Dr. Rao is the director of the Laboratory for Theory and Mathematical Modeling and professor at the Medical College of Georgia at Augusta University.
Until 2012, he held a permanent faculty position at Indian Statistical Institute, Kolkata. He has taught and/or performed research at several premier institutions including, the Indian Statistical Institute (ISI), University of Oxford (Oxford, UK), the Indian Institute of Science (Bengaluru, India) and the University of Guelph, (Guelph, Canada) prior to his arrival at Augusta University.
Dr. Rao developed the first AI model in the world for the identification of COVID-19 through apps. His recent works on partition theorem in populations, Chicken-walk models, Rao's Partition Theorem in Populations, and Rao-Carey Fundamental Theorem in stationary populations, Multilevel Contours, and Bundles of Complex Planes are well received. His recent EDLM (Exact Deep Learning Machines) are anticipated as a game changer in AI related experiments.
Areas of Expertise (4)
Invited Speaker for American Mathematical Society (January 2024) (professional)
Invited Speaker for American Mathematical Society Special Session at The Joint Mathematics Meetings in San Francisco.
Invited Speaker for American Mathematical Society (January 2023) (professional)
Invited to be the speaker for the Special Session at The Joint Mathematics Meetings in Boston.
GIAN, MED, Govt of India, External Foreign Faculty lecturer (June 2022) (professional)
Delivered a presentation hosted by the Department of Education in Science and Mathematics within the National Council of Educational Research and Training of India. The presentation, “United Nation’s Annual Country-Wise Human Development Index,” was part of the group’s National Student Outreach Program and the Pravasi Bharatiya Academic and Scientific Sampark’s series “Integrating Indian Diaspora with the Motherland.”
John H Conway Invited Lecture Speaker (2022) (professional)
John Conway Spirited Seminar series started in 2021 by the Department of Mathematics at Syed Babar Ali School of Science and Engineering-LUMS, Pakistan. These seminars allow mathematicians from all over the World to indulge in productive discussions on recently evolved theories, expositions, and research.
member of AI-enabled Technologies & Systems Domain Expert Group (DEG) (2001) (professional)
Currently serving as a member of AI-enabled Technologies & Systems Domain Expert Group (DEG), constituted in 2021 by The Council of Scientific & Industrial Research (CSIR), Government of India.
ISCB23 Conference Awards for Scientists 2002, Dijon, France
An award presented by The International Society for Clinical Biostatistics.
Heiwa Nakajima Foundation Award, Tokyo
An awared presented by Heiwa Nakajima Foundation
Gold Medal for M.Sc Topper
An award presented by Andhra University
Deemed University, Internation: Doctoral degree, Demography and Population Stud
Andhra University: Master's degree, Pre-Medicine/Pre-Medical Studi
- Handbook of Statistics (Elsevier/North-Holland, Amsterdam)
- Demography India
- Complex Analysis and Operator Theory (CAOT), Springer
- Handbook of Statistics, Elsevier
- Demography, Duke University Press
- Journal of Indian Institute of Science (JIISc), Springer
- Journal of the Indian Society for Probability and Statistics, Springer
- Journal of Mathematical Analysis and Applications (JMAA), Elsevier
Media Appearances (6)
Reevaluating the United Nations' Human Development Index: A Critique of Historical Context and Colonialism's Impact on Wealth and Development
Arni S.R. Srinivasa Rao, PhD, has presented a talk on the United Nations Development Programme's (UNDP) Human Development Index (HDI) as part of a National Student Outreach Program in India. Rao's presentation, "United Nation’s Annual Country-Wise Human Development Index," criticized the HDI's lack of historical context and its failure to account for the impact of colonialism on countries' wealth. Rao argued that the HDI should consider the economic, social, and developmental history of countries over the past 5,000 years, rather than starting from 1990.
MCG: Math model shows millions of COVID cases may have gone unreported
Researchers found millions of COVID cases may have gone unreported in the first two and half years of the pandemic. According to the World Health Organization, there have been more than half a billion COVID cases and more than 6 million deaths reported worldwide. As staggering as that sounds, a new study, including research from a Medical College of Georgia professor found the true number of COVID cases is very likely much higher than we realize. “Back when the pandemic started, people were at home, and then some of them were not sure of about the symptoms,” said Dr. Arni Rao.
