concluding remarks \n The New Jersey Big Data Alliance Advances Computing Innovation and Education in New Jersey
URL:https://njbda.org/event/big-data-the-future-of-aviation/
LOCATION:Online
CATEGORIES:workshops
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201022T140000
DTEND;TZID=America/New_York:20201022T163000
DTSTAMP:20260415T132040
CREATED:20201014T183135Z
LAST-MODIFIED:20201014T184550Z
UID:549-1603375200-1603384200@njbda.org
SUMMARY:Sipping Big Data! A Tasty Conversation with ABInBev\, the World's #1 Brewer
DESCRIPTION:New Jersey Big Data Alliance Presents… \nSipping Big Data! A Tasty Conversation with ABInBev\, the World’s #1 Brewer \nOctober 22: 2:00-4:30pm EST \n \nProgram Overview \n\nThis half-day workshop will be hosted virtually in connection with the Newark site of Anheuser-Busch InBev.\nThe event will feature speakers from Anheuser-Busch working in advanced analytics\, companies in the Newark area\, and faculty experts.\n\nHosts: \nAdam Spunberg\, Global Tech Exploration Lead\, ABInBev\nRashmi Jain\, Professor\, Montclair State University; Vice President\, NJBDA\nAnthony Russo\, President\, Commerce and Industry Association of NJ \n\nPresentations will focus on strategic development and innovation in the application of big data and data analytics solutions such as AI\, ML\, and advanced manufacturing in beverage production.\n\nRegister Now\nFor inquiries\, please contact: jainra@montclair.edu \nEvent Sponsored by the US Economic Development Administration \nDownload PDF flyer
URL:https://njbda.org/event/sipping-big-data/
LOCATION:Online
CATEGORIES:workshops
END:VEVENT
END:VCALENDAR