phd-portfolio

An ultra-minimalist, Jekyll-based portfolio template for academics.

Complete scRNA-seq analysis pipeline for 10x Genomics PBMC 3k dataset using Python and Scanpy.

Explainable transfer-learning framework for plant disease classification and model reliability analysis.

Hybrid BM25 and dense-retrieval investor recommender with reciprocal-rank fusion, evaluation and LLM-ready reranking.

A research-style Python project for geographic question answering: natural language questions are mapped to explicit semantic representations and executed over a geographic knowledge graph, with rule-based parsing, evaluation metrics, and error analysis.

Explainable deep-learning research pipeline for dental cavity detection from smartphone images.

Python simulation of BPSK, OFDM, and Alamouti STBC — physical layer wireless communications from scratch

Research portfolio connecting my work on multimodal learning, retrieval systems, contrastive learning, embedding geometry, and AI evaluation.

Research-level implementation of unsupervised anomaly detection using KMeans, DBSCAN, Isolation Forest, and deep Autoencoders. Applied to IoT sensors, financial fraud, network intrusion, and time-series fault detection. Built for PhD-oriented ML portfolios.

Research-grade reinforcement learning framework for robot navigation, covering discrete, obstacle-aware, continuous-control, and multi-agent environments with PPO and DQN, full evaluation pipeline, reproducible experiments, and LaTeX paper template for PhD-level research.

Research-oriented natural-language data analysis system with intent detection, guarded execution and reproducible evaluation.

Reproducible Earth Observation pipeline for multi-temporal forest change detection and uncertainty-aware evaluation.

Personal portfolio of Dr. Mallikarjuna Thippana — computational biologist specializing in genomics & epigenomics (bulk RNA-seq, ChIP-seq, ATAC-seq, CUT&RUN), single-cell & spatial multi-omics, and statistical ML for biological data.

Open-source ML pipeline for Pueraria isoflavone bioactivity prediction. v0.6.1: 5-target SAR (ER-α/β, PI3K, AKT1, MMP-9), CV AUC 0.91-0.95. PhD application portfolio 2027.