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Welcome to the Abbasi Lab Homepage!

We are a research team at the University of California, San Francisco where we investigate the role of advanced computational tools in understanding brain functions and its related disorders. More specifically, our research program revolves around the development of interpretable machine learning tools to (1) integrate multi-modal data collected from the brain (and body) in both microscopic and macroscopic resolutions, (2) predict functions of biological systems in different resolutions, and (3) determine the functional differences across neurological disorders. We are a part of the Neuroscape Center and Cognitive Neuroscience division at UCSF and our team is supported through funding from NIH National Institute of Mental Health, NIH National Institute of Aging, Weill Neurohub, Sandler Program for Breakthrough in Biomedical Research, UCSF Innovation Ventures, and Google.

We are hiring!

We have multiple openings for postdoctoral fellows to work with us at UCSF on exciting applied problems at the intersection of machine learning and computational/clinical neuroscience. Please see Openings for more information or directly contact Reza via Reza.AbbasiAsl@ucsf.edu to learn more!

News and Events

July 2024: UCSF News coverage of our recent paper: Breakthrough: How AI Uses Videos to Gauge Parkinson’s Severity.

July 2024: New software released: 7T MRI synthesization from 3T acquisitions.

July 2024: New perspective piece on arXiv: Opportunities in deep learning methods development for computational biology.

June 2024: New software released: Biomedical question answering robustness benchmark.

June 2024: Gavin’s paper is now accepted in MICCAI 2024: 7T MRI Synthesization from 3T Acquisitions.

June 2024: Congrats Alex for receiving the UCSF GRFM fellowship!

June 2024: Naomi joined the lab! Welcome Naomi!

June 2024: New paper out in the Journal of Neural Engineering: Structurally-constrained encoding framework using a multi-voxel reduced-rank latent model for human natural vision.

June 2024: Maria‘s paper is highlighted as Editor’s Choice in Brain: Motor network gamma oscillations in chronic home recordings predict dyskinesia in Parkinson’s disease.

May 2024: New paper accepted in npj Parkinson’s Disease: Interpretable Video-Based Tracking and Quantification of Parkinsonism Clinical Motor States.

May 2024: New paper accepted in GEN Biotechnolgy: Machine Learning for Uncovering Biological Insights in Spatial Transcriptomics Data.

May 2024: New preprint out: Data-driven fine-grained region discovery in the mouse brain with transformers.

May 2024: Reza presented an Invited Lecturer at the Caltech Bioengineering Lecture Series.

March 2024: New preprint out: 7T MRI Synthesization from 3T Acquisitions.

Feb 2024: New preprint out: Zero-shot sampling of adversarial entities in biomedical question answering.

Jan 2024: Congrats Arefeh for winning the second place prize in the Vesuvius challenge!

Jan 2024: Maryam joined the team! Welcome Maryam!

Jan 2024: Maria’s paper is published in Brain: Motor network gamma oscillations in chronic home recordings predict dyskinesia in Parkinson’s disease

Jan 2024: Reza is presenting an invited talk at ETH Zurich.

Dec 2023: Reza is organizing an invited session at CMStatistics 2023 on Interpretable Machine Learning for Scientific Discovery!

Nov 2023: New software released: https://github.com/abbasilab/Video-Tracking-PD.

Nov 2023: New preprint out: Interpretable Video-Based Tracking and Quantification of Parkinsonism Clinical Motor States.

Nov 2023: New preprint led by R. Carhart-Harris: The entropic heart: Tracking the psychedelic state via heart rate dynamics.

Nov 2023: Max joined the lab! Welcome Max!

Nov 2023: Alex is presenting his work at MLCB 2023.

Nov 2023: Kara is presenting her work at the SfN nanosymposium on Neural Decoding and Neuroprosthetics.

Sep 2023: Our book chapter on Interpretable Prototype Discovery in Deep Learning-based Time Series Classification accepted to be published by Springer.

Aug 2023: Reconstructing visual experience from a large-scale biologically realistic model of mouse primary visual cortex abstract now published in Journal of Vision.

Aug 2023: Congrats to Aelx for his recognition as BMI Discovery Fellow! 

Aug 2023: Congrats to Alex for winning the UCSF ARCS Scholarship award!

July 2023: Abstracts from the lab accepted in ISMB 2023, OCNS 2023, and RECOMB-Seq 2023 on our models for spatial transcriptomics data.

July 2023: Reza is appointed as the Executive Committee Member for the Bakar Computational Health Sciences Institute.

June 2023: Reza presented lab’s recent work at the 9th Annual BRAIN Initiative Meeting.

June 2023: Kara presented her work at 10th Annual BCI meeting.

May 2023: Russell joined the lab! Welcome Russell!

