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Its origins are rooted in the pioneering vision of Prof. Eduardo Caianiello, a key figure who established neural network and cybernetics research in Italy and was instrumental in founding institutions like CNR's Laboratory of Cybernetics, and the Italian Institute of Advanced Scientific Studies (IASS) in Vietri sul Mare, SIREN's historical base. Continuing this legacy, SIREN has hosted the annual Italian Workshop on Neural Networks (WIRN) since its foundation, also promoting scientific dissemination, educational training and advanced research by participating in European research projects. The society is currently led by President Prof. Anna Esposito (University of Campania “Luigi Vanvitelli”)\u003C/span>\u003C/p>","2025-05-06T20:08:30.458Z","2025-05-06T20:08:35.913Z","2025-05-06T20:08:35.907Z","730",{"id":989,"name":990,"alternativeText":16,"caption":16,"width":991,"height":992,"formats":993,"hash":1006,"ext":19,"mime":20,"size":1007,"url":1008,"previewUrl":16,"provider":23,"provider_metadata":16,"createdAt":1009,"updatedAt":1009},176,"siren.png",503,214,{"small":994,"thumbnail":1000},{"ext":19,"url":995,"hash":996,"mime":20,"name":997,"path":16,"size":998,"width":913,"height":999},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/small_siren_af15b50165.png","small_siren_af15b50165","small_siren.png",28.23,213,{"ext":19,"url":1001,"hash":1002,"mime":20,"name":1003,"path":16,"size":1004,"width":38,"height":1005},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/thumbnail_siren_af15b50165.png","thumbnail_siren_af15b50165","thumbnail_siren.png",11.84,104,"siren_af15b50165",6.06,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/siren_af15b50165.png","2025-05-06T20:01:59.290Z",{"id":195,"variation":46,"button":1011},[1012],{"id":717,"label":859,"size":50,"color":51,"style":16,"icon":860,"iconPosition":52,"url":1013,"newWindow":8,"downloadable":8,"shape":16},"https://www.siren-neural-net.it/","-518",{"id":214,"name":1016,"description":1017,"createdAt":1018,"updatedAt":1019,"publishedAt":1020,"url_path_id":1021,"logo":1022,"website":1052,"url_path":1057},"AUBAY ITALIA SPA","\u003Cp style=\"text-align:justify;\">Aubay is a Innovation Company, leader in Europe in the areas of Management Consulting,\u003Cbr>Information & Communication Technology. The group is listed on the NYSE Euronext market in\u003Cbr>Paris, has 7500 employees in 7 countries and 540,3M€ turnover in 2024. We are key player in the\u003Cbr>main market areas, our value is our customers who we support and guide in the path of change.\u003Cbr>Aubay Group is present in Italy with Aubay Italia S.p.A., a company which offers ICT and\u003Cbr>Management Consulting professional services and solutions through experienced professionals\u003Cbr>with a strong know-how in ICT consulting, System Integration and Business Consulting.\u003Cbr>Our project is inspired by a strong entrepreneur attitude which is the base for a real business\u003Cbr>project.\u003Cbr>Continuous growth, profitability and a solid financial position are the key ingredients that\u003Cbr>guarantee us extraordinary and continuous growth. Our financial position is solid: margins are\u003Cbr>always reinvested in the company and there is no middle or long-term debt.\u003Cbr>In more than 20 years of history we built a sound company which is the base for a promising and\u003Cbr>reliable future, we aim to continue to improve a company model with a strong social role and able\u003Cbr>to generate growth, welfare and employment.\u003C/p>","2025-05-29T20:17:59.083Z","2025-05-30T13:43:36.558Z","2025-05-29T20:18:01.616Z","762",{"id":1023,"name":1024,"alternativeText":16,"caption":16,"width":1025,"height":1026,"formats":1027,"hash":1048,"ext":1029,"mime":1032,"size":1049,"url":1050,"previewUrl":16,"provider":23,"provider_metadata":16,"createdAt":1051,"updatedAt":1051},190,"LOGO_AUBAY.jpg",825,874,{"small":1028,"medium":1035,"thumbnail":1041},{"ext":1029,"url":1030,"hash":1031,"mime":1032,"name":1033,"path":16,"size":1034,"width":872,"height":913},".jpg","https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/small_LOGO_AUBAY_80089f7825.jpg","small_LOGO_AUBAY_80089f7825","image/jpeg","small_LOGO_AUBAY.jpg",21.66,{"ext":1029,"url":1036,"hash":1037,"mime":1032,"name":1038,"path":16,"size":1039,"width":1040,"height":920},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/medium_LOGO_AUBAY_80089f7825.jpg","medium_LOGO_AUBAY_80089f7825","medium_LOGO_AUBAY.jpg",37.56,708,{"ext":1029,"url":1042,"hash":1043,"mime":1032,"name":1044,"path":16,"size":1045,"width":1046,"height":1047},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/thumbnail_LOGO_AUBAY_80089f7825.jpg","thumbnail_LOGO_AUBAY_80089f7825","thumbnail_LOGO_AUBAY.jpg",4.93,147,156,"LOGO_AUBAY_80089f7825",44.93,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/LOGO_AUBAY_80089f7825.jpg","2025-05-29T20:22:37.101Z",{"id":464,"variation":46,"button":1053},[1054],{"id":1055,"label":859,"size":50,"color":51,"style":16,"icon":860,"iconPosition":52,"url":1056,"newWindow":8,"downloadable":8,"shape":16},55,"https://www.aubay.it/","-545",{"id":66,"name":1059,"description":63,"createdAt":1060,"updatedAt":1061,"publishedAt":1062,"url_path_id":1063,"logo":1064,"website":16,"url_path":1095},"Intesa