HR job title
- Associate Professor of the Practice
- Contact Info
- 1 979 458 2336
Erraguntla, Madhav Associate Professor of the Practice
Positions
- Associate Professor of the Practice, Industrial and Systems Engineering, College of Engineering
- Overview
- Publications
- Research
- Teaching
- Background
- Contact
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Overview
Publications
selected publications
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academic article
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Yu, M., Pasman, H., Erraguntla, M., Quddus, N., & Kravaris, C.
(2022).
A framework to identify and respond to weak signals of disastrous process incidents based on FRAM and machine learning techniques. Process Safety and Environmental Protection.
158, 98-114.
-
Dave, D., DeSalvo, D. J., Haridas, B., McKay, S., Shenoy, A., Koh, C. J., Lawley, M., & Erraguntla, M.
(2021).
Feature-Based Machine Learning Model for Real-Time Hypoglycemia Prediction. J Diabetes Sci Technol.
15(4), 842-855.
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Curry, G. L., Moya, H., Erraguntla, M., & Banerjee, A.
(2021).
Transient Queueing Analysis for Emergency Hospital Management. IISE Transactions on Healthcare Systems Engineering.
1-20.
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Yu, M., Erraguntla, M., Quddus, N., & Kravaris, C.
(2021).
A data-driven approach of quantifying function couplings and identifying paths towards emerging hazards in complex systems. Process Safety and Environmental Protection.
150, 464-477.
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Zapletal, J., Najmitabrizi, N., Erraguntla, M., Lawley, M. A., Myles, K. M., & Adelman, Z. N.
(2021).
Making gene drive biodegradable. Philosophical Transactions of the Royal Society B: Biological Sciences.
376(1818), 20190804-20190804.
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Erraguntla, M., Dave, D., Zapletal, J., Myles, K., Adelman, Z. N., Pohlenz, T. D., & Lawley, M.
(2021).
Predictive model for microclimatic temperature and its use in mosquito population modeling. Scientific Reports.
11(1), 18909.
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Aljihmani, L., Kerdjidj, O., Zhu, Y., Mehta, R. K., Erraguntla, M., Sasangohar, F., & Qaraqe, K.
(2020).
Classification of Fatigue Phases in Healthy and Diabetic Adults Using Wearable Sensor. Sensors.
20(23), 6897-6897.
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Tyagi, O., Zhu, Y., Johnson, C., Mehta, R. K., Sasangohar, F., Erraguntla, M., & Qarake, K.
(2020).
Neuromuscular Fatigue mechanisms in Type 1 diabetic men and women. Proceedings of the Human Factors and Ergonomics Society Annual Meeting.
64(1), 960-961.
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Tyagi, O., Zhu, Y., Johnson, C., Mehta, R. K., Sasangohar, F., Erraguntla, M., & Qaraqe, K.
(2020).
Neural Signatures of Handgrip Fatigue in Type 1 Diabetic Men and Women. Frontiers in Human Neuroscience.
14, 564969.
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Zhu, Y., Mehta, R. K., Erraguntla, M., Sasangohar, F., & Qaraqe, K.
(2020).
Quantifying Accelerometer-Based Tremor Features of Neuromuscular Fatigue in Healthy and Diabetic Adults. IEEE Sensors Journal.
20(19), 11183-11190.
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Zahed, K., Sasangohar, F., Mehta, R., Erraguntla, M., & Qaraqe, K.
(2020).
Diabetes Management Experience and the State of Hypoglycemia: National Online Survey Study. JMIR Diabetes.
5(2), e17890-e17890.
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Zhu, Y., Jayagopal, J. K., Mehta, R. K., Erraguntla, M., Nuamah, J., McDonald, A. D., Taylor, H., & Chang, S
(2020).
Classifying Major Depressive Disorder Using fNIRS During Motor Rehabilitation. IEEE Trans Neural Syst Rehabil Eng.
28(4), 961-969.
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Abbas, H. T., Alic, L., Erraguntla, M., Ji, J. X., Abdul-Ghani, M., Abbasi, Q. H., & Qaraqe, M. K.
(2019).
Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test. PLoS One.
14(12), e0219636-e0219636.
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Nuamah, J. K., Sasangohar, F., Erraguntla, M., & Mehta, R. K.
