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.

 

 

 

 

 

 

Noor Saeed Khan | Mathematics | Research Citation Excellence Award

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Assist. Prof. Dr. Noor Saeed Khan | Mathematics | Research Citation Excellence Award

Assist. Prof. Dr. Noor Saeed Khan, University of Education Lahore, Attock Campus, Attock, Pakistan

Assistant Professor Dr. Noor Saeed Khan is currently serving as an Assistant Professor of Mathematics at the University of Education Lahore, Attock Campus, Pakistan. His academic and professional career highlights his expertise in fluid mechanics, mathematical modeling, and numerical analysis. Dr. Khan’s teaching portfolio includes various undergraduate and postgraduate mathematics courses, and he has significantly contributed to student supervision at multiple academic levels.

Education:

SSC
Institution: Govt. High School Sabir Abad, Karak, Khyber Pakhtunkhwa, Pakistan

FSc
Institution: Govt. Degree College Sabir Abad, Karak, Khyber Pakhtunkhwa, Pakistan

BSc
Institution: Govt. Post Graduate College Karak, Khyber Pakhtunkhwa, Pakistan

MSc Mathematics
Institution: Department of Mathematics, University of Peshawar, Peshawar, Khyber Pakhtunkhwa, Pakistan

MS Mathematics
Institution: FAST-National University of Computer and Emerging Sciences, Hayat Abad, Peshawar Campus

PhD Mathematics
Institution: Department of Mathematics, Abdul Wali Khan University, Mardan, Khyber Pakhtunkhwa, Pakistan

Post-Doctorate
Title: A Novel Assessment of Nanofluids Thermodynamics Stability Simulated Through Homotopy Analysis Method
Institution: KMUTT-Fixed Point Research Laboratory, Fixed Point Theory and Applications Research Group, Theoretical and Computational Science Center (TaCS), Faculty of Science, King Mongkut’s University of Technology Thonburi (KMUTT), Bangkok, Thailand

Professional Profiles:

Google Scholar

Professional Experience:

Dr. Khan began his career as an Assistant Professor at the University of Education Lahore in 2020 and has served at both the Multan and Attock campuses. His cumulative teaching and research experience span multiple years, contributing to both academic growth and institutional development.

Research Interests:

Dr. Khan’s research areas include:

Fluid Mechanics and Thermodynamics: Expertise in Newtonian and non-Newtonian fluids, nanofluids, and gyrotactic microorganisms.

Numerical Methods and Modeling: Application of advanced mathematical techniques like HAM for solving complex fluid mechanics problems.

He has also led a Higher Education Commission (HEC)-funded research project worth PKR 1 million, focusing on enhanced thermal conductivity in nanofluids.

Publications:

Thin film flow of a second-grade fluid in a porous medium past a stretching sheet with heat transfer
Published in: Alexandria Engineering Journal (Elsevier), Vol. 57, Issue 2, Pages 1019-1031 (2017)
Impact Factor: 6.2
DOI: 10.1016/j.aej.2017.01.036

Thermophoresis and thermal radiation with heat and mass transfer in a magnetohydrodynamic thin film second-grade fluid of variable properties past a stretching sheet
Published in: European Physical Journal Plus (Springer Nature), 132, 11 (2017)
Impact Factor: 2.8
DOI: 10.1140/epjp/i2017-11277-3

Brownian motion and thermophoresis effects on MHD mixed convective thin film second-grade nanofluid flow with Hall effect and heat transfer past a stretching sheet
Published in: Journal of Nanofluids (American Scientific Publisher), 6(5): 812-829 (2017)
Impact Factor: Pending for 2025
DOI: 10.1166/jon.2017.1383

Magnetohydrodynamic nanoliquid thin film sprayed on a stretching cylinder with heat transfer
Published in: Applied Sciences (MDPI), 7(3), 271 (2017)
Impact Factor: 2.5
DOI: 10.3390/app7030271

Mixed convection in gravity-driven thin film non-Newtonian nanofluids flow with gyrotactic microorganisms
Published in: Results in Physics (Elsevier BV), 7:4033-4049 (2017)
Impact Factor: 4.4
DOI: 10.1016/j.rinp.2017.10.017

Flow and heat transfer in water-based liquid film fluids dispensed with graphene nanoparticles
Published in: Results in Physics (Elsevier BV), 8:1143-1157 (2018)
Impact Factor: 4.4
DOI: 10.1016/j.rinp.2018.01.032

Non-Newtonian nanoliquids thin film flow through a porous medium with magnetotactic microorganisms
Published in: Applied Nanoscience (Springer Nature), 8:1523-1544 (2018)
Impact Factor: 3.674
DOI: 10.1007/s13204-018-0834-5

Magnetohydrodynamic second-grade nanofluid flow containing nanoparticles and gyrotactic microorganisms
Published in: Computational and Applied Mathematics (Springer Nature), 37:6332-6358 (2018)
Impact Factor: 2.5
DOI: 10.1007/s40314-018-0683-6

