CAIN 2023 Contributors

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Results (166)

A Rui AbreuAbreu, Rui Faculty of Engineering, University of PortoUnited States Tenet: A Flexible Framework for Machine Learning-based Vulnerability Detection
Ankit AgrawalAgrawal, Ankit University of Notre DameUnited States Web Chair in Organizing Committee for CAIN 2023 PC Member in Program Committee for CAIN 2023
Christopher AkikiAkiki, Christopher Leipzig UniversityGermany Exploring Hyperparameter Usage and Tuning in Machine Learning Research
Hani AldebesAldebes, Hani TU DarmstadtGermany Towards Code Generation from BDD Test Case Specifications: A vision
B Atta BadiiBadii, Atta University of Reading, United Kingdom Enabling Machine Learning in Software Architecture Frameworks
Earl T. BarrBarr, Earl T. University College LondonUnited Kingdom PC Member in Program Committee for CAIN 2023
Hector Barron-EscobarBarron-Escobar, Hector Leidos AustraliaAustralia Developing ethical and trustworthy AI/ML in the warfare domain
Nelly BencomoBencomo, Nelly Durham UniversityUnited Kingdom PC Member in Program Committee for CAIN 2023
Amel BennaceurBennaceur, Amel The Open University, UKUnited Kingdom PC Member in Program Committee for CAIN 2023
Justus BognerBogner, Justus University of StuttgartGermany Proceedings Chair in Organizing Committee for CAIN 2023 PC Member in Program Committee for CAIN 2023 Session Chair of Data & Model Optimization (part of CAIN Papers) A Case Study on AI Engineering Practices: Developing an Autonomous Stock Trading System Design Patterns for AI-based Systems: A Multivocal Literature Review and Pattern Repository
Markus BorgBorg, Markus CodeSceneSweden Automotive Perception Software Development: An Empirical Investigation into Data, Annotation, and Ecosystem Challenges
Jan BoschBosch, Jan Chalmers University of TechnologySweden Steering Committee Member in Steering Committee for CAIN 2023 General Chair in Organizing Committee for CAIN 2023 Session Chair of Panel - Online (part of CAIN Papers)
Laurent BouéBoué, Laurent MicrosoftUnited States Automatically Resolving Data Source Dependency Hell in Large Scale Data Science Projects
Lionel BriandBriand, Lionel University of Luxembourg; University of OttawaLuxembourg PC Member in Program Committee for CAIN 2023
Bowman BrownBrown, Bowman University of Southern California, USAUnited States Reproducibility Requires Consolidated Artifacts
C Christian CabreraCabrera, Christian Department of Computer Science and Technology, Univesity of Cambridge Prevalence of Code Smells in Reinforcement Learning Projects
Nicolás CardozoCardozo, Nicolás Universidad de los AndesColombia Prevalence of Code Smells in Reinforcement Learning Projects
Moharram ChallengerChallenger, Moharram University of AntwerpBelgium Enabling Machine Learning in Software Architecture Frameworks
Wing-Kwong ChanChan, Wing-Kwong City University of Hong Kong, Hong KongChina PC Member in Program Committee for CAIN 2023
Leon ChemnitzChemnitz, Leon TU DarmstadtGermany Towards Code Generation from BDD Test Case Specifications: A vision
S. C. CheungCheung, S. C. PC Member in Program Committee for CAIN 2023
Muhammed Tawfiq ChowdhuryChowdhury, Muhammed Tawfiq University of Notre DameUnited States Engineering Challenges for AI-Supported Computer Vision in Small Uncrewed Aerial Systems
Hilary CinisCinis, Hilary Salesforce Ignite APACAustralia Helping companies de-risk the integration of first, second and third-party AI models into a customer engagement platform
Jane Cleland-HuangCleland-Huang, Jane University of Notre DameUnited States Steering Committee Member in Steering Committee for CAIN 2023 PC Member in Program Committee for CAIN 2023 Engineering Challenges for AI-Supported Computer Vision in Small Uncrewed Aerial Systems
Maxime CordyCordy, Maxime University of Luxembourg, Luxembourg Towards Understanding Model Quantization for Reliable Deep Neural Network Deployment
Eduard Costel PinconschiCostel Pinconschi, Eduard Instituto Superior Técnico, University of Lisboa & INESC-IDPortugal Tenet: A Flexible Framework for Machine Learning-based Vulnerability Detection
