News

  • Sept 2026: How much detail can ultra-low-field MRI recover? Our new study quantifies the information ceiling and shows that tested super-resolution models generate fine structures unsupported by the measurements.
  • Sept 2026: Inspired by the AdS/CFT correspondence, Holographic Generative Flows introduces a physics-based framework for generative flow matching, accepted in Machine Learning: Science and Technology.
  • Aug 2026: A new stochastic PDE preprint derives logarithmic derivatives of solution distributions for variational and singular stochastic partial differential equations.
  • July 2026: Accepted for an oral presentation at MICCAI’s Off-Grid workshop, K-NeAS reconstructs multiple tissue types from sparse CT measurements using neural surface representations.
  • May 2026: Hessian Matching improves coarse-grained molecular simulations by teaching neural potentials the curvature of the free-energy landscape.
  • March 2026: Spinverse uses differentiable physics to reconstruct tissue boundaries and permeability from simulated diffusion MRI measurements.
  • Feb 2026: Introducing RibPull: continuous neural representations of CT ribcages that enable extraction of their skeletal structure.
  • Dec 2025: Published in The Journal of Physical Chemistry B, our molecular dynamics benchmark enables reproducible comparisons across nine proteins using weighted ensemble sampling.
  • Dec 2025: Generating brain scans with controllable clinical attributes: this paper combines a 3D VAE-GAN with diffusion-based feature sampling.
  • Oct 2025: Latent Spaces for Langevin Dynamics establishes conditions for correct molecular sampling in coarse-grained representations, including learned latent spaces.
  • Sept 2025: By requesting new training data when trajectories reach poorly sampled configurations, our active learning framework improves coarse-grained molecular simulations.
  • Sept 2025: What happens when energy supervision complements force matching? This study investigates its effect on learned molecular free-energy landscapes.
  • Aug 2025: Malliavin calculus provides a foundation for score-based diffusion models in infinite dimensions, with score formulas for generative modelling in function spaces.
  • July 2025: A new paper in Scientific Reports combines modular machine learning models, imaging, genetics, and cognitive tests for Alzheimer’s detection, including when some data modalities are missing.
  • May 2025: Prathamesh presented a posted in ISMRM poster on ReMiDi: Microstructure reconstruction using a differentiable MRI simulator.
  • March 2025: Malliavin Calculus can can be applied to AI-based Diffusion Models! Check our two new pre-prints: Malliavin Calculus for Diffusion Models (more applied, contains numerical results) and A Malliavin calculus approach to score functions in diffusion generative models (more theoretical, covering the general case)
Older news

Recent Publications

Projects

Project’s we’re currently focusing on: Molecular Dynamics, Differentiable simulators, ML Compositionality, Generative Modelling, Mars Climate Simulations, Brain Digital Twins and Differentiable Cryptanalysis. For prospective students, look at these in particular.

ML for Molecular Dynamics

Using AI/ML for scaling Molecular Dynamics simulations of proteins.

Brain Digital Twins

Using multimodal MRI and differentiable simulators to infer virtual brains.

Simulating a Virtual Cell

Simulating a virtual cell using ML-derived coarse-grained potentials

Image Reconstruction

Using ML and compressed sensing techniques to improve the quality, speed and cost of medical scans.

AI for Material Science

AI and Quantum Simulations for Discovering Novel Materials

Mars Climate Simulations

Combining classical Mars climate simulations, neural network surrogates, and differentiable simulators for parameter calibration.

Cryptanalysis through Differentiable Cyphers

Developing cryptanalytic attacks using soft differentiable cyphers and gradient-based key optimization.

ML Compositionality

ML Compositionality refers to the idea of building a large ML model from modular and reusable building blocks, just like LEGO.

Generative Modelling

Develop models for generation, reconstruction and manipulation of images, text or other high-dimensional data.

ML Benchmarks

We build benchmarks and organize community challenges on key medical prediction problems.

Disease progression modelling

Modelling the progression of Alzheimer’s disease and related neurodegenerative diseases

Differentiable Simulators

Building MRI/PET/Diffusion/MD simulators in PyTorch that can enable us to perform backpropagation through the entire simulator

Medical Visualisation

Building software and ML models for the visualisation of medical images. An example project is BrainPainter.

Lab members

PhD students



Masters and Undergraduate students

  • Cyrus Correll: AI for Molecular Dynamics
  • Sameera (Sam) Kashyap: Mars climate simulations
  • Nilufer Sagat: Optimization of photovoltaic materials



