Scalable Likelihood-Free Parameter Inference of Stochastic Simulation Models
Our research group is dedicated to addressing the complex challenges of parameter inference in stochastic simulation models, especially when we have limited observed data in the form of time series. We specialise in situations where traditional likelihood functions are either unavailable or computationally infeasible. These processes can be computationally demanding, particularly for large-scale problems with a high number of parameters to infer. To overcome these challenges, we integrate machine learning techniques such as neural networks for generating summary statistics and neural density estimators as surrogate models. Our ultimate goal is to develop scalable, efficient, and accurate methods for parameter inference, focusing on questions like efficient parameter selection, accurate comparison of high-dimensional time series, and efficient posterior distribution estimation.
People: Mayank Nautiyal, Csongor Horváth, Andreas Hellander, Prashant Singh
Federated Learning: Distributed Optimization and Applications
Federated machine learning (FedML) involves the development of algorithms and applications that enable distributed machine learning across multiple devices or data sources while maintaining data privacy and security. We study FedML broadly in the group. Some specific recent topics include distributed optimization and developing novel approaches for federated learning that can address challenges such as communication and computation efficiency, data heterogeneity, and model drift. Ultimately, our research aims to advance the state-of-the-art in federated optimization and enable its practical deployment in real-world applications.
People: Li Ju, Salman Toor, Andreas Hellander
Statistical Sampling for Large-Scale Scientific Experiments
In this research project we address the challenges of scale and complexity while conducting large-scale scientific experiments, with particular focus on phenotypic drug and drug combination screening experiments in close collaboration with the Pharmaceutical Bioinformatics research group at Uppsala University.
To successfully fight a disease and have less severe side effects, people are trying to find existing drug compounds that work together in synergy. But this leads to combinatorial explosion if one decides to study the efficacy of all possible drug combinations from a given set of compounds. The setting fits nicely into the framework of Bayesian optimization which focuses on the balance between exploration and exploitation. Hence, we are designing a decision-making algorithm that will assist the experiments on drug combination in a robotic laboratory. This method will employ Bayesian approach to 1. combine statistical learning with assumptions from different biological models, 2. make predictions with uncertainty estimates important for the exploration-exploitation tradeoff, and 3. will be scalable to be applied to large datasets.
People: Aleksandr Karakulev, Dhanushki Mapitigama, Prashant Singh
Secure and Privacy-Preserving Federated Machine Learning
Federated machine learning (FedML) is a promising approach that enables multiple participants to collaboratively train a shared machine learning model while allowing them to keep their data private. However, due to its decentralized nature, FedML is vulnerable to various security and privacy threats. Under this project, we study various security / privacy challenges and their impact in production grade federated machine learning systems in depth. Our research aim is to design practical solutions that can be used in production to help counter these threats.
People: Usama Zafar, Salman Toor
Data-driven Vulnerability Analysis for Critical Infrastructures
Software applications and reliable communication are key to offer regular
and mission critical services online. Mission critical services are part of critical
infrastructure sectors like healthcare, finance, communications, and power
generation and distribution. To support a reliable, secure, and efficient
offering of software services for critical infrastructures, the cybersecurity and
trustworthiness of software and computing infrastructures are vital. It is of
utmost importance to take all possible measures to secure the computing
systems supporting critical infrastructures and ensure uninterrupted
availability of services. To secure these systems, our project proposes to
design and develop a comprehensive and proactive vulnerability analysis
framework for software applications running on critical infrastructures. The
proposed project, with its well-defined focus areas, is a cross-disciplinary
effort within data science, machine learning, and cybersecurity with the
purposes.
People: Zhenlu Sun, Salman Toor
Inference from Longitudinal Human Biological Data
A common approach in biological research involves collecting data from the same participants at multiple time points. This longitudinal format is prevalent in fields such as microbiomics, exposomics, and genomics. Typically, such data are high-dimensional and sparse, reflecting the vast number of biological features and their varying prevalence across individuals. These studies often involve a limited number of participants, and data can be incomplete due to missed appointments or collection failures. The goal of our project is to impute missing data to increase the number of complete observations, thereby enabling more robust downstream analysis. We then propose suitable methods for further data classification and interpretation. Current applications include predicting postpartum depression or food allergies from microbiome data, as well as analyzing and modeling exposome data.
People: Andrey Shternshis
Statistical Learning Theory and Large-Scale Optimization
We explore core questions in statistical learning: what enables genuine generalization beyond memorization, how non-convex optimization converges in practice, what makes learning adaptive and data-efficient, and how to ensure robustness. These challenges are not only crucial for building effective learning systems, but also speak to the foundational principles that make learning from data possible.
People: Aleksandr Karakulev, Csongor Horváth, Li Ju, Prashant Singh