LAFI 2026 Contributors

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Results (73)

A Shubh AgrawalAgrawal, Shubh Northeastern University Towards an Equational Calculus of Interventions
Pedro H. Azevedo de AmorimAzevedo de Amorim, Pedro H. University of OxfordUnited Kingdom Committee Member in Program Committee within the LAFI-track
B Jialu BaoBao, Jialu Northeastern University Towards an Equational Calculus of Interventions Committee Member in Program Committee within the LAFI-track
Kevin BatzBatz, Kevin Generating Functions Meet Occupation Measures: Invariant Synthesis for Probabilistic Loops
Kevin BatzBatz, Kevin RWTH Aachen UniversityGermany Verifying Sampling Algorithms via Distributional Invariants
Guillaume BaudartBaudart, Guillaume InriaFrance Session Chair of Fourth Session (part of LAFI)
Eli BinghamBingham, Eli Broad Institute Typed Abstractions for Causal Probabilistic Programming
Maddy BowersBowers, Maddy Massachusetts Institute of TechnologyUnited States Sequential Monte Carlo Program Synthesis with Refinement Proposals
C Simon CastellanCastellan, Simon University of Rennes; Inria; CNRS; IRISAFrance Semantic Foundations for Laziness in Discrete Probabilistic Programming
Karen ChungChung, Karen Massachusetts Institute of TechnologyUnited States A Design Proposal for GraPPL: Probabilistic Programming with Low-Level, High-Performance GPU Programmable Inference
Jürgen CitoCito, Jürgen TU WienAustria Committee Member in Program Committee within the LAFI-track
Elias Rojas CollinsCollins, Elias Rojas MIT A Design Proposal for GraPPL: Probabilistic Programming with Low-Level, High-Performance GPU Programmable Inference
Katherine CollinsCollins, Katherine MIT Language-Model Probabilistic Programming for Improving Autoformalization via Cycle Consistency and Incremental Type-Checking
Breandan ConsidineConsidine, Breandan United States A Word Sampler for Well-Typed Functions
Raphaëlle CrubilléCrubillé, Raphaëlle On Contextual Distances in Randomized Programming: Amplification and Lower Bounds
Jack CzenszakCzenszak, Jack Yale UniversityUnited States Nominal Semantics for First-class Automatic Differentiation
D Mauricio Barba da Costada Costa, Mauricio Barba MIT Language-Model Probabilistic Programming for Improving Autoformalization via Cycle Consistency and Incremental Type-Checking Sequential Monte Carlo Program Synthesis with Refinement Proposals
F Claudia FaggianFaggian, Claudia CNRS, Université Paris Cité Towards Compiling Higher-Order Programs to Bayesian Networks
Ole FenskeFenske, Ole Towards Representation Agnostic Probabilistic Programming
Jack FeserFeser, Jack BasisUnited States Typed Abstractions for Causal Probabilistic Programming
Cameron FreerFreer, Cameron Massachusetts Institute of TechnologyUnited States Session Chair of Third Session (part of LAFI) Language-Model Probabilistic Programming for Improving Autoformalization via Cycle Consistency and Incremental Type-Checking Committee Member in Steering Committee within the LAFI-track
G Adrian GallusGallus, Adrian RWTH Aachen University Generating Functions Meet Occupation Measures: Invariant Synthesis for Probabilistic Loops
Poorva GargGarg, Poorva University of California, Los AngelesIndia Committee Member in Program Committee within the LAFI-track
Ilias GarnierGarnier, Ilias France Committee Member in Program Committee within the LAFI-track
Hong GeGe, Hong University of Cambridge Multi-Agent Systems for Traceable Bayesian Workflow
Dominik GeißlerGeißler, Dominik Technische Universität BerlinGermany Probabilistic Programming Meets Automata Theory: Exact Inference using Weighted Automata
Matin GhavamiGhavami, Matin Massachusetts Institute of TechnologyUnited States A Design for Massively Parallel Gibbs Sampling on the GPU via Static and Dynamic Analysis of Probabilistic Programs