Sanpaolo","2025-05-30T12:58:23.355Z","2025-05-30T12:58:26.612Z","2025-05-30T12:58:26.605Z","763",{"id":1065,"name":1066,"alternativeText":16,"caption":16,"width":1067,"height":1068,"formats":1069,"hash":1091,"ext":1029,"mime":1032,"size":1092,"url":1093,"previewUrl":16,"provider":23,"provider_metadata":16,"createdAt":1094,"updatedAt":1094},210,"INTESA SANPAOLO_COL.jpg",2103,236,{"large":1070,"small":1076,"medium":1081,"thumbnail":1086},{"ext":1029,"url":1071,"hash":1072,"mime":1032,"name":1073,"path":16,"size":1074,"width":906,"height":1075},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/large_INTESA_SANPAOLO_COL_6a2878bba1.jpg","large_INTESA_SANPAOLO_COL_6a2878bba1","large_INTESA SANPAOLO_COL.jpg",20.8,112,{"ext":1029,"url":1077,"hash":1078,"mime":1032,"name":1079,"path":16,"size":1080,"width":913,"height":539},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/small_INTESA_SANPAOLO_COL_6a2878bba1.jpg","small_INTESA_SANPAOLO_COL_6a2878bba1","small_INTESA SANPAOLO_COL.jpg",8.86,{"ext":1029,"url":1082,"hash":1083,"mime":1032,"name":1084,"path":16,"size":1085,"width":920,"height":673},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/medium_INTESA_SANPAOLO_COL_6a2878bba1.jpg","medium_INTESA_SANPAOLO_COL_6a2878bba1","medium_INTESA SANPAOLO_COL.jpg",15.05,{"ext":1029,"url":1087,"hash":1088,"mime":1032,"name":1089,"path":16,"size":1090,"width":38,"height":348},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/thumbnail_INTESA_SANPAOLO_COL_6a2878bba1.jpg","thumbnail_INTESA_SANPAOLO_COL_6a2878bba1","thumbnail_INTESA SANPAOLO_COL.jpg",3.36,"INTESA_SANPAOLO_COL_6a2878bba1",50.57,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/INTESA_SANPAOLO_COL_6a2878bba1.jpg","2025-05-30T12:58:19.967Z","-546",{"id":292,"name":1097,"description":1098,"createdAt":1099,"updatedAt":1100,"publishedAt":1101,"url_path_id":1102,"logo":1103,"website":1119,"url_path":1125},"IEEE Power & Energy Society","\u003Cp>\u003Cspan style=\"background-color:rgb(255,255,255);color:rgb(26,32,38);\">The Power & Energy Society (PES) provides the world’s largest forum for sharing the latest in technological developments in the electric power industry, for developing standards that guide the development and construction of equipment and systems, and for educating members of the industry and the general public. Members of the Power & Energy Society are leaders in this field, and they — and their employers — derive substantial benefits from involvement with this unique and outstanding association.\u003C/span>\u003C/p>","2025-06-09T18:55:44.049Z","2025-06-09T18:55:53.457Z","2025-06-09T18:55:53.451Z","768",{"id":1104,"name":1105,"alternativeText":16,"caption":16,"width":1106,"height":1107,"formats":1108,"hash":1115,"ext":1029,"mime":1032,"size":1116,"url":1117,"previewUrl":16,"provider":23,"provider_metadata":16,"createdAt":1118,"updatedAt":1118},215,"PES.jpg",357,248,{"thumbnail":1109},{"ext":1029,"url":1110,"hash":1111,"mime":1032,"name":1112,"path":16,"size":1113,"width":1114,"height":1047},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/thumbnail_PES_3653cd5618.jpg","thumbnail_PES_3653cd5618","thumbnail_PES.jpg",10.08,225,"PES_3653cd5618",21.69,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/PES_3653cd5618.jpg","2025-06-09T18:54:57.178Z",{"id":1120,"variation":46,"button":1121},54,[1122],{"id":1123,"label":859,"size":50,"color":51,"style":16,"icon":860,"iconPosition":52,"url":1124,"newWindow":8,"downloadable":8,"shape":16},57,"https://ieee-pes.org/","-551",{"pagination":1127},{"page":5,"pageSize":641,"pageCount":5,"total":292},{"id":396,"heading":391,"pageHeader":1129,"sections":1130},{"id":396,"description":16,"showPageHeader":8,"backgroundColor":68,"image":16},[1131],{"id":5,"__component":1132,"componentVariation":1133,"contactsVariation":1134,"styles":16,"header":16,"sessionsGroup":1135},"content.sessions","Sessions Base","Card Contact Full",[1136],{"id":5,"groupTitle":1137,"sessions":1138},"IJCNN 2025 Keynotes",[1139,1193,1241,1290,1328,1381,1422,1461],{"id":317,"session":1140},{"id":1141,"title":1142,"teaser":63,"body":1143,"createdAt":1144,"updatedAt":1145,"publishedAt":1146,"url_path_id":1147,"contacts":1148,"url_path":1192},71,"Recent Results Towards Reasoning","\u003Cp style=\"text-align:justify;\">\u003Cspan style=\"background-color:rgb(255,255,255);color:rgb(34,34,34);font-family:Arial, Helvetica, sans-serif;\">In this presentation, I will go over a few recent topics towards understanding the limits of reasoning capabilities of large language models. I will start with an analysis of the hardness of problems like syllogisms when tackling them with the Transformer architecture; I will propose a metric to measure that hardness, and an approach to make it easier for LLMs to reduce such hardness. I will show that these limitations are not specific to the textual domain and also exist for other domains like visual tasks. Finally, I will discuss recent results in better measuring the capabilities of LLMs for math problems.