René's URL Explorer Experiment


Title: Willie Neiswanger

Description: Willie Neiswanger is an Assistant Professor at USC studying AI and Machine Learning.

Keywords:

direct link

Domain: willieneis.github.io

Hey, it has json ld scripts: 
    {
      "@context": "https://schema.org",
      "@type": "Person",
      "name": "Willie Neiswanger",
      "alternateName": "willie neiswanger",
      "url": "https://willieneis.github.io",
      "sameAs": [
        "https://twitter.com/willieneis",
        "https://x.com/willieneis",
        "https://www.linkedin.com/in/willieneiswanger",
        "https://viterbi.usc.edu/directory/faculty/Neiswanger/Willie",
        "https://scholar.google.com/citations?user=QwKHApEAAAAJ"
      ],
      "image": "https://willieneis.github.io/img/me_garden.jpg",
      "description": "Willie Neiswanger is a computer scientist and AI researcher.",
      "jobTitle": "Assistant Professor of Computer Science",
      "affiliation": {
        "@type": "Organization",
        "name": "University of Southern California"
      }
    }
authorWillie Neiswanger

Links:

Willie Neiswangerhttps://willieneis.github.io/index.html
http://github.com/willieneis
http://twitter.com/willieneis
https://scholar.google.com/citations?user=QwKHApEAAAAJ
https://willieneis.github.io/pdf/cv_neiswanger.pdf
USC https://usc.edu/
Viterbi https://viterbischool.usc.edu/
Computer Science https://www.cs.usc.edu/
Computer Sciencehttps://www.cs.usc.edu/
University of Southern California (USC)https://usc.edu/
Viterbi Schoolhttps://viterbischool.usc.edu/
School of Advanced Computinghttps://sac.usc.edu/
Stefano Ermonhttps://cs.stanford.edu/~ermon/
PhDhttps://willieneis.github.io/pdf/willie_thesis.pdf
Eric Xinghttp://www.cs.cmu.edu/~epxing/
Jeff Schneiderhttps://www.cs.cmu.edu/~schneide/
Barnabás Póczoshttp://www.cs.cmu.edu/~bapoczos/
New paperhttps://arxiv.org/abs/2405.13954
New paperhttps://pubs.rsc.org/en/content/articlelanding/2025/dd/d5dd00325c
New paperhttps://arxiv.org/abs/2312.00267
New paperhttps://royalsocietypublishing.org/rsta/article/383/2305/20240041/234826/Bayesian-machine-learning-for-inverse-design-of
New paperhttps://arxiv.org/abs/2406.19314
LiveBenchhttps://livebench.ai/
New paperhttps://arxiv.org/abs/2402.02392
DeLLMahttps://dellma.github.io/
METAGENE-1https://metagene.ai/
New paperhttps://www.nature.com/articles/s41524-024-01326-2
New paperhttps://arxiv.org/abs/2404.01266
New paperhttps://ojs.aaai.org/index.php/AAAI/article/view/27785
New paperhttps://iopscience.iop.org/article/10.1088/1741-4326/ad22f5/meta
New paperhttps://arxiv.org/abs/2310.05674
codehttps://github.com/leopard-ai/betty
New paperhttps://arxiv.org/abs/2309.11600
Differentiable Almost Everything Workshophttps://differentiable.xyz/
New paperhttps://arxiv.org/abs/2207.02849
New paperhttps://arxiv.org/abs/2212.09510
New paperhttps://arxiv.org/abs/2203.12023
New paperhttps://arxiv.org/abs/2303.02569
New paperhttps://arxiv.org/abs/2210.04714
Workshop on Gaussian Processes and Decision-making Systemshttps://gp-seminar-series.github.io/neurips-2022/
New paperhttps://arxiv.org/abs/2210.04642
codehttps://github.com/fusion-ml/trajectory-information-rl
New paperhttps://arxiv.org/abs/2210.01383
Real World Experiment Design and Active Learning Workshophttp://realworldml.github.io/icml2022/
New paperhttps://arxiv.org/abs/2206.13035
websitehttps://lfbo-ml.github.io/
New paperhttps://arxiv.org/abs/2206.11468
New paperhttps://arxiv.org/abs/2112.05244
blog posthttps://blog.ml.cmu.edu/2022/05/06/barl/
New paperhttps://arxiv.org/abs/2112.09126
websitehttps://is-count.github.io/
New paperhttps://arxiv.org/abs/2011.09588
codehttps://github.com/YoungseogChung/calibrated-quantile-uq
explainable machine learninghttps://arxiv.org/abs/2106.12543
personalized benchmarkinghttps://arxiv.org/abs/2111.04260
Polluxhttps://arxiv.org/abs/2008.12260
OSDI'21https://www.usenix.org/conference/osdi21
New paperhttps://arxiv.org/abs/2104.09460
websitehttps://willieneis.github.io/bax-website
Machine Learning for Data (Creation, Privacy, Bias) Workshophttps://sites.google.com/view/ml4data
New paperhttps://arxiv.org/abs/2008.12260
AdaptDLhttps://github.com/petuum/adaptdl
New paperhttps://arxiv.org/abs/2006.07368
codehttps://github.com/willieneis/gp-martingales
New paperhttps://doi.org/10.1063/5.0044989
New paperhttps://arxiv.org/abs/2012.06046
codehttps://github.com/benbo/interactive-weak-supervision
Uncertainty Toolboxhttps://github.com/uncertainty-toolbox/uncertainty-toolbox
New paperhttps://arxiv.org/abs/1910.11858
codehttps://github.com/naszilla/naszilla
METAGENE-1https://metagene.ai/
LiveBenchhttps://livebench.ai/
LLM360https://www.llm360.ai/
Bettyhttps://github.com/leopard-ai/betty
Bayesian Algorithm Execution (BAX)https://willieneis.github.io/bax-website
Uncertainty Toolboxhttps://github.com/uncertainty-toolbox/uncertainty-toolbox
Modern EXD/ALhttps://realworldml.github.io/
Naszillahttps://github.com/naszilla/naszilla
AdaptDLhttps://github.com/petuum/adaptdl
CASL Projecthttps://www.casl-project.ai/
ProBOhttps://willieneis.github.io/research/probo/index.html
Bayesian Optimization and DOEhttps://willieneis.github.io/research/bayesOpt/index.html
Prior Swappinghttps://willieneis.github.io/research/priorSwapping/index.html
Embarrassingly Parallel VIhttps://willieneis.github.io/research/embParVI/index.html
Embarrassingly Parallel MCMChttps://willieneis.github.io/research/embParMCMC/index.html
Fast Function-based Regressionhttps://willieneis.github.io/research/fastFuncToFunc/index.html
GPU for Time-varying PYPshttp://jmlr.org/papers/v18/10-231.html
Parallel Frank-Wolfe Optimizationhttp://arxiv.org/abs/1409.6086
LRO Models for Link Predictionhttp://www.auai.org/uai2014/proceedings/individuals/206.pdf
DDP Object Tracking and Modelinghttp://jmlr.org/proceedings/papers/v33/neiswanger14.pdf
Cell Motility Analysishttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC4323926/
found herehttps://scholar.google.com/citations?user=QwKHApEAAAAJ&hl=en
GitHubhttps://github.com/willieneis
Twitterhttps://twitter.com/willieneis
CV.https://willieneis.github.io/pdf/cv_neiswanger.pdf
Dan F-Mhttp://dfm.io
URLs of crawlers that visited me.