World's Best Scientists 2026 revealed!

Mark Podolskij publication distribution in Mathematics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mathematics in 2026. The highlighted bar marks where Mark Podolskij sits on this spectrum.

42–46 publications: 3 scientists 47–51 publications: 5 scientists 52–56 publications: 7 scientists 57–61 publications: 20 scientists 62–66 publications: 14 scientists 67–71 publications: 25 scientists 72–76 publications: 19 scientists 77–81 publications: 35 scientists 82–86 publications: 50 scientists 87–91 publications: 60 scientists 92–96 publications: 86 scientists 97–101 publications: 84 scientists 102–106 publications: 83 scientists 107–111 publications: 90 scientists 112–116 publications: 99 scientists 117–121 publications: 90 scientists 122–126 publications: 91 scientists 127–131 publications: 109 scientists 132–136 publications: 110 scientists 137–141 publications: 98 scientists 142–146 publications: 112 scientists 147–151 publications: 102 scientists 152–156 publications: 88 scientists 157–161 publications: 106 scientists 162–166 publications: 83 scientists 167–171 publications: 102 scientists 172–176 publications: 77 scientists 177–181 publications: 81 scientists 182–186 publications: 78 scientists 187–191 publications: 71 scientists 192–196 publications: 92 scientists 197–201 publications: 64 scientists 202–206 publications: 69 scientists 207–211 publications: 64 scientists 212–216 publications: 62 scientists 217–221 publications: 58 scientists 222–226 publications: 53 scientists 227–231 publications: 50 scientists 232–236 publications: 46 scientists 237–241 publications: 46 scientists 242–246 publications: 46 scientists 247–251 publications: 43 scientists 252–256 publications: 29 scientists 257–261 publications: 45 scientists 262–266 publications: 30 scientists 267–271 publications: 33 scientists 272–276 publications: 34 scientists 277–281 publications: 30 scientists 282–286 publications: 31 scientists 287–291 publications: 21 scientists 292–296 publications: 34 scientists 297–301 publications: 26 scientists 302–306 publications: 10 scientists 307–311 publications: 17 scientists 312–316 publications: 23 scientists 317–321 publications: 13 scientists 322–326 publications: 16 scientists 327–331 publications: 26 scientists 332–336 publications: 13 scientists 337–341 publications: 13 scientists 342–346 publications: 16 scientists 347–351 publications: 17 scientists 352–356 publications: 12 scientists 357–361 publications: 18 scientists 362–366 publications: 18 scientists 367–371 publications: 9 scientists 372–376 publications: 11 scientists 377–381 publications: 8 scientists 382–386 publications: 8 scientists 387–391 publications: 9 scientists 392–396 publications: 9 scientists 397–401 publications: 8 scientists 402–406 publications: 11 scientists 407–411 publications: 6 scientists 412–416 publications: 6 scientists 417–421 publications: 9 scientists 422–426 publications: 8 scientists 427–431 publications: 5 scientists 432–436 publications: 8 scientists 437–441 publications: 8 scientists 442–446 publications: 4 scientists 447–451 publications: 4 scientists 452–456 publications: 4 scientists 457–461 publications: 2 scientists 462–466 publications: 2 scientists 467–471 publications: 4 scientists 472–476 publications: 3 scientists 477–481 publications: 3 scientists 482–486 publications: 6 scientists 487–491 publications: 3 scientists 492–496 publications: 5 scientists 497–501 publications: 5 scientists 502–506 publications: 1 scientists 507–511 publications: 6 scientists 512–516 publications: 4 scientists 517–521 publications: 1 scientists 522–526 publications: 3 scientists 527–531 publications: 1 scientists 532–536 publications: 4 scientists 537+ publications: 100 scientists
42 publications 537+

The last bar groups every scientist with 537 publications or more.

Mark Podolskij D-index placement in Mathematics in 2026

The chart shows the D-index (discipline H-index) distribution of Mathematics scientists ranked by Research.com in 2026. The highlighted bar marks where Mark Podolskij sits on this spectrum.