Luís CruzCruz, Luís Delft University of TechnologyNetherlands PC Member in Program Committee for CAIN 2023 Maintaining and Monitoring AIOps Models Against Concept Drift Towards Understanding Machine Learning Testing in Practise Uncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AI
D Fabiano DalpiazDalpiaz, Fabiano Utrecht UniversityNetherlands PC Member in Program Committee for CAIN 2023
Ivana DusparicDusparic, Ivana Trinity College Dublin, Ireland Prevalence of Code Smells in Reinforcement Learning Projects
E Hakan ErdogmusErdogmus, Hakan Carnegie Mellon UniversityUnited States Tenet: A Flexible Framework for Machine Learning-based Vulnerability Detection
F Aleksander FabijanFabijan, Aleksander MicrosoftUnited States PC Member in Program Committee for CAIN 2023
Daniel FeitosaFeitosa, Daniel University of GroningenNetherlands Uncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AI
Michael FeldererFelderer, Michael German Aerospace Center (DLR)Germany PC Member in Program Committee for CAIN 2023
Iordanis FostiropoulosFostiropoulos, Iordanis University of Southern California, USAUnited States Reproducibility Requires Consolidated Artifacts
Xavier FranchFranch, Xavier Universitat Politècnica de CatalunyaSpain PC Member in Program Committee for CAIN 2023
Yoshiaki FukazawaFukazawa, Yoshiaki Waseda UniversityJapan Extensible Modeling Framework for Reliable Machine Learning System Analysis
G Ilche GeorgievskiGeorgievski, Ilche University of Stuttgart, GermanyGermany AI Planning Software Development Lifecycle
Smita GhaisasGhaisas, Smita TCS ResearchIndia PC Member in Program Committee for CAIN 2023
Arda GoknilGoknil, Arda SINTEF Digital Replay-Driven Continual Learning for the Industrial Internet of Things
Ian GortonGorton, Ian Northeastern University – Seattle, USAUnited States PC Member in Program Committee for CAIN 2023
Marcel GroteGrote, Marcel A Case Study on AI Engineering Practices: Developing an Autonomous Stock Trading System
Muhammad Ali GulzarGulzar, Muhammad Ali Virginia TechUnited States PC Member in Program Committee for CAIN 2023
Siyuan GuoGuo, Siyuan Max Planck Institute for Intelligent SystemsGermany Dataflow graphs as complete causal graphs
Yuejun GUoGUo, Yuejun University of Luxembourg Towards Understanding Model Quantization for Reliable Deep Neural Network Deployment
Stephan G¨unnemannG¨unnemann, Stephan School of Computation, Information and Technology, Technical University of Munich, Munich, GermanyGermany Enabling Machine Learning in Software Architecture Frameworks
H Khan Mohammad HabibullahHabibullah, Khan Mohammad University of GothenburgSweden Automotive Perception Software Development: An Empirical Investigation into Data, Annotation, and Ecosystem Challenges
Rachel HarrisonHarrison, Rachel Oxford Brookes University United Kingdom PC Member in Program Committee for CAIN 2023
Marius HauserHauser, Marius University of Stuttgart, Germany Design Patterns for AI-based Systems: A Multivocal Literature Review and Pattern Repository
Petra HeckHeck, Petra Fontys University of Applied SciencesNetherlands PC Member in Program Committee for CAIN 2023 Defining Quality Requirements for a Trustworthy AI Wildflower Monitoring Platform
Lukas HeilandHeiland, Lukas University of Stuttgart, Germany Design Patterns for AI-based Systems: A Multivocal Literature Review and Pattern Repository
Fatemeh Hendijani FardHendijani Fard, Fatemeh University of British ColumbiaCanada Algorithm Debt: Challenges and Future Paths
Hans-Martin HeynHeyn, Hans-Martin University of Gothenburg & Chalmers University of TechnologySweden Automotive Perception Software Development: An Empirical Investigation into Data, Annotation, and Ecosystem Challenges
Jati Hiliamsyah HusenHiliamsyah Husen, Jati Waseda UniversityJapan Extensible Modeling Framework for Reliable Machine Learning System Analysis
Helena Holmström OlssonHolmström Olsson, Helena Malmö UniversitySweden Steering Committee Member in Steering Committee for CAIN 2023 Program Chair in Organizing Committee for CAIN 2023 PC Chair in Program Committee for CAIN 2023 Session Chair of Keynote - Dr. Ken Power (part of CAIN Papers) Chair in Papers Committee within the CAIN Papers-track
