Additive Combinatorics Notes

Leart Ajvazaj

University of Cambridge • January 2024

Comprehensive notes from an Additive Combinatorics class at Cambridge with Julia Wolf. Covers Fourier-analytic techniques, Bogolyubov's lemma, Roth's theorem, and modern developments in arithmetic progressions and sum-product phenomena.

Combinatorics Notes

Leart Ajvazaj

University of Cambridge • October 2023

Notes from Bela Bollobas' Part III Combinatorics course at Cambridge. Covers basic results on chains, antichains, EKR theorem, Katona's Circle method, Kruskal-Katona, Sumsets, Alon's Combinatorial Nullstellensatz, etc.

Galois Theory Notes

Leart Ajvazaj

Yale University • January 2021

Notes from a Fields and Galois Theory class at Yale. Covers field extensions, construction problems, problems of solvability, cyclotomic extensions, and fundamental theorems of Galois theory.

Modern Algebra Notes

Leart Ajvazaj

Yale University • 2020

Notes on modern algebra covering group theory, ring theory, and module theory. Includes fundamental concepts and advanced topics in abstract algebra.

Teoria e Numrave per Olimpiada Matematikore

Leart Ajvazaj

Educational Resource

Shenime te perdorura ne trajnimin e nxenesve fitues te olimpiades matematikore te Kosoves. Perfshin bazat e teorise se numrave per olimpiada.

Computational Complexity

Leart Ajvazaj

University of Cambridge • January 2024

Notes from Timothy Gowers' Computational Complexity class taught at Part III. Topics including complexity classes, completeness, probabilistic algorithms, and some fundamental results in theoretical computer science.

ML-Based (Re)Prioritization of Transmissions from Remote Monitoring of CIEDS

91Life

Heart Rhythm Journal • 2025

Develop and evaluate a machine learning (ML) classification model that outperforms rule-based protocols in prioritizing TSMs generated from remote monitoring (RM) of CIEDs–using data found only within each given TSM.

AI Models to Automate Archiving of Not-Relevant ILR Transmissions

91Life

Heart Rhythm O2 • 2025

This study aims to develop and validate a machine based model, with artificial intelligence, that accurately predicts which ILR transmissions may be automatically archived - based on criteria and adjudication by technicians who triage these reports.