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Open to interdisciplinary research collaborations and selected AI systems roles

Applied AI Researcher and AI Systems Engineer

Shaurav Khadka

Researching and building dependable AI systems for complex real-world data.

Applied AI researcher with a systems-engineering background. My current priority is AI-assisted quantum device characterisation, supported by scientific machine learning, temporal reasoning, and computational modelling. I build and evaluate dependable systems that turn research questions into testable evidence.

Sydney, NSW, Australia · From research question to defensible evidence and dependable implementation.

Explore Research DirectionsView Applied SystemsRésumé
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Research-Led Focus

Scientific questions lead. Applied systems provide the evidence base for testing ideas honestly.

Research Priorities

Current research direction

  • Artificial Intelligence
  • AI-Assisted Quantum Device Characterisation
  • Scientific Machine Learning
  • Quantum Computing
  • Open Quantum Systems
  • Non-Markovian Dynamics
  • Computational Physics
  • Computational Mathematics

Applied Evidence Base

Built and evaluated systems

  • Trustworthy AI
  • Document Intelligence
  • Semantic Retrieval
  • Temporal Graph Learning
  • Computer Vision
  • Robotics and Sim2Real
  • Reinforcement Learning
  • AI Reliability

Research Profile

Research Directions

My primary direction is AI-assisted quantum device characterisation: using rigorous artificial intelligence, scientific machine learning, and mathematical modelling to study noisy quantum systems. I am especially interested in research where temporal dependencies, interpretability, and practical mitigation decisions meet.

I want to contribute to research that is mathematically grounded, experimentally honest, physically informed, interpretable where possible, and useful beyond a controlled demonstration.

Discuss a research opportunity
Primary research focus

AI-Assisted Quantum Device Characterisation

My current priority is the characterisation and mitigation of non-Markovian noise in quantum hardware: combining physical reasoning, artificial intelligence, temporal modelling, and interpretable representations to connect measurement data with practical mitigation decisions.

Primary focus

Questions I want to pursue

  • How can process-tensor and tensor-network representations make memory effects measurable and interpretable?
  • Where can temporal AI methods improve characterisation without obscuring the underlying physics?
  • How can optimisation support parameter tuning or gate-sequence decisions for mitigation?

Active focus

Trustworthy AI Systems and Production Reliability

Evaluation methods for AI pipelines where traceability, robustness, auditability, confidence handling, latency, and cost matter alongside model accuracy.

Questions I want to pursue

  • How should reliability be measured across the full decision pipeline?
  • How can failure analysis distinguish model, data, and system faults?

Active focus

Temporal Learning for Dynamic and Relational Data

Learning systems for data that evolves over time: temporal graphs, sequential evidence, changing relationships, and non-static risk signals.

Questions I want to pursue

  • When does temporal modelling materially outperform static baselines?
  • How should time-dependent behaviour be evaluated and explained?

Active focus

Sim2Real Perception and Autonomous Systems

Robust perception under deployment shift, confidence-aware decisions, and vision-to-action systems that must behave safely outside curated datasets.

Questions I want to pursue

  • How can deployment adaptation be designed in from the beginning?
  • How should confidence thresholds shape downstream actions?

Research map

Interdisciplinary research landscape

A transparent map of the scientific domains and methods I am actively developing. These are research priorities and learning directions, not inflated claims of completed expertise.

Quantum systems, characterisation and control

The physical and computational language needed to study noisy quantum devices and practical mitigation strategies.

  • Quantum computing
  • Open quantum systems
  • Non-Markovian dynamics
  • Quantum control
  • Quantum error mitigation

Mathematical modelling and scientific AI

Methods for turning partially observed physical systems into testable models, interpretable evidence, and reproducible analysis.

  • Scientific machine learning
  • Process tensors
  • Tensor networks
  • Inverse problems
  • System identification
  • Uncertainty quantification

Broader scientific interests

Adjacent fields that sharpen how I think about dynamic, complex, and partially observed systems.

