Understand behavior
Learn patterns in user, device, or system activity over time, then investigate meaningful deviations from that baseline.
Learning from data
AI & Machine Learning
Models built around the problem.
The starting point is a question that data can help answer. What happens next? What behavior is unusual? Which signals matter? I approach machine learning through data quality, measurable baselines, and experiments that compare usefulness with the cost of running a model.
Explore the capabilities- Predictive modeling
- Deep learning
- Behavioral modeling
- Anomaly detection
- Model evaluation
01
Problems worth solving
Anticipate what comes next
Use historical and contextual signals to explore demand, equipment condition, operational risk, and future events.
Make models practical
Evaluate whether a model can meet its accuracy, latency, memory, and privacy requirements on the hardware available.
02
Explore the capabilities
01Predictive modeling & structured data
Translate a business or operational question into a prediction target. Compare simple, interpretable baselines with more flexible models using evaluation splits that reflect deployment.
Methods & tools
- Linear and logistic models
- Tree ensembles
- Feature engineering
- Calibration
Example deliverables
- Data and target definition
- Baseline comparison
- Error analysis and decision thresholds
02Time series & behavioral modeling
Represent ordered events and changing state. Compare architectures by how well they capture useful context and how much time and memory they require.
Methods & tools
- Statistical baselines
- LSTM and GRU
- Transformers
- State-space models
Example deliverables
- Event representation
- Sequence evaluation protocol
- Latency and memory measurements
03Pattern discovery & anomaly detection
Explore normal variation and unusual activity when labels are scarce. An anomaly score is a signal for investigation; its usefulness depends on false positives, context, and the response it enables.
Methods & tools
- Clustering
- Isolation forests
- Autoencoders
- Sequence scoring
Example deliverables
- Baseline behavior analysis
- Threshold calibration
- False-positive investigation
04Deep learning & representation learning
Explore learned representations for sequences, text, images, and relationships. Deep learning is one part of the ML toolkit; architecture choice follows the data and evaluation question.
Methods & tools
- Neural networks
- Embeddings
- Graph neural networks
- Transfer learning
Example deliverables
- Representation experiments
- Architecture comparisons
- Ablation studies
05Evaluation & deployment
Measure generalization and operating cost together. Design the path from an experiment to repeatable inference, with clear model versions, input contracts, and monitoring.
Methods & tools
- Temporal holdouts
- Precision and recall
- Profiling
- Drift monitoring
Example deliverables
- Reproducible evaluation
- Deployment constraints
- Model monitoring plan
03
From question to working system
- 01
Frame the question
Define the entity, prediction or detection task, and what a useful result would change.
- 02
Inspect the data
Check coverage, quality, labels, leakage, and how the data is produced.
- 03
Compare approaches
Start with a baseline and test competing methods under the same conditions.
- 04
Measure in context
Evaluate errors, inference cost, and operational limits before deciding what to deploy.
Start with the question your data should answer.
Discuss the data you have, the decision you need to make, and the constraints a useful model must satisfy.