On this recent article in the Quanta magazine, Alex Dixon, who wrote in Haskell the first solver for the problem, commented:

For the past 50 years, Vector Addition Systems—a simple but powerful computational model—have been a topic of great interest in theoretical CS. The reachability problem in that model asks whether we can get from some configuration to another.

The problem sounds relatively easy on a first glance, and an exponential lower bound held firm for over 40 years. Work by excellent theoreticians, including familiar names from Warwick DCS, finally closed the difficulty of the problem in 2021, concluding that it is very, very difficult indeed.

We are happy to announce that Dr Paris Giampouras has joined the Department of Computer Science as an Assistant Professor. Originally from Greece, he has relocated to Warwick from Baltimore, where he spent four years working as a Postdoctoral Fellow and later as a Research Faculty member at the Mathematical Institute for Data Science at Johns Hopkins University. His expertise lies in machine learning theory and its applications in image processing and computer vision. More specifically, his research has focused on exploring parsimonious representations to address various inverse problems and adversarial robustness.

Currently, he is focusing on two main areas: a) leveraging structured representation in Generative AI applications, and b) developing algorithms that enable continual learning of various tasks for deep learning systems. His goal is to contribute to the foundational understanding of AI algorithms, with a focus on robustness, applications of AI in medicine, and climate change.

We welcome him to the department!

We are happy to announce that Dr Fanghui Liu and Dr Debmalya Mandal have joined the Department of Computer Science as Assistant Professors.

Fanghui finished his PhD degree at Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University in 2019. Then he gained the research experience in Europe as a postdoc researcher, at ESAT-STADIUS, KU Leuven from 2019 to 2021 and Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland from 2021 to 2023, respectively. His research interests include machine learning, statistical learning theory as well as applications in trustworthy machine learning systems.

Debmalya completed his Master’s degree in Computer Science and Automation from Indian Institute of Science, and his PhD in Computer Science from Harvard University, where he was also part of the EconCS group. He then gained experience as a postdoctoral researcher at the Data Science Institute of Columbia University, and at the Max Planck Institute of Software Systems. He is broadly interested in the theoretical foundations of AI/ML systems and often incorporate tools from computational social choice, game theory, and machine learning theory in his research. He is currently working on fairness in machine learning, reinforcement learning, and the exciting problem of AI alignment.

We welcome them both to the department!