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:

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.

 

 

E.Laxmi Lydia | Computer Science | Best Researcher Award

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Prof. E.Laxmi Lydia, Computer Science, Best Researcher Award

Professor at Velagapudi Ramakrishna Siddhartha Engineering College, Siddhartha Academy of Higher Education (SAHE), India

Summary:

Dr. E. Laxmi Lydia is a seasoned educator and researcher with over 20 years of experience in teaching, training, and research. Currently serving as a Professor and Dean of R&D at Vignan’s Institute of Information Technology, she has significantly contributed to the academic and research community. Dr. Lydia is recognized for her exceptional interpersonal skills, commitment to student development, and active involvement in curriculum design and institutional accreditation processes. Her extensive expertise and numerous accolades, including the Best Researcher Award for five consecutive years, highlight her dedication to advancing knowledge and fostering innovation in the field of computer science and engineering.

Professional Profile:

πŸ‘©β€πŸŽ“Education:

Ph.D. in Computer Science and Engineering (Year not specified)

Master of Computer Applications (MCA) (Year not specified)

Bachelor of Science (B.Sc.) (Year not specified)

🏒 Professional Experience:

Professor & Dean of R&D Vignan’s Institute of Information Technology (A), July 2019 – Present
Dr. E. Laxmi Lydia leads the Research and Development department, overseeing various research initiatives and fostering collaborations both within and outside the institution. She plays an integral role in curriculum development and significantly contributes to the institution’s growth and academic excellence.

Associate Professor Vignan’s Institute of Information Technology (A), 2015 – 2019
In her tenure as an Associate Professor, Dr. Lydia taught both undergraduate and postgraduate courses, guided numerous research projects, and actively participated in various academic committees. Her efforts were pivotal in advancing the research capabilities and academic standards of the institution.

Associate Professor Raghu Engineering College, 2011 – 2015
At Raghu Engineering College, Dr. Lydia conducted lectures, supervised research activities, and contributed significantly to the academic community through her publications and participation in conferences. Her work helped in elevating the research profile and educational quality of the college.

Assistant Professor Ravindra and Rajendra PG College for MCA, 2003 – 2009
During her tenure as an Assistant Professor, Dr. Lydia was responsible for teaching MCA students, developing comprehensive course materials, and mentoring students. Her dedication to teaching and mentorship helped shape the careers of many students in the field of computer science and engineering.

Honors & Certifications:

  • Oracle Certified (2009)
  • Microsoft Certified Solution Developer (MCSD)
  • Outcome-Based Education (OBE) Certified
  • Engineering Education Certification
  • EPICS-Engineering Projects in Community Services Certified
  • Reviewer for JEET Scopus Journal
  • Best Researcher Award (2018, 2019, 2020, 2021, 2022) at Vignan’s Institute of Information Technology
  • Distinguished Faculty in CSE-2021 by Ambitions, an educational entity

Professional Affiliations:

  • Member of the Board of Studies (BOS) at Vignan’s Institute of Information Technology
  • Member of the International Accreditation Council of Quality Education & Research (IACQER)
  • Member of the Computer Society of India

Research Interests:

Dr. Lydia’s research interests encompass a wide range of topics within computer science and engineering, including but not limited to:

  • Artificial Intelligence and Machine Learning
  • Data Science and Big Data Analytics
  • Cybersecurity and Information Assurance
  • Software Engineering and Development
  • Internet of Things (IoT) and Smart Systems

Top Noted Publication:

An Optimal Least Square Support Vector Machine Based Earnings Prediction of Blockchain Financial Products

  • Authors: M Sivaram, EL Lydia, IV Pustokhina, DA Pustokhin, M Elhoseny, GP Joshi
  • Journal: IEEE Access
  • Volume: 8
  • Pages: 120321-120330
  • Year: 2020
  • Citations: 97

Concept of Electronic Document Management System (EDMS) as an Efficient Tool for Storing Document

  • Authors: ATR Rosa, IV Pustokhina, EL Lydia, K Shankar, M Huda
  • Journal: Journal of Critical Reviews
  • Volume: 6
  • Issue: 5
  • Pages: 85-90
  • Year: 2019
  • Citations: 91

