Victor Orlov | Mathematics | Best Research Article Award

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Victor Orlov | Mathematics | Best Research Article Award

 Moscow State University of Civil Engineering | Russia

Professor Orlov Viktor Nikolaevich is a distinguished mathematician with over five decades of academic and research experience. He began his career after graduating in Mathematics from Chuvash State University in 1973. Over the years, he earned advanced degrees including a Ph.D. in Physical and Mathematical Sciences (1989) and a Doctor of Sciences in Mathematical Modeling (2011). His academic journey has seen him hold prominent positions at institutions like Cheboksary State Agricultural Academy, Russian State Social University, and Kazan Federal University. Currently, he serves as a professor at the Department of Higher Mathematics. An expert in mathematical modeling and nonlinear differential equations, Prof. Orlov has authored over 260 publications, including monographs, textbooks, patents, and articles indexed in WoS and Scopus. He also serves as an editorial board member and reviewer for multiple international journals. His dedication to mathematics has significantly shaped the Russian and international scientific communities.

Publication Profile

Orcid

Education

Orlov Viktor Nikolaevich began his academic journey at Chuvash State University, where he completed his undergraduate degree in Mathematics in 1973. He then pursued postgraduate studies at the Herzen Leningrad State Pedagogical Institute, completing them in 1982. In 1989, he successfully defended his Candidate of Sciences (Ph.D.) dissertation in Differential Equations at Belarusian State University (BSU), Minsk. This was followed by the conferment of the Associate Professor title in 1992. Driven by a desire for further specialization, he enrolled in the doctoral program at Moscow State University for the Humanities in 2009. In April 2011, he earned the prestigious Doctor of Physical and Mathematical Sciences degree in the field of Mathematical Modeling, Numerical Methods, and Software Packages, with his dissertation focusing on nonlinear differential equations with moving singularities. His education forms the foundation of his longstanding scientific excellence and teaching acumen.

Experience

Professor Orlov’s rich professional journey spans over 52 years in academia. From 2003–2006, he served as Head of the Department of Computer Science and Computer Engineering at Cheboksary State Agricultural Academy. From 2007–2011, he led the Department of Mathematics, Computer Science, and Modeling at the Russian State Social University (Cheboksary branch). In 2012, he became Head of the Department of Algebra and Geometry at I. Ya. Yakovlev Cheboksary State Pedagogical University. From 2015, he headed the Department of Mathematics and Methods of Teaching Mathematics at Kazan Federal University’s Yalta branch. Between 2017–2021, he worked at Moscow State University of Civil Engineering, where he continues today as Professor in the Department of Higher Mathematics. His leadership extended to education quality control as an expert for Rosobrnadzor from 2015 to 2018. Prof. Orlov has played key roles in enhancing mathematical education and research infrastructure in Russia.

Honors and Awards

Professor Orlov has been consistently recognized for his outstanding contributions to science and education. He has received 5 patents for inventions and 9 certificates for algorithms and software programs, showcasing his commitment to applied research. He received three competitive research grants from the National Research University Moscow State University of Civil Engineering in 2022, all completed ahead of schedule. In 2023, he led another successful grant project. He is a highly sought-after academic editor and reviewer, contributing to over 90 reviews for MDPI journals such as Mathematics (Q1) and Axioms (Q2), and serves on the editorial boards of multiple journals including Bulletin of ChSPU and Brest State Technical University. He was the Editor-in-Chief of Axioms special issues in 2022 and 2023. His impact is evidenced by his Russian Hirsch index of 18 and foreign index of 9, marking him as a respected figure in global academia.

Research Focus

Professor Orlov’s research expertise lies at the intersection of analytical theory of differential equations, fractional calculus, and computational mathematics. His landmark work revolves around the mathematical modeling of nonlinear differential equations with moving singularities, which was the basis of his doctoral dissertation. He has advanced the theory and applications of differential equations with fractional derivatives, a critical area in modeling complex physical and engineering systems. His contributions also span numerical methods, scientific software development, and simulation of dynamic systems. With over 260 publications, including 223 scientific articles, 39 educational works, 4 monographs, and multiple patents, his research addresses both theoretical advancements and real-world applications. He actively engages in interdisciplinary research, often collaborating across physics, engineering, and computer science. Prof. Orlov’s work not only enhances theoretical understanding but also contributes practical solutions, particularly in the fields of mechanical systems and deformable body mechanics.

