Cytotoxic T-Cells: Nature's Assassins or something more?
2021-12-15 11:00 - 12:00 UTC
Date: Wednesday, December 15Time: 11:00am – 12:00pm (lunch provided to in-person attendees)Location: Discovery I, Room 331 (virtual option available)
Register for in-person (limited seating) at https://bit.ly/3d0yb6GRegister for virtual attendance at https://bit.ly/31d8ld6
Seminar Description:Cytotoxic (CD8+) T-cells are mainly known for their role in killing infected and cancerous cells however, recent evidence suggest that this cell population also plays a major role in the post-myocardial infarction (MI) remodeling process. Our lab has indicted that these cells likely have both a beneficial and detrimental role possibly due to temporal-dependent heterogeneity. Our goal is to dissect the role of cytotoxic T-cells over the post-MI time course and determine targets as a potential therapy to prevent development of heart failure.
Speaker Info:Dr. DeLeon-Pennell is an Assistant Professor in the Division of Cardiology at the Medical University of South Carolina (MUSC). She received her PhD in 2011 at Baylor University and was trained in cardiac physiology at the University of Texas Health Science Center San Antonio and the University of Mississippi Medical Center. Her research is focused on dissecting the interplay of pre-existing variables such as chronic inflammation, sex, and age on cardiovascular events. This includes dissecting the influence of the innate and adaptive immune system on cardiovascular remodeling, understanding the dynamics and function of bio-molecules involved in cardiac remodeling, and utilizing the knowledge acquired to develop therapeutic strategies to prevent, slow, or reverse the progression to heart failure.

Disparities in radon testing and the role of black residential segregation: A longitudinal study in an urban environment
2022-01-26 11:00 - 12:00
January 2022 Seminar: Disparities in radon testing and the role of black residential segregation: A longitudinal study in an urban environment by Dr. Dajun Dai. This seminar is hosted by the BDHSC Geospatial Core.
Seminar Description:Radon is a naturally released colorless and odorless radioactive gas. Exposure to indoor radon is the second leading cause of lung cancer after smoking. Short-term testing, the first line of protecting the public from potential residential exposure, remains exasperatingly low nationwide despite inexpensive and simple testing procedure. The goal of this study was to understand radon potential and promote geographically focused testing using geospatial technologies and a mixed-method approach. Working on a site in Atlanta categorized as the highest radon potential zone by the U.S. Environmental Protection Agency, this study addressed (1) the predictability of housing features and geology on indoor radon potential, (2) the characteristics of communities with low testing rates, and (3) the development of action-oriented health messaging that is culturally relevant to focused communities.
Speaker: Dr. Dajun Dai is a tenured professor at Department of Geosciences at Georgia State University (GSU) jointly appointed at GSU School of Public Health. He is an affiliated faculty member at the Injury Prevention Research Center at Emory University and Georgia Health Policy Center. His research addresses access to healthcare, neighborhood effects, social and physical difference and their implication on public health, planning, and public policy. His research has been supported by the World Health Organization, the National Institutes of Health, the National Science Foundation, the U.S. Geological Survey, and the Robert Wood Johnson Foundation, among others.
The in-person portion of this event has been canceledRegister for virtual attendance (via Zoom) here, https://bit.ly/3HyZVMN

National Big Data Health Science Conference
2022-02-11 08:00 - 2022-02-12 18:00
The University of South Carolina Big Data Health Science Center is pleased to announce that registration for its 3rd annual National Big Data Health Science Conference is now open! The theme of the conference, which will take place February 11-12, 2022, is “Unlocking the Power of Big Data in Health: Developing an Interdisciplinary Response for Health Equity”. Both in-person and virtual options for attendance are available. This conference will bring together leaders from academia, government, industry, and healthcare systems to focus on and forge new discussions about the role of interdisciplinary collaboration in Big Data applications and advancements in the health sciences. The agenda includes plenary addresses from 7 renowned experts, 6 in-depth breakout sessions ranging from electronic health records research to social media analysis (with over 30 contributing panelists from around the world), workshops in SAS Viya, computational modeling and more, and a wide variety of poster presentations.
Important information regarding the agenda, COVID-19 policies, and other FAQ can be viewed on the conference website at https://www.sc-bdhs-conference.org/
Register ($50) here, 2022 National Big Data Health Science Conference (touchnet.net).