April 2023: Diurnal Step Count Patterns in Progressive Multiple Sclerosis abstract now published in Neurology.

April 2023: Congrats to Alex for passing his qualifying exam!

April 2023: Congrats to Daniel for receiving 2023 URAP Summer Fellowship Award!

March 2023: Congrats to Alex for receiving the Honorable Mention on his NSF GRFP application!

March 2023: New preprint out: Machine Learning for Uncovering Biological Insights in Spatial Transcriptomics Data.

March 2023: New software released: https://github.com/abbasilab/osNMF

March 2023: New preprint on unsupervised pattern discovery in large-scale spatial gene expression datasets.

March 2023: New editorial published in Front. Comput. Neurosci.

March 2023: Kara joined the lab! Welcome Kara!

Feb 2023: Reza will be giving a talk at ITA 2023 in San Diego.

Feb 2023: Congrats to Rob for getting admitted into the UCSF BMI PhD program!

Feb 2023: One abstract accepted for presentation at AAN 2023.

Feb 2023: One accepted oral presentation at VSS 2023.

Jan 2023: We received the Kunal Patel Catalyst Award for our project on camera-based tracking of PD in collaboration with the UCSF Movement Disorders center!

Dec 2022: We are now a part of UC Berkeley/UCSF joint graduate program in Computational Precision Health. Consider applying if you want to join our lab! Read more here.

Dec 2022: Reza will be presenting an invited talk at CMStatistics 2022.

Dec 2022: Reza will be presenting as a panelist at the NIH Brain Initiative Workshop on High Throughput Imaging Characterization of Brain Cell Types & Connectivity.

Nov 2022: Jen joined the lab as our new research data analyst!

Nov 2022: Congrats to Gavin for passing his qual exam!

Nov 2022: Reza gave the UC San Diego NanoEngineering/Chemical Engineering seminar.

Nov 2022: We will be at SfN in San Diego. Reach out if you are around!

Nov 2022: Maria will be presenting a poster at SfN 2022 titled Neural spectral features capture severity of dyskinesia in Parkinson’s Disease.

Nov 2022: Thank you Google for funding our project on the applications of wearables in Parkinson’s disease!

Nov 2022: Reza is featured in the Neuroscape Network newsletter.

Aug 2022: Alex joined the lab as a new PhD student!

July 2022: We received the lab’s first NIH/NIMH R01 grant to investigate the role of ML in integrating structural and functional neural data!

July 2022: Reza will give a talk at CNS 2022 on the functional characterization of an entire column in mouse V1.

July 2022: Reza will present recent work on the compression of voxel-wise models at FENS 2022.

July 2022: Reza received the UCSF Neurology N-RIP award!

July 2022: New preprint on Compression-enabled interpretability of voxel-wise encoding models.

May 2022: Rob, Maria, and Zhinoos joined the lab!

May 2022: Reza will speak at the 24th International Conference on Computational Statistics in August.

April 2022: Reza will speak as the community speaker at the American Association of Neurological Surgeons Annual Meeting 2022.

March 2022: Two accepted oral presentations at Sharif Neuroscience Symposium.

Feb 2022: Reza will present as the plenary speaker at the ICRTB 2022.

Feb 2022: Our paper on the robust registration of MRIs is now published in Algorithms.

Jan 2022: We have been awarded a new multi-institution grant from Weill Neurohub to develop ML-based computational tools for the integrated analysis of 7T and 3T MRIs.

Jan 2022: We have received a second New Frontiers Research Award from the Sandler Program for Breakthrough Biomedical Research.

Dec 2021: Reza is presenting as an invited speaker at the CMStatistics 2021.

Oct 2021: Roozbeh Farhoodi joins the lab as a new postdoctoral scholar. Welcome, Roozbeh!

August 2021: Congrats to Austin Jang and Shiladitya Dutta, two undergraduate students in the lab, for winning the UC Berkeley URAP fellowship for their research projects!

August 2021: New paper published in Frontiers in Big Data: Structural Compression of Convolutional Neural Networks with Applications in Interpretability.

June 2021: New paper on arXiv: Multi-Modal Prototype Learning for Interpretable Multivariable Time Series Classification.

June 2021: Congrats to Austin Jang and Shiladitya Dutta, for winning the URAP summer fellowship!

May 2021: We got awarded the New Frontiers Research Award from the Sandler Program for Breakthrough Biomedical Research.

May 2021: We are hiring a fully-funded postdoc fellow to lead efforts for our new Neuroscape-based project on computational models of multi-modal biosensor data! Reach out if you are interested!

May 2021: Gavin Cui joins the lab as a bioengineering PhD student. Welcome Gavin!