\u003C/span>\u003C/p>","2024-12-20T19:13:22.342Z","2025-05-29T20:29:53.488Z","2024-12-20T19:13:27.826Z","525",[1149],{"id":1150,"name":1151,"committee":16,"position":16,"affiliation":1152,"email":16,"biography":1153,"createdAt":1154,"updatedAt":1154,"url_path_id":1155,"contactPhoto":1156,"socialLinks":1190,"url_path":1191},405,"Samy Bengio","Apple","\u003Cp style=\"text-align:justify;\">Samy Bengio (PhD in computer science, University of Montreal, 1993) is a senior director of machine learning research at Apple since 2021. Before that, he was a distinguished scientist at Google Research since 2007 where he was heading part of the Google Brain team, and at IDIAP in Switzerland in the early 2000s where he co-wrote the well-known open-source Torch machine learning library.\u003C/p>\u003Cp style=\"text-align:justify;\">\u003Cbr>His research interests span many areas of machine learning such as deep architectures, representation learning, vision and language processing and more recently, reasoning.\u003C/p>\u003Cp style=\"text-align:justify;\">\u003Cbr>He is action editor of the Journal of Machine Learning Research and on the board of the NeurIPS foundation. He was on the editorial board of the Machine Learning Journal, has been program chair (2017) and general chair (2018) of NeurIPS, program chair of ICLR (2015, 2016), general chair of BayLearn (2012-2015), MLMI (2004-2006), as well as NNSP (2002), and on the program committee of several international conferences such as NeurIPS, ICML, ICLR, ECML and IJCAI.\u003C/p>\u003Cp style=\"text-align:justify;\">\u003Cbr>More details can be found at http://bengio.abracadoudou.com.\u003C/p>","2024-12-20T19:12:02.114Z","524",{"id":1157,"name":1158,"alternativeText":16,"caption":16,"width":1159,"height":1160,"formats":1161,"hash":1186,"ext":19,"mime":20,"size":1187,"url":1188,"previewUrl":16,"provider":23,"provider_metadata":16,"createdAt":1189,"updatedAt":1189},133,"samy_apple_2021.png",1872,2183,{"large":1162,"small":1168,"medium":1174,"thumbnail":1180},{"ext":19,"url":1163,"hash":1164,"mime":20,"name":1165,"path":16,"size":1166,"width":1167,"height":906},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/large_samy_apple_2021_598b1626e4.png","large_samy_apple_2021_598b1626e4","large_samy_apple_2021.png",1654.13,858,{"ext":19,"url":1169,"hash":1170,"mime":20,"name":1171,"path":16,"size":1172,"width":1173,"height":913},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/small_samy_apple_2021_598b1626e4.png","small_samy_apple_2021_598b1626e4","small_samy_apple_2021.png",440.98,429,{"ext":19,"url":1175,"hash":1176,"mime":20,"name":1177,"path":16,"size":1178,"width":1179,"height":920},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/medium_samy_apple_2021_598b1626e4.png","medium_samy_apple_2021_598b1626e4","medium_samy_apple_2021.png",942.84,643,{"ext":19,"url":1181,"hash":1182,"mime":20,"name":1183,"path":16,"size":1184,"width":1185,"height":1047},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/thumbnail_samy_apple_2021_598b1626e4.png","thumbnail_samy_apple_2021_598b1626e4","thumbnail_samy_apple_2021.png",50.32,134,"samy_apple_2021_598b1626e4",1475.18,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/samy_apple_2021_598b1626e4.png","2024-12-20T19:11:03.285Z",[],"-319","-320",{"id":290,"session":1194},{"id":851,"title":1195,"teaser":63,"body":1196,"createdAt":1197,"updatedAt":1198,"publishedAt":1199,"url_path_id":1200,"contacts":1201,"url_path":1240},"AI Ethics and safety in the era of generative AI","\u003Cp style=\"text-align:justify;\">\u003Cspan style=\"background-color:rgb(255,255,255);color:rgb(34,34,34);font-family:Arial, Helvetica, sans-serif;\">AI is going to bring huge benefits in terms of scientific progress, human wellbeing, economic value, and the possibility of finding solutions to major social and environmental problems. Supported by AI, we will be able to make more grounded decisions and to focus on the main values and goals of a decision process rather than on routine and repetitive tasks. However, such a powerful technology also raises some concerns, related for example to the black-box nature of some AI approaches, the possible discriminatory decisions that AI algorithms may recommend, the spread of misinformation, data privacy/handling issues, and the impact on jobs and societal structures. As AI capabilities advance, from symbolic AI to machine learning to generative AI to agentic AI, new and amplified ethics issues arise and need to be identified and addressed.\u003C/span>\u003Cbr>\u003Cspan style=\"background-color:rgb(255,255,255);color:rgb(34,34,34);font-family:Arial, Helvetica, sans-serif;\">These concerns are among the obstacles that hold AI back or that cause worry for current AI users, adopters, and policy makers. Without answers to these questions, many will not trust AI, and therefore will not fully adopt it nor get its positive impact.\u003C/span>\u003Cbr>\u003Cspan style=\"background-color:rgb(255,255,255);color:rgb(34,34,34);font-family:Arial, Helvetica, sans-serif;\">In this talk I will present the main issues around AI ethics and safety, some of the proposed technical and non-technical solutions, as well as practical actions and regulations being defined for AI development, deployment, and use.