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Anshul Singh
I am a Research Associate at the IACV Lab, IISc
Bangalore, advised by Prof. Soma
Biswas. My research centers on Large Language Models, with a particular focus on
multimodal systems, visual reasoning, and document understanding. My work explores how to enhance
the reasoning capabilities of language and vision models. Specifically, I have worked on:
- Adversarial Robustness and Safety of Multimodal LLMs and Diffusion
Models
- Multimodal Analysis of open source information using LLM-guided Active Learning
- Multimodal reasoning for multi-tabular data using Vision-Language Models
- Multilingual alignment for low-resource Indic languages
- Vision Transformers and Signal analysis for fault detection and vocal pattern recognition
Previously, I was a research intern at the LT Research Group, University of Hamburg, with Prof. Chris
Biemann and Jan Strich, and a MITACS
Globalink Research Intern at Dalhousie
University. I completed my Bachelor of Engineering in Information Technology at Panjab University. During my undergraduate studies, I also worked
as a machine learning research intern at IIT Roorkee and
served as the ML/AI Lead at Google Developer Student Clubs.
Research Interests: Multimodal and Multilingual Reasoning | Vision-Language Models
| Interpretability of LLMs | Information Retrieval
Hobbies: Outside of research, I enjoy traveling, reading, writing blogs,
minimalist living, and tinkering with small experiments. I'm always happy to connect for a
discussion or collaboration!
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Experience & Education
Research Associate
IISc, Bangalore
Aug 2025 – Present
Research Intern
LT Group, UHH
Jan 2025 – May 2025
Undergrad Researcher
Dalhousie University, Halifax
Oct 2024 – April 2025
Research Intern
MITACS, Canada
June 2024 – Sep 2024
ML Research Intern
IIT, Roorkee
June 2023 – July 2023
B.E Information Technology
Panjab University
Sep 2021 – June 2025
News
Research
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MTabVQA: Evaluating Multi-Tabular
Reasoning of Language
Models in Visual Space
Anshul Singh, Chris Biemann, Jan
Strich
Empirical Methods of Natural Language Processing (EMNLP), 2025 Findings
Paper / Dataset / Poster
In this work, we address a critical gap in Vision-Language Model (VLM) evaluation by introducing
MTabVQA, a novel benchmark for multi-tabular visual question answering. Our benchmark comprises
3,745 complex question-answer pairs that require multi-hop reasoning across several visually
rendered table images, simulating real-world documents. We benchmark state-of-the-art VLMs,
revealing significant limitations in their ability to reason over complex visual data. To address
this, we release MTabVQA-Instruct, a large-scale instruction-tuning dataset. Our experiments
demonstrate that fine-tuning with our dataset substantially improves VLM performance, bridging the
gap between existing benchmarks that rely on single or non-visual tables.
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Comparative Analysis of
State-of-the-Art Attack Detection Models
Priyanka Kumari, Veenu Mangat, and Anshul Singh
14th International Conference on Computing Communication and Networking Technologies (ICCCNT),
2023
Paper
In this work, we address the growing security challenges in IoT networks by conducting a
comprehensive comparative analysis of machine learning classifiers for intrusion detection. We
evaluated five distinct models on two real-world IoT network traffic datasets to identify the most
effective algorithms for detecting malicious activity. Our findings show that tree-based models,
specifically Random Forest and Decision Trees, deliver outstanding performance, achieving accuracies
exceeding 99%. This research provides a clear benchmark and practical guidance for developing robust
and high-performance security systems to protect vulnerable IoT environments.
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Reflections
My DIC Journey
Research at IIT
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