  • Data science
  • Scientific computing
  • Dynamical systems
  • Astrophysics
  • Cosmology
  • Complex systems

Research Method

From research question to defensible evidence.

A research-led loop for turning complex questions into models, experiments, evidence, and dependable systems.

  1. 01

    Question

    Frame the scientific or operational question, assumptions, constraints, and falsifiable evidence.

  2. 02

    Model

    Choose representations and computational methods that preserve the structure of the problem.

  3. 03

    Test

    Design reproducible experiments, compare baselines, inspect edge cases, and challenge assumptions.

  4. 04

    Translate

    Connect experimental findings to interpretable decisions, robust systems, and practical constraints.

  5. 05

    Communicate

    Document trade-offs, limitations, and decisions clearly enough to act on.

Measured Highlights

Three results worth remembering.

These are benchmark anchors, not a separate project list. Open any card for context, then inspect the complete six-system portfolio below.

Measured highlight 01 / 03

2.38% → 95.24%

Robot-image accuracy after deployment-specific Sim2Real adaptation

The model looked strong on curated data and degraded sharply on robot-camera images. The recovery came from treating domain shift as a deployment problem, not a footnote.

Inspect case study Evidence index
Baseline
2.38% before deployment-specific adaptation.
Measured
95.24% robot-image accuracy after targeted collection, augmentation, and fine-tuning.
Conditions
Robot-camera inputs with lighting, viewpoint, scale, and background differences.
Public scope
Collaborative team-level result with exported notebook figures and explicit attribution.

Measured highlight 02 / 03

300 → 1,925

AirRaid PPO mean reward after temporal observation changes

Observation design materially changed what the policy could learn. Frame skipping and frame stacking improved the benchmark result without pretending algorithm choice was the only lever.

Inspect case study Evidence index

Measured highlight 03 / 03

P@5 = 0.68 · R@5 = 0.68

RedditPulse semantic retrieval quality

The retrieval layer was measured before generation was treated as useful. That matters because grounded insight quality depends on which evidence the system surfaces first.

Inspect case study Evidence index

Inspectable Systems

Six distinct systems. One research-led portfolio.

The benchmark highlights above stay as concise entry points. The six systems below expand the portfolio across production reliability, temporal graph learning, conversational AI, semantic retrieval, machine-learning evaluation, and responsible-AI analysis.

06

Distinct systems

03

Benchmark anchors

09

Total case-study routes

Production AI · Document Intelligence

Public method case study

01

Production AI Reliability and Document Intelligence

Problem: Document intelligence can fail long before or after OCR. Real reliability depends on the complete path from ingestion to extraction, transformation, validation, and review.

Contribution: Built repeatable evaluation workflows across OCR configurations, mappings, confidence scores, error codes, and reruns while preserving traceability and review boundaries.

OCR → transform → validate → trace

AI/ML Research and Development Intern

  • Python
  • pandas
  • AWS S3
  • boto3
  • Azure Document Intelligence
  • JSON
Inspect case study

Temporal Graph Learning

Temporal graph-learning research build

02

Temporal GNN for Blockchain Fraud Detection

Problem: Fraud is relational and time-dependent. Static tabular features can miss how transactions evolve across a network.

Contribution: Built a TGAT-style pipeline with temporal encodings, attention-based message passing, dual-task learning, and an XGBoost comparison path.

t0 → t1 → t2

Research build

  • PyTorch
  • NetworkX
  • TGAT
  • Temporal GNNs
  • XGBoost
Inspect case study

Generative AI · Conversational Systems

Scoped conversational-AI prototype

03

LLM-Based Financial Assistant Prototype

Problem: Conversational assistants can produce fluent but poorly scoped responses. This prototype explores structured prompting, model comparison, synthetic profiles, and explicit safety boundaries.

