I am a PhD candidate at Stony Brook University, advised by
Dimitris Samaras.
My work revolves around the rich image priors encoded in deep generative denoising models.
I am interested in applying these priors to the digital histopathology and satellite image domains.
I also interned at Microsoft Research where with my mentor
Nebojsa Jojic
we worked on interesting generative prior problems.
Fast constrained sampling in pre-trained diffusion models
NeurIPS, 2025
We speed up training-free inference in diffusion models using an approximation to Newton's method. This is the algorithm that powers ZoomLDM. We also made a short game that uses this algorithm to generate diffusion images in real-time [Gameplay 1 / Gameplay 2].
PixCell: A generative foundation model for digital histopathology images
Preprint
The first generative foundation model for digital histopathology images. We demonstrate the ability to preserve privacy by utilizing synthetic data, and solve generative tasks such as virtual staining.
ZoomLDM: Latent Diffusion Model for multi-scale image generation
CVPR, 2025
We propose a multi-scale diffusion model to make generation of massive pathology images feasible.
Learned representation-guided diffusion models for large-image generation
CVPR, 2024
We use self-supervised models to provide annotations for diffusion models in digital pathology. Exploiting the unique structure of the data, we synthesize large images with only a patch-based model.
Conditional Generation from Unconditional Diffusion Models using Denoiser Representations
BMVC, 2023
We show that we can learn to control generation with as few as 20 examples using the existing denoiser representations.
Diffusion models as plug-and-play priors
NeurIPS 2022
Diffusion models can be used in inference tasks without any additional training. You can even solve the traveling salesman problem with a diffusion model (left).
Single-Image Based Food Volume Estimation
HCI International 2020
Training and applying monocular depth estimation models to estimate the food volume from a given RGB image.
Monocular Depth Estimation using Generative Adversarial Networks
Undergraduate Thesis, 2018
Adding GAN losses to monocular depth estimation models improves predicted depth accuracy.
Eyes of the beholder: attention-guided metamers reveal divergent scene representations
Ritik Raina, Abe Leite, Alexandros Graikos, Seoyoung Ahn, Gregory J Zelinsky
CCN 2026
Poppy: Polarization-based Plug-and-Play Guidance for Enhancing Monocular Normal Estimation
Irene Kim, Sai Tanmay Reddy Chakkera, Alexandros Graikos, Dimitris Samaras, Akshat Dave
ECCV 2026
Structured image representation learning for flow-matching models
Alexandros Graikos,
Kostas Triaridis,
Nikolay Malkin,
Dimitris Samaras
ICLR 2026 2nd Workshop on Deep Generative Model in Machine Learning: Theory, Principle and Efficacy
GriDiT: Factorized Grid-Based Diffusion for Efficient Long Image Sequence Generation
Snehal Singh Tomar,
Alexandros Graikos,
Arjun Krishna,
Dimitris Samaras,
Klaus Mueller
TMLR 2026
Gen-SIS: Generative Self-augmentation Improves Self-supervised Learning
Varun Belagali*,
Srikar Yellapragada*,
Alexandros Graikos,
Saarthak Kapse,
Zilinghan Li,
Tarak Nath Nandi,
Ravi K Madduri,
Prateek Prasanna,
Joel Saltz,
Dimitris Samaras
DataCV Workshop @ CVPR 2026
Generating metamers of human scene understanding
Ritik Raina,
Abe Leite,
Alexandros Graikos,
Seoyoung Ahn,
Dimitris Samaras,
Greg Zelinsky
ICLR 2026
Fast constrained sampling in pre-trained diffusion models
Alexandros Graikos,
Nebojsa Jojic,
Dimitris Samaras
NeurIPS 2025
PixCell: A generative foundation model for digital histopathology images
Alexandros Graikos*, S. Yellapragada*, Z. Li, K. Triaridis, V. Belagali, T. N. Nandi, K. Bai, B. S. Knudsen, T. Kurc, R. R. Gupta, P. Prasanna, R. K Madduri, J. Saltz, Dimitris Samaras
Preprint
Pathology Image Compression with Pre-trained Autoencoders
Srikar Yellapragada,
Alexandros Graikos,
Kostas Triaridis,
Zilinghan Li,
Tarak Nath Nandi,
Ravi K Madduri,
Prateek Prasanna,
Joel Saltz,
Dimitris Samaras
MICCAI 2025
PathSegDiff: Pathology Segmentation using Diffusion model representations
Sachin Kumar Danisetty,
Alexandros Graikos,
Srikar Yellapragada,
Dimitris Samaras
5th Deep Generative Models Workshop @ MICCAI 2025
Seen2Scene: a generative model of fixation-by-fixation scene understanding
Ritik Raina,
Abe Leite,
Alexandros Graikos,
Seoyoung Ahn,
Greg Zelinsky
CCN 2025
ZoomLDM: Latent Diffusion Model for multi-scale image generation
Alexandros Graikos*,
Srikar Yellapragada*,
Kostas Triaridis,
Prateek Prasanna,
Rajarsi R. Gupta,
Joel Saltz,
Dimitris Samaras
CVPR 2025
∞-Brush: Controllable Large Image Synthesis with Diffusion Models in Infinite Dimensions
Minh-Quan Le*,
Alexandros Graikos*,
Srikar Yellapragada,
Rajarsi Gupta,
Joel Saltz,
Dimitris Samaras
ECCV 2024
Diffusion-Refined VQA Annotations for Semi-Supervised Gaze Following
Qiaomu Miao,
Alexandros Graikos,
Jingwei Zhang,
Sounak Mondal,
Minh Hoai,
Dimitris Samaras
ECCV 2024
Learned representation-guided diffusion models for large-image generation
Alexandros Graikos*,
Srikar Yellapragada*,
Minh-Quan Le,
Saarthak Kapse,
Prateek Prasanna,
Joel Saltz,
Dimitris Samaras
CVPR 2024
PathLDM: Text conditioned Latent Diffusion Model for Histopathology
Srikar Yellapragada*,
Alexandros Graikos*,
Prateek Prasanna,
Tahsin Kurc,
Joel Saltz,
Dimitris Samaras
WACV 2024
Conditional Generation from Unconditional Diffusion Models using Denoiser Representations
Alexandros Graikos,
Srikar Yellapragada,
Dimitris Samaras
BMVC 2023
S-volsdf: Sparse multi-view stereo regularization of neural implicit surfaces
Haoyu Wu,
Alexandros Graikos,
Dimitris Samaras
ICCV 2023
GFlowNet-EM for learning compositional latent variable models
Edward Hu,
Nikolay Malkin,
Moksh Jain,
Katie Everett,
Alexandros Graikos,
Yoshua Bengio
ICML 2023
Diffusion models as plug-and-play priors
Alexandros Graikos,
Nikolay Malkin,
Nebojsa Jojic,
Dimitris Samaras
NeurIPS 2022