Prashant Singh
Associate senior lecturer/Assistant Professor at Department of Information Technology; Division of Scientific Computing
- Telephone:
- +46 18 471 54 12
- E-mail:
- prashant.singh@it.uu.se
- Visiting address:
- Hus 10, Lägerhyddsvägen 1
- Postal address:
- Box 337
751 05 UPPSALA
- Academic merits:
- Docent
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Short presentation
Prashant Singh is an Assistant Professor, Docent at the Division of Scientific Computing, Department of Information Technology. He is also affiliated to the Science for Life Laboratory, Uppsala University as a SciLifeLab fellow. His research interests involve developing machine learning and optimization methods to enable fast, data-efficient analysis and processing of scientific data, particularly in the domain of life sciences.
Keywords
- active learning
- bayesian inference
- machine learning
- optimization
- scientific computing
- surrogate models
- systems biology
Biography
Prior to joining Uppsala University, Prashant Singh was an Assistant Professor at the Department of Computing Science, Umeå University. Prashant was a postdoctoral researcher at the Division of Scientific Computing, Department of Information Technology, Uppsala University between 2017 and 2020, where his research explored machine learning and statistical sampling methods within the domain of computational biology. Prashant Singh obtained the degree of PhD in Computer Science Engineering from Ghent University, Belgium in May 2016, where he specialized in model-based optimization and active learning. Prashant received his MSc degree in Computer Science from the University of Delhi, India in 2011, with specialization in data mining and supervised machine learning.
Research
Prashant's research interests broadly span optimization and machine learning. He is particularly interested in developing efficient methods for data-scarce or computationally expensive problems. Some related topics include constrained multi-objective optimization, surrogate modeling, parameter inference, inverse modeling, active learning, sequential sampling, design of experiments, etc.
Publications
Recent publications
- Adaptive Robust Learning using Latent Bernoulli Variables (2024)
- Efficient Resource Scheduling for Distributed Infrastructures using Negotiation Capabilities (2023)
- Systematic comparison of modeling fidelity levels and parameter inference settings applied to negative feedback gene regulation (2022)
- Scalable federated machine learning with FEDn (2022)
- To test, or not to test (2022)
All publications
Articles
- Systematic comparison of modeling fidelity levels and parameter inference settings applied to negative feedback gene regulation (2022)
- Identification of dynamic mass-action biochemical reaction networks using sparse Bayesian methods (2022)
- Convolutional Neural Networks as Summary Statistics for Approximate Bayesian Computation (2022)
- Epidemiological modeling in StochSS Live! (2021)
- Accelerated regression-based summary statistics for discrete stochastic systems via approximate simulators (2021)
- Scalable machine learning-assisted model exploration and inference using Sciope (2021)
- Automated line-based sequential sampling and modeling algorithm for EMC near-field scanning (2017)
- Multi-objective geometry optimization of a gas cyclone using triple-fidelity co-Kriging surrogate models (2017)
- Constrained multi-objective antenna design optimization using surrogates (2017)
- A pipeline for systematic comparison of model levels and parameter inference settings applied to negative feedback gene regulation
Conferences
- Adaptive Robust Learning using Latent Bernoulli Variables (2024)
- Efficient Resource Scheduling for Distributed Infrastructures using Negotiation Capabilities (2023)
- Scalable federated machine learning with FEDn (2022)
- To test, or not to test (2022)
- Robust and integrative Bayesian neural networks for likelihood-free parameter inference (2022)
- Proactive Autoscaling for Edge Computing Systems with Kubernetes (2021)
- Towards Smart e-Infrastructures, A Community Driven Approach Based on Real Datasets (2020)
- Multi-objective optimization driven construction of uniform priors for likelihood-free parameter inference (2018)
- Hyperparameter optimization for approximate Bayesian computation (2018)
- Learning surrogate models of document image quality metrics for automated document image processing (2018)
- Surrogate assisted model reduction for stochastic biochemical reaction networks (2017)
- Automatic document image binarization using Bayesian optimization (2017)