Contribution: Built a structured prompt workflow, lightweight GPT-2 and DistilGPT-2 comparison, synthetic profile inputs, and a Gradio interface.

profile → prompt → compare → respond

Individual prototype

  • Transformers
  • GPT-2
  • DistilGPT-2
  • Prompt Design
  • Gradio
Inspect case study

NLP · Information Retrieval

Modular retrieval research toolkit

04

Semantic Search and Information Retrieval Engine

Problem: Keyword matching is transparent but limited when meaning varies across phrasing. The system needed a modular comparison path from classical retrieval to dense semantic search.

Contribution: Built reusable text preprocessing, TF-IDF baselines, embedding-based retrieval, ranking logic, and evaluation hooks for comparing relevance trade-offs.

clean → encode → rank → evaluate

Individual modular research build

  • TF-IDF
  • Sentence Transformers
  • Vector Search
  • Ranking
Inspect case study

Machine Learning · Data Science

Reusable experimental evaluation pipeline

05

Machine-Learning Evaluation and Data-Science Pipeline

Problem: A model result is only useful when the path from raw data to evaluation is reproducible, comparable, and explicit about failure cases.

Contribution: Built repeatable workflows for cleaning, exploratory analysis, feature engineering, supervised comparison, clustering, validation, hyperparameter tuning, and error analysis.

data → features → compare → inspect

Individual experimentation toolkit

  • scikit-learn
  • pandas
  • EDA
  • Cross-validation
  • Clustering
  • Error Analysis
Inspect case study

Responsible AI · Governance

Responsible-AI research and analysis portfolio

06

Responsible AI, Governance and Human-Centred Analysis

Problem: AI systems can be technically capable and still fail users, organisations, or communities when accountability, transparency, risk, and human oversight are treated as afterthoughts.

Contribution: Analysed responsible-AI principles, governance frameworks, human-centred design questions, adoption constraints, and technical-communication requirements across applied AI contexts.

risk → explain → govern → improve

Research analysis and technical communication

  • Responsible AI
  • Governance
  • Risk Analysis
  • Human-Centred AI
  • Technical Communication
Inspect case study

Research Evidence

Selected evidence and public scope.

A concise record of published benchmarks, method descriptions, and selected artifacts supporting the work shown above.

Project

Vision and ROS2 Sim2Real

Claim

Robot-image accuracy recovery

Evidence

verified endpoint comparison

Scope

public benchmark
Inspect

Project

RL Benchmark Suite

Claim

RL curves and endpoint evaluation

Evidence

exported notebook figures

Scope

public benchmark
Inspect

Project

RedditPulse

Claim

Semantic retrieval evaluation

Evidence

verified metrics and evaluation charts

Scope

public benchmark
Inspect

Project

Production AI Reliability

Claim

Synthetic invoice reliability trace

Evidence

sanitised method illustration

Scope

public method
Inspect

Project

Temporal GNN Fraud Detection

Claim

TGAT versus XGBoost trade-off benchmark

Evidence

synthetic-validation table and charts

Scope

synthetic validation
Inspect

Project

LLM Financial Assistant Prototype

Claim

Prompt-driven conversational workflow

Evidence

scoped prototype architecture

Scope

exploration
Inspect

Project

Semantic Search and IR

Claim

Classical-to-dense retrieval comparison path

Evidence

retrieval-method architecture

Scope

public method
Inspect

Project

ML Evaluation and Data-Science Pipeline

Claim

Reproducible evaluation workflow

Evidence

experimental pipeline architecture

Scope

public method
Inspect

Project

Responsible AI and Governance Analysis

Claim

Risk-to-governance analysis path

Evidence

governance-analysis framework

Scope

public method
Inspect

Experience

Research and Technical Work, Ordered by Signal

Applied research, production-oriented AI R&D, technical leadership, and software engineering. Supporting operations experience stays visible without dominating the research narrative.

  1. TRUUTH

    AI/ML Research and Development Intern

    Feb 2026 — Present

    Sydney, NSW, Australia · Hybrid

    Production-oriented document intelligence, fraud-detection evaluation, and AI reliability analysis. Built repeatable OCR-evaluation workflows across layouts, configuration choices, confidence scores, field mappings, and error codes while documenting traceability, reproducibility, validation dependencies, latency, and cost considerations.

