Data Scientist · Developer
I am a data engineering student and researcher working at the intersection of machine learning, mathematics, and applied systems. I focus on translating deep theoretical intuition into tangible engineering solutions.
About
Who I am and what I'm currently working on.
I am a Data Engineering student and aspiring MSc candidate at the Technion, motivated by a desire to understand the full lifecycle of data. Whether I am building foundational data pipelines or extracting insights through advanced machine learning, my focus is on turning raw information into intelligent, automated systems. I operate best at the intersection of applied systems and theoretical research, where big ideas have to be proven in practice.
I approach my work with a meticulous, investigative mindset. The most rewarding part of engineering is the opportunity to step into unmapped domains, ask the right questions, and build a solution that leaves no stone unturned. I love the iterative grind of the research process—the cycle of designing an experiment, identifying the weaknesses, and engineering a system from the ground up until it is undeniably solid.
Education
BSc Data Engineering
Technion — Israel Institute of Technology · Dean's Award of Excellence
Darski High School
Graduated with honours
Experience
Research Assistant — Prof. Avi Gal & Prof. Assaf Shohar
Technion · Modeling legal statutes as database constraints (in collaboration with the University of Haifa), and representing neural networks as linear functions using classical linear algebra
Soldier & Medic — Givati Brigade
Israel Defense Forces · Developed methodical documentation, planning, and operational execution skills
Skills
Projects
Data science projects, ML experiments, and applications.
Applied large language models to classify transcripts of people with Alzheimer's disease. Investigated the linguistic subtleties — specific patterns in speech and word choice — that distinguish Alzheimer's transcripts from healthy controls. Supervised by Prof. Roy Reichert.
Investigated whether large language models can memorize information from books and how this depends on the linguistic and literary properties of the source text. Found correlations between specific textual characteristics and a model's ability to recall book content. Supervised by Eyal Ben David.
Integrated learned heuristics into classical planning problems using graph neural networks. The GNN is trained to predict the quality of planning states, guiding the planner toward more efficient search trajectories and reducing the number of states explored. Supervised by Prof. Erez Karpas.
Explored computationally lightweight observation models for robot decision-making that remain accurate enough for reliable decisions. Analysed the error bounds introduced by model simplification and demonstrated that a robot can make sound decisions under simplified — yet bounded — uncertainty models. Supervised by Prof. Vadim Indelman.
Research assistant project in collaboration with the University of Haifa. Models state laws as formal constraints on relational databases and builds an interface through which a database instance can be evaluated and repaired to comply with regulatory requirements. Explores the intersection of legal reasoning and database theory. Supervised by Prof. Avi Gal.
Research assistant project building on Prof. Shohar's recent publication. Represents trained neural networks as linear functions using tools from classical linear algebra. The framework enables direct inspection of the features learned by a network, offering a new lens for neural network interpretability without requiring additional tooling. Supervised by Prof. Assaf Shohar.
Research
Academic papers and writings.
Research Assistant · Technion — in collaboration with the University of Haifa
This project models state laws as formal constraints on relational databases. The core challenge is translating legal language — which is often ambiguous and context-dependent — into precise formal constraints that a database system can enforce and repair. The work sits at the intersection of legal reasoning, formal methods, and database theory.
Research Assistant · Technion
This work represents trained neural networks as linear functions using tools from classical linear algebra. Rather than treating a neural network as a black box, the framework exposes the network's learned features directly through algebraic decomposition. The goal is to investigate what a network has actually learned — and why — in a way that is interpretable, rigorous, and does not require additional tooling or probes.
Contact
Open to research collaborations, data science discussions, and new opportunities. Feel free to reach out.