Work in Progress

This is a living document. Use cases are documented progressively as part of an ongoing effort to make applied work accessible and reproducible. Check back for updates.

End-to-end pipeline diagram: Sentinel-2 stack (multi-temporal revisits) → Spectral indices (NDWI · MNDWI · AWEI) → Geometry filters (man-made pond shapes) → Expansion alerts (Indonesia + Vietnam coasts)
Object DetectionCoastal MonitoringChange DetectionTime-Series SatellitePatent

Aquaculture pond detection and change analysis

Filed Patent IDS000010594, Issued June 2025 · Lead Developer

Catching illegal shrimp farms from orbit before regulators arrive, hours, not field-survey years.

PythonGoogle Earth EngineNDWIMNDWIAWEI +1
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End-to-end pipeline diagram: Conflict benchmark (EN · HI · TE · multi-domain) → Open VLM cohort (multilingual evaluation) → Linear probes (residual-stream readout) → Cross-lingual steering (text-override eliminated)
Vision-Language ModelsMultilingual AIAI SafetyActivation SteeringCross-Modal ConflictApart ResearchHackathon

Do Multilingual VLMs Abstain Under Cross-Modal Conflict in Low-Resource Languages?

Apart Research Global South AI Safety Hackathon (2026) · Co-First Author, Benchmark & Steering Lead

A model that trusts its eyes in English becomes a caption-follower in Telugu. We measured it, localized it, and steered it back.

InternVL3-2B/8BQwen2.5-VL-3B/7BQwen3-VL-8BLLaVA-OneVision-7BGLM-4.1V-9B-Thinking +16
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End-to-end pipeline diagram: Clean + attacked + Benign-prefix control + Held-out family → Hidden-state capture (read-only, single forward pass) → Control-aware probe (logistic regression, one layer) → Conformal FPR gate (flag above calibrated threshold)
Mechanistic InterpretabilityPrompt InjectionActivation ProbingConformal PredictionAI SecurityHeron Fellowship

Hidden-State Detection of In-Context Goal Hijacking with a Conformal False-Positive Guarantee

Heron AI Security Research Fellowship, Work-Test Prototype (2026) · Sole Researcher

Output monitoring sees nothing when a hijack attempt fails. The residual stream sees it almost every time.

Qwen2.5-Instruct 0.5B-7BLogistic regression probeSplit conformal predictionResidual-stream activation cachingDeconfounded AUC +4
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End-to-end pipeline diagram: SimilarWeb + Traackr (competitive intel) + Client media data (BigQuery · dbt) → XGBoost (SHAP-validated forecasts) → Split conformal (distribution-free intervals) → Planning dashboards (Streamlit · APAC markets)
ForecastingConformal PredictionXGBoostGCP Vertex AIdbtMLOpsUncertainty Quantification

Share of Voice forecasting system, Fortune 500 APAC (Artefact)

Artefact, Senior Data Scientist (Feb 2025, Nov 2025) · Lead ML Engineer (Founding Technical Member, Jakarta Office)

Turned media gut-feel into calibrated prediction intervals across 6 APAC markets for a Fortune 500.

XGBoostConformal Prediction (split)GCP Vertex AIBigQueryGCS +12
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End-to-end pipeline diagram: Login surge traffic (major bank clients) → MobileNet · OpenVINO (CPU-optimised runtime) → Verification API (sync + async load-tested) → Credit scorecard (points-based · explainable)
BiometricsFace VerificationAnti-SpoofingLiveness DetectionCredit ScoringFintechMLOpsRegulatory Compliance

Biometric authentication and alternative credit scoring at scale (GDP Labs)

GDP Labs (GLAIR.ai), Senior Data Scientist / ML Engineer (2021, 2023) · Lead ML Engineer, Promoted to Senior within 12 months

1M+ daily inferences at 99.99% uptime. Credit for the borrowers banks wouldn't score.

PyTorchscikit-learnXGBoostIntel OpenVINOMobileNet +14
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End-to-end pipeline diagram: Kafka stream (Redis online features) → Rule pre-filters (velocity · geo · device) → XGBoost + graph (layered anomaly detectors) → Case routing (drift-monitored scores)
Fraud DetectionAnomaly DetectionGraph NetworksReal-Time InferenceKafkaRedisFintech

Real-time fraud detection pipeline (GDP Labs)

GDP Labs (GLAIR.ai), Lead ML Engineer (2021, 2023) · Lead ML Engineer

Three fraud attack types, one pipeline, rule filters, XGBoost, and graph anomaly detection.

XGBoostgradient boostinggraph anomaly detectionApache KafkaRedis +5
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End-to-end pipeline diagram: JAKI · Qlue reports (citywide citizen data) → Prophet ensemble (vs ARIMA · SARIMA) → DiD analysis (plastic-bag ban rollout) → Route planning (Tableau · per district)
Time Series ForecastingSmart CityProphetLSTMCausal InferencePolicy Analysis

Municipal waste logistics forecasting, Jakarta Smart City

Jakarta Smart City, Data Scientist (Jan 2021, June 2021) · Lead Data Scientist

Forecasted Jakarta's trash so trucks follow where waste actually piles up instead of a fixed weekly schedule.

PythonRFacebook ProphetARIMASARIMA +9
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End-to-end pipeline diagram: Traffic video QA (challenge corpus) → Dataset re-pack (per-step memory cut) → LoRA adapters (Qwen2.5-VL-3B) → ZeRO-2 training (on a commodity V100)
Vision-Language ModelsLoRA Fine-tuningQwen2.5-VLLLaMA-FactoryDeepSpeedVLM Training

Qwen VL Fine-tuning for AI City Challenge 2026 Track 2

AI City Challenge 2026, Track 2 (2026) · Lead ML Engineer

Fine-tuning a 3B VLM on a 14GB GPU, every memory constraint hit, diagnosed, and solved.

Qwen2.5-VL-3B-InstructLoRALLaMA-FactoryDeepSpeed ZeRO-2PyTorch 2.5.1+cu121 +4
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End-to-end pipeline diagram: VLN-DUET + VLN-HAMT + Recurrent VLN-BERT → Confidence rescaling (divide by residual confidence) → Episode-max calibration (whole-trajectory coverage proof) → Help-seeking policy (operator queried on flagged steps)
Conformal PredictionVision-Language NavigationUncertainty QuantificationVLN-DUETVLN-HAMTHuman-in-the-Loop

Conformal Prediction for Vision-and-Language Navigation in Discrete Graphs

Thesis Research, VLN-DUET Uncertainty Propagation (2026) · Lead Researcher

Navigation agents are confidently wrong. A parameter-free rescaling of the model's own softmax turns that overconfidence into a calibrated stop sign.

PyTorchDeepSpeed ZeRO-2VLN-DUETVLN-HAMTRecurrent VLN-BERT +11
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