Deadline for R25 Fellow Applications
2022-03-14
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
Background: 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.
Program 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:
Aim 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.
Program Benefits and Support:The program will provide the following support to Fellow during the training year:

25% salary support for one year (subject to NIH salary cap)
Support for participation in grant writing bootcamp
Matched with a mentoring team
Participation in a funded Big Data research project
Support in NIH grant preparation and submission

Inquires: For questions related to various aspects of the Big Data Fellow program, please contact any of the following individuals:
Xiaoming Li, Ph.D., xiaoming@maillbox.sc.edu
Jiajia Zhang, Ph.D., jzhang@mailbox.sc.edu
Miranda Nixon, MA, mc95@mailbox.sc.edu

Full RFA Available Here
Click Here

Studying Substance use From Social Media Using Natural Language Processing and Machine Learning Methods
2022-03-23 11:00 - 12:00
Where: Virtual via Zoom (registration required)
How: Register at https://us02web.zoom.us/webinar/register/WN_f4s0gmlYTYO6h4S0sceSjw
Seminar 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.
About 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.

Deadline for Pilot Project Proposals
2022-04-15
Announcement Release Data: January 10, 2022Application Receipt Date: April 15, 2022 by 5 p.m.Notification of Outcome: All applicants will receive notification by June 30, 2022Earliest Project Start Date: August 16, 2022
Purpose: The purpose of this RFP is to invite pilot project applications on the utilization of Big Data analytics in health-related research. The primary goals of this pilot project program will be to promote interdisciplinary collaboration in Big Data health science research and support meritorious applications that can leverage existing data to address critical issues related to health behavior, clinical care, healthcaredelivery, and population health. Interdisciplinary research that involves linking and integrating data sets from multiple sources are particularly encouraged. Existing data may include, but are not limited to electronic health records data, social media data, geospatial data, genomic data, or bio-nanomaterial data.
Background: Radical transformation is required in the US healthcare system to promote more effective and affordable approaches to personalized medicine and population health. Healthcare costs have outgrown the overall economy for several decades, yet health remains suboptimal, and the number of people with multiple chronic conditions continues to escalate. Life expectancy in the US has declined for three years in a row compared to other developed countries. The NIH-Wide Strategic Plan (2016-2020) asserts that our nation and the world stand at a unique moment of opportunity in biomedical research, and data science is an integral contributor. The generation of massive, rich data sets in healthcare (e.g., electronic health records, genomic data) and the emergence of advanced information, communication, and computational technologies — collectively referred to as “Big Data analytics” — offer an invaluable opportunity to improve the quality and efficiency of healthcare.
NIH issued its first Strategic Plan for Data Science in May 2018 and suggested that the Big Data approach will advance uniquely our understanding of disease prevention, identification, control, treatment, and delivery in the coming decades and will be key to reducing national and global health disparities. However, several critical gaps exist in utilizing such an approach, including the growing costs of managing data, “siloed” data resources with limited integration and interconnectivity, and an underutilization of Big Data approaches for clinical decision-making and research. A key reason for these gaps is a lack of data-science talent and limited leadership in the development, implementation, and evaluation of Big Data health analytics. The shortage of data scientists is projected to increase from 100,000 in 2012 to 240,000 by 2020. In response to this talent gap, academic institutions across the US, including several Ivy league universities and our aspirant peer institutions, have created data-science programs housed either in a Business or Engineering School, but few, if any, are currently focused on healthcare analytics.
With the support of UofSC excellence Initiative, the UofSC Big Data Health Science Center (BDHSC) aims to transform UofSC into a global leader in the focused field of Big Data health science analytics. To accomplish this mission, BDHSC has developed program activities around the following five strategic objectives: (1) Infrastructural and capacity building; (2) Professional development; (3) Community Engagement; (4) Academic training; and (5) Methodological advances. This pilot project program will serve the BDHSC mission by supporting and promoting interdisciplinary Big Data Health-related research across UofSC system. A total of 20 pilot projects were funded in 2020 and 2021 through a two-layer review process. The funded pilot projects can be found at BDHSC website: https://bigdata.sc.edu/
Research Objectives and Scope: The purpose of the pilot project program is to stimulate and promote interdisciplinary research in Big Data health sciences by supporting meritorious applications that utilize existing data sources in order to address critical issues related to health behavior, patient care, healthcare delivery, and population health. The program will support research that uses a variety of data sources, including electronic health records data, social media data, geospatial data, genomic data, bio-nanomaterial data, and other publicly available or acquirable data. The issues to be addressed by the pilot projects can also include a variety of health outcomes at individual, community, health system or population levels.
For questions regarding this RFP and proposal submission, please contact Ms. Miranda Nixon at mc95@mailbox.sc.edu; 803-777-5027

