Portfolio item number 1
Short description of portfolio item number 1
Posts by Collection
portfolio
Portfolio item number 2
Short description of portfolio item number 2
publications
Paper Title Number 1
Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1).
Download Paper | Download Slides | Download Bibtex
Paper Title Number 2
Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2).
Download Paper | Download Slides
Paper Title Number 3
Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3).
Download Paper | Download Slides
Paper Title Number 4
Published in GitHub Journal of Bugs, 2024
This paper is about fixing template issue #693.
Recommended citation: Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3).
Download Paper
Paper Title Number 5, with math \(E=mc^2\)
Published in GitHub Journal of Bugs, 2024
This paper is about a famous math equation, \(E=mc^2\)
Recommended citation: Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3).
Download Paper
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Advanced Machine Learning
MSc Course, CuCEng, 2025
Course Description
This Master’s level course dives deep into the theoretical and practical foundations of modern machine learning, focusing primarily on Deep Learning (DL) architectures, advanced optimization techniques, and probabilistic models. The course adopts a seminar and project-based structure, requiring students to engage critically with seminal papers and implement sophisticated models.
Computational Linguistics
MSc Course, CuCEng, 2025
Course Objective
Theoretical foundations of modern language models, deep linguistic analysis, and LLM architectures. Unlike standard NLP engineering courses, this course focuses on the “Why” and “How” of linguistic theories as applied to deep learning architectures, moving from sub-word morphology to the latest large language model alignment techniques.
Discrete Mathematics
Undergraduate Course, CuCEng, 2025
Course Objectives
This course introduces the mathematical foundations of computer science, covering topics such as formal logic, set theory, mathematical induction, methods of counting, discrete probability, and elementary graph theory.
Graph Theory
PhD Course, CuCEng, 2025
Course Description
This course is designed for PhD students to explore the mathematical foundations of graph theory, analyze complex networks, and transition into modern graph representation learning (GNNs). The course follows a seminar-based format. Students are expected to read the assigned seminal paper or book chapter before class and participate in critical discussions.
Introduction to Machine Learning
Undergraduate Course, CuCEng, 2025
Course Objectives
This course introduces the fundamental principles of machine learning and their real-world applications, enabling students to design and evaluate intelligent systems capable of learning from data.
Natural Language Processing
Undergraduate Course, CuCEng, 2025
Course Objectives
In this course, the main goal is to define the methods and approaches used in Natural Language Processing.
Text Vectorization
PhD Course, CuCEng, 2025
Course Objective
By the end of this course, students will understand the theoretical foundations behind transforming textual data into numerical representations that can be processed by machine learning models.
Starting from classical Vector Space Models and TF-IDF, the course explores a wide spectrum of embedding techniques up to modern Transformer-based contextual models. Students will gain both theoretical insight and practical implementation skills.
Theory of Computation
Undergraduate Course, CuCEng, 2025
Course Objectives
In this course, the main goal is to define the language classes in terms of grammars and automata.