April 2021: Our NIH grant in collaboration with Neuroscape PIs (Ted Zanto and David Ziegler) on “Neural markers of impending task performance” just got funded!

Feb 2021: One abstract accepted at VSS 2021 on our large-scale standardized survey of neural receptive fields in an entire column in mouse V1.

Feb 2021: We are hiring two new postdoc fellows to join us at UCSF and lead our efforts on developing a computational pipeline to analyze spatial gene expression in mice.

Feb 2021: Reza will be giving a talk at the faculty seminar at the Kavli Institute.

Jan 2021: Reza becomes a Weill Neurohub Investigator.

Jan 2021: We got awarded the Weill Neurohub Next Great Ideas Fund! Our proposed project is focused on spatial gene expression analysis in mice.

Nov 2020: Reza will give an invited talk at the 2020 Basic and Clinical Neuroscience.

Oct 2020: One abstract accepted at CSHL NAISys meeting on interpretable models of neurons in visual cortex.

August 2020: One abstract accepted at JSM 2020 on Revealing Spatial Gene Patterns and Interactions in Mouse Brain via Stability-Driven NMF.

May 2020: One abstract accepted at CNS 2020 on compressed models of neurons in V4!

May 2020: Our paper featured on the cover of Neuron!

March 2020: Reza is now a joint faculty at the Department of Bioengineering and Therapeutic Sciences at UCSF.

March 2020: We are now officially affiliated with Neuroscape!

March 2020: The dataset from our recent eNeuro paper is now available on the CRCNS data repository.

March 2020: Here is a nice read from Allen Institute on our recent Neuron paper.

March 2020: Due to the recent Corona Virus outbreak, the lab has moved to fully remote mode! Hope everyone is staying safe and healthy!

March 2020: We are happy to welcome to the lab Oluwaseun Adegbite as rotation PhD student, Luke Chen as an RA, and five UC Berkeley undergraduates through the URAP program: Kevin Lin, Rohan Divate, Austin Jang, Anmol Parande, Felix Ching!

Feb 2020: Reza will give an invited talk at ITA 2020 on interpretation of neural networks with applications in comp neuroscience.

Jan 2020: We will be present at Cosyne 2020 with one invited talk and one accepted abstract on compressed interpretable models of neurons. Reza will give the invited talk at the workshop on “Scrutinizing models of brain function: from in-silico stimulus synthesis to direct brain perturbation”.

Jan 2020: Our paper titled “Systematic Integration of Structural and Functional Data into Multi-Scale Models of Mouse Primary Visual Cortex” has been accepted at Neuron.

Jan 2020: Our paper titled “Superficial bound of the depth limit of 2-photon imaging in mouse brain” appeared on eNeuro.

Jan 2020: We are now affiliated with Bakar Computational Health Sciences Institute at UCSF! Reza will be a faculty affiliate at the institute.

Dec 2019: Welcome to the lab Neel! Welcome Neel!

Nov 2019: Welcome to the lab Ahyeon and Franklin!

Nov 2019: Reza joined the UC Berkeley-UCSF graduate program in bioengineering. We have multiple openings for rotation students!

Oct 2019: Our paper titled “Definitions, methods, and applications in interpretable machine learning” appeared on PNAS.

Oct 2019: Reza is presenting at SfN on functional imaging of excitatory neurons in an entire column in mouse visual cortex. Here is the abstract.

Sep 2019: Abbasi Lab officially started!

Research

Our lab’s vision is to better understand brain functions and its related disorders by designing specialized interpretable tools from machine learning and statistics. More specifically, our research programs revolve around the development of transparent supervised and unsupervised machine learning tools to integrate multi-modal data collected from brain (and body) in both microscopic and macroscopic resolutions. Some of the specific projects in the lab include:

Functional modeling in the brain

We develop models and algorithms based on advanced machine learning principles to understand the functions of neurons in the brain and their relationship with spatially resolved gene expression profiles. To achieve this, we study large-scale datasets with a variety of modalities such as electrophysiology, calcium imaging, electron microscopy, and spatial transcriptomic and gene expression profiles.

Specific projects:

Defining the function of any organ including the brain depends on defining and describing its building blocks: tissues, tissue domains, and cell types. Genome-wide characterization of gene expression and machine learning approaches have transformed the understanding of cell types that build the nervous system. However, the precise arrangement of cells types and their differences across the brain areas will require interpretation of spatial gene expression data. In collaboration with Hongkui Zeng and Bosiljka Tasic at the Allen Institute and Bin Yu at UC Berkeleythis project aims to integrate spatial gene expression and neural connectivity data to reveal building blocks of spatial gene expression profiles in the mouse and human brain. These building blocks will partition the brain into completely data-driven functional 3D brain areas and establish local gene networks. The project involves designing and validating unsupervised and interpretable machine learning frameworks and statistical tools. 

