@yale.edu
Assistant Professor
Pawar is an Assistant Professor (tenure-track) in Department of Computer Science and Biology at Claflin University (HBCU) ( with a Masters in Computer Science and a Ph.D. from Georgia State University, Atlanta. He’s also an co-founder of a Connecticut based AI company, ChestAi (, and a research affiliate at Yale University School of Medicine (. Until last year, he was an Associate Research Scientist at Yale Center for Genome Analysis. His dissertation was focused on big data (next-generation sequencing, microarrays, X-ray crystallography etc.) analysis with machine learning techniques (Neural networks, SVM’s, Restricted boltzmann machines, Clustering algorithms etc.). If you share similar interests feel free to contact. You can browse some of the recent machine learning applications on GitHub in projects. AOL: pawarshrikant@
ORCID iD
Georgia State University Ph.D. (GPA 3.6) 2019
Continuing Studies Program (2023-24): Stanford University, USA.
Georgia State University MS Biology (GPA 3.6)
Georgia State University MS Computer Science (GPA 3.6)
Western Kentucky University MS Bioinformatics,
Field of Research/Study: Bioinformatics; Computational Biology; Data Science; Artificial Intelligence; Computer Vision; Biomarker study.
Pawar is an Assistant Professor (tenure-track) in Department of Computer Science and Biology at Claflin University (HBC) with a Masters in Computer Science and a Ph.D. from Georgia State University. He's also an co-founder of a Connecticut based AI company, ChestAi, and a research affiliate at Yale.
https://scepscor.org/research-expertise-profiles-table/ https://scepscor.org/pawar-shrikant-research-focus/ https://www.crunchbase.com/organization/chestai https://www.f6s.com/company/chestai https://aws.amazon.com/marketplace/seller-profile?id=seller-b6otd3wry7lkk https://github.com/spawar2/Chest-X-ray-Neural-Nets https://www.ai4hlth.org/product-profiles/ChestAi https://www.chestai.org/ & https://www.crunchbase.com/person/shrikant-pawar-f817 https://tracxn.com/d/companies/chestai/__MB57iTXFVTO2fhlmUZpozcbLBTim3zBYEfD1xFox4mI https://theorg.com/org/chestai https://www.startupranking.com/chestai https://www.sideprojectors.com/project/41226/chestai https://startupbuffer.com/compare-atlantica-vs-chestai https://www.saashub.com/chestai-alternatives https://www.saashub.com/georgia-research-consulting-grc-llc-alternatives https://www.f6s.com/profile/5298554 https://www.crunchbase.com/organization/georgia-research-consulting-grc-llc https://www.claflin.edu/academics-research/faculty-research
Project: https://www.claflin-computation.com/_files/ugd/81dd80_0fc28fef39d94861ad5b18e83391a167.docx?dn=JoshuaKipronoThesisDraft.docx PPT: https://www.claflin-computation.com/_files/ugd/81dd80_8bb6fb5a75384bd5888fd7d8f43d51af.pdf Project: https://www.claflin-computation.com/_files/ugd/81dd80_1ad94ed63c6a49c887177419bdb46567.docx?dn=Etha%20project%20report.docx Project: https://www.claflin-computation.com/_files/ugd/81dd80_88f5decdd4c44ce497f7f2f71018c63b.docx?dn=Building%20a%20Responsive%20SmartHome.docx PPT: https://www.claflin-computation.com/_files/ugd/81dd80_65cf61cfe8b8429fae9cfcb31f099f15.pptx?dn=Building%20a%20Responsive%20Smart%20Home.pptx Project: https://www.claflin-computation.com/_files/ugd/81dd80_5add3a1670ff4c68a3cb307ddbe7e811.docx?dn=Priscilla%20Fatokun%27s%20Thesis.docx PPT: https://www.claflin-computation.com/_files/ugd/81dd80_9ea49363c6704e198ccbd724c57a3fdc.pptx?dn=Priscilla%20Fatokun%27s%20Presentation.pptx Project: https://campuspress.yale.edu/shrikantpawar/
https://campuspress.yale.edu/shrikantpawar/files/2024/05/ICICT-2024.pptx https://www.youtube.com/watch?v=Y6skvhHVR2w&ab_channel=ShrikantPawar https://campuspress.yale.edu/shrikantpawar/files/2024/05/IWBBIO-Pawar-23.pptx https://campuspress.yale.edu/shrikantpawar/files/2024/05/WorldS4-2023.pptx https://www.youtube.com/watch?v=PtjHqf4xlbI&ab_channel=ShrikantPawar https://medicine.yale.edu/news-article/why-does-covid-19-cause-severe-illness-in-some-patients-but-not-others/ ChestAi Demo: https://youtu.be/5BfRpzYU-T8
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Benjamin S. Haslund-Gourley, Kyra Woloszczuk, Jintong Hou, Jennifer Connors, Gina Cusimano, Mathew Bell, Bhavani Taramangalam, Slim Fourati, Nathan Mege, Mariana Bernui,et al.
