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RESEARCH TASKS

Open research questions, experimental techniques, and academic collaboration opportunities.

Last Updated: November 2025

Open Research Questions

1. Causal Factors in Aviation Accidents 🔬

Question: Can we identify causal relationships (not just correlations) between pilot experience, weather, and accident severity using causal inference?

Motivation: Traditional statistical models identify correlations, but aviation safety requires understanding true causality to inform policy.

Approach:

  • Use DoWhy library for causal inference
  • Build causal DAG: weather → pilot decisions → accident severity
  • Estimate treatment effects: ATE of pilot experience on fatality risk
  • Refute estimates: placebo tests, random common cause

Expected Outcome: Quantify causal effects (e.g., "1000 additional flight hours reduces fatality risk by 15%")

Timeline: 3-4 months Collaborators: Aviation safety researchers, causal inference experts Deliverable: Research paper for Safety Science or Reliability Engineering & System Safety

2. Transfer Learning for Aviation NLP 🤖

Question: Can we fine-tune general-purpose LLMs (GPT-4, Claude) on aviation accident reports to create specialized models with 90%+ accuracy?

Motivation: General LLMs lack aviation domain knowledge. Fine-tuned models could outperform SafeAeroBERT.

Approach:

  • Fine-tune GPT-3.5/GPT-4 on 10K+ NTSB narratives
  • Compare to BERT-based models (SafeAeroBERT, DistilBERT)
  • Evaluate on classification, NER, and summarization tasks
  • Measure cost-benefit: accuracy vs API costs

Expected Outcome: Specialized aviation LLM with 92%+ accuracy (vs 87-91% for BERT)

Timeline: 2-3 months Budget: $500-1000 (OpenAI fine-tuning costs) Deliverable: Model on Hugging Face Hub, paper for NLP in Aviation workshop

3. Geospatial Risk Prediction 📍

Question: Can we predict future accident hotspots using spatiotemporal models (e.g., Gaussian Processes, spatial LSTMs)?

Motivation: Current geospatial analysis identifies historical hotspots. Predictive models could enable proactive safety measures.

Approach:

  • Build spatiotemporal dataset: (lat, lon, time, features)
  • Train Gaussian Process regression with RBF kernel
  • Train spatial LSTM (ConvLSTM) for sequence prediction
  • Validate on 2023-2024 data: predict accident density for 2025

Expected Outcome: Identify 5-10 future hotspots with 70%+ precision

Timeline: 4-5 months Collaborators: Geospatial data scientists, aviation safety analysts Deliverable: Paper for Transportation Research or GeoInformatica

4. Explainable AI for Aviation Safety ✨

Question: How can we improve SHAP explanations for aviation ML models to be interpretable by non-technical stakeholders (pilots, regulators)?

Motivation: SHAP values are technical. Aviation stakeholders need natural language explanations.

Approach:

  • Generate SHAP values for 1000+ predictions
  • Use LLM to convert SHAP to natural language: "This accident was predicted as fatal because the aircraft was 30 years old (20% contribution), the pilot had low hours (15% contribution), and weather was IMC (12% contribution)"
  • Evaluate with aviation experts: comprehension, trust, actionability
  • Compare to LIME, anchor explanations

Expected Outcome: Natural language explanations with 85%+ expert approval rating

Timeline: 2-3 months Collaborators: HCI researchers, aviation safety experts Deliverable: Paper for XAI conference (IUI, CHI, ExSS)

5. Multi-modal Learning: Text + Images 🖼️

Question: Can we improve accident classification by combining textual narratives with accident scene images?

Motivation: NTSB reports include images (wreckage, damage). Multi-modal models could capture visual cues missed by text-only models.

Approach:

  • Collect accident images from NTSB reports (5K+ images)
  • Train vision model: ResNet, Vision Transformer (ViT)
  • Train multi-modal model: CLIP, ViLT, or custom fusion
  • Compare to text-only baseline (SafeAeroBERT)

Expected Outcome: Multi-modal model achieves 93%+ accuracy (vs 87-91% text-only)

Timeline: 5-6 months Challenges: Limited labeled image data, image quality varies Deliverable: Paper for Computer Vision in Transportation (CVPR, ICCV workshops)

6. Survival Analysis for Occupant Injury Prediction 🏥

Question: Can we predict occupant injury severity (Kaplan-Meier survival curves) based on accident characteristics?

Motivation: Current models predict accident severity. Occupant-level predictions could inform safety design (seatbelts, airbags, crashworthiness).