As few as 1 in 5 COVID cases may have been counted worldwide, mathematical models suggest
Medical Xpres online
Mathematical models indicate that as few as one in five cases of COVID-19 which occurred during the first 29 months of the pandemic are accounted for in the half billion cases officially reported. The World Health Organization reported 513,955,910 cases from Jan. 1, 2020 to May 6, 2022 and 6,190,349 deaths, numbers which already moved COVID-19 to a top killer in some countries, including the United States, just behind heart disease and cancer, according to the Centers for Disease Control and Prevention. Still mathematical models indicate overall underreporting of cases ranging from 1 in 1.2 to 1 in 4.7, investigators report in the journal Current Science. That underreporting translates to global pandemic estimates between 600 million and 2.4 billion cases.
Mathematical modeling draws more accurate picture of coronavirus cases
Augusta University Jagwire online
Mathematical modeling can take what information is reported about the coronavirus, including the clearly underreported numbers of cases, factor in knowns like the density and age distribution of the population in an area, and compute a more realistic picture of the virus’ infection rate, numbers that will enable better prevention and preparation, modelers say.
App, AI work together to provide rapid at-home assessment of coronavirus risk
Augusta University Jagwire
A coronavirus app coupled with machine intelligence will soon enable an individual to get an at-home risk assessment based on how they feel and where they’ve been in about a minute, and direct those deemed at risk to the nearest definitive testing facility, investigators say.
Professor develops new system to track spread of coronavirus
Augusta University Jagwire online
The death toll from the coronavirus has risen to nearly 500, and the number of virus cases has climbed to almost 25,000, including 11 in the United States. As public health officials work to stop the spread of the illness, an Augusta University researcher has developed a mathematical model and algorithm to help health organizations track the spread of potential outbreaks.
Stationary status of discrete and continuous age-structured population modelsMathematical Biosciences
Rao, A.S.R.S*. and Carey J.R.
From Leonhard Euler to Alfred Lotka and in recent years understanding the stationary process of the human population has been of central interest to scientists. Population reproductive measure NRR (net reproductive rate) has been widely associated with measuring the status of population stationarity and it is also included as one of the measures in the millennium development goals. This article argues how the partition theorem-based approach provides more up-to-date and timely measures to find the status of the population stationarity of a country better than the NRR-based approach. We question the timeliness of the value of NRR in deciding the stationary process of the country. We prove associated theorems on discrete and continuous age distributions and derive measurable functional properties. The partitioning metric captures the underlying age structure dynamic of populations at or near stationarity. As the population growth rates for an ever-increasing number of countries trend towards replacement levels and below, new demographic concepts and metrics are needed to better characterize this emerging global demography.
Indian courts of law can benefit immensely by adopting artificial intelligence methods in bail applications for speedy and accurate justiceHandbook of Statistics
Rao, A. S. R. S.*, & Gore, A. P.
A new thrust has emerged a few years ago under the leadership of Justice Dr. D. Y. Chandrachud of the Supreme Court of India (He is currently the Chief Justice of India) to digitize the work of courts (Government.com, 2023). Much progress has been made with consequent relief to plaintiffs and other participants. The possibility of using AI has also been mooted by (retired) Chief Justice of the Supreme Court Mr. Sharad Bobade (Mr. Sharad Bobade's statement, 2023). Fortunately, there is clarity that AI is to assist and not substitute judges. This aspect of technology does not seem to have made any serious inroads into the judicial system. Whatever the complexities involved in the courts of law, the judges needed to go through a set of rules and constitutional points before passing a judgment on a particular case. In the implementation of a set of rules through AI for passing a judgment for a specific case, there shouldn't be any scope for chance factors. Mathematical logical constructions are like having well-defined rules of law and no scope for subjectivity in implementing the law. Through this article, we provide a proof of concept of adapting deep learning techniques, especially exact deep learning techniques that can benefit Indian courts of law. Such techniques can be very much useful in relatively simple and routine but very numerous cases to expedite judgments. Even in complicated cases involving arguments and counterarguments, these approaches can play a constructive role. We recommend using such automation for producing draft judgments in a more expeditious and yet equally effective manner in various courts of law in India.
