Ph.D., IIT Madras · Data Science, Caterpillar India
Deciding well when the data is scarce and the tails matter.
Welcome, and thanks for stopping by. I'm a quantitative researcher and a working data scientist, and everything here comes out of one question.
How choices should be made when the evidence is thin.
- The problem — consequences are asymmetric, and the model informing the choice is itself estimated rather than specified.
- The methods — uncertainty quantification, probabilistic risk assessment, robust and multi-objective optimisation.
- They travel — the same question turns up in business planning and in engineering reliability.
Applications
| Business | Operational risk · Demand planning · Resource allocation · Decision support · Forecasting |
| Engineering | Reliability · Fatigue life · Robust design · Motor design · Tail risk · Surrogate modelling |
Toolboxes, research studies and the analyses behind the papers. The writing lives on my site — see below.
Methods and software
LMomFit fitting ·
L_UQ uncertainty ·
RDO robust design ·
PRA risk assessment
Research studies
lmrd-selection-bias ·
lmrd-set-coverage ·
lmoment-pot-reliability ·
conformal-dro-aviation ·
asrs-atc-detection-gaps ·
predict-explain-prescribe ·
ai-driven-product-design
Applied work
fatigue-life-screening ·
CPM_prediction ·
Timeseries ·
Optimization ·
Visuvalization ·
DeepanJayaraman.github.io — this site
Python R MATLAB scikit-learn XGBoost TensorFlow LangChain Ollama SQL Power BI Azure
On the website
| Research | The doctoral contribution and the current threads, with the methods drawn out |
| Publications | Journal articles, manuscripts under review, conferences and invited talks — filterable by type, domain and rating |
| Industry | Caterpillar, Onward Technologies and Siemens, with the stack and application areas per project |
| Teaching | Courses taught and courses I can offer |