Approach:

  • Build occupant-level dataset: seat position, restraints, impact forces
  • Train Cox proportional hazards model
  • Identify risk factors: age, seat position, aircraft type
  • Validate with medical trauma scoring systems (ISS, AIS)

Expected Outcome: Predict occupant fatality risk with 75%+ C-index

Timeline: 3-4 months Collaborators: Medical researchers, aviation safety engineers Deliverable: Paper for Journal of Trauma or Aviation, Space, and Environmental Medicine

7. Knowledge Graph Reasoning 🧠

Question: Can we use graph neural networks (GNNs) to predict missing relationships in the aviation knowledge graph?

Motivation: Knowledge graph has 20%+ disconnected nodes. Link prediction could infer implicit relationships.

Approach:

  • Train GNN on knowledge graph: GraphSAGE, GAT, or R-GCN
  • Predict missing links: (Aircraft A) -[SIMILAR_TO]-> (Aircraft B)
  • Evaluate with link prediction metrics: MRR, Hits@10
  • Compare to rule-based approaches (Cypher queries)

Expected Outcome: Predict 1000+ new relationships with 70%+ precision

Timeline: 4-5 months Collaborators: Graph ML researchers, Neo4j experts Deliverable: Paper for Graph Learning in Transportation (KDD, ICML workshops)

8. Federated Learning for Privacy-Preserving Analysis 🔒

Question: Can we train ML models on distributed NTSB data (across multiple agencies) without sharing raw data?

Motivation: Federated learning enables privacy-preserving collaboration. Could combine NTSB (US) + EASA (EU) + CASA (Australia) data.

Approach:

  • Simulate federated setting: split data by region
  • Train federated XGBoost: aggregate model updates (not raw data)
  • Compare to centralized model: accuracy, convergence time
  • Evaluate privacy guarantees: differential privacy

Expected Outcome: Federated model achieves 90%+ centralized accuracy with formal privacy

Timeline: 6-8 months Collaborators: Privacy researchers, international aviation agencies Deliverable: Paper for Privacy in ML (CCS, USENIX Security) or Aviation Safety

9. Real-time Anomaly Detection 🚨

Question: Can we detect anomalous accident patterns in real-time (e.g., sudden spike in engine failures)?

Motivation: Early detection of emerging safety issues could trigger investigations before fatalities increase.

Approach:

  • Implement streaming anomaly detection: Isolation Forest, LSTM autoencoder
  • Monitor incoming accident reports (monthly NTSB updates)
  • Define anomalies: 2σ above expected rate, novel occurrence codes
  • Alert system: Slack/email when anomaly detected

Expected Outcome: Detect 80%+ of anomalies within 24 hours

Timeline: 2-3 months Dependencies: Phase 5 (real-time Kafka pipeline) Deliverable: Production system + paper for Anomaly Detection in Time Series

10. Counterfactual Explanations for "What-If" Analysis 🔮

Question: Can we generate counterfactual explanations: "If pilot had 1000 more hours, would accident still be fatal?"

Motivation: Counterfactuals help stakeholders understand how to prevent future accidents.

Approach:

  • Use DICE (Diverse Counterfactual Explanations) library
  • Generate counterfactuals for 1000+ accidents
  • Validate feasibility: are counterfactuals realistic?
  • Compare to causal inference results (DoWhy)

Expected Outcome: Generate 3-5 actionable counterfactuals per accident

Timeline: 2-3 months Collaborators: Explainable AI researchers Deliverable: Tool for investigators + paper for XAI conference

Experimental Techniques

Model Architecture Experiments

1. Transformer Models for Tabular Data

  • Hypothesis: TabTransformer outperforms XGBoost on accident prediction
  • Approach: Train TabTransformer on 100+ engineered features
  • Baseline: XGBoost (91% accuracy)
  • Target: 92%+ accuracy
  • Effort: 20 hours

2. Graph Neural Networks for Severity Prediction

  • Hypothesis: GNN leveraging knowledge graph improves predictions
  • Approach: Encode accident as graph node, aggregate neighbor features
  • Baseline: XGBoost with tabular features
  • Target: 2-3% accuracy improvement
  • Effort: 30 hours

3. Ensemble Methods: Stacking vs Voting

  • Hypothesis: Stacking ensemble (meta-learner) beats voting ensemble
  • Approach: Stack XGBoost + RF + LSTM with Logistic Regression meta-learner
  • Baseline: Voting ensemble (91% accuracy)
  • Target: 92%+ accuracy
  • Effort: 15 hours

