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Dmitry Bagaev

Company

TU/e

Role

Postdoctoral Researcher

Software Architect Bayesian Inference Statistical Analysis Probabilistic Modelling
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Dmitry Bagaev's Activity

Events Attended

1

since 2026

Groups Joined

2

communities

Groups Owned

0

as organizer

Active Communities

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attended 2+ events

Talks Given

2

presentations

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Milestones

First Event

PyData Meetup @ Bright Cape

April 2026

First Group

PyData Eindhoven

January 2021

First Talk

Maintaining 3 Julia packages

February 2026

Groups You’re In

PyData Eindhoven banner
PyData Eindhoven

This Meetello Group is a place for technical people to come and hear technical talks, and network with likeminded people in the Eindhoven region interested in Python. No Sales, No Recruting, just technical talks.

PyData Eindhoven logo
Yawen Wang
Dmitry Bagaev
Gareth Thomas
organized by:
Gareth Thomas
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JuliaLang Eindhoven banner
JuliaLang Eindhoven

his meetup is a place where people who are passionate about Julia the programming language can come together and share experiences in the Eindhoven region. No sales, no marketing, no recruiting, just technical people sharing how they solve technical problems with Julia.

JuliaLang Eindhoven logo
Dmitry Bagaev
Gareth Thomas
organized by:
Gareth Thomas

Events Attending

2026

2026

1 event attended

PyData Meetup @ Bright Cape banner
PyData Meetup @ Bright Cape
Apr
02
03:30 PM
Eindhoven

PyData Eindhoven & Bright Cape We’re excited to announce our upcoming meetup in collaboration with Bright Cape, focused on “Scalable AI & Cloud Deployments.” The event will be hosted in the Stage Room at Fifth NRE. Mark your calendar and join us for an evening of insights, networking, and discussions on cutting-edge technology with fellow members of the data and AI community.

Powered by Bright Cape Fifth NRE

Talks Given

Keep Calm and Trust the AI

At PyData Meetup @ Bright Cape — Apr 02, 2026

Large language models are powerful, but when asked for facts or numerical answers, they can hallucinate with surprising confidence. In this talk, we take a different approach: instead of asking an LLM to guess, we let it orchestrate real probabilistic inference. Using RxInfer and an MCP server, we connect a language interface to a Bayesian linear regression model running on an actual dataset. The LLM translates user intent into structured computation, the regression model performs principled inference, and the result is grounded in data—not generated from patterns alone. You’ll see how combining probabilistic programming with tool calling creates AI systems that are transparent, verifiable, and dramatically more reliable. Sometimes, the best way to trust the AI is to make sure it does the math.

Maintaining 3 Julia packages

At Embracing Change — Sep 22, 2022