Synergic Deep Learning Model–Based Automated Detection and Classification of Brain Intracranial Hemorrhage Images in Wearable Networks

  • Author: EL Lydia
  • Journal: Personal and Ubiquitous Computing
  • Year: 2022
  • Citations: 90

Optimal Deep Learning Based Image Compression Technique for Data Transmission on Industrial Internet of Things Applications

  • Authors: B Sujitha, VS Parvathy, EL Lydia, P Rani, Z Polkowski, K Shankar
  • Journal: Transactions on Emerging Telecommunications Technologies
  • Volume: 32
  • Issue: 7
  • Article: e3976
  • Year: 2021
  • Citations: 84

Data Encryption for Internet of Things Applications Based on Catalan Objects and Two Combinatorial Structures

  • Authors: MH SaračeviΔ‡, SZ AdamoviΔ‡, VA MiΕ‘kovic, M Elhoseny, ND Maček, EL Lydia
  • Journal: IEEE Transactions on Reliability
  • Volume: 70
  • Issue: 2
  • Pages: 819-830
  • Year: 2020
  • Citations: 83

 

Anurag Upadhyay | Machine Learning Award | Best Researcher Award

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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

 

 

Zakria Qadir | Computer Engineering

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Dr. Zakria Qadir: Leading Researcher in Computer Engineering

πŸŽ‰ Congratulations Dr. Zakria QadirΒ on Winning the Most Reader’s Article Award! πŸ† Your dedication to research, mentorship, and collaboration with international teams is truly commendable. This award is a testament to your outstanding work and the impact it has on the broader community.

Professional Profile:

πŸ”¬ Research Focus: Enthusiastic PostDoc Research Associate at UNSW Artificial Intelligence Institute, dedicated to pushing boundaries in DIGITECH. Research spans Artificial Intelligence, Machine Learning, Wireless Communication, IoT, and Cybersecurity. Highly cited Young STEM Researcher.

πŸŽ“ Education:

  • Ph.D. in Electrical and Computer Engineering (Western Sydney University).
    • Research: Smart UAVs for disaster relief, AI, ML, IoT applications.
  • Master’s in Sustainable Environment and Energy System (METU).
    • Thesis: Neural Network-Based Prediction Algorithms for Hybrid PV-Wind System.
  • Bachelor of Science in Electronic Engineering (UET Taxila).
    • Gold Medal Award for securing First Position.

πŸ† Achievements:

  • Google Scholar: Citations 1000+, H-Index 17, Total Papers 40, Cumulative Impact Factor >100+.
  • Keynote Speaker at Core A conferences.
  • Fully Funded ARC Research Discovery Scholarship for Ph.D.
  • Various scholarships and awards for academic excellence.

πŸ‘¨β€πŸ’» Professional Experience:

  • Post-Doc Research Associate at UNSW, collaborating with the Department of Defence Australia.
  • Research: Drones-aided AI Algorithms for Battlefield Scenarios.
  • Research Assistant at UNSW, collaborating with Cisco, focusing on Intelligent Transportation Systems.
  • Sessional Lecturer at Victoria University, teaching Data Science, AI, Computer Science, Business Analysis, Networking.
  • Casual Lecturer at Western Sydney University (WSU) and Melbourne Institute of Technology (MIT).
  • Lecturer at National University of Technology, teaching IoT, AI, and Machine Learning.
  • Senior Research Scientist at Imam Abdulrahman Bin Faisal University.
  • Graduate Teaching Assistant at Middle East Technical University (METU).
  • Lab Engineer at National University of Science and Technology (NUST).

Publication Top Noted:

  • Towards 6G Internet of Things: Recent advances, use cases, and open challenges
  • A Hybrid Deep Learning Approach for Bottleneck Detection in IoT
  • A strong construction of S-box using Mandelbrot set an image encryption scheme
  • Resource optimization in UAV-assisted wireless networksβ€”A comprehensive survey
  • Autonomous UAV Path-Planning Optimization Using Metaheuristic Approach for Predisaster Assessment

πŸ“š Skills:

  • Programming Languages: MATLAB, Python, C++.
  • Metaheuristic Algorithms: PSO, ACO, DGBCO, GWO.
  • Machine Learning (AI): Deep learning, Feature Extraction, CNN, FRNN, YOLO.
  • Understanding of Arduino, Raspberry Pi, Proteus, Lucid Chart, VOSViewer, LaTeX.

