Merge pull request #817 from neurodata/bib-update

update pub, profiles

content/bibs/people.bib

@@ -492,8 +492,8 @@ @incollection{kareefullah
 	userw = {Cup Stacker},
 	month = {9},
 	year = {2021},
-	number = {09/21 –- },
-	series = {},
+	number = {09/21 – 08/24},
+	series = {2024},
 	abstract = {Assisted with fixing issues in graspologic and hyppo},
 	userb = {},
 	userc = {BME, JHU},
@@ -504,7 +504,7 @@ @jhu.edu
 	url = {},
 	usere = {},
 	file = {kareef_ullah.jpg},
-	priority = {4.5}
+	priority = {6.0}
 },
 
 @incollection{hopeugwuoke,

content/bibs/pubs.bib

@@ -4171,4 +4171,16 @@ @article{zuzuk2024dynamic
 	journal={Management Science},
 	publisher={INFORMS},
 	keywords={peer-reviewed},
+},
+
+@article{madhyastha2024trex,
+	title={T-Rex (Tree-Rectangles): Reformulating Decision Tree Traversal as Hyperrectangle Enclosure},
+	author={Madhyastha, Meghana and Budavari, Tamas and Braverman, Vladmir and Vogelstein, Joshua and Burns, Randal},
+	author+an={4=highlight},
+	year={2024},
+	abstract={Tree ensembles, random forests and gradient boosted trees, are useful in resource-limited machine learning deployments. However, traversing tree data structures is not cache friendly, which results in high latency during inference or regression. Tree traversal incurs random I/Os making inference memory bound. We present a system that trades many random I/Os for few sequential I/O by remapping a forest of trees into a single spatial index. It builds on the observation that each leaf in the forest encodes a hyperrectangle in the feature space. We make queries I/O efficient through pruning and space-filling curves. We then optimize computation through quantization of hyperrectangle boundaries and vectorization of enclosure queries. Our evaluation on a diverse set of benchmark datasets shows that the system reduces inference latency by 2 times in memory and 10 times for external memory with no detectable loss of accuracy},
+	url={https://doi.org/10.1109/ICDE60146.2024.00145,
+	journal={2024 IEEE 40th International Conference on Data Engineering (ICDE)},
+	publisher={IEEE},
+	keywords={peer-reviewed},
 },

content/bibs/talks.bib

@@ -1,3 +1,13 @@
+@inproceedings{jovo-schiz2024,
+ title = {\href{https://docs.google.com/presentation/d/1fBfVKEf3XbT9klRJO08Hm_CxPb4WPoqXNiOnVvTDHuc/pub?start=false&loop=false&delayms=60000&slide=id.p}{Learning the way that animals leaned to learn}},
+ author = {Vogelstein, Joshua T},
+ author+an = {1=highlight},
+ address = {Schizophrenia Center, Johns Hopkins University, Baltimore, MD, USA},
+ year = {2024},
+ month = {September},
+ keywords = {local}
+}
+
 @inproceedings{sampan-gyss2022,
  title = {Nonparametric MANOVA via Independence Testing},
  author = {Panda, Sambit and Shen, Cencheng and Vogelstein, Joshua T},