Shahar Lando - Data Science & Machine Learning

Shahar Lando

I’m Shahar Lando, a Data Science student at Afeka in Israel, with a background in sound design.

What I’m drawn to

I enjoy the space where mathematics, statistics, and code meet: building models, designing experiments, and finding useful structure in complex data. My audio background also brings a particular interest in sound and machine learning.

What I’m looking for

Student opportunities in data science, data analysis, and machine learning, with room to learn, contribute, and work on meaningful problems.

AudioDNA

Teaching machines to listen.
A sound effect classifier and similarity search engine.

From sound to searchable features

An end-to-end pipeline for classifying sound effects across six categories. Acoustic features - including MFCCs, spectral features, and zero-crossing rate - turn audio into a representation a model can learn from.

Classification meets discovery

Random Forest classification pairs with cosine-similarity retrieval. A Streamlit interface lets users upload a sound, predict its category, and find related samples.

Behavioral
experiments

From a question about human decisions to a controlled experiment.

Designing the data, then analyzing it

A behavioral economics experimentation platform exploring anchoring, framing, and the decoy effect, with randomized treatment assignment, anonymized data collection, consent, and debriefing.

Looking beyond a p-value

Analysis brings together hypothesis tests, effect sizes, confidence intervals, bootstrap methods, and power analysis to investigate how experimental conditions influence decisions.

Reach out.

Interested in working together? I’d love to hear about student opportunities and collaborations in data science, machine learning, and audio.

Based in Israel · Data Science student at Afeka · Background in sound design