Publications

  •  Development of a Mathematical Modeling Method for Nonlinear Differential Equations with Moving Singularities

  • Analytical Approach to Differential Equations with Fractional Derivatives

  • Computational Algorithms for Solving Boundary Value Problems in Mechanics

  • Numerical Simulation of Complex Dynamic Systems in Engineering

  • Mathematical Modeling of Physical Systems with Variable Parameters

  •  On the Convergence of Numerical Methods in Differential Equations

  •  Modeling of Heat Transfer Using Fractional Calculus

  • Nonlinear Oscillations in Mechanical Structures: A Simulation Study

  • Software Packages for Analytical Solutions of Nonlinear Equations

Chiara Amorino | Mathematics | Young Scientist Award

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Assist. Prof. Dr. Chiara Amorino | Mathematics | Young Scientist Award

Assist. Prof. Dr. Chiara Amorino, Universitat Pompeu Fabra, Spain

Assist. Prof. Dr. Chiara Amorino is an Assistant Professor at Universitat Pompeu Fabra in Barcelona, a position she has held since April 2024. She previously served as a postdoctoral researcher at the University of Luxembourg in Prof. Mark Podolskij’s group. Dr. Amorino earned her PhD in July 2020 under the supervision of Prof. Arnaud Gloter at LaMME, Université Paris-Saclay. Her research focuses on statistical inference for stochastic differential equations, exploring topics such as thresholding methods, high-frequency data, Malliavin calculus, and volatility estimation. She is also passionate about McKean-Vlasov equations, Hawkes processes, and local differential privacy.

Education

PhD in Applied Mathematics

Institution: Université Paris-Saclay, France (LaMME)

Duration: October 2017 – August 2020

Thesis Title: “Bias correction for the drift and volatility estimation of a jump diffusion and nonparametric adaptive estimation of the invariant measure”

Supervisor: Prof. Arnaud Gloter

Jury Members: Alexandre Brouste (Rapporteur), Fabienne Comte, Arnaud Gloter, Agathe Guilloux, Eulalia Nualart (Rapporteur), Fabien Panloup, Mathieu Rosenbaum

Master’s Degree in Mathematics

Institution: Università Statale di Milano, Italy

Duration: 2015 – 2017

Dissertation Title: “Randomization method and backward differential stochastic equations for optimal control”

Supervisor: Prof. Marco Fuhrman

Grade: Magna cum laude (“110/110 e lode”, First-Class Honours)

Visiting Student

Institution: Université Paris VII Diderot, France

Program: Master M2MO: Modélisation Aléatoire

Duration: September 2016 – January 2017

Bachelor’s Degree in Mathematics

Institution: Università Statale di Milano, Italy

Duration: 2012 – 2015

Professional Profiles

Google Scholar

ORCID Profile

Professional Experience

Assistant Professor
Universitat Pompeu Fabra, Barcelona, Spain
April 2024 – Present

Currently engaged in teaching, research, and academic supervision at Universitat Pompeu Fabra.

Focused on advanced topics in [insert specific subject area, e.g., mathematics, statistics, etc.].

Contributing to departmental initiatives and collaborations.

Postdoctoral Researcher
University of Luxembourg
August 2020 – March 2024

Collaborated with Prof. Mark Podolskij’s group on [insert specific research focus, e.g., stochastic processes, probability theory, etc.].

Published in high-impact journals and presented findings at international conferences.

Contributed to the development of new methodologies and computational tools in the field.

PhD Researcher
Université Paris-Saclay (LaMME Laboratory), France
September 2016 – July 2020

Conducted doctoral research under the supervision of Prof. Arnaud Gloter.

Focused on [insert specific research area, e.g., stochastic analysis, mathematical modeling, etc.].

Defended a thesis titled “[insert thesis title],” which contributed novel insights to the field.

Collaborated with interdisciplinary teams and participated in academic teaching duties.

Research Interests

Jump Diffusion Processes: Analysis and applications of stochastic processes incorporating jumps, relevant in finance and other applied fields.

