Yogesh Verma

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About Me

I’m a Doctoral Researcher at (Qurious) Quantum Machine Learners, Aalto University, working with Prof. Vikas Garg, Prof. Samuel Kaski and Dr. Markus Heinonen. I am also a recipient of Nokia Scholarship and a DAAD AI-Net Fellow.

My research interests include physics-inspired deep learning for modeling dynamical systems, (geometric and topological) deep learning, generative models and robust bayesian inference.

More about me here.

Publications

  1. Y. Verma*, G. Mercatali*, A. Freitas and V. Garg. “Diffusion Twigs with Loop Guidance for Conditional Graph Generation”, 38th Conference on Neural Information Processing Systems (NeurIPS 2024).
  2. Y. Verma, A. Souza and V. Garg. “Topological Neural Networks go Persistent, Equivariant and Continuous”, 41st International Conference on Machine Learning (ICML 2024). arxiv
  3. Y. Verma, M. Heinonen and V. Garg. “ClimODE: Climate Forecasting With Physics-informed Neural ODEs”, Oral (Top 1%) , 12th International Conference on Learning Representations (ICLR 2024). arxiv
  4. Y. Verma, M. Heinonen and V. Garg. “AbODE: Ab initio Antibody Design using Conjoined ODEs.”, 40th International Conference on Machine Learning (ICML 2023). arxiv
  5. Y. Verma, S. Kaski, M. Heinonen and V. Garg. “Modular Flows: Differential Molecular Generation.”, 36th Conference on Neural Information Processing Systems (NeurIPS 2022). arxiv
  6. Y. Verma, M. Aggarwal, V. Joshi, A. Sharma. “Characterization Study of a Button BPM with an Approach to Automated Measurements”, 9th International Beam Instrumentation Conference, IBIC2020., arxiv

Preprints

  1. A. Dumitrescu, D. Korpela, M. Heinonen, Y. Verma, V. Iakovlev, V. Garg, H. Lähdesmäki. “Field-based Molecule Generation”, preprint
  2. Y. Verma and S. Jena. “Particle Track Reconstruction using Geometric Deep Learning “, preprint
  3. Y. Verma and S. Jena. “Jet characterization in Heavy Ion Collisions by QCD-Aware Graph Neural Networks”, preprint
  4. Y. Verma and S. Jena. “Shower Identification in Calorimeter using Deep Learning “, preprint

Workshops:

  1. Y. Verma, M. Heinonen and V. Garg. “AbODE: Ab initio Antibody Design using Conjoined ODEs.”, New Frontiers in Learning, Control, and Dynamical Systems at ICML 2023.
  2. Y. Verma, S. Kaski, M. Heinonen and V. Garg. “Modular Flows: Differential Molecular Generation.”, Symbiosis of Deep Learning and Differential Equations, Workshop at NeurIPS 2022.
  3. Y. Verma, S. Kaski, M. Heinonen and V. Garg. “Modular Flows: Differential Molecular Generation.”, New Frontiers in Graph Learning, Workshop at NeurIPS 2022.

Services

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Contact

Email:
firstname.lastname@aalto.fi

Address:
A338, Tietotekniikka-talo (CS building)
Konemiehentie 2
FI-02150
Espoo
Finland