Tarik El Moudden | AI | Best Researcher Award

Posted on by Best Paper Award

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:

πŸ‘©β€πŸŽ“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 (DiplΓ΄me d’Γ‰tudes SupΓ©rieures 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 cityβ€”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.

 

 

Balaji Srinivasan | Machine Learning Method | Best Researcher Award

Posted on by Best Paper Award

Mr. Balaji Srinivasan | Machine Learning Method | Best Researcher Award

Balaji Srinivasan at Engineers India Limited, India

Summary:

Balaji Srinivasan is a seasoned analyst with over 16 years of experience in the refinery and petrochemical industries. Specializing in asset lifecycle management and local stress analysis, he has played a pivotal role in ensuring the safety and reliability of complex systems. His expertise in finite element analysis and advanced technology development has earned him recognition in the field.

Professional Profile:

πŸ‘©β€πŸŽ“Education:

Balaji Srinivasan holds a solid educational foundation in engineering, specializing in fields pertinent to the refinery and petrochemical industries. His education laid the groundwork for a successful career in managing complex projects and conducting advanced technology development activities.

🏒 Professional Experience:

With over 16 years of experience, Balaji Srinivasan has made significant contributions to the refinery and petrochemical sectors. He has been with Engineers India Limited in New Delhi since September 2007, where he specializes in local stress analysis. His expertise extends to conducting advanced technology development activities, including transient thermal and creep-fatigue interaction studies.

Balaji has executed finite element, fatigue, creep, and creep-fatigue analyses for pressure vessels and piping components, including high-pressure and high-temperature reactors, coke drums, dryers, and agitator vessels. He has led complex equipment troubleshooting exercises, ensuring safe and reliable plant operations through numerical simulations to identify root causes of fault sequences and performing fitness-for-service assessments on critical components.

Balaji’s knowledge of FEA software like ABAQUS and ANSYS has been instrumental in solving unconventional, complex, and multidisciplinary problems arising from pre- and post-commissioning activities. His interactive management approach has enhanced output, maximized quality, and increased employee satisfaction.

Research Interests:

Balaji’s research interests lie in asset lifecycle management, design basis assessment, local stress analysis, and advanced technology development for the refinery and petrochemical industries. He is particularly focused on enhancing the reliability and safety of critical infrastructure through innovative analysis and simulation techniques.

Author Metric:

Balaji Srinivasan has contributed to several industry-related publications and research projects. His work is well-regarded in the field of stress analysis and lifecycle management, with a focus on enhancing system reliability and safety. His publications have garnered citations, reflecting the impact of his research on the industry.

Top Noted Publication:

1. Strain engineering and one-dimensional organization of metal–insulator domains in single-crystal vanadium dioxide beams

  • Authors: J. Cao, E. Ertekin, V. Srinivasan, W. Fan, S. Huang, H. Zheng, J.W.L. Yim, et al.
  • Journal: Nature Nanotechnology
  • Volume: 4
  • Issue: 11
  • Pages: 732-737
  • Year: 2009
  • DOI: 10.1038/nnano.2009.266
  • Citations: 676

2. Mechanism of thermal reversal of the (fulvalene) tetracarbonyldiruthenium photoisomerization: toward molecular solar–thermal energy storage

  • Authors: Y. Kanai, V. Srinivasan, S.K. Meier, K.P.C. Vollhardt, J.C. Grossman
  • Journal: Angewandte Chemie International Edition
  • Volume: 49
  • Issue: 47
  • Pages: 8926-8929
  • Year: 2010
  • DOI: 10.1002/anie.201003643
  • Citations: 136

3. Proton momentum distribution in water: an open path integral molecular dynamics study

  • Authors: J.A. Morrone, V. Srinivasan, D. Sebastiani, R. Car
  • Journal: The Journal of Chemical Physics
  • Volume: 126
  • Issue: 23
  • Pages: 234504
  • Year: 2007
  • DOI: 10.1063/1.2746330
  • Citations: 90

4. Interplay between intrinsic defects, doping, and free carrier concentration in SrTiO₃ thin films

  • Authors: E. Ertekin, V. Srinivasan, J. Ravichandran, P.B. Rossen, W. Siemons, et al.
  • Journal: Physical Review Bβ€”Condensed Matter and Materials Physics
  • Volume: 85
  • Issue: 19
  • Article: 195460
  • Year: 2012
  • DOI: 10.1103/PhysRevB.85.195460
  • Citations: 56

5. Exploring the potential of fulvalene dimetals as platforms for molecular solar thermal energy storage: computations, syntheses, structures, kinetics, and catalysis