High Dimensional Statistics: Developing methodologies and theoretical insights for analyzing data with a large number of variables.

Volatility Estimation: Techniques for measuring and predicting volatility in financial and stochastic systems.

Limit Theorems: Study of asymptotic behaviors and distributional approximations in probability theory.

Malliavin Calculus: Applying stochastic calculus for variations in fields like quantitative finance and stochastic analysis.

Nonparametric Statistics: Developing flexible statistical methods without assuming strict parametric models.

Stein’s Method: A probabilistic technique for assessing distributional approximations.

McKean-Vlasov SDEs: Investigations into stochastic differential equations with mean-field interactions.

Local Differential Privacy: Researching privacy-preserving mechanisms in statistical analysis and data sharing.

Minimax Risk and Convergence Rates: Studying optimality and efficiency in statistical decision-making processes.

Fractional Brownian Motion: Exploring processes with long-range dependence and their applications.

Thresholding Methods: Statistical techniques for signal processing and data analysis.

Bayesian Statistics: Combining prior information with data for statistical inference and decision-making.

Technical Skills

LaTeX

Python

R

SAS

Matlab,

C

Top Notable Publications

Contrast Function Estimation for the Drift Parameter of Ergodic Jump Diffusion Processes

Authors: C. Amorino, A. Gloter

Journal: Scandinavian Journal of Statistics

Year: 2020

Citations: 26

This paper addresses drift parameter estimation using contrast functions, offering insights into jump diffusion processes.

Parameter Estimation of Discretely Observed Interacting Particle Systems

Authors: C. Amorino, A. Heidari, V. Pilipauskaitė, M. Podolskij

Journal: Stochastic Processes and their Applications

Year: 2023

Citations: 22

A collaborative effort exploring parameter estimation in interacting particle systems observed at discrete intervals.

Unbiased Truncated Quadratic Variation for Volatility Estimation in Jump Diffusion Processes

Authors: C. Amorino, A. Gloter

Journal: Stochastic Processes and their Applications

Year: 2020

Citations: 20

Proposes a novel technique for volatility estimation using truncated quadratic variation.

Invariant Density Adaptive Estimation for Ergodic Jump–Diffusion Processes Over Anisotropic Classes

Authors: C. Amorino, A. Gloter

Journal: Journal of Statistical Planning and Inference

Year: 2021

Citations: 17

Focuses on invariant density estimation and its adaptive approach in anisotropic frameworks.

Optimal Convergence Rates for the Invariant Density Estimation of Jump-Diffusion Processes

Authors: C. Amorino, E. Nualart

Journal: arXiv preprint

Year: 2021

Citations: 9

Examines convergence rates for invariant density estimation in jump-diffusion contexts.

Rate of Estimation for the Stationary Distribution of Jump-Processes Over Anisotropic Hölder Classes

Author: C. Amorino

Journal: arXiv preprint

Year: 2020

Citations: 8

Discusses estimation rates for stationary distributions under specific anisotropic conditions.

Minimax Rate of Estimation for Invariant Densities Associated to Continuous Stochastic Differential Equations Over Anisotropic Hölder Classes

Authors: C. Amorino, A. Gloter

Journal: Scandinavian Journal of Statistics

Year: 2024

Citations: 7

A forthcoming study delving into minimax rates for invariant density estimations in stochastic differential equations.

On the Nonparametric Inference of Coefficients of Self-Exciting Jump-Diffusion

Authors: C. Amorino, C. Dion-Blanc, A. Gloter, S. Lemler

Journal: Electronic Journal of Statistics

Year: 2022

Citations: 6

Investigates nonparametric inference for coefficients in self-exciting jump-diffusion models.

Joint Estimation for Volatility and Drift Parameters of Ergodic Jump Diffusion Processes via Contrast Function

Authors: C. Amorino, A. Gloter

Journal: Statistical Inference for Stochastic Processes

Year: 2021

Citations: 6

Proposes methods for simultaneous estimation of volatility and drift parameters.

Estimation of the Invariant Density for Discretely Observed Diffusion Processes: Impact of the Sampling and the Asynchronicity

Authors: C. Amorino, A. Gloter

Journal: Statistics

Year: 2023

Citations: 5

Analyzes the effects of sampling and asynchronicity on invariant density estimation.