Bioconvection in second-grade nanofluid flow containing nanoparticles and gyrotactic microorganisms
Published in: Brazilian Journal of Physics, 48(4):227-241 (2018)
Impact Factor: 1.5
DOI: 10.1007/s13538-018-0567-7

Study of two-dimensional boundary layer flow of a thin film fluid with variable thermo-physical properties in three-dimensional space
Published in: AIP Advances, 8(10):105318 (2018)
Impact Factor: 2.6
DOI: 10.1063/1.5053808

Conclusion:

Assist. Prof. Dr. Noor Saeed Khan is a strong candidate for the Research Citation Excellence Award, thanks to his prolific contributions to applied mathematics and fluid dynamics. His research is widely published and cited, demonstrating relevance and innovation. To enhance his candidacy, increased community engagement, leadership in funded projects, and interdisciplinary collaborations could further strengthen his impact.

 

 

 

 

 

 

Aviral Srivastava | Mathematics | Academic Citation Trailblazer Award

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Mr. Aviral Srivastava | Mathematics | Academic Citation Trailblazer Award

Graduate research assistant at Pennsylvania State University, United States

Actively seeking teaching faculty and lecturer positions in Cybersecurity, Computer Science, Machine Learning/AI, and Data Science, Aviral Srivastava is available to commence duties starting Spring 2025. He is currently a graduate student pursuing a Master’s program at Penn State University, with an impressive portfolio of more than 20 publications and two awarded patents. He has co-edited a book on multidisciplinary research and co-authored a textbook on IoT. Aviral has been the recipient of numerous prestigious awards, including Cybersecurity Innovator of the Year, Best International Researcher, and Outstanding Researcher in the fields of Computer Science, Cybersecurity, and Neural Cryptography. He has also received the Distinguished Researcher Award, Asia’s Most Promising Researcher Award in Computer Science in 2023, and the InRes Academic Excellence Award 2023 for demonstrating exceptional research capabilities and innovation. His expertise includes leveraging Machine Learning, Deep Learning, Reinforcement Learning, and Neural Networks to address complex cybersecurity issues.

Professional Profiles:

📚 Education:

Mr. Aviral Srivastava is currently pursuing a Master of Science in Cybersecurity Analytics and Operations at Pennsylvania State University, University Park, PA, with an expected graduation date of May 2025. He maintains an exemplary GPA of 4.0/4.0 in his graduate studies. Prior to this, he earned his Bachelor of Technology in Computer Science from Amity University, India, graduating in July 2023 with a GPA of 3.4/4.0.

Teaching  Experience:

Aviral Srivastava has gained valuable teaching experience at Pennsylvania State University, within the College of Information Sciences and Technology. In Spring 2024, he served as a Teaching Assistant for CYBER 440: Cybersecurity Analytics and Operations Capstone. In this role, he facilitated advanced cybersecurity analysis and guided students in applying industry-prevalent analytic frameworks, thereby enhancing their capability to analyze and address complex cybersecurity incidents. He also supported student learning by conducting office hours, offering personalized guidance, and assisting students in developing clear, effective communication and report-writing skills tailored for both technical and executive audiences. In Summer 2024, Aviral continued his teaching assistantship at Pennsylvania State University, this time for CYBER 100: Computer Systems Literacy. His responsibilities included assisting in the development and enhancement of the course curriculum to ensure it remained up-to-date with current industry standards and technological advancements. He adeptly resolved student queries and explained complex concepts related to computer systems, covering hardware, software, and basic cybersecurity principles. Additionally, he supported student learning by conducting office hours, providing personalized guidance, and fostering an inclusive learning environment.

Research :

During the summer of 2025, Aviral Srivastava gained significant research experience as a Summer Research Intern at the Cybersecurity Lab at Pennsylvania State University, under the supervision of Dr. Peng Liu. He led the development of a Cryptography Capture The Flag (CTF) challenge generation project using GPT-4, which aimed to enhance educational tools for learning cryptography and security problem-solving skills. Additionally, Aviral spearheaded a project focused on improving Content Management Systems (CMS) using advanced Natural Language Processing (NLP) techniques, with an emphasis on enhancing user interaction and content accessibility.

Technical Skills :

Aviral Srivastava possesses a diverse and robust set of technical skills across various domains. In programming languages, he is proficient in Python, C/C++, JavaScript, SQL, Bash, and PowerShell. His cybersecurity expertise includes penetration testing, vulnerability assessment, incident response, malware analysis, red teaming, and cryptography. He is adept with tools such as Burp Suite, Metasploit, Nmap, Nessus, OWASP Tools, and has knowledge in network security, firewalls, IPS/IDS, and forensics tools like Autopsy, EnCase, FTK, and Volatility. He is also familiar with frameworks such as the Cyber Kill Chain and the MITRE ATT&CK Framework. In terms of development tools, Aviral is skilled in using VS Code, Jupyter Notebook, Git, Docker, and Jenkins. He has experience with AI/ML technologies, including TensorFlow, PyTorch, Sci-kit Learn, Pandas, and Numpy. Additionally, he has proficiency in cloud technologies, with hands-on experience in AWS, Azure, and Google Cloud.

