Research & Projects

A current open challenge in the mathematical programming field is optimization with black-box models and/or data-driven models. These models pose a fundamental difficulty as they lack closed-form analytical expressions, rendering conventional optimization algorithms ineffective or even inapplicable. To address this challenge, I have co-developed an optimization algorithm which uses advanced mathematical programming concepts such as underestimating functions, spatial branch-and-bound heuristics, lipschitz continuity etc., and state-of-the-art ML methods.

The initial beta version, V1.0, has been released as open-source software on GitHub, and can be found here. The updated version V2.0 is already in the pipeline, will be released soon!

Relevant publications

Suryateja Ravutla, and Fani Boukouvala, "Data-Driven Lipschitz-Informed Convex Underestimators for Branch-And-Bound Optimization of Black-Box Functions." Journal of Global Optimization, 2024, under review.

Suryateja Ravutla, Jianyuan Zhai, and Fani Boukouvala, "Hybrid Modeling and Multi-Fidelity Approaches for Data-Driven Branch-and-Bound Optimization." In Computer Aided Chemical Engineering, vol. 52, pp. 1313-1318. Elsevier, 2023.

While there is a rapid deployment of AI/ML systems in all fields, bias may arise as an unintended consequence, due to data-dependent parameters of the models. Many AI and ML models, especially deep learning models, are often considered black boxes. Explaining the decisions made by these models can be challenging. Hybrid modeling and physics-informed ML can play a crucial role as an approach to addressing these problems. I'm working on utilizing hybrid modeling approaches to improve the ML model fit and generalizability, and simultaneously incorporates physics/constraint knowledge.

Relevant publications

Suryateja Ravutla, and Fani Boukouvala, "Effects of Surrogate Hybridization and Adaptive Sampling for Simulation-Based Optimization." Industrial & Engineering Chemistry Research, 2025, accepted.

Suryateja Ravutla, Jianyuan Zhai, and Fani Boukouvala, "Hybrid Modeling and Multi-Fidelity Approaches for Data-Driven Branch-and-Bound Optimization." In Computer Aided Chemical Engineering, vol. 52, pp. 1313-1318. Elsevier, 2023.

In my most recent work, I have been working on merging optimization and hybrid ML for data-driven model identification. I’m combining a hybrid modeling paradigm with sparse regression, with the goal of simultaneous hybrid model development and model identification. Motivating applications in engineering include non-linear dynamic control, bioprocesses, and kinetic rate laws modeling, and there are relevant applications of this work in fields such as economic forecasting etc. 

Relevant publications

Suryateja Ravutla, and Fani Boukouvala, "A Two-stage Hybrid Modeling Approach for Data-Driven Process Model Discovery." In preparation.

Suryateja Ravutla, and Fani Boukouvala, "Integrating Hybrid Modeling and Multifidelity Approaches for Data-Driven Process Model Discovery." LAPSE, Systems and Control Transactions (2024), Volume 3: pg 351 - 358

During my time at Georgia Tech, I engaged in a range of projects collaborating with peers from diverse backgrounds and researchers from various universities. These projects encompassed the development of machine learning models for process optimization and screening of adsorbent materials. An example application of this work was optimizing the design and operation of a vacuum pressure swing adsorption system for carbon capture.

Relevant publications

Yuya Takakura, Suryateja Ravutla, Kim Jinsu, Keisuke Ikeda, Hiroshi Kajiro, Tomoyuki Yajima, Junpei Fujiki, Fani Boukouvala, Matthew Realff, Yoshiaki Kawajiri, "Surrogate model optimization of vacuum pressure swing adsorption using a flexible metal organic framework with hysteretic sigmoidal isotherms." International Journal of Greenhouse Gas Control, accepted.

Kim, Sun Hye, Héctor Octavio Rubiera Landa, Suryateja Ravutla, Matthew J. Realff, and Fani Boukouvala, "Data-driven simultaneous process optimization and adsorbent selection for vacuum pressure swing adsorption." Chemical Engineering Research and Design 188 (2022): 1013-1028

As an undergraduate researcher at the Indian Institute of Technology Hyderabad, I worked on computationally developing a physics-based model for the calcium dynamics in hippocampal neurons, by understanding the calcium signaling pathway. This model served as a basis for understanding how neurons communicate using calcium signals, providing valuable insights into their functioning. A few other projects I worked on involved modeling biological system pathways and modeling of cell-to-cell variability in viral infection.

Relevant publications

Saxena, Abha, Suryateja Ravutla, Kishalay Mitra, Jayanta Chakraborty, David Murhammer, and Lopamudra Giri,"Evolution of a single-cell predictive model for packaging and budding of viruses based on TEM based measurements." Authorea Preprints (2021).

Saxena, Abha, Suryateja Ravutla, Vikas Upadhyay, Soumya Jana, David Murhammer, and Lopamudra Giri, "Statistical modeling of cell-to-cell variability in viral infection during passaging in suspension cell culture: Application in Monte-Carlo simulation." Biotechnology and bioengineering 117, no. 5 (2020): 1483-1501.

Vikas Upadhyay, Suryateja Ravutla, Vaibhav Dhyani, Kevin George, Sarpras Swain, Kishalay Mitra, and Lopamudra Giri, "A model screening framework for the generation of Ca2+ oscillations in hippocampal neurons using differential evolution." The 9th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 961-964. IEEE, 2019.

Sarpras Swain, Sathish Ande, Suryateja Ravutla, Soumya Jana, and Lopamudra Giri, "Spatially resolved calcium spiking in hippocampal neurons: Estimation via confocal imaging and model-based simulation." The 8th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 279-283. IEEE, 2017