CV

Experience

2019 - Present: Staff Machine Learning Scientist, Layer 6 AI at TD
- Lead Scientist for Trustworthy AI
- Manager of team of machine learning scientists

Education

Ph.D in Theoretical Physics, University of Toronto, 2019
M.Sc. in Physics, University of Toronto, 2015
H.B.Sc. in Physics, University of British Columbia, 2014
Exchange Program - National University of Singapore, 2013

Skills

Languages: Python, Java, Scala
Deep Learning Libraries: Pytorch, Huggingface - Diffusers, Transformers, Datasets
Frameworks: Azure, Kubernetes, Databricks, Spark, Hadoop
Data Science Libraries: Scikit-learn, Pandas, Numpy, Scipy
Scientific Languages: Mathematica, Maple, Matlab

Publications

Trustworthy AI Must Account for Interactions

Jesse C. Cresswell. Trustworthy AI Must Account for Interactions. In Advances in Neural Information Processing Systems, volume 39, 2026

On the Burden of Achieving Fairness in Conformal Prediction

Ziang Gao, Pengqi Liu, Archer Yi Yang, Mouloud Belbahri, Jesse C. Cresswell, Masoud Asgharian. On the Burden of Achieving Fairness in Conformal Prediction. In Advances in Neural Information Processing Systems, volume 39, 2026

Conformal Agent Error Attribution

Naihe Feng, Yi Sui, Shiyi Hou, Ga Wu, Jesse C. Cresswell. Conformal Agent Error Attribution. In Advances in Neural Information Processing Systems, volume 39, 2026

A Gradient Perspective on RLVR Stability and Winner Advantage Policy Optimization

Prasanth YSS, Zhichen Ren, Rasa Hosseinzadeh, Ilan Gofman, Yuqi Chen, Zhaoyan Liu, Guangwei Yu, Jesse C. Cresswell, Satya Krishna Gorti. A Gradient Perspective on RLVR Stability and Winner Advantage Policy Optimization. EMNLP 2026

Unifying Conformal Language Tasks with In-Context Ensembles

Xiao Shi Huang, Chen-Yuan Lin, Bruce Kuwahara, Kin Kwan Leung, Jesse C. Cresswell. Unifying Conformal Language Tasks with In-Context Ensembles. Findings of EMNLP 2026

Chartographer: Counterfactual Chart Generation for Evaluating Vision-Language Models

Yifan Jiang, Dae Yon Hwang, Jesse C. Cresswell, Freda Shi. Chartographer: Counterfactual Chart Generation for Evaluating Vision-Language Models. EMNLP 2026

LakeQuest: A Three-Domain Benchmark for Grounded Question Answering across Data Lakes

Michael Solodko, Steven Gong, Guangwei Yu, Satya Krishna Gorti, Jesse C. Cresswell, Victor Zhong. LakeQuest: A Three-Domain Benchmark for Grounded Question Answering across Data Lakes. Conference on Language Modeling 2026

Self-Supervised Representation Learning as Mutual Information Maximization

Akhlaqur Rahman Sabby, Yi Sui, Tongzi Wu, Jesse C. Cresswell, Ga Wu. Self-Supervised Representation Learning as Mutual Information Maximization. TMLR 2026

Causal Foundation Models

Christopher Stith, Hossein Rahmani, Jesse C. Cresswell. Causal Foundation Models. arXiv preprint: 2609.03003

TabPATE: Differentially Private Tabular In-Context Learning Without Public Data

Dariush Wahdany, Matthew Jagielski, Jesse C. Cresswell, Adam Dziedic, Franzsika Boenisch. TabPATE: Differentially Private Tabular In-Context Learning Without Public Data. ICML 2026 Workshop on Foundation Models for Structured Data

Dataset Inference for Data Provenance and Privacy Auditing in Tabular Foundation Models

Dariush Wahdany, Jesse C. Cresswell, Naiqing Guan, Atiyeh Ashari Ghomi, Franzsika Boenisch, Adam Dziedzic. Dataset Inference for Data Provenance and Privacy Auditing in Tabular Foundation Models. ICML 2026 Workshop on Foundation Models for Structured Data

Training Fair Tabular Foundation Models

Patrik Kenfack, Jesse C. Cresswell, Anthony L. Caterini, Samira Ebrahimi Kahou, Ulrich Aïvodji. Training Fair Tabular Foundation Models. ICML 2026 Workshop on Foundation Models for Structured Data