Springer Science and Business Media LLC
AbstractThe glycosylation of IgG plays a critical role during human severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, activating immune cells and inducing cytokine production. However, the role of IgM N-glycosylation has not been studied during human acute viral infection. The analysis of IgM N-glycosylation from healthy controls and hospitalized coronavirus disease 2019 (COVID-19) patients reveals increased high-mannose and sialylation that correlates with COVID-19 severity. These trends are confirmed within SARS-CoV-2-specific immunoglobulin N-glycan profiles. Moreover, the degree of total IgM mannosylation and sialylation correlate significantly with markers of disease severity. We link the changes of IgM N-glycosylation with the expression of Golgi glycosyltransferases. Lastly, we observe antigen-specific IgM antibody-dependent complement deposition is elevated in severe COVID-19 patients and modulated by exoglycosidase digestion. Taken together, this work links the IgM N-glycosylation with COVID-19 severity and highlights the need to understand IgM glycosylation and downstream immune function during human disease.
Al Ozonoff, Naresh Doni Jayavelu, Shanshan Liu, Esther Melamed, Carly E. Milliren, Jingjing Qi, Linda N. Geng, Grace A. McComsey, Charles B. Cairns, Lindsey R. Baden,et al.
Springer Science and Business Media LLC
AbstractPost-acute sequelae of SARS-CoV-2 (PASC) is a significant public health concern. We describe Patient Reported Outcomes (PROs) on 590 participants prospectively assessed from hospital admission for COVID-19 through one year after discharge. Modeling identified 4 PRO clusters based on reported deficits (minimal, physical, mental/cognitive, and multidomain), supporting heterogenous clinical presentations in PASC, with sub-phenotypes associated with female sex and distinctive comorbidities. During the acute phase of disease, a higher respiratory SARS-CoV-2 viral burden and lower Receptor Binding Domain and Spike antibody titers were associated with both the physical predominant and the multidomain deficit clusters. A lower frequency of circulating B lymphocytes by mass cytometry (CyTOF) was observed in the multidomain deficit cluster. Circulating fibroblast growth factor 21 (FGF21) was significantly elevated in the mental/cognitive predominant and the multidomain clusters. Future efforts to link PASC to acute anti-viral host responses may help to better target treatment and prevention of PASC.
Yingzhao Jin, Cui Guo, Mohammadreza Abbasian, Mitra Abbasifard, J. Haxby Abbott, Auwal Abdullahi, Aidin Abedi, Hassan Abidi, Hassan Abolhassani, Eman Abu-Gharbieh,et al.
Elsevier BV
Marita Cross, Kanyin Liane Ong, Garland T Culbreth, Jaimie D Steinmetz, Ewerton Cousin, Hailey Lenox, Jacek A Kopec, Lydia M Haile, Peter M Brooks, Deborah R Kopansky-Giles,et al.
Elsevier BV
Jorge R Ledesma, Jianing Ma, Meixin Zhang, Ann V L Basting, Huong Thi Chu, Avina Vongpradith, Amanda Novotney, Kate E LeGrand, Yvonne Yiru Xu, Xiaochen Dai,et al.
Elsevier BV
Michael Brauer, Gregory A Roth, Aleksandr Y Aravkin, Peng Zheng, Kalkidan Hassen Abate, Yohannes Habtegiorgis Abate, Cristiana Abbafati, Rouzbeh Abbasgholizadeh, Madineh Akram Abbasi, Mohammadreza Abbasian,et al.
Elsevier BV
Stein Emil Vollset, Hazim S Ababneh, Yohannes Habtegiorgis Abate, Cristiana Abbafati, Rouzbeh Abbasgholizadeh, Mohammadreza Abbasian, Hedayat Abbastabar, Abdallah H A Abd Al Magied, Samar Abd ElHafeez, Atef Abdelkader,et al.