    • Document Intelligence
    • OCR Evaluation
    • AWS S3
    • Azure Document Intelligence
    • Reliability

  2. Picpoint Nepal Pvt. Ltd.

    Chief Technology Officer

    Jun 2021 — Dec 2024

    Kathmandu, Nepal · Hybrid

    Technical leadership across operational systems, digital workflows, and data-informed decision support. Led the technical roadmap and maintained systems supporting remote workflows, business coordination, web operations, and market-intelligence tooling.

    • Technical Leadership
    • Operations Systems
    • Data Workflows
    • Web Systems

  3. Thakur International

    Jr. Full Stack Developer

    Jun 2019 — May 2020

    Kathmandu, Nepal · On-site

    Application development, API integration, debugging, and backend-data quality within an agile engineering team. Implemented and maintained web and mobile components while improving maintainability through structured debugging, refactoring, and performance tuning.

    • PHP
    • Python
    • JavaScript
    • REST APIs
    • Debugging

Additional Australian operations experience

Ingleburn Convenience Store · Operations and Digital Support Assistant · Part-time

Oct 2024 — Jun 2026

Supported transaction accuracy, inventory records, POS troubleshooting, digital administration, and customer-facing operations while completing postgraduate study in Australia.

Capabilities

Capability Atlas

The broader portfolio inventory, grouped by research and systems domain. These capabilities support three measured highlights and six different inspectable systems without turning every subtask into a separate project card.

Foundation

Education and Selected Credentials

Formal study, applied leadership programs, and focused technical learning.

Education

Macquarie University

Master of Information Technology · Artificial Intelligence

2024 — Present · Sydney, NSW, Australia

Relevant work: NLP and LLM systems, graph machine learning, advanced computer vision and action, reinforcement learning, AI governance, and an industry AI/ML R&D internship.

Education

London Metropolitan University · Islington College

BSc Computer Science · First Class Honours

2017 — 2021 · Kathmandu, Nepal

Ranked among the top 10 students in the cohort. Built recommendation, trip-planning, and database-backed systems across Python, PHP/MySQL, Oracle, C#, Java, and GUI development.

Selected Credentials

  • Global Leadership Program

    Macquarie University

    Structured co-curricular leadership development focused on global engagement and professional growth.

  • MQ Incubator × KPMG Design Thinking

    MQ Incubator × KPMG

    Entrepreneurship, innovation, and human-centred problem solving.

  • UPG Sustainability Leadership · 2024

    UPG

    Selected as one of 500 participants from a global applicant pool.

  • CS50x Computer Science

    Harvard University

  • Elements of AI

    University of Helsinki

  • Ethics and Governance of AI for Health

    World Health Organization

Independent Publishing

Books & Independent Publishing

Explore my authored and collaborative publishing catalogue: technology and well-being, children’s storytelling, illustration, editing, and creative production.

The catalogue below includes the seven distinct works listed on my Goodreads Author profile. Purchase links lead to Amazon Australia where a verified listing is available; Goodreads links provide the public catalogue record.

07

Works

Amazon

Buy

Goodreads

Discover

Shop Amazon Author StoreGoodreads Author ProfileBrowse collaborative titles
Featured authored publication

The Digital Equilibrium

Navigating Technological Advancement for Optimal Well-Being

An independent authored work exploring how technological progress can be balanced with human well-being and intentional living.

Buy on AmazonView on Goodreads

Catalogue

Complete publication catalogue

Authored and collaborative publishing work, presented with direct reading and purchase paths.

Illustrator · Editor

02

Children Stories

Bal Katha

A children’s-story collection created with Gokul Khadka, with illustration and editorial contribution by Shaurav Khadka.

Buy on Amazon Goodreads

Illustrator · Creative contributor

03

Joyful Stories

Joyful Stories

An illustrated story collection listed in the Goodreads Author catalogue.

