Kolya Malkin's homepage

Bayesian machine learning • neurosymbolic AI • AI for science and scientific reasoning

From July 2024: Chancellor's Fellow (≈ Lecturer (UK) ≈ Asst.Prof. (US)) @ Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh
Until May 2024: Postdoc @ Mila – Québec AI institute and Department of Informatics and Operations Research, Université de Montréal

Call for students

I am looking for motivated students to fill fully-funded PhD positions in the School of Informatics, University of Edinburgh, ideally to begin in January, May, or September 2025. The research topics are in the areas of Bayesian machine learning (including probabilistic reasoning in language, generative models, and neurosymbolic methods) and applications in the sciences. I am especially happy to work with students who have a strong background in mathematics and an interest in robust and interpretable AI. Please see my research interests and recent publications for examples of the kind of work we might do together.

The studentships include:

• Funding for tuition and living expenses for the duration of the PhD, as well as discretionary travel and research funds and access to some of the most advanced computing facilities in Britain.
• A supportive and stimulating research environment in a large, leading institution in computer science and AI – with particular strengths in NLP and probabilistic machine learning, as well as theoretical CS, robotics, and other areas – and a major new investment in generative AI.
• The opportunity to collaborate with researchers at Mila and other world-class institutions.
• A chance to explore one of the world's most beautiful, historic, and livable cities.

The typical length of a PhD in the UK is 3-4 years. All students have at least one secondary supervisor – please let me know if you have a specific person or lab in mind – and there are many opportunities for collaboration.

Students from all countries are eligible. Those from any backgrounds and identities underrepresented in AI are particularly encouraged and welcome.

Interested students should contact me to discuss research directions and the application process.

Research

I mainly work on algorithms for deep-learning-based reasoning and their applications. I am specifically interested in the following subjects:

• Generative models, in particular, induction of compositional structure in generative models and modeling of posteriors over high-dimensional explanatory variables (including with continuous-time (diffusion) generative models). Much of my recent work is on generative flow networks, which are a path towards inference machines that build structured, uncertainty-aware explanations for observed data.
• Applications to natural language processing and reasoning in language: what large language models can do, what they cannot do, and how to overcome their limitations with improved inference procedures. I view human-like symbolic, formal, and mathematical reasoning as a long-term aspiration for artificial intelligence.
• Applications to computer vision: notably, below you can find my work on AI for remote sensing (land cover mapping and change detection), which can be used for tracking land use patterns over time and monitoring the effects of climate change.

In Montreal, I am fortunate to work with Prof. Yoshua Bengio and also to collaborate with Profs. Aaron Courville, Gauthier Gidel, and Guillaume Lajoie, among others. I have also been lucky to work with many fellow postdoctoctoral researchers (including Kilian Fatras, Alex Hernández-García, Pablo Lemos, Alex Tong) and M.S./Ph.D. students and interns (including Tristan Deleu, Edward Hu, Moksh Jain, Minsu Kim, Salem Lahlou, Jarrid Rector-Brooks, Alexandra Volokhova, Dinghuai Zhang, among many others).

I was formally trained as a pure mathematician: at the University of Washington (Seattle) (B.S., 2015) and Yale University (M.S. and Ph.D., 2021). In addition, I believe many individuals and societies could benefit from a dose of friendly mathematical education. Some organizations I have been involved in: UW Math Circles (Seattle), Math-M-Addicts (New York City). I am also a coauthor of this collection of problems and puzzles.

Publications and preprints (also here and here)

2024

2023

2022

2021

2020