Elsevier BV
Alize J Ferrari, Damian Francesco Santomauro, Amirali Aali, Yohannes Habtegiorgis Abate, Cristiana Abbafati, Hedayat Abbastabar, Samar Abd ElHafeez, Michael Abdelmasseh, Sherief Abd-Elsalam, Arash Abdollahi,et al.
Elsevier BV
Austin E Schumacher, Hmwe Hmwe Kyu, Amirali Aali, Cristiana Abbafati, Jaffar Abbas, Rouzbeh Abbasgholizadeh, Madineh Akram Abbasi, Mohammadreza Abbasian, Samar Abd ElHafeez, Michael Abdelmasseh,et al.
Elsevier BV
Jeremy P Gygi, Anna Konstorum, Shrikant Pawar, Edel Aron, Steven H Kleinstein, and Leying Guan
Oxford University Press (OUP)
Abstract Motivation Predictive biological signatures provide utility as biomarkers for disease diagnosis and prognosis, as well as prediction of responses to vaccination or therapy. These signatures are identified from high-throughput profiling assays through a combination of dimensionality reduction and machine learning techniques. The genes, proteins, metabolites, and other biological analytes that compose signatures also generate hypotheses on the underlying mechanisms driving biological responses, thus improving biological understanding. Dimensionality reduction is a critical step in signature discovery to address the large number of analytes in omics datasets, especially for multi-omics profiling studies with tens of thousands of measurements. Latent factor models, which can account for the structural heterogeneity across diverse assays, effectively integrate multi-omics data and reduce dimensionality to a small number of factors that capture correlations and associations among measurements. These factors provide biologically interpretable features for predictive modeling. However, multi-omics integration and predictive modeling are generally performed independently in sequential steps, leading to suboptimal factor construction. Combining these steps can yield better multi-omics signatures that are more predictive while still being biologically meaningful. Results We developed a supervised variational Bayesian factor model that extracts multi-omics signatures from high-throughput profiling datasets that can span multiple data types. Signature-based multiPle-omics intEgration via lAtent factoRs (SPEAR) adaptively determines factor rank, emphasis on factor structure, data relevance and feature sparsity. The method improves the reconstruction of underlying factors in synthetic examples and prediction accuracy of coronavirus disease 2019 severity and breast cancer tumor subtypes. Availability and implementation SPEAR is a publicly available R-package hosted at https://bitbucket.org/kleinstein/SPEAR.
Jaimie D Steinmetz, Katrin Maria Seeher, Nicoline Schiess, Emma Nichols, Bochen Cao, Chiara Servili, Vanessa Cavallera, Ewerton Cousin, Hailey Hagins, Madeline E Moberg,et al.
Elsevier BV
Ai-Min Wu, Marita Cross, James M Elliott, Garland T Culbreth, Lydia M Haile, Jaimie D Steinmetz, Hailey Hagins, Jacek A Kopec, Peter M Brooks, Anthony D Woolf,et al.
Elsevier BV
, Konrad Pesudovs, Van Charles Lansingh, John H. Kempen, Ian Tapply, Arthur G. Fernandes, Maria Vittoria Cicinelli, Alessandro Arrigo, Nicolas Leveziel, Serge Resnikoff,et al.
Springer Science and Business Media LLC
Konrad Pesudovs, Van Charles Lansingh, John H. Kempen, Ian Tapply, Arthur G. Fernandes, Maria V. Cicinelli, Alessandro Arrigo, Nicolas Leveziel, Paul Svitil Briant, Theo Vos,et al.
Springer Science and Business Media LLC
Abstract Background To estimate global and regional trends from 2000 to 2020 of the number of persons visually impaired by cataract and their proportion of the total number of vision-impaired individuals. Methods A systematic review and meta-analysis of published population studies and gray literature from 2000 to 2020 was carried out to estimate global and regional trends. We developed prevalence estimates based on modeled distance visual impairment and blindness due to cataract, producing location-, year-, age-, and sex-specific estimates of moderate to severe vision impairment (MSVI presenting visual acuity <6/18, ≥3/60) and blindness (presenting visual acuity <3/60). Estimates are age-standardized using the GBD standard population. Results In 2020, among overall (all ages) 43.3 million blind and 295 million with MSVI, 17.0 million (39.6%) people were blind and 83.5 million (28.3%) had MSVI due to cataract blind 60% female, MSVI 59% female. From 1990 to 2020, the count of persons blind (MSVI) due to cataract increased by 29.7%(93.1%) whereas the age-standardized global prevalence of cataract-related blindness improved by −27.5% and MSVI increased by 7.2%. The contribution of cataract to the age-standardized prevalence of blindness exceeded the global figure only in South Asia (62.9%) and Southeast Asia and Oceania (47.9%). Conclusions The number of people blind and with MSVI due to cataract has risen over the past 30 years, despite a decrease in the age-standardized prevalence of cataract. This indicates that cataract treatment programs have been beneficial, but population growth and aging have outpaced their impact. Growing numbers of cataract blind indicate that more, better-directed, resources are needed to increase global capacity for cataract surgery.