\u003C/span>\u003C/p>","2024-12-20T19:19:05.631Z","2025-06-09T15:19:11.430Z","2024-12-20T19:19:08.604Z","527",[1202],{"id":1203,"name":1204,"committee":16,"position":16,"affiliation":1205,"email":16,"biography":1206,"createdAt":1207,"updatedAt":1208,"url_path_id":1209,"contactPhoto":1210,"socialLinks":1238,"url_path":1239},406,"Francesca Rossi","IBM Reseach, USA","\u003Cp style=\"text-align:justify;\">Francesca Rossi is an IBM Fellow and the IBM AI Ethics Global Leader.\u003C/p>\u003Cp style=\"text-align:justify;\">\u003Cbr>After 25 years in academia, she is now based at the T.J. Watson IBM Research Lab, New York, USA, where she leads research projects and she co-chairs the IBM AI Ethics board.\u003C/p>\u003Cp style=\"text-align:justify;\">\u003Cbr>Her research interests focus on artificial intelligence, with special focus on constraint reasoning, preferences, multi-agent systems, computational social choice, neuro-symbolic AI, cognitive architectures, and value alignment.\u003C/p>\u003Cp style=\"text-align:justify;\">\u003Cbr>On these topics, she has published over 220 scientific articles in journals and conference proceedings, and as book chapters. She is a fellow of both AAAI and EurAI, and she has been the president of IJCAI. She is in the steering committee of the AAAI/ACM Conference on AI, Ethics, and Society, that she chaired in 2023. She is the current president of AAAI.\u003Cbr>Besides her AI research activities, Francesca also work with many stakeholders on AI ethics and governance. She has been a member of the European Commission High Level Expert Group on AI, she is a member of the scientific advisory board of the Future of Life Institute and of the board of the Partnership on AI.  She co-chairs the Responsible AI working group of the Global Partnership on AI and the OECD Expert group on AI Futures.\u003C/p>","2024-12-20T19:18:21.089Z","2025-01-06T15:00:58.487Z","526",{"id":1185,"name":1211,"alternativeText":16,"caption":16,"width":1212,"height":1212,"formats":1213,"hash":1234,"ext":1029,"mime":1032,"size":1235,"url":1236,"previewUrl":16,"provider":23,"provider_metadata":16,"createdAt":1237,"updatedAt":1237},"22-06-29_RLAVINE_YORKTOWN_RESEARCH_FR_053_CROPPED.jpg",3900,{"large":1214,"small":1219,"medium":1224,"thumbnail":1229},{"ext":1029,"url":1215,"hash":1216,"mime":1032,"name":1217,"path":16,"size":1218,"width":906,"height":906},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/large_22_06_29_RLAVINE_YORKTOWN_RESEARCH_FR_053_CROPPED_def3310d40.jpg","large_22_06_29_RLAVINE_YORKTOWN_RESEARCH_FR_053_CROPPED_def3310d40","large_22-06-29_RLAVINE_YORKTOWN_RESEARCH_FR_053_CROPPED.jpg",144.77,{"ext":1029,"url":1220,"hash":1221,"mime":1032,"name":1222,"path":16,"size":1223,"width":913,"height":913},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/small_22_06_29_RLAVINE_YORKTOWN_RESEARCH_FR_053_CROPPED_def3310d40.jpg","small_22_06_29_RLAVINE_YORKTOWN_RESEARCH_FR_053_CROPPED_def3310d40","small_22-06-29_RLAVINE_YORKTOWN_RESEARCH_FR_053_CROPPED.jpg",35.77,{"ext":1029,"url":1225,"hash":1226,"mime":1032,"name":1227,"path":16,"size":1228,"width":920,"height":920},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/medium_22_06_29_RLAVINE_YORKTOWN_RESEARCH_FR_053_CROPPED_def3310d40.jpg","medium_22_06_29_RLAVINE_YORKTOWN_RESEARCH_FR_053_CROPPED_def3310d40","medium_22-06-29_RLAVINE_YORKTOWN_RESEARCH_FR_053_CROPPED.jpg",78.34,{"ext":1029,"url":1230,"hash":1231,"mime":1032,"name":1232,"path":16,"size":1233,"width":1047,"height":1047},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/thumbnail_22_06_29_RLAVINE_YORKTOWN_RESEARCH_FR_053_CROPPED_def3310d40.jpg","thumbnail_22_06_29_RLAVINE_YORKTOWN_RESEARCH_FR_053_CROPPED_def3310d40","thumbnail_22-06-29_RLAVINE_YORKTOWN_RESEARCH_FR_053_CROPPED.jpg",5.12,"22_06_29_RLAVINE_YORKTOWN_RESEARCH_FR_053_CROPPED_def3310d40",2257.72,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/22_06_29_RLAVINE_YORKTOWN_RESEARCH_FR_053_CROPPED_def3310d40.jpg","2025-01-06T15:00:55.641Z",[],"-321","-322",{"id":1242,"session":1243},87,{"id":1244,"title":1245,"teaser":63,"body":1246,"createdAt":1247,"updatedAt":1248,"publishedAt":1249,"url_path_id":1250,"contacts":1251,"url_path":1289},85,"xLSTM: LSTM is so back","\u003Cp style=\"text-align:justify;\">Long Short-Term Memory (LSTM) networks have withstood the test of time, forming the foundation of many early deep learning breakthroughs—including the first generation of Large Language Models (LLMs). However, the rise of Transformers has since overshadowed LSTMs, establishing them as the dominant architecture for LLMs.\u003C/p>\u003Cp style=\"text-align:justify;\">We revisited the potential of LSTMs and ask: \u003Ci>Can LSTMs be scaled up to compete with Transformers?\u003C/i>\u003Cbr>We introduce \u003Cstrong>xLSTM\u003C/strong>, a significantly enhanced version of LSTM featuring \u003Cstrong>exponential gating\u003C/strong> and a novel \u003Cstrong>matrix memory with covariance-based updates\u003C/strong>. Our kernel implementations of xLSTM adheres to scaling laws and demonstrates faster training than Transformers. More crucially, xLSTM has \u003Cstrong>linear-time inference \u003C/strong>in the number of produced tokens, in stark contrast to the quadratic complexity of attention mechanisms, making it highly efficient for deployment.