Rasool Taban | Statistical Modeling and Simulation | Best Researcher Award

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Dr. Rasool Taban | Statistical Modeling and Simulation | Best Researcher Award

University of Lisbon | Portugal

Dr. Rasool Taban, Ph.D, is a distinguished Data Scientist currently affiliated with Technical University Institute – University of Lisbon, where he continues to advance the frontiers of Artificial Intelligence and Data Science. His academic journey began in Computer Engineering and evolved into a profound focus on Artificial Intelligence during his M.Sc. studies at the University of Tehran, where he graduated with honors in Artificial Intelligence and Robotics. His early research centered on developing an automated screening system designed to assist in diagnosing Autism Spectrum Disorder in children, demonstrating his ability to merge technology with meaningful social impact. Dr. Taban recently earned his Industrial Ph.D. at Institute – University of Lisbon, funded by the prestigious Marie Curie BIGMATH project, where his research specialized in addressing one of the most persistent challenges in statistical learning-imbalanced data. He successfully developed three novel balancing techniques, each tailored to optimize performance across different variable classes, making significant contributions to data reliability and analytical accuracy in machine learning models. With two published journal papers indexed in Scopus and SCI, Dr. Taban’s scholarly work reflects both academic rigor and applied innovation. He has also participated in multiple research and industry projects, collaborating with institutions such as the SDG Group, CIF/N26, Evenco International, and CTAD–Tehran Autism Center. His involvement as part of the editorial team for the International Conference on Robotics and Mechatronics (ICRoM) further underscores his leadership in advancing interdisciplinary research. Dr. Taban’s primary research interests include imbalanced data, statistical learning, data science, and financial data modeling. His contributions have not only expanded methodological knowledge in statistical computing but have also bridged the gap between theoretical frameworks and real-world data-driven applications, reflecting his commitment to excellence in both academia and industry.

Profiles:  Google Scholar | Linked In

Featured Publications

Taban, R., Nunes, C., & Oliveira, M. R. (2023). RM-SMOTE: A new robust balancing technique.

Taban, R., Nunes, C., & Oliveira, M. R. (2025). Mixed-robROSE: A novel balancing technique tailored for mixed-type datasets.

Bozorgnia, F., Arakelyan, A., & Taban, R. (2023). Graph-based semi-supervised learning for classification of imbalanced data. Submitted to Conference ENUMATH.

Shahri, M. A., & Taban, R. (2021). ML revolution in NLP: A review of machine learning techniques in natural language processing. Journal of Applied Intelligent Systems & Information Sciences (JAISIS), 2(1), 2.

Taban, R., Parsa, A., & Moradi, H. Tip-toe walking detection using CPG parameters from skeleton data gathered by Kinect. In International Conference on Ubiquitous Computing and Ambient Intelligence (pp. 9).

Martin Ferrand| Statistical Modeling and Simulation | Best Researcher Award

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Dr. Martin Ferrand | Statistical Modeling and Simulation | Best Researcher Award

EDF R&D | France

Dr. Martin Ferrand is an accomplished researcher and engineer whose academic and professional journey has been defined by excellence in fluid dynamics, smoothed particle hydrodynamics (SPH), and computational modelling, and he is currently affiliated with the University of Manchester in the MACE Department, where he has undertaken advanced research as part of his MPhil studies, focusing on SPH with the specialized software SPARTACUS developed at EDF R&D; his academic foundations were laid at École des Ponts ParisTech, one of France’s most prestigious engineering institutions, where he specialized in mechanics, fluid mechanics, and data processing, complemented by intensive preparatory studies in advanced mathematics and physics at Lycée du Parc, Lyon, following his French Scientific Baccalaureate with distinction in 2004, which marked the start of a career shaped by intellectual rigor and scientific curiosity. Professionally, Ferrand has gained diverse and impactful experience, including at EDF R&D in Chatou, France, where he developed turbulence models incorporating buoyancy effects and enhanced the hydrodynamic reproduction of the Berre lagoon using TELEMAC3D, and at Imperial College London, where he expanded his expertise into stochastic processes by designing models to reproduce rainfall patterns for optimizing sewage infrastructure, while his immersion at Bouygues Construction gave him early exposure to industry practices, broadening his adaptability. His academic impact is evident through a strong research record comprising 70 scholarly documents, collectively cited 749 times, with an h-index of 11, reflecting the recognition, influence, and sustained relevance of his work in computational and environmental fluid mechanics; his expertise extends across theoretical, computational, and applied domains, establishing him as a versatile contributor to advancing both scientific understanding and engineering practice. In addition to his technical accomplishments, Ferrand is multilingual, fluent in French and English with working knowledge of German, enabling him to collaborate effectively in international research environments, while his interests outside academia, including football, cooking, and model train construction, showcase his creativity, discipline, and appreciation for teamwork, all of which complement his professional excellence. Overall, Martin Ferrand stands as a dedicated scholar and engineer whose combination of intellectual achievement, technical expertise, and international experience continues to make a significant contribution to his field and positions him as a rising figure in the global scientific community.

Profiles: Scopus | Google Scholar | Orcid

Featured Publications

“Unified semi‐analytical wall boundary conditions for inviscid, laminar or turbulent flows in the meshless SPH method”

“Unified semi-analytical wall boundary conditions applied to 2-D incompressible SPH”

“An innovative method based on CFD to simulate the influence of photovoltaic panels on the microclimate in agrivoltaic conditions”

“A time-step-robust algorithm to compute particle trajectories in 3-D unstructured meshes for Lagrangian stochastic methods”

“Unsteady open boundaries for SPH using semi-analytical conditions and Riemann solver in 2D”

Saikat Biswas | Operations Research and Statistical Optimization | Best Researcher Award

Published on by Statistics

Dr. Saikat Biswas | Operations Research and Statistical Optimization | Best Researcher Award

IIT Roorkee | India

Dr. Saikat Biswas is an Indian chemical engineer and academic whose research primarily focuses on computational fluid dynamics (CFD), multiphase flow, and microfluidics, with a special emphasis on droplet dynamics including breakup, splitting, and the transition from dripping to jetting in complex microchannel geometries. He earned his PhD in Chemical Engineering from the Indian Institute of Technology Guwahati (2016–2023), where his doctoral work investigated droplet breakup dynamics in confined microscale flows, and previously completed both his M.Tech and B.Tech in Chemical Engineering at the National Institute of Technology Agartala. Throughout his academic journey, he has published 14 documents, accumulating 41 citations and achieving an h-index of 3, reflecting his growing impact in the field. His contributions include both numerical and computational studies, such as two-dimensional and three-dimensional simulations of droplet splitting at T-junctions and multifurcating channels, investigations of flow-focusing geometries, and analyses of the role of viscosity ratio, surface tension, and channel design in influencing microfluidic droplet behaviour. Skilled in advanced tools such as ANSYS Fluent, COMSOL Multiphysics, OpenFOAM, and MATLAB, he integrates computational methods with engineering applications to address fundamental and applied challenges. Recognized as hard-working, adaptable, and collaborative, Biswas continues to contribute to the advancement of microfluidics and multiphase flow research.

Profiles: Scopus | Google Scholar | Orcid

Featured Publications

“Digital electronic based portable device for colorimetric quantification of ketones and glucose level in human urine”

“Droplet splitting in multifurcating microchannel: A three-dimensional numerical simulation study”

“3D simulation of dripping and jetting phenomena in a flow-focusing geometry”

“A computational study on transition mechanism of dripping to jetting flow in a flow focusing geometry”

“Influence of microchannel geometry on droplet breakup dynamics: A computational study”