Shrikant Pawar
Springer Nature Singapore
, Lydia M. Haile, Aislyn U. Orji, Kelly M. Reavis, Paul Svitil Briant, Katia M. Lucas, Fares Alahdab, Till Winfried Bärnighausen, Arielle Wilder Bell, Chao Cao,et al.
Ovid Technologies (Wolters Kluwer Health)
Objectives: This article describes key data sources and methods used to estimate hearing loss in the United States, in the Global Burden of Disease study. Then, trends in hearing loss are described for 2019, including temporal trends from 1990 to 2019, changing prevalence over age, severity patterns, and utilization of hearing aids. Design: We utilized population-representative surveys from the United States to estimate hearing loss prevalence for the Global Burden of Disease study. A key input data source in modeled estimates are the National Health and Nutrition Examination Surveys (NHANES), years 1988 to 2010. We ran hierarchical severity-specific models to estimate hearing loss prevalence. We then scaled severity-specific models to sum to total hearing impairment prevalence, adjusted estimates for hearing aid coverage, and split estimates by etiology and tinnitus status. We computed years lived with disability (YLDs), which quantifies the amount of health loss associated with a condition depending on severity and creates a common metric to compare the burden of disparate diseases. This was done by multiplying the prevalence of severity-specific hearing loss by corresponding disability weights, with additional weighting for tinnitus comorbidity. Results: An estimated 72.88 million (95% uncertainty interval (UI) 68.53 to 77.30) people in the United States had hearing loss in 2019, accounting for 22.2% (20.9 to 23.6) of the total population. Hearing loss was responsible for 2.24 million (1.56 to 3.11) YLDs (3.6% (2.8 to 4.7) of total US YLDs). Age-standardized prevalence was higher in males (17.7% [16.7 to 18.8]) compared with females (11.9%, [11.2 to 12.5]). While most cases of hearing loss were mild (64.3%, 95% UI 61.0 to 67.6), disability was concentrated in cases that were moderate or more severe. The all-age prevalence of hearing loss in the United States was 28.1% (25.7 to 30.8) higher in 2019 than in 1990, despite stable age-standardized prevalence. An estimated 9.7% (8.6 to 11.0) of individuals with mild to profound hearing loss utilized a hearing aid, while 32.5% (31.9 to 33.2) of individuals with hearing loss experienced tinnitus. Occupational noise exposure was responsible for 11.2% (10.2 to 12.4) of hearing loss YLDs. Conclusions: Results indicate large burden of hearing loss in the United States, with an estimated 1 in 5 people experiencing this condition. While many cases of hearing loss in the United States were mild, growing prevalence, low usage of hearing aids, and aging populations indicate the rising impact of this condition in future years and the increasing importance of domestic access to hearing healthcare services. Large-scale audiometric surveys such as NHANES are needed to regularly assess hearing loss burden and access to healthcare, improving our understanding of who is impacted by hearing loss and what groups are most amenable to intervention.
George A. Mensah, Valentin Fuster, Christopher J.L. Murray, Gregory A. Roth, George A. Mensah, Yohannes Habtegiorgis Abate, Mohammadreza Abbasian, Foad Abd-Allah, Ashkan Abdollahi, Mohammad Abdollahi,et al.
Journal of the American College of Cardiology Elsevier BV
Tiffany K Gill, Manasi Murthy Mittinty, Lyn M March, Jaimie D Steinmetz, Garland T Culbreth, Marita Cross, Jacek A Kopec, Anthony D Woolf, Lydia M Haile, Hailey Hagins,et al.
Elsevier BV
Madeline E Moberg, Erin B Hamilton, Scott M Zeng, Dana Bryazka, Jeff T Zhao, Rachel Feldman, Yohannes Habtegiorgis Abate, Mohsen Abbasi-Kangevari, Ame Mehadi Abdurehman, Aidin Abedi,et al.
