Dr. Sumit K. Jha is Associate Professor of Computer Science at the University of Central Florida, Orlando. His research focusses on formal methods. He has applied formal methods to problems in computational systems biology, cyber-physical systems, cyber-security, computational finance, emerging computer architectures, hybrid and stochastic systems.Dr. Jha received his Ph.D. in Computer Science with Dr. Christopher James Langmead at Carnegie Mellon University. Before joining Carnegie Mellon, he graduated with B.Tech (Honors) in Computer Science and Engineering from the Indian Institute of Technology Kharagpur. Dr. Jha has worked on R Address: EECS Department University of Central Florida Orlando FL 32816
Abstract—Exhaustive state space exploration based verification of embedded system designs remains... more Abstract—Exhaustive state space exploration based verification of embedded system designs remains a challenge despite three decades of active research into Model Checking. On the other hand, simulation based verification of even critical embedded system designs is often subject to financial budget considerations in practice. In this paper, we suggest an algorithm that minimizes the overall cost of producing an embedded system including the cost of testing the embedded system and expected losses from an incompletely tested ...
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine - BCB '12, 2012
Abstract Computational Systems Modeling could play a significant role in improving and speeding u... more Abstract Computational Systems Modeling could play a significant role in improving and speeding up of the drug development process. By the incorporation of cellular modeling into a High Information Content Drug Screening platform the information content of the pharmacological test could be significantly increased through a deeper understanding of cellular pathways and signaling mechanisms. Unfortunately, many of the cellular signaling pathways in the cells are yet to be explored. Moreover, which is an even larger problem, ...
2012 IEEE 2nd International Conference on Computational Advances in Bio and medical Sciences (ICCABS), 2012
Abstract Stochastic models are often used to study the behavior of biochemical systems and biomed... more Abstract Stochastic models are often used to study the behavior of biochemical systems and biomedical devices. While the structure of such models is often readily available from first principles, several quantitative features of the model are not easily determined. These quantitative features are often incorporated into the model as parameters. The algorithmic discovery of parameter values from experimentally observed facts (including extreme-scale data) remains a challenge for the computational systems biology community. In this paper, ...
2012 IEEE 2nd International Conference on Computational Advances in Bio and medical Sciences (ICCABS), 2012
Abstract Stochastic Differential Equation (SDE) models are often used to model the dynamics of co... more Abstract Stochastic Differential Equation (SDE) models are often used to model the dynamics of complex biological systems. The stochastic nature of these models means that some behaviors are more likely than others. It is often the case that a model's primary purpose is to study rare but interesting or important behaviors, such as the formation of a tumor, or the failure of a cyber-physical system. Unfortunately, due to the limited availability of analytic methods for SDEs, stochastic simulations are the most common means for ...
International Journal of Bioinformatics Research and Applications, 2014
Stochastic Differential Equation (SDE) models are used to describe the dynamics of complex system... more Stochastic Differential Equation (SDE) models are used to describe the dynamics of complex systems with inherent randomness. The primary purpose of these models is to study rare but interesting or important behaviours, such as the formation of a tumour. Stochastic simulations are the most common means for estimating (or bounding) the probability of rare behaviours, but the cost of simulations increases with the rarity of events. To address this problem, we introduce a new algorithm specifically designed to quantify the likelihood of rare behaviours in SDE models. Our approach relies on temporal logics for specifying rare behaviours of interest, and on the ability of bit-vector decision procedures to reason exhaustively about fixed-precision arithmetic. We apply our algorithm to a minimal parameterised model of the cell cycle, and take Brownian noise into account while investigating the likelihood of irregularities in cell size and time between cell divisions.
ABSTRACT The formal verification of large probabilistic models is important and challenging. Expl... more ABSTRACT The formal verification of large probabilistic models is important and challenging. Exploiting the concurrency that is often present is one way to address this problem. Here we study a restricted class of asynchronous distributed probabilistic systems in which the synchronizations determine the probability distribution for the next moves of the participating agents. The key restriction we impose is that the synchronizations are deterministic, in the sense that any two simultaneously enabled synchronizations must involve disjoint sets of agents. As a result, this network of agents can be viewed as a succinct and distributed presentation of a large global Markov chain. A rich class of Markov chains can be represented this way. We define an interleaved semantics for our model in terms of the local synchronization actions. The network structure induces an independence relation on these actions, which, in turn, induces an equivalence relation over the interleaved runs in the usual way. We construct a natural probability measure over these equivalence classes of runs by exploiting Mazurkiewicz trace theory and the probability measure space of the associated global Markov chain. It turns out that verification of our model, called DMCs (distributed Markov chains), can often be efficiently carried out by exploiting the partial order nature of the interleaved semantics. To demonstrate this, we develop a statistical model checking (SMC) procedure and use it to verify two large distributed probabilistic networks.
