Pratham Sunkad | Scientific Machine Learning | Best Researcher Award

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Mr. Pratham Sunkad | Scientific Machine Learning | Best Researcher Award 

Mr. Pratham Sunkad, at IIT Madras, India.

Pratham Sunkad is an aspiring mechanical engineer and AI researcher from the Indian Institute of Technology, Madras (IIT Madras). With a strong foundation in computational mechanics and artificial intelligence, he has worked on cutting-edge projects in Variational Physics-Informed Neural Networks (VPINNs), numerical mathematics, and fluid-structure interaction. His interdisciplinary approach combines machine learning with computational physics to solve real-world problems. He has interned at American Express, the Indian Institute of Science (IISc), and Weierstrass Institute, Berlin, contributing to AI-driven optimization and scientific computing. Passionate about physics, engineering, and artificial intelligence, Pratham is actively involved in research that bridges machine learning with classical numerical methods. He has published in reputed journals and arXiv preprints, making significant contributions to computational science. Beyond academics, he enjoys music, sports, and mentoring underprivileged students for JEE Advanced.

Professional Profile

ORCID

🎓 Education

Pratham is currently pursuing a B.Tech in Mechanical Engineering with a Minor in Artificial Intelligence at IIT Madras (2021–2025), maintaining an impressive CGPA of 8.67/10. He completed his Class XII (96.2%) at National Public School, Rajajinagar, Bangalore, excelling in Mathematics and Computer Science. His coursework spans diverse fields, including heat transfer, fluid mechanics, machine learning, finite element analysis, and computational fluid dynamics.

Throughout his academic journey, he has displayed a strong aptitude for multivariable calculus, probability & statistics, differential equations, and deep learning techniques. His technical expertise includes programming languages such as Python, C++, C, Java, SQL, and MATLAB, along with proficiency in TensorFlow, PyTorch, NumPy, and Scikit-learn. His rigorous academic training, complemented by hands-on research, has prepared him to tackle interdisciplinary engineering and AI challenges.

💼 Experience

Pratham has gained valuable industry and research experience through internships at leading institutions:

  • 🔹 Data Analyst Intern, American Express (May–July 2024): Optimized merchant outreach strategies using predictive analytics and machine learning. Developed algorithms to identify high-risk cases for email engagement.
  • 🔹 Research Intern, AiREX LAB, IISc Bangalore (Dec 2023–Dec 2024): Worked on Variational Physics-Informed Neural Networks (VPINNs), integrating finite elements with machine learning for scientific computing.
  • 🔹 Research Intern, Weierstrass Institute, Berlin (April–Dec 2024): Implemented Streamline-Upwind Petrov-Galerkin (SUPG) methods to enhance VPINNs for convection-dominated problems. Developed an adaptive indicator function to improve accuracy.

These experiences have strengthened his skills in computational data science, numerical mathematics, AI-driven modeling, and scientific computing.

🔬 Research Interests

Pratham’s research focuses on combining machine learning with numerical methods to solve complex engineering problems. His key interests include:

  • ⚡ Physics-Informed Neural Networks (PINNs) & VPINNs – Bridging finite element methods with deep learning for better physical modeling.
  • 🌊 Computational Fluid Dynamics (CFD) & Fluid-Structure Interaction (FSI) – Using advanced numerical methods like Lattice Boltzmann to model fluid mechanics problems.
  • 🧠 Deep Learning & AI for Engineering Applications – Applying deep learning models to optimize structural and fluid simulations.
  • 🔍 Optimization & Uncertainty Quantification – Studying how machine learning can improve accuracy in numerical simulations.

His projects on medical image segmentation, wavelet-based PDE solvers, and reinforcement learning further extend his expertise in AI-driven scientific computing.

🏆 Awards & Achievements

Pratham has received multiple national-level accolades, highlighting his academic excellence:

  • 🥇 JEE Advanced (2021): AIR 1866 among 1M+ candidates.
  • 🥈 JEE Mains (2021): AIR 1548 and 100 percentile in Physics among 1M+ candidates.
  • 🥉 KVPY Fellowship (2020): AIR 1227, among the top 0.6% of candidates.
  • 🏅 INPhO Finalist: Ranked among the top 200 students in the Indian National Physics Olympiad.
  • 🎵 Trinity College London – Grade 6 Piano (Distinction).
  • 🥋 Black Belt (Dan 1) in Karate from Kaishogun Karate Do India.

