Update DeepLense Projects

Author: ereinha

_gsocproposals/2026/proposal_DEEPLENSE2.md

@@ -1,50 +1,51 @@
 ---
-title: Unsupervised Super-Resolution and Analysis of Real Lensing Images
+title: Hybrid Quantum-Classical Representation Learning for Dark Matter Substructure Classification
 layout: gsoc_proposal
 project: DEEPLENSE
-project size: 175hr/350hr
+project size: 350hr
 year: 2026
 organization:
   - Alabama
   - MIT
   - Florida
+  - PSL
 ---
 
 ## Description
-This project’s aims are twofold: developing an unsupervised super-resolution architecture to upscale the quality of lensing images constructed using real galaxy sources, and to obtain insight about the lenses themselves. An unsupervised super-resolution technique could be very valuable for lensing studies as access to high resolution lensing images for training and study can be limited, especially given potential lensing data from upcoming surveys such as Euclid and LSST. The overall goal of this project is to develop an architecture that can better study the characteristics of the gravitational lenses and their substructure.
+
+Strong gravitational lensing is a powerful tool for studying dark matter and the large-scale structure of the universe. Strong gravitational lensing images encode subtle signatures of dark matter substructure that can distinguish between competing theoretical models (Cold Dark Matter, Axion or Fuzzy Dark Matter, no substructure). While classical deep learning has shown promise in this classification task, the high dimensional feature spaces and complex correlations in lensing images may benefit from quantum computational approaches.
+This project proposes the development of Quantum Machine Learning (QML) models for dark matter substructure classification. Quantum neural networks leverage quantum phenomena such as superposition and entanglement to explore exponentially large Hilbert spaces, potentially capturing correlations that classical networks miss. Hybrid quantum classical architectures, where quantum circuits serve as trainable feature extractors within classical pipelines, offer a practical near term approach compatible with current NISQ (Noisy Intermediate Scale Quantum) devices.
 
 ## Duration
+
 Total project length: 175/350 hours.
 
 ## Difficulty level
-Intermediate/Advanced
+
+Advanced
 
 ## Task ideas
- * Start with unsupervised SR of simulated images and think of ways to bridge the gap to real images.
- * Try then integrating into the pipeline modules that study characteristics of the lenses.
 
 ## Expected results
- * A more capable architecture that can operate on a wider variety of lensing images, including lensing images created with real galaxy datasets.
- * Insight into the lensing systems, and their sub-structures.
 
 ## Requirements
-Python, PyTorch, experience with machine learning, knowledge of computer vision techniques, familiarity with autoencoders.
+ * Strong background in Machine Learning & Deep Learning.
+ * Knowledge of Quantum Computing (VQAs, Quantum Optimization).
+ * Proficiency in Python & Pennylane or Qiskit.
+ * Ability to work independently on research projects.
 
-<!---
-## Test
-Please use this [link](https://docs.google.com/document/d/1a-5JiHph3K59gV3-kEZWzKYTFMvDeYiJvoE0U2I4x0w/edit?usp=sharing) to access the test for this project.
---->
 
 ## Mentors
   * [Michael Toomey](mailto:[email protected]) (Massachusetts Institute of Technology)
   * [Sergei Gleyzer](mailto:[email protected]) (University of Alabama)
   * [Pranath Reddy](mailto:[email protected]) (University of Florida)
+  * [Rajat Shinde](mailto:[email protected]) (University of Alabama in Huntsville)
+
 
 Please DO NOT contact mentors directly by email. Instead, please email [[email protected]](mailto:[email protected]) with Project Title and include **your CV** and **test results**. The relevant mentors will then get in touch with you. 
 
 
 ## Links
   * [Paper 1](https://arxiv.org/abs/2008.12731)
   * [Paper 2](https://arxiv.org/abs/1909.07346)
-  * [Paper 3](https://iopscience.iop.org/article/10.1088/2632-2153/ad76f8/meta)
-
+  

_gsocproposals/2026/proposal_DEEPLENSE3.md

@@ -40,6 +40,7 @@ Please use this [link](https://docs.google.com/document/d/1a-5JiHph3K59gV3-kEZWz
 ## Mentors
   * [Michael Toomey](mailto:[email protected]) (Massachusetts Institute of Technology)
   * [Sergei Gleyzer](mailto:[email protected]) (University of Alabama)
+  * [Ashutosh Ojha](mailto:[email protected]) (Indian Institute of Technology (Indian School of Mines), Dhanbad)
 