30 D-Index: 174 scientists 31 D-Index: 151 scientists 32 D-Index: 174 scientists 33 D-Index: 117 scientists 34 D-Index: 136 scientists 35 D-Index: 127 scientists 36 D-Index: 145 scientists 37 D-Index: 153 scientists 38 D-Index: 150 scientists 39 D-Index: 150 scientists 40 D-Index: 138 scientists 41 D-Index: 136 scientists 42 D-Index: 93 scientists 43 D-Index: 108 scientists 44 D-Index: 115 scientists 45 D-Index: 112 scientists 46 D-Index: 103 scientists 47 D-Index: 75 scientists 48 D-Index: 59 scientists 49 D-Index: 67 scientists 50 D-Index: 60 scientists 51 D-Index: 57 scientists 52 D-Index: 59 scientists 53 D-Index: 62 scientists 54 D-Index: 60 scientists 55 D-Index: 50 scientists 56 D-Index: 42 scientists 57 D-Index: 54 scientists 58 D-Index: 50 scientists 59 D-Index: 42 scientists 60 D-Index: 41 scientists 61 D-Index: 35 scientists 62 D-Index: 40 scientists 63 D-Index: 21 scientists 64 D-Index: 31 scientists 65 D-Index: 27 scientists 66 D-Index: 29 scientists 67 D-Index: 19 scientists 68 D-Index: 25 scientists 69 D-Index: 17 scientists 70 D-Index: 18 scientists 71 D-Index: 12 scientists 72 D-Index: 14 scientists 73 D-Index: 13 scientists 74 D-Index: 18 scientists 75 D-Index: 9 scientists 76 D-Index: 11 scientists 77 D-Index: 10 scientists 78 D-Index: 9 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 10 scientists 82 D-Index: 5 scientists 83 D-Index: 5 scientists 84 D-Index: 13 scientists 85 D-Index: 6 scientists 86+ D-Index: 99 scientists
30 D-Index 86+

The last bar groups every scientist with 86 D-Index or more.

Overview

Mark Podolskij is affiliated with the University of Luxembourg in Luxembourg. Their research primarily spans the fields of Economics, Econometrics and Finance, with a secondary focus on Mathematics. Their work intersects several subfields including Finance, Statistics and Probability, Mathematical Physics, Management Science and Operations Research, and Economics and Econometrics.

The main topics covered in their research explore areas such as stochastic processes and financial applications, financial risk and volatility modeling, statistical methods and inference, stochastic processes and statistical mechanics, probability and risk models, Markov chains and Monte Carlo methods, and complex systems and time series analysis.

Mark Podolskij has published numerous papers in a range of academic journals and publication venues. Frequent venues include arXiv (Cornell University), Electronic Journal of Statistics, Stochastic Processes and their Applications, Open Repository and Bibliography (University of Luxembourg), and Electronic Journal of Probability.

Examples of recent publications include:

  • "Semiparametric estimation of McKean-Vlasov SDEs" (2023), Annales de l Institut Henri Poincaré Probabilités et Statistiques
  • "Parameter estimation of discretely observed interacting particle systems" (2023), Stochastic Processes and their Applications
  • "Polynomial rates via deconvolution for nonparametric estimation in McKean-Vlasov SDEs" (2024), Probability Theory and Related Fields
  • "On Dantzig and Lasso estimators of the drift in a high dimensional Ornstein-Uhlenbeck model" (2020), Electronic Journal of Statistics
  • "A Berry-Esseén theorem for partial sums of functionals of heavy-tailed moving averages" (2020), Electronic Journal of Probability

Collaboration is a notable aspect of their research career, with frequent coauthors including Chiara Amorino, Vytautė Pilipauskaitė, Dmytro Marushkevych, Akram Heidari, and Gabriela Ciołek.