In another project supported by an R01 grant from NIH/NIMH, we aim to characterize the relationship between neural function and connectivity in visual sensory processing. In order to explore this relationship and in collaboration with the Allen Institute, we are analyzing recorded visual responses from pan-excitatory neurons within the 1 mm cube region of the primary visual cortex, spanning all visual layers from pia to white matter. This includes 750 2-photon and 35 3-photon calcium imaging planes spaced by ~16 um. Our goal is to examine the single-cell and population activity in the primary visual cortex integrated with Electron Microscopic reconstruction of neural morphology and connectivity from the same tissue to understand the principles of functional connectivity at the single-cell level.

Models based on the deep neural networks are effective in predicting single neuron responses in the primate visual cortex. Despite their high predictive accuracy, these models are generally difficult to interpret. This limits their applicability in characterizing neuron function. We investigate methods and algorithms to elicit interpretation and visualization of models of neurons based on deep neural networks. To achieve this, we develop frameworks based on stability, compression, and sparsity.

Computational clinical neuroscience

As a part of the UCSF Neuroscape Center and in order to bridge our understanding of the brain to patient data, we build tools to study and visualize large-scale neurological clinical datasets. Our goals are to characterize the relationship between multi-modal patient data and disease processes to enable efficient biomarker identification.  This involves analyzing and visualizing longitudinal medical images, genomic data, biosensor data, and other relevant datasets collected from patients.

Specific projects:

Multiple Sclerosis (MS) is the leading cause of nontraumatic neurological disability in young adults, and it is estimated that 3 in 1000 US adults, or nearly 1 million individuals, are affected. The development of effective therapeutics for MS represents one of the great success stories of modern molecular medicine, with near-complete suppression of clinical attacks and focal brain inflammation now possible for most patients.  However, the more disabling neurodegenerative component of the disease – progressive MS – remains poorly treated, and the development of potent therapeutics for progressive MS has been limited by an inability to efficiently extract clinically meaningful MRI data corresponding to changes in white matter lesions, global and regional atrophy, and neurodegeneration. Various data modalities are used to assess the progression of MS including brain MRIs, behavioral measures, and genomic data. The multi-modal nature of the large-scale data collected from these patients demand for automated computational tools processing methods. In collaboration with the UCSF Multiple Sclerosis (MS) Center, this project aims to systematically integrate and analyze longitudinal MRI and genomics data from hundreds of patients with MS to better understand the disease process and its biomarkers. More specifically, this project leverages one-of-a-kind UCSF EPIC and ORIGINS MS datasets to build state-of-the-art interpretable machine learning pipelines to characterize dynamic changes over time and their relationship to MS progression. Additionally, the project involves the development of state-of-the-art and transparent machine learning tools to build computational pipelines for 3T and 7T MRI.

A deeper understanding of an individual’s state (e.g, stress, mood, attention, arousal, awareness) requires recording continuous data across multiple modalities and integrating these signals to generate meaningful and predictive composite measures. In collaboration with the UCSF Neuroscape Center, this project aims to systematically integrate hundreds of different biosensor data collected from the brain and body to predict the emotional state in humans. The data consists of high-density EEG, EMG, ECG, EOG, Continuous blood pressure, Trans Radial Electrical Impedance Velocimetry, respiration, EDA/GSR, and accelerometers readings. This project involves the development of interpretable machine learning tools to integrate and analyze large-scale time-series data. 

Advanced Parkinson’s disease (PD) is characterized by motor and non-motor symptoms which are highly disabling and significantly impair quality of life. Optimizing dopaminergic medication, Deep Brain Stimulation (DBS), and lifestyle interventions to minimize symptoms is central to the management of PD. Achieving this goal requires accurate measures of symptom severity and fluctuations. At-home objective symptom quantification introduces a potential solution, but due to technical limitations, wearable tracking devices have not yet been readily adopted into clinical practice for PD. In collaboration with the UCSF Movement Disorders and Neuromodulation Center and Google Research, this project aims to develop the next generation of at-home video-assisted technology to track and diagnose PD. This solution is enabled with advanced machine learning tools and has the potential to significantly enhance diagnostic accuracy and optimize personalized medical therapy in PD.

Interpretable Machine Learning

Guided by scientific questions from neuroscience and biomedicine, we are interested in the general problem of interpreting machine learning models. In the past decade, research in machine learning has been principally focused on the development of algorithms and models with high predictive capabilities. However, interpreting these models remains a challenge, primarily because of the large number of parameters involved. We investigate methods based on statistical principles to build more interpretable machine learning models. 