 
 Please DO NOT contact mentors directly by email. Instead, please email [[email protected]](mailto:[email protected]) with Project Title and include **your CV** and **test results**. The relevant mentors will then get in touch with you. 
@@ -49,3 +50,4 @@ Please DO NOT contact mentors directly by email. Instead, please email [ml4-sci@
   * [Paper 1](https://arxiv.org/abs/2008.12731)
   * [Paper 2](https://arxiv.org/abs/1909.07346)
   * [Paper 3](https://ml4physicalsciences.github.io/2024/files/NeurIPS_ML4PS_2024_78.pdf)
+  * [Paper 4](https://ml4physicalsciences.github.io/2025/files/NeurIPS_ML4PS_2025_252.pdf)

_gsocproposals/2026/proposal_DEEPLENSE4.md

@@ -24,9 +24,9 @@ Total project length: 175/350 hours.
 Intermediate/Advanced
 
 ## Task ideas
- * Start with architectures previously explored within the DeepLense project and optimise them for the lens finding task.
- * Perform the lens search in the real observational data and analyse the properties of the detected lens candidates.
- * Evaluate model performance on different surveys.
+ * Start with architectures previously explored within the DeepLense project and optimise them for the lens finding task
+ * Perform the lens search in the real observational data and analyse the properties of the detected lens candidates
+ * Evaluate model performance on different surveys
 
 ## Expected results
  *  Increase the number of known strong lenses.

_gsocproposals/2026/proposal_DEEPLENSE5.md

@@ -1,5 +1,5 @@
 ---
-title: Diffusion Models for Gravitational Lensing Simulation
+title: Physics-Informed Diffusion Models for Gravitational Lensing Simulation
 layout: gsoc_proposal
 project: DEEPLENSE
 project size: 175hr/350hr
@@ -12,25 +12,25 @@ organization:
 
 ## Description
 
-Strong gravitational lensing is a promising probe of the substructure of dark matter to better understand its underlying nature. Deep learning methods have the potential to accurately identify images containing substructure and differentiate WIMP particle dark matter from other well-motivated models, including axions and axion-like particles, warm dark matter, etc.
-
-Traditional simulations of gravitational lensing are time-consuming and require extensive computational resources. This project proposes the use of diffusion models, a class of generative models known for their ability to produce high-quality, detailed images from a distribution of noise, to simulate strong gravitational lensing images. We aim to generate realistic simulations of gravitational lensing events that can be used to augment datasets for machine learning models and facilitate the development of better domain adaptation and self-supervised models aimed at bridging the gap between simulated and real images of gravitational lensing. Furthermore, we will also investigate leveraging conditional diffusion models to generate gravitational lensing simulations by conditioning the model on specific parameters related to the lensing events, such as the mass distribution of the lensing galaxy, orientation, and the redshift of both the source and the lens.
+Strong gravitational lensing is a promising probe of the substructure of dark matter to better understand its underlying nature. Deep learning methods have the potential to accurately identify images containing substructure and differentiate WIMP particle dark matter from other well-motivated models, including axions and axion-like particles, warm dark matter, etc. 
+This project proposes the development of physics-informed diffusion and flow matching models for simulating strong gravitational lensing images. Gravitational lensing is governed by well-defined physical laws and symmetries. We aim to explore strategies for encoding these physical structures directly into generative models so that outputs are not merely statistically realistic but physically consistent. Possible directions include incorporating symmetry-aware architectures, parameterizing the generation process through physically meaningful intermediate representations (e.g., convergence and shear maps), enforcing governing equations as soft or hard constraints, or other approaches the contributor may propose.
+The resulting model is intended to produce high-fidelity, physics-compliant lensing simulations to augment training data for substructure detection and dark matter classification, while offering stronger generalization and a narrower sim-to-real gap compared to purely data-driven generative approaches.
 
 ## Duration
 
 Total project length: 175/350 hours.
 
 ## Difficulty level
-
-intermediate
 Intermediate/Advanced
 
 ## Task ideas
  * Explore diffusion models for the generation of strong gravitational lensing images.
+ * Benchmark generated images against traditional simulations for physical consistency and downstream task utility.
  * Create a diverse dataset of simulated gravitational lensing images under various astrophysical conditions.
 
