Dmitry V. Bagaev • Eindhoven University of Technology
2023
Bayesian inference realizes optimal information processing through a full commitment to reasoning by probability theory. The Bayesian framework is a crucial technology at the core of modern AI with applications such as speech and image recognition and generation, biomedical analysis, robot navigation, and more. This dissertation focuses on the realization of efficient Bayesian inference in large-scale probabilistic models, targeting real-time signal processing and control applications under real-world conditions. We present a practical architecture based on reactive message passing-based inference in a factor graph representation of the probabilistic model under study.
M Goncharov, D Bagaev, D Shcherbinin, I Zvyagin, D Bolotin, PG Thomas, ...
Nature methods 19 (9), 1017-1019 • 2022
Ironically, my most impactful contributions turned out to be in biochemistry and immunology, despite never having studied biology or chemistry (and still not knowing them well). This is largely due to the significant differences in citation patterns between fields. My contributions in these areas are primarily software-based - developing tools and databases like VDJdb that enable other researchers to conduct their biological research more effectively. It's much harder to accumulate citations in pure mathematics, where papers are read by a smaller, specialized community, compared to biology fields where research involves many collaborators and reaches a broader audience. While my overall percentage contribution in biology papers is lower due to larger author lists, the visibility and impact of the software tools I've built are significantly higher, as they serve as essential infrastructure for the broader research community.
M Shugay, DV Bagaev, MA Turchaninova, DA Bolotin, OV Britanova, ...
PLoS computational biology 11 (11), e1004503 • 2015
M Shugay, DV Bagaev, IV Zvyagin, RM Vroomans, JC Crawford, G Dolton, ...
Nucleic acids research 46 (D1), D419-D427 • 2018
DV Bagaev, RMA Vroomans, J Samir, U Stervbo, C Rius, G Dolton, ...
Nucleic acids research 48 (D1), D1057-D1062 • 2020
AS Shomuradova, MS Vagida, SA Sheetikov, KV Zornikova, D Kiryukhin, ...
Immunity 53 (6), 1245-1257. e5 • 2020
M Shugay, AR Zaretsky, DA Shagin, IA Shagina, IA Volchenkov, ...
PLoS computational biology 13 (5), e1005480 • 2017
MV Pogorelyy, AD Fedorova, JE McLaren, K Ladell, DV Bagaev, ...
Genome medicine 10 (1), 1-14 • 2018
DV Bagaev, IV Zvyagin, EV Putintseva, M Izraelson, OV Britanova, ...
BMC genomics 17 (1), 453 • 2016
İ Şenöz, T van de Laar, D Bagaev, B de Vries
Entropy 23 (7), 807 • 2021
D Bagaev, B de Vries
Scientific Programming 2023 (1), 6601690 • 2023
D Bagaev, A Podusenko, B De Vries
Journal of Open Source Software 8 (84), 5161 • 2023
D Bagaev, F Grigoriev, I Kapyrin, I Konshin, V Kramarenko, A Plenkin
Russian Supercomputing Days, 265-277 • 2019
D Bagaev, I Konshin, K Nikitin
Russian supercomputing days, 54-66 • 2017
D Bagaev, B van Erp, A Podusenko, B de Vries
Software Impacts 12, 100299 • 2022
A Podusenko, B van Erp, D Bagaev, İ Şenöz, B de Vries
2021 IEEE 31st International Workshop on Machine Learning for Signal Processing • 2021