People

Reza Abbasi-Asl

Principal Investigator

Reza is an Assistant Professor in the Department of Neurology and the Department of Bioengineering and Therapeutic Sciences at UCSF. He is a core faculty member at the UCSF Neuroscape Center, a Weill Neurohub Investigator, and the Director of Data Analytics at the UCSF Weill Institute for Neuroscience. He is a core member of the UC Berkeley/UCSF Bioengineering and Computational Precision Health graduate programs and the UCSF Bioinformatics graduate program. Before joining UCSF, Reza was a scientist at the Allen Institute for Brain Science in Seattle. He completed his PhD and MSc in Electrical Engineering and Computer Sciences at UC Berkeley in 2018, where he developed interpretable machine learning tools with applications in computational neuroscience. Reza received his MSc in Biomedical Engineering from Sharif University of Technology in 2013 and BSc in Electrical Engineering from Amirkabir University of Technology (Tehran Polytechnic) in 2010. He is the recipient of the 2023 Kunal Patel Catalyst Award, New Frontiers Research Award from the Sandler Program for Breakthrough Biomedical Research (PBBR) in 2021, and 2022, and the Eli Jury Award from UC Berkeley, Department of Electrical Engineering and Computer Sciences in 2018.


Arefeh Sherafati

Postdoctoral scholar

Arefeh is a postdoctoral scholar at the Abbasi Lab working on computational methods for integrating structural and functional data in the mouse primary visual cortex. Her research will include the use of machine learning and deep learning to identify spatially-selective and motion-selective neurons in the V1 area. Arefeh got her PhD from Washington University in St. Louis in physics. Her Ph.D. focused on denoising and signal processing methods for high-density diffuse optical tomography (HD-DOT). After that, she worked as a postdoctoral research associate at the Biophotonics Research Center at Washington University School of Medicine. She is currently developing computational models for fMRI and HD-DOT data to find the neural correlates of the impacts of brain implants such as cochlear implants in speech processing or deep brain stimulators in Parkinson’s disease or essential tremor during resting state.


Patrick Xian

Postdoctoral Scholar

Patrick is a postdoctoral scholar at the Abbasi Lab. His main interests are scientific machine learning, which bridges the gap between theories and experiments through computational models, and imaging informatics. His background is in statistics, chemical physics, and quantum information. He conducted his PhD research at the Max Planck Institute, University of Toronto, and University of Hamburg to understand molecular dynamics at the fundamental spatiotemporal scale. During the Covid pandemic, he worked at Northwestern University and University College London on X-ray imaging of brains and lungs. Prior to joining UCSF, he has developed and used machine learning models for spatial and network data in different scientific contexts. Besides research, he likes traveling and visiting museums.


Maryam Bijanzadeh

Assistant Professional Researcher, Neuroscape

Maryam is an Assistant Professional Researcher in our team and a core member of the Neuroscape Center at UCSF. Her research focus is to examine how emotional and affective behavior is encoded in the brain and body using multidimensional neuro- and physiological readouts. Prior to joining the team, she worked as a Machine Learning Scientist at iRhythm Technologies, where she developed ML models to automatically detect cardiac arrhythmias. During her Postdoctoral training in the Chang lab at UCSF (2017-2021), she spearheaded a project focused on understanding neural markers of naturalistic affective behaviors using large neurophysiological datasets sampled from more than 100 sensors across multiple brain regions of consented patients. She also led a collaboration between different laboratories at UCSF in designing a battery of tasks to study neural and physiological mechanisms underlying emotional experiences in patients with epilepsy. She completed her PhD in Neuroscience at the University of Utah where she used laminar electrode arrays in non-human primates to understand how global and local sensory information is processed across layers of the visual cortex.


Rahul Biswas

Postdoctoral Scholar

Rahul is a Postdoctoral Scholar at Abbasi Lab in UCSF Neurology. Prior to this, he completed his PhD in Electrical and Computer Engineering at the University of Washington, Seattle, based at the NeuroAI Lab there. His research interests are in Computational Neuroscience, Causal Inference, Machine Learning, and Data-driven Time Series Analysis. His doctoral research focuses on inferring the causal pathway of information flow in the brain from brain signals. Prior to doctoral studies, he completed MS in Statistics from the University of Washington, Seattle, and Indian Statistical Institute, Kolkata.


Gavin Cui

PhD Candidate, Bioengineering

Gavin is a PhD student in the UC Berkeley-UCSF joint Bioengineering program. His project in the lab is focused on building computational platforms to analyze imaging and genomics data in Multiple Sclerosis. Before joining to the program, Gavin was an undergraduate researcher at McGill University, where he generated MRI-based simulations of transcranial Direct Current Stimulation. Gavin’s background in Neuroscience and Medical Imaging prompts him to explore projects that apply machine learning methods for predicting neurological disorder prognosis.


