Geeling Chau
@GeelingC
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Computation and Neural Systems PhD student @Caltech | Brain Computer Interfaces, Neural Signal Decoding | https://t.co/438WcGkN1l
Joined November 2020
RT @BciSociety: With over 500 delegates in attendance, the BCI Meeting was a tremendous success. A heartfelt thank you to all of you for th….
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RT @Maitreyee_W: Our brain-to-voice synthesis brain-computer interface paper was published in @Nature today! This neuroprosthesis synthesiz….
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RT @SergeyStavisky: 📢 Heads-up to the neuro-ML/BCI community that we'll launch the Brain-to-Text '25 competition in ~a month, and it'll run….
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In #Banff for the @BCISociety #BCI2025 meeting! . How can Machine Learning benefit BCIs? .🤝Would love to meet! (DM me) .📍Poster: Jun 4 (Wed) 4-6pm. P2-G-68.🗣Talk: Jun 5 (Thurs) Morning. W21: Large neural data models for BCIs. Organized by @_JoelYe🙌.
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RT @lpjiang97: I'm excited to share our latest work — "Data Heterogeneity Limits the Scaling Effect of Pretraining in Neural Data Transform….
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RT @synchroninc: Thoughts are now an input device. Today, @Apple announced its new BCI Human Interface Device (#HID) protocol—and Synchron….
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RT @BTolooshams: We have released VARS-fUSI: Variable sampling for fast and efficient functional ultrasound imaging (fUSI) using neural ope….
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If you missed us, we'll be speaking at 4:06pm (Friday) in Garnet 216-218! Hope to see you there! :).
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Come find us in Hall 3! Can’t wait to meet you and talk about modeling brain data across heterogeneous datasets!
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It was so fun to be a part of this! Huge thanks to the organizers for putting it together!.
The recordings from the 🌐🧠 Neuro Foundation Model workshop are up on the workshop website! . Thanks again to our speakers, and everyone who attended. And thanks to the entire team @cole_hurwitz, @nandahkrishna, @averyryoo, @evadyer and @tyrell_turing for making this happen 🙌
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Looking forward to joining the FM for neuro conversation at the #COSYNE2025 workshops! . I will be sharing some of our experiences scaling models for iEEG datasets with variable layouts and high-resolution temporal signals. Hope to see you there!
How can large-scale models + datasets revolutionize neuroscience 🧠🤖🌐? We are excited to announce our workshop: “Building a foundation model for the brain: datasets, theory, and models” at @CosyneMeeting #COSYNE2025. Join us in Mont-Tremblant, Canada from March 31 - April 1.
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Would like to especially shout out @yisongyue, @SaberaTalukder, @_abarbu_, and Boris Katz for their valuable advice and unwavering support through @YueLabCaltech and @MIT_CBMM. @su1001v and Saraswati for indispensable help. And @czlwang for marching through this with me. 🤗🎉.
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🎉Excited to share: My first ML conference paper, Population Transformer 🧠, is an Oral at #ICLR2025! This work has truly evolved since its first appearance as a workshop paper last year. So thankful to have worked with the best advisors + collaborators! 🤗 More soon!.
How can we train models on more brains and sensor layouts? . We present Population Transformer (PopT) which learns population-level interactions on intracranial electrodes, with 🔥decoding and interpretability benefits. See our poster at #ICML2024 @AI_for_Science 12pm
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RT @neur_reps: We are excited to hold our post-workshop social jointly with @unireps, @NeuroAI_NeurIPS, and @newthe….
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Catch me and @czlwang presenting our poster today (12/14) 3:30-5:30pm at the #NeurIPS2024 NeuroAI Workshop! 🧠 Paper:
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At #NeurIPS2024 and thrilled to see so many brilliant minds in one place! 🤩🧠 .If you’re exploring #MachineLearning for #Neurotech or #Neuroscience, let’s connect! Always up for discussions on brain decoding, BCIs, and scaling models for neuroscience. 🧠✨.
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@czlwang @SaberaTalukder @su1001v @yisongyue @_abarbu_ It was great seeing all the talks at the Chen AI for Neuro Workshop and having lively discussions after! Thanks to @UeliRutishauser, @PietroPerona, and @ChenInstitute for organizing! 🧠
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I will touch on some work we've (@czlwang, @SaberaTalukder, @su1001v, Saraswati, @yisongyue, Boris Katz, @_abarbu_) done on pretraining transformers across variable input layouts for 🔥 decoding and interpretability. Read more here:
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