I am a Ph.D. student in machine learning with Arno Solin's research group at Aalto University, Finland. Recently, I have had the priviledge of collaborating with Amit Sharma (Microsoft Research), Atanu R. Sinha (Adobe Research) and Prof. Seth Flaxman and Elizaveta Semenova (University of Oxford).
My research interests span generative machine learning, probabilistic modeling, and efficient inference techniques. Recently, I have explored reasoning and planning in retrieval augmented generation (RAG), LLM guided causal discovery, and sequential decision-making models that require computational efficiency and well-calibrated uncertainty.
I earned my Master's degree in Machine Learning, Data Science and Artificial Intelligence from Aalto University, Finland (2019-2021). In 2021-2022, alongside Aalto University, I worked with SpectacularAI as a consultant for an electronics firm developing methods to integrate uncertainty in their deep learning models, making them robust. During 2016-2019, I worked in the R&D team of TomTom, responsible for devising, developing, and bringing into production innovative new technologies. My work mainly revolved around machine learning, image processing, and automation, focusing on revolutionizing map data.
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News
- [March 2025] Prakhar Verma, Sukruta Prakash Midigeshi, Gaurav Sinha, Arno Solin, Nagarajan Natarajan, Amit Sharma. Plan*RAG: Efficient Test-Time Planning for Retrieval Augmented Generation accepted in Workshop on Reasoning and Planning for Large Language Models at ICLR 2025. Link
- [September 2024] Completed a 3 months Research Internship at Adobe Research.
- [June 2024] Completed a 3 months Research Internship at Microsoft Research.
- [January 2024] Prakhar Verma, Vincent Adam, Arno Solin. Variational Gaussian Process Diffusion Processes accepted in the Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 2024. Link
- [October 2023] Elizaveta Semenova, Prakhar Verma, Max Cairney-Leeming, Arno Solin, Samir Bhatt, Seth Flaxman. PriorCVAE: Scalable MCMC parameter inference with Bayesian deep generative modelling Link
- [September 2023] Completed a 3 months research visit at University of Oxford.
- [May 2023] Paul E. Chang*, Prakhar Verma*, ST John, Arno Solin, Mohammad Emtiyaz Khan. Memory-based dual Gaussian processes for sequential learning accepted in International Conference on Machine Learning (ICML), 2023 (Oral Presentation). Link
- [March 2023] Prakhar Verma, Vincent Adam, Arno Solin. Gaussian Variational Inference for Diffusion Processes Revisited accepted as a poster in "BayesComp" 2023. Poster
- [November 2022] Prakhar Verma, Paul E. Chang, Arno Solin, Mohammad Emtiyaz Khan. Sequential Learning in GPs with Memory and Bayesian Leverage Score accepted in Asian Conference in Machine Learning (ACML) workshop "Continual Lifelong Learning" 2022 (Contributed talk). Link Slide
- [October 2022] Paul E. Chang, Prakhar Verma, ST John, Victor Picheny, Henry Moss, Arno Solin. Fantasizing with Dual GPs in Bayesian Optimization and Active Learning accepted in NeurIPS workshop "Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems" 2022. Link Poster