Full RFP Available Here
Click Here

Canceled: An Overview of the South Carolina Alzheimer’s Disease Registry
2022-08-26 11:00 - 12:00 | Room 140, Discovery I, 915 Greene Street, Columbia, SC, 29208, United States
An Overview of the South Carolina Alzheimer’s Disease Registry by Dr. Maggi Miller (UofSC).
Register for in-person attendance (boxed lunch provided) at https://forms.gle/enuNSyA7owBGWvnx7.
Register for virtual attendance at https://us02web.zoom.us/webinar/register/WN_NTzdK2VXTQeihlx23Wvm1g.

NIMH Division of AIDS Research Funding Opportunities and Priorities in Data Science
2022-09-28 11:00 - 12:00 | Koger Center for the Arts, Gallery Room, 1051 Greene St, Columbia, SC, 29201, United States
NIMH Division of AIDS Research Funding Opportunities and Priorities in Data Science by Dr. Lori Scott-Sheldon (Chief of Data Science and Emerging Methodologies in HIV at the National Institute of Mental Health).
Register at https://forms.gle/RbVP3yd44BNwf5uL9
Boxed lunch provided on first come first served basis.
No virtual option available but check our website in the weeks following for a recording.

Big Data Health Science Center Annual Retreat 2022
2022-09-30 09:00 - 13:00 | Campus Room, Capstone Hall, 902 Barnwell St, Columbia, SC, 29208, United States
ePlease see the below for information about the upcoming BDHSC retreat and networking luncheon (9/30). This event is intended for BDHSC affiliates and stakeholders to gather and discuss extramurally funded research, opportunities for collaboration, and challenges in data acquisition. Students must seek permission prior to registering. BDHSC faculty affiliates and core members are encouraged to attend.
Register (in-person only) at BDHSC Annual Retreat Registration (google.com).

