Major Faculty Research Areas

Area 1: Artificial Intelligence and Machine Learning

Research interests: capitalize on our prior successes and invigorate collective efforts under the broad umbrella of Artificial Intelligence.

  1. Designing and leveraging innovative computing hardware informed by and optimized for the fundamental principles and algorithms of AI.
  2. Developing fundamental machine learning (ML) and data mining methods, including reinforcement learning, energy-based methods, and deep learning.
  3. Autonomous Systems: object tracking, semantic and 3D scene understanding, federated learning, robot motion planning and control, multiple robot coordination, and embedded AI.
  4. Human-assistive AI: AI techniques such as natural language processing (NLP) and visualization combined with human-computer interaction (HCI) techniques including augmented reality (AR) and virtual reality (VR) will augment human capability by enabling people to interact naturally and leverage AI systems intuitively and transparently.
  5. Ethical, Explainable, and Trustworthy AI.

Some SDS faculty who work in this area: Minwoo Lee, Liyue Fan, Siddharth Krishnan, Xi Niu, Hamed Tabkhi, Benjamin Radford, Albert Park, Jason Windett, Wlodek Zadrozny, etc.

Sample Grants/Projects

  1. Minwoo Lee (co-PI): SocialBit: Establishing the accuracy of a wearable sensor to detect social interactions after stroke. NIH AREA R15 (#1R01HD099176-01A1)
  2. Xi Niu (PI): “CHS: Small: Promoting Unexpected Information Discovery: An Interactive Framework for Computational Serendipity”; Xi Niu (PI), Mary Lou Maher (Co-PI), Jingfeng Xia (Co-PI); NSF CISE IIS core program; Award Number: 1910696; $496,041; 2019 – 2022
  3. Jason Windett (PI): National Science Foundation, Resource Implementation for Data-Intensive Research (RIDIR), Collaborative Research: DAPPR: Diffusion Analytics for Public Policy Research.” SES-1636695, Fall 2016-2019. $808,129

Sample publications

  1. Giang Dao and Minwoo Lee (2019). Relevant Experiences in Experience Replay. IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL).
  2. Niu, X., Fan, X., & Zhang, T. (2019). Understanding Faceted Search from Data Science and Human Factor Perspectives. ACM Transactions on Information Systems (TOIS), 37(2)
  3. M. Mendieta and H. Tabkhi (2021). CARPe Posterum: A Convolutional Approach for Real-time Pedestrian Path. Prediction”, Thirty-fifth AAAI Conference on Artificial Intelligence.

Area 2: Leadership Reimagined: Science-based Solutions for Inclusive Organizations

Research interests: to fundamentally redefine leadership and in turn its application across countless leader development and training programs in business and society.

  1. Building theoretically grounded taxonomies of leader behaviors that are gender and race neutral;
  2. Developing leadership training and development initiatives that are scientifically based and applicable to leaders regardless of background
  3. Developing machine learning algorithms that minimize the perceptual and implicit biases in typical leadership ratings.

SDS faculty who work in this area: George Banks and Scott Tonidandel

Sample Grants/Projects

  1. George Banks (PI), Wenwen Dou (Co-PI): “FW-HTF-P: Collaborative Research: Artificial Intelligence-Supported Development of Future Organizational Leaders”. National Science Foundation. Oct. 1, 2021 – Sept. 30, 2022. $114,096
  2. Scott Tonidandel, Co-PI, $475,000, three-year grant from the National Science Foundation titled “When Team Diversity Facilitates Performance: Understanding and Overcoming Fractured Behavioral Patterns”, 2015-18.
  3. Scott Tonidandel, Co-PI, $1.38 million five-year grant from the National Institute of Drug Abuse titled “Social Influences on Drug-Seeking Behavior”, 2012-2016.

Sample publications

  1. Banks, G. C., Barnes, C., & Jiang, K. (in press). Changing the conversation on the science-practice gap: An adherence-based approach. Journal of Management
  2. Gooty, J, Banks, G. C., Loignon, A. C., Tonidandel, S. & Williams, C. E. (in press). Meta-Analyses as a Multi-Level Model. Organizational Research Methods,
  3. Tonidandel, S., King, E. B., & Cortina, J. M. (2018). Big data methods: Leveraging modern data analytic techniques to build organizational science. Organizational Research Methods, 21, 525-547. doi: 10.1177/1094428116677299

Area 3: Nanoscale Science and Materials

Research interests:

  1. Developing mathematical models for the anomalous protein diffusion near folding/unfolding transition, and mathematical models for protein aggregation.
  2. Developing empirical models that fit calorimetry data, and quantify stability/flexibility relationships to inform protein design, and elucidate protein evolution.
  3. Applying molecular dynamics simulation and employ machine learning to glean mechanistic insights
  4. Developing new therapeutics based on manipulating protein-protein interactions

SDS faculty whose research is in this area: Donald Jacob (, Harish Cherukuri, Shan Yan, etc.

