Prashant Singh
Biträdande universitetslektor vid Institutionen för informationsteknologi; Beräkningsvetenskap
- Telefon:
- 018-471 54 12
- E-post:
- prashant.singh@it.uu.se
- Besöksadress:
- Hus 10, Regementsvägen 10
- Postadress:
- Box 337
751 05 UPPSALA
- Akademiska meriter:
- Docent
Nyckelord
- scientific computing
- systems biology
- machine learning
- optimization
- active learning
- bayesian inference
- surrogate models
Publikationer
Senaste publikationer
Bayesian polynomial neural networks and polynomial neural ordinary differential equations
Ingår i PloS Computational Biology, 2024
- DOI för Bayesian polynomial neural networks and polynomial neural ordinary differential equations
- Ladda ner fulltext (pdf) av Bayesian polynomial neural networks and polynomial neural ordinary differential equations
Adaptive Robust Learning using Latent Bernoulli Variables
Ingår i Proceedings of the 41st International Conference on Machine Learning, s. 23105-23122, 2024
Transfer Learning-Assisted Inverse Modeling in Nanophotonics Based on Mixture Density Networks
Ingår i IEEE Access, s. 55218-55224, 2024
- DOI för Transfer Learning-Assisted Inverse Modeling in Nanophotonics Based on Mixture Density Networks
- Ladda ner fulltext (pdf) av Transfer Learning-Assisted Inverse Modeling in Nanophotonics Based on Mixture Density Networks
Efficient Resource Scheduling for Distributed Infrastructures using Negotiation Capabilities
Ingår i 2023 IEEE 16th International Conference on Cloud Computing (CLOUD), s. 486-492, 2023
To test, or not to test: A proactive approach for deciding complete performance test initiation
Ingår i BIG-DATA 2022, s. 4758-4767, 2022
Alla publikationer
Artiklar i tidskrift
Bayesian polynomial neural networks and polynomial neural ordinary differential equations
Ingår i PloS Computational Biology, 2024
- DOI för Bayesian polynomial neural networks and polynomial neural ordinary differential equations
- Ladda ner fulltext (pdf) av Bayesian polynomial neural networks and polynomial neural ordinary differential equations
Transfer Learning-Assisted Inverse Modeling in Nanophotonics Based on Mixture Density Networks
Ingår i IEEE Access, s. 55218-55224, 2024
- DOI för Transfer Learning-Assisted Inverse Modeling in Nanophotonics Based on Mixture Density Networks
- Ladda ner fulltext (pdf) av Transfer Learning-Assisted Inverse Modeling in Nanophotonics Based on Mixture Density Networks
Identification of dynamic mass-action biochemical reaction networks using sparse Bayesian methods
Ingår i PloS Computational Biology, 2022
- DOI för Identification of dynamic mass-action biochemical reaction networks using sparse Bayesian methods
- Ladda ner fulltext (pdf) av Identification of dynamic mass-action biochemical reaction networks using sparse Bayesian methods
Convolutional Neural Networks as Summary Statistics for Approximate Bayesian Computation
Ingår i IEEE/ACM Transactions on Computational Biology & Bioinformatics, s. 3353-3365, 2022
Ingår i PloS Computational Biology, 2022
- DOI för Systematic comparison of modeling fidelity levels and parameter inference settings applied to negative feedback gene regulation
- Ladda ner fulltext (pdf) av Systematic comparison of modeling fidelity levels and parameter inference settings applied to negative feedback gene regulation
Ingår i BMC Bioinformatics, 2021
- DOI för Accelerated regression-based summary statistics for discrete stochastic systems via approximate simulators
- Ladda ner fulltext (pdf) av Accelerated regression-based summary statistics for discrete stochastic systems via approximate simulators
Scalable machine learning-assisted model exploration and inference using Sciope
Ingår i Bioinformatics, s. 279-281, 2021
- DOI för Scalable machine learning-assisted model exploration and inference using Sciope
- Ladda ner fulltext (pdf) av Scalable machine learning-assisted model exploration and inference using Sciope
Epidemiological modeling in StochSS Live!
Ingår i Bioinformatics, s. 2787-2788, 2021
Ingår i Journal of Optimization Theory and Applications, s. 172-193, 2017
Constrained multi-objective antenna design optimization using surrogates
Ingår i International journal of numerical modelling, 2017
Automated line-based sequential sampling and modeling algorithm for EMC near-field scanning
Ingår i IEEE transactions on electromagnetic compatibility (Print), s. 704-709, 2017
Konferensbidrag
Adaptive Robust Learning using Latent Bernoulli Variables
Ingår i Proceedings of the 41st International Conference on Machine Learning, s. 23105-23122, 2024
Efficient Resource Scheduling for Distributed Infrastructures using Negotiation Capabilities
Ingår i 2023 IEEE 16th International Conference on Cloud Computing (CLOUD), s. 486-492, 2023
To test, or not to test: A proactive approach for deciding complete performance test initiation
Ingår i BIG-DATA 2022, s. 4758-4767, 2022
Scalable federated machine learning with FEDn
Ingår i 2022 22nd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2022), s. 555-564, 2022
Robust and integrative Bayesian neural networks for likelihood-free parameter inference
Ingår i 2022 International Joint Conference on Neural Networks (IJCNN), s. 1-10, 2022
Proactive Autoscaling for Edge Computing Systems with Kubernetes
2021
Towards Smart e-Infrastructures, A Community Driven Approach Based on Real Datasets
Ingår i Proceedings of the 2020 IEEE Green Technologies Conference (GreenTech), s. 109-114, 2020
Learning surrogate models of document image quality metrics for automated document image processing
Ingår i Proc. 13th IAPR International Workshop on Document Analysis Systems, s. 67-72, 2018
Hyperparameter optimization for approximate Bayesian computation
Ingår i Proc. 50th Winter Simulation Conference, s. 1718-1729, 2018
Ingår i Proc. 32nd European Simulation and Modelling Conference, s. 22-27, 2018
Surrogate assisted model reduction for stochastic biochemical reaction networks
Ingår i Proc. 49th Winter Simulation Conference, s. 1773-1783, 2017
Automatic document image binarization using Bayesian optimization
Ingår i Proc. 4th International Workshop on Historical Document Imaging and Processing, s. 89-94, 2017