PUBLICATIONS

Books

  1. N M Anoop Krishnan, Hariprasad Kodamana, Ravinder Bhattoo., 2024, Machine Learning for Materials Discovery: Numerical Recipes and Practical Applications (Springer International Publishing)

Peer Reviewed Publications

Google Scholar Profile
  1. Sharma, S., Kumar, A., Bakshi, B., Kodamana, H. and Ramteke, M. 2026. Sustainable Agricultural Residue Management in the Indo-Gangetic Plains for Cost-Effective Climate and Water Resilience.Industrial & Engineering Chemistry Research .
  2. Maity, D., Kodamana, H. and Ramteke, M., 2026. A Framework for Computationally Efficient Process Simulation with Machine Learning Aided Flash Calculations. Chemical Engineering Research and Design.
  3. Gupta, M., Jain, S., Ramani, V., Kodamana, H. and Ranu, S., 2026. Position: Graph Condensation Needs a Reset—Move Beyond Full-dataset Training and Model-Dependence. arXiv preprint arXiv:2605.18893.
  4. Alam, M.N., Bhat, S.U., Kodamana, H. and Rathore, A.S., 2026. Integrating mechanistic modelling with a multi-agent reinforcement learning framework for stage-wise optimization of protein production in E. coli. Computers & Chemical Engineering, p.109692.
  5. Kundu, K., Kumar, A., Kodamana, H. and Pant, K.K., 2026. Data-driven optimization of catalyst composition and reaction temperature for hydrogen-rich syngas yield from biomass gasification: Experimental validation and characterization. Energy Conversion and Management, 354, p.121243.
  6. Kumar, D., Anwer, S., Alhajaj, A., Ramteke, M. and Kodamana, H., 2026. Thermophysical Property Prediction and Optimization of CO2–Binding Organic Liquids Using Kolmogorov–Arnold Networks and Optimized Quantum Descriptors. Industrial & Engineering Chemistry Research, 65(11), pp.6375-6392.
  7. Dixit, J., Kodamana, H., Sandeep, S. and AchutaRao, K.M., 2026. Deep Learning–Based Correction of Decadal Predictions of the PDO and TAG Indices. IEEE Geoscience and Remote Sensing Letters
  8. Arjun, M., Tandon, R., Gupta, A., Kodamana, H. and Ramteke, M. 2026. MIRACLE: Model-free Imitation and Reinforcement Learning for Adaptive Cut-Selection. The Fourteenth International Conference on Learning Representations (ICLR 2026)
  9. Kumar, A., Nazemi, F., Kodamana, H., Ramteke, M. and Bakshi, B., 2025. Large Language Model-based Framework to Retrieve Life Cycle Inventory and Environmental Impact Data from Scientific Literature. Environmental Science & Technology
  10. Alam, M.N., Bhat, S.U., Kodamana, H. and Rathore, A.S., 2025. A data-driven symbolic regression framework for modelling and multi-objective optimization of a microbial fermentation system. Industrial & Engineering Chemistry Research
  11. Arjun, M., You, F., Ramteke, M. and Kodamana, H., 2025. Hierarchical Reinforcement Learning with Dynamic Meta Agent for Adaptive Cut Selection in Integer Programming with Applications to Sensor Network Design. Industrial & Engineering Chemistry Research
  12. Arjun, M., Ramteke, M. and Kodamana, H., 2025. Comparison of Actor-Critic Reinforcement Learning Methods for Dynamic Cut Selection for Integer Programming with Applications to Sensor Network Design. Computers & Chemical Engineering
  13. Goswami, U., Kumar, D., Kodamana, H. and Ramteke, M., 2025. Multi-objective Optimization of Hydrocracking Processes Using Graph Neural Differential Equations. Chemical Engineering Research & Design
  14. Anto, A., Kumar, D., Kodamana, H. and Ramteke, M., 2025. Adaptive Fault Detection via Machine Unlearning. Computers & Chemical Engineering
  15. Rani, J., Tripura, T., Kodamana, H. and Chakraborty, S., 2025. Generative adversarial wavelet neural operator with applications to fault detection and isolation of multivariate time series data. Control Engineering Practice
  16. Sharma, S., K.M. Anirudh., Sandeep, S., Bakshi, B., Ramteke, M. and Kodamana, H., 2025. Multi-objective Optimization for Sustainable Agricultural Strategies Considering Future Climatic Scenarios: A Study in the Context of India Climatic Scenarios. Computers & Chemical Engineering
  17. Goswami, U., Kumar, D., Kodamana, H. and Ramteke, M., 2025. Change Point Detection of Processes using Graph Neural Differential Convolutional Network with Contrastive Loss. Process Safety and Environmental Protection
  18. Rathore, R., Bakshi, B.R., Kodamana, H. and Ramteke, M., 2025. Multi-Objective Optimization of Crop Land Allocation for Sustainability within the Food-Energy-Water-Land-Fertilizer (FEWLF) Nexus. Computers & Chemical Engineering, p.109207.