Best Publications

  • Microstructure Noise in the Continuous Case: The Pre-Averaging Approach ∗

    Jean Jacod;Yingying Li;Per A. Mykland;Mark Podolskij

  • Realized range-based estimation of integrated variance

    Kim Christensen;Mark Podolskij

  • Estimation of volatility functionals in the simultaneous presence of microstructure noise and jumps

    Mark Podolskij;Mathias Vetter

  • Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data

    Kim Christensen;Silja Kinnebrock;Mark Podolskij;Mark Podolskij

  • Estimation of Volatility Functionals in the Simultaneous Presence of Microstructure Noise and Jumps

    Mark Podolskij;Mathias Vetter;Margit Sommer

  • A Central Limit Theorem for Realised Power and Bipower Variations of Continuous Semimartingales

    Ole E. Barndorff-Nielsen;Svend Erik Graversen;Jean Jacod;Mark Podolskij

  • Fact or friction: Jumps at ultra high frequency☆

    Kim Christensen;Roel C.A. Oomen;Roel C.A. Oomen;Mark Podolskij

  • Realised quantile-based estimation of the integrated variance

    Kim Christensen;Roel Oomen;Roel Oomen;Mark Podolskij;Mark Podolskij

  • A central limit theorem for realised power and bipower variations of continuous semimartingales

    Ole E. Barndorff–Nielsen;Svend Erik Graversen;Jean Jacod;Mark Podolskij

  • Bipower-type estimation in a noisy diffusion setting☆

    Mark Podolskij;Mathias Vetter

  • Pre-averaging based estimation of quadratic variation in the presence of noise and jumps: Theory, implementation, and empirical evidence

    Nikolaus Hautsch;Mark Podolskij

  • Limit theorems for moving averages of discretized processes plus noise

    Jean Jacod;Mark Podolskij;Mathias Vetter

  • Power variation for Gaussian processes with stationary increments

    Ole E. Barndorff-Nielsen;José Manuel Corcuera;Mark Podolskij

  • On covariation estimation for multivariate continuous Itô semimartingales with noise in non-synchronous observation schemes

    Kim Christensen;Mark Podolskij;Mathias Vetter

  • Understanding limit theorems for semimartingales: a short survey

    Mark Podolskij;Mathias Vetter

  • New tests for jumps in semimartingale models

    M. Podolskij;D. Ziggel

  • Multipower Variation for Brownian Semistationary Processes

    Ole E. Barndorff-Nielsen;José Manuel Corcuera;Mark Podolskij

  • Quantitative Breuer-Major Theorems

    Ivan Nourdin;Giovanni Peccati;Mark Podolskij

  • Limit theorems for functionals of higher order differences of Brownian semi-stationary processes

    Ole Barndorff-Nielsen;José Manuel Corcuera;Mark Podolskij

  • Quantitative Breuer-Major Theorems

    Mark Podolskij

Frequent Co-Authors

Ole E. Barndorff-Nielsen
Ole E. Barndorff-Nielsen Aarhus University
Jean Jacod
Jean Jacod Sorbonne University
Nakahiro Yoshida
Nakahiro Yoshida University of Tokyo
Holger Dette
Holger Dette Ruhr University Bochum
Per A. Mykland
Per A. Mykland University of Chicago
Giovanni Peccati
Giovanni Peccati University of Luxembourg
Nikolaus Hautsch
Nikolaus Hautsch University of Vienna
Ivan Nourdin
Ivan Nourdin University of Luxembourg
David Nualart
David Nualart University of Kansas
Neil Shephard
Neil Shephard Harvard University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Pursuing a Mathematics degree in the USA opens doors to versatile career opportunities, especially when combined with complementary fields. For students looking to expand their business acumen alongside quantitative skills, exploring a one year mba can be an efficient way to accelerate career growth without a lengthy time commitment.

Many professionals prefer flexible learning paths, which makes online mba programs that accept transfer credits attractive. These programs allow students to build on prior coursework, saving time and tuition while enhancing leadership skills crucial in data-driven industries.

Given the surge in demand for data expertise, combining mathematics with an ms in data analytics offers a strong competitive edge. This blend equips graduates with both theoretical and applied analytical tools highly sought after in tech, finance, and healthcare sectors.

For those seeking accessible options, exploring mba programs easy to get into can provide valuable credentials with less competitive barriers, helping quickly pivot into managerial roles that benefit from mathematical expertise.

Best Scientists Citing Mark Podolskij

Trending Scientists

Recently Published Articles