+
 ## Expected results
- *  A diffusion model capable of generating realistic simulations of strong gravitational lensing phenomena.
+ *  A diffusion model capable of generating realistic, physically consistent simulations of strong gravitational lensing phenomena.
 
 ## Requirements
  * Python, PyTorch and relevant past experience in Machine Learning.
@@ -45,6 +45,7 @@ Please use this [link](https://docs.google.com/document/d/1a-5JiHph3K59gV3-kEZWz
   * [Michael Toomey](mailto:[email protected]) (Massachusetts Institute of Technology)
   * [Pranath Reddy](mailto:[email protected]) (University of Florida)
   * [Sergei Gleyzer](mailto:[email protected]) (University of Alabama)
+  * [Hamees Sayed](mailto:[email protected]) (IIT Madras)
 
 Please DO NOT contact mentors directly by email. Instead, please email [[email protected]](mailto:[email protected]) with Project Title and include **your CV** and **test results**. The relevant mentors will then get in touch with you. 
 
@@ -53,3 +54,5 @@ Please DO NOT contact mentors directly by email. Instead, please email [ml4-sci@
   * [Paper 1](https://arxiv.org/abs/2008.12731)
   * [Paper 2](https://arxiv.org/abs/1909.07346)
   * [Paper 3](https://arxiv.org/abs/2112.12121)
+  * [Paper 4](https://arxiv.org/abs/2203.17003)
+  * [Paper 5](https://arxiv.org/abs/2211.03812)

_gsocproposals/2026/proposal_DEEPLENSE7.md

@@ -0,0 +1,65 @@
+---
+title: Agentic AI for Autonomous Gravitational Lensing Simulation Workflows
+layout: gsoc_proposal
+project: DEEPLENSE
+project size: 350hr
+year: 2026
+organization:
+  - Alabama
+  - MIT
+---
+
+## Description
+
+Strong gravitational lensing is a powerful tool for studying dark matter and the large-scale structure of the universe. Current gravitational lensing simulation pipelines (e.g., [DeepLenseSim](https://github.com/mwt5345/DeepLenseSim) built on Lenstronomy) require substantial manual intervention: researchers must configure parameters, submit cluster jobs, validate outputs, retrain downstream ML models, and iterate on failures. This creates bottlenecks in large-scale dataset generation, limits exploration of parameter space, and consumes researcher time on engineering tasks rather than scientific analysis.
+This project proposes the development of an Agentic AI framework to autonomously orchestrate gravitational lensing simulation workflows. Unlike traditional automation scripts that follow rigid rules, agentic systems employ LLM-powered agents that can reason about scientific objectives, adapt to failures, and coordinate complex multi-step workflows with minimal human supervision.
+
+
+## Duration
+
+Total project length: 175/350 hours.
+
+## Difficulty level
+
+Advanced
+
+## Task ideas
+ * Survey agent frameworks and orchestration patterns
+ * Design multi-agent system with specialized agents
+ * Integrate with DeepLenseSim and SLURM
+ * Evaluate automation gains and dataset quality
+
+
+## Expected results
+ * Working multi-agent system for autonomous workflow orchestration
+ * Significant reduction in manual intervention with adaptive behavior
+ * Benchmark study comparing against traditional pipelines
+
+
+## Requirements
+ * Prior experience with LLMs and local inference tools such as Llama.cpp or Ollama
+ * Prior experience with agentic AI frameworks such as Orchestral AI or Pydantic AI 
+ * Prior experience with Reinforcement Learning (RL) and its application to AI agents, planning, or autonomous workflow optimization is highly preferred.
+ * Familiarity with gravitational lensing is preferred but not mandatory.
+
+
+<!---
+## Test
+Please use this [link](https://docs.google.com/document/d/1a-5JiHph3K59gV3-kEZWzKYTFMvDeYiJvoE0U2I4x0w/edit?usp=sharing) to access the test for this project.
+--->
+
+## Mentors
+  * [Michael Toomey](mailto:[email protected]) (Massachusetts Institute of Technology)
+  * [Sergei Gleyzer](mailto:[email protected]) (University of Alabama)
+  * [Pranath Reddy](mailto:[email protected]) (University of Florida)
+  * [Rajat Shinde](mailto:[email protected]) (University of Alabama in Huntsville)
+
+
+Please DO NOT contact mentors directly by email. Instead, please email [[email protected]](mailto:[email protected]) with Project Title and include **your CV** and **test results**. The relevant mentors will then get in touch with you. 
+
+
+## Links
+  * [Paper 1](https://arxiv.org/abs/2008.12731)
+  * [Paper 2](https://arxiv.org/abs/1909.07346)
+  * [Paper 3](https://arxiv.org/pdf/2512.15867)
+  