  19. Kumar, D., Dixit, V., Ramteke, M. and Kodamana, H., 2025. Learning System Physics Using Symbolic Neural Integration (SyNISM) with Applications to Chemical Processes. Industrial & Engineering Chemistry Research, 4 (23), 11441-11458
  20. Sahu, P.L., Sandeep, S. and Kodamana, H., 2025. Evaluating global machine learning models for tropical cyclone dynamics and thermodynamics. Journal of Geophysical Research: Machine Learning and Computation, 2(2), p.e2025JH000594.
  21. Anirudh, K.M., Raj, P., Sandeep, S., Kodamana, H. and Sabeerali, C.T., 2025. A skillful prediction of monsoon intraseasonal oscillation using deep learning. Journal of Geophysical Research: Machine Learning and Computation, 2(2), p.e2024JH000504.
  22. Arjun, M., Ramteke, M., and Kodamana, H., 2025. Benchmarking of Multi-Agent Reinforcement Learning Strategies for Optimizing Cutting Plane Selection. IFAC Papers Online (14th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems).
  23. Gupta, N., Kandath, H. and Kodamana, H., 2025. An adversarial twin-agent inverse proximal policy optimization guided by model predictive control. Computers & Chemical Engineering, 199, p.109124.
  24. Kumar, A., Jami, H.C., Bakshi, B.R., Ramteke, M. and Kodamana, H., 2025. An evolutionary study on technologies for polyethylene terephthalate waste recycling using natural language processing. Computers & Chemical Engineering, p.109011.
  25. Jain, N., Roy, S., Kodamana, H. and Nair, P., 2025. Scaling the predictions of multiphase flow through porous media using operator learning. Chemical Engineering Journal, 503, p.157671.
  26. Alam, M.N., Anurag, A., Gangwar, N., Ramteke, M., Kodamana, H. and Rathore, A.S., 2025. Physics-informed neural networks guided modelling and multiobjective optimization of a mAb production process. The Canadian Journal of Chemical Engineering.
  27. Rani, J., Goswami, U., Kodamana, H. and Tamboli, P.K., 2025. Reconstruction error-based fault detection of time series process data using generative adversarial auto-encoders. The Canadian Journal of Chemical Engineering, 103(3), pp.1213-1228.
  28. Kumar, A. and Kodamana, H., 2025. Process Modeling and Optimal Evaluation Analysis for Direct CO2 Conversion to Methanol.Comprehensive Methanol Science, p. 190-210
  29. Gupta, M., Jain, S., Ramani, V., Kodamana, H. and Ranu, S., 2025. Bonsai: Gradient-free Graph Distillation for Node Classification. Thirteenth International Conference on Learning Representations 2025 (ICLR 2025).
  30. Jami, H.C., Singh, P.R., Kumar, A., Bakshi, B.R., Ramteke, M. and Kodamana, H., 2024. CCU-Llama: A Knowledge Extraction LLM for Carbon Capture and Utilization by Mining Scientific Literature Data. Industrial & Engineering Chemistry Research, 63(41), pp.17585-17598.
  31. Kumar, D., Ramteke, M. and Kodamana, H., 2024. A framework for model maintenance using kernel-based forward propagating neural networks. Chemical Engineering Research and Design, 210, pp.352-364.
  32. Kundu, K., Kumar, A., Kodamana, H. and Pant, K.K., 2024. Obtaining high H2-rich syngas yield and carbon conversion efficiency from biomass gasification: From characterization to process optimization using machine learning with experimental validation. Fuel, 378, p.132931.