2011 IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), 2011
Abstract The development of mechanistic computational models of biological systems continues to b... more Abstract The development of mechanistic computational models of biological systems continues to be an iterative manual process that requires considerable insight and patience from the modeler. An abundance of high throughput experimental methods has caused a rapid explosion of factual information about the behavior of biochemical systems. A signature based symbolic approach for automatic synthesis of biological circuits is suggested and experimental evidence that such an approach would be computationally ...
2012 ASE/IEEE International Conference on BioMedical Computing (BioMedCom), 2012
ABSTRACT Modern epidemiology has made use of a number of mathematical models, including ordinary ... more ABSTRACT Modern epidemiology has made use of a number of mathematical models, including ordinary differential equation (ODE) based models and agent based models (ABMs) to describe the dynamics of how a disease may spread within a population and enable the rational design of strategies for intervention that effectively contain the spread of the disease. Although such predictions are of fundamental importance in preventing the next global pandemic, there is a significant gap in trusting the outcomes/predictions solely based on such models. Hence, there is a need to develop approaches such that mathematical models can be calibrated against historical data. In addition, there is a need to develop rigorous uncertainty quantification approaches that can provide insights into when a model will fail and characterize the confidence in the (possibly multiple) model outcomes/predictions, when such retrospective analysis cannot be performed. In this paper, we outline an approach to develop uncertainty quantification approaches for epidemiological models using formal methods and model checking. By specifying the outcomes expected from a model in a suitable spatio-temporal logic, we use probabilistic model checking methods to quantify the probability with which the epidemiological model satisfies a given behavioral specification. We argue that statistical model checking methods can solve the uncertainty quantification problem for complex epidemiological models.
International Journal of Bioinformatics Research and Applications, 2014
Stochastic models are increasingly used to study the behaviour of biochemical systems. While the ... more Stochastic models are increasingly used to study the behaviour of biochemical systems. While the structure of such models is often readily available from first principles, unknown quantitative features of the model are incorporated into the model as parameters. Algorithmic discovery of parameter values from experimentally observed facts remains a challenge for the computational systems biology community. We present a new parameter discovery algorithm that uses simulated annealing, sequential hypothesis testing, and statistical model checking to learn the parameters in a stochastic model. We apply our technique to a model of glucose and insulin metabolism used for in-silico validation of artificial pancreata and demonstrate its effectiveness by developing parallel CUDA-based implementation for parameter synthesis in this model.
Abstract—Exhaustive state space exploration based verification of embedded system designs remains... more Abstract—Exhaustive state space exploration based verification of embedded system designs remains a challenge despite three decades of active research into Model Checking. On the other hand, simulation based verification of even critical embedded system designs is often subject to financial budget considerations in practice. In this paper, we suggest an algorithm that minimizes the overall cost of producing an embedded system including the cost of testing the embedded system and expected losses from an incompletely tested ...
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine - BCB '12, 2012
Abstract Computational Systems Modeling could play a significant role in improving and speeding u... more Abstract Computational Systems Modeling could play a significant role in improving and speeding up of the drug development process. By the incorporation of cellular modeling into a High Information Content Drug Screening platform the information content of the pharmacological test could be significantly increased through a deeper understanding of cellular pathways and signaling mechanisms. Unfortunately, many of the cellular signaling pathways in the cells are yet to be explored. Moreover, which is an even larger problem, ...
2012 IEEE 2nd International Conference on Computational Advances in Bio and medical Sciences (ICCABS), 2012
Abstract Stochastic models are often used to study the behavior of biochemical systems and biomed... more Abstract Stochastic models are often used to study the behavior of biochemical systems and biomedical devices. While the structure of such models is often readily available from first principles, several quantitative features of the model are not easily determined. These quantitative features are often incorporated into the model as parameters. The algorithmic discovery of parameter values from experimentally observed facts (including extreme-scale data) remains a challenge for the computational systems biology community. In this paper, ...
2012 IEEE 2nd International Conference on Computational Advances in Bio and medical Sciences (ICCABS), 2012
Abstract Stochastic Differential Equation (SDE) models are often used to model the dynamics of co... more Abstract Stochastic Differential Equation (SDE) models are often used to model the dynamics of complex biological systems. The stochastic nature of these models means that some behaviors are more likely than others. It is often the case that a model's primary purpose is to study rare but interesting or important behaviors, such as the formation of a tumor, or the failure of a cyber-physical system. Unfortunately, due to the limited availability of analytic methods for SDEs, stochastic simulations are the most common means for ...