\u003C/p>\u003Cp style=\"text-align:justify;\">A 7-billion parameter xLSTM model achieves \u003Cstrong>comparable performance to state-of-the-art Transformer models\u003C/strong>, while offering significantly faster inference. We are currently developing distilled xLSTM variants from large Transformer models with accelerated inference. Additionally, xLSTM \u003Cstrong>time-series foundation models\u003C/strong> are constructed, which already outperform leading approaches such as \u003Cstrong>Chronos (Amazon)\u003C/strong>, \u003Cstrong>TimesFM (Google)\u003C/strong>, and \u003Cstrong>Moirai (Salesforce)\u003C/strong>.\u003C/p>\u003Cp style=\"text-align:justify;\">xLSTM is already seeing real-world adoption: companies like \u003Cstrong>Spleenlab\u003C/strong> and \u003Cstrong>Festo\u003C/strong> have successfully integrated it into commercial products. \u003C/p>","2025-01-09T14:55:57.040Z","2025-05-28T12:41:08.325Z","2025-01-09T14:55:59.020Z","597",[1252],{"id":1253,"name":1254,"committee":16,"position":16,"affiliation":1255,"email":16,"biography":1256,"createdAt":1257,"updatedAt":1258,"url_path_id":1259,"contactPhoto":1260,"socialLinks":1287,"url_path":1288},463,"Sepp Hochreiter","JKU Linz, NXAI GmbH","\u003Cp style=\"text-align:justify;\">Sepp Hochreiter heads the Institute for Machine Learning, the LIT AI Lab, and is Director of the ELLIS Unit at the Johannes Kepler University Linz. He is the founder of the company NXAI, which advances new deep learning technologies such as xLSTM. He is considered a pioneer of deep learning due to his fundamental work and historical milestones, the development of Long Short- Term Memory (LSTM) and the analysis of the Vanishing Gradient. Dr. Hochreiter's groundbreaking work on LSTM was at the core of the first large language models that later led to ChatGPT and similar models.\u003C/p>","2025-01-09T14:55:17.366Z","2025-05-29T20:03:55.241Z","596",{"id":1261,"name":1262,"alternativeText":16,"caption":16,"width":1263,"height":1264,"formats":1265,"hash":1283,"ext":19,"mime":20,"size":1284,"url":1285,"previewUrl":16,"provider":23,"provider_metadata":16,"createdAt":1286,"updatedAt":1286},187,"Sepp_Hochreiter.png",720,758,{"small":1266,"medium":1272,"thumbnail":1277},{"ext":19,"url":1267,"hash":1268,"mime":20,"name":1269,"path":16,"size":1270,"width":1271,"height":913},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/small_Sepp_Hochreiter_e9ea53d671.png","small_Sepp_Hochreiter_e9ea53d671","small_Sepp_Hochreiter.png",551.12,475,{"ext":19,"url":1273,"hash":1274,"mime":20,"name":1275,"path":16,"size":1276,"width":710,"height":920},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/medium_Sepp_Hochreiter_e9ea53d671.png","medium_Sepp_Hochreiter_e9ea53d671","medium_Sepp_Hochreiter.png",1172.17,{"ext":19,"url":1278,"hash":1279,"mime":20,"name":1280,"path":16,"size":1281,"width":1282,"height":1047},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/thumbnail_Sepp_Hochreiter_e9ea53d671.png","thumbnail_Sepp_Hochreiter_e9ea53d671","thumbnail_Sepp_Hochreiter.png",60.53,148,"Sepp_Hochreiter_e9ea53d671",306.59,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/Sepp_Hochreiter_e9ea53d671.png","2025-05-29T20:03:52.748Z",[],"-390","-391",{"id":1291,"session":1292},88,{"id":799,"title":1293,"teaser":63,"body":1294,"createdAt":1295,"updatedAt":1296,"publishedAt":1297,"url_path_id":1298,"contacts":1299,"url_path":1327},"The Mirror Mechanism: The Double Life of the Motor and Emotional Systems: Production and Understanding","\u003Cp style=\"margin-left:0in;text-align:justify;\">Mirror mechanism is a basic neural mechanism that transforms sensory representations of others’ actions into motor representations of the same actions in the brain of the observer. In the first part of my talk I will describe the functions of the mirror mechanism located in the parieto-fontal network of monkeys and humans. I will show that this mechanism enables one to understand others in an immediate, phenomenological way, without recourse to cognitive inferential processing. In the second part of my talk I will discuss the role of the mirror mechanism in understanding vitality forms and basic emotions. The data on emotions will lead me to the last part of my talk where I will present stereo-EEG data on action and emotion recognition. I will conclude discussing the clinical application of the mirror mechanism (action observation therapy, AOT)\u003C/p>","2025-01-14T18:25:53.218Z","2025-06-03T16:07:20.697Z","2025-01-14T18:25:55.383Z","602",[1300],{"id":1301,"name":1302,"committee":16,"position":16,"affiliation":1303,"email":16,"biography":63,"createdAt":1304,"updatedAt":1305,"url_path_id":1306,"contactPhoto":1307,"socialLinks":1325,"url_path":1326},467,"Giacomo Rizzolatti ","Università degli Studi di