Mohammed Omer

• Developed Python-based data processing and analysis pipelines for large-scale longitudinal datasets, including data cleaning, participant matching, timestamp processing, duplicate handling, and automated quality checks. • Performed statistical analysis and data visualization in Python. • Contributed to the replication of AdSent, an LLM-based fake-news detection framework, implementing LLM-based text generation, adversarial data augmentation, and transformer-based classification for robustness evaluation.

• Guided students through algorithmic problems on biological sequence data, including sequence comparison, string matching, indexing, and genome assembly. • Supported the analysis and discussion of solutions involving dynamic programming, string algorithms, suffix trees, and sequence-based algorithms. • Explained advanced computational concepts including RNA structure prediction, phylogenetic algorithms, and genome-related data processing, helping students troubleshoot and improve their solutions.

• Assessed weekly algorithmic problem sets, evaluating solutions for correctness, computational complexity, and soundness of reasoning. • Supported students in solving advanced problems involving approximation, parameterized and exact algorithms, randomized algorithms, complexity theory, and linear programming/primal-dual techniques. • Explained algorithm design, correctness proofs, and computational complexity, helping students develop efficient and theoretically sound solutions.