  33. Mandal, S., Balraj, K., Kodamana, H., Arora, C., Clark, J.M., Kwon, D.S. and Rathore, A.S., 2024. Weakly supervised large-scale pancreatic cancer detection using multi-instance learning. Frontiers in Oncology, 14, p.1362850.
  34. Goswami, U., Kodamana, H. and Ramteke, M., 2024. Fault detection using Graph Neural Differential Auto-encoders (GNDAE). Computers & Chemical Engineering, 189, p.108775.
  35. Haq, A., Bakshi, B.R., Kodamana, H. and Ramteke, M., 2024. Assessing the effectiveness of improving urban air quality with solutions based on technology, nature and policy. Sustainable Cities and Society, 110, p.105549.
  36. Gupta, N., Anand, S., Kumar, D., Ramteke, M., Kandath, H. and Kodamana, H., 2024. A Twin Agent Reinforcement Learning Framework by Integrating Deterministic and Stochastic Policies. Industrial & Engineering Chemistry Research, 63(24), pp.10692-10703.
  37. Alam, M.N., Anupa, A., Kodamana, H. and Rathore, A.S., 2024. A deep learning-aided multi-objective optimization of a downstream process for production of monoclonal antibody products. Biochemical Engineering Journal, 208, p.109357.
  38. Keerthiveena, B., Sheikh, M.T., Kodamana, H. and Rathore, A.S., 2024. DeepDepth: Prediction of O (6)-methylguanine-DNA methyltransferase genotype in glioblastoma patients using multimodal representation learning based on deep feature fusion. Neural Computing and Applications, 36(19), pp.11507-11523.
  39. Sharma, R., Bakshi, B.R., Ramteke, M. and Kodamana, H., 2024. Quantifying ecosystem services from trees by using i-tree with low-resolution satellite images. Ecosystem Services, 67, p.101611.
  40. Goswami, U., Rani, J. and Kodamana, H., 2024. Neural Ordinary Differential Equations Auto-Encoder for Fault Detection in Process Systems. In Computer Aided Chemical Engineering (Vol. 53, pp. 1867-1872). Elsevier.
  41. Alam, M.N., Bhat, S.U., Kodamana, H. and Rathore, A.S., 2024. DL based real-time prediction of product formation in biopharmaceutical manufacturing. In Computer Aided Chemical Engineering (Vol. 53, pp. 3061-3066). Elsevier.
  42. Kumar, D., Dixit, V., Ramteke, M. and Kodamana, H., 2024. Learning Interpretable Representation of Koopman Operator for Non-linear Dynamics. In Computer Aided Chemical Engineering (Vol. 53, pp. 2773-2778). Elsevier.
  43. Agrawal, A., Bakshi, B.R., Kodamana, H. and Ramteke, M., 2024. Multi-objective optimization of food-energy-water nexus via crops land allocation. Computers & Chemical Engineering, 183, p.108610.
  44. Gupta, M., Manchanda, S., Kodamana, H. and Ranu, S., 2024. Mirage: Model-agnostic graph distillation for graph classification. Twelfth International Conference on Learning Representations 2024 (ICLR 2024).
  45. Goswami, U., Rani, J., Kodamana, H., Tamboli, P.K. and Vaswani, P.D., 2024. A graph embedding based fault detection framework for process systems with multi-variate time-series datasets. Digital Chemical Engineering, 10, p.100135.
  46. Sharma, R., Haq, A., Bakshi, B.R., Ramteke, M. and Kodamana, H., 2024. Designing synergies between hybrid renewable energy systems and ecosystems developed by different afforestation approaches. Journal of Cleaner Production, 434, p.139804.
  47. Haq, A., Sharma, R., Bakshi, B.R., Kodamana, H. and Ramteke, M., 2023. Forecasting sustainable power generation profiles to achieve net zero emissions using multi-objective techno-ecological framework: A study in the context of India. Computers & Chemical Engineering, 179, p.108439.
  48. Kumar, D., Goswami, U., Kodamana, H., Ramteke, M. and Tamboli, P.K., 2023. Variance-capturing forward-forward autoencoder (VFFAE): A forward learning neural network for fault detection and isolation of process data. Process Safety and Environmental Protection, 178, pp.176-194.
  49. Kumar, A., Bakshi, B.R., Ramteke, M. and Kodamana, H., 2023. Recycle-BERT: extracting knowledge about plastic waste recycling by natural language processing. ACS Sustainable Chemistry & Engineering, 11(32), pp.12123-12134.
