Research Interests

My research is focused on pushing the boundaries of artificial intelligence through the following areas:

• Large Language Models (LLMs): Developing more efficient, robust, and capable foundation models.
• Recommendation Systems: Designing large-scale personalization frameworks that balance complex objectives and constraints.
• Neuroscience & Deep Learning: Exploring the intersection of biological intelligence and computational models to build brain-inspired AI.
• Causal Inference: Learning treatment effects and optimizing policies in sequential, dynamic environments.

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Publications

Causal-Informed Hybrid Online Adaptive Optimization for Ad Load Personalization in Large-Scale Social Networks

Aakash Mishra, Qi Xu, Zhigang Hua, Vishwanath Sangale, Jizhe Zhang, et al. Accepted at NeurIPS 2025 Workshop on Constrained Optimization for Machine Learning (COML). [arXiv]

Treatment Effect Learning Under Sequential Randomization

Friedberg, R., Mudd, R., Johnstone, P., Pothen, M., Vaingankar, V., Vishwanath Sangale, & Zaidi, A. arXiv preprint, 2025. [arXiv]

NxtPost: User to Post Recommendations in Facebook Groups

Sangale, V., et al. Proc. ACM SIGKDD (KDD), 2022, pp. 3792–3800. [DOI]

TeTrIS: Template Transformer Networks for Image Segmentation With Shape Priors

Sangale, V., et al. IEEE Transactions on Medical Imaging (TMI), 38(11):2596–2606, 2019. [DOI]

Localization of a Mobile Autonomous Robot Using Extended Kalman Filter

Sangale, V., et al. IEEE ICACC, 2013. [IEEE Xplore]