Causal Foundation Models with Continuous Treatments

Christopher Stith, Medha Barath, Vahid Balazadeh, Jesse C. Cresswell, Rahul G. Krishnan. Causal Foundation Models with Continuous Treatments. ICML 2026 Workshop on Foundation Models for Structured Data

Beyond Procedure: Substantive Fairness in Conformal Prediction

Pengqi Liu, Zijun Yu, Mouloud Belbahri, Arthur Charpentier, Masoud Asgharian, Jesse C. Cresswell. Beyond Procedure: Substantive Fairness in Conformal Prediction. International Conference on Machine Learning 2026

Agentic Monte Carlo: Simulating Reinforcement Learning for Black-Box Agents

Dae Yon Hwang, Raunaq Suri, Valentin Villecroze, Anthony L. Caterini, Jesse C. Cresswell, Noël Vouitsis, Brendan Leigh Ross. Agentic Monte Carlo: Simulating Reinforcement Learning for Black-Box Agents. International Conference on Machine Learning 2026

RankJudge: A Multi-Turn LLM-as-a-Judge Synthetic Benchmark Generator

Zhenwei Tang, Zhaoyan Liu, Rasa Hosseinzadeh, Tongzi Wu, Keyvan Golestan, Jesse C. Cresswell. RankJudge: A Multi-Turn LLM-as-a-Judge Synthetic Benchmark Generator. arXiv preprint: 2605.21748

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems

Brendan Leigh Ross, Noël Vouitsis, Atiyeh Ashari Ghomi, Rasa Hosseinzadeh, Ji Xin, Zhaoyan Liu, Yi Sui, Shiyi Hou, Kin Kwan Leung, Gabriel Loaiza-Ganem, Jesse C. Cresswell. Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems. ICLR 2026

Classifying and Addressing the Diversity of Errors in Retrieval-Augmented Generation Systems

Kin Kwan Leung, Mouloud Belbahri, Yi Sui, Alex Labach, Xueying Zhang, Stephen Anthony Rose, Jesse C. Cresswell. Classifying and Addressing the Diversity of Errors in Retrieval-Augmented Generation Systems. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), 2026

TabDPT: Scaling Tabular Foundation Models on Real Data

Junwei Ma, Valentin Thomas, Rasa Hosseinzadeh, Hamidreza Kamkari, Alex Labach, Jesse C. Cresswell, Keyvan Golestan, Guangwei Yu, Anthony L. Caterini, Maksims Volkovs. TabDPT: Scaling Tabular Foundation Models on Real Data. In Advances in Neural Information Processing Systems, volume 38, 2025

Document Summarization with Conformal Importance Guarantees

Bruce Kuwahara, Chen-Yuan Lin, Xiao Shi Huang, Kin Kwan Leung, Jullian Arta Yapeter, Ilya Stanevich, Felipe Perez, Jesse C. Cresswell. Document Summarization with Conformal Importance Guarantees. In Advances in Neural Information Processing Systems, volume 38, 2025

CausalPFN: Amortized Causal Effect Estimation via In-Context Learning

Vahid Balazadeh Meresht, Hamidreza Kamkari, Valentin Thomas, Junwei Ma, Bingru Li, Jesse C. Cresswell, Rahul G. Krishnan. CausalPFN: Amortized Causal Effect Estimation via In-Context Learning. In Advances in Neural Information Processing Systems, volume 38, 2025

CaloChallenge 2022: A community challenge for fast calorimeter simulation

Claudius Krause et al. CaloChallenge 2022: a community challenge for fast calorimeter simulation. Rep. Prog. Phys. 88 116201

MaskSQL: Safeguarding Privacy for LLM-Based Text-to-SQL via Abstraction

Sepideh Abedini, Shubhankar Mohapatra, D. B. Emerson, Masoumeh Shafieinejad, Jesse C. Cresswell, Xi He. MaskSQL: Safeguarding Privacy for LLM-Based Text-to-SQL via Abstraction. arXiv preprint: 2509.23459

Response Quality Assessment for Retrieval-Augmented Generation via Conditional Conformal Factuality

Naihe Feng, Yi Sui, Shiyi Hou, Jesse C. Cresswell, Ga Wu. Response Quality Assessment for Retrieval-Augmented Generation via Conditional Conformal Factuality. SIGIR 2025

MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL Translation

Satya Krishna Gorti, Ilan Gofman, Zhaoyan Liu, Jiapeng Wu, Noël Vouitsis, Guangwei Yu, Jesse C. Cresswell, Rasa Hosseinzadeh. MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL Translation. NAACL 2025