Parma","2025-01-14T18:13:40.476Z","2025-05-29T20:06:10.046Z","601",{"id":1308,"name":1309,"alternativeText":16,"caption":16,"width":1310,"height":1310,"formats":1311,"hash":1321,"ext":19,"mime":20,"size":1322,"url":1323,"previewUrl":16,"provider":23,"provider_metadata":16,"createdAt":1324,"updatedAt":1324},188,"Giacomo_Rizzolatti.png",661,{"small":1312,"thumbnail":1317},{"ext":19,"url":1313,"hash":1314,"mime":20,"name":1315,"path":16,"size":1316,"width":913,"height":913},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/small_Giacomo_Rizzolatti_575f31cdc8.png","small_Giacomo_Rizzolatti_575f31cdc8","small_Giacomo_Rizzolatti.png",165.62,{"ext":19,"url":1318,"hash":1319,"mime":20,"name":1320,"path":16,"size":1034,"width":1047,"height":1047},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/thumbnail_Giacomo_Rizzolatti_575f31cdc8.png","thumbnail_Giacomo_Rizzolatti_575f31cdc8","thumbnail_Giacomo_Rizzolatti.png","Giacomo_Rizzolatti_575f31cdc8",77.59,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/Giacomo_Rizzolatti_575f31cdc8.png","2025-05-29T20:06:07.409Z",[],"-395","-396",{"id":1329,"session":1330},115,{"id":1075,"title":1331,"teaser":63,"body":1332,"createdAt":1333,"updatedAt":1334,"publishedAt":1335,"url_path_id":1336,"contacts":1337,"url_path":1380},"Neurosymbolic AI : combining data with knowledge","\u003Cp style=\"margin-left:0px;text-align:justify;\">The focus in AI today is very much on using just data for learning, but one should not learn what one already knows. The challenge therefore is to use the avaible knowledge to guide and constrain the learning, and to reason with the resulting models in a trustworthy manner. This requires the integration of symbolic AI with machine learning, which is the focus of neurosymbolic AI, often touted as the next wave in AI. \u003Cbr>I will argue that  Neurosymbolic AI = Logic + Probability + Neural Networks. This will allow me to specify a high-level recipe for incorporating background knowledge into any neural network approach. The recipe starts from neural networks, interprets them at the symbol level by viewing them as “neural predicates” (or relations) and adding logical knowledge layers on top of them. The glue is provided by a probabilistic interpretation of the logic.  Probability is interpreted broadly,  it provides the quantitative differentiable component necessary to connect the symbolic and subsymbolic levels. \u003C/p>","2025-05-21T07:16:57.029Z","2025-06-12T16:42:12.169Z","2025-05-21T07:17:01.672Z","736",[1338],{"id":1339,"name":1340,"committee":16,"position":16,"affiliation":1341,"email":16,"biography":1342,"createdAt":1343,"updatedAt":1343,"url_path_id":1344,"contactPhoto":1345,"socialLinks":1378,"url_path":1379},565,"Luc De Raedt","KU Leuven (Belgium) and Örebro University (Sweden)","\u003Cp style=\"margin-left:0px;text-align:justify;\">Prof. Dr. Luc De Raedt is Director of Leuven.AI, the KU Leuven Institute for AI, full professor of Computer Science at KU Leuven, and  guest professor at Örebro University (Sweden) at the Center for Applied Autonomous Sensor Systems in the Wallenberg AI, Autonomous Systems and Software Program. He is working on the integration of machine learning and machine reasoning techniques, also known under the term neurosymbolic AI.  He has chaired  the main European and International Machine Learning and Artificial Intelligence conferences (IJCAI, ECAI, ICML and ECMLPKDD) and is a fellow of EurAI,  AAAI and ELLIS, and member of Royal Flemish Academy of Belgium. He received ERC Advanced Grants in 2015 and in 2023. His team has received distinguished paper awards at IJCAI, AAAI, ECMLPKDD, ICART,  etc.\u003C/p>\u003Cp style=\"text-align:justify;\">\u003Cbr> \u003C/p>","2025-05-21T06:51:25.528Z","735",{"id":1346,"name":1347,"alternativeText":16,"caption":16,"width":1348,"height":1349,"formats":1350,"hash":1374,"ext":1029,"mime":1032,"size":1375,"url":1376,"previewUrl":16,"provider":23,"provider_metadata":16,"createdAt":1377,"updatedAt":1377},184,"luc.jpg",2848,3560,{"large":1351,"small":1357,"medium":1363,"thumbnail":1369},{"ext":1029,"url":1352,"hash":1353,"mime":1032,"name":1354,"path":16,"size":1355,"width":1356,"height":906},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/large_luc_d9167f118c.jpg","large_luc_d9167f118c","large_luc.jpg",64.53,800,{"ext":1029,"url":1358,"hash":1359,"mime":1032,"name":1360,"path":16,"size":1361,"width":1362,"height":913},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/small_luc_d9167f118c.jpg","small_luc_d9167f118c","small_luc.jpg",17.15,400,{"ext":1029,"url":1364,"hash":1365,"mime":1032,"name":1366,"path":16,"size":1367,"width":1368,"height":920},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/medium_luc_d9167f118c.jpg","medium_luc_d9167f118c","medium_luc.jpg",36.68,600,{"ext":1029,"url":1370,"hash":1371,"mime":1032,"name":1372,"path":16,"size":1373,"width":842,"height":1047},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/thumbnail_luc_d9167f118c.jpg","thumbnail_luc_d9167f118c","thumbnail_luc.jpg",2.76,"luc_d9167f118c",661.9,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/luc_d9167f118c.jpg","2025-05-21T06:50:51.219Z",[],"-520","-521",{"id":1382,"session":1383},139,{"id":1384,"title":1385,"teaser":63,"body":1386,"createdAt":1387,"updatedAt":1388,"publishedAt":1389,"url_path_id":1390,"contacts":1391,"url_path":1421},136,"The AI-Driven Hospital of the Future and Beyond","\u003Cp style=\"text-align:justify;\">\u003Cspan style=\"background-color:rgb(255,255,255);color:rgb(34,34,34);font-family:Arial, Helvetica, sans-serif;\">AI today can pass the Turing test and is in the process of transforming science, technology, society, humans, and beyond. Surprisingly modern AI is built out of two very simple and old ideas, rebranded as deep learning: neural networks and gradient descent learning.  