Sample Grants/Projects

  1. Harish Cherukuri, PI: “Advanced Modeling and Simulation of Thermal Kinetics in Additive Manufacturing”, DOD DA Army Research Office. 9/2018-8/2021, $598,562
  2. Harish Cherukuri, PI: “North Carolina Consortium for Self-Aware Machining and Metrology (CSAM),” NC-ROI. 08/2018-07/2022. $1,665,502
  3. Shan Yan (PI): “Mechanism of APE1 in DNA damage response”, NIH/NCI – 1R01CA225637-01. 02/13/18-01/31/23. $1.9 million
  4. Shan Yan (co-PI): “Understanding the reciprocal regulation between Hsp70 and the DNA damage response,” NIH/NCI – 1R01CA225637-01, 12/03/20-11/30/24. $1,179,511.

Sample Publications

  1. Nguyen KTP, Druhan LJ, Avalos BR, Zhai L, Rauova L, Nesmelova IV, Dréau D. (2020). “CXCL12-CXCL4 heterodimerization prevents CXCL12-driven breast cancer cell migration”, 2020, Cell Signal., 66:109488. PMID: 31785332.
  2. Tyler Grearg, Chris Averyg, John Patterson, Donald Jacobs (2021). Molecular function recognition by supervised projection pursuit machine learning, Nature: Scientific Reports, 11:4247.
  3. Ha, A., Lin, Y, and Yan, S. (2020). A non-canonical role for the DNA glycosylase NEIL3 in suppressing APE1 endonuclease-mediated ssDNA damage. J Biol Chem. 295 (41): 14222-14235.

Area 4: Online Misinformation Behavior, Impact, Detection, and Mitigation

Research Interests:

  1. Understanding online misinformation strategies and misinformation dissemination or sharing behavior (e.g., through social network analysis, visualization, graph analysis)
  2. Building machine learning models for automated online misinformation detection (e.g., fake news detection, fake online consumer product review detection; phishing detection; online fraud detection)
  3. Developing effective methods for misinformation intervention
  4. Focusing on misinformation in a variety of application domains, including, but not limited to, business, healthcare, politics, and entertainment.

Some SDS faculty who work in this area: Dongsong Zhang (, Frederico Batista Pereira, Lina Zhou, Shi Chen, Wenwen Dou, and Siddharth Krishnan, Shannon Reid, etc.

Sample grants/projects:

  1. Lina Zhou (PI) and Dongsong Zhang (Co-PI): “SBE: Small: Behavioral Control of Deceivers in Online Attacks”. National Science Foundation (NSF). Award #: SES 1527684. September 2015 – Aug. 2021. $499,912.
  2. Frederico Batista Pereira (co-PI). Political Elites and the Appeal of Fake News in Brazil. Facebook Research Award on Misinformation and Polarization, 2021.
  3. Wenwen Dou (PI): “Deception Detection, Tracking and Factuality Assessment in Social and News Media”, Pacific Northwest National Laboratory. 2017-2018. $50,000.

Sample Publications:

  1. Safarnejad, L., Xu, Q., Ge, Y., Krishnan, S., Bagarvathi, A., Chen, S. (2020). Contrasting Real and Misinformation Dissemination Network Structures on Social Media during the 2016 Zika Epidemic. American Journal of Public Health, 110: S340-347.
  2. Shan, G., Zhou, L., & Zhang, D. (2021). From Conflicts and Confusion to Doubts: Examining Review Inconsistency for Fake Review Detection. Decision Support Systems. 144.
  3. Zhang, D., Zhou, L., Kehoe, L. J., and Kilic, I. (2016). What Online Reviewer Behaviors Really Matter? A Study of Effects of Verbal and Nonverbal Behaviors on Online Fake Review Detection. Journal of Management Information Systems. 33(2). p.456-481
  4. Ryan Wesslen, Sashank Sathanam, Alireza Karduni, Isaac Cho, Samira Shaikh, Wenwen Dou. Investigating Effects of Visual Anchors on Decision-Making about Misinformation. Computer Graphics Forum 38 (3), 161-171, 2019

Area 5: Smart and sustainable cities

Research Interests: Mitigate socio-economic gaps, enable upward mobility for underserved communities, and chart a path of sustainable, environmentally friendly growth.