(2019).
Correction to: The past, present and future of opioid withdrawal assessment: a scoping review of scales and technologies. BMC Med Inform Decis Mak.
19(1), 126.
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Nuamah, J. K., Sasangohar, F., Erraguntla, M., & Mehta, R. K.
(2019).
The past, present and future of opioid withdrawal assessment: a scoping review of scales and technologies. BMC Med Inform Decis Mak.
19(1), 113.
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Sands, L. P., Savla, J., Erraguntla, M., & Lawley, M.
(2019).
CHANGE IN UNMET NEEDS FOR DAILY MOBILITY AND SELF-CARE ACTIVITIES AND FUTURE HEALTH OUTCOMES. Innovation in Aging.
3(Supplement_1), S202-S202.
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McGowan, C. J., Biggerstaff, M., Johansson, M., Apfeldorf, K. M., Ben-Nun, M., Brooks, L., ... Influenza Forecasting Working Group, ..
(2019).
Collaborative efforts to forecast seasonal influenza in the United States, 2015-2016. Scientific Reports.
9(1), 683.
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Erraguntla, M., Zapletal, J., & Lawley, M.
(2019).
Framework for Infectious Disease Analysis: A comprehensive and integrative multi-modeling approach to disease prediction and management. Health Informatics J.
25(4), 1170-1187.
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Sultana, I., Erraguntla, M., Kum, H., Delen, D., & Lawley, M.
(2019).
Post-acute care referral in United States of America: a multiregional study of factors associated with referral destination in a cohort of patients with coronary artery bypass graft or valve replacement. BMC Med Inform Decis Mak.
19(1), 223.
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Zapletal, J., Gupta, H., Erraguntla, M., Adelman, Z. N., Myles, K. M., & Lawley, M. A.
(2019).
Predicting aquatic development and mortality rates of Aedes aegypti. PLoS One.
14(5), e0217199-e0217199.
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Hosseinian, S. M., Zhu, Y., Mehta, R. K., Erraguntla, M., & Lawley, M. A
(2019).
Static and Dynamic Work Activity Classification from a Single Accelerometer: Implications for Ergonomic Assessment of Manual Handling Tasks. IISE Trans Occup Ergon Hum Factors.
7(1), 59-68.
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Zahed, K., Sasangohar, F., Mehta, R., Erraguntla, M., Lawley, M., & Qaraqe, K.
(2018).
Investigating the Efficacy of Using Hand Tremors for Early Detection of Hypoglycemic Events: A Scoping Literature Review. Proceedings of the Human Factors and Ergonomics Society Annual Meeting.
62(1), 1211-1215.
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Zhu, Y., Zahed, K., Mehta, R. K., Sasangohar, F., Erraguntla, M., Lawley, M., Abbas, H. T., & Qaraqe, K.
(2018).
Non-invasive Wearable System for Hypoglycemia Detection: A Proof of Concept User-Centered Design Process. Proceedings of the Human Factors and Ergonomics Society Annual Meeting.
62(1), 1052-1056.
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Madanagopal, K., Erraguntla, M., & Mayer, R.
(2018).
MySidewalk – Incentivized crowdsourcing to improve walkable communities. Gerontechnology.
17(s), 96-96.
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Zapletal, J., Erraguntla, M., Adelman, Z. N., Myles, K. M., & Lawley, M. A.
(2018).
Impacts of diurnal temperature and larval density on aquatic development of Aedes aegypti. PLoS One.
13(3), e0194025-e0194025.
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Zapletal, J., Gupta, H., Erraguntla, M., Adelman, Z. N., Myles, K. M., & Lawley, M. A.
(2018).
Predicting Aquatic Development and Mortality Rates of Aedes Aegypti. 407502.
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Delen, D., Erraguntla, M., Mayer, R. J., & Wu, C.
(2011).
Better management of blood supply-chain with GIS-based analytics. Annals of Operations Research.
185(1), 181-193.
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Benjamin, P. C., Erraguntla, M., Mayer, R. J., & Marshall, C.
(1999).
Toolkit for enabling analysis and modeling of adaptive workflow (TEAMWORK). ACM SIGGROUP Bulletin.
20(3), 9-9.