A Geometric Framework for Understanding Memorization in Generative Models

Brendan Leigh Ross, Hamidreza Kamkari, Zhaoyan Liu, Tongzi Wu, George Stein, Gabriel Loaiza-Ganem, Jesse C. Cresswell. A Geometric Framework for Understanding Memorization in Generative Models. ICLR 2025

Conformal Prediction Sets Can Cause Disparate Impact

Jesse C. Cresswell, Bhargava Kumar, Yi Sui, and Mouloud Belbahri. Conformal Prediction Sets Can Cause Disparate Impact. ICLR 2025

Zero-Execution Retrieval-Augmented Configuration Tuning of Spark Applications

Raunaq Suri, Ilan Gofman, Guangwei Yu, Jesse C. Cresswell. Zero-Execution Retrieval-Augmented Configuration Tuning of Spark Applications. arXiv preprint 2503.03826

Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples

Noël Vouitsis, Rasa Hosseinzadeh, Brendan Leigh Ross, Valentin Villecroze, Satya Krishna Gorti, Jesse C. Cresswell, Gabriel Loaiza-Ganem. Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples. NeurIPS 2024 Workshop on Fine-Tuning in Modern Machine Learning

DRESS: Disentangled Representation-based Self-Supervised Meta-Learning for Diverse Tasks

Wei Cui, Tongzi Wu, Jesse C. Cresswell, Yi Sui, Keyvan Golestan. DRESS: Disentangled Representation-based Self-Supervised Meta-Learning for Diverse Tasks. NeurIPS 2024 Workshop on Self-Supervised Learning - Theory and Practice

A Geometric View of Data Complexity: Efficient Local Intrinsic Dimension Estimation with Diffusion Models

Hamidreza Kamkari, Brendan Leigh Ross, Rasa Hosseinzadeh, Jesse C. Cresswell, Gabriel Loaiza-Ganem. A Geometric View of Data Complexity: Efficient Local Intrinsic Dimension Estimation with Diffusion Models. In Advances in Neural Information Processing Systems, volume 37, 2024

Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections

Gabriel Loaiza-Ganem, Brendan Leigh Ross, Rasa Hosseinzadeh, Anthony L. Caterini, esse C. Cresswell. Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections. TMLR 2024

Benchmarking Robust Self-Supervised Learning Across Diverse Downstream Tasks

Antoni Kowalczuk, Jan Dubiński, Atiyeh Ashari Ghomi, Yi Sui, George Stein, Jiapeng Wu, Jesse C. Cresswell, Franziska Boenisch, Adam Dziedzic. Robust Self-Supervised Learning Across Diverse Downstream Tasks. ICML 2024 Workshop on Foundation Models in the Wild

Scaling Up Diffusion and Flow-based XGBoost Models

Jesse C. Cresswell, Taewoo Kim. Scaling Up Diffusion and Flow-based XGBoost Models. ICML 2024 Workshop on AI for Science

A Geometric Explanation of the Likelihood OOD Detection Paradox

Hamidreza Kamkari, Brendan Leigh Ross, Jesse C. Cresswell, Anthony L. Caterini, Rahul G. Krishnan, Gabriel Loaiza-Ganem. A Geometric Explanation of the Likelihood OOD Detection Paradox. International Conference on Machine Learning 2024

Conformal Prediction Sets Improve Human Decision Making

Jesse C. Cresswell, Yi Sui, Bhargava Kumar, and Noël Vouitsis. Conformal Prediction Sets Improve Human Decision Making. International Conference on Machine Learning 2024

Augment then Smooth: Reconciling Differential Privacy with Certified Robustness

Jiapeng Wu, Atiyeh Ashari Ghomi, David Glukhov, Jesse C. Cresswell, Franziska Boenisch, and Nicholas Papernot. Augment then Smooth: Reconciling Differential Privacy with Certified Robustness. TMLR 2024

Data-Efficient Multimodal Fusion on a Single GPU

Noël Vouitsis, Zhaoyan Liu, Satya Krishna Gorti, Valentin Villecroze, Jesse C. Cresswell, Guangwei Yu, Gabriel Loaiza-Ganem, and Maksims Volkovs. Data-Efficient Multimodal Fusion on a Single GPU. Computer Vision and Pattern Recognition Conference 2024

Self-supervised Representation Learning from Random Data Projectors

Yi Sui, Tongzi Wu, Jesse C. Cresswell, Ga Wu, George Stein, Xiao Shi Huang, Xiaochen Zhang, Maksims Volkovs. Self-supervised Representation Learning from Random Data Projectors. International Conference on Learning Representations 2024