Elsevier BV
Mahdi Safdarian, Eugen Trinka, Vafa Rahimi-Movaghar, Aljoscha Thomschewski, Amirali Aali, Gdiom Gebreheat Abady, Semagn Mekonnen Abate, Foad Abd-Allah, Aidin Abedi, Denberu Eshetie Adane,et al.
Elsevier BV
Yael Rosenberg-Hasson, Tyson H. Holmes, Joann Diray-Arce, Jing Chen, Ryan Kellogg, Michael Snyder, Patrice M. Becker, Al Ozonoff, Nadine Rouphael, Elaine F. Reed,et al.
The American Association of Immunologists
Abstract The clinical trajectory of COVID-19 may be influenced by previous responses to heterologous viruses. We examined the relationship of Abs against different viruses to clinical trajectory groups from the National Institutes of Health IMPACC (Immunophenotyping Assessment in a COVID-19 Cohort) study of hospitalized COVID-19 patients. Whereas initial Ab titers to SARS-CoV-2 tended to be higher with increasing severity (excluding fatal disease), those to seasonal coronaviruses trended in the opposite direction. Initial Ab titers to influenza and parainfluenza viruses also tended to be lower with increasing severity. However, no significant relationship was observed for Abs to other viruses, including measles, CMV, EBV, and respiratory syncytial virus. We hypothesize that some individuals may produce lower or less durable Ab responses to respiratory viruses generally (reflected in lower baseline titers in our study), and that this may carry over into poorer outcomes for COVID-19 (despite high initial SARS-CoV-2 titers). We further looked at longitudinal changes in Ab responses to heterologous viruses, but found little change during the course of acute COVID-19 infection. We saw significant trends with age for Ab levels to many of these viruses, but no difference in longitudinal SARS-CoV-2 titers for those with high versus low seasonal coronavirus titers. We detected no difference in longitudinal SARS-CoV-2 titers for CMV seropositive versus seronegative patients, although there was an overrepresentation of CMV seropositives among the IMPACC cohort, compared with expected frequencies in the United States population. Our results both reinforce findings from other studies and suggest (to our knowledge) new relationships between the response to SARS-CoV-2 and Abs to heterologous viruses.
Dongze Wu, Yingzhao Jin, Yuhan Xing, Melsew Dagne Abate, Mohammadreza Abbasian, Mohsen Abbasi-Kangevari, Zeinab Abbasi-Kangevari, Foad Abd-Allah, Michael Abdelmasseh, Mohammad-Amin Abdollahifar,et al.
Elsevier BV
Rachel J Black, Marita Cross, Lydia M Haile, Garland T Culbreth, Jaimie D Steinmetz, Hailey Hagins, Jacek A Kopec, Peter M Brooks, Anthony D Woolf, Kanyin Liane Ong,et al.
Elsevier BV
Gisela Robles Aguilar, Lucien R. Swetschinski, Nicole Davis Weaver, Kevin S. Ikuta, Tomislav Mestrovic, Authia P. Gray, Erin Chung, Eve E. Wool, Chieh Han, Anna Gershberg Hayoon,et al.
Elsevier BV
William M Gardner, Christian Razo, Theresa A McHugh, Hailey Hagins, Victor M Vilchis-Tella, Conor Hennessy, Heather Jean Taylor, Nandita Perumal, Kia Fuller, Kelly M Cercy,et al.
Elsevier BV
Shrikantpawar3@ pawarshrikant@ pawarshrikant@
South Carolina Established Program for Stem Cooperative Research (SC EPSCoR), AI-enabled Devices for the Advancement of Personalized and Transformative Healthcare in South Carolina (ADAPT), Research Expertise:
ChestAi Demo:
2022: Assistant Professor (tenure-track) in Department of Computer Science and with a joint appointment in Biology at Claflin University (, Teaching-student-advising:
Faculty Profile:
2022: Research affiliate at Yale University School of Medicine
2020: Yale University, New Haven, USA. Title: Associate Research Scientist (, current projects in collaboration with Dr. Christian Griñán Ferré, Univesity of Barcelona; Dr. Kleinstein, Yale University; Dr. Uduman, Dana Farber Harvard; Dr. George Tegos, Gamma Therapeutics; Dr. Lahiri, Sunway University; Dr. Allen. Bale, Dr. Hui. Zhang, DNA Diagnostic Lab at Yale University; Dr. Insoo. Kang; Dr. Kei-Hoi Chung; and Dr. Leying Guan at Yale University
2023: Managing research effectively, 2023 online summer school by Narayanan A, Lahiri C & Pawar S, Center for Research, Innovation & Translation, Department of Biotechnology, Atmiya University, India. . Topic: .
