Associate Professor, Department of Chemical Engineering, (Jointly with) Yardi School of AI
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Cited by
Cited by
Predicting Young's modulus of oxide glasses with sparse datasets using machine learning
S Bishnoi, S Singh, R Ravinder, M Bauchy, NN Gosvami, H Kodamana, ...
Journal of Non-Crystalline Solids 524, 119643, 2019
Gaussian process modelling with Gaussian mixture likelihood
A Daemi, H Kodamana, B Huang
Journal of Process Control 81, 209-220, 2019
Process monitoring using a generalized probabilistic linear latent variable model
R Raveendran, H Kodamana, B Huang
Automatica 96, 73-83, 2018
Deep learning aided rational design of oxide glasses
R Ravinder, KH Sridhara, S Bishnoi, HS Grover, M Bauchy, H Kodamana, ...
Materials horizons 7 (7), 1819-1827, 2020
Approaches to robust process identification: A review and tutorial of probabilistic methods
H Kodamana, B Huang, R Ranjan, Y Zhao, R Tan, N Sammaknejad
Journal of Process Control 66, 68-83, 2018
A gap metric based multiple model approach for nonlinear switched systems
K Hariprasad, S Bhartiya, RD Gudi
Journal of process control 22 (9), 1743-1754, 2012
Reinforcement learning based optimization of process chromatography for continuous processing of biopharmaceuticals
N Saxena, A Tiwari, D Sonawat, H Kodamana, AS Rathore
Chemical Engineering Science 230, 116171, 2020
Mixtures of Probabilistic PCA With Common Structure Latent Bases for Process Monitoring
H Kodamana, R Raveendran, B Huang
IEEE Transactions on Control Systems Technology, 2018
Scalable Gaussian processes for predicting the optical, physical, thermal, and mechanical properties of inorganic glasses with large datasets
S Bishnoi, R Ravinder, HS Grover, H Kodamana, NMA Krishnan
Materials advances 2 (1), 477-487, 2021
Multi-objective dynamic optimization of hybrid renewable energy systems
R Sharma, H Kodamana, M Ramteke
Chemical Engineering and Processing-Process Intensification 170, 108663, 2022
Twin actor twin delayed deep deterministic policy gradient (TATD3) learning for batch process control
T Joshi, S Makker, H Kodamana, H Kandath
Computers & Chemical Engineering 155, 107527, 2021
Semi‐supervised dynamic latent variable modeling: I/O probabilistic slow feature analysis approach
L Fan, H Kodamana, B Huang
AIChE Journal 65 (3), 964-979, 2019
Prediction of ENSO beyond spring predictability barrier using deep convolutional LSTM networks
M Gupta, H Kodamana, S Sandeep
IEEE Geoscience and Remote Sensing Letters 19, 1-5, 2020
Identification of robust probabilistic slow feature regression model for process data contaminated with outliers
L Fan, H Kodamana, B Huang
Chemometrics and Intelligent Laboratory Systems, 2018
A novel approach to process operating mode diagnosis using conditional random fields in the presence of missing data
M Fang, H Kodamana, B Huang, N Sammaknejad
Computers & Chemical Engineering 111, 149-163, 2018
Robust Identification of Nonlinear Errors-in-variables Systems with Parameter Uncertainties Using Variational Bayesian Approach
F Guo, H Kodamana, Y Zhao, B Huang, Y Ding
IEEE Transactions on Industrial Informatics, 2017
A stabilizing sub-optimal model predictive control for quasi-linear parameter varying systems
S Mate, H Kodamana, S Bhartiya, PSV Nataraj
IEEE Control Systems Letters 4 (2), 402-407, 2019
A computationally efficient robust tube based MPC for linear switched systems
K Hariprasad, S Bhartiya
Nonlinear Analysis: Hybrid Systems 19, 60-76, 2016
An adaptive, interacting, cluster-based model for predicting the transmission dynamics of COVID-19
R Ravinder, S Singh, S Bishnoi, A Jan, A Sharma, H Kodamana, ...
Heliyon 6 (12), 2020
Reinforcement learning based control of batch polymerisation processes
V Singh, H Kodamana
IFAC-PapersOnLine 53 (1), 667-672, 2020
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