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X-WR-CALNAME:USC Big Data Health Science Center
X-ORIGINAL-URL:https://bigdata.sc.edu
X-WR-CALDESC:Events for USC Big Data Health Science Center
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DTSTART:20210101T000000
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DTSTART;VALUE=DATE:20220314
DTEND;VALUE=DATE:20220315
DTSTAMP:20260610T064356
CREATED:20220223T171913Z
LAST-MODIFIED:20220223T172034Z
UID:4351-1647216000-1647302399@bigdata.sc.edu
SUMMARY:Deadline for R25 Fellow Applications
DESCRIPTION:Announcement Release Date: January 18\, 2022Application Receipt Date: March 14\, 2022 by 5 p.m. Notification of Outcome: All applicants will receive notification by April 30\, 2022Earliest Project Start Date: May 16\, 2022  \nBackground: Supported by NIAID (R25AI164581-01)\, the UofSC Big Data Health Science Center (BDHSC) has been implementing a Big Data Health Science Fellow (“Big Data Fellow”) program since 2021. The multiple\, massive\, and rich Big Data streams in healthcare (e.g.\, electronic health records\, mobile technologies\, wearable devices\, genomic data) and the emergence of advanced information and computational technologies (e.g.\, machine learning and artificial intelligence) offer an invaluable opportunity for applying innovative Big Data science research in NIAID focus areas of infectious diseases such as HIV/AIDS and COVID-19. Big Data science has the potential to identify high-risk individuals and communities and prioritize them for early biomedical or public health interventions\, predict long-term clinical outcomes and disease progression\, and evaluate public health policy impact. Key to addressing these complexities is a critical mass of health researchers with adequate knowledge\, competencies\, and skills to unlock important answers from Big Data to better understand\, treat\, and ultimately prevent these diseases and related comorbidities. However\, there is a nationwide shortage of talent with such knowledge\, competencies\, and skills\, especially in traditional academic settings. While junior faculty\, as part of the generations of digital learners\, have the greatest potential to develop their Big Data health science research agenda\, many face multiple structural barriers to conducting Big Data science research. Such barriers include the lack of protected time to initiate new interdisciplinary Big Data research\, opportunity to participate in funded Big Data research\, and adequate mentoring. The Big Data Fellow program\, as part of the BDHSC’s professional development mission\, is designed to address these gaps and promote Big Data health science research at UofSC. \nProgram Goals and Aims: The program will recruit about 4 UofSC health science junior faculty per year and provide them with salary support (25%) to participate in the training program with the following specific aims: \nAim 1: Provide courses for competency and skills development in BDS research. Each trainee will complete 2 formal or informal courses (one per semester) in BDS areas that are appropriate for their background and research interests.Aim 2: Engage trainees in hands-on research and proposal development. Trainees will participate in ongoing NIH-funded Big Data research projects that utilize existing large data sources (e.g.\, NIH COVID-19 Cohort Collaborative [N3C] Data\, SC statewide HIV and COVID-19 data and VA system-wide HIV and COVID-19 data). Aim 3: Provide trainees with rich mentoring experience in BDS research and professional development. Each trainee will be mentored by a team of NIAID-funded investigators who have complementary knowledge and skills from multiple domains (clinical medicine\, public health\, biostatistics\, computing\, geospatial science\, social media\, etc.\,) and will engage in contextual mentoring and peer-to-peer mentoring. \nProgram Benefits and Support:The program will provide the following support to Fellow during the training year: \n\n25% salary support for one year (subject to NIH salary cap)\nSupport for participation in grant writing bootcamp\nMatched with a mentoring team\nParticipation in a funded Big Data research project\nSupport in NIH grant preparation and submission\n\nInquires: For questions related to various aspects of the Big Data Fellow program\, please contact any of the following individuals: \nXiaoming Li\, Ph.D.\, xiaoming@maillbox.sc.edu \nJiajia Zhang\, Ph.D.\, jzhang@mailbox.sc.edu \nMiranda Nixon\, MA\, mc95@mailbox.sc.edu \n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Full RFA Available Here \n				Click Here
URL:https://bigdata.sc.edu/event/deadline-for-r25-fellow-applications/
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BEGIN:VEVENT
DTSTART;TZID=UTC:20220323T110000
DTEND;TZID=UTC:20220323T120000
DTSTAMP:20260610T064356
CREATED:20220223T165853Z
LAST-MODIFIED:20220223T165853Z
UID:4343-1648033200-1648036800@bigdata.sc.edu
SUMMARY:Studying Substance use From Social Media Using Natural Language Processing and Machine Learning Methods
DESCRIPTION:Where: Virtual via Zoom (registration required) \nHow: Register at https://us02web.zoom.us/webinar/register/WN_f4s0gmlYTYO6h4S0sceSjw \nSeminar Description: In this talk\, I will outline the progress we have made over recent years in utilizing social media data via natural language processing (NLP) and machine learning methods for studying substance use. Substance use and the associated overdose epidemic has been continuing in the United States (US) for decades. Over 270 people on average die every day from substance-related overdoses. However\, surveillance mechanisms are laggy and we only get to know about the state of the epidemic once substantial damage has already been done. For example\, in February 2022\, we only have provisional estimates from early 2021. To address this lag\, we are working towards utilizing social media data for automatically tracking and estimating substance use trends (including opioids\, benzodiazepines\, and stimulants). The talk will mostly focus on our NIH/NIDA funded project that focuses on prescription drugs (R01DA046619). I will also present some updates from our research collaborations with the CDC\, which focus specifically on substance use trends during COVID-19. \nAbout the Speaker: Dr. Abeed Sarker is an assistant professor at the Department of Biomedical Informatics\, School of Medicine\, Emory University. He also serves as program faculty at the Department of Computer Science\, Emory University and Department of Biomedical Engineering\, Emory University and Georgia Institute of Technology.  His research interests lie at the intersection of natural language processing\, applied machine learning and social media. Much of his recent work has focused on utilizing social media big data to study substance use\, including nonmedical use of prescription medications.  He is currently leading a number of research projects in this space funded primarily by the National Institute on Drug Abuse of the National Institutes of Health\, the Centers for Disease Control and Prevention and Emory University.
URL:https://bigdata.sc.edu/event/studying-substance-use-from-social-media-using-natural-language-processing-and-machine-learning-methods/
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