4. Few-Shot Learning for Rare Accident Types

  • Hypothesis: Few-shot learning improves classification for rare accidents (<100 examples)
  • Approach: Use Prototypical Networks or MAML
  • Baseline: Standard classification (poor on rare classes)
  • Target: 70%+ accuracy on rare classes
  • Effort: 25 hours

Data Augmentation

5. Synthetic Narrative Generation

  • Hypothesis: GPT-4 can generate synthetic accident narratives to augment training data
  • Approach: Generate 5000+ synthetic narratives, train SafeAeroBERT
  • Baseline: 87-91% accuracy (real data only)
  • Target: 90-93% accuracy (with synthetic data)
  • Effort: 20 hours
  • Budget: $200-500 (GPT-4 API)

6. SMOTE for Imbalanced Classes

  • Hypothesis: SMOTE (Synthetic Minority Over-sampling) improves rare accident classification
  • Approach: Apply SMOTE to minority classes, retrain XGBoost
  • Baseline: F1-score 0.75 on minority classes
  • Target: F1-score 0.82+
  • Effort: 10 hours

A/B Testing Candidates

1. RAG Retrieval Strategies

  • A: Dense retrieval (FAISS)
  • B: Hybrid retrieval (BM25 + FAISS)
  • Metric: Precision@5, Recall@10
  • Hypothesis: Hybrid beats dense by 10-15%

2. ML Model Serving

  • A: XGBoost v1.0
  • B: XGBoost v1.1 (tuned with Optuna)
  • Metric: Accuracy, inference latency
  • Hypothesis: v1.1 improves accuracy by 1-2%

3. Dashboard UI

  • A: Current Streamlit layout
  • B: Redesigned layout (user testing)
  • Metric: Time to complete task, user satisfaction
  • Hypothesis: Redesign reduces task time by 20%

4. Prompt Engineering for RAG

  • A: Simple prompt: "Answer based on these reports"
  • B: Chain-of-thought prompt: "First identify relevant reports, then analyze patterns, finally provide recommendations"
  • Metric: Answer quality (ROUGE, human evaluation)
  • Hypothesis: CoT prompt improves quality by 15%

Academic Collaboration Opportunities

Universities

  1. MIT - International Center for Air Transportation

    • Focus: Aviation safety, operations research
    • Potential collaboration: Causal inference, predictive modeling
    • Contact: Prof. John Hansman

  2. Embry-Riddle Aeronautical University

    • Focus: Aviation safety, human factors
    • Potential collaboration: Pilot behavior analysis, survival analysis
    • Contact: Dr. David Esser (Aviation Safety)

  3. Stanford - AI Lab

    • Focus: Explainable AI, causal inference
    • Potential collaboration: XAI for aviation, counterfactual explanations
    • Contact: Prof. Percy Liang

  4. Carnegie Mellon - Software Engineering Institute

    • Focus: ML engineering, MLOps
    • Potential collaboration: Production ML systems, monitoring
    • Contact: Dr. Grace Lewis

Research Labs

  1. NASA - Aviation Safety Reporting System (ASRS)

    • Focus: Incident reporting, NLP
    • Potential collaboration: Text analysis, topic modeling
    • Dataset sharing: ASRS reports (complementary to NTSB)

  2. FAA - Office of Aerospace Medicine

    • Focus: Human factors, medical certification
    • Potential collaboration: Occupant injury analysis, survival models
    • Access to medical data

  3. EASA - European Union Aviation Safety Agency

    • Focus: International aviation safety standards
    • Potential collaboration: Federated learning, cross-border analysis
    • Dataset sharing: European accident data

Industry Partners

  1. Boeing - Safety Analytics

    • Focus: Commercial aviation, fleet safety
    • Potential collaboration: Predictive maintenance, ML models
    • Data access: Flight data recorder (FDR) data

  2. Airbus - Flight Safety

    • Focus: Aircraft design, crashworthiness
    • Potential collaboration: Multi-modal learning (text + images), safety design
    • Access to engineering data

  3. FlightSafety International

    • Focus: Pilot training, simulation
    • Potential collaboration: Pilot behavior modeling, training effectiveness
    • Simulator data

Conference Deadlines (2025-2026)

Machine Learning

  • NeurIPS - May 2025 (notification: Sep 2025)
  • ICML - January 2026 (notification: May 2026)
  • AAAI - August 2025 (notification: November 2025)
  • KDD - February 2026 (notification: May 2026)

Aviation Safety

  • International Conference on Aviation Safety - June 2025
  • Aerospace Technology Congress - October 2025
  • Flight Safety Foundation Annual Safety Summit - November 2025

