Mustakim
Shikalgar

AI legal assistant (industry capstone, contract) with two user modes, guided learning and direct help. Rebuilt the LangGraph pipeline from 6 nodes to 5, cutting LLM calls per message from 2 to 1. Implemented FSPR knowledge profiling, lawyer-style probing behavior, and rolling conversation summarization for coherent long-session context retention.

Fault-tolerant distributed KV store implementing Raft consensus for leader election and log replication across a 5-node cluster. Tunable consistency supporting CP vs AP trade-offs via configurable quorum-based reads.

Binary classification system for predicting semiconductor wafer pass/fail on a severely imbalanced dataset. Two-stage pipeline using L1 regularization for dimensionality reduction followed by Random Forest with balanced class weighting.

Semantic segmentation of road scenes for autonomous driving. Trained and compared three architectures — U-Net, SegNet, and DeepLabV3+ — on the Lyft/Udacity Carla simulator dataset (13 semantic classes). Fixed 7 critical bugs in the original codebase including broken IoU metric, mask decoding, and softmax/logits mismatch.

Knowledge graph integrating 10,000+ NamUs records using semantic web technologies. Replaced a $50/month GraphDB/Azure backend with FastAPI + RDFLib achieving sub-100ms SPARQL queries at zero infrastructure cost.

Genome assembler achieving 99.9% coverage on phiX174 (5,386 bp) using de Bruijn graphs and Eulerian cycle traversal. Implemented error correction including tip removal and bubble detection with multi-format support.