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Benjamin, P. C., Erraguntla, M., & Mayer, R. J.
(1995).
Using Simulation for Robust System Design. SIMULATION.
65(2), 116-128.
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Mayer, R., Erraguntla, M., Menzel, C., & Hwang, J. C.
(1994).
Formal model of single agent planning situations. Journal of Systems Integration.
4(3), 219-241.
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Dave, D., Erraguntla, M., Lawley, M., DeSalvo, D., Haridas, B., McKay, S., & Koh, C.
Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study. JMIR Diabetes.
6(2), e26909-e26909.
-
Yu, M., Pasman, H., Erraguntla, M., Quddus, N., & Kravaris, C.
(2022).
A framework to identify and respond to weak signals of disastrous process incidents based on FRAM and machine learning techniques. Process Safety and Environmental Protection.
158, 98-114.
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chapter
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Qaraqe, M., Erraguntla, M., & Dave, D.
(2021).
AI and Machine Learning in Diabetes Management: Opportunity, Status, and Challenges. Multiple Perspectives on Artificial Intelligence in Healthcare.
(pp. 129-141).
Springer International Publishing.
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Tomasulo, P., Erraguntla, M., & Kamel, H.
(2012).
Blood Donation: An Approach to Donor Vigilance. Hemovigilance.
(pp. 75-98).
Wiley-Blackwell.
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Ramachandran, S., Erraguntla, M., & Benjamin, P.
(2002).
A Knowledge Based Framework for the Design of Soft-Computing Systems. GRID-BASED PROBLEM SOLVING ENVIRONMENTS.
Intelligent Information Processing.
(pp. 129-140).
Springer US.
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Qaraqe, M., Erraguntla, M., & Dave, D.
(2021).
AI and Machine Learning in Diabetes Management: Opportunity, Status, and Challenges. Multiple Perspectives on Artificial Intelligence in Healthcare.
(pp. 129-141).
Springer International Publishing.
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conference paper
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Islam, M. S., Qaraqe, M. K., Abbas, H. T., Erraguntla, M., & Abdul-Ghani, M.
(2020).
The Prediction of Diabetes Development: A Machine Learning Framework. 1-6.
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Islam, M. S., Qaraqe, M. K., Abbas, H. T., Erraguntla, M., Abdul-Ghani, M., & IEEE, ..
(2020).
The Prediction of Diabetes Development: A Machine Learning Framework. 154-159.
-
Aljihmani, L., Abbas, H., Zhu, Y., Mehta, R. K., Sasangohar, F., Erraguntla, M., ... Qaraqe, K.
(2019).
Features of Physiological Tremor in Diabetic Patients. 268-271.
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Aljihmani, L., Zhuz, Y., Abbas, H. T., Mehta, R., Sasangohar, F., Erraguntla, M., Abbasi, Q. H., & Qaraqe, K.
(2019).
Spectral Analysis of Hand Tremors Induced During a Fatigue Test. 2019 IEEE 32ND INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS).
658-663.
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Freeze, J., Erraguntla, M., & Verma, A.
(2018).
Data Integration and Predictive Analysis System for Disease Prophylaxis: Incorporating Dengue Fever Forecasts. Proceedings of the 50th Hawaii International Conference on System Sciences (2017).
913-922.
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Zahed, K., Sasangohar, F., Zhu, Y., Mehta, R., Erraguntla, M., Lawley, M., & Qaraqe, K.
(2018).
NonInvasive Wearable Sensors to detect onset of hypoglycemia: A Literature Review
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Gonzalez, K., Sasangohar, F., Mehta, R. K., Lawley, M., & Erraguntla, M.
(2017).
Measuring Fatigue through Heart Rate Variability and Activity Recognition: A Scoping Literature Review of Machine Learning Techniques. Proceedings of the Human Factors and Ergonomics Society Annual Meeting.
1748-1752.
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Erraguntla, M., Freeze, J., Delen, D., Madanagopal, K., Mayer, R., & Khojasteh, J.
(2017).
Data Integration and Predictive Analysis System for Disease Prophylaxis. Proceedings of the 50th Hawaii International Conference on System Sciences (2017).
1132-1141.
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Benjamin, P., Madanagopal, K., Erraguntla, M., & Corlette, D.
(2016).
