Surgan Jandial

Surgan Jandial

I am a research associate at Adobe MDSR Labs, Noida. Prior to this in 2021, I earned my Bachelors degree in Computer Science from Indian Institute of Technology (IIT), Hyderabad, where I was fortunate to work with Prof. Vineeth N Balasubramanian.

My research interests are in making AI systems/pipelines:

Resource-Efficient for widespread access: efficient model training, efficient model selection, efficient model size, data efficiency via synthetic data;

Safe for long-term usage or deployment: model fairness, model security.

My experiences have convinced me that in our quest for efficiency, we often overlook the aspects of safety. Thus, I strive to simultaneously achieve both. I have begun this exploration by focusing on models that are both compact and fair.

When I am not working on any of the above, I am likely researching interesting Computer Vision, LLM, and VLM applications.

Email / List of Patents / Preprints / Some personal interests

Google Scholar / Linkedin / Twitter

Conferences

(* denotes equal contribution)

Publications Component

	All Should Be Equal in the Eyes of Language Models: Counterfactually Aware Fair Text Generation Pragyan Banerjee^, Abhinav Java^, Surgan Jandial^, Simra Shahid ^, Shaz Furniturewala, Balaji Krishnamurthy, Sumit Bhatia AAAI, 2024 Model Fairness Large Language Models
	Retro-KD: Past States for Regularizing Targets in Teacher-Student Learning Surgan Jandial^, Yash Khasbage^, Arghya Pal, Balaji Krishnamurthy, Vineeth N Balasubramanian CODS-COMAD, 2023 (Oral) Knowledge Distillation Model Compression
	One-Shot Doc Snippet Detection: Powering Search in Document Beyond Text Abhinav Java^, Shripad Deshmukh^, Milan Aggarwal, Surgan Jandial, Mausoom Sarkar, Balaji Krishnamurthy WACV, 2023 Applications Computer Vision
	Distilling the Undistillable: Learning from a Nasty Teacher Surgan Jandial, Yash Khasbage, Arghya Pal, Vineeth N Balasubramanian, Balaji Krishnamurthy ECCV, 2022 Knowledge Distillation Model Stealing Model Security
	SAC: Semantic Attention Composition for Text-Conditioned Image Retrieval Surgan Jandial^, Pinkesh Badjatiya^, Pranit Chawla^, Ayush Chopra^, Mausoom Sarkar, Balaji Krishnamurthy WACV, 2022 Applications Computer Vision Vision Language Models
	Retrospective Loss: Looking Back to Improve Training of Deep Neural Networks Surgan Jandial^, Ayush Chopra^, Mausoom Sarkar, Piyush Gupta, Balaji Krishnamurthy, Vineeth N Balasubramanian KDD, 2020 Efficient Model Training
	SieveNet: A Unified Framework for Robust Image-Based Virtual Try-On Surgan Jandial^, Ayush Chopra^, Kumar Ayush^, Mayur Hemani, Balaji Krishnamurthy, Abhijeet Halwai WACV, 2020 Also presented at Workshop on AI for Content Creation, CVPR 2020* Media Coverage: Venturebeat / Beebom / WWD Applications Computer Vision
Workshops (* denotes equal contribution)
	Towards Fair Knowledge Distillation using Student Feedback Abhinav Java ^, Surgan Jandial^ , Chirag Agarwal Workshop on Efficient Systems for Foundation Models, ICML 2023 Under review at a Top-Tier ML Conference Model Fairness Knowledge Distillation Vision Language Models
	Gatha: Relational Loss for enhancing text-based style transfer Surgan Jandial, Shripad Deshmukh, Abhinav Java, Simra Shahid, Balaji Krishnamurthy 6th Workshop on Computer Vision for Fashion, Art, and Design, CVPR 2023 (Oral) Synthetic Data Generation Vision Language Models
	Self-supervised Autoencoder for Correlation-Preserving in Tabular GANs Siddarth Ramesh^, Surgan Jandial^, Gauri Gupta^, Piyush Gupta, Balaji Krishnamurthy Data-centric Machine Learning Research (DMLR) Workshop, ICML 2023* Synthetic Data Generation Tabular Data
	Contextual Alchemy: A Framework for Enhanced Readability through Cross-Domain Entity Alignment Simra Shahid, Nikitha Srikanth, Surgan Jandial, Balaji Krishnamurthy Workshop on Machine Learning for Creativity and Design, Neurips 2023 Applications Large Language Models
	On Conditioning the Input Noise for Controlled Image Generation with Diffusion Models Vedant Singh^, Surgan Jandial ^, Ayush Chopra, Siddarth Ramesh, Balaji Krishnamurthy, Vineeth N Balasubramanian Workshop on AI for Content Creation, CVPR 2022 Synthetic Data Generation
	Leveraging Style and Content features for Text Conditioned Image Retrieval Pranit Chawla , Surgan Jandial, Pinkesh Badjatiya, Ayush Chopra, Mausoom Sarkar, Balaji Krishnamurthy Workshop on Computer Vision for Fashion, Art and Design, CVPR 2022 Applications Computer Vision Vision Language Models
	AdvGAN++: Harnessing latent layers for adversary generation Puneet Mangla^, Surgan Jandial^, Sakshi Varshney^, Vineeth N Balasubramanian Neural Architect Workshop, ICCV 2019* Robustness Computer Vision
	Robust Cloth Warping via Multi-Scale Patch Adversarial Loss for VirtualTry-On Framework Kumar Ayush^, Surgan Jandial^, Ayush Chopra^, Mayur Hemani, Balaji Krishnamurthy Workshop on Human Behaviour Understanding, ICCV 2019* Applications Computer Vision
	Powering Virtual Try-On via Auxiliary Human Segmentation Learning Kumar Ayush^, Surgan Jandial^, Ayush Chopra^, Balaji Krishnamurthy Workshop on Computer Vision for Fashion, Art and Design, ICCV 2019* Applications Computer Vision
Preprints (* denotes equal contribution)
	Thinking Fair and Slow: On the Efficacy of Structured Prompts for Debiasing Language Models Shaz Furniturewala^, Surgan Jandial^, Abhinav Java, Pragyan Banerjee, Simra Shahid, Sumit Bhatia, Kokil Jaidka Under review at a Top-Tier ML Conference Model Fairness Large Language Models
	Leveraging style-based relations for text-conditioned style transfer Surgan Jandial^, Silky Singh^, Simra Shahid^*, Abhinav Java, Shripad Deshmukh Under review at a Top-Tier ML Conference Synthetic Data Generation Style Transfer Vision Language Models
Patents

