Fenil Bardoliya

I study how language becomes structure: text-to-table generation, information extraction, and multimodal reasoning—building systems that produce interpretable tables and graphs from text and measuring how LLMs and MLLMs behave under distribution shift, augmentation, and real deployment constraints.

I work at the Complex Data Reasoning & Analysis Lab (CoRAL) with Dr. Vivek Gupta. My recent projects include schema-guided text-to-table pipelines, interactive NL-to-SQL systems over sports data, and large-scale evaluation of vision–language models for grounding and robustness.

At ASU, I worked in DREAMS Lab under Prof. Jnaneshwar Das, interned at Samsung Semiconductor India Research, and completed my B.E. in Computer Science at BITS Pilani. I care about reproducible benchmarks, clear failure modes, and tools researchers can actually use.

🔬 Research interests

🧪 Multimodal LLMs Evaluation and Benchmarking
🧠 Agentic Reasoning
⚙️ Post-training & Unlearning
🎬 Video Understanding
✨ Generative AI

✨ Open to full-time roles · Available immediately

Tempe, Arizona, USA

📰 News

Apr 2026

Presenting The Perceptual Observatory at Voxel51 Best of WACV (30 Apr). Project page →

🤝 Service

Reviewer — ICML, CVPR, ACL workshops; CVPR 2026 (CogVL); ACL 2026 (SURGeLLM); ICML 2026 (AI4Science).
Teaching & mentoring — CSE 576 NLP (mentor, informal TA), CSE 575 grader, thesis mentor — ASU.

📚 Publications

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💼 Experience

NLP Researcher Complex Data Reasoning & Analysis Lab (CoRAL) Aug 2024 – Present · Tempe, AZ, USA

Built a scalable evaluation framework for MLLMs under 30+ perturbations across 62K+ samples (scalable to 1M+), supporting deployment-facing reliability analysis.
Designed Map&Make, a schema-guided and agentic text-to-table pipeline; evaluated frontier LLMs with 5+ structural and semantic metrics.
Implemented distributed post-training with LoRA, DPO, and GRPO on A100/H100/H200 clusters, improving GSM8K and MATH by 20-40%.
Co-developed SPORTSQL over live EPL data; contributed 1,793 benchmark queries and reached up to 80% exact match and 94% LLM-as-judge accuracy.
Built annotation and quality-control protocols for human and LLM-assisted evaluation, improving reproducibility and failure analysis.

Computer Vision Research Aide Distributed Robotic Exploration and Mapping Systems (DREAMS) Lab · Arizona State University · Prof. Jnaneshwar Das March 2024 – Jul 2024 · Tempe, AZ, USA

Developed 3D reconstruction and scene-modeling pipelines for geological formations using Gaussian Splatting and NeRF.
Recreated Apollo lunar landing sites for immersive simulation environments, improving spatial fidelity and neural-rendering robustness.

Assistant Engineer Samsung Semiconductor India Research Jan 2023 – Jul 2023 · Bengaluru, India

Built automation pipelines for network log capture and analysis.
Developed an LSTM-based anomaly detector for SIP and IMS traces over VoLTE, reaching ~80% accuracy across 25+ failure modes.