syeda5688.github.io Open in urlscan Pro
2606:50c0:8000::153  Public Scan

Form analysis
0 forms found in the DOM

Text Content

Toggle navigation
 * about (current)
 * blog
 * publications
 * cv
 * ctrl k
 * 




SYED A. RIZVI

PhD student at Yale University.

Yale University

New Haven, CT

I am a first-year PhD student in Computer Science at Yale University, advised by
Prof. David van Dijk. I am also currently working as a Student Researcher at
Google for Summer 2024 under the supervision of Dr. Bryan Perozzi. I work on
building and applying Graph Neural Networks, Large Language Models, and
Foundational models to large-scale biological data.

Before joining Yale, I had the privilege of working as an undergraduate research
student at the University of Houston, Rice University, and Houston Methodist. I
received my bachelor’s degree in Computer Science from the University of Houston
in 2022.

Here is my Google Scholar and LinkedIn.


NEWS

May 02, 2024 Our paper Cell2Sentence: Teaching Large Language Models the
Language of Biology has been accepted at ICML 2024. Congratulations to all of
the co-authors! See the blog post here. Jan 17, 2024 Our paper BrainLM: A
Foundational Model for Brain Activity Recordings has been accepted at ICLR 2024.
Congratulations to all of the co-authors!


LATEST POSTS

Jun 09, 2024 Basics of Graph Neural Networks


SELECTED PUBLICATIONS

 1. BrainLM: A foundation model for brain activity recordings
    Josue Ortega Caro, Antonio Henrique Oliveira Fonseca, Syed A Rizvi, and 8
    more authors
    In , 2024
    
    Abs Bib HTML
    
    We introduce the Brain Language Model (BrainLM), a foundation model for
    brain activity dynamics trained on 6,700 hours of fMRI recordings. Utilizing
    self-supervised masked-prediction training, BrainLM demonstrates proficiency
    in both fine-tuning and zero-shot inference tasks. Fine-tuning allows for
    the accurate prediction of clinical variables like age, anxiety, and PTSD as
    well as forecasting of future brain states. Critically, the model
    generalizes well to entirely new external cohorts not seen during training.
    In zero-shot inference mode, BrainLM can identify intrinsic functional
    networks directly from raw fMRI data without any network-based supervision
    during training. The model also generates interpretable latent
    representations that reveal relationships between brain activity patterns
    and cognitive states. Overall, BrainLM offers a versatile and interpretable
    framework for elucidating the complex spatiotemporal dynamics of human brain
    activity. It serves as a powerful "lens" through which massive repositories
    of fMRI data can be analyzed in new ways, enabling more effective
    interpretation and utilization at scale. The work demonstrates the potential
    of foundation models to advance computational neuroscience research.
    
    @inproceedings{caro2023brainlm,
      title = {BrainLM: A foundation model for brain activity recordings},
      author = {Caro, Josue Ortega and de Oliveira Fonseca, Antonio Henrique and Rizvi, Syed A and Rosati, Matteo and Averill, Christopher and Cross, James L and Mittal, Prateek and Zappala, Emanuele and Dhodapkar, Rahul Madhav and Abdallah, Chadi and others},
      url = {https://openreview.net/pdf?id=RwI7ZEfR27},
      journal = {ICLR 2024},
      year = {2024},
    }

 2. Cell2sentence: Teaching large language models the language of biology
    Daniel Levine, Sacha Lévy, Syed Asad Rizvi, and 8 more authors
    ICML 2024, 2024
    
    Abs Bib HTML
    
    We introduce Cell2Sentence (C2S), a novel method to directly adapt large
    language models to a biological context, specifically single-cell
    transcriptomics. By transforming gene expression data into ”cell sentences,”
    C2S bridges the gap between natural language processing and biology. We
    demonstrate cell sentences enable the finetuning of language models for
    diverse tasks in biology, including cell generation, complex celltype
    annotation, and direct data-driven text generation. Our experiments reveal
    that GPT-2, when fine-tuned with C2S, can generate biologically valid cells
    based on cell type inputs, and accurately predict cell types from cell
    sentences. This illustrates that language models, through C2S finetuning,
    can acquire a significant understanding of single-cell biology while
    maintaining robust text generation capabilities. C2S offers a flexible,
    accessible framework to integrate natural language processing with
    transcriptomics, utilizing existing models and libraries for a wide range of
    biological applications.
    
    @article{levine2023cell2sentence,
      title = {Cell2sentence: Teaching large language models the language of biology},
      author = {Levine, Daniel and L{\'e}vy, Sacha and Rizvi, Syed Asad and Pallikkavaliyaveetil, Nazreen and Chen, Xingyu and Zhang, David and Ghadermarzi, Sina and Wu, Ruiming and Zheng, Zihe and Vrkic, Ivan and others},
      url = {https://www.biorxiv.org/content/biorxiv/early/2024/02/15/2023.09.11.557287.full.pdf},
      journal = {ICML 2024},
      year = {2024},
    }

Feel to reach out via email or social profiles!
© Copyright 2024 Syed A. Rizvi. Powered by Jekyll with al-folio theme. Hosted by
GitHub Pages.