Jennifer HorkoffHorkoff, Jennifer Chalmers and the University of GothenburgSweden Automotive Perception Software Development: An Empirical Investigation into Data, Annotation, and Ecosystem Challenges
Qiang HuHu, Qiang University of Luxembourg Towards Understanding Model Quantization for Reliable Deep Neural Network Deployment
Erik Johannes HusomHusom, Erik Johannes SINTEF Digital Replay-Driven Continual Learning for the Industrial Internet of Things
Waqar HussainHussain, Waqar Monash UniversityAustralia Industry Co-Chair in Organizing Committee for CAIN 2023 Session Chair of Industry Talks (part of CAIN Papers)
I Yuta IshimotoIshimoto, Yuta Kyushu UniversityJapan An Initial Analysis of Repair and Side-effect Prediction for Neural Networks
Minna IsomursuIsomursu, Minna University of OuluFinland AI Living Lab: Quality Assurance for AI-based Health systems
David Issa MattosIssa Mattos, David Volvo CarsSweden PC Member in Program Committee for CAIN 2023
Laurent IttiItti, Laurent University of Southern California, USAUnited States Reproducibility Requires Consolidated Artifacts
J Aurelie JacquetJacquet, Aurelie How international standards are defining best practice and helping implement AI responsibly: Dr. Aurelie Jacquet
Limin JiaJia, Limin Carnegie Mellon University Tenet: A Flexible Framework for Machine Learning-based Vulnerability Detection
Brittany JohnsonJohnson, Brittany George Mason UniversityUnited States PC Member in Program Committee for CAIN 2023
K Yasutaka KameiKamei, Yasutaka Kyushu UniversityJapan PC Member in Program Committee for CAIN 2023 An Initial Analysis of Repair and Side-effect Prediction for Neural Networks
Foutse KhomhKhomh, Foutse Polytechnique MontréalCanada PC Member in Program Committee for CAIN 2023 Engineering Trustworthy AI Systems - Foutse Khomh
Ensiye KiyamousaviKiyamousavi, Ensiye GATE InstituteBulgaria How Federated Machine Learning Helps Increase the Mutual Benefit of Data-Sharing Ecosystems
Alessia KnaussKnauss, Alessia Zenseact ABSweden Automotive Perception Software Development: An Empirical Investigation into Data, Annotation, and Ecosystem Challenges
Eric KnaussKnauss, Eric Chalmers \| University of GothenburgSweden PC Member in Program Committee for CAIN 2023 Automotive Perception Software Development: An Empirical Investigation into Data, Annotation, and Ecosystem Challenges
Nikolay KolyadaKolyada, Nikolay Exploring Hyperparameter Usage and Tuning in Machine Learning Research
Masanari KondoKondo, Masanari Kyushu UniversityJapan An Initial Analysis of Repair and Side-effect Prediction for Neural Networks
Rrezarta KrasniqiKrasniqi, Rrezarta University of North Carolina at CharlotteUnited States PC Member in Program Committee for CAIN 2023 Session Chair of Training & Learning (part of CAIN Papers)
Iva KrastevaKrasteva, Iva Sofia University, GATE InstituteBulgaria How Federated Machine Learning Helps Increase the Mutual Benefit of Data-Sharing Ecosystems
Boris KraychevKraychev, Boris GATE InstituteBulgaria How Federated Machine Learning Helps Increase the Mutual Benefit of Data-Sharing Ecosystems
Pratap KunireddyKunireddy, Pratap MicrosoftUnited States Automatically Resolving Data Source Dependency Hell in Large Scale Data Science Projects
Christian KästnerKästner, Christian Carnegie Mellon University PC Member in Program Committee for CAIN 2023 A Meta-Summary of Challenges in Building Products with ML Components -- Collecting Experiences from 4758+ Practitioners
L Filippo LanubileLanubile, Filippo University of BariItaly PC Member in Program Committee for CAIN 2023
Neil D. LawrenceLawrence, Neil D. Department of Computer Science and Technology, Univesity of Cambridge Dataflow graphs as complete causal graphs
Yves Le TraonLe Traon, Yves University of Luxembourg, LuxembourgLuxembourg Towards Understanding Model Quantization for Reliable Deep Neural Network Deployment
John LeeLee, John PC Member in Program Committee for CAIN 2023
Noah LeeLee, Noah MetaUnited States PC Member in Program Committee for CAIN 2023
Valentina LenarduzziLenarduzzi, Valentina University of OuluFinland AI Living Lab: Quality Assurance for AI-based Health systems
Grace LewisLewis, Grace Carnegie Mellon Software Engineering InstituteUnited States PC Member in Program Committee for CAIN 2023 Session Chair of AI System Design (part of CAIN Papers) A Meta-Summary of Challenges in Building Products with ML Components -- Collecting Experiences from 4758+ Practitioners
Heng LiLi, Heng Polytechnique MontréalCanada PC Member in Program Committee for CAIN 2023
Polly Jing LiLi, Polly Jing Kognic ABSweden Automotive Perception Software Development: An Empirical Investigation into Data, Annotation, and Ecosystem Challenges
Yue LiuLiu, Yue CSIRO's Data61 & University of New South WalesAustralia Towards Concrete and Connected AI Risk Assessment (C2AIRA): A Systematic Mapping Study
Qinghua LuLu, Qinghua CSIRO’s Data61Australia Towards Concrete and Connected AI Risk Assessment (C2AIRA): A Systematic Mapping Study
Lucy Ellen LwakatareLwakatare, Lucy Ellen University of HelsinkiFinland PC Member in Program Committee for CAIN 2023
M Jie M. ZhangM. Zhang, Jie King's College LondonUnited Kingdom PC Member in Program Committee for CAIN 2023
Lei MaMa, Lei University of AlbertaCanada PC Member in Program Committee for CAIN 2023
Wei MaMa, Wei Nanyang Technological University, Singapore Towards Understanding Model Quantization for Reliable Deep Neural Network Deployment
Ken MatsuiMatsui, Ken Kyushu University An Initial Analysis of Repair and Side-effect Prediction for Neural Networks
Tim MenziesMenzies, Tim North Carolina State UniversityUnited States PC Member in Program Committee for CAIN 2023
Andreas MetzgerMetzger, Andreas University of Duisburg-EssenGermany PC Member in Program Committee for CAIN 2023
Mira MeziniMezini, Mira TU DarmstadtGermany Towards Code Generation from BDD Test Case Specifications: A vision
Tommi MikkonenMikkonen, Tommi University of JyvaskylaFinland PC Member in Program Committee for CAIN 2023
Armin MoinMoin, Armin University of California, Santa BarbaraUnited States Enabling Machine Learning in Software Architecture Frameworks
Henry MucciniMuccini, Henry University of L'Aquila, ItalyItaly Steering Committee Member in Steering Committee for CAIN 2023 PC Member in Program Committee for CAIN 2023 Session Chair of Roadmap and Closing Remarks (part of CAIN Papers)
Kazuki MunakataMunakata, Kazuki Fujitsu Limited, Tokyo, JapanJapan Extensible Modeling Framework for Reliable Machine Learning System Analysis
Simon Myklebust NielsenMyklebust Nielsen, Simon University of Oslo, NorwayNorway Replay-Driven Continual Learning for the Industrial Internet of Things
N Nadia NaharNahar, Nadia Carnegie Mellon UniversityBangladesh A Meta-Summary of Challenges in Building Products with ML Components -- Collecting Experiences from 4758+ Practitioners
Krishna NarasimhanNarasimhan, Krishna TU DarmstadtGermany Towards Code Generation from BDD Test Case Specifications: A vision
Mariam NaveedNaveed, Mariam TU DarmstadtGermany Towards Code Generation from BDD Test Case Specifications: A vision
Phuong T. NguyenNguyen, Phuong T. University of L’AquilaItaly PC Member in Program Committee for CAIN 2023
Tien N. NguyenNguyen, Tien N. University of Texas at DallasUnited States PC Member in Program Committee for CAIN 2023
O Ipek OzkayaOzkaya, Ipek Carnegie Mellon UniversityUnited States Steering Committee Member in Steering Committee for CAIN 2023 Program Chair in Organizing Committee for CAIN 2023 PC Chair in Program Committee for CAIN 2023 Session Chair of Realizing the Promise of AI: Challenges and Visions (part of CAIN Papers) Session Chair of Keynote - Dr. Foutse Khomh (part of CAIN Papers) Welcome Chair in Papers Committee within the CAIN Papers-track
P Andrei PaleyesPaleyes, Andrei Department of Computer Science and Technology, Univesity of Cambridge Dataflow graphs as complete causal graphs
Mike PapadakisPapadakis, Mike University of Luxembourg, LuxembourgLuxembourg PC Member in Program Committee for CAIN 2023 Towards Understanding Model Quantization for Reliable Deep Neural Network Deployment
Frances PaulischPaulisch, Frances Siemens Healthineers PC Member in Program Committee for CAIN 2023
Harsha PereraPerera, Harsha CSIRO's Data61 & University of New South WalesAustralia Towards Concrete and Connected AI Risk Assessment (C2AIRA): A Systematic Mapping Study
Lorena Poenaru-OlaruPoenaru-Olaru, Lorena TU DelftNetherlands Maintaining and Monitoring AIOps Models Against Concept Drift
Martin PotthastPotthast, Martin Leipzig UniversityGermany Exploring Hyperparameter Usage and Tuning in Machine Learning Research
Ken PowerPower, Ken MotionalUnited States AI Engineering in Autonomous Vehicle Development - Ken Power
Rafael PrikladnickiPrikladnicki, Rafael School of Technology at PUCRS UniversityBrazil PC Member in Program Committee for CAIN 2023
Corina S. PăsăreanuPăsăreanu, Corina S. Carnegie Mellon UniversityUnited States PC Member in Program Committee for CAIN 2023
R Chaiyong RagkhitwetsagulRagkhitwetsagul, Chaiyong Mahidol University, ThailandThailand PC Member in Program Committee for CAIN 2023
Mona RahimiRahimi, Mona Northern Illinois UniversityUnited States Posters Chair in Organizing Committee for CAIN 2023 PC Member in Program Committee for CAIN 2023 Session Chair of Poster - Online (part of CAIN Papers) Chair in Posters Committee within the CAIN Posters-track
Hridesh RajanRajan, Hridesh Iowa State UniversityUnited States PC Member in Program Committee for CAIN 2023
Orna RazRaz, Orna IBM PC Member in Program Committee for CAIN 2023
David ReichenbachReichenbach, David TU Darmstadt, GermanyGermany Towards Code Generation from BDD Test Case Specifications: A vision
Sofia ReisReis, Sofia Instituto Superior Técnico, U. Lisboa & INESC-IDPortugal Tenet: A Flexible Framework for Machine Learning-based Vulnerability Detection
S Jan S. RellermeyerS. Rellermeyer, Jan Leibniz University HannoverGermany Maintaining and Monitoring AIOps Models Against Concept Drift
June SallouSallou, June Delft University of TechnologyNetherlands Uncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AI
Leonardo Sastoque PinillaSastoque Pinilla, Leonardo Centro de Fabricación Avanzada Aeronáutica (CFAA)Spain Replay-Driven Continual Learning for the Industrial Internet of Things
Andras SchmelczerSchmelczer, Andras Leiden UniversityUnited Kingdom Trustworthy and Robust AI Deployment by Design: A framework to inject best practice support into AI deployment pipelines
Gerard SchoutenSchouten, Gerard Fontys University of Applied SciencesNetherlands Defining Quality Requirements for a Trustworthy AI Wildflower Monitoring Platform
Bernhard SchölkopfSchölkopf, Bernhard MPI Tuebingen Dataflow graphs as complete causal graphs
Sagar SenSen, Sagar Replay-Driven Continual Learning for the Industrial Internet of Things
Arumoy ShomeShome, Arumoy Delft University of TechnologyNetherlands Towards Understanding Machine Learning Testing in Practise
Norbert SiegmundSiegmund, Norbert Leipzig UniversityGermany PC Member in Program Committee for CAIN 2023
Norbert SiegmundSiegmund, Norbert Leipzig UniversityGermany Exploring Hyperparameter Usage and Tuning in Machine Learning Research
Emmanuel Iko-Ojo SimonSimon, Emmanuel Iko-Ojo Australian National UniversityAustralia Algorithm Debt: Challenges and Future Paths
Sebastian SimonSimon, Sebastian Leipzig UniversityGermany Exploring Hyperparameter Usage and Tuning in Machine Learning Research
Benno SteinStein, Benno Bauhaus-University WeimarGermany Exploring Hyperparameter Usage and Tuning in Machine Learning Research
Pavle SuboticSubotic, Pavle Microsoft AzureSerbia Automatically Resolving Data Source Dependency Hell in Large Scale Data Science Projects
T Hironori TakeuchiTakeuchi, Hironori Musashi UniversityJapan Extensible Modeling Framework for Reliable Machine Learning System Analysis
Hiroshi TanakaTanaka, Hiroshi Fujitsu Limited, Tokyo, JapanJapan Extensible Modeling Framework for Reliable Machine Learning System Analysis
Hnin Thandar TunTun, Hnin Thandar Waseda University, JapanJapan Extensible Modeling Framework for Reliable Machine Learning System Analysis
Simeon TverdalTverdal, Simeon SINTEF Digital Replay-Driven Continual Learning for the Industrial Internet of Things
U Naoyasu UbayashiUbayashi, Naoyasu Kyushu UniversityJapan An Initial Analysis of Repair and Side-effect Prediction for Neural Networks
V Karthik VaidhyanathanVaidhyanathan, Karthik IIIT HyderabadIndia Posters Chair in Organizing Committee for CAIN 2023 PC Member in Program Committee for CAIN 2023 Session Chair of Poster - Online (part of CAIN Papers) Chair in Posters Committee within the CAIN Posters-track
Arie van Deursenvan Deursen, Arie Delft University of TechnologyNetherlands Maintaining and Monitoring AIOps Models Against Concept Drift Towards Understanding Machine Learning Testing in Practise Uncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AI
Melina VidoniVidoni, Melina Australian National UniversityAustralia Algorithm Debt: Challenges and Future Paths
Joost VisserVisser, Joost Leiden UniversityNetherlands Trustworthy and Robust AI Deployment by Design: A framework to inject best practice support into AI deployment pipelines
W Song WangWang, Song York UniversityCanada PC Member in Program Committee for CAIN 2023
Hironori WashizakiWashizaki, Hironori Waseda UniversityJapan Extensible Modeling Framework for Reliable Machine Learning System Analysis
Jon WhittleWhittle, Jon CSIRO's Data61 and Monash UniversityAustralia Towards Concrete and Connected AI Risk Assessment (C2AIRA): A Systematic Mapping Study
X Boming XiaXia, Boming CSIRO's Data61 & University of New South WalesAustralia Towards Concrete and Connected AI Risk Assessment (C2AIRA): A Systematic Mapping Study
Xin XiaXia, Xin HuaweiChina PC Member in Program Committee for CAIN 2023
Xiaofei XieXie, Xiaofei Singapore Management UniversitySingapore Towards Understanding Model Quantization for Reliable Deep Neural Network Deployment
Zhenchang XingXing, Zhenchang CSIRO’s Data61; Australian National UniversityAustralia Industry Chair in Organizing Committee for CAIN 2023 Session Chair of Trust (part of CAIN Papers) Session Chair of Welcome and Keynote by Dr. Aurelie Jacquet (part of CAIN Papers)
Zhenchang XingXing, Zhenchang Towards Concrete and Connected AI Risk Assessment (C2AIRA): A Systematic Mapping Study
Zhenchang XingXing, Zhenchang CSIRO’s Data61; Australian National UniversityAustralia Chair in Industrial Talks Committee within the CAIN Industrial Talks-track
Y Tim YarallyYarally, Tim Delft University of Technology Uncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AI
Nobukazu YoshiokaYoshioka, Nobukazu Waseda University, JapanJapan Extensible Modeling Framework for Reliable Machine Learning System Analysis
Z Marc ZellerZeller, Marc Siemens AGGermany safe.trAIn – Engineering and Assurance of a Driverless Regional Train
Chi ZhangZhang, Chi Tenet: A Flexible Framework for Machine Learning-based Vulnerability Detection
Haoran ZhangZhang, Haoran Carnegie Mellon University, USAUnited States A Meta-Summary of Challenges in Building Products with ML Components -- Collecting Experiences from 4758+ Practitioners
Tianyi ZhangZhang, Tianyi Purdue UniversityUnited States PC Member in Program Committee for CAIN 2023
Xiangyu ZhangZhang, Xiangyu Purdue UniversityUnited States PC Member in Program Committee for CAIN 2023
Shurui ZhouZhou, Shurui University of Toronto, Canada A Meta-Summary of Challenges in Building Products with ML Components -- Collecting Experiences from 4758+ Practitioners
Liming ZhuZhu, Liming CSIRO’s Data61Australia PC Member in Program Committee for CAIN 2023
Liming ZhuZhu, Liming The University of New South WalesAustralia Towards Concrete and Connected AI Risk Assessment (C2AIRA): A Systematic Mapping Study
Roshanak Zilouchian MoghaddamZilouchian Moghaddam, Roshanak MicrosoftUnited States PC Member in Program Committee for CAIN 2023
Thomas ZimmermannZimmermann, Thomas Microsoft ResearchUnited States Steering Committee Member in Steering Committee for CAIN 2023 PC Member in Program Committee for CAIN 2023