  50. Rani, J., Tripura, T., Goswami, U., Kodamana, H. and Chakraborty, S., 2023. Fault detection using Fourier neural operator. In Computer Aided Chemical Engineering (Vol. 52, pp. 1897-1902). Elsevier.
  51. Gupta, N., Anand, S., Kumar, D., Ramteke, M. and Kodamana, H., 2023. Proximal policy optimization for the control of mAB production. In Computer Aided Chemical Engineering (Vol. 52, pp. 1903-1908). Elsevier.
  52. Goswami, U., Rani, J., Kumar, D., Kodamana, H. and Ramteke, M., 2023. Energy out-of-distribution based fault detection of multivariate time-series data. In Computer Aided Chemical Engineering (Vol. 52, pp. 1885-1890). Elsevier.
  53. Kumar, A. and Kodamana, H., 2023. An NLP-based framework for extracting the catalysts involved in Hydrogen production from scientific literature. In Computer Aided Chemical Engineering (Vol. 52, pp. 1457-1462). Elsevier.
  54. Gupta, N., Anand, S., Joshi, T., Kumar, D., Ramteke, M. and Kodamana, H., 2023. Process control of mab production using multi-actor proximal policy optimization. Digital Chemical Engineering, 8, p.100108.
  55. Gupta, M., Kodamana, H. and Ranu, S., 2023. Frigate: Frugal spatio-temporal forecasting on road networks. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 649-660).
  56. Kumar, A., Upadhyayula, S. and Kodamana, H., 2023. A Convolutional Neural Network-based gradient boosting framework for prediction of the band gap of photo-active catalysts. Digital Chemical Engineering, 8, p.100109.
  57. Goswami, U., Rani, J., Kodamana, H., Kumar, S. and Tamboli, P.K., 2023. Fault detection and isolation of multi-variate time series data using spectral weighted graph auto-encoders. Journal of the Franklin Institute, 360(10), pp.6783-6803.
  58. Rani, J., Tripura, T., Kodamana, H., Chakraborty, S. and Tamboli, P.K., 2023. Fault detection and isolation using probabilistic wavelet neural operator auto-encoder with application to dynamic processes. Process Safety and Environmental Protection, 173, pp.215-228.
  59. Joshi, T., Kodamana, H., Kandath, H. and Kaisare, N., 2023. TASAC: A twin-actor reinforcement learning framework with a stochastic policy with an application to batch process control. Control Engineering Practice, 134, p.105462.
  60. Rani, J., Roy, A.A., Kodamana, H. and Tamboli, P.K., 2023. Fault detection of pressurized heavy water nuclear reactors with steady state and dynamic characteristics using data-driven techniques. Progress in nuclear energy, 156, p.104516.
  61. Kumar, A., Pant, K.K., Upadhyayula, S. and Kodamana, H., 2022. Multiobjective Bayesian optimization framework for the synthesis of methanol from syngas using interpretable Gaussian process models. ACS omega, 8(1), pp.410-421.
  62. Gupta, N., De, R., Kodamana, H. and Bhartiya, S., 2022. Batch-to-Batch Adaptive Iterative Learning Control- Explicit Model Predictive Control Two-Tier Framework for the Control of Batch Transesterification Process. ACS omega, 7(45), pp.41001-41012.
  63. Agrawal, A., Bakshi, B.R., Kodamana, H. and Ramteke, M., 2022. Renewables-integrated energy systems can provide electricity at lower cost with less environmental and social damage. ACS Sustainable Chemistry & Engineering, 10(40), pp.13390-13401.
  64. Srujan, K.S., Sandeep, S., Suhas, E. and Kodamana, H., 2022. A Dynamical Linkage Between Western North Pacific Tropical Cyclones and Indian Monsoon Low-Pressure Systems. Geophysical Research Letters, 49(11), p.e2022GL098597.
  65. Kumar, A., Ganesh, S., Gupta, D. and Kodamana, H., 2022. A text mining framework for screening catalysts and critical process parameters from scientific literature-A study on Hydrogen production from alcohol. Chemical Engineering Research and Design, 184, pp.90-102.
