Natural Language Processing Laboratory

Our mission is to develop and implement Natural Language Processing (NLP) technologies to apply to the electronic medical record.  These technologies include core NLP tasks such as relation extraction, coreference resolution, and parsing, and make use of statistical machine learning methods.  In order to use many machine learning methods, manually labeled (annotated) domain- and task-specific data is required.  To that end, we are heavily involved in many different clinical document annotation projects.  Since manual annotation is a time-consuming, painstaking, expensive process, it is also our goal to develop and use algorithms that minimize the required amount of labeled data required while maximizing the use of existing labeled data.

The Programmatic Outbreak Response and [socio-]Technical Analytics Lab (PORTAL)

PORTAL uses probabilistic modeling, data science, and “systems epidemiology” in the context of public health, with a focus on causal inference to advance infectious disease surveillance using digital disease data (e.g. search trends; news and social media).

The Registry and Informatics R&D (RIR&D) Group

The RIR&D Group is focused on the development and application of innovative, open-source, data-driven solutions to challenges faced by longitudinal disease registries and related studies. A principal focus of our group is informatics-based strategies to enhance the collection and sharing of clinical research data within the 75+ site Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry, serving as a paradigm for other similar longitudinal chronic disease registries. Members of the CHIP RIR&D Group include specialists in software architecture and development for multi-site/multi-source regulatory-grade clinical research data warehousing, research data coordination, research analytics and computable phenotyping, and related IRB and regulatory/legal matters for clinical trials.

The Manrai Lab

The Manrai Lab is a team of machine learning scientists, clinicians, and biomedical data scientists working to improve medical decision making by developing computational approaches that incorporate rich and deep representations of clinical state and an individual's identity into care. Active projects include: improving genetic variant classification and quantifying risk ("penetrance") for inherited heart disease, measuring "normal" variation for blood lab biomarkers across populations focused on creatinine and kidney disease, developing semi-supervised learning approaches for multi-modal imaging, and modeling reproducibility in integrative biomedical studies using meta-science ("science of science") approaches.

The Pediatric Therapeutics and Regulatory Science Initiative

The Pediatric Therapeutics and Regulatory Science Initiative provides a forum for collaboration with academia, patients, policymakers, and industry. Its aim is to advance the development and evidence-based use of novel therapeutics for children globally.

Inclusion Body Myositis Research

The Inclusion Body Myositis Research group is dedicated to understanding and finding treatment for sporadic inclusion body myositis (s-IBM), dermatomyositis, polymyositis, and other inflammatory myopathies.


SMART Health IT advances medicine, discovery and public health through parsimonious, open standards, application programming interfaces, laws and regulation, and is best known for the SMART on FHIR API. 

Laboratory for Neuroinformatics and Neurodiagnostics (LNN)

The LNN develops computational methods for extracting digital biomarkers for brain disorders from EEG measurements. Our approach to EEG signal analysis is based in complex dynamical systems theory. Entropy measures, recurrence plot and recurrence network analysis, neural synchronization, and tensor data structures all play a role. 

Computational Epidemiology Lab

The Computational Epidemiology Lab conducts a diverse set of projects to predict patterns of disease, analyze patterns of epidemics, use of satellite data and public health surveillance tools. 

Avillach Lab

The Avillach Lab investigates translational bioinformatics, specifically in integrating multiple heterogeneous sources of clinical and genomics data in a meaningful way. 

Translational Omics Medicine Lab

The Translational Omics Medicine Lab develops methods to molecularly characterize patients for research and discovery. 

Machine Intelligence Lab

The Machine Intelligence Lab has a multidisciplinary research agenda. The research involves the conception and implementation of machine intelligence analytics tools, capable of predicting unobserved events in health care in the immediate or near future. 

Predictive Medicine Group

The Predictive Medicine Group works to develop novel approaches for predicting human health. Our diverse group of researchers, clinicians, mathematicians, computer scientists and biologists develop advanced predictive models for a wide range of applicants, including disease risk prediction, predictive pharmacovigilance, predictive health system dynamics and real-time public health surveillance. 


Diao JA, Powe NR, Manrai AK. Race-Free Equations for eGFR: Comparing Effects on CKD Classification. Journal of the American Society of Nephrology : JASN 2021.

Plana D, Tian E, Cramer AK, Yang H, Carmack MM, Sinha MS, Bourgeois FT, Yu SH, Masse P, Boyer J, Kim M, Mo J, LeBoeuf NR, Li J, Sorger PK. Assessing the filtration efficiency and regulatory status of N95s and nontraditional filtering face-piece respirators available during the COVID-19 pandemic. BMC infectious diseases 2021.