Extramural Funding & Internal Awards: ORCID iD:
Extramural Funding & Internal Awards (Selected academic and non-academic): SC Independent Colleges & Universities (SCICU) Undergraduate Student/Faculty Research Symposium, Spartanburg.
[**30][**39]
4. Entrepreneurship Foundation Fund (2020), USA: “CHEST-AI: AI tool for detection of lung diseases from chest X- ray data”:
5. Culinda Technologies (2020), Texas, USA ( Machine learning solutions for medical devices that provide deep insight into IoT and IoMT applications
6. Microsoft for Startups Founders Hub Azure Credits (2022) $25,000 ( “CHEST-AI: AI tool for detection of lung diseases from chest X- ray data”.
7. SCICU Undergraduate Student/Faculty Research Program (2022): Utilizing Natural Language Processing (NLP) to increase the effectiveness of finding career opportunities. $7,169. . App Deploy:
8. Claflin University Smart Home, Center of Excellence Seed Grant (2022), $19,919.88
9. Oracle for Research Award:
10. SCICU Undergraduate Student/Faculty Research Program (2023): A prospective, community-based observational study for creating a comprehensible electronic health record (EHR) database using wearable devices in African American cohort. $7,000.
11. Google HBCU Career Readiness Capacity Grant (2023): $20,000
12. SC INRE EPSCoR Research Experience for Teachers (RET) program (2022):
2020: ChestAi, New Haven, USA. Title: Founder ( ChestAi Demo:
2019: Karyosoft, Indianapolis, USA. Title: Genomics Data Scientist, worked on angular, nodejs, flask, AWS, MongoDB, Rabbit-mq, Nginx webserver, Jbrowse techniques for data mining and software visualization
2019: Synergy (Plus+) . Atlanta, USA. Title: Data Scientist, worked on web development, statistical and data mining techniques for platform optimization
2018: Georgia State University, Department of Biology, Atlanta, USA. Title: Instructor of Record
2018: Georgia State University, Department of Computer Science & Biology, Atlanta, USA. Title: Ahmed T. Abdelal Fellow in Molecular Genetics and Biotechnology ,
2013: Freie Universität Berlin, Berlin, Germany. Title: Center for International Collaborative (CIC) Research Fellow
The Department of Defense (DoD), Army Materiel Command (AMC) HBCU/MI Equipment/Instrumentation award (2024–2025), Claflin University, Orangeburg, South Carolina, USA. Total Award (direct and indirect), Role: PI, 100% $410,596. Basic, Applied, and Advanced Research in Science and Engineering: Intel Xeon NVIDIA GPU Server for Enhancing Computational Capabilities at Claflin
.
5. NSF RII Track-1 Award: Total Award (direct and indirect) $20,000,000.00, Claflin Sub-award $414,000.00. A multi-institutional grant collaboration between Clemson University in partnership with Benedict College, The Citadel, Claflin University, College of Charleston, Francis Marion University, Medical University of South Carolina, South Carolina Research Authority, South Carolina . Thrust II ( Funding supports Claflin University students Caliese J. Beckford and Mr. Sabb, Dinari. Techniques of computer vision for image analysis. Project Implementation Team (, Education and Workforce Development (, SC EPSCoR Research Expertise: . Role Co-PI. [*9][**38, 40, 42, 43]
,
6. Honored Listee, Marquis Who's Who, ,
Thesis presentation: Thesis presentation:
1. Center for Excellence in Teaching and Learning (CETL) Hardware Grant (2018), Georgia State University, USA: .
2. Create-X, Startup Launch Grant (2019), Georgia Institute of Technology, USA: “Utilization of convolutional neural networks for security against password cracking”:
3. Yale University, Rothberg Fund (2020), USA: “CHEST-AI: AI tool for detection of lung diseases from chest X- ray data”: . [*2023:1, 4, 9; 2021: 17, 23][**21, 23, 29, 30]
4. Claflin University Summer Seed Research Grant (2023): Utilization of Machine Learning Techniques for Aiding Detection of Ischemic Stroke Lesion, Infarct Volumes, and Small-artery Occlusions: $10,000. Role-PI [**33] [*2024:9][**2024:36] ORCID iD: