Faculty

Institution

Unit

Venue

Mark Neubauer

Professor

UIUC

Affiliations

The Grainger College of Engineering

Department of Physics

Physics

Mark Neubauer is currently serving as a Physics Professor at Uiuc.

Honors

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Experiences

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Education

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Mark Neubauer

UIUC

Professor

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high energy physics particle physics computer science collider physics elementary particles experimental physics physics data science computer engineering machine learning high performance computer architecture neural network (machine learning) hardware acceleration computational intelligence deep learning artificial intelligence scientific data scientific computing machine learning research accelerator physics parallel computing data management field programmable gate array physic aware machine learning tracking systems graph neural networks detector physic intelligent computing particle beam nuclear science domain-specific accelerator neutrino physics fpga design neutrino astronomy theoretical physics machine vision heavy flavour physic machine learning models electroweak interactions high-energy nuclear reaction sudbury neutrino observatory data-intensive computing fair principles knowledge discovery microarchitecture databases convolutional neural networks explainable ai radiation detection ai foundation neutrino properties fast tracker geometric learning high-performance data analytics massive data processing interpretability ethics of artificial intelligence responsible data management responsible ai algorithmic fairness analysis preservation fair ai research ai workshop cms data relativistic astrophysics high-energy astrophysics nuclear decays neutral boson new particle a search particle physics community data science community residual pressure tagging tools beam-induced backgrounds full trackml dataset

Video Presentations

Notable Scholarly Works

Notable Federal Grants

Collaborators

Evidential Deep Learning for Uncertainty Quantification and Out-Of-Distribution Detection in Jet Identification Using Deep Neural Networks. Machine Learning Science and Technology (2025)

Ayush KhotXiwei WangAvik RoyVolodymyr KindratenkoMark Neubauer

uncertainty quantificationneural network (machine learning)machine learningmachine learning modelsevidential deep learningartificial intelligencedeep learningbayesian analysisdata sciencecomputational intelligenceanomaly detectioncomputer sciencephysic aware machine learning

From Signals to Features to Insights: Multi-Level Novelty Detection for Fast Scientific Discovery. (2025)

Devashri NaikNastaran DarabiSina TayebatiDinithi JayasuriyaShamma NasrinDanush ShekarCorrinne MillsBenjamin ParpillonFarah FahimMark S. NeubauerAmit Ranjan Trivedi

novelty detectionfast scientific discoverydata miningmulti-level novelty detectionscientific datastatisticsmachine learning researchknowledge discoverydata sciencediscovery techniqueunsupervised machine learningmachine learning

HIGTR: High-Performance Fpga Implementation of Complete Gnn-Based Trajectory Reconstruction for Hep. (2025)

Yun-Chen YangHsuan-Wei YuBo-Cheng LaiShih-Chieh HsuMark NeubauerSantosh Parajuli

graph constructionneural network (machine learning)fpga designcomputer engineeringgraph neural networkshardware accelerationtrajectory reconstructiondeep learningfield programmable gate arraydomain-specific acceleratorcomputer science

Fair AI Models in High Energy Physics. EPJ Web of Conferences (2024)

Haoyang LiJavier DuarteAvik RoyRuike ZhuE. A. HuertaDaniel DiazPhilip HarrisRaghav KansalDaniel S. KatzIshaan H. KavooriVolodymyr V. KindratenkoFarouk MokhtarMark S. NeubauerSang Eon ParkMelissa QuinnanRoger RusackZhizhen Zhao

scientific computingneural network (machine learning)fair ai modelsgraph neural networksai modelsartificial intelligenceintelligent computingmachine learning researchai foundationdata sciencecomputational intelligencemachine learningphysic aware machine learningcomputer science

Smart Pixel Sensors: Towards On-Sensor Filtering of Pixel Clusters With Deep Learning. Machine Learning Science and Technology (2024)

Jieun YooJennet DickinsonMorris SwartzGiuseppe Di GuglielmoAlice BeanDouglas BerryManuel Blanco ValentinKarri DiPetrilloFarah FahimLindsey GrayJames HirschauerShruti R KulkarniRon LiptonPetar MaksimovicCorrinne MillsMark S NeubauerBenjamin ParpillonGauri PradhanChinar SyalNhan TranDahai WenAaron Young

physicshigh energy physicsdetector physicparticle physicsexperimental physicsdeep learningcustom readoutparticle beamradiation detectionsignal processingapplied physicsphysic aware machine learning

Software Citation in Hep: Current State and Recommendations for the Future. EPJ Web of Conferences (2024)

Matthew FeickertDaniel S. KatzMark S. NeubauerElizabeth Sexton-KennedyGraeme A. Stewart

open-source softwareopen sciencescientometricsopen-source software developmentinformetricsscientific communicationsoftware engineeringsoftware citationnuclear physicshep software foundationdata sciencebibliometricscitation analysiscomputer science

Accelerating the Inference of the Exa.Trkx Pipeline. Journal of Physics Conference Series (2023)

Alina LazarXiangyang JuDaniel MurnanePaolo CalafiuraSteven FarrellYaoyuan XuMaria SpiropuluJean-Roch VlimantGiuseppe CeratiLindsey GrayThomas KlijnsmaJim KowalkowskiMarkus AtkinsonMark NeubauerGage DeZoortSavannah ThaisShih-Chieh HsuAdam AurisanoJeremy HewesAlexandra BallowNirajan AcharyaChun-yi WangEmma LiuAlberto Lucas

deep learningmachine learning researchdata scienceconvolutional neural networksparallel computinggeometric learningneural network (machine learning)machine learningparallel programminggraph processingcomputer visioncomputational sciencegraph neural networkshigh performance computer architecturemachine visionexa.trkx pipelinetrackml benchmark datasethardware accelerationcomputer science

A Detailed Study of Interpretability of Deep Neural Network Based Top Taggers. Machine Learning Science and Technology (2023)

Ayush KhotMark S NeubauerAvik Roy

deep neural networkinterpretabilitytop jetsneural network (machine learning)convolutional neural networkstop tagging performancedeep learningmachine learning researchdata sciencecomputational intelligencemachine learningexplainable aicomputer science

Codebase Release 2.0 for Ufomanager. SciPost Physics Codebases (2023)

Mark S. NeubauerAvik RoyZijun Wang

scientific computingcodebase release 2.0e-sciencehigh energy physicshigh energy density physicsparticle physicsdata integrationscientific datascience gatewaydata scienceufo modelscomputer sciencefair principlescollider physics

CORRIGENDUM: Applications and Techniques for Fast Machine Learning in Science. Frontiers in Big Data (2023)

Allison McCarn DeianaNhan TranJoshua AgarMichaela BlottGiuseppe Di GuglielmoJavier DuartePhilip HarrisScott HauckMia LiuMark S. NeubauerJennifer NgadiubaSeda Ogrenci-MemikMaurizio PieriniThea AarrestadSteffen BährJürgen BeckerAnne-Sophie BertholdRichard J. BonventreTomás E. Müller BravoMarkus DiefenthalerZhen DongNick FritzscheAmir GholamiEkaterina GovorkovaDongning GuoKyle J. HazelwoodChristian HerwigBabar KhanSehoon KimThomas KlijnsmaYaling LiuKin Ho LoTri NguyenGianantonio PezzulloSeyedramin RasoulinezhadRyan A. RiveraKate ScholbergJustin SeligSougata SenDmitri StrukovWilliam TangSavannah ThaisKai Lukas UngerRicardo VilaltaBelina von KrosigkShen WangThomas K. Warburton

learning classifier systemmachine learningcomputational sciencedata miningclassification methodmultiple classifier systemmachine learning researchdata classificationdata scienceapplied mathematicscomputational intelligencecomputational statisticscomputer sciencefast machine learning

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Mark Neubauer

Professor

UIUC

Affiliations

The Grainger College of Engineering

Department of Physics

Physics

Mark Neubauer is currently serving as a Physics Professor at Uiuc.

Honors

Create free account to unlock more features

Experiences

Create free account to unlock more features

Education

Create free account to unlock more features

Mark Neubauer

UIUC

Professor

You can click a keyword to filter the displayed items

Video Presentations

Notable Scholarly Works

Notable Federal Grants

Collaborators

Evidential Deep Learning for Uncertainty Quantification and Out-Of-Distribution Detection in Jet Identification Using Deep Neural Networks. Machine Learning Science and Technology (2025)

Ayush KhotXiwei WangAvik RoyVolodymyr KindratenkoMark Neubauer

From Signals to Features to Insights: Multi-Level Novelty Detection for Fast Scientific Discovery. (2025)

Devashri NaikNastaran DarabiSina TayebatiDinithi JayasuriyaShamma NasrinDanush ShekarCorrinne MillsBenjamin ParpillonFarah FahimMark S. NeubauerAmit Ranjan Trivedi

HIGTR: High-Performance Fpga Implementation of Complete Gnn-Based Trajectory Reconstruction for Hep. (2025)

Yun-Chen YangHsuan-Wei YuBo-Cheng LaiShih-Chieh HsuMark NeubauerSantosh Parajuli

Fair AI Models in High Energy Physics. EPJ Web of Conferences (2024)

Smart Pixel Sensors: Towards On-Sensor Filtering of Pixel Clusters With Deep Learning. Machine Learning Science and Technology (2024)

physicshigh energy physicsdetector physicparticle physicsexperimental physicsdeep learningcustom readoutparticle beamradiation detectionsignal processingapplied physicsphysic aware machine learning

Software Citation in Hep: Current State and Recommendations for the Future. EPJ Web of Conferences (2024)

Matthew FeickertDaniel S. KatzMark S. NeubauerElizabeth Sexton-KennedyGraeme A. Stewart

Accelerating the Inference of the Exa.Trkx Pipeline. Journal of Physics Conference Series (2023)

A Detailed Study of Interpretability of Deep Neural Network Based Top Taggers. Machine Learning Science and Technology (2023)

Ayush KhotMark S NeubauerAvik Roy

Codebase Release 2.0 for Ufomanager. SciPost Physics Codebases (2023)

Mark S. NeubauerAvik RoyZijun Wang

CORRIGENDUM: Applications and Techniques for Fast Machine Learning in Science. Frontiers in Big Data (2023)

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