Bethel Melesse
Tessema

AI Team, 3Billion — Seoul, South Korea

• Forward Synthesis Evaluation Model: Developed a neural network to score synthetic feasibility of reaction pathways in an RL-based molecular generation pipeline. Optimized training with PyTorch, RDKit, and DeepChem, achieving a 24× speedup (days → 1 hour). Benchmarked with AUROC, AUPRC, and Tanimoto similarity alongside experimental chemists.
• End-to-End Protein-Ligand Interaction Prediction: Built an E2E model predicting binding pockets, ligand poses, and binding affinities for structure-based drug discovery. Led data preprocessing and graph construction from PDB/MOL2/SDF formats; designed multi-task GNN architectures with attention; implemented distributed multi-GPU training with MLflow tracking.

DBDC Lab, Ajou University — Suwon, South Korea

• Information Retrieval for Low-Resource Languages: Trained a BERT-like transformer for information retrieval in African languages, benchmarking against TF-IDF and BM25.
• Generalized Output Spaces for Classifiers: Improved cross-domain and cross-task generalization in multi-modal classifiers by fusing class/task descriptions, achieving state-of-the-art zero-shot performance on CIFAR, RCV1, and 20NG.
• Low-Resource Datasets for LLM Adaptation: Processed 1000+ TB of Common Crawl data for under $2, creating monolingual datasets for 7 low-resource languages, and fine-tuned multilingual LLMs with QLoRA on consumer GPUs.