Neural Implicit Manifold Learning for Topology-Aware Generative Modelling

Brendan Leigh Ross, Gabriel Loaiza-Ganem, Anthony L. Caterini, and Jesse C. Cresswell. Neural Implicit Manifold Learning for Topology-Aware Generative Modelling. TMLR 2024

Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models

George Stein, Jesse C. Cresswell, Rasa Hosseingzadeh, Yi Sui, Brendan Leigh Ross, Valentin Villecroze, Anthony L. Caterini, J. Eric T. Taylor, Gabriel Loaiza-Ganem. Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models. In Advances in Neural Information Processing Systems, volume 36, 2023

Decentralized Federated Learning through Proxy Model Sharing

Shivam Kalra, Junfeng Wen, Jesse C. Cresswell, Maksims Volkovs, and Hamid R. Tizhoosh. Decentralized federated learning through proxy model sharing. Nature Communications 14, 2899, 2023.

Verifying the Union of Manifolds Hypothesis for Image Data

Bradley C.A. Brown, Anthony L. Caterini, Brendan Leigh Ross, Jesse C. Cresswell, and Gabriel Loaiza-Ganem. Verifying the Union of Manifolds Hypothesis for Image Data. International Conference on Learning Representations 2023.

Disparate Impact in Differential Privacy from Gradient Misalignment

Maria S. Esipova, Atiyeh Ashari Ghomi, Yaqiao Luo, and Jesse C. Cresswell. Disparate Impact in Differential Privacy from Gradient Misalignment. International Conference on Learning Representations 2023

Denoising Deep Generative Models

Gabriel Loaiza-Ganem, Brendan Leigh Ross, Luhuan Wu, John P. Cunningham, Jesse C. Cresswell, and Anthony L. Caterini. Denoising Deep Generative Models. NeurIPS 2022 Workshop on Understanding Deep Learning Through Empirical Falsification.

CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds

Jesse C. Cresswell, Brendan Leigh Ross, Gabriel Loaiza-Ganem, Humberto Reyes-Gonzalez, Marco Letizia, and Anthony L. Caterini. CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds. NeurIPS 2022 Workshop on Machine Learning and the Physical Sciences.

Find Your Friends: Personalized Federated Learning with the Right Collaborators

Yi Sui, Junfeng Wen, Yenson Lau, Brendan Leigh Ross, and Jesse C. Cresswell. Find Your Friends: Personalized Federated Learning with the Right Collaborators. NeurIPS 2022 Workshop on Federated Learning: Recent Advances and New Challanges

Diagnosing and Fixing Manifold Overfitting in Deep Generative Models

Gabriel Loaiza-Ganem, Brendan Leigh Ross, Jesse C. Cresswell, and Anthony L. Caterini. Diagnosing and Fixing Manifold Overfitting in Deep Generative Models. TMLR 2022

Federated Learning and Differential Privacy for Medical Image Analysis

Mohammed Adnan, Shivam Kalra, Jesse C. Cresswell, Graham W. Taylor, and Hamid R. Tizhoosh. Federated Learning and Differential Privacy for Medical Image Analysis. Nature Scientific Reports, 12, 1953, 2022

Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows

Brendan Leigh Ross and Jesse C. Cresswell. Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows. In Advances in Neural Information Processing Systems, volume 34, 2021

C-Learning: Horizon-Aware Cumulative Accessibility Estimation

Panteha Naderian, Gabriel Loaiza-Ganem, Harry J. Braviner, Anthony L. Caterini, Jesse C. Cresswell, Tong Li, Animesh Garg. C-Learning: Horizon-Aware Cumulative Accessibility Estimation. International Conference on Learning Representations

Operational symmetries of entangled states

Ilan Tzitrin, Aaron Z. Goldberg, and Jesse C. Cresswell, Operational symmetries of entangled states. J. Phys. A: Math. Theor. 53 095304, 2021

Quantum Information Approaches to Quantum Gravity

Jesse C. Cresswell, Quantum Information Approaches to Quantum Gravity. University of Toronto Doctoral Thesis

Holographic relations for OPE blocks in excited states

Jesse C. Cresswell, Ian T. Jardine, and Amanda W. Peet. Holographic relations for OPE blocks in excited states. JHEP 2019 3, 58

Perturbative expansion of entanglement negativity using patterned matrix calculus

Jesse C. Cresswell, Ilan Tzitrin, and Aaron Z. Goldberg. Perturbative expansion of entanglement negativity using patterned matrix calculus. Phys. Rev. A 99 012322, 2019