Distributed information gathering, exploration and sensemaking toolkit (DIGEST). 449-455.
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Erraguntla, M., Tomasulo, P., Land, K., Kamel, H., Bravo, M., Whitaker, B., Mayer, R., & Khaire, S.
(2014).
Data Mining to Improve Safety of Blood Donation Process. 2014 47TH HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES (HICSS).
789-795.
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Erraguntla, M., May, L., Gopal, B., Mayer, R. J., & Benjamin, P. C.
(2012).
Open source text based biovigilance. 417-422.
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Erraguntla, M., Gopal, B., Ramachandran, S., & Mayer, R.
(2012).
Inference of Missing ICD 9 Codes Using Text Mining and Nearest Neighbor Techniques. 2012 45th Hawaii International Conference on System Sciences.
1060-1069.
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Kamel, H., Townsend, M., Schroeder, K., Bravo, M., Erraguntla, M., Whitaker, B., Tomasulo, P. A., & Land, K.
(2011).
United States Donor Hemovigilance System: Experience of 3 Blood Centers. Transfusion.
32A-32A.
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Erraguntla, M., Ramachandran, S., Wu, C., Mayer, R. J., & IEEE, ..
(2010).
Avian Influenza Datamining Using Environment, Epidemiology, and Etiology Surveillance and Analysis Toolkit (E3SAT). 2014 47TH HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES (HICSS).
1152-1158.
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Erraguntla, M., Ramachandran, S., Wu, C., & Mayer, R. J.
(2010).
Avian Influenza Datamining Using Environment, Epidemiology, and Etiology Surveillance and Analysis Toolkit (E3SAT). 1-7.
-
Erraguntla, M., & Mayer, R. J.
(2007).
Medical material management support using data mining and analytics. 1578-1588.
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Ramachandran, S., Erraguntla, M., Mayer, R., & Benjamin, P.
(2007).
Data Mining in Military Health Systems - Clinical and Administrative Applications. 158-163.
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Ramachandran, S., Erraguntla, M., Mayer, R., Benjamin, P., & IEEE, ..
(2007).
Data mining in military health systems - Clinical and administrative applications. 2007 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING, VOLS 1-3.
584-589.
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Graul, M., Benjamin, P., Patki, M., & Erraguntla, M.
(2006).
A framework for modeling and simulation at multiple levels of abstraction. 35th European Mask and Lithography Conference (EMLC 2019).
622706-622706-9.
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Painter, M., Erraguntla, M., Hogg, G., & Beachkofski, B.
(2006).
Using Simulation, Data Mining, and Knowledge Discovery Techniques for Optimized Aircraft Engine Fleet Management. 2007 Winter Simulation Conference.
1253-1260.
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Benjamin, P., Ramachandran, S., & Erraguntla, M.
(2005).
Towards a method for ontology driven systems reengineering (ODSR). 143-150.
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Benjamin, P., Koola, P., Ramachandran, S., Erraguntla, M., & Fernandes, R.
(2004).
Adaptive Knowledge Discovery Framework (AKDF). 80-82.
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Benjamin, P., Graul, M., & Erraguntla, M.
(2002).
Toolkit for Enabling Adaptive Modeling and Simulation (TEAMS). 1994 WINTER SIMULATION CONFERENCE PROCEEDINGS.
763-771.
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Benjamin, P., Ramachandran, S., Erraguntla, M., & Delen, D.
(2000).
Towards a Knowledge Discovery Wizard (KDW). 267-273.
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Delen, D., Benjamin, P. C., & Erraguntla, M.
(1999).
An integrated toolkit for enterprise modeling and analysis. 1994 WINTER SIMULATION CONFERENCE PROCEEDINGS.
289-297.
-
Benjamin, P., Erraguntla, M., Mayer, R., Painter, M., & Marshall, C.
(1998).
A framework for adaptive process modeling and execution (FAME). 3-9.
-
Benjamin, P. C., Delen, D., & Erraguntla, M.
(1998).
An introduction to using PROSIM for business process simulation and analysis. 1994 WINTER SIMULATION CONFERENCE PROCEEDINGS.
315-321 vol.1-321.
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Delen, D., Benjamin, P. C., & Erraguntla, M.
(1998).
