Nitish Shukla

2335, Engineering Building

Michigan State University

East Lansing, MI, 48864

Ph.D. student at Michigan State University in the iPRoBe lab with Prof. Arun Ross.

My research lies at the intersection of representation learning and model reliability, with a focus on how neural systems encode, reason over, and sometimes fail to disentangle fine-grained visual information in security-critical settings. Concretely, I work on generative face demorphing — recovering the constituent identities hidden inside a face morph using diffusion and GAN-based models, benchmarks, and evaluation protocols — and on multimodal large language models (MLLMs). For MLLMs, my work addresses two core questions: (a) how to optimize MLLMs for multi-image contexts without relying on extensive human annotations, and (b) how to leverage MLLMs to learn richer representations that improve performance on downstream retrieval and generation tasks.

Before that, I studied M.Sc. Computer Science at Chennai Mathematical Institute and M.Sc. Mathematics at IIT Guwahati. At CMI, I worked with Prof. KV and Madhavan Mukund studying the robustness properties of DNNs induced by prototypes.

Outside academia, I find joy in cooking (My Instagram), watching movies, and playing video games during my free time. Beyond that, I’m an enthusiastic hiker and hold a deep admiration for snowy mountains. If there’s anything specific you’d like to discuss or share, feel free to contact me!

news

Apr 07, 2026	Paper titled “S2H-DPO: Hardness-Aware Preference Optimization for Vision–Language Models” has been accepted to ACL 2026!. 🎊
Sep 11, 2025	Awarded IAPR Best Biometrics Student Paper Award (BBSPA) at IEEE International Joint Conference on Biometrics (IJCB), 2025. 🎊
Jul 12, 2025	Paper titled “Facial Demorphing from a Single Morph Using a Latent Conditional GAN” accepted at IEEE International Joint Conference on Biometrics (IJCB), 2025.
May 20, 2025	Paper titled “diffDemorph: Extending Reference-Free Demorphing to Unseen Faces” accepted at IEEE International Conference on Image Processing (ICIP), 2025.
May 12, 2025	Started summer internship at Adobe Research. 🎊

selected publications

arXiv
Enhancing Single-Image Facial Demorphing using Multimodal Large Language Models

Nitish Shukla, and Arun Ross

arXiv preprint arXiv:2605.25442, 2026

Abs arXiv Bib

A reference-free facial demorphing framework that leverages Multimodal Large Language Models (MLLMs) to guide a coupled diffusion-based reconstruction. Semantic embeddings extracted from intermediate MLLM layers condition the demorphing, providing high-level reasoning about facial attributes and identity cues that complement low-level pixel information. Both constituent faces are synthesized jointly through a denoising diffusion model operating directly in the RGB domain. The method achieves restoration accuracies exceeding 96% at 0.1% FMR on landmark-based morphs, with PSNR gains of 6–9 dB over existing methods.
@article{shukla2026llmdemorph, title = {Enhancing Single-Image Facial Demorphing using Multimodal Large Language Models}, author = {Shukla, Nitish and Ross, Arun}, journal = {arXiv preprint arXiv:2605.25442}, year = {2026}, }
ACL
S2H-DPO: Hardness-Aware Preference Optimization for Vision–Language Models

Nitish Shukla, Surgan Jandial, and Arun Ross

In Findings of the Association for Computational Linguistics (ACL), 2026

Abs Bib HTML

A Simple-to-Hard (S2H) learning framework that systematically constructs multi-image preference data across three hierarchical reasoning levels: single-image localized reasoning, multi-image localized comparison, and global visual search. Unlike prior work relying on model-specific attributes such as hallucinations or attention heuristics, the approach leverages prompt-driven complexity to create chosen/rejected pairs applicable across different models. Evaluations on LLaVA and Qwen-VL show significant gains in multi-image reasoning while preserving single-image performance.
@inproceedings{shukla2026s2hdpo, title = {S2H-DPO: Hardness-Aware Preference Optimization for Vision--Language Models}, author = {Shukla, Nitish and Jandial, Surgan and Ross, Arun}, booktitle = {Findings of the Association for Computational Linguistics (ACL)}, year = {2026}, }
IJCB
Facial Demorphing from a Single Morph Using a Latent Conditional GAN

Nitish Shukla, and Arun Ross

In IEEE International Joint Conference on Biometrics (IJCB), 2025

Awarded Abs arXiv Bib Code

IAPR Best Biometrics Student Paper Award (BBSPA)

The method decomposes a morph in latent space, allowing it to demorph images created from unseen morph techniques and face styles. Trained on morphs created from synthetic faces and tested on morphs created from real faces using different morph techniques, the method outperforms existing methods by a considerable margin and produces high-fidelity demorphed face images.
@inproceedings{shukla2025lcgan, title = {Facial Demorphing from a Single Morph Using a Latent Conditional GAN}, author = {Shukla, Nitish and Ross, Arun}, booktitle = {IEEE International Joint Conference on Biometrics (IJCB)}, year = {2025}, }
ICIP
diffDeMorph: Extending Reference-Free Demorphing to Unseen Faces

Nitish Shukla, and Arun Ross

In IEEE International Conference on Image Processing (ICIP), 2025

Abs arXiv Bib

A diffusion-based approach, diffDeMorph, that disentangles component images from a composite morph with high visual fidelity. It is the first to generalize across morph techniques and face styles, beating the prior state of the art by at least 59.46% under a common training protocol across all datasets tested. The model is trained on morphs from synthetically generated faces and tested on real morphs.
@inproceedings{shukla2025diffdemorph, title = {diffDeMorph: Extending Reference-Free Demorphing to Unseen Faces}, author = {Shukla, Nitish and Ross, Arun}, booktitle = {IEEE International Conference on Image Processing (ICIP)}, year = {2025}, }