  • Authors: K. BΓΆrjesson, D. Δ†oso, V. Gray, J.C. Grossman, J. Guan, C.B. Harris, et al.
  • Journal: Chemistry–A European Journal
  • Volume: 20
  • Issue: 47
  • Pages: 15587-15604
  • Year: 2014
  • DOI: 10.1002/chem.201402857
  • Citations: 44

ShivaDutt Jangampeta | Machine Learning | Industry Innovation Research Award

Posted on by Best Paper Award

Mr. ShivaDutt Jangampeta, Machine Learning, Industry Innovation Research Award

ShivaDutt Jangampeta at JP Morgan Chase, United States

Mr. ShivaDutt Jangampeta appears to be a highly experienced professional with a strong background in cybersecurity, specifically in Security Information and Event Management (SIEM). However, determining his suitability for the “Research for Community Impact Award” requires an assessment of how his work impacts the community.

Relevant Criteria for the Research for Community Impact Award:

Community Impact: The award typically recognizes research that has a significant positive impact on the community, either through direct application or through contributions to knowledge that benefit the community.

Innovation and Advancement: Contributions to advancing the field and introducing innovative solutions or methodologies.

Collaboration and Dissemination: Engagement with stakeholders and dissemination of research findings to a broader audience.

Evaluation of Mr. Jangampeta’s Suitability:

Strengths:

Professional Experience:

  • Extensive experience in managing and optimizing SIEM systems.
  • Leadership roles in cybersecurity teams at major organizations like JPMorgan Chase and PEPSICO.
  • Development and implementation of advanced cybersecurity measures that could indirectly benefit community security by protecting sensitive data and reducing vulnerabilities.

Research Contributions:

  • Authored several publications related to data security, SIEM, and compliance, indicating a contribution to academic knowledge.
  • Research topics like anomaly detection in SIEM and the role of data security in compliance suggest a focus on enhancing cybersecurity frameworks, which can have broad implications for community safety and trust in digital systems.

Innovative Solutions:

  • Implementation of advanced technologies and automation in cybersecurity, contributing to more resilient and efficient security infrastructures.

Areas to Consider:

Direct Community Impact:

  • While Mr. Jangampeta’s work in cybersecurity is crucial, the direct impact on the community might be less apparent compared to other fields such as public health or environmental science.
  • It would be beneficial to highlight specific examples or case studies where his work directly protected community members or contributed to public safety.

Engagement and Dissemination:

  • The extent of his engagement with community stakeholders and efforts to disseminate his research findings to a broader, non-technical audience would strengthen his case for this award.

Conclusion:

Mr. Jangampeta’s extensive experience and contributions to the field of cybersecurity are impressive and certainly valuable. His work indirectly impacts the community by enhancing the security of digital infrastructures, which is increasingly important in today’s interconnected world. To bolster his application for the Research for Community Impact Award, it would be advantageous to emphasize any direct community benefits resulting from his cybersecurity initiatives and any outreach efforts he has made to educate or involve the community in his work.

If his research and professional activities can be framed to clearly demonstrate significant, tangible benefits to the community, Mr. Jangampeta could be a suitable candidate for the award.

 

Anurag Upadhyay | Machine Learning Award | Best Researcher Award

Posted on by Best Paper Award

Mr. Anurag Upadhyay, Machine Learning Award, Best Researcher Award

Anurag Upadhyay at DGI GREATER NOIDA, India

Summary:

Mr. Anurag Upadhyay is a dedicated academician and experienced educator with over 15 years of teaching experience in the field of computer science and technology. He holds a Master’s degree (M.Tech) in Computer Science from IFTM University, Moradabad, and a Bachelor’s degree (B.Tech) in Computer Science from MIT Moradabad (UPTU).

Throughout his career, Mr. Upadhyay has served in various teaching positions at esteemed institutions such as IFTM University Moradabad, KITPS Moradabad, and RIMT Bareilly, among others. Currently, he is an Assistant Professor at Dronacharya Group of Institutions, Greater Noida, where he continues to impart his knowledge and expertise to aspiring students.

Professional Profile:

Google Scholar Profile

πŸ‘©β€πŸŽ“Education & Qualification:

Professional Experience: Β 

Mr. Anurag Upadhyay has over 15 years of experience in the field of teaching. He has held various positions in different educational institutions, including lecturer positions at Govt Girls Polytechnic Bareilly, FIET Bareilly, and CET IFTM Moradabad. He served as an Assistant Professor at IFTM University Moradabad, KITPS Moradabad, RIMT Bareilly, IIMT Greater Noida, and Dronacharya Group of Institutions Greater Noida. Throughout his career, Mr. Upadhyay has taught a wide range of subjects, including Machine Learning, Cloud Computing, Operating System, Information Technology, Computer Organization, Distributed System, Data Mining & Data Warehousing, Computer Concepts & Programming in ‘C,’ Soft Computing, Design of Algorithm, Software Engineering, and Compiler. He is an active member of professional organizations such as CSTA, ACM, and IAENG. Additionally, Mr. Upadhyay has undergone training and short-term courses in various areas related to computer science and technology.