Maria Olaru

PhD Candidate, Neuroscience

Maria is a PhD candidate in the UCSF Neuroscience program working with time-series data from neural implants, iEEG, and wearable sensors jointly advised by Reza and Phil Starr. Currently, she is developing neuro-electrophysiological predictive algorithms for continuous symptom measures in patients with Parkinson’s Disease that are physiologically interpretable. She is also developing closed-loop deep brain stimulation algorithms using direct EEG signal feedback that update patient stimulation parameters to alleviate symptoms and adverse stimulation-related effects. Prior to starting her PhD, she worked in the Neuroradiology Department at UCSF under the supervision of Dr. Leo Sugrue. During this time, she designed a neuroimaging framework that allows for real-time quantification of language hemisphere localization for surgical patients to guide resection procedures and developed lab-wide statistical tools for biological and behavioral data. In her spare time, she enjoys running long distances, climbing outdoors, and reading comprehensive documentation.


Alex Lee

PhD Candidate, Bioinformatics

Alex is a PhD student in the UCSF BMI program. He is interested in interpretable machine learning and is currently working on developing models for multimodal data integration and pattern identification in molecular imaging data. Previously, Alex worked as a research assistant in the Seeley lab at UCSF and at Vivani Medical. Outside of research, he enjoys cooking and then eating.


Kara Presbrey

PhD Candidate, Neuroscience

Kara is a PhD student in the UCSF Neuroscience program jointly advised by Reza Abbasi-Asl, Doris Wang and Phil Starr. Kara studies the neural representation of sequential movement in Parkinson’s Disease – assessing how the cortico-basal ganglia circuit implements ongoing sequential movements across phases of learning, extracting biomarkers of consolidation during sleep that promote performance improvements, and evaluating the effect of levodopa and deep brain stimulation on these processes.


Russell Ro

PhD Student, Bioengineering

Russell is a PhD student in the UC Berkeley-UCSF Joint Bioengineering program. He is interested in time-series analysis, dynamics and control, and healthcare decision-making systems. Prior to starting his graduate studies, Russell graduated from UC San Diego where he studied the neural plasticity mechanisms underlying stroke recovery. He also worked as an engineer at Novartis where he designed tissue culture and assay systems for drug discovery applications. In his free time, you may find Russell bricking mid-rangers in pickup, talking too much about economics without knowing much about economics, or drinking obscene amounts of boba.


Maxine Collard

MD/PhD Student, Neuroscience

Maxine is an MD/PhD student in the Neuroscience program at UCSF. Maxine studies how to describe relational structure in a substrate-independent way, and how to characterize the processes that can shape its evolution. Her research focuses on using applied category theory to model these structures and processes, in order to understand convergent themes resembling “internal models of the outside world” that pervade neuroscience, the biology of non-neural tissues, the emerging study of cognition in organisms across the phylogenetic tree, psychoanalysis, and machine learning. Maxine’s clinical goal is to develop new ways to durably alleviate suffering and promote flourishing by giving folks a greater agency to change their world models.


Naomi Donovan

PhD Student, Neuroscience

Naomi is a PhD student in the Neuroscience program at UCSF. She is interested in developing neural network models based on the visual cortex. Previously, Naomi spent her undergrad at Hopkins and studied the impact of hearing loss on the auditory cortex of mice. She also spent a summer at the Blue Brain Project, implementing a model of thalamocortical projections to the barrel cortex. In her free time, she enjoys running, trying new foods, and sitting in the sun outside.



Daniel Deng

Undergraduate researcher

Daniel is an undergraduate student studying Computer Science and Molecular and Cellular Biology at UC Berkeley. At the Abbasi Lab, he is interested in using signal processing and interpretable machine learning to advance our understanding of neurodegenerative diseases. His current project is on computer vision-aided assessment of Parkinson’s disease motor symptoms. Before joining the Abbasi Lab, he was a research assistant at the Cardiac Vision Lab at UCSF, where he worked on numerical simulation and predictive modeling of cardiac dynamics. In his free time, he enjoys cooking and oil painting.



Former core members


Rob Cahill, Research Specialist
Currently: Co-Founder and President at Junevity

Neelroop Parikshak, Neurology Resident
Currently: Associate Director, Neurology, Therapeutic Area Genetics at Regeneron

Roozbeh Farhoodi, Postdoctoral scholar
Currently: Software Engineer, Google

Jennifer Townsend, Research Data Scientist
Currently: Research Scientist at Vu Lab (UCSF)

Vincent Wang, Research Specialist



Former undergraduate researchers


Shiladitya Dutta

Austin Jang

Gaurav Ghosal

Zeyu Yun

Chirag Sharma

Meera Mehta

Anmol Parande

Franklin Heng

Ahyeon Hwang

Kevin Chen

Arbaaz Muslim

Rohan Divate



Join us!

