Prashant Singh

Prashant is an Assistant Professor at the Division of Scientific Computing, Department of Information Technology, Uppsala University and a SciLifeLab fellow. His research interests broadly span non-convex optimization, data-efficient learning, statistical sampling/design of experiments, and inverse problems, particularly within the life sciences domain.

###### Postdoctoral Researcher

Andrey Shternshis

Andrey joined the group in October 2023 as a postdoctoral researcher. His current research explores fairness-aware statistical experiments and machine learning. His research interest also spans quatitative finance, in particular statistical methods in finance and predictability analysis. Andrey received his PhD in the program “Computational methods and Mathematical Models for Sciences and Finance” in September 2023 from Scuola Normale Superiore (SNS), Pisa, Italy. Andrey received a master’s degree in 2019, and a bachelor’s degree in 2016 at the Novosibirsk State University in the program of applied mathematics and computer science. Publications: scholar.google.com/citations?user=3grli6AAAAAJ

###### PhD Students

Aleksandr Karakulev

Aleksandr joined the group in August 2022 as a PhD student. Aleksandr holds Masters and Bachelor degrees in Applied Mathematics and Physics from the Moscow Institute of Physics and Technology (MIPT). His doctoral research explores novel statistical design of experiments for drug repurposing. His research interests also include inverse problems and optimization.

Mayank Nautiyal

Mayank joined the group in September 2022 as a PhD student. Mayank holds a Masters degree in Signal Processing from the Indian Institute of Technology (IIT) Gandhinagar, India. Mayank is working in the area of likelihood-free parameter inference of stochastic models. His prior research addressed inverse problems in distributed sensor networks.

*YOU!*

We currently have 2 open PhD positions:

–PhD position in Scientific Machine Learning (keywords: variational inference, large-scale optimization, robust learning, Bayesian inference). Apply here by April 2, 2024!

– PhD position in Deep Learning for Drug Repurposing (keywords: Bayesian neural networks, large-scale optimization, active learning, drug discovery). Apply here by March 22, 2024! Please select Project 11 titled ‘Adaptive Deep Learning of Drug Combination Mechanics for Accelerated Repurposing‘.

###### Graduate Students

Csongor Horváth

Csongor is pursuing his Masters degree in Computational Science at Uppsala University, and holds a Bachelors’ degree in Mathematics. His thesis project investigates solution/minima quality for ill-posed optimization problems, and large-scale optimization problems in machine learning.

Mina Badri

Mina is pursuing her Masters degree in Data Science at Uppsala University. Her thesis project explores Bayesian deep learning focussed on drug repurposing experimental design pipelines.

Ema Duljkovic

Ema is pursuing her Masters degree in Data Science at Uppsala University, and holds a Bachelors’ degree in Mathematics. She is pursuing a project course at the group in the area of Bayesian active learning for drug repurposing.

Dhanushki Mapitigama

Dhanushki is pursuing her Masters degree in Data Science at Uppsala University, and holds a Bachelors’ degree in Computer Engineering. She is pursuing a project course at the group in the area of Bayesian active learning for drug repurposing.

Stefanos Tsampanakis

Stefanos is pursuing his Masters degree in Data Science at Uppsala University. His thesis project is a joint collaboration with Vahid Azimi Mousolou at Dept. of Physics and Astronomy. The subject of his thesis is characterising quantum entanglement as an optimization problem, to be solved in a data-efficient manner using Bayesian optimization.

Sajad Sharhani

Sajad is pursuing his Masters degree in Computational Science at Uppsala University. As part of his thesis project, he is exploring variational autoencoders for denoising and de-biasing applications in scientific data.

###### Alumni

- Liang Cheng; 2023, MSc Data Science, Thesis: ‘Autoencoder-Based Likelihood-Free Parameter Inference of Gene Regulatory Network‘, now Doctoral Student at University of Oslo
- Ansar Siddiqui; 2023, BSc Computer Science, Thesis: ‘Parameter Inference for Stochastic Models of Gene Expression in Eukaryotic Cells‘, now Cloud Developer at Scania
- Max Andersson, Carl Löfkvist, Edward Glöckner; 2023, BSc Engineering Physics, Thesis: ‘Dark Matter signals at the Large Hadron Collider with Deep Learning‘
- Mona Babikir; 2019, MSc Computer Science, Thesis: ‘Machine Learning for Cloud: Modeling Cluster Health using Usage Parameters‘, now Software Engineer at Tink AB
- Iliam Barkino; 2019, MSc Engineering Physics, Thesis: ‘Summary Statistic Selection with Reinforcement Learning‘, now Deep Tech VC at Industrifonden
- Mattias Åkesson; 2018, MSc Engineering Physics, Thesis: ‘Learning Phantom Dose Distribution using Regression Artificial Neural Networks‘, now Senior Federated Learning Engineer at Scaleout Systems AB

email: firstname.lastname@it.uu.se

visiting address: Lägerhyddsvägen 1, Hus 10, 752 37 Uppsala