Hey!
My name is Eloy Geenjaar and I am an Electrical Engineering & Computer Engineering Ph.D. student, Mathematics M.S. student, and CNTP scholar at the Georgia Institute of Technology under the supervision of Prof. Vince Calhoun at the TReNDS center.
My research spans several areas; temporal and multimodal representation learning, neurodevelopmental research, generative disentanglement, and the brain as a dynamical system. My goal is to leverage multimodal whole-brain data to uncover low-dimensional dynamics that are linked to personal mental health. Namely, to improve our ability to treat mental health issues, quantifying dynamical brain motifs that are linked to or precede mental disorders can aid mental health practitioners.
Email: egeenjaar at gatech.edu
Research
- Chromatic fusion: generative multimodal neuroimaging data fusion provides multimodal-informed insights into schizophrenia. Eloy Geenjaar, Noah Lewis, Alex Fedorov, Lei Wu, Judith Ford, Adrian Preda, Sergey Plis, and Vince Calhoun
- CommsVAE: Learning the brain's macroscale communication dynamics using coupled sequential VAEs Eloy Geenjaar, Noah Lewis, Amrit Kashyap, Robyn Miller, and Vince Calhoun
- Neurodevelopmental Trajectories in Children With Internalizing, Externalizing and Emotion Dysregulation Symptoms Elisabet Blok*, Eloy Geenjaar*, Eloise Geenjaar*, Vince Calhoun, and Tonya White
- Fusing multimodal neuroimaging data with a variational autoencoder Eloy Geenjaar*, Noah Lewis, Zening Fu, Rohan Venkatdas, Sergey Plis, and Vince Calhoun
- Variational voxelwise rs-fMRI representation learning: Evaluation of sex, age, and neuropsychiatric signatures Eloy Geenjaar*, Tonya White, and Vince Calhoun
- Tasting the cake: evaluating self-supervised generalization on out-of-distribution multimodal MRI data Alex Fedorov*, Eloy Geenjaar, Lei Wu, Thomas P. DeRamus, Vince D. Calhoun, and Sergey M. Plis
- Self-Supervised Multimodal Domino: in Search of Biomarkers for Alzheimer's Disease Alex Fedorov*, Tristan Sylvain, Eloy Geenjaar, Margaux Luck, Lei Wu, Thomas P. DeRamus, Alex Kirilin, Dmitry Bleklov, Vince D. Calhoun, and Sergey M. Plis
- Environment-Wide Association Study (EnWAS) of Prenatal and Perinatal Factors Associated With Autistic Traits: A Population-Based Study Masoud Amiri*, Sander Lamballais, Eloy Geenjaar, Laura M. E. Blanken, Hanan El Marroun, Henning Tiemeier, and Tonya White
2023, currently being published in Human Brain Mapping
2022, oral presentation (top 10%) at the Medical Imaging meets NeurIPs workshop
2021, Frontiers in Psychiatry
2021, EMBC 2021
2021, presented at the ML for Biomedical and Medical Imaging Big Data workshop at BIBM 2021
2021, presented at the RobustML workshop at ICML 2021
2020, best paper award at ICHI 2021
2020, Autism Research
Background and Motivation
I started my Bachelor's degree in Industrial Design Engineering at the Delft University of Technology in 2014. During my minor at the Erasmus Medical Center in Rotterdam, I started working with Dr. White. Under Dr. White's supervision, I started to develop an interest in neuroimaging research and decided to pursue a double Master's degree in Biomedical Engineering and Embedded Systems.
Interests
Two of my main interests are gastronomy and vinology. With gastronomy, I am referring to the broadest definition of the word, from Southern barbeque to haute cuisine and from Indian to Dutch food. I enjoy going out of my way to try traditional or fusion cuisines and explore a piece of food culture from places around the world. In terms of wine, I have been awarded two wine degrees, SDEN 3 and WSET 3, but do not shy away from supermarket wine. I like teaching family, friends, and acquaintances about the history behind regional wines and help them distinguish flavors in a wine. Every wine was made with a purpose and there are always flavor profiles or characteristics to explore.
Books I have recently read or am reading that I enjoyed (in no particular order)
- Human kind: A Hopeful History by Rutger Bregman
- Chaos: Making a New Science by James Gleick
- The procedure by Harry Mulisch
- The Art of Doing Science and Engineering: Learning to learn by Richard Hamming
- Normal people by Sally Rooney
Courses, lectures, or talks I found especially inspiring or interesting (in no particular order)
- Information theory pattern recognition and neural networks by David Mackay at Cambridge University
- Deep Unsupervised Learning by Pietter Abbeel at UC Berkeley
- Principles of fMRI by Martin Lindquist and Tor Wager at Johns Hopkins University
- Computational Models of the Neocortex by Thomas Dean at Stanford University
- Thousand Brains Theory of Intelligence by Jeff Hawkins on the Lex Fridman Podcast
- Vicarious Robot AI by Dileep George on the Brain Inspired podcast
- Artificial Consciousness and the Nature of Reality by Joscha Bach on the Lex Fridman Podcast
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