πŸŽ“ Teaching Experience:

  • Lectured and supervised students at various universities.
  • Lesson planning, preparation, and research in diverse areas.

πŸ… Honors and Awards:

  • Graduate Teaching Assistant Scholarship at METU.
  • Gold Medal Award for securing First Position in Bachelors.
  • Best Engineering Project Award at UET Taxila.

πŸ† Funding and Recognition:

  • Awarded ARC Research Discovery Scholarship, Research Candidate Support Funding, Teaching Assistant Scholarships, Travel Grants, and Research Grants.
  • Recognition from Core A conferences and Australia’s Natural Hazard Research.

 

The paper “A Prototype of an Energy-Efficient MAGLEV Train: A Step Towards Cleaner Train Transport” focuses on the development and evaluation of a prototype Magnetic Levitation (MAGLEV) train with an emphasis on energy efficiency. Below are some key points and important content from the paper:

Abstract:

  • Focus: Development and assessment of an energy-efficient MAGLEV train prototype.
  • Goal: Contributing to cleaner and more sustainable train transportation.

Introduction:

  • Motivation: Addressing the need for environmentally friendly and energy-efficient transportation solutions.
  • Importance of MAGLEV: Highlighting the advantages of MAGLEV technology, such as reduced friction and energy consumption.

Key Features of the MAGLEV Prototype:

  • Energy Efficiency Measures: Description of features and technologies incorporated to enhance energy efficiency.
  • Magnetic Levitation System: Explanation of the MAGLEV technology used in the prototype.
  • Propulsion System: Details about the propulsion mechanism and its role in energy savings.

Performance Evaluation:

  • Energy Consumption Analysis: Quantitative assessment of energy consumption compared to traditional train systems.
  • Environmental Impact: Discussion on the potential reduction in carbon footprint and environmental benefits.

Results and Findings:

  • Energy Savings Percentage: Presentation of the achieved energy savings compared to conventional trains.
  • Operational Stability: Evaluation of the MAGLEV prototype’s stability during operations.

Conclusion:

  • Significance: Emphasizes the significance of developing energy-efficient transportation solutions.
  • Future Implications: Discusses the potential widespread adoption of MAGLEV technology for cleaner and sustainable train transport.

Impact and Citations:

  • Citation Count: Indicates the paper’s impact and recognition within the research community.
  • Reader’s Count: Reflects the broader readership and interest in the paper’s findings.

Innovation and Contribution:

  • Novelty: Highlights any novel approaches, technologies, or methodologies introduced in the MAGLEV prototype.
  • Contribution to the Field: Describes how the research contributes to advancements in cleaner and energy-efficient transportation.

This summary provides a glimpse into the essential content of the paper, focusing on its goals, methodology, findings, and impact on the field of transportation and energy efficiency.

 

 

 

 

 

Yogesh | Artificial Intelligence

Posted on by Best Paper Award
Dr. Yogesh: Leading Researcher in Artificial Intelligence

Congratulations to Dr. Yogesh on Winning the Best Researcher Award! Dr. Yogesh is a dedicated researcher known for his impactful contributions to the field of Artificial Intelligence. His commitment to research, mentorship, and collaboration with international teams has earned him this prestigious recognition.

Dr. Yogesh is a distinguished researcher in the field of Artificial Intelligence, recognized for his outstanding contributions and achievements. Currently serving as Assistant Professor-III in the Department of Computer Science and Engineering at Chitkara University, Punjab, he brings a wealth of experience and expertise to his role.

Professional Profile:

πŸŽ“ Educational Qualifications:

  • Ph.D.: Amity University Uttar Pradesh, Noida, 2021
  • M. Tech: Amity University Uttar Pradesh, Noida, 2013 (84.7%)
  • B. Tech: Magadh University, Patna, 2007 (76%)

Gayan KIncy Kulatilleke | Machine learning

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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)