About

This site contains articles, notes and tutorials on topics in several technical domains that I find interesting and relevant for my work and hobbies. This site will serve as an archive and provide reference material that I may need to revisit and possibly expand on later. I am making these documents public in the hope that it will be useful for you too.

I have a general interest in all things compute. This includes algorithms (data structures, optimization, heuristics, meta-heuristics), programming languages (functional, declarative), software engineering, software development tools and libraries. I have formal training in electronics engineering. Even though I did not take that route and have forgotten much, if not most, of what I learned, I still have an interest in hardware. Specifically I also dabble in a little circuit design and prototyping, programming embedded systems with the Arduino MCU and a little VHDL programming using a Xilinx FPGA board. Many a time I come across several other topics of interest that include maths (for example statistics), 3D graphics, animation and visualization. The interest in graphics was born from my final year licentiate project in Radiosity. I also have an interest in AI planning and ILP that I used in my PhD work. I may include some references and notes on some of these topics.

By day, I am an applied Artificial Intelligence (AI) and machine learning (ML) "researcher". What does this mean? It means I try to apply AI/ML to industrial problems. Examples of domains include manufacturing, IT services and retail. I work especially on cases where it has not been tried before or did not attain the desired results. Many times, my colleagues and I, must adapt or extend existing ML algorithms (online learning, batch learning, lifelong learning, few-shot/one shot learning, unsupervised and self-supervised learning) or methods (for example model ensembles) to reach our goals. Topics I deal with include data acquisition (networking, sensors, data sampling, ETL, ELT, manual labelling), knowledge management (controlled vocabularies, dictionaries, glossaries, taxonomies, ontologies), data and ML model management (data pipelines, MLOps, AutoML), data quality assurance (data cleaning, data consolidation) and data management (data architectures, data frameworks and systems integration). I hope to include some material on these topics also.

Feedback and corrections are welcome. You can contact via: tech4rdhq at gmail dot com