Awards and Honors:

Aviral Srivastava has earned several notable certifications and accolades in the field of cybersecurity and computer science. His certifications include the Certified Ethical Hacker Practical (CEH) from EC-Council, obtained in October 2022, and the Certified Penetration Testing Expert (CPTE) from Pristine Infosolutions Pvt. Ltd., earned in November 2021. Additionally, he holds the ISO/IEC 27001 Information Security Associate certification from SkillFront, obtained in May 2021, and the Gremlin Certified Chaos Engineering Practitioner (GCCEP) from Gremlin, received in June 2021. Aviral’s achievements reflect his exceptional skills and dedication to his field. He ranked 237th out of 64,000 hackers in the Ring0 CTF and holds the “PRO HACKER” rank in HackTheBox, with a global ranking of 150 and a ranking of 2 in India. He has been recognized with multiple awards, including the Young Researcher Award from the Institute of Management Bhubaneswar and the Young Researcher Award for the paper “StegoDOG” from the Institute of Scholars. He received the ISSN Best International Researcher Award in Computer Science and Cybersecurity and was named the Cybersecurity Innovator of the Year at Bsides Bangalore 2023. Further accolades include the ISSN International Outstanding Researcher Award in Neural Cryptography and recognition as the Most Promising Researcher in Cybersecurity and Cryptography in Asia. In 2023, he was honored with the Best Young Researcher Award from the International Journal for Modern Trends in Science and Technology, the Distinguished Researcher Award of the Year at the 9th International Millennium Impact Awards, and the Academic Excellence Award from the Institute of Researchers.

 

📚Publications :

Adaptive Cyber Defense: Leveraging Neuromorphic Computing for Advanced Threat Detection and Response

Authors: A Srivastava, V Parmar, S Patel, A Chaturvedi

Conference: 2023 International Conference on Sustainable Computing and Smart Systems

Citations: 3

Year: 2023

Cauchy Grasshopper Optimization Algorithm with Deep Learning Model for Cloud Enabled Cyber Threat Detection System

Authors: CN Ravi, TS Karthik, K Manikandan, P Kalaivaani, PN Chopkar, A Srivastava

Conference: 2023 7th International Conference on Intelligent Computing and Control

Citations: 1

Year: 2023

Swarm Intelligence for Network Security: A New Approach to User Behavior Analysis

Authors: A Srivastava

Journal: International Research Journal of Engineering and Technology (IRJET) 1 (09)

Citations: 1

Year: 2023

Detection of Covid-19 from X-ray Images using Deep Learning Techniques

Authors: A Srivastava, VV Kumar, TR Mahesh

Journal: International Journal of Data Informatics and Intelligent Computing 1 (2), 1-7

Citations: 1

Year: 2022

Stego Dog: Image Steganography Tool for Confidentiality and Integrity

Authors: V Parmar, D Gandhi, A Srivastava, S Sharma

Conference: 2022 International Conference on Sustainable Computing and Data

Citations: 1

Year: 2022

Imperceptible Malware: Bypassing Modern AV-Engines by AI-Assisted Code

Authors: A Srivastava, D Thakkar, P Verma, P Patel, SSS Student

Journal: International Journal of Engineering Applied Sciences and Technology 6 (6)

Citations: 1

Year: 2021

Anticipated Network Surveillance–An Extrapolated Study to Predict Cyber-Attacks Using Machine Learning and Data Analytics

Authors: A Srivastava, D Thakkar, DS Valiveti, DP Shah, DG Raval

Preprint: arXiv preprint arXiv:2312.17270

Citations: 0

Year: 2023

Multimodal Sensor Data Fusion Based Cyberattack Detection in Industrial Internet of Things Environment

Authors: R Nithya, JJA Sundari, MS Balamurugan, R Sindhuja, A Srivastava

Conference: 2023 7th International Conference on Electronics, Communication and

Citations: 0

Year: 2023

Intelligent False Data Injection Attack Detection Using Soft Computing in Cyber-Physical Power Systems

Authors: PN Khairnar, KV Bindu, MAA Walid, S Jothimani, B Subha, A Srivastava

Conference: 2023 7th International Conference on Electronics, Communication and

Citations: 0

Year: 2023

Securing IoT-Edge Networks: Federated Deep Learning for Botnet Detection

Authors: S Nagasundaram, R Sindhuja, BR Kanna, S Rajalakshmi, G Shobana, A Srivastava

Conference: 2023 7th International Conference on Electronics, Communication and

Citations: 0

Year: 2023

Hybrid Multimodal Machine Learning Driven Android Malware Recognition and Classification Model

Authors: K Aggarwal, R Karthikeyan, S Kayalvili, S Srimathi, A Srivastava

Conference: 2023 7th International Conference on Electronics, Communication and

Citations: 0

Year: 2023

Digital Power Play: Unraveling the Evolution of State-Sponsored Cyber Operations

Authors: A Srivastava, V Parmar, P Sanghavi, S Rani

Conference: 2023 16th International Conference on Security of Information and Networks

Citations: 0

Year: 2023