Smita Krishnaswamy - Cellular Trajectories & Regulatory Networks w/ Neural & Graph ODE Models, 2/2 @IPAMUCLA

Uploaded February 2026 | Updated September 2026, 3 weeks ago

Recorded 24 February 2026. Smita Krishnaswamy of Yale University presents "Virtual Talk: Inferring Dynamic Cellular Trajectories and Underlying Cellular Regulatory Networks with Neural and Graph ODE Models. Pt. 2/2" at IPAM's Mathematics of Cancer: Open Mathematical Problems Workshop.
Abstract: This two-part tutorial. The first part will cover our data diffusion geometry-preserving PHATE method for embedding cellular data into a low dimensional state space, and then to infer cellular trajectories within that space using our biologically principled neural ODE framework called MIOflow (manifold interpolating flows). In the second part, we will cover our graph ODE framework called RITINI (Regulatory Interaction Network Inference) to infer regulatory mechanisms underlying these dynamic cellular trajectories. We will also cover a case study in breast cancer, showing how cells transform from cancer stem cell states to aggressive metastatic states, and how these methods could be used to discover potential state-gating targets.
Learn more online at: https://www.ipam.ucla.edu/programs/workshops/mathematics-of-cancer-open-mathematical-problems/

Smita Krishnaswamy - Cellular Trajectories & Regulatory Networks w/ Neural & Graph ODE Models, 2/2

Max Tegmark - Neural network interpretability: symmetry, geometry and formal verification

Dimitris Giannakis - Operator-theoretic approach, feature extraction & statistical model of climate

$Artur Izmaylov - Quantum SENiority-based Subspace Expansion (Q-SENSE) and Its Extensions Recorded 18 February 2026. Artur Izmaylov of the University of Toronto presents Quantum SENiority-based Subspace Expansion (Q-SENSE) and Its Extensions at IPAMs Bridging the Gap Between NISQ and FTQC Workshop. Abstract: Quantum SENiority-based Subspace Expansion (Q-SENSE) is a hybrid quantum-classical algorithm that interpolates between the Variational Quantum Eigensolver (VQE) and Configuration Interaction (CI) methods. It constructs Hamiltonian matrix elements on a quantum device and solves the resulting eigenvalue problem classically. Unlike other expansion-based methods — such as Quantum Subspace Expansion (QSE), Quantum Krylov Algorithms, and the Non-Orthogonal Quantum Eigensolver — Q-SENSE introduces seniority operators as artificial symmetries to construct orthogonal basis states. This seniority-symmetry-based approach reduces one of the primary limitations of VQE on near-term quantum hardware — circuit depth — at the cost of measuring additional matrix elements. The artificial symmetries also reduce the number of Hamiltonian terms that must be measured, as only a small fraction of the terms couple basis states in different seniority subspaces. With all these merits, Q-SENSE offers a scalable and resource-efficient route to quantum advantage on near-term quantum devices and in the early fault-tolerant regime. Learn more online at: https://www.ipam.ucla.edu/programs/workshops/bridging-the-gap-between-nisq-and-ftqc/$

Ciaran Ryan-Anderson - Experimental Quantum Error Correction at Quantinuum - IPAM at UCLA

Zhiyan Ding - Ground State Preparation for Near-Term Devices using System-Bath Coupling Dynamics

Miguel Garcia-Garibay - What is being transformed by AI in Higher Education? (Everything)

Ionut-Gabriel Farcas - A tutorial on multi-fidelity methods for fusion energy research, Pt. 1/2

Sebastien Bubeck - A Combinatorics Problem - IPAM at UCLA

Samantha Riesenfeld - Quantum-Classical Algorithms for Biomarker Discovery in Multimodal Cancer Data

Shancong Mou - Derivative-Informed Training of Neural Operators on the Fly - IPAM at UCLA

Chimnay Nirkhe - Making the leap to Quantum PCPs - IPAM at UCLA