BDHSC Pilot Project Showcase
2022-10-28 11:00 - 12:00 | Room 140, Discovery I, 915 Greene Street, Columbia, SC, 29208, United States
Join us as we hear about the projects of four 2021 Big Data Health Science Center Pilot Project Recipients!
Friday, October 28 from 11-12pm
In-person: Discovery 1, Room 140 and virtual option available
Registration required. Register at in-person: https://docs.google.com/forms/d/e/1FAIpQLScNdjmGrrp3u2bfyZtsGMvvV7VAJ7T7Y0FXT2qm0jSOvo2w9w/viewform?usp=sf_link
Virtual: https://us02web.zoom.us/webinar/register/WN_znlpwqA5SKuS7277fz2lrg or by scanning the QR code below.
Lunch provided.
Dezhi Wu, Ph.D., College of Engineering and Computing
Exploring Sentiment and Communication Exchange Patterns of Substance Use Disorder (SUD) Associated with Pregnant Women on Twitter Before and During COVID-19 Pandemic
Substance use among pregnant women is a significant public health concern. Recent studies have shown an alarming increase in substance use; however, it is still unknown how the COVID-19 pandemic has impacted vulnerable pregnant women. We applied NLP, text mining and machine learning techniques to extract just-in-time substance use (SU) data among pregnant women on Twitter, and then explored their communication patterns, risky health perceptions, sentiment, and maternal and fetal health outcomes. Our study findings are informative to perinatal healthcare practice and related public health policy changes.
Peiyin Hung, Ph.D., Arnold School of Public Health
Rural and Racial Disparities in Maternal Telehealth Health Care Access during the COVID-19 Pandemic.
Ensuring equitable telehealth uptake in prenatal care is critical for the optimal outcomes of newly developed prenatal care guidelines. This study aims to quantify the trends of prenatal care via telehealth before and during the COVID-19 pandemic, and assess the variations by maternal race, ethnicity, and residence. We used a statewide Medicaid claims data for Medicaid-insured women in South Carolina to assess prenatal telehealth uptake before and during the COVID-19 pandemic. Using an interrupted time series approach, we found that prenatal care visits via telehealth uptake increased substantially at the beginning of the COVID-19 pandemic, decreased within a few months, but remained more prevalent than the pre-pandemic rates. This study highlights the disproportionate telehealth access among rural women and women from racial minority groups who have historically faced barriers to accessing prenatal care.
Greg Trevors, Ph.D., College of Education
Identifying Optimal Vaccine Promotion Messages for Vulnerable Subgroups from Large-Scale Gamified Interventions
The long-term goal of this project is to inform the design of subsequent digital health promotion interventions that will dynamically personalize to participants based on empirical insights gained from machine learning analyses on our large-scale digital intervention data. To accomplish these long-term goals, the immediate objectives in this proposal are (1) identify distinct vaccine hesitancy subgroups from our prior intervention participants; and (2) identify optimal content that is associated with an increase likelihood of healthy behavioral intentions and attitudes, including vaccine intent and confidence, for each VH subgroup.
Chen Liang, Ph.D., Arnold School of Public Health
Deep Phenotyping individuals with PASC using a Graph representational model of S3C
Studies found 47-87% of patients with persistent symptoms and multi-system organ dysfunction after the acute phase of COVID-19, denoted as Post-Acute Sequela of SARS-CoV-2 infection (PASC). Leveraging a biomedical informatics and data science approach, we will design an innovative Graph representational model on top of a multi-center clinical dataset [South Carolina COVID-19 Cohort (S3C), NIH/NIAID R01A127203-4S1]. Using this Graph model, we will develop and pilot test a new semi-supervised machine-learning deep phenotyping algorithm to identify individuals with PASC and characterize their electronic phenotypes. This study will result in a clinically plausible, interpretable, generalizable, and high-throughput deep phenotyping algorithm application for identifying possible PASC cases at COVID-19 recovery clinics, supporting patient referral, outreach, and therapeutics development.

Alzheimer's Disease Registry Seminar
2022-11-16 10:30 - 11:30 | Room 140, Discovery I, 915 Greene Street, Columbia, SC, 29208, United States
An Overview of the South Carolina Alzheimer’s Disease Registry by Dr. Maggi Miller (USC).
Register at https://forms.gle/LVZDeiz1SmtNHc19A.