Issued Cloth Warping Using Multi-Scale Patch Adversarial Loss
Application granted on 06/08/2021. US Patent number 11080817
Issued Accurately Generating Virtual Try-On Images Utilizing a Unified Neural Network Framework
Application granted on 08/03/2021. US Patent number 11030782
Issued Text-Conditioned Image Search with Transformation, Aggregation, and Composition of Visio-Linguistic Features
Application granted on 08/08/2023. US Patent number 11720651
Issued Model Training with Retrospective Loss
Application granted on 10/24/2023. US Patent number 11797823
Filed Text-Conditioned Image Search Based on Dual-Disentangled Feature Composition
Filled at the US Patent Office on 1/28/2021
Filed Regularizing Targets in Model Distillation Utilizing Past State Knowledge of Students
Filled at the US Patent Office on 8/9/2022
Filed Diffusion Model Image Generation
Filled at the US Patent Office on 8/31/2022
Filed Systems and Methods for Data Augmentation
Filled at the US Patent Office on 10/11/2022
Filed Systems and Methods for Machine Learning Transferability
Filled at the US Patent Office on 3/3/2023
Filed Form Structure Similarity Detection
Filled at the US Patent Office on 3/27/2023
Filed Personalized Form Error Correction Propagation
Filled at the US Patent Office on 4/27/2023
Filed Knowledge Distillation Using Contextual Semantic Noise
Filled at the US Patent Office on 2/22/2023
Filed Systems and Methods for Generating Synthetic Tabular Data for Machine Learning and Other Applications
Filled at the US Patent Office on 4/3/2023
Filed One-Shot Document Snippet Search
Filled at the US Patent Office on 6/30/2023
Filed Generating Alternative Examples for Content
Filled at the US Patent Office on 11/3/2023
In-Filing A Novel Method and Apparatus for Text-Guided Style Transfer
Internally approved at Adobe Inc. in June 2023 for filing
In-Filing A Novel Framework for Bias Aware Distillation using Student Feedback
Internally approved at Adobe Inc. in December 2023 for filing
Submitted A Novel Framework for Counterfactually Aware Fair Text Generation
Awaiting internal approval at Adobe Inc.
Submitted Mask-CLIPstyler: Localized text-based style transfer in images
Awaiting internal approval at Adobe Inc.

this made my life easy.