cv
PhD Student, Computer Vision & Multimodal Learning — Michigan State University
General Information
| Full Name | Nitish Shukla |
| Position | PhD Student, Computer Science (Computer Vision & Multimodal Learning) |
| Affiliation | iPRoBe Lab, Michigan State University |
| shuklan3@msu.edu | |
| Languages | English, Hindi |
Research Overview
- I study efficient, interpretable, and reliable visual AI through multimodal learning, representation analysis, and generative modeling. My research examines how pretrained models encode visual evidence and how their internal representations can support reasoning, retrieval, and detection with less computation or human annotation. A central application is facial demorphing, where I develop diffusion models, GANs, and identity-preserving decomposition methods to recover contributing identities from a single morphed image. This work emphasizes generalization across faces and morphing techniques, supported by evaluation protocols that assess identity recovery rather than reconstruction quality alone.
Education
-
2024 - Present PhD in Computer Science
Michigan State University, MI, USA - Advised by Prof. Arun Ross in the iPRoBe Lab.
- Face demorphing, biometric security, and multimodal large language models.
-
2020 - 2022 MSc in Computer Science
Chennai Mathematical Institute (CMI), Chennai, India - Studied robustness properties of DNNs induced by prototypes with Prof. K. V. Subramanyam and Prof. Madhavan Mukund.
-
2017 - 2019 MSc in Mathematics
Indian Institute of Technology (IIT) Guwahati, India -
2014 - 2017 BSc in Physics, Mathematics and Computer Applications
Kanpur University, India
Experience
-
May 2025 - Aug 2025 PhD Research Intern
Adobe Research - Developed an attribute-aware style validation framework using zero-shot MLLM-induced embeddings for fine-grained artistic style analysis and retrieval.
- Proposed an attribute-based image decomposition strategy with FAISS-backed vector search that improved Recall@K by 2–3% over baseline representations. (Manuscript under review.)
-
Aug 2022 - Aug 2023 Data Scientist II
Micron Technology - Designed a synthetic data generation pipeline in Python and PyTorch to fine-tune a proprietary defect segmentation system for semiconductor die inspection.
- Synthetic augmentation improved defect shape mIoU by 9%, enabling robust detection of rare failure modes.
-
2019 - 2020 Research and Development Engineer
Next Education India Pvt. Ltd.
Honors and Awards
-
2025 - IAPR Best Biometrics Student Paper Award (BBSPA), IEEE International Joint Conference on Biometrics (IJCB) 2025.
Ongoing Research Projects
-
2026 Enhancing Single-Image Facial Demorphing using MLLMs
- Leveraged MLLM-derived semantic embeddings of morphs to condition a custom diffusion-based inpainting model, achieving 2–3% improvement in face recognition and biometric verification accuracy over baseline demorphing. (Under review, T-PAMI.)
-
2025 Style-Aware Open-Set Image Retrieval via MLLMs
- Introduced an open-set image retrieval framework leveraging zero-shot MLLM reasoning and attention to extract attribute-disentangled embeddings (brushstrokes, color palette, composition) via contrastive learning, achieving 2–3% mAP gains without supervised fine-tuning. (Under review.)
-
2026 Recursive Visual Pruning for Efficient Multi-Image Reasoning
- Designed an attention-guided recursive visual token pruning framework for vision-language models that reduces visual context via per-image budget allocation, enabling efficient multi-image inference without sacrificing reasoning accuracy. (In preparation.)
Selected Publications
-
2026 S2H-DPO: Hardness-Aware Preference Optimization for Vision–Language Models
- Nitish Shukla, Surgan Jandial, Arun Ross. Findings of the Association for Computational Linguistics (ACL).
-
2025 Facial Demorphing from a Single Morph Using a Latent Conditional GAN
- Nitish Shukla, Arun Ross. IEEE IJCB. IAPR Best Biometrics Student Paper Award.
-
2025 diffDeMorph: Extending Reference-Free Demorphing to Unseen Faces
- Nitish Shukla, Arun Ross. IEEE ICIP.
-
2025 dc-GAN: Dual-Conditioned GAN for Face Demorphing From a Single Morph
- Nitish Shukla, Arun Ross. IEEE FG.
-
2025 Metric for Evaluating Performance of Reference-Free Demorphing Methods
- Nitish Shukla, Arun Ross. IEEE/CVF WACV Workshops.
-
2024 Facial Demorphing via Identity Preserving Image Decomposition
- Nitish Shukla, Arun Ross. IEEE IJCB.
-
2023 Generating Adversarial Attacks in the Latent Space
- Nitish Shukla, Sudipta Banerjee. IEEE/CVF CVPR Workshops.
-
2023 SDeMorph: Towards Better Facial De-morphing from Single Morph
- Nitish Shukla. IEEE IJCB.
Technical Skills
-
Languages & Frameworks
- Python, PyTorch, OpenCV, HuggingFace Transformers, Scikit-learn
-
Computer Vision
- Face recognition, liveness detection, biometric verification, face demorphing, morph attack detection, image retrieval, inpainting
-
Deep Learning
- GANs, diffusion models, vision transformers (ViT), attention mechanisms, contrastive learning, few-shot learning, fine-tuning, RLHF, preference optimization
-
Multimodal Models
- CLIP, MLLMs, vision-language representation learning, zero-shot reasoning
-
Search & Retrieval
- FAISS, vector search, embedding-based retrieval
-
Experimentation
- MLflow, Pandas, large-scale benchmarking
-
Mathematics
- Bayesian methods, graph theory, linear algebra
References
- Arun Ross (Michigan State University) — rossarun@msu.edu
- K. V. Subramanyam (Chennai Mathematical Institute) — kv@cmi.ac.in
- Sudipta Banerjee (University of Wyoming) — sbanerj3@uwyo.edu