  66. Ghosh, D., Chakraborty, S., Kodamana, H. and Chakraborty, S., 2022. Application of machine learning in understanding plant virus pathogenesis: trends and perspectives on emergence, diagnosis, host-virus interplay and management. Virology Journal, 19(1), p.42.
  67. Sharma, R., Agrawal, D. and Kodamana, H., 2022. Data reconciliation frameworks for dynamic operation of hybrid renewable energy systems. ISA transactions, 128, pp.424-43.
  68. Chakraborty, S., Kodamana, H. and Chakraborty, S., 2022. Deep learning aided automatic and reliable detection of tomato begomovirus infections in plants. Journal of Plant Biochemistry and Biotechnology, 31(3), pp.573-580.
  69. Sharma, R., Kodamana, H. and Ramteke, M., 2022. Multi-objective dynamic optimization of hybrid renewable energy systems. Chemical Engineering and Processing-Process Intensification, 170, p.108663.
  70. Nair, A., Srujan, K.S., Kulkarni, S.R., Alwadhi, K., Jain, N., Kodamana, H., Sandeep, S. and John, V.O., 2021. A deep learning framework for the detection of tropical cyclones from satellite images. IEEE Geoscience and Remote Sensing Letters, 19, pp.1-5.
  71. Joshi, T., Makker, S., Kodamana, H. and Kandath, H., 2021. Twin actor twin delayed deep deterministic policy gradient (TATD3) learning for batch process control. Computers & Chemical Engineering, 155, p.107527.
  72. Agrawal, D., Sharma, R., Ramteke, M. and Kodamana, H., 2021. Hierarchical two-tier optimization framework for the optimal operation of a network of hybrid renewable energy systems. Chemical Engineering Research and Design, 175, pp.37-50.
  73. Sinha, A., Gupta, M., Srujan, K.S., Kodamana, H. and Sandeep, S., 2021. Prediction of synoptic-scale sea level pressure over the Indian monsoon region using deep learning. IEEE Geoscience and Remote Sensing Letters, 19, pp.1-5.
  74. Roy, A., Dhawan, H., Upadhyayula, S. and Kodamana, H., 2021. Insights from principal component analysis applied to Py-GCMS study of Indian coals and their solvent extracted clean coal products. International Journal of Coal Science & Technology, 8(6), pp.1504-1514.
  75. Singh, S., Agrawal, A., Kodamana, H. and Ramteke, M., 2021. Multi-objective optimization based recursive feature elimination for process monitoring. Neural Processing Letters, 53, pp.1081-1099.
  76. Nikita, S., Tiwari, A., Sonawat, D., Kodamana, H. and Rathore, A.S., 2021. Reinforcement learning based optimization of process chromatography for continuous processing of biopharmaceuticals. Chemical Engineering Science, 230, p.116171.
  77. Ravinder, R., Singh, S., Bishnoi, S., Jan, A., Sharma, A., Kodamana, H. and Krishnan, N.A., 2020. An adaptive, interacting, cluster-based model for predicting the transmission dynamics of COVID-19. Heliyon, 6(12).
  78. Bishnoi, S., Ravinder, R., Singh, H., Kodamana, H. and Krishnan, N.M., 2020. Scalable Gaussian processes for predicting the properties of inorganic glasses with large datasets. Materials advances, 2(1), pp.477-487.
  79. Gupta, M., Kodamana, H. and Sandeep, 2020. Prediction of ENSO beyond spring predictability barrier using deep convolutional LSTM networks. IEEE Geoscience and Remote Sensing Letters, 19, pp.1-5.
  80. Joshi, T., Goyal, V. and Kodamana, H., 2020. A novel dynamic just-in-time learning framework for modeling of batch processes. Industrial & Engineering Chemistry Research, 59(43), pp.19334-19344.
  81. Atmaram, L.L. and Kodamana, H., 2020. Successive Linearization based Stochastic Model Predictive Control for batch processes described by DAES. IFAC-PapersOnLine, 53(1), pp.380-385.
  82. Singh, V. and Kodamana, H., 2020. Reinforcement learning based control of batch polymerisation processes. IFAC-PapersOnLine, 53(1), pp.667-672.
  83. Ravinder, R., Sridhara, K.H., Bishnoi, S., Grover, H.S., Bauchy, M., Kodamana, H. and Krishnan, N.A., 2020. Deep learning aided rational design of oxide glasses. Materials horizons, 7(7), pp.1819-1827.