Markov Chain Models for Cardiac Rhythm Dynamics in Patients Undergoing Catheter Ablation of Atrial FibrillationBulletin of Mathematical Biology
Lee, T.J., Berman, A.E. & Rao, A.S.R.S*.
We have developed a novel Markov Chain modeling system that considers vectors of patients with atrial fibrillation (AF) by their AF status over a period of time. Our model examines the impact of catheter ablation of AF upon the dynamics of a patient’s AF status and their potential return to sinus rhythm. We prove several theorems to determine the probabilities of patients achieving sinus rhythm or progressing to permanent AF. Additionally, we observed aggregation of patients within the paroxysmal AF state in simulation. The aggregating property of Markov chains illustrated the potential benefits of catheter ablation on healthcare resource allocation.
Exact deep learning machinesDeep Learning, Handbook of Statistics
Incorporating actual intelligence into the machines and making them think and perform like humans is impossible. In this chapter, a new kind of machine called the EDLM (exact deep learning machine) is introduced. Such EDLMs can achieve the target with probability one and could be the best alternative for originally designed artificial intelligence models of the mid-20th century by Alan Turing and others that have so far not seen reality. In the current context, achieving a target is defined as detecting a given object accurately.
Randomness and Uncertainty Are Central in Most Walks of LifeJournal of Indian Institute of Science
The Journal of Indian Institute of Science (J IISc) inviting to edit a special issue on statistics and probability is timely as the world has recently celebrated the birth centenary of most celebrated Indian American statistician C. R. Rao (1920–)1,2,3. I had an opportunity to work and closely interact with two of the most famous statisticians of the twentieth century C. R. Rao and British statistician David R. Cox (1924–2022)4, 5. One of the commonalities among these two legends was that they saw the greater importance of both the development of theories in statistics and probability and their applications to the real world and providing leadership in science. Applications of randomness and uncertainty can be seen in most walks of life. Building theories on these concepts have been fascinating to scientists. The insights generated by these two concepts enrich several research domains not only within statistics and probability but also in engineering and other basic sciences. The two legends mentioned above did fundamental theoretical contributions while understanding randomness and uncertainty in nature. The articles in the current special issue of J IISc did cover these concepts substantially.
The formula to compute country-wise Human Development Index by the UNDP is illogicalSocArXiv Papers
Mathematically the geometric rule of computing the human development index by the UNDP launched in 1990 could be valid. However, the three components of the index and its expanded versions like Oxfam's Multidimensional Poverty Index (MPI) stand illogical as long as the wealth of the nations looted and lost during the colonial era is not adjusted
Mathematical analysis and topology of SARS-CoV-2, bonding with cells and unbondingJournal of Mathematical Analysis and Applications
Srinivasa Rao, Arni S. R*.; Krantz, Steven G.
We consider the structure of the novel coronavirus (SARS-Cov-2) in terms of the number of spikes that are critical in bonding with the cells in the host. Bonding formation is considered for selection criteria with and without any treatments. Functional mappings from the discrete space of spikes and cells and their analysis are performed. We found that careful mathematical constructions help in understanding the treatment impacts, and the role of vaccines within a host. Smale's famous 2-D horseshoe examples inspired us to create 3-D visualizations and understand the topological diffusion of spikes from one human organ to another organ. The pharma industry will benefit from such an analysis for designing efficient treatment and vaccine strategies.
Disease Modelling and Public Health,Herb Tandree Philosophy Bks.
Disease Modelling and Public Health, Part A, Volume 36 addresses new challenges in existing and emerging diseases with a variety of comprehensive chapters that cover Infectious Disease Modeling, Bayesian Disease Mapping for Public Health, Real time estimation of the case fatality ratio and risk factor of death, Alternative Sampling Designs for Time-To-Event Data with Applications to Biomarker Discovery in Alzheimer's Disease, Dynamic risk prediction for cardiovascular disease: An illustration using the ARIC Study, Theoretical advances in type 2 diabetes, Finite Mixture Models in Biostatistics, and Models of Individual and Collective Behavior for Public Health Epidemiology. As a two part volume, the series covers an extensive range of techniques in the field. It present a vital resource for statisticians who need to access a number of different methods for assessing epidemic spread in population, or in formulating public health policy.