Amin Nejatbakhsh
Research Scientist @META | Guest Researcher @FlatironCCN | Visiting Scholar @NYU | PhD @ZuckermanBrain @Columbia | Neuro & AI Enthusiast
- I'm very excited about this work which can open up new ways for neural data analysis. If you're in NeurIPS consider checking Victor's poster.
- At #NeurIPS2025! 🎉 Excited to present Conditionally Linear Dynamical Systems (CLDS). We leverage the dependence of neural dynamics on task covariates to yield an interpretable, flexible model of dynamics. Come meet and check it out! 📍: Poster #2209, Hall C,D,E on Thu Dec 4, 11 am–2 pm, PST. 🧵/6
- Pleased to announce that our paper on "Identifying Neural Dynamics Using Interventional State Space Models" has been selected for a poster presentation in #ICML2025. Please check the thread for paper details (0/n). Presentation info: icml.cc/virtual/2025....
- In neuroscience we often ask which dynamical system model generated the data? However, our ability to distinguish between dynamical hypotheses from data is hindered by model non-identifiability. For example, the two systems below are indistinguishable using observational data (1/n).
- Excited to announce that our paper on "Comparing noisy neural population dynamics using optimal transport distances" has been selected for an oral presentation in #ICLR2025 (1.8% top papers). Check the thread for paper details (0/n). Presentation info: iclr.cc/virtual/2025....
- A central question in AI is to understand how hidden representations are shaped in models. A useful paradigm is to define metric spaces that quantify differences in representations across networks. Given two networks, the goal is to compare the high-dimensional responses to the same inputs (1/n).