Former students

Show former students and research descriptions
  • Sanjit Shashi: AI for science, holographic generative modelling, and AI-driven molecular dynamics
  • Manolis Nikolakakis: Image reconstruction, brain CT, and neural skeletons
  • Najmeh Mashhadi: Machine learning compositionality and generative models in medicine
  • Daniel Sabo: AI models for coarse-grained molecular dynamics
  • Ian Terry
  • Anderson Compalas
  • Anusha Pai: Molecular dynamics data processing
  • Daksh Shah: Scalable Multi-Material CT Reconstruction Using Neural SDFs (MICCAI paper)
  • Arthur Wei
  • Justin Bui: Gaussian splatting
  • Ariel Raizman: Hidden Markov models for nanopore methylation analysis
  • Jane Choi
  • Alex Feghhi
  • Jason Zhang: Machine learning for peptide simulations
  • Akshitha Nagaraj: DNA simulation and data pipelines
  • Clayton Lau: Molecular dynamics preprocessing and ligand simulations
  • Bora Dursun: Differentiable programming and cryptanalysis
  • Akshat Tiwari
  • Amisha Kandi: Differentiable programming
  • Anish Pahilajani
  • Philip Bizimis: Differentiable programming and cryptanalysis
  • Sreevani Suvarna: Compositional machine learning with neural network graphs
  • Jueqi Wang: MICCAI publication; external student from St. Francis Xavier University, Canada
  • Junya Ihira: Exchange student at UCSC
  • Bhrigu Garg: Compositional machine learning and brain image generation
  • Rahul Nadkarni: Gaussian splatting
  • Jonathan Vengosh: Gaussian splatting
  • Decker Krogh: Differentiable programming and cryptanalysis
  • Mario Brenes: Language models for mesh generation; diffusion MRI simulation
  • Peter Cai: Explainable CT denoising
  • Abhishek Chavan: High-resolution brain MRI simulation
  • Avani Kinikar: Brain slice reconstruction from diffusion MRI
  • Ritesh Kumar: Explainable CT denoising
  • Pakhi Sinha: MRI super-resolution
  • Ashwani Rathee: Evaluation of brain tissue reconstruction from MRI
  • Mohit Agrawal: Brain digital twins from MRI
  • Douglas Lin: Machine learning for ligand dynamics
  • Stephanie Lin: Summer visiting student from UC Merced; machine learning for DNA simulation
  • Tamanna Iyyani: MRI super-resolution
  • Joshua Li: Brain digital twins from MRI
  • Iman Yael Schaefer: Videography for the Maglite MRI project
  • Dyuthi Vijay: Brain digital twins from MRI
  • Alexander Aghili: Energy matching and weighted ensemble molecular dynamics; ICML workshop paper; SIAM Best Poster Award
  • Zahra Petiwala: Diffusion MRI simulation; Dean’s Award
  • Paul Kim: Machine learning for protein simulations
  • Arshia Kapil: Molecular dynamics
  • Aidan Maldonado: Molecular dynamics
  • Ish Khandel: Compositional machine learning
  • Annie Liu: MRI research
  • Srilekha Vutukuru: Compositional machine learning capstone project
  • Vedu Mallela: Mouse brain visualization with BrainPainter; arXiv technical report
  • Thomas Liao: Whole brain emulation with Randall Koene and Carboncopies
  • Jonathan Morris: MRI scanner hardware and magnetic field mapping
  • Julia Wong: Gaussian splatting for CT images
  • Logan: 3D modelling and visualization for the Maglite MRI project
  • Amber Borjigin: Passive magnetic field shimming for MRI
  • Tisha Gangar: Weighted ensemble molecular dynamics
  • Shreya Handa: Hepatitis B virus protein simulations
  • Ben Hess: Passive magnetic field shimming for MRI
  • Neil Karkhanis: Gradient coil design for open MRI
  • Eric Kimbrell: Deep learning for whole slide imaging
  • Edison Kuo: MRI console software and website development
  • Sneha Kupili: Brain MRI data collection
  • Nina Lane: MRI gradient coils and radiofrequency switching
  • Ember Lu: Machine learning for Hepatitis B virus molecular dynamics simulations
  • Maximilian Miller: Magnetic field mapping for MRI
  • Victoria Pacheco: Active magnetic field shimming and MRI gradients
  • Eliot Watchell: Radiofrequency hardware for the Maglite MRI project
  • Justin Xu: MRI sequences and evaluation of brain tissue reconstruction
  • Dhruvanshi Shah: Brain digital twins from MRI
  • Ashish Srivastava: Brain digital twins from MRI
  • Mason Brown: Deep learning for whole slide imaging and explainable CT reconstruction
  • Shri Prathaa: External student from IIT Madras; spectral autoencoders for diffusion MRI
  • Russel Elliot: Language models for mesh generation
  • Phil Liu: Finite element computation for diffusion MRI simulation
  • Sreyes Venkatesh: Spiking neural networks with Jason Eshraghian; NICE conference paper

Recent & Upcoming Talks

Teaching

Lecture Recordings

CSE140 Intro to AI, Winter 2023 (17 lectures):

CSE242 Machine Learning, Fall 2022 (18 lectures):

Media

Romanian TVR (Oct 2023)

Join My Lab

  • Postdocs: Email me directly. I hire all year round.
  • PhD students: Submit a PhD application through UCSC grad amissions. The application deadline is in early January of every year. You are welcome to email me earlier to enquire about our lab or to visit us.
  • UCSC undergraduates/Masters students: I can supervise you on a thesis project if we come up with a very interesting idea, or if you propose your own project. You’re welcome to email me or my PhD students and ask them to suggest a project, or to brainstorm ideas. Another option is to start by helping one of my exiting PhD students on their project. Also see the projects and their associated readings.
  • Visiting students: If you’re a PhD student or postdoc from another research lab and want to visit us for 6-months or 1-year to gain more skills or collaborate, send me an email.

Contact