  84. Liu, Z.,Kodamana, H., and Huang, Afacan, A, 2019. A GMM-MRF based image segmentation approach for interface level estimation. IFAC-PapersOnLine, 52(1), pp.28-33.
  85. Rivera, J., Berjikian, J., Ravinder, R., Kodamana, H., Das, S., Bhatnagar, N., Bauchy, M. and Krishnan, Ν.Α., 2019. Glass fracture upon ballistic impact: new insights from peridynamics simulations. Frontiers in Materials, 6, p.239.
  86. Bishnoi, S., Singh, S., Ravinder, R., Bauchy, M., Gosvami, N.N., Kodamana, H. and Krishnan, N.A., 2019. Predicting Young's modulus of oxide glasses with sparse datasets using machine learning. Journal of Non-Crystalline Solids, 524, p.119643.
  87. Mate, S., Kodamana, H., Bhartiya, S. and Nataraj, P.S.V., 2019. A stabilizing sub-optimal model predictive control for quasi-linear parameter varying systems. IEEE Control Systems Letters, 4(2), pp.402-407.
  88. Daemi, A., Kodamana, H. and Huang, B., 2019. Gaussian process modelling with Gaussian mixture likelihood. Journal of Process Control, 81, pp.209-220.
  89. Fang, M., Kodamana, H. and Huang, B., 2019. Real-time mode diagnosis for processes with multiple operating conditions using switching conditional random fields. IEEE Transactions on Industrial Electronics, 67(6), pp.5060-5070.
  90. Fang, M., Ibrahim, F., Kodamana, H., Huang, B., Bell, N. and Nixon, M., 2019. Hierarchically distributed monitoring for the early prediction of gas flare events. Industrial & Engineering Chemistry Research, 58(26), pp.11352-11363.
  91. Liu, Z., Kodamana, H., Afacan, A. and Huang, B., 2019. Dynamic prediction of interface level using spatial temporal Markov random field. Computers & Chemical Engineering, 128, pp.301-311.
  92. Fan, L., Kodamana, H. and Huang, B., 2019. Semi-supervised dynamic latent variable modeling: I/O probabilistic slow feature analysis approach. AIChE Journal, 65(3), pp.964-979.
  93. Raveendran, R., Kodamana, H. and Huang, B., 2018. Process monitoring using a generalized probabilistic linear latent variable model. Automatica, 96, pp.73-83.
  94. Kodamana, H., Huang, B., Ranjan, R., Zhao, Y., Tan, R. and Sammaknejad, N., 2018. Approaches to robust process identification: A review and tutorial of probabilistic methods. Journal of Process Control, 66, pp.68-83.
  95. Alipouri, Y., Huang, B. and Kodamana, H., 2018. MV bound and MV controller for convex-non-linear systems with input constraints. IET Control Theory & Applications, 12(6), pp.761-769.
  96. Fang, M., Kodamana, H., Huang, B. and Sammaknejad, N., 2018. A novel approach to process operating mode diagnosis using conditional random fields in the presence of missing data. Computers & Chemical Engineering, 111, pp.149-163.
  97. Fang, M., Kodamana, H. and Huang, B., 2018, December. Switching conditional random field approach to process operating mode diagnosis for multi-modal processes. In 2018 IEEE Conference on Decision and Control (CDC) (pp. 5146-5151). ΙΕΕΕ.
  98. Fan, L., Kodamana, H. and Huang, B., 2018. Identification of robust probabilistic slow feature regression model for process data contaminated with outliers. Chemometrics and Intelligent Laboratory Systems, 173, pp.1-13.
  99. Kodamana, H., Raveendran, R. and Huang, B., 2017. Mixtures of probabilistic PCA with common structure latent bases for process monitoring. IEEE Transactions on Control Systems Technology, 27(2), pp.838-846.
  100. Guo, F., Kodamana, H., Zhao, Y., Huang, B. and Ding, Y., 2017. Robust identification of nonlinear errors-in-variables systems with parameter uncertainties using variational Bayesian approach. IEEE Transactions on Industrial Informatics, 13(6), pp.3047-3057.
  101. Guo, F., Hariprasad, K., Huang, B. and Ding, Y.S., 2017. Robust identification for nonlinear errors-in-variables systems using the EM algorithm. Journal of Process Control, 54, pp.129-137.