I will describe several applications of AI to problems in biomedicine developed in my laboratory, from the molecular level to the patient level using omic data, imaging data, clinical data, and beyond.\u003C/span>\u003Cbr>\u003Cspan style=\"background-color:rgb(255,255,255);color:rgb(34,34,34);font-family:Arial, Helvetica, sans-serif;\">Examples include the analysis of circadian rhythms in gene expression data, the identification of polyps in colonoscopies, and the prediction of post-operative outcomes. I will discuss the opportunities and challenges for developing, integrating, and deploying AI in the first AI-driven hospitals of the future and present two frameworks for addressing some of the most pressing societal issues related to AI research.\u003C/span>\u003C/p>","2025-06-05T19:26:23.410Z","2025-06-05T19:26:26.022Z","2025-06-05T19:26:26.017Z","765",[1392],{"id":1393,"name":1394,"committee":16,"position":16,"affiliation":1395,"email":16,"biography":1396,"createdAt":1397,"updatedAt":1398,"url_path_id":1399,"contactPhoto":1400,"socialLinks":1419,"url_path":1420},566,"Pierre Baldi","University of California","\u003Cp style=\"text-align:justify;\">Pierre Baldi earned MS degrees in Mathematics and Psychology from the University of Paris, and a PhD in Mathematics from the California Institute of Technology. He is currently Distinguished Professor in the Department of Computer Science, Founding Director of the AI in Science Institute,  and Associate Director of the Center for Machine Learning and Intelligent Systems  at the University of California Irvine. The long term focus of his research is on understanding intelligence in brains and machines. He has made several contributions to the theory of AI and deep learning, and  developed and applied AI and deep learning methods for the natural sciences, to address problems in physics, chemistry, and bio-medicine. He recently published his fifth book: Deep Learning in Science, Cambridge University Press (2021). His honors include the 1993 Lew Allen Award at JPL, the 2010 E. R. Caianiello Prize for research in machine learning, the 2023 Dennis Gabor Award of the International Neural Network Society, and election to Fellow of the AAAS, AAAI, IEEE, ACM, and ISCB.  He serves as Associated Editor for Artificial Intelligence, Neural Networks, and the IEEE/ACM Transactions in Computational Biology and Bioinformatics. He has mentored ~100 graduate students and postdoctoral fellows and co-founded several startup companies. At UCI, he has introduced several new courses, including the course: Neural Networks and Deep Learning and the course: AI Frontiers: Technical, Ethical, and Societal.\u003Cbr> \u003C/p>","2025-06-05T19:25:04.342Z","2025-06-13T20:44:43.823Z","764",{"id":1401,"name":1402,"alternativeText":16,"caption":16,"width":1403,"height":1403,"formats":1404,"hash":1415,"ext":1029,"mime":1032,"size":1416,"url":1417,"previewUrl":16,"provider":23,"provider_metadata":16,"createdAt":1418,"updatedAt":1418},217,"Pierre.jpg",619,{"small":1405,"thumbnail":1410},{"ext":1029,"url":1406,"hash":1407,"mime":1032,"name":1408,"path":16,"size":1409,"width":913,"height":913},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/small_Pierre_f364b4f006.jpg","small_Pierre_f364b4f006","small_Pierre.jpg",22.51,{"ext":1029,"url":1411,"hash":1412,"mime":1032,"name":1413,"path":16,"size":1414,"width":1047,"height":1047},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/thumbnail_Pierre_f364b4f006.jpg","thumbnail_Pierre_f364b4f006","thumbnail_Pierre.jpg",3.47,"Pierre_f364b4f006",31.7,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/Pierre_f364b4f006.jpg","2025-06-13T20:44:41.301Z",[],"-547","-548",{"id":921,"session":1423},{"id":1424,"title":1425,"teaser":63,"body":1426,"createdAt":1427,"updatedAt":1428,"publishedAt":1429,"url_path_id":1430,"contacts":1431,"url_path":1460},137,"Tensor Networks for AGI, Deep Neural Networks and Machine Learning","\u003Cp style=\"text-align:justify;\">\u003Cspan style=\"color:black;\">Tensors and tensor networks are powerful mathematical tools that expand upon the idea of vectors, and matrices to multiway arrays, which allow efficiently handling and manipulating multidimensional data. Tensors are the essential building blocks of today’s leading machine learning and deep neural networks frameworks. In fact, modern Machine Learning methods are using tensor representations to power the next generation of AI algorithms.  