We have several openings for post-docs, graduate and undergraduate students and software developers. Contact Reza directly at Reza.AbbasiAsl@ucsf.edu to learn more!

Publications

Pre-Prints


Data-driven fine-grained region discovery in the mouse brain with transformers
A. J. Lee, S. Yao, N. Lusk, H. Zeng, B. Tasic, R. Abbasi-Asl
arXiv preprint, 2024.


Zero-shot sampling of adversarial entities in biomedical question answering.
R. Patrick Xian, Alex J. Lee, Vincent Wang, Qiming Cui, Russell Ro, Reza Abbasi-Asl
arXiv preprint, 2024.


The entropic heart: Tracking the psychedelic state via heart rate dynamics
F. E. Rosas, P. A. M. Mediano, C. Timmermann, A. I Luppi, D. Candia-Rivera, R. Abbasi-Asl, A. Gazzaley, M. L. Kringelbach, S. D. Muthukumaraswamy, D. Bor, S. Garfinkel, R. Carhart-Harris
bioRxiv preprint, 2023. (WEB)


Unsupervised pattern discovery in spatial gene expression atlas reveals mouse brain regions beyond established ontology
R. Cahill*, Y. Wang*, Patrick Xian, A. Lee, H. Zeng, B. Yu, B. Tasic, R. Abbasi-Asl
bioRxiv preprint, 2023. (WEB, Code)


Compression-enabled interpretability of voxel-wise encoding models
F Kamali, AA Suratgar, M Menhaj, R Abbasi-Asl
bioRxiv preprint, 2022. (WEB)


Multi-Modal Prototype Learning for Interpretable Multivariable Time Series Classification
Gaurav Ghosal, R. Abbasi-Asl
arXiv preprint, 2021. (WEB)


The DeepTune framework for modeling and characterizing neurons in visual cortex area V4
R. Abbasi-Asl*, Y. Chen*, A. Bloniarz, M. Oliver, Ben Willmore, J. L. Gallant and B. Yu
bioRxiv preprint, 2019. (WEB)



Peer- Reviewed Publications


Interpretable Video-Based Tracking and Quantification of Parkinsonism Clinical Motor States
D. Deng, J. L. Ostrem, V. Nguyen, D. D. Cummins, J. Sun, A. Pathak, S. Little*, R. Abbasi-Asl*
npj Parkinson’s Disease (in press), 2024. (WEB, Code)


7T MRI Synthesization from 3T Acquisitions
Q. Cui, D. Tosun, P. Mukherjee, R. Abbasi-Asl
MICCAI, 2024.


Structurally-Constrained Encoding Framework using a Multi-Voxel Reduced-Rank Latent Model for Human Natural Vision
A. Ranjbar, A. Suratgar, M. Menhaj, R. Abbasi-Asl
Journal of Neural Engineering (in press), 2024.


Practical considerations for machine learning-enabled discoveries in spatial transcriptomics
A. Lee, R. Cahill, R. Abbasi-Asl
GEN Beiotechnology (in press), 2024. (WEB)


Motor network gamma oscillations in chronic home recordings predict dyskinesia in Parkinson’s disease
M. Olaru,  S. Cernera,  A. Hahn,  T. A Wozny,  J. Anso, C. de Hemptinne,  S. Little,  W. Neumann,  R. Abbasi-Asl,  P. A Starr
Brain, 2024. (WEB)


Interpretable prototype discovery in deep learning based time series classification
G. Ghosal, R. Abbasi-Asl,
Integrated Systems: Innovations and Applications, Springer book chapter, 2023 (in press).


Functional Microcircuits in the Brain and in Artificial Intelligent Systems
J. H. Lee, Y. Choe, S. Ardid, R. Abbasi-Asl, M. McCarthy, B. Hu
Frontiers in Comp. Neuro., 2023. (WEB)


Robust registration of medical images in the presence of spatially-varying noise
R. Abbasi-Asl, A. Ghaffari and E. Fatemizade
Algorithms, 2022. (WEB)


Structural Compression of Convolutional Neural Networks with Applications in Interpretability
R. Abbasi-Asl and B. Yu
Frontiers in Big Data, 2021. (WEB)


Systematic Integration of Structural and Functional Data into Multi-Scale Models of Mouse Primary Visual Cortex
Y.N. Billeh, B. Cai, S.L. Gratiy, K. Dai, R. Iyer, N.W. Gouwens, R. Abbasi-Asl, X. Jia, J.H. Siegle, S.R. Olsen, C. Koch, S. Mihalas, and A. Arkhipov
Neuron, 2020. (Featured cover Image, WEB, bioRxiv version, DATA, Allen Institute Coverage)