Research Interest:

Machine Learning: Exploring algorithms and techniques to enable computers to learn from and make predictions or decisions based on data.

Cloud Computing: Investigating cloud-based services, architectures, and technologies for efficient data storage, processing, and management.

Operating Systems: Studying the design, implementation, and optimization of operating systems to enhance system performance and resource utilization.

Information Technology: Researching advancements in IT infrastructure, applications, and management practices to support organizational objectives and improve efficiency.

Data Mining & Data Warehousing: Analyzing large datasets to discover patterns, trends, and insights that can drive informed decision-making in various domains.

Computer Organization: Investigating the architecture and design of computer systems, including hardware components and their interactions, to optimize performance and functionality.

Software Engineering: Exploring methodologies, tools, and best practices for the systematic development, maintenance, and evolution of software systems.

Compiler Design: Studying the theory and implementation of compilers, which translate high-level programming languages into machine-readable code for execution on hardware platforms.

Publication Top Noted:

Title: Empirical Comparison by data mining Classification algorithms (C 4.5 & C 5.0) for thyroid cancer data set

  • Authors: A. Upadhayay, S. Shukla, S. Kumar
  • Journal: International Journal of Computer Science & Communication Networks
  • Volume: 3
  • Issue: 1
  • Page: 64
  • Year: 2013
  • Citation Count: 36

Title: A fresh loom for multilevel feedback queue scheduling algorithm

  • Authors: R.K. Yadav, A. Upadhayay
  • Journal: International Journal of Advances in Engineering Sciences
  • Volume: 2
  • Issue: 3
  • Pages: 21-23
  • Year: 2012
  • Citation Count: 19

Title: Animal husbandry practices in Pithoragarh district of Uttarakhand state

  • Authors: S. Shukla, D.P. Tiwari, A. Kumar, B.C. Mondal, A.K. Upadhayay
  • Journal: Indian Journal of Animal Sciences
  • Volume: 77
  • Issue: 11
  • Page: 1201
  • Year: 2007
  • Citation Count: 12

Title: Evaluation of fish curry from farmed and wild caught Indian major carps of Tarai Region, Uttarakhand

  • Authors: M. Gupta, A.K. Upadhayay, N.N. Pandey, P. Kumar
  • Publisher: Society of Fisheries Technologists (India) Cochin
  • Year: 2013
  • Citation Count: 2

Title: Cryptography and Network Security

  • Author: Anurag Upadhyay
  • Publisher: Lambert Publishing House
  • Volume: 1
  • ISBN: 978-620-2-02336-8
  • Year: 2017

 

 

Gayan KIncy Kulatilleke | Machine learning

Posted on by Best Paper Award
Dr. Gayan KIncy Kulatilleke: Leading Researcher in Machine learning, AI

Dr. Gayan Kincy Kulatilleke is a distinguished researcher and expert in the field of Artificial Intelligence and Machine Learning. Holding a Ph.D. in AI, M.Sc. degrees in Big Data and Networking, and a B.Sc. in Engineering, he has garnered recognition for his outstanding contributions to the field.

As a leading academic at the University of Queensland, Brisbane, Dr. Kulatilleke serves as a Sessional Academic and Research Assistant, specializing in Computer Networks and High-Performance Computing. He also plays a pivotal role as an AI/ML Mentor for Summer and Internship Projects, showcasing his dedication to nurturing the next generation of innovators.

πŸŽ“ Education

  • PhD(AI), MSc(Big Data), MSc(Networking), BSc(Eng.), ACMA(UK), CGMA(USA), AMIE(SL)

πŸ† Awards

  • Best Paper: Microsoft Award (Cybersecurity, HFES 2020)
  • Best Paper: CSIRO Award (AJCAI 2022)
  • “Global Knowledge Sharing” Award for Best Technical Publication (Virtusa 2003)
  • Runner Up: cmbHack.js 2014 (48-hour JavaScript Hackathon)
  • Multiple Australian Scholarships: UQ RTP, ARC LP for PhD

Professional Profiles:

Publication Top Noted

  • XG-BoT: An explainable deep graph neural network for botnet detection and forensics
  • HFE and cybercrime: Using systems ergonomics to design darknet marketplace interventions
  • Efficient block contrastive learning via parameter-free meta-node approximation
  • Inspection-L: self-supervised GNN node embeddings for money laundering detection in bitcoin
  • DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection

Research FocusΒ 

Dr. Gayan Kincy Kulatilleke’s research encompasses a broad spectrum within the realms of Artificial Intelligence (AI) and Machine Learning (ML). While specific details of his research focus might require more recent information, the provided details indicate several areas of interest and expertise:

  1. Botnet Detection and Forensics
  2. Human Factors Engineering (HFE) and Cybercrime
  3. Contrastive Learning for Efficient Block Analysis
  4. Self-Supervised Learning for Money Laundering Detection
  5. Hybrid Deep Learning for Network Anomaly Detection

πŸ’Ό Work Experience

University of Queensland, Brisbane

  • Sessional Academic & Research Assistant (01/2019 – Present)
    • Lead Tutor: Computer Networks, High-Performance Computing
    • AI/ML Mentor for Summer and Internship Projects
    • Presenter: High-Performance Compute GPU Clusters, ML Pipelines

Freelance AI ML and Software Consultant/Developer, Remote (01/2006 – Present)

  • Advanced skills in C++, OS, Automation, Integration, DevOps, Cloud Management, Google Technologies, Data Engineering, Software Development, AI/ML.

Mailot – Founder CTO, Brisbane (01/2023 – 06/2023)

  • GPT-3 Productivity SaaS Startup
  • NLP and Human Inputs for Augmented Responses
  • Integration of OpenAI, Langchain, Vector Databases with Node.js on Vercel

AMZ Consulting Pty Ltd, Sydney – Consultant/Trainer (05/2023 – Present)

  • PowerBI, Modelling, DAX, Integrations

TeleMARS, Brisbane – Lead AI ML Engineer and Researcher (04/2023 – Present)

  • AI Architecture Design, AI Analytics Solution Design
  • Development and Solution Delivery Management

Space Platform, Brisbane – Head ML (06/2019 – 06/2020)

  • Privacy Preserving Query and Intent Identification Algorithms
  • Research on IOS and Android’s Randomized MAC Addresses
  • Data Engineering, ETL Operations, ML Model Design and Implementation

Capital Staffing, London – Software Developer (10/2017 – 07/2018)

  • C#, .NET MSSQL Server, DevOps, Cloud Services

Central Bank of Sri Lanka – Senior Assistant Director (05/2005 – 10/2022)

  • AI ML Big Data Initiatives
  • Data Science and Analysis Teams Coaching
  • Implementation of Strategic IT Needs
  • Surveillance, Authentication, Access Control Systems Oversight

Virtusa Corporation, Colombo – Software Engineer (R&D) (05/2004 – 08/2004)

  • Managed R&D Portal, Global Technology Center Consultation
  • “Global Knowledge Sharing” Award for Best Technical Whitepaper

MillenniumIT (London Stock Exchange Group) – Application Engineer (08/2003 – 05/2004)

  • Global Support, Technical Advice to Capital Market Clients
  • Performance Benchmarking, Real-time Multithreaded C/C++ Code Development

πŸŽ“ Educational/Professional Qualifications

  • PhD Candidate, Tutor, Research Assistant, University of Queensland (2019 – 2023)
  • MSc Big Data Science, Queen Mary, University of London (2017 – 2018)
  • MSc Computer Science, Networking, University of Moratuwa, Sri Lanka (2004 – 2006)
  • BSc Computer Science & Engineering, University of Moratuwa, Sri Lanka (1999 – 2003)

πŸ† Special Achievements/Publications

  • Google Research Profile: Link
  • “Microsoft Best Paper Award: Cybersecurity Technical Group” (2020)
  • “Global Knowledge Sharing” Award for Best Technical Publication (Virtusa 2003)

🀝 Positions of Responsibility

  • International Reviewer – Multiple AI ML Journals (2022 – Onwards)
  • Operational Committee Member – WiT Cyber Review Committee, Queensland (2023 – 2024)
  • Coordinator – Chess & Drafts Games, Central Bank Recreation Club (2010 – 2017)
  • Chief Examiner – Institute of Bankers, Sri Lanka (2009 – 2017)
  • Volunteer Work with ADB to Create Computer-Aided Educational Software for Schools (2004)

πŸ“š Lateral Courses Completed

  • Macro Econometric Forecasting, IMF, Institute for Capacity Development, 2016
  • Financial Programming and Policies, Part 2: Program Design, IMF, 2012

πŸ† Skills

  • Data, AI, Machine Learning Engineering
  • Software Development (Python, C++, C#)
  • AI ML (GPTx, Langchain Models, TensorFlow)
  • DevOps and Cloud Management (Azure, AWS)
  • Google Technologies (Data Studio, Firebase, Cloud Firestore)
  • Data Engineering (PowerBi, Tableau, DAX, Python-based Web Scraping)

πŸŽ‰ Awards and Recognitions

  • Best Paper Awards (Microsoft, CSIRO)
  • Global Knowledge Sharing Award (Virtusa)
  • Australian Scholarships (UQ RTP, ARC LP)