International Journal of Bioinformatics Research and Applications, 2014
Stochastic Differential Equation (SDE) models are used to describe the dynamics of complex system... more Stochastic Differential Equation (SDE) models are used to describe the dynamics of complex systems with inherent randomness. The primary purpose of these models is to study rare but interesting or important behaviours, such as the formation of a tumour. Stochastic simulations are the most common means for estimating (or bounding) the probability of rare behaviours, but the cost of simulations increases with the rarity of events. To address this problem, we introduce a new algorithm specifically designed to quantify the likelihood of rare behaviours in SDE models. Our approach relies on temporal logics for specifying rare behaviours of interest, and on the ability of bit-vector decision procedures to reason exhaustively about fixed-precision arithmetic. We apply our algorithm to a minimal parameterised model of the cell cycle, and take Brownian noise into account while investigating the likelihood of irregularities in cell size and time between cell divisions.
ABSTRACT The formal verification of large probabilistic models is important and challenging. Expl... more ABSTRACT The formal verification of large probabilistic models is important and challenging. Exploiting the concurrency that is often present is one way to address this problem. Here we study a restricted class of asynchronous distributed probabilistic systems in which the synchronizations determine the probability distribution for the next moves of the participating agents. The key restriction we impose is that the synchronizations are deterministic, in the sense that any two simultaneously enabled synchronizations must involve disjoint sets of agents. As a result, this network of agents can be viewed as a succinct and distributed presentation of a large global Markov chain. A rich class of Markov chains can be represented this way. We define an interleaved semantics for our model in terms of the local synchronization actions. The network structure induces an independence relation on these actions, which, in turn, induces an equivalence relation over the interleaved runs in the usual way. We construct a natural probability measure over these equivalence classes of runs by exploiting Mazurkiewicz trace theory and the probability measure space of the associated global Markov chain. It turns out that verification of our model, called DMCs (distributed Markov chains), can often be efficiently carried out by exploiting the partial order nature of the interleaved semantics. To demonstrate this, we develop a statistical model checking (SMC) procedure and use it to verify two large distributed probabilistic networks.
2011 IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), 2011
Abstract The development of mechanistic computational models of biological systems continues to b... more Abstract The development of mechanistic computational models of biological systems continues to be an iterative manual process that requires considerable insight and patience from the modeler. An abundance of high throughput experimental methods has caused a rapid explosion of factual information about the behavior of biochemical systems. A signature based symbolic approach for automatic synthesis of biological circuits is suggested and experimental evidence that such an approach would be computationally ...
2012 ASE/IEEE International Conference on BioMedical Computing (BioMedCom), 2012
ABSTRACT Modern epidemiology has made use of a number of mathematical models, including ordinary ... more ABSTRACT Modern epidemiology has made use of a number of mathematical models, including ordinary differential equation (ODE) based models and agent based models (ABMs) to describe the dynamics of how a disease may spread within a population and enable the rational design of strategies for intervention that effectively contain the spread of the disease. Although such predictions are of fundamental importance in preventing the next global pandemic, there is a significant gap in trusting the outcomes/predictions solely based on such models. Hence, there is a need to develop approaches such that mathematical models can be calibrated against historical data. In addition, there is a need to develop rigorous uncertainty quantification approaches that can provide insights into when a model will fail and characterize the confidence in the (possibly multiple) model outcomes/predictions, when such retrospective analysis cannot be performed. In this paper, we outline an approach to develop uncertainty quantification approaches for epidemiological models using formal methods and model checking. By specifying the outcomes expected from a model in a suitable spatio-temporal logic, we use probabilistic model checking methods to quantify the probability with which the epidemiological model satisfies a given behavioral specification. We argue that statistical model checking methods can solve the uncertainty quantification problem for complex epidemiological models.
International Journal of Bioinformatics Research and Applications, 2014
Stochastic models are increasingly used to study the behaviour of biochemical systems. While the ... more Stochastic models are increasingly used to study the behaviour of biochemical systems. While the structure of such models is often readily available from first principles, unknown quantitative features of the model are incorporated into the model as parameters. Algorithmic discovery of parameter values from experimentally observed facts remains a challenge for the computational systems biology community. We present a new parameter discovery algorithm that uses simulated annealing, sequential hypothesis testing, and statistical model checking to learn the parameters in a stochastic model. We apply our technique to a model of glucose and insulin metabolism used for in-silico validation of artificial pancreata and demonstrate its effectiveness by developing parallel CUDA-based implementation for parameter synthesis in this model.
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Papers by Sumit K Jha