Inflationary Flows: Calibrated Bayesian Inference with Diffusion-Based Models
De Albuquerque, D., & Pearson, J.(2024). "Inflationary Flows: Calibrated Bayesian Inference with Diffusion-Based Models." arXiv: 2407.08843 [cs, stat]
CV
Education
- M.D & Ph.D in Electrical and Computer Engineering (ECE), Duke University, 2026 (expected)
- M.S. in Electrical and Computer Engineering (ECE), Duke University, 2022
- B.S. in Neuroscience, Duke University, 2015
Honors & Awards
- Ruth L. Kirschstein Pre-Doctoral NRSA, NIMH, 2023-2025
- Outstanding Graduate Student Teaching Assistant, Spring 2023 & 2024
- Selected to attend in-person, Oxford’s Machine Learning in Medicine Summer School, Summer 2023
- Runner-up for Gertrude B. Elion Research Award, Triangle Community Foundation, Spring 2023
- One out of three Duke finalists, Apple’s AI/ML PhD Fellowship selection, Fall 2021
- Medical Scientist Training Program (MSTP) trainee award through Duke, 2017-present
- Post-Baccalaureate Intramural Research Fellowsip Award, NIMH, 2015-2017
- Elected Member of Phi Beta Kappa, Summer 2015
- Graduated with Distinction in Neuroscience, Summer 2015
- Graduated Magna Cum Laude, Summer 2015
- Duke’s Neuroscience Program of Research Fellowship Award, Summer 2014
- Valedictorian of 1000 graduating seniors, Fall 2010 (Brazil)
Research Experience
- Fall 2019 - present: Brains & Computation Lab
- Duke University
- Summary: Research focus is on developing novel statistical machine learning techniques and in applying these to provide more flexible and reliable modeling and analysis methods for neuroscience data broadly (e.g., fMRI, EEG, ECoG).
- PI: John Pearson
- Fall 2015 - Summer 2017: Clinical and Translational Neuroscience Branch (CTNB)
- National Institute of Mental Health (NIMH)
- Summary: Main project involved combining genome-wide association analysis and neuroimaging methodologies in an agnostic, high-throughput voxel-wise and genome-wide approach to investigate genetic determinants of working memory function.
- PIs: Karen Berman & Joseph Callicott
- Spring 2013 - Spring 2015: Woldorff Laboratory
- Duke University
- Summary: Main project involved leveraging the high temporal resolution of electroencephalography (EEG) and a previously validated translational sustained attention paradigm to investigate the effects of a visual, global distractor on the temporal cascade of electrical brain processes associated with ongoing sustained attention efforts.
- PI: Marty Woldorff
Skills
- Programming
- Pytorch, Python, C, C++, SQL, R, Julia, Latex
- Spoken Languages
- English (fluent), Portuguese (native), Spanish (advanced)
- Biomedical Data Analysis
- Electroencephalography (EEG) data analysis (EPR and time-frequency methods)
- Neuroimaging (fMRI, DTI, MR spectrostcopy) data analysis (SPM, FSL, Afni, NiBabel, Pydicom, Nilearn)
- Genomics Data Analysis
Publications
Deep Generative Analysis for Task-Based Functional MRI Experiments
De Albuquerque, D., Goffinet, J., Wright, R., & Pearson, J. (2021). "Deep Generative Analysis for Task-Based Functional MRI Experiments." Journal of Machine Learning Research. 149(1-29).
The effects of distraction on brain processes underlying signal detection
Demeter, E., DeAlbuquerque, D., & Worldorff, M. (2016). "The Effects of ongoing distraction on the neural processes underlying signal detection." Neuropsychologia. 89(335-343).
Talks & Presentations
Deep Generative Analyses for Task-Based fMRI Experiments
Poster at Computational and Systems Neuroscience (CoSyNe) 2021 Annual Meeting, Virtual
Genetic Determinants of Working Memory: a High-Throughput Voxel-Wise GWAS Approach
Talk at Society of Biological Psychiatry (SoBP) 2017 Annnual Meeting, San Diego, California
Assessing Genetic Determinants of normal brain function through genome wide association studies (GWASs): a quantitative intermediate phenotype approach
Poster at Society of Biological Psychiatry (SoBP) 2016 Annnual Meeting, Atlanta, Georgia
The effects of distraction on brain processes underlying signal detection
Poster at Cognitive Neuroscience Society 2015 Annual Meeting, San Francisco, California
Teaching
Service and leadership
- Creative Computing Educational Program
- Role: Co-Founder, Instructor
- Active Time: Spring 2023 - present
- Description: This program is aimed at teaching 2nd and 3rd graders about basic principles of computer programming logic using Scratch, a free, high-level programming language developed by the MIT Media Lab for educational purposes.
- Boost Beyond Program (Duke University School of Medicine)
- Role: Mentor
- Active Time: Spring 2021 - Fall 2023
- Description: This program is aimed at mentoring and supporting high-school students from minority or under-privileged backgrounds interested in pursuing careers in STEM throughout their college application process.
- Hand2Hold Student Group (Duke University School of Medicine)
- Role: Executive Board Member; Educator
- Active Time: Fall 2017 - Fall 2018
- Description: This is a student group at the Duke SOM with two main goals: 1) prototyping and constructing customized 3D-printed “wearables” for low-income patients with small hand and/or finger amputations; 2) providing educational outreach to minority students interested in STEM (through Duke’s Boost Program) by introducing these students to basics of hand anatomy, 3D printing, and engineering design.
- NIMH’s Schizophrenia Ward Clinical Program
- Role: Volunteer
- Active Time: Fall 2015 - Summer 2017
- Description: This program is aimed at helping researchers at NIMH gain direct experience working with schizophrenia patients, the clinical teams, and patient families, at NIMH’s adult inpatient psychiatric unit.