Boston, US
Available for work
Every individual is a spreadsheet, and the world? One giant, messy but pretty database.
While most kids dreaded math, I found joy in the certainty that every equation had an answer. That love for logic eventually led me to Data Science.
I’ve studied, moved countries, studied a bit more, crunched data, built a lot of AI related things, in pursuit of knowledge, and even completed an MS in Data Science. All driven by one core belief: data isn’t just numbers, it’s potential waiting to be decoded.
Now, I’m diving deeper into how data shapes industries and looking to collaborate on any cool idea you might have.
If it involves math, models, or making sense of chaos - I’m in.
Too Much to Trust? Measuring the Security and Cognitive Impacts of Explainability in AI-Driven SOCs
April 2025
A study found that SOC analysts value contextual, evidence-backed AI explanations over raw accuracy or dashboard outputs. Role-aware, context-rich XAI designs aligned with SOC workflows improve analyst trust, comprehension, and triage efficiency.
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Survey Perspective: The Role of Explainable AI in Threat Intelligence
March 2025
This paper presents findings from an industry survey on how SOC analysts handle AI-generated alerts, revealing key challenges in trust and efficiency, and highlighting the need for XAI-driven features like attribution, confidence scores, and feature explanations to enhance alert interpretability and triage.
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Neuroevolution Neural Architecture Search for Evolving RNNs in Stock Return Prediction and Portfolio Trading
October 2024
This work uses the EXAMM algorithm to evolve RNNs for stock return forecasting, enabling a simple long-short strategy that outperforms the DJI and S&P 500 in both bear (2022) and bull (2023) markets.
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