Superficial bound of the depth limit of 2-photon imaging in mouse brain
K. Takasaki, R. Abbasi-Asl, J. Waters
eNeuro, 2020. (WEB, bioRxiv version , DATA)


Interpretable machine learning: definitions, methods, and applications
J. Murdoch*, C. Singh*, K. Kumbier†, R. Abbasi-Asl†, and B. Yu
PNAS, 2019. (WEB, NEWS)


Brain-Computer Interface in Virtual Reality
R. Abbasi-Asl, M. Keshavarzi, D. Y. Chan
Proceedings of International IEEE EMBS Conference on Neural Engineering (NER), 2019. (WEB, arXiv version)


3-Photon Calcium Imaging of Deep Cortical Layers for Functional Connectomics
K. Takasaki, J. Larkin, R. Abbasi-Asl, D. Denman, D. Millman, S. de Vries, M. Takeno, N. M da Costa, R C. Reid, J. Waters
Optics and the Brain, 2019. (WEB)


Visual physiology of the Layer 4 cortical circuit in silico
A. Arkhipov, N. W. Gouwens, Y. N. Billeh, S. Gratiy, R. Iyer, Z. Wei, Z. Xu, R. Abbasi-Asl, J. Berg, M. Buice, N. Cain, N. da Costa, S. de Vries, D. Denman, S. Durand, D. Feng, T. Jarsky, J. Lecoq, B. Lee, L. Li, S. Mihalas, G. K. Ocker, S. R. Olsen, R. C. Reid, G. Soler-Llavina, S. A. Sorensen, Q. Wang, J. Waters, M. Scanziani, C. Koch
PLOS Comp. Bio., 2018. (WEB, DATA)


Interpreting Convolutional Neural Networks Through Compression
R. Abbasi-Asl and B. Yu
Proceedings of NeurIPS IML Symposium, 2017. (WEB)


Do retinal ganglion cells project natural scenes to their principal subspace and whiten them?
R. Abbasi-Asl, C. Pehlevan, B. Yu and D. B. Chklovskii. Proceedings of Asilomar IEEE Conference on Signals, Systems and Computers (ACSSC), 2016. (WEB, arXiv version)


Hammerstein-Wiener Model: A New Approach to the Estimation of Formal Neural Information
R. Abbasi-Asl, R. Khorsandi, B. Vosooghi Vahdat.
Basic and Clinical Neuroscience. 2012, (WEB)


Automatic B-spline Image Registration Using Histogram-based Landmark Extraction
A. Ghanbari, R. Abbasi-Asl, A. Ghaffari, E. Fatemizadeh
Proceedings of IEEE-EMBS Conference on Biomedical Engineering & Sciences (IECBES), 2012. (WEB)


Estimation of Muscle Force with EMG Signals Using Hammerstein-Wiener Model
R. Abbasi-Asl, R. Khorsandi, Sh. Farzampour, E. Zahedi
IFMBE Proceedings of International Conference on Biomedical Engineering (BIOMED), 2011. (WEB)


Selected Published Abstracts

Diurnal Step Count Patterns in Progressive Multiple Sclerosis
D. Navani, V. Block, B. Cree, R. Abbasi-Asl
Neurology, 2023. (WEB)


Reconstructing visual experience from a large-scale biologically realistic model of mouse primary visual cortex
R. Abbasi-Asl, Y. Chi, H. Yang, K. Dai, A. Arkhipov
Journal of Vision, 2023. (WEB)


A large-scale standardized survey of neural receptive fields in an entire column in mouse V1
R. Abbasi-Asl, A. Muslim, J. Larkin, K. Takasaki, D. Millman, D. Denman, J. Lecoq, A, Arkhipov, N. W Gouwens, J. Waters, R C. Reid, S. EJ de Vries
Journal of Vision, 2021. (WEB)


Towards multipurpose bio-realistic models of cortical circuits
A. Arkhipov, Y. N Billeh, B. Cai, S. L Gratiy, K. Dai, R. Iyer, N. W Gouwens, R. Abbasi-Asl, X. Jia, J. H Siegle, S. R Olsen, S. Mihalas, C. Koch
BMC Neuroscience, 2020. (WEB)


Systematic integration of experimental data in biologically realistic models of the mouse primary visual cortex: Insights and predictions
Y. Billeh, B. Cai, S, Gratiy, K. Dai, R. Iyer, N. Gouwens, R. Abbasi-Asl, X. Jia, J. Siegle, S. Olsen, C. Koch, S. Mihalas, A. Arkhipov
BMC Neuroscience, 2019. (WEB)