These achievements underscore his proficiency in competitive problem-solving, scientific research, and extracurricular excellence.

📚 Top Noted Publications 

Pratham has co-authored high-impact research papers in AI-driven scientific computing.

1. Improving hp-Variational Physics-Informed Neural Networks for Steady-State Convection-Dominated Problems

Authors: Thivin Anandh, Divij Ghose, Himanshu Jain, Pratham Sunkad, Sashikumaar Ganesan, Volker John

This paper proposes two significant enhancements to the FastVPINNs framework for tackling convection-dominated convection-diffusion-reaction problems:

  • SUPG Stabilization Term: Incorporation of a term inspired by Streamline Upwind/Petrov-Galerkin (SUPG) stabilization into the loss functional. The network architecture is designed to predict spatially varying stabilization parameters.

  • Adaptive Indicator Functions: Introduction of a network architecture that learns optimal parameters for a class of indicator functions, improving the enforcement of Dirichlet boundary conditions.

Numerical studies demonstrate that these enhancements lead to more accurate solutions compared to existing methods.

arXiv

2. FastVPINNs: Tensor-Driven Acceleration of VPINNs for Complex Geometries

Authors: Thivin Anandh, Divij Ghose, Himanshu Jain, Sashikumaar Ganesan

This work introduces FastVPINNs, a tensor-based advancement aimed at reducing computational overhead and improving scalability of hp-VPINNs, especially in complex geometries. Key features include:

  • Optimized Tensor Operations: Utilization of optimized tensor operations to achieve significant reductions in training time.

  • Enhanced Performance: With appropriate hyperparameter selection, FastVPINNs outperform traditional PINNs in both speed and accuracy, particularly for problems with high-frequency solutions.

Conclusion

Pratham Sunkad is a highly promising researcher with strong academic credentials, technical expertise, and diverse research experience. His international collaborations and AI-driven physics research are notable strengths. However, to be a top contender for the Best Researcher Award, he should focus on publishing in top journals, leading independent research, and translating work into patents or industry solutions.

Tarik El Moudden | AI | Best Researcher Award

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Mr. Tarik El Moudden | AI | Best Researcher Award

Tarik El Moudden at Ibn Tofail University, Kenitra, Morocco, Morocco

Summary:

Dr. Tarik El Moudden is a Moroccan-based data scientist and AI specialist, currently serving as a Senior Web Application Developer and Data Analyst at Zenithsoft and a lecturer at Ibn Tofail University. He has extensive experience in neural network frameworks, computer vision, and predictive analytics, leveraging tools such as Python, TensorFlow, and Keras. In addition to his research, he is dedicated to mentoring the next generation of AI professionals and data scientists. He combines a strong academic background with hands-on industry experience, working on complex problems in machine learning, AI integration, and big data analytics.

Professional Profile:

👩‍🎓Education:

Dr. Tarik El Moudden earned his Doctorate in Predictive Modeling using AI and Big Data Analysis from the Computer Science Research Laboratory at Ibn Tofail University, Kenitra, Morocco, in 2024. He holds a DESA (Diplôme d’Études Supérieures Approfondies) in Advanced Study in Telecommunication and Informatics from the same university, completed in 2008. Dr. El Moudden has also pursued a number of professional certifications, including specialized skills in Power BI, Artificial Intelligence (AI), Python, Data Science, Machine Learning, Deep Learning, and Big Data, powered by IBM Developer Skills Network (2024). He holds certifications from NASA’s Applied Remote Sensing Training (ARSET) program, covering large-scale machine learning applications for agriculture solutions and spectral indices for land and aquatic applications. Additionally, he is certified as a Professional Drone Pilot and in Project Management with AI.

🏢 Professional Experience:

Dr. El Moudden has been a Senior Web Application Developer and Senior Data Analyst and AI Models Integration Specialist at Zenithsoft, Rabat, Morocco, from 2020 to 2024. During his tenure, he developed expertise in neural networks, deep learning, and machine learning models for predictive analytics and data-driven solutions. At Ibn Tofail University, he has taught various modules across different levels, such as Power BI, Data Science, Applied Mathematics, Python Programming, and Machine Learning. He has been involved in teaching these subjects at the Master’s and Professional License levels in fields like Big Data, Artificial Intelligence (AI), Engineering, and Applied Mathematics from 2019 to 2024. His teaching portfolio extends to subjects like Numerical Methods with Python for Master’s students in Partial Differential Equations and Complex Geometry, as well as Applied Mathematics and Optimization for Engineering students.

Research Interests:

Dr. El Moudden’s research primarily focuses on AI integration in predictive modeling, machine learning applications for large-scale agriculture solutions, computer vision, neural networks (CNNs, RNNs, GANs), and data analysis. His work spans image classification, object detection, and image segmentation using Python, TensorFlow, Keras, and PyTorch. He is also passionate about exploring AI’s potential in various industry-specific applications, particularly Big Data, deep learning models, and cloud-based solutions through platforms like Microsoft Azure.

Author Metrics:

  • ORCID: 0000-0002-6963-6686
  • Published Articles: Dr. El Moudden has contributed to scientific publications and is a regular reviewer in the fields of AI, predictive analytics, and machine learning. His research focuses on enhancing AI’s impact on real-world applications, particularly in agriculture and big data. He continues to publish research papers in both local and international conferences.

Top Noted Publication:

Artificial intelligence for assessing the planets’ positions as a precursor to earthquake events

  • Authors: T.E. Moudden, M. Amnai, A. Choukri, Y. Fakhri, G. Noreddine
  • Journal: Journal of Geodynamics, 2024, Volume 162, Article 102057
  • This article explores the use of artificial intelligence to analyze planetary positions in relation to earthquake occurrences, contributing valuable insights into the role of celestial mechanics in earthquake prediction.

New unfreezing strategy of transfer learning in satellite imagery for mapping the diversity of slum areas: A case study in Kenitra city—Morocco

  • Authors: T.E. Moudden, M. Amnai, A. Choukri, Y. Fakhri, G. Noreddine
  • Journal: Scientific African, 2024, Volume 24, Article e02135
  • This open access research focuses on a novel transfer learning approach to analyze satellite imagery for detecting slum areas in Kenitra, Morocco. It highlights advancements in AI and satellite technology for urban mapping.

Building an efficient convolution neural network from scratch: A case study on detecting and localizing slums

  • Authors: T.E. Moudden, M. Amnai
  • Journal: Scientific African, 2023, Volume 20, Article e01612
  • This article presents a case study on developing an effective convolutional neural network (CNN) from scratch, specifically designed for slum detection and localization.

Slum image detection and localization using transfer learning: a case study in Northern Morocco

  • Authors: T. El Moudden, R. Dahmani, M. Amnai, A.A. Fora
  • Journal: International Journal of Electrical and Computer Engineering, 2023, Volume 13(3), Pages 3299–3310
  • This article applies transfer learning techniques to detect and localize slums using satellite imagery, focusing on Northern Morocco as a case study.

Nutrient removal performance within the biological treatment of the Marrakech wastewater treatment plant and characterization of the aeration and non-aeration process

  • Authors: M. Tahri, T. El Moudden, B. Bachiri, M. El Amrani, A. Elmidaoui
  • Journal: Desalination and Water Treatment, 2022, Volume 257, Pages 117–130
  • This article investigates the efficiency of nutrient removal during the biological treatment processes at the Marrakech wastewater treatment plant, providing key insights into water treatment technologies.

Conclusion:

Dr. Tarik El Moudden is a deserving candidate for the Best Researcher Award due to his significant contributions to the field of AI, data science, and machine learning, with a strong focus on practical applications in agriculture, urban development, and disaster prediction. His academic achievements, coupled with his industry expertise, reflect a researcher who is poised to make transformative impacts in the AI landscape. With a bit more focus on expanding his international collaborations and enhancing the visibility of his work, Dr. El Moudden’s research can become even more influential in shaping AI’s future in solving complex, real-world problems.