fixing broken links?

Author: slinderman

_alumni/mackay_matt.md

@@ -1,6 +1,6 @@
 ---
 name: Matthew MacKay
-link: https://scholar.google.com/citations?user=37kPUpIAAAAJ&hl=en
+link: 
 type: PhD Student (Statistics, 2020-23). Now Technical Staff at Anthropic
 pic: images/mackay_matt.jpg
 email: 

_includes/home.html

@@ -13,7 +13,7 @@ <h1>Linderman Lab</h1>
                         <h2>Stanford University</h2>
                         <p>
                             Welcome to the Linderman Lab! We belong to the <a href="https://statistics.stanford.edu/">Statistics Department</a> and the <a href="https://neuroscience.stanford.edu/">Wu Tsai Neurosciences Institute</a> at Stanford University.
-                            Our work bridges <strong>computational neuroscience</strong>, <strong>artificial intelligence</strong>, and <strong>Bayesian statistics</strong>, developing models and algorithms for complex data. Check out some of our research
+                            Our work blends <strong>computational neuroscience</strong>, <strong>artificial intelligence</strong>, and <strong>Bayesian statistics</strong> to develop models and algorithms for complex data. Check out some of our research
                             below, and reach out if you'd like to learn more!
                         </p>
                     </header>

_includes/research.html

@@ -24,7 +24,7 @@ <h3>Computational Neuroscience</h3>
                             Modern recording and tracking technologies let us measure thousands of neurons and quantify behavior with unprecedented precision, but the resulting data are noisy, high-dimensional time series.
                             We develop state space models tailored to this challenge, like the rSLDS (<a href="#linderman2017recurrent">Linderman et al., 2017</a>), gpSLDS (<a href="#hu2024modeling">Hu et al., 2024</a>), and Keypoint MoSeq (<a href="#weinreb2024keypoint">Weinreb et al., 2024</a>).
                             Working closely with experimental collaborators, we have used tools like these to study neural dynamics underlying emotional states (<a href="#nair2023approximate">Nair et al., 2023</a>; <a href="#vinograd2024intrinsic">Vinograd et al., 2024</a>), the neural basis of natural behavior (<a href="#markowitz2023spontaneous">Markowitz et al., 2023</a>), and how behavior changes throughout the lifespan (<a href="#bedbrook2026lifelong">Bedbrook et al., 2025</a>).
-                            We also build open-source software &mdash; including <a href="https://github.com/lindermanlab/ssm">SSM</a> and <a href="https://probml.github.io/dynamax/">Dynamax</a> (<a href="#linderman2024dynamax">Linderman et al., 2024</a>) &mdash; to make these tools broadly accessible.
+                            We also build open-source software &mdash; including <a href="https://github.com/lindermanlab/ssm">SSM</a> and <a href="https://probml.github.io/dynamax/">Dynamax</a> (<a href="#linderman2025dynamax">Linderman et al., 2025</a>) &mdash; to make these tools broadly accessible.
                         </p>
                     </div>
                 </div>
@@ -40,7 +40,7 @@ <h3>Artificial Intelligence</h3>
                         <p>
                             Deep state space models have seen a resurgence within machine learning, and they are now widely used for sequential data like language, audio, and video.
                             We helped pioneer this area with S5 (<a href="#smith2023simplified">Smith et al., 2023a</a>,<a href="#smith2023convolutional">b</a>), and we continue to develop theory and algorithms for parallelizing deep SSMs with nonlinear dynamics (<a href="#gonzalez2024towards">Gonzalez et al., 2024</a>, <a href="#gonzalez2025predictability">2025</a>, <a href="#gonzalez2026unifying">2026</a>).
-                            We have studied cost-efficient collaboration between on-device and cloud models (<a href="#narayan2025minions">Narayan et al., 2025</a>), information-theoretic principles for agentic system design (<a href="#he2026information">He et al., 2026</a>), and informed correctors for discrete diffusion models (<a href="#zhao2025informed">Zhao et al., 2025</a>).
+                            Beyond SSMs, we have also developed cost-efficient collaboration protocols between on-device and cloud models (<a href="#narayan2025minions">Narayan et al., 2025</a>), information-theoretic principles for agentic system design (<a href="#he2026information">He et al., 2026</a>), and informed correctors for discrete diffusion models (<a href="#zhao2025informed">Zhao et al., 2025</a>).
                         </p>
                     </div>
                 </div>

cv/cv.bib

@@ -530,7 +530,7 @@ @inproceedings{beller2022looking
   booktitle={Proceedings of the Annual Meeting of the Cognitive Science Society},
   volume={44},
   year={2022},
-  arxiv={psyarxiv.com/h2r8d},
+  arxiv={https://psyarxiv.com/h2r8d},
   link={https://escholarship.org/content/qt7gk617ss/qt7gk617ss.pdf},
 }
 
@@ -705,7 +705,7 @@ @article{glaser2020multi
   journal={Advances in Neural Information Processing Systems (NeurIPS)},
   year={2020},
   biorxiv={https://www.biorxiv.org/content/10.1101/2020.10.21.349282v1},
-  code={https://github.com/lindermanlab/ssm/blob/master/notebooks/Multi-Population%20rSLDS.ipynb},
+  code={https://github.com/lindermanlab/ssm/blob/master/notebooks/Multi-Population-rSLDS.ipynb},
   link={https://papers.nips.cc/paper/2020/hash/aa1f5f73327ba40d47ebce155e785aaf-Abstract.html}
   }
 
@@ -892,14 +892,6 @@ @article{
 code={https://github.com/google/gumbel_sinkhorn}
 }
 
-@article{linderman2017latenthawkes,
-    title={{B}ayesian inference for latent {H}awkes processes},
-    author={Scott W. Linderman and Yixin Wang and David M. Blei},
-    journal={Advances in Approximate Bayesian Inference Workshop at the 31st Conference on Neural Information Processing Systems},
-    year={2017},
-    link={http://approximateinference.org/2017/accepted/LindermanEtAl2017.pdf}
-}
-
 @article{buchanan2017quantifying,
     title={Quantifying the behavioral dynamics of {C}. elegans with autoregressive hidden {M}arkov models},
     author={E. Kelly Buchanan and Akiva Lipschitz and Scott W. Linderman and Liam Paninski},
@@ -932,7 +924,6 @@ @inproceedings{naesseth2018variational
     author={Naesseth, Christian A. and Scott W. Linderman and Ranganath, Rajesh and Blei, David M.},
     booktitle={Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS)},
     year = {2018},
-    code = {https://github.com/blei-lab/vsmc},
     arxiv = {https://arxiv.org/abs/1705.11140},
     link = {http://proceedings.mlr.press/v84/naesseth18a/naesseth18a.pdf},
 }
@@ -1001,14 +992,6 @@ @inproceedings{naesseth2017rejection
     code={https://github.com/blei-lab/ars-reparameterization}
 }
 
-@article{naesseth2016rejectionwkshp,
-    title={Rejection sampling variational inference},
-    author={Naesseth, Christian A. and Ruiz, Francisco J. R. and Scott W. Linderman and Blei, David M.},
-    journal={Advances in Approximate Bayesian Inference Workshop at the 30th Conference on Neural Information Processing Systems},
-    year={2016},
-    link={http://approximateinference.org/2016/accepted/NaessethEtAl2016.pdf}
-}
-
 
 @article{chen2016bayesian,
     author = {Zhe Chen and Scott W. Linderman and Matthew A. Wilson},