Kiran GopinathanGopinathan, Kiran BasisUnited States Basis — A Programming Languages Take on Principled Foundations for AI
Andrew D. GordonGordon, Andrew D. Cogna and University of EdinburghUnited Kingdom Multi-Agent Systems for Traceable Bayesian Workflow
H Darion HaaseHaase, Darion RWTH Aachen University Generating Functions Meet Occupation Measures: Invariant Synthesis for Probabilistic Loops
Tom HirschowitzHirschowitz, Tom Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LAMA, 73000 ChambéryFrance Semantic Foundations for Laziness in Discrete Probabilistic Programming
Steven HoltzenHoltzen, Steven Northeastern UniversityUnited States Towards an Equational Calculus of Interventions Committee Member in Steering Committee within the LAFI-track
Mathieu HuotHuot, Mathieu MIT A Design Proposal for GraPPL: Probabilistic Programming with Low-Level, High-Performance GPU Programmable Inference
K Benjamin Lucien KaminskiKaminski, Benjamin Lucien Saarland University; University College LondonGermany Generating Functions Meet Occupation Measures: Invariant Synthesis for Probabilistic Loops
Ohad KammarKammar, Ohad University of EdinburghUnited Kingdom Typed Abstractions for Causal Probabilistic Programming
Madhav KandaKanda, Madhav University of Illinois Urbana-Champaign Grammar-Constrained LLM Generation for Reliable and Efficient Probabilistic Program Synthesis
Joost-Pieter KatoenKatoen, Joost-Pieter RWTH Aachen UniversityGermany Generating Functions Meet Occupation Measures: Invariant Synthesis for Probabilistic Loops Verifying Sampling Algorithms via Distributional Invariants
Thomas KirsteKirste, Thomas Towards Representation Agnostic Probabilistic Programming
Lutz KlinkenbergKlinkenberg, Lutz RWTH Aachen University Generating Functions Meet Occupation Measures: Invariant Synthesis for Probabilistic Loops
L Michael LeeLee, Michael University of Cambridge, UK Typed Abstractions for Causal Probabilistic Programming
Alexander K. LewLew, Alexander K. Yale UniversityUnited States Session Chair of First Session (part of LAFI) Welcome Language-Model Probabilistic Programming for Improving Autoformalization via Cycle Consistency and Incremental Type-Checking Nominal Semantics for First-class Automatic Differentiation Sequential Monte Carlo Program Synthesis with Refinement Proposals Incremental Density Computation for Efficient Programmable Inference Committee Member in Organizing Committee within the LAFI-track Co-chair in Program Committee within the LAFI-track
M Dougal MaclaurinMaclaurin, Dougal Committee Member in Steering Committee within the LAFI-track
Vikash MansinghkaMansinghka, Vikash Massachusetts Institute of Technology A Design for Massively Parallel Gibbs Sampling on the GPU via Static and Dynamic Analysis of Probabilistic Programs A Design Proposal for GraPPL: Probabilistic Programming with Low-Level, High-Performance GPU Programmable Inference Sequential Monte Carlo Program Synthesis with Refinement Proposals Incremental Density Computation for Efficient Programmable Inference
Vikash K. MansinghkaMansinghka, Vikash K. Massachusetts Institute of TechnologyUnited States Language-Model Probabilistic Programming for Improving Autoformalization via Cycle Consistency and Incremental Type-Checking
Houssein MansourMansour, Houssein ISAE-SUPAERO On Contextual Distances in Randomized Programming: Amplification and Lower Bounds
Sasa MisailovicMisailovic, Sasa University of Illinois at Urbana-ChampaignUnited States Grammar-Constrained LLM Generation for Reliable and Efficient Probabilistic Program Synthesis
O Timothy J. O'DonnellO'Donnell, Timothy J. Language-Model Probabilistic Programming for Improving Autoformalization via Cycle Consistency and Incremental Type-Checking