Buy on Amazon Goodreads

Illustrator · Editor

04

Joyful Stories

Mazzako Katha

A colourful illustrated collection designed for younger readers and created with Gokul Khadka.

Buy on Amazon Goodreads

Illustrator · Creative contributor

05

Joyful Stories

Mazzako Katha · Alternate edition

An alternate catalogue edition of the illustrated Mazzako Katha collection.

Buy on Amazon Goodreads

Illustrator · Editor

06

Words of Wisdom

Amritvani

A collaborative illustrated publication centred on devotional reflections and words of wisdom.

Buy on Amazon Goodreads

Illustrator · Creative contributor

07

2 in 1 Joyful, Children Stories

Combined children’s-story edition

A combined illustrated edition bringing together children’s stories in a single collection.

Buy on Amazon Goodreads

About

Research-Led Systems Work

I am an applied AI researcher and AI systems engineer interested in scientifically grounded methods for complex, dynamic, and imperfectly observed systems.

My current direction is AI-assisted quantum device characterisation, with a focus on non-Markovian dynamics, temporal reasoning, interpretable models, and mitigation-oriented analysis. I care about the full path from research question to defensible evidence: defining the problem clearly, selecting representations carefully, testing assumptions, evaluating limitations, and translating results into dependable systems.

My background in software development, technical operations, leadership programs, and independent publishing is an execution advantage. It helps me approach research not as an isolated model exercise, but as a disciplined process of experimentation, explanation, implementation, and responsibility.

Toolkit

Research Methods and Applied Toolkit

A research-first view of the methods I am developing and the implementation tools I use to test ideas reproducibly.

Actively developing

Research methods in development

14 methods

Mathematical, computational, and physically informed methods I am actively developing for scientific AI and quantum-device characterisation.

  • Artificial Intelligence
  • Scientific Machine Learning
  • Process-Tensor Reasoning
  • Tensor Networks
  • Computational Mathematics
  • Computational Physics
  • Numerical Linear Algebra
  • Probability and Statistical Inference
  • Inverse Problems
  • System Identification
  • Uncertainty Quantification
  • Temporal Modelling
  • Signal Processing
  • Optimisation

Used in applied systems

Applied engineering toolkit

29 tools

Languages, libraries, platforms, and workflow tools used to build, evaluate, and communicate applied AI systems.

  • Python
  • SQL
  • Java
  • C#
  • PHP
  • pandas
  • NumPy
  • Matplotlib
  • scikit-learn
  • TensorFlow
  • PyTorch
  • NetworkX
  • Hugging Face Transformers
  • Sentence Transformers
  • FAISS
  • Streamlit
  • Jupyter
  • Gymnasium
  • Stable-Baselines3
  • ROS2
  • Docker
  • Linux
  • AWS S3
  • boto3
  • Azure Document Intelligence
  • JSON
  • MySQL
  • Oracle
  • Git

Research Writing

Research Notes and Engineering Decisions

Short reflections on research questions, evaluation choices, and the engineering decisions that shape dependable scientific work.

Primary Research Direction

Why I am exploring process tensors for non-Markovian quantum-noise characterisation

A research-preparation note on memory effects in quantum hardware, process-tensor reasoning, tensor-network representations, and where AI may help without obscuring the physics.

Read note

Research Note

Evaluating AI reliability beyond headline accuracy

Why confidence calibration, error-code analysis, and traceability often matter more than a single accuracy number when systems leave the demo.

Read note

Research Note

What sparse rewards teach us about system design

Lessons from reward shaping in MountainCar and continuous-control tasks — and how sparse feedback reshapes how we structure learning systems.

Read note

Research Note

From keyword matching to semantic retrieval

Comparing TF-IDF baselines with transformer embeddings and vector search, and the practical trade-offs of moving to semantic retrieval.

Read note

Contact

Let’s investigate and build something consequential.

I am open to interdisciplinary research collaborations and selected AI systems roles spanning AI-assisted quantum device characterisation, scientific machine learning, trustworthy AI, and temporal reasoning.

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