Prakhar Verma

I am a research intern at Adobe Research, and a machine learning Ph.D. student with Arno Solin's research group at Aalto University, Finland. Recently, I have had the priviledge of collaborating with Amit Sharma from Microsoft Research, and Prof. Seth Flaxman, Elizaveta Semenova from University of Oxford.

I am broadly interested in generative machine learning, probabilistic modeling, and efficient inference techniques. For my master’s thesis, I researched on developing variational inference techniques for non-linear SDEs. Recently, my work has focused on sequential decision-making models that need computationally efficient and well-calibrated uncertainty.

I graduated from Aalto University, Finland, with a Master's in Machine Learning, Data Science and Artificial Intelligence as major and Mathematics as minor (2019-2021).

In 2021-2022, alongside Aalto University, I also 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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  • [June 2024] Started as a Research Intern at Adobe Research.
  • [March 2024] Started as a Research Intern 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 (preprint. Under review.). Link
  • [July 2023] Started as a Visiting Researcher 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


Prakhar Verma (2021). Sparse Gaussian processes for stochastic differential equations. Master’s thesis. Aalto University. PDF

Prakhar Verma (2016). Development of automated GIS Tools on various platforms. Bachelor's thesis. Uttarakhand Technical University, TomTom India. PDF