Integrated Modeling and Analysis Generator Environment (IMAGE): a decision support tool. 1994 WINTER SIMULATION CONFERENCE PROCEEDINGS.
1401-1408 vol.2-1408.
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Benjamin, P., Erraguntla, M., Delen, D., & Mayer, R.
(1998).
Simulation modeling at multiple levels of abstraction. 1994 WINTER SIMULATION CONFERENCE PROCEEDINGS.
391-398 vol.1-398.
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Dalal, M. A., Erraguntla, M., & Benjamin, P.
(1997).
An introduction to using ProSim for business process simulation and analysis. 1994 WINTER SIMULATION CONFERENCE PROCEEDINGS.
718-724.
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Cecil, J., Erraguntla, M., & Mayer, R.
(1994).
A fuzzy inferential approach for the diagnosis of defects in electronics assembly. 70-74 vol.1-74.
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Erraguntla, M., Benjamin, P. C., & Mayer, R. J.
(1994).
An architecture of a knowledge-based simulation engine. 1994 WINTER SIMULATION CONFERENCE PROCEEDINGS.
673-680.
-
Islam, M. S., Qaraqe, M. K., Abbas, H. T., Erraguntla, M., & Abdul-Ghani, M.
(2020).
The Prediction of Diabetes Development: A Machine Learning Framework. 1-6.
-
institutional repository document
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Ziyadidegan, S., Razavi, M., Pesarakli, H., Javid, A. H., & Erraguntla, M.
(2021).
Factors affecting the COVID-19 risk in the US counties: an innovative approach by combining unsupervised and supervised learning
-
Ziyadidegan, S., Razavi, M., Pesarakli, H., Javid, A., & Erraguntla, M.
(2021).
Factors affecting the COVID-19 risk in the US counties: an innovative approach by combining unsupervised and supervised learning
-
Dave, D., Erraguntla, M., Lawley, M., DeSalvo, D., Haridas, B., McKay, S., & Koh, C.
(2021).
Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study (Preprint)
-
Zahed, K., Sasangohar, F., Mehta, R., Erraguntla, M., & Qaraqe, K.
(2020).
Diabetes Management Experience and the State of Hypoglycemia: National Online Survey Study (Preprint)
-
Abbas, H., Alic, L., Erraguntla, M., Ji, J., Abdul-Ghani, M., Abbasi, Q., & Qaraqe, M.
Predicting long-term Type 2 Diabetes with Support Vector Machine using Oral Glucose Tolerance Test
-
Ziyadidegan, S., Razavi, M., Pesarakli, H., Javid, A. H., & Erraguntla, M.
(2021).
Factors affecting the COVID-19 risk in the US counties: an innovative approach by combining unsupervised and supervised learning
Research
principal investigator on
co-principal investigator on
- SenSE: Multimodal noninvasive wearable sensors and machine learning for predicting critical glycemic events awarded by Directorate for Engineering - (Arlington, Virginia, United States) 2020 - 2023
- IPA for Madhav Erraguntla awarded by United States Department of Veterans Affairs - (Washington D.C., District of Columbia, United States) 2016 - 2017
Teaching
teaching activities
- ISEN302 Econ Anly Engr Project Instructor
- ISEN310 Uncertainty Modeling For Ie Instructor
- ISEN340 Operations Research Ii Instructor
- ISEN413 Advanced Data Analytics Instructor
- ISEN491 Hnr-research Instructor
- ISEN613 Engr Data Analysis Instructor
- ISEN667 Engineering Economy Instructor
- ISEN684 Professional Internship: In-ab Instructor
- ISEN691 Research Instructor
- ISEN692 Professional Study Instructor
Background
education and training
- Ph.D. in Industrial Engineering, Texas A&M University - (College Station, Texas, United States) 1996
- M.Tech. in Industrial Engineering, National Institute of Industrial Engineering - (Mumbai, Maharashtra, India) 1989
- B.T. in Mechanical Engineering, Sri Venkateswara University - (Tirumala - Tirupati, Andhra Pradesh, India) 1987
Contact
first name
- Madhav
has last name
- Erraguntla
mailing address
-
Texas A&M University
Industrial Engineering
3131 TAMU
College Station, TX 77843-3131
USA
Other
- merraguntla@tamu.edu