I am a 4th year PhD student at Duke University, advised by Katherine A. Heller. I have been a visiting student researcher at UC Berkeley with Michael I. Jordan since August 2018. I have also been a student researcher at Google Brain since January 2019.
I am interested in the design and theoretical analysis of probabilistic inference algorithms. More generally, I strive to better understand uncertainty quantification which I believe is crucial for robust decision making and efficient lifelong learning in a dynamic environment mirroring our physical world.
I have been fortunate to receive the following fellowships: Facebook Emerging Scholar (2019-2021, declined), Pratt-Gardner Graduate Fellowship (2017-2019).
During my Ph.D., I have spent wonderful summers interning at Google Brain (2019) and Google Research NY (2018).
Before moving to Duke, I graduated with a Bachelor's and a Master's degree in Computer Science from Princeton University, where I was advised by
Barbara E. Engelhardt.
- Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence
G Jerfel*, S Wang*, C Fannjiang, KA Heller, YA Ma, MI Jordan; accepted at AABI 2021; submitted to AISTATS 2021.
- Deep Uncertainty and the Search for Proteins
Z Mariet, G Jerfel, Z Wang, C Angermueller, D Belanger, S Vora, M Bileschi, L Colwell, D Sculley, D Tran, J Snoek; accepted at ML 4 Molecules @ NeurIPS 2020; submitted to ICML 2021.
- Combining Ensembles and Data Augmentation Can Harm Your Calibration
Y Wen*, G Jerfel*, R Muller, MW Dusenberry, J Snoek, B Lakshminarayanan, D Tran; ICLR; 2021.
- Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors
MW Dusenberry*, G Jerfel*, Y Wen, YA Ma, J Snoek, K Heller, B Lakshminarayanan, D Tran; ICML ; [Code]; 2020.
- Improving Calibration of BatchEnsemble with Data Augmentation
Y Wen*, G Jerfel*, R Muller, MW Dusenberry, J Snoek, B Lakshminarayanan, D Tran; Workshop on Uncertainty and Robustness at ICML ; [Code]; 2020.
- Analyzing the Role of Model Uncertainty for Electronic Health Records
MW Dusenberry, D Tran, E Choi, J Kemp, J Nixon, G Jerfel, K Heller, AM Dai; ACM Conference on Health, Inference, and Learning (CHIL); 2020.
- Reconciling meta-learning and continual learning with online mixtures of tasks
G Jerfel*, E Grant*, T Griffiths, KA Heller; NeurIPS Spotlight; 2019.
- AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles
C Weill, J Gonzalvo, V Kuznetsov ... G Jerfel, V Macko, B Adlam, M Mohri, C Cortes; Automated Machine Learning at ICML; [Code]; 2019.
- Measuring Calibration in Deep Learning
J Nixon, M Dusenberry, L Zhang, G Jerfel, D Tran; Uncertainty and Robustness in Deep Visual Learning at CVPR; 2019.
- Dynamic Collaborative Filtering with Compound Poisson Factorization
G Jerfel, ME Basbug, BE Engelhardt; AISTATS; 2017.