Learning Individualized Treatment Rules with Many Treatments
2022-12-07 11:00 - 12:00 | Virtual
Please join us on December 7 for a virtual seminar “Learning Individualized Treatment Rules with Many Treatments” by Dr. Yufeng Liu, the University of North Carolina at Chapel Hill. This seminar is hosted by our Electronic Health Records Core.
Registration required. Please REGISTER HERE

Deadline for Abstracts Submission - 2023 Annual Big Data Health Science Conference
2022-12-12
The Big Data Health Science Center is seeking research and program-based abstracts for oral and poster presentations responsive to its 4th annual conference theme, “Unlocking the Power of Big Data in Health: Translating Data Science into Program Development and Implementation”. This conference will be held in-person at the Pastides Alumni Center in Columbia, SC on February 10-11,2023. Accepted abstracts will be published in conference proceedings Research and program-based abstracts in the areas of infectious diseases, AI for Sensing and Diagnosis, EHR, social media data, genomics analysis, geospatial research, GIS, infrastructural and capacity development, professional development, community/industry engagement, academic training, and methodological advances are desired.
Learn more at https://www.sc-bdhs-conference.org/.

BDHSC Pilot Project Showcase Part 2
2023-01-25 13:00 - 14:00 | Room 140, Discovery I, 915 Greene Street, Columbia, SC, 29208, United States
Please join us for a Big Data seminar showcasing 4 of our 2021 pilot project recipients. Speakers will discuss their research questions, data, methods, and future directions. This event is free and boxed lunch will be provided on a first come first served basis. Register at https://forms.gle/RoryZogDVET3FCRF9.

National Big Data Health Science Student Case Competition 2023
2023-02-03 - 2023-02-05 | Virtual

National Big Data Health Science Conference 2023
2023-02-10 - 2023-02-11 | Pastides Alumni Center, Columbia, SC, 900 Senate St, Columbia, SC, 29201, United States

Deadline for NIH-funded T35 Research Training Program Applications
2023-02-15

Deadline for 2023 Health GIS Scholars Program
2023-03-01

An Overview of the Federal Statistical Research Data Centers
2023-03-22 12:00 - 13:00 | Zoom Webinar

Deadline for 2023 Pilot Project Proposals
2023-04-14
University of South Carolina Big Data Health Science Center (BDHSC) is seeking proposals for the conduct and uptake of pilot research projects focusing on the Big Data analytics in addressing critical issues related to health behavior, healthcare, and population health.
The purpose of this RFP is to invite pilot project applications on the utilization of Big Data analytics in health-related research. The primary goals of this pilot project program will be to promote interdisciplinary collaboration in Big Data health science research and support meritorious applications that can leverage existing data to address critical issues related to health behavior, clinical care, healthcare delivery, and population health. Interdisciplinary research that involves linking and integrating data sets from multiple sources are particularly encouraged. Existing data may include, but are not limited to electronic health records data, social media data, geospatial data, genomic data, or artificial intelligence for sensing and diagnosis. To get more information, please check out the full 2023 Pilot Project RFP here.
For questions regarding this RFP and proposal submission, please contact Ms. Miranda Nixon at mc95@mailbox.sc.edu; 803-777-5027.

NSF ACCESS HPC Monthly Workshop: Machine Learning and Big Data
2023-05-23 11:00 - 2023-05-24 17:30
Research Computing and the USC Big Health Data Science Center will jointly co-host an in-person NSF ACCESS HPC Monthly Workshop on “Machine Learning and Big Data” sponsored by ACCESS and presented by the Pittsburgh Supercomputing Center. The workshop will be held on May 23-24, 2023, from 11AM-5PM on May 23 and 11AM-5:30PM on May 24.
This workshop will focus on topics including big data analytics and machine learning with Spark, and deep learning using Tensorflow. This will be an IN-PERSON event hosted by various satellite sites, there WILL NOT be a direct to desktop option for this event. Registration is on a first come first served basis. Interested applicants must first have an ACCESS ID. If you do not have an ACCESS ID, please visit this page to create one:
ACCESS USER REGISTRATION
Once you have an ACCESS ID, please complete the following registration page by Sunday, May 21 at Noon Eastern time:
Eventbrite Reservation
Classroom location and further details will be provided once your registration has been processed.

