Curriculum vitae

Education

• Ph.D student at IPST, University of Maryland, College Park, 2021-present
• BS-MS with Physics major, Indian Institute of Science Education & Research, Pune, 2016-2021
  • Thesis: Signal propagation and Initialization in Deep Neural Networks (link)

Research experience

• Graduate Research Assistant: UMD, Summer 2022- Present
  • Supervisor: Professor Maissam Barkeshli
  • Deep learning theory
• Graduate Research Assistant: UMD, Spring 2022
  • Supervisor: Professor Michelle Girvan
  • Information bottleneck for dynamical systems
• Group Rotation II: UMD, Spring 2022
  • Supervisor: Professor Victor Albert
  • Foundations of Deep Learning (reading project)
• Group Rotation I: UMD, Fall 2021
  • Supervisor: Professor Michelle Girvan
  • Predicting the dynamics of multi-scale systems with reservoir computing
• Master’s thesis project: IISER Pune, Aug. 2020-July 2021
  • Supervisor: Dr. G J Sreejith
  • Signal propagation and initialization in Deep Neural Networks
• Semester project: IISER Pune, Spring 2020
  • Supervisor: Dr. G J Sreejith
  • Learning non-local order parameter of Dimer model with Convolutional Neural Networks
• Semester project: IISER Pune, Fall 2019
  • Supervisor: Professor MS Santhanam
  • Predicting dynamics of long-range correlated time series using Recurrent Neural Networks
• Long-term project: IISER Pune, Spring 2019 - Aug. 2021
  • Supervisor: Professor MS Santhanam
  • Analyzing correlations in extreme events of a time series with power law correlations
• Summer project: IISER Pune, Summer 2018
  • Supervisor: Professor Prasad Subrmanian
  • Automated detection of solar radio bursts
  • Detection of HI emission from Sagittarius A galaxy
• Long-term project: IISER Pune, Fall 2016 - Present
  • Supervisor: Dr. MS Madhusudhan
  • Large scale motif prediction in protein sequences
  • Functional prediction of uncharacterized protein sequences

Summer schools attended

• IAIFI Summer School: IAIFI, Tufts University, Aug. 2023
• Princeton Machine Learning Summer School: Princeton, Jun. 2023
• IAIFI Summer School: IAIFI, Tufts University, Aug. 2022
• Princeton Machine Learning Summer School: Princeton, Jun. 2022

Conferences attended

• Quantum-inspired machine learning: ITS CUNY, Oct. 2020
• Statistical physics of machine learning: ICTS, Bangalore, Jan. 2020

Skills

• Machine learning
  • Feed-forward networks
  • Convolutional neural networks
  • Recurrent neural networks
  • Reservoir computing
  • Tensorflow, Keras
• Cluster computing
  • PBS
  • SLURM

• Monte Carlo simulations

• Programming languages
  • Python
  • C/C++
  • any language after a week
• Others
  • Shell scripting: bash, zsh
  • LaTeX
  • Github
  • MySQL

Publications

Initializing ReLU networks in an expressive subspace of weights

Dayal Singh and G J Sreejith, Under review

Inferring long memory using extreme events

Dayal Singh Kalra and M. S. Santhanam, Chaos 31, 113131 (2021)

Finding SeMo: large scale linear motif detection and protein function annotation

Dayal Singh, Ankit Roy, Sanjana Nair, MS Madhusudhan, Under review

Automated Detection of Solar Radio Bursts Using a Statistical Method

Dayal Singh, Sasikumar Raja, Prasad Subramanian, R. Ramesh, and Christian Monstein, Solar Physics 294, 112 (2019)

Talks

The mean-field theory of deep neural networks?

January 01, 2022

Future talk at Your insititute, Your location, USA

Teaching

• Teaching Assitant: Spring 2023
  • PHYS 786: Machine learning for physicists
  • Contributed to creating programming assignments