P Hugo PaquetPaquet, Hugo Inria, École Normale SupérieureFrance Session Chair of Second Session (part of LAFI) Welcome Semantic Foundations for Laziness in Discrete Probabilistic Programming Committee Member in Organizing Committee within the LAFI-track Co-chair in Program Committee within the LAFI-track
Romir PatelPatel, Romir MIT Language-Model Probabilistic Programming for Improving Autoformalization via Cycle Consistency and Incremental Type-Checking
Maximilian PopkoPopko, Maximilian Towards Representation Agnostic Probabilistic Programming
R McCoy Reynolds BeckerReynolds Becker, McCoy MIT A Design Proposal for GraPPL: Probabilistic Programming with Low-Level, High-Performance GPU Programmable Inference
Martin C. RinardRinard, Martin C. Massachusetts Institute of TechnologyUnited States A Design for Massively Parallel Gibbs Sampling on the GPU via Static and Dynamic Analysis of Probabilistic Programs
S William S. MosesS. Moses, William University of Illinois Urbana-ChampaignUnited States Committee Member in Program Committee within the LAFI-track
Feras SaadSaad, Feras Carnegie Mellon UniversityUnited States Committee Member in Program Committee within the LAFI-track
Armando Solar-LezamaSolar-Lezama, Armando Massachusetts Institute of TechnologyUnited States Sequential Monte Carlo Program Synthesis with Refinement Proposals
Dario SteinStein, Dario A Welcome to Causal Probabilistic Programming
Dario SteinStein, Dario University of Oxford Typed Abstractions for Causal Probabilistic Programming
Xianda SunSun, Xianda University of Cambridge Multi-Agent Systems for Traceable Bayesian Workflow
T Christine TassonTasson, Christine Sorbonne Université — LIP6France Committee Member in Steering Committee within the LAFI-track
Joshua B. TenenbaumTenenbaum, Joshua B. Massachusetts Institute of TechnologyUnited States Language-Model Probabilistic Programming for Improving Autoformalization via Cycle Consistency and Incremental Type-Checking Sequential Monte Carlo Program Synthesis with Refinement Proposals
Jean-Baptiste TristanTristan, Jean-Baptiste AmazonUnited States Committee Member in Steering Committee within the LAFI-track
U Shubham UgareUgare, Shubham MetaUnited States Grammar-Constrained LLM Generation for Reliable and Efficient Probabilistic Program Synthesis
V Gabriele VanoniVanoni, Gabriele IRIF, Université Paris Cité Towards Compiling Higher-Order Programs to Bayesian Networks
Matthijs VákárVákár, Matthijs Utrecht University Committee Member in Steering Committee within the LAFI-track
W David WalkerWalker, David Princeton UniversityUnited States Monte Carlo Analysis of Probabilistic Programs
Theo WangWang, Theo University of Cambridge, University of OxfordUnited Kingdom Typed Abstractions for Causal Probabilistic Programming
Xiaoyan WangWang, Xiaoyan Massachusetts Institute of TechnologyUnited States Sequential Monte Carlo Program Synthesis with Refinement Proposals
Tobias WinklerWinkler, Tobias RWTH Aachen UniversityGermany Generating Functions Meet Occupation Measures: Invariant Synthesis for Probabilistic Loops Probabilistic Programming Meets Automata Theory: Exact Inference using Weighted Automata Verifying Sampling Algorithms via Distributional Invariants
Y Jeremy YallopYallop, Jeremy University of CambridgeUnited Kingdom Typed Abstractions for Causal Probabilistic Programming
Z Fabian ZaiserZaiser, Fabian MIT Language-Model Probabilistic Programming for Improving Autoformalization via Cycle Consistency and Incremental Type-Checking Incremental Density Computation for Efficient Programmable Inference Committee Member in Program Committee within the LAFI-track
Yizhou ZhangZhang, Yizhou University of WaterlooCanada Committee Member in Program Committee within the LAFI-track
A. ZhaoZhao, A. Princeton Monte Carlo Analysis of Probabilistic Programs
Daniel ZilkenZilken, Daniel Verifying Sampling Algorithms via Distributional Invariants