Cromwell EA, Osborne JCP, Unnasch TR, Basáñez MG, Gass KM, Barbre KA, Hill E, Johnson KB, Donkers KM, Shirude S, Schmidt CA, Adekanmbi V, Adetokunboh OO, Afarideh M, Ahmadpour E, Ahmed MB, Akalu TY, Al-Aly Z, Alanezi FM, Alanzi TM, Alipour V, Andrei CL, Ansari F, Ansha MG, Anvari D, Appiah SCY, Arabloo J, Arnold BF, Ausloos M, Ayanore MA, Baig AA, Banach M, Barac A, Bärnighausen TW, Bayati M, Bhattacharyya K, Bhutta ZA, Bibi S, Bijani A, Bohlouli S, Bohluli M, Brady OJ, Bragazzi NL, Butt ZA, Carvalho F, Chatterjee S, Chattu VK, Chattu SK, Cormier NM, Dahlawi SMA, Damiani G, Daoud F, Darwesh AM, Daryani A, Deribe K, Dharmaratne SD, Diaz D, Do HT, El Sayed Zaki M, El Tantawi M, Elemineh DA, Faraj A, Fasihi Harandi M, Fatahi Y, Feigin VL, Fernandes E, Foigt NA, Foroutan M, Franklin RC, Gubari MIM, Guido D, Guo Y, Haj-Mirzaian A, Hamagharib Abdullah K, Hamidi S, Herteliu C, Hidru HD, Higazi TB, Hossain N, Hosseinzadeh M, Househ M, Ilesanmi OS, Ilic MD, Ilic IM, Iqbal U, Irvani SSN, Jha RP, Joukar F, Jozwiak JJ, Kabir Z, Kalankesh LR, Kalhor R, Karami Matin B, Karimi SE, Kasaeian A, Kavetskyy T, Kayode GA, Kazemi Karyani A, Kelbore AG, Keramati M, Khalilov R, Khan EA, Khan MNN, Khatab K, Khater MM, Kianipour N, Kibret KT, Kim YJ, Kosen S, Krohn KJ, Kusuma D, La Vecchia C, Lansingh VC, Lee PH, LeGrand KE, Li S, Longbottom J, Magdy Abd El Razek H, Magdy Abd El Razek M, Maleki A, Mamun AA, Manafi A, Manafi N, Mansournia MA, Martins-Melo FR, Mazidi M, McAlinden C, Meharie BG, Mendoza W, Mengesha EW, Mengistu DT, Mereta ST, Mestrovic T, Miller TR, Miri M, Moghadaszadeh M, Mohammadian-Hafshejani A, Mohammadpourhodki R, Mohammed S, Mohammed S, Moradi M, Moradzadeh R, Moraga P, Mosser JF, Naderi M, Nagarajan AJ, Naik G, Negoi I, Nguyen CT, Nguyen HLT, Nguyen TH, Nikbakhsh R, Oancea B, Olagunju TO, Olagunju AT, Omar Bali A, Onwujekwe OE, Pana A, Pourjafar H, Rahim F, Rahman MHU, Rathi P, Rawaf S, Rawaf DL, Rawassizadeh R, Resnikoff S, Reta MA, Rezapour A, Rubagotti E, Rubino S, Sadeghi E, Saghafipour A, Sajadi SM, Samy AM, Sarmiento-Suárez R, Sawhney M, Schipp MF, Shaheen AA, Shaikh MA, Shamsizadeh M, Sharafi K, Sheikh A, Shetty BSK, Shin JI, Shivakumar KM, Simonetti B, Singh JA, Skiadaresi E, Soheili A, Soltani S, Spurlock EE, Sufiyan MB, Tabuchi T, Tapak L, Thompson RL, Thomson AJ, Traini E, Tran BX, Ullah I, Ullah S, Uneke CJ, Unnikrishnan B, Uthman OA, Vinkeles Melchers NVS, Violante FS, Wolde HF, Wonde TE, Yamada T, Yaya S, Yazdi-Feyzabadi V, Yip P, Yonemoto N, Yousof HSA, Yu C, Yu Y, Yusefzadeh H, Zaki L, Zaman SB, Zamanian M, Zhang ZJ, Zhang Y, Ziapour A, Hay SI, Pigott DM. Predicting the environmental suitability for onchocerciasis in Africa as an aid to elimination planning. PLoS neglected tropical diseases 2021.

Hutch MR, Liu M, Avillach P, , Luo Y, Bourgeois FT. National Trends in Disease Activity for COVID-19 Among Children in the US. Frontiers in pediatrics 2021.