Summer Workshops in Bioinformatics and Data Analytics
2023-06-12 10:00 - 2023-07-14 16:00
Please use the below form to register for the workshops.
https://forms.gle/2q98LD1BKtVr9b7z9

Utilizing Big Spatiotemporal Data to Understand COVID and its Impact
2023-08-30 10:30 - 11:30 | Room 140, Discovery I, 915 Greene Street, Columbia, SC, 29208, United States
Utilizing Big Spatiotemporal Data to Understand COVID and its Impacts by Dr. Chaowei Phil Yang

Event Time: Wednesday August 30, 2023 @10:30-11:30am
Event Address: Discovery Building, Rm 140 (915 Greene St, Columbia, SC 29201)
Please register here.Spatiotemporal

Big Data Health Science Center Annual Retreat 2023
2023-09-29 09:00 - 13:00 | Campus Room, Capstone Hall, 902 Barnwell St, Columbia, SC, 29208, United States
Please join us for the 2023 BDHSC Annual Retreat. See below for full details. Please help us to get an accurate head count for food by registering here.

This retreat is geared towards faculty. Post docs and doctoral candidates welcome.

The Rhino Federated Computing Platform Unlocks the World's Data Silos for Collaborative Research
2023-10-25 11:30 - 12:30 | Virtual
Please join us for our October 25th (11:30-12:30) Big Data seminar featuring Dr. Malhar Patel, Director of Clinical Engagement at Rhino Health.
Register for this free virtual event at https://us02web.zoom.us/webinar/register/WN_yYq0S83RT0693cpOXjkiig

Place Matters: Utility of Geospatial Data in Health Research
2023-11-29 11:00 - 12:00 | Virtual
Registration required. Register here. https://us02web.zoom.us/webinar/register/WN_899MmYukTsSvvV5RYkRDkg.

The increasing focus on health equity research will require projects that examine and address the geographic contexts where we live, work, and play. Such contexts are important drivers of health inequities given that the characteristics of neighborhoods and other spaces have been driven by historic and current structural inequities. This seminar will provide an orientation to spatial framing and methods, including traditional and emerging spatial methodologies such as global positioning systems (GPS) and location aware technologies.
Dr. Katherine Theall is a Professor at Tulane University’s School of Public Health and Tropical Medicine, Senior Director of the Tulane Violence Prevention Institute and Director of the Mary Amelia Center for Women’s Health Equity Research. As a social and spatial epidemiologist, her research focuses on reducing health inequities by understanding and altering neighborhood environments and social policies in underserved populations locally, nationally, and internationally. She has been PI or co-PI of more than 25 federally- and privately-supported research and training awards and has a highly interdisciplinary background, with educational and practical experience in social/spatial epidemiology and community health and prevention sciences. Her work involves close collaboration with both state and city governments as well as international partners, where she has been involved in research, programming, and translational efforts to improve health equity and well-being.

A Bayesian Machine Learning Approach for Estimating Heterogeneous Survivor Causal Effects: Applications To a Critical Care Trial
2023-12-07 10:00 - 11:00 | Koger Center for the Arts, Gallery Room, 1051 Greene St, Columbia, SC, 29201, United States
Registration is required. Please register here.

National Big Data Health Science Student Case Competition 2024
2024-01-26 - 2024-01-28 | Virtual

National Big Data Health Science Conference 2024
2024-02-02 - 2024-02-03 | Columbia Metropolitan Convention Center, 1101 Lincoln St, Columbia, SC, 29201, United States
Funding for this conference was made possible (in part) by R13LM014347 from the National Library of Medicine. The views expressed in written conference materials or publications and by speakers and moderators do not necessarily reflect the official policies of the Department of Health and Human Services; nor does mention by trade names, commercial practices, or organizations imply endorsement by the U.S. Government.

Deadline for Big Data Analytics Emerging Scholar Training Program for URM Undergrads in SC
2024-03-01
The USC Big Data Health Science Center is now accepting applications for the BDHSC’s newly funded training program, Big Data Analytics Emerging Scholar Training Program for URM Undergrads in South Carolina (1R25AI172761-01). Please click for full details about the program, including eligibility, application process, FAQ, etc. The application deadline is March 1, 2024.