_gsocproposals/2026/proposal_DEEPLENSE8.md

@@ -0,0 +1,98 @@
+---
+title: Neural Operators for Fast Simulation of Strong Gravitational Lensing
+layout: gsoc_proposal
+project: DEEPLENSE
+project size: 350hr
+year: 2026
+organization:
+  - Alabama
+  - MIT
+---
+
+## Description
+
+Strong gravitational lensing is a powerful probe of dark matter substructure and cosmological structure formation. Current simulation pipelines (e.g., Lenstronomy-based ray tracing) require solving the lens equation repeatedly for different mass distributions and source configurations. While accurate, these simulations are computationally expensive and limit large-scale parameter sweeps, uncertainty quantification, and real-time inference.
+This project proposes the development of a Neural Operator framework to learn the functional mapping between mass distributions and lensed images. Unlike traditional neural networks that operate on finite-dimensional vectors, neural operators learn mappings between infinite-dimensional function spaces. This makes them particularly well-suited for approximating solutions of physical systems governed by partial differential equations.
+The goal is to train a neural operator that directly maps:
+  * Lens mass distribution → Lensed image
+  or
+  * (Mass distribution, source light profile) → Observed lensed image
+
+
+Such a model would serve as a fast surrogate simulator capable of producing high-fidelity lensing outputs at a fraction of the computational cost of traditional ray-tracing solvers.
+This would represent the first exploration of neural operators within the ML4SCI DeepLense ecosystem.
+
+
+## Duration
+
+Total project length: 175/350 hours.
+
+## Difficulty level
+
+Advanced
+
+## Task ideas
+1. Literature Study
+    1. Study Fourier Neural Operators (FNO)
+    2. Study DeepONets
+    3. Review operator learning in physical systems
+2. Neural Operator Implementation
+    1. Implement Fourier Neural Operator for 2D fields
+    2. Compare with DeepONet-style architectures
+    3. Explore spectral vs spatial operator parameterizations
+3. Evaluation
+    1. Compare speed vs traditional solver
+    2. Measure pixel-wise reconstruction error
+    3. Evaluate preservation of physical invariants
+    4. Test generalization across:
+        1. Different mass profiles
+        2. Different redshifts
+        3. Different source morphologies
+4. Extensions (if time permits)
+    1. Conditional neural operators
+    2. Uncertainty-aware operator learning
+    3. Physics-informed operator constraints
+    4. Hybrid operator + diffusion refinement
+
+
+
+
+## Expected results
+ * A neural operator capable of approximating the lensing simulation operator.
+ * Significant speedup compared to ray-tracing pipelines.
+ * Demonstration of generalization across varying astrophysical conditions.
+ * A benchmark study comparing neural operators with CNN-based surrogate models.
+
+
+
+## Requirements
+Python, PyTorch, experience with machine learning and deep learning.
+Partial understanding of:
+  * Representation learning
+  * Spectral methods
+  * Partial differential equations (basic familiarity)
+  * Scientific machine learning concepts
+Familiarity with gravitational lensing is preferred but not mandatory.
+
+
+
+<!---
+## Test
+Please use this [link](https://docs.google.com/document/d/1a-5JiHph3K59gV3-kEZWzKYTFMvDeYiJvoE0U2I4x0w/edit?usp=sharing) to access the test for this project.
+--->
+
+## Mentors
+  * [Michael Toomey](mailto:[email protected]) (Massachusetts Institute of Technology)
+  * [Sergei Gleyzer](mailto:[email protected]) (University of Alabama)
+  * [Pranath Reddy](mailto:[email protected]) (University of Florida)
+  * [Ashutosh Ojha](mailto:[email protected]) (Indian Institute of Technology (Indian School of Mines), Dhanbad)
+
+
+Please DO NOT contact mentors directly by email. Instead, please email [[email protected]](mailto:[email protected]) with Project Title and include **your CV** and **test results**. The relevant mentors will then get in touch with you. 
+
+
+## Links
+  * [Paper 1](https://arxiv.org/abs/2010.08895)
+  * [Paper 2](https://arxiv.org/abs/2108.08481)
+  * [Paper 3](https://arxiv.org/abs/1910.03193)
+