Enterprise AI
Production-grade AI delivery
5+ years' experience delivering enterprise AI, GenAI, and applied AI solutions across telecommunications, energy, transportation, healthcare, and urban planning.
AI consultant delivering production-grade enterprise AI systems.
Dilantha Haputhanthri
Data Scientist & AI Consultant @ PS Hummingbird
I design and deliver Azure-native AI, GenAI, and agentic workflow automation solutions that move beyond prototypes into secure, scalable enterprise workflows.
Enterprise AI Azure-native Architecture Azure AI Agentic Workflows LLM Orchestration Cloud Delivery DevOps
About
Research Depth, Production Delivery.
I’m a Data Scientist based in Victoria, Australia, working at the intersection of applied AI research and production enterprise systems. My work spans data science, machine learning, Generative AI, analytics platforms, and cloud-based AI delivery.
My background combines a PhD in Artificial Intelligence, published research in energy-efficient machine learning, and hands-on delivery of production-grade AI and analytics systems across telecommunications, energy, transportation, and healthcare.
Research Credibility
PhD in AI with 500+ Google Scholar citations
Combines a PhD in Artificial Intelligence with published AI and Data Science research, bringing applied research depth into enterprise AI delivery.
Azure-native
Secure cloud architecture
Designing Azure-native AI architecture with Azure OpenAI, Azure AI Foundry, DevOps, CI/CD, production deployment patterns, and enterprise workflow automation.
GenAI Products
Customer-facing enablement
Delivered GenAI capabilities across large-scale digital products, including RAG-based customer support, LLM evaluation, search enhancement, and FAQ automation.
Experience
Applied AI, Research, and Analytics delivery across Industry and Academia.
Current and recent roles across PS Hummingbird, La Trobe University, Telstra, and the CDAC.
2020
2021
2022
2023
2024
2025
2026
Select a dot or role label to view details.
Selected Work
High-impact AI and Data Systems.
Representative project themes across Azure-native agentic AI, enterprise GenAI enablement, and energy analytics platforms.
Data Scientist / Azure AI Implementation
Urban Planning AI Platform
Designed and delivered a production Azure-native multi-agent AI solution to automate core urban planning workflows, including Azure architecture design, Azure DevOps setup, production deployment approach, and agentic workflow implementation.
Azure OpenAI Azure AI Foundry Agentic AI Multi-Agent Systems Azure DevOps
Data Science Specialist
Enterprise GenAI Enablement
Delivered GenAI capabilities across multiple telecommunications digital products, including RAG-based customer support, LLM evaluation, search enhancement, and FAQ automation for large-scale customer-facing use cases.
GenAI RAG LLM Evaluation Search Enhancement FAQ Automation
Data Scientist
Energy Analytics & Forecasting Platform
Led solar generation analytics and machine learning pipeline development for an energy analytics platform, supporting forecasting, performance monitoring, and net-zero decision-making.
Energy Analytics Forecasting Machine Learning Solar Generation Net-Zero Analytics
Research
Applied AI research with a production mindset.
Research areas include Sparse AI, Sparse Distributed Representations, Vector Symbolic Architectures, energy-efficient machine learning, and forecasting.
532
Citations
Current Google Scholar cited-by count.
10
h-index
Google Scholar author impact metric.
10
i10-index
Google Scholar count of publications with 10+ citations.
PhD Thesis
DOI 10.26181/32441283
Sparse Distributed Representations for Energy-Efficient Artificial Intelligence
PhD thesis on Sparse Distributed Representations for energy-efficient AI, with research spanning vector data classification, manifold learning, Sparse Distributed Representations, Vector Symbolic Architectures, and sparse deep learning.
- Degree
- Doctor of Philosophy thesis
- Institution
- La Trobe University
- School
- La Trobe Business School
- Year Awarded
- 2025
Artificial intelligence Energy-efficient computing Artificial life and complex adaptive systems
Sparse Distributed Representations
Energy-efficient representations for vector data classification, topology preservation, and scalable AI systems.
Vector Symbolic Architectures
Hyperdimensional computing methods for representation, binding, bundling, unsupervised learning, and efficient symbolic computation.
Energy & Forecasting AI
Solar generation forecasting, energy analytics, demand forecasting, and machine learning for net-zero infrastructure.
Applied AI for Complex Domains
Research outputs across energy, healthcare, transportation, social data analysis, and intelligent infrastructure.
Featured Publications
Research outputs spanning Sparse AI and energy systems.
2026 - Neurocomputing
Parametrization of sparse distributed representations for vector data classification
Sparse Distributed Representation research focused on vector data classification and efficient representation learning.
SDR Vector Classification Efficient AI
2026 - International Journal of Electrical Power & Energy Systems
Solar power forecasting with sparse deep learning for fast frequency response ancillary services
Applied energy AI research using sparse deep learning for solar power forecasting and grid support use cases.
Sparse Deep Learning Forecasting Energy Systems
2024 - IEEE Transactions on Neural Networks and Learning Systems
Hyperseed: Unsupervised Learning With Vector Symbolic Architectures
Vector Symbolic Architecture research for unsupervised learning and topology-preserving feature representation.
VSA Unsupervised Learning Hyperdimensional Computing
Education
La Trobe University
Doctor of Philosophy - PhD, Artificial Intelligence
2022 - 2025
University of Moratuwa
Master of Science - MS, Computer Science
2020 - 2021
University of Moratuwa
Bachelor of Science (Hons.), Computer Science and Engineering
2015 - 2019
Teaching
Postgraduate teaching across AI, analytics, and cloud platforms.
Sessional academic work focused on practical, implementation-oriented learning for data, analytics, artificial intelligence, and hyperautomation topics.
2024 - Present
BUS5WB - Data Warehousing and Big Data Analytics
La Trobe University
Postgraduate subject covering enterprise data warehouse architecture, dimensional modelling, ETL lifecycles, OLAP, big data analytics, data lakes, PySpark, Hive metastore, Databricks, Delta Lake, medallion architecture, and NoSQL.
Data Warehousing Big Data Analytics Dimensional Modelling SQL ETL OLAP MDX PySpark Databricks Delta Lake Medallion Architecture NoSQL
2023 - 2025
BUS5PR1 - Artificial Intelligence and Hyperautomation
La Trobe University
Industry-based, employability-focused subject advancing analytics capability into Artificial Intelligence and Hyperautomation through an analytics pitch, scrum-based group assignment, and final insights presentation for an organisational context.
Generative AI Predictive AI NLP Ethics Data Security Deep Learning Analytics Communication Scrum-based Delivery Hyperautomation
2022 - 2025
BUS5001 - Cloud Platforms and Analytics
La Trobe University
Cloud analytics subject covering cloud platform fundamentals, cloud architectures, adoption, security and governance, version control, DevOps, serverless computing, cloud data platforms, end-to-end data solutions, AI in the cloud, RPA, conversational agents, and data ethics.
Cloud Platforms Cloud Architecture Cloud Adoption Security and Governance Version Control DevOps Serverless Computing Cloud Data Platforms AI in the Cloud RPA Conversational Agents Data Ethics
Contact
Consulting, collaboration, and technical discussions.
For consulting, collaboration, or technical discussions, feel free to get in touch.
Email Send an email Best for consulting, collaboration, or technical conversations. LinkedIn Connect on LinkedIn Professional updates, consulting context, and collaboration. GitHub View GitHub Code, experiments, and technical project work. La Trobe Scholars Academic collaboration Research, education, supervision, and university collaboration context.