I build data and machine learning systems that make messy, large-scale infrastructure easier to reason about.
Currently at IBM Research Zürich, after an MSc in Computer Science from ETH Zürich, I work around data systems, ML systems, and applied optimization.
I am equally drawn to clean abstractions, precise writing, good arguments, music, performance, and the quiet discipline of training.
About Me
I am a research-oriented software engineer working across data systems, ML systems, and applied machine learning. I like problems where models meet infrastructure: query engines, memory constraints, cost models, forecasting pipelines, and optimization systems that have to work outside the comfort of a notebook.
At IBM Research Zürich, after completing my MSc in Computer Science at ETH Zürich, my recent work has focused on provisioning-aware query routing for heterogeneous lakehouse engines. The project studies how to route queries across systems such as Spark and PrestoDB by jointly reasoning about latency, memory, and cloud cost.
Before ETH, I worked as a Data Scientist at AB InBev, building forecasting and integer-programming systems for supply-chain analytics across thousands of distribution routes. That industry experience shaped how I think about research: elegant abstractions matter, but so do telemetry, failure modes, handovers, and the strange details that appear only when systems are used.
I graduated Department Rank 1 and Gold Medalist from IIT Bombay, where I also spent years in student journalism, public speaking, and campus analytics. Outside technical work, I write, perform poetry, play music, and train — habits that keep me close to language, rhythm, and discipline.
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