Due to their versatility and efficiency in handling large scale data, they play a crucial role in popular machine learning frameworks such as Tensorflow, PyTorch/TensorLy-Torch and are kernels in GPUs and TPUs (Tensor Processing Units), specifically designed for parallelizing and accelerating matrix/tensor operations.  Moreover, \u003C/span>\u003Cspan style=\"background-color:white;color:black;\">tensor networks will be crucial for Artificial General Intelligence (AGI) because they offer a powerful and versatile framework for representing and processing complex, multi-modal, high-dimensional data, which is essential for building multi-intelligence AGI systems that would possess the ability to reason, adapt to new situations, and solve problems across various domains.  Their ability to capture intricate complex correlations and patterns within multi-modal data, along with their potential for efficient computation, makes them a promising direction for AGI research. \u003C/span>\u003Cspan style=\"color:black;\">    I will critically overview fundamental low-rank tensor decompositions and tensor networks. Special emphasis will be given to tensorization and quantization. I will discuss their applications to compress deep neural networks, optimize multimodal attentions and generate lightweight neural networks for specific tasks, like image/video reconstruction, super resolution, time series forecasting  and brain computer interfaces. \u003C/span>\u003C/p>","2025-06-09T13:42:42.539Z","2025-06-09T21:07:03.733Z","2025-06-09T13:42:45.225Z","767",[1432],{"id":1433,"name":1434,"committee":16,"position":16,"affiliation":1435,"email":16,"biography":1436,"createdAt":1437,"updatedAt":1438,"url_path_id":1439,"contactPhoto":1440,"socialLinks":1458,"url_path":1459},567,"Andrzej Cichocki","Systems Research Institute of Polish  Academy of Science, Poland","\u003Cp style=\"text-align:justify;\">Andrzej Cichocki received the M.Sc. (with honors), Ph.D. and Dr.Sc. (Habilitation) degrees, all in electrical engineering from the Warsaw University of Technology (Poland). He spent 4 years at University Erlangen (Germany) as an Alexander-von-Humboldt Research Fellow and Guest Professor. He was a Senior Team Leader and Head of the laboratories at RIKEN BSI (Brain Science Institute) in Japan form 1995 till 2018.  He established and conducted in Riken BSI three laboratories: Open Information Systems, Artificial Brain Systems, and Cichocki Laboratory for Advanced Brain Signal Processing. Currently he is a Professor in Systems Research Institute of Polish Academy of Science in Warsaw, and adjunct professor in \u003Cspan style=\"background-color:white;color:#4D5156;\">Nicolaus Copernicus University\u003C/span> in Torun, Poland. He is author of more than 800 technical journal papers and 6 monographs (books) in English (3 of them translated to Chinese). He served as Associated Editor of \u003Ci>IEEE Transactions\u003C/i> \u003Ci>on Signals Processing\u003C/i>,\u003Ci> IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, Journal of NeuroscienceMethods \u003C/i>and he is founding Editor in Chief for \u003Ci>Journal Computational Intelligence\u003C/i> \u003Ci>and Neuroscience (CIN), AI in Health (AIH) and Neural Engineering and Neurofeedback (NEN)\u003C/i>. Currently, his research focus on AI, Brain Computer Interfaces, tensor decompositions and tensor networks for Machine Learning, and Signal Processing for biomedical applications. His publications currently report over 70,000 citations according to Google Scholar, with an h-index of 125. He is Fellow of the IEEE since 2013. \u003Cspan style=\"background-color:white;color:#333333;\">Dr. Cichocki has been selected in 2021-2023 by Web of Science (Clarivate) for his multiple top publications on the list of\u003C/span>\u003Cspan style=\"color:#333333;\"> \u003C/span>\u003Cspan style=\"background-color:white;color:#333333;\">Highly Cited Researchers. \u003C/span>\u003Cspan style=\"background-color:white;color:#202122;\">He is one of the most cited scientists working in electrical engineering and computer science affiliated with Poland.  \u003C/span>\u003Cspan style=\"background-color:white;color:black;\">\u003Cstrong>He is \u003C/strong>\u003C/span>\u003Cstrong>honored to be the 2024 recipient of the Dennis Gabor Award, \u003C/strong>\u003Ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://www.inns.org/awards\">\u003Cspan style=\"color:windowtext;\">\u003Cstrong>given by the International Neural Networks Society (INNS), for \"\u003C/strong>\u003Ci>\u003Cstrong>Outstanding Achievements in Neural Engineering\u003C/strong>\u003C/i>\u003Cstrong>\"\u003C/strong>\u003C/span>\u003C/a>\u003Cstrong>.\u003C/strong>\u003C/p>\u003Cp style=\"text-align:justify;\">\u003Cspan style=\"background-color:white;color:#202122;\">More information is available on \u003C/span>\u003C/p>\u003Cp style=\"text-align:justify;\">\u003Ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://en.wikipedia.org/wiki/Andrzej_Cichocki\">https://en.wikipedia.org/wiki/Andrzej_Cichocki\u003C/a> \u003C/p>\u003Cp style=\"text-align:justify;\">\u003Ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://scholar.google.com/citations?hl=en&user=wpZDx1cAAAAJ&view_op=list_works&sortby=pubdate\">https://scