Prashant Singh

Prashant is an Assistant Professor, Docent 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.

Dhanushki Mapitigama

Dhanushki joined the group in September 2024 as a PhD student. Dhanushki holds a Masters degree in Data Science from Uppsala University, Sweden. Her doctoral project explores Bayesian active learning approaches for drug discovery and repurposing. She is generally interested in Bayesian machine learning and optimization.

Csongor Horváth

Csongor joined the group in September 2024 as a PhD student. Csongor holds a Masters degree in Computational Science from Uppsala University, Sweden. His doctoral work focusses on various aspects of solving ill-posed inverse problems, and in general non-convex optimization. He is also interested in deep learning, graph theory and the intersection of machine learning and optimization.

###### Visiting Students

Emily Morgan

Emily Morgan is a Master’s student in Bioinformatics at Rhodes University, where she participates in research led by Prof. Özlem Tastan-Bishop. With a background in Computer Science and Biochemistry, Emily’s focus is on computational biology, particularly molecular dynamics simulations, dynamic residue network analysis, and machine learning methods for studying viral proteins. Her research interests include structural biology, machine learning applications in bioinformatics, and developing predictive models to understand protein dynamics and mutations.

Kristiana Stefa

Kristiana is a Master’s student in Data Science at the IT University of Copenhagen, Denmark. Kristiana’s graduate project is in collaboration with Prof. Ida-Maria Sintorn, and explores uncertainty quantification in large-scale generative models.

Stela Arranz Gheorghe

Stela is a Master’s student in Data Science at the IT University of Copenhagen, Denmark. Stela’s graduate project is in collaboration with Prof. Ida-Maria Sintorn, and explores robust guided attention.

###### Graduate Students

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.

*YOU!*

We always look forward to hearing from talented and motivated future colleagues at all levels – Masters/PhD/PostDoc.

###### Alumni

- Ema Duljkovic, 2023, MSc Data Science, now R&D intern at Ericsson.
- Mina Badri, 2023, MSc Data Science, Data Science intern at Volvo Cars.
- 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