  1. Harnessing the recent technology advances of big data cloud computing, AI, Internet-of-Things (IoT), 5G communication, and low-cost sensors, to allow for fine-grained information processing to enable optimal decision-making across a range of city services.
  2. Developing community-led solutions to meet local needs while addressing privacy and safety concerns of the above-listed technologies.
  3. Public safety, public transit Equity, sustainable energy, accessible health, and resilience of power distribution.
  4. Creating a “Smart City Research Ecosystem” to enable AI for social good.

SDS faculty: Hamed Tabkhivayghan (, Shannon Reid

Sample Grants/Projects:

  1. The $2 million NSF Smart and Connected Communities (SCC) project led by Hamed Tabkhivayghan (ECE) on building safe and secure communities through real-time edge video analytics. The project formalizes, and models public safety and security events to be machine-detectable, reducing biases, and enabling broad-based community support and trust.
  2. The $500K NSF Cyber-Physical System project led by Tabkhi (ECE) along with faculty from ETCM on AI for highway worker safety. The project uses deep learning algorithms, edge computing, and assisted reality systems to enable real-time prediction of work zone intrusions and notification of highway workers.

Sample publications:

  1. S. Rogers, J, Slycord, M. Baharani, and H. Tabkhi, “gem5-SALAM: A System Architecture for LLVM-based Accelerator Modeling”, to appear in IEEE/ACM International Symposium on Microarchitecture, Athens, Greece, October 17–21, 2020.
  2. C. Neff, M. Mendieta, S. Mohan, M. Baharani, S. Rogers, and H. Tabkhi, “REVAMP2T: Realtime Edge Video Analytics for Multi-camera Privacy-aware Pedestrian Tracking”, IEEE Internet of Things Journal, November 2019.
  3. J. Sanchez, A. Sawant, C. Neff, H. Tabkhi, “AWARE-CNN: Automated Workflow for Application-aware Real-time Edge Acceleration of CNNs”, IEEE Internet of Things Journal, May 2020, DOI: 10.1109/JIOT.2020.2990215.
  4. S. Rogers, H. Tabkhi, “Locality-Aware Memory Assignment and Tiling”, IEEE/ACM Design Automation Conference (DAC), San Francisco, CA, USA, Jun 2018.

Area 6: Urban health

Research Interests: understanding and addressing the primary underlying causes of health inequities – the social determinants of health

  1. Using action research methods, community-based participatory research, and clinical research methods to improve health outcomes in high-risk clinical populations
  2. Implementing community-based chronic disease prevention and management programs
  3. Advancing health policy reform, and
  4. Promoting racial equity

SDS Faculty: Michael Dulin (, Laura Gunn, Shi Chen, Yuqi Guo, Albert Park, Cheryl Brown, Paul Rajib, Monika Sawhney, Jean-Claude Thill, etc.

Sample Grants/Projects:

  1. Michael Dulin, site PI: “Implementing Best Practice Care for Sepsis Survivors to Reduce Morbidity & Mortality,” NIH R01NR018434-01A1
  2. Michael Dulin, PI: “Transdisciplinary Approach to the Evaluation of Social Determinants of Health,” R01 MD006127-05, National Institute on Minority Health and Health Disparities (NIMHD)
  3. Laura Gunn, co-PI: “The SKyRoCKeT Study: Surface-Knit and Reformulate CADENCE-Kids for Translation,” NIH: National Institute of Child Health & Human Development (NICHD) R01 Grant. May 1, 2022 – March 31, 2027. $2,896,873.
  4. Monika Sawhney, Co-PI: “Substance Abuse and Mental Health Services Administration (SAMSHA),” U.S. Department of Health and Human Services, $945,000, Sept. 2015 – Aug. 2018.

Sample Publications:

  1. Xu, M., et al. (2021). Accurately Differentiating COVID-19, Other Viral Infections, and Healthy Individuals Using Multimodal Features via Late Fusion. Journal of Medical Internet Research, doi: 10.2196/25535.
  2. Chen, S., Zhou L, Song Y, Xu Q, Wang P, Wang K, Ge Y, and Janies D. 2020. Comparative Analysis of Viral COVID-19 Sina Weibo and Twitter Contents with a Novel Feature Extraction and Machine Learning Workflow. Journal of Medical Internet Research, doi: 10.2196/24889.
  3. Coffman MJ, Scott VC*, Schuch C*, Mele C, Mayfield C*, Balasubramanian V*, Stevens A, Dulin M. Postpartum Depression Screening and Referrals in Special Supplemental Nutrition Program for Women, Infants, and Children Clinics. J Obstet Gynecol Neonatal Nurs. 2020 Jan; 49(1):27-40.
  4. Pescheny JV, Gunn LH, Randhawa G, Pappas Y. (2019). The Impact of the Luton Social Prescribing Programme on Energy Expenditure: A Quantitative Before-and-After Study. BMJ Open, 9:e026862.