PhD students Dekel Zak Dekel is working on semantic approaches to program synthesis. She is developing new techniques that understand the code and learns from their mistakes. On the application side, her work focuses on cybersecurity. Lorenzo Theunissen Lorenzo is working on new program synthesis techniques for automatically configuring the functionality of programmable network devices -- new generation network devices have switched to progammable data planes. Reuben Gardos Reid Reuben is working on scientific discovery through program synthesis and probabilistic programs as a representation of scientific models -- they allow us to focus on causal links and capture all stochasticity and uncertainty. Tilman Hinnerichs Tilman is working on neuro-symbolic approaches to knowledge representation and program synthesis, developing formal languages that combine symbols and distributed representations as well as techniques to learn such models. Issa Hanou Issa is exploring the interations between learning, the ideas from program synthesis, with special attention to robust planning. Jonas Witt Jonas is exploring how analogical reasoning, tied wtih problem decomposition, can help program synthesis techniques to solve more complex tasks (at Siemens and University of Bamberg) Research engineers Pamela Wöchner Pamela is helping us make Herb.jl as good as possible! Master students Alperen Guncan Discovering generalised conflicts in program synthesis Ivo Yordanov Joint project with ASML and Kshitij Goyal Danila Bren Using motifs to discover biological qualitative networks Hidde Leistra Drone design with program synthesis Stef Rasing Learning within a single synthesis problem Han Heijmans Probabilistic programming and MDP solving (with Frans Oliehoek) Lola Dekhuijsen Incremental inference in probabilistic programming Alumni Postdocs Liang Wang Liang is working on simulation-based inference with probabilistic programming. In particular, Liang focuses on exploring how can probabilistic programming help us to do better inference with coupled simulators. PhD students Aldo Pareja Probabilistic programming and simulators (with MIT-IBM Watson Lab) Kshitij Goyal Machine learning under constraints (at KU Leuven, with Hendrik Blockeel) Master students Ole Poeth Generalising conflict analysis in program synthesis Richard Wijers Program synthesis for chemical reaction networks (with Jana Weber) Sebastien van Tiggele Program synthesis for agent-based biochemical modelling (with Walter Fontana and Jérôme Feret) Matteo Bertorotta Improving program synthesis with e-graphs (with Andreea Costea) Pallabi Sree Parker Program refactoring and forgetting in planning problems Avi Halevy Improving reasoning in Large Language Models with probabilistic logic programming and proof assistants (with Tina Nane) Mara Coman Better proposals in probabilistic programming through constraint solving Claire Kuhlkin Improving medical guidelines with ILP (with Annette ten Teije) Luka Janjić Program synthesis for dependently typed programs (with Jesper Cockx) Bart Swinkels Constraint Propagation in Program Synthesis Timo Jugariu Program synthesis for programmable data planes Jord Molhoek Incorporating prior knowledge in MDPs with probabilistic programming Reuben Gardos Reid Reasoning with a Rail Network Simulator Jaap de Jong Unifying example- and specification-based program synthesis Pepijn Klop Using Large Language Models to generate examples for program synthesis Gautham Venkataraman Using Probabilistic Programming to guide Combinatorial Optimization in Dynamic Environments Koen du Buf System on Chip early power estimation using Machine Learning Alexander Freeman Exploiting modularity in program synthesis Marc Droogh AI for chip topographical optimisation Dirk van Bokkem Economical greenhouse decision support model Research assistants Stef Rasing Stef Rasing Honours students (Bsc) Piotr Cichoń Evolving search procedures in program synthesis Nicolae Filat Evolving search procedures in program synthesis Ivan Bozhanin Learning distances for distance-guided program synthesis Tudor Magirescu Learning distances for distance-guided program synthesis