NLP & AI

  • ACL - February 2025 (notification: May 2025)
  • EMNLP - June 2025 (notification: September 2025)
  • NAACL - December 2025 (notification: March 2026)

Explainable AI

  • XAI Workshop @ NeurIPS - September 2025
  • IUI (Intelligent User Interfaces) - October 2025
  • CHI (Computer-Human Interaction) - September 2025

Paper Draft Timelines

Paper 1: "Causal Inference in Aviation Accidents" 🎯

  • Target: Safety Science (Q1 journal, IF: 4.2)
  • Timeline:
    • Q2 2025: Data analysis, causal models (DoWhy)
    • Q3 2025: Write draft, internal review
    • Q4 2025: Submit to journal
    • Q1 2026: Revisions, acceptance
  • Authors: Lead Data Scientist, Aviation Safety Expert, Causal Inference Researcher
  • Expected Citations: 20-50 in first 2 years

Paper 2: "SafeAeroBERT: Fine-tuned BERT for Aviation NLP" 🤖

  • Target: EMNLP 2025 or ACL 2026
  • Timeline:
    • Q3 2025: Fine-tune BERT, evaluate on test set
    • Q4 2025: Write draft, internal review
    • Q1 2026: Submit to conference
    • Q2 2026: Presentation (if accepted)
  • Authors: NLP Specialist, Data Scientist
  • Code: Open-source on Hugging Face
  • Expected Citations: 50-100 in first 2 years (high-impact venue)

Paper 3: "Knowledge Graphs for Aviation Safety Analysis" 🧠

  • Target: KDD 2026 or ICDM 2026
  • Timeline:
    • Q4 2025: Build knowledge graph, run graph algorithms
    • Q1 2026: GNN experiments, link prediction
    • Q2 2026: Write draft, submit to conference
    • Q3 2026: Presentation (if accepted)
  • Authors: AI Engineer, Graph ML Researcher
  • Code: Open-source Neo4j schema + queries
  • Expected Citations: 30-60 in first 2 years

Dataset Releases

1. Cleaned NTSB Dataset 📊

  • Format: Parquet files (compressed, 10x smaller than CSV)
  • Size: ~2GB (100K accidents, 200+ features)
  • Platform: Kaggle, Hugging Face Datasets
  • License: CC BY 4.0 (public domain data)
  • Timeline: Q2 2025 (after Phase 1 complete)
  • Expected Downloads: 1000+ in first year

2. Aviation Accident Narratives Corpus 📝

  • Format: Hugging Face Datasets
  • Size: 10K+ narratives with labels (severity, causes)
  • Use case: NLP research, BERT fine-tuning
  • License: CC BY 4.0
  • Timeline: Q3 2025 (after SafeAeroBERT training)
  • Expected Downloads: 500+ in first year

3. Knowledge Graph Dump 🗂️

  • Format: Neo4j dump, RDF triples
  • Size: 50K+ entities, 100K+ relationships
  • Platform: Zenodo, GitHub
  • License: CC BY 4.0
  • Timeline: Q1 2026 (after Phase 4 complete)
  • Expected Downloads: 200+ in first year

Open-Source Contributions

1. ntsb-sdk (Python Package)

  • Description: Python SDK for NTSB analytics API
  • Features: Authentication, rate limiting, pagination
  • Platform: PyPI
  • Timeline: Q1 2026 (Phase 5)
  • Expected Downloads: 500+ monthly

2. SafeAeroBERT (Model)

  • Description: Fine-tuned BERT for aviation accident classification
  • Platform: Hugging Face Hub
  • Timeline: Q3 2025 (Phase 4)
  • Expected Downloads: 100+ monthly

3. Aviation Feature Engineering Library

  • Description: Feature engineering pipelines for aviation ML
  • Features: NTSB code extraction, temporal/spatial features
  • Platform: PyPI
  • Timeline: Q3 2025 (Phase 3)
  • Expected Downloads: 200+ monthly

Estimated Research Budget

Category Cost
LLM API costs (fine-tuning, RAG) $1,000 - $2,000
Cloud compute (GPU training) $500 - $1,000
Conference travel (2-3 conferences) $4,000 - $6,000
Open-access publication fees $1,500 - $3,000
Dataset hosting (Zenodo, S3) $100 - $500
Total $7,100 - $12,500

Funding Sources:

  • NSF grants (SBIR/STTR)
  • Academic partnerships (equipment, cloud credits)
  • Industry sponsorship (Boeing, Airbus)
  • Crowdfunding (Patreon, GitHub Sponsors)

Last Updated: November 2025 Version: 1.0