Probability · Queueing theory · Applied mathematics
El'mira Yu.
Kalimulina
Stochastic networks and industrial modelling
I work on the mathematics of stochastic networks: queueing systems, random graphs, reliability and probabilistic dynamics. Across research and industry, I build and optimise networked systems with random demand, failures, mobility and evolving structure — particularly in telecommunications and transport, distributed computing and financial networks, and reliability-critical infrastructure.
research / industry20+ years of experience
focusNetworks under uncertainty
20+years of experience in research and teaching
50+research publications
50+degree projects supervised
2025patent and registered software
01 / VALUE
What I do with
stochastic networks
I retain the randomness that actually governs performance — traffic bursts, failures and repairs, mobility, routing and structural change — and turn it into quantities that can be proved, computed and used in a design decision.
Build the stochastic model
Queueing networks, Markov processes, random graphs and reliability models for systems with time-varying load, failures, mobility and changing topology.
Establish operating limits
Stability and ergodicity criteria, convergence rates, delay and throughput estimates, overload probabilities, bottlenecks and phase transitions.
Optimise the real system
Analytical bounds, simulation and data analysis to compare architectures, validate control policies and translate mathematical results into engineering choices.
Mathematical core
Probability theory · Queueing theory · Markov processes · Random graphs · Reliability · Simulation
Application domains
Telecommunications · Transport · Distributed computing · Financial networks · Reliability-critical infrastructure
02 / INDUSTRY
Industry:
models that reach the real system
Experience across search and ad tech, transport Wi-Fi, quantitative finance and technology assessment — without an artificial boundary between fundamental and applied mathematics.
01
2016—present
MIRWIFI JSC
R&D Expert · Telecommunications Platforms
Analytical and simulation models of performance and reliability for large transport data networks operating under mobility, unstable channels and variable demand.
02
2020—2022
TWIM · UAE
Quantitative Analyst
Financial mathematics, development and backtesting of trading strategies, and ongoing monitoring and analysis of production algorithms.
03
2007—2009
Yandex
Research Engineer · Search Quality
Machine-learning models for web-spam detection, classification and forecasting for contextual advertising, using Hadoop, MapReduce, Java, Python and R.
04
since 2025
Scientific & Technical Review
Expert for the Ministry of Industry and Trade
Expert assessment of scientific equipment and technology proposals at the interface between research requirements and engineering feasibility.
Protected result · 2025
Intelligent control of network-traffic aggregation
Co-inventor of patent RU 2843669 C1 and co-author of registered software for modelling buffer dynamics in networks with mobile aggregators. The solution targets stable, high-performance transmission under unreliable channels.
View patentTransport · 2022
Predicting failures of locomotive equipment
Supervised an applied student project commissioned by Russian Railways: problem formulation, forecasting models and translation of analytics into an operational setting.
Research leadership
Principal Investigator of an RFBR grant
Led a project on analytical models, methods and algorithms for optimising distributed systems with evolving structure.
03 / RESEARCH
Systems that evolve
under uncertainty
My work centres on large random systems: proving stability, estimating convergence to stationarity and turning asymptotic theory into an engineering benchmark.
01±
Stochastic networks
Ergodicity, stability and quantitative convergence rates for queueing networks with dynamic or random structure.
02±
Reliability and performance
Models of failure, repair, delay and throughput for telecommunications, computing and transport systems.
03±
Random processes on graphs
Interacting systems, zero-range processes, random walks and asymptotic properties of large networks.
04±
Information and logic
Weighted Chernoff information, context-sensitive hypothesis testing and many-valued logic for complex-system analysis.
Current academic base
Lomonosov Moscow State University
Senior Research Fellow at the Laboratory of Large Random Systems, Faculty of Mechanics and Mathematics, since 2024.
Current research appointment
Kharkevich Institute for Information Transmission Problems, RAS
Senior Research Fellow at the Dobrushin Mathematics Laboratory: ergodicity of Markov processes and applied stochastic models, with a focus on the theory of dynamic networks.
Research foundation
ICS RAS · 2009—2024
Fifteen years of research in reliability, queueing systems and network modelling, together with editorial and organisational work.
04 / TEACHING
Student recognition
2023 / 2024
“Best Lecturer, Higher School of Engineering”
Russian University of Transport. I teach rigorous probability as a working language for data analysis, engineering and decision-making.
Teaching principle
A strong course does not simplify mathematics — it reveals why the mathematics matters and how to use it.
Author-designed courses at MSU
From probability to models of real networks
Stochastic Networks
Author-designed course · lectures · 34 hours
Faculty of Mechanics and Mathematics, MSU · Department of ProbabilityStochastic Networks and Their Applications in Complex Systems
Elective course · seminars · 34 hours
Faculty of Mechanics and Mathematics, MSUStatistical Practicum
Specialised course · lectures · 36 hours
Faculty of Chemistry, MSUProbability Theory and Mathematical Statistics
Core university course
Lomonosov Moscow State University50+degree projects
Supervision in credit scoring, financial risk, network systems, NLP, failure monitoring and transport analytics.
20+years of teaching
MSU, RUT (MIIT), MTUCI and Yandex School of Data Analysis: Bachelor’s and Master’s teaching, staff development and research supervision.
Previously taught
Machine learning · optimisation for ML · distributed systems · Hadoop & Spark · NoSQL & Neo4j · data mining in R/Python · mathematical models in economics · Wolfram Mathematica · reliability theory.
05 / PUBLICATIONS
Publications —
from reliability to information
More than 50 research publications. This selected trajectory moves from reliability of telecommunications systems through dynamic networks and many-valued logic to contemporary information theory.
2026Information theoryWeighted Chernoff Information and Optimal Loss Exponent in Context-Sensitive Hypothesis TestingEntropy · 28(5), 536 · with Mark Kelbert2026Mobile networksMarkov-modulated queueing network for mobile traffic aggregation with threshold-controlled buffersMathematical Modelling and Numerical Simulation with Applications · 6(1)2024Many-valued logicFiniteness of One-Valued Function Classes in Many-Valued LogicFractal and Fractional · 8(1)2022Algebraic structureLattice Structure of Some Closed Classes for Three-Valued Logic and Its ApplicationsMathematics · 10(1)2020Queueing networksOn Exponential Convergence of Dynamic Queueing Network and Its ApplicationsSpringer · Communications in Computer and Information Science 13372016ReliabilityAnalysis of System Reliability with Control, Dependent Failures, and Arbitrary Repair TimesInternational Journal of System Assurance Engineering and Management
06 / CREDENTIALS
Research depth. Engineering range.
Formal training in telecommunications and systems analysis, advanced probability, and an early foundation in machine learning.
Education
PhD / Candidate of Technical Sciences
MTUCI · analytical reliability models for distributed telecommunications networks.
Diploma with honours
MTUCI · Information Technology.
Advanced training
Stochastic analysis
Markov processes, random fields, stochastic differential equations and optimal stopping.
Machine Learning · MIPT
Pattern recognition, optimisation, statistics, algorithms and parallel computing.
Tools selected for the problem
PythonRSparkHadoopNumPy / SciPy / pandasWolfram MathematicaDockerNeo4j / SQLsimulationLaTeX
Research · R&D · teaching · expert work