  102. Guo, F., Wu, O., Kodamana, H., Ding, Y. and Huang, B., 2017. An augmented model approach for identification of nonlinear errors-in-variables systems using the EM algorithm. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(11), pp.1968-1978.
  103. Kodamana, H. and Huang, B., 2017, Commentary on the article Statistical process monitoring with independent component analysis, Virtual Special Issue on the 25th Anniversary of Journal of Process Control.
  104. Wu, O., Kodamana, H., Li, J., Huang, B. and Forbes, J.F., 2017, December. Identification of nonlinear errors-in-variables systems in state-space form: A linear parameter varying approach. In 2017 11th Asian Control Conference (ASCC) (pp. 1417-1422). IEEE.
  105. Fan, L., Kodamana, H. and Huang, B., 2017. Robust identification of switching Markov ARX models using EM algorithm. IFAC-PapersOnLine, 50(1), pp.9772-9777.
  106. Wu, O., Kodamana, H., Jan, N.M., Tan, R. and Huang, B., 2017. Robust soft sensor development using multi-rate measurements. IFAC-PapersOnLine, 50(1), pp.10190-10195.
  107. Wu, O., Hariprasad, K., Huang, B. and Forbes, J.F., 2016, December. Identification of linear dynamic errors-in-variables systems with a dynamic uncertain input using the EM algorithm. In 2016 IEEE 55th Conference on Decision and Control (CDC) (pp. 1229-1234). ΙΕΕΕ.
  108. Hariprasad, K. and Bhartiya, S., 2016. An efficient and stabilizing model predictive control of switched systems. IEEE Transactions on Automatic Control, 62(7), pp.3401-3407.
  109. Vignesh, S.V., Hariprasad, K., Athawale, P., Siram, V. and Bhartiya, S., 2016. Optimal strategies for transitions in simulated moving bed chromatography. Computers & Chemical Engineering, 84, pp.83-95.
  110. Hariprasad, K. and Bhartiya, S., 2016. A computationally efficient robust tube based MPC for linear switched systems. Nonlinear Analysis: Hybrid Systems, 19, pp.60-76.
  111. Vignesh, S.V., Hariprasad, K., Athawale, P. and Bhartiya, S., 2016. An optimization-driven novel operation of simulated moving bed chromatographic separation. IFAC-PapersOnLine, 49(7), pp.165-170.
  112. Sharma, G., Vignesh, S.V., Hariprasad, K. and Bhartiya, S., 2015. Control-relevant multiple linear modeling of simulated moving bed chromatography. IFAC-PapersOnLine, 48(8), pp.477-482.
  113. Hariprasad, K. and Bhartiya, S., 2014, December. Adaptive robust model predictive control of nonlinear systems using tubes based on interval inclusions. In 53rd IEEE conference on decision and control (pp. 2032-2037). ΙΕΕΕ.
  114. Hariprasad, K. and Bhartiya, S., 2014. A computationally efficient stabilizing model predictive control of switched systems. IFAC Proceedings Volumes, 47(1), pp.607-613.
  115. Hariprasad, K. and Bhartiya, S., 2013. A dual-terminal set based robust tube MPC for switched systems. IFAC Proceedings Volumes, 46(32), pp.93-98.
  116. Athawale, P., Hariprasad, K., Vinod, S. and Bhartiya, S., 2013. Optimal operating strategies for SMBC. IFAC Proceedings Volumes, 46(32), pp.457-462.
  117. Hariprasad, K., Bhartiya, S. and Gudi, R.D., 2012. A multiple linear modeling approach for nonlinear switched systems. IFAC Proceedings Volumes, 45(15), pp.63-68.
  118. Hariprasad, K., Bhartiya, S. and Gudi, R.D., 2012. A gap metric based multiple model approach for nonlinear switched systems. Journal of process control, 22(9), pp.1743-1754.
  119. Balasubramanian, G., Hariprasad, K., Sivakumaran, N. and Radhakrishnan, T.K., 2009. Adaptive control of multivariable process using recurrent neural networks. Instrumentation Science and Technology, 37(6), pp.615-630.
  120. Balasubramanian, G., Hariprasad, K., Sivakumaran, N. and Radhakrishnan, T.K., 2009. Adaptive control of neutralization process using recurrent neural networks. Instrumentation Science and Technology, 37(4), pp.383-396.