Girishkumar Ponkiya

• Evaluated 5 NER tools against internal ESG data; integrated spaCy, CoreNLP, and Diffbot into the production NiFi pipeline.
• Improved the pipeline's relevance classifier from 82%+ to 85%+ accuracy by replacing a spaCy-based component with a tuned scikit-learn model (feature engineering + hyperparameter search).
• Contributed to Japanese-language expansion raising relevance from ~9% to ~33%.
• Devised a novel threshold identification approach enabling mathematical confidence bounds on relevance and false positive rates — previously unmeasurable.
• Redesigned entity linking around a Memgraph knowledge graph for better adaptability to dynamic entity universes.
• Deployed LLMs (including Gemma3:27B) with FP8 quantization, kv-cache quantization, and multi-GPU tensor parallelism. Identified a CPU-level bottleneck (missing AVX2/AVX-512) leading to a hardware upgrade.
• Built AI-driven document processing pipeline for thematic index: PDF → Markdown (Docling) → section filtering → LLM summarisation.
• Developed aspect-based sentiment analysis for the AI-driven STOXX sentiment index using E3 APIs and Vespa-backed vector embeddings.

The thesis addressed Noun Compound Interpretation (NCI): automatically uncovering implicit semantic relations between component nouns (e.g., student protest → "student is the doer of the protest"). The work spans two distinct axes:

1. Automatic interpretation: Multiple approaches of increasing sophistication — (a) supervised LSTM-based prepositional paraphrasing; (b) unsupervised fill-in-the-blank using BERT/RoBERTa; (c) unsupervised free-form paraphrasing using T5 — the unsupervised models outperformed prior supervised baselines.

2. Linguistic contribution: Proposed FrameNet-based semantic relation labels for noun compounds, and demonstrated that FrameNet data can be leveraged to generalize the label space — enabling interpretation models to handle unseen relation labels via zero-shot learning (ConvE embeddings over FrameNet).

Relevant courses: Foundation of ML, NLP and the Web, Artificial Intelligence, Topics in NLP (Machine Translation).

Research originated during a 2014 TRDDC internship. Internship mentor Girish K. Palshikar became Ph.D. co-supervisor — a direct intellectual lineage from internship to completed PhD.

Addressed the Q-MER (Query Maximal Empty Rectangle) problem in external memory — finding the largest axis-parallel empty rectangle around a query point among massive point sets that exceed main memory.

Contributions: (1) Proposed algorithm for an external priority search tree with linear space in log-linear time; (2) Showed all standard range queries can be answered efficiently; (3) Algorithm to solve MER with O(log n) extra space in main memory.

Relevant topics: Probabilistic Reasoning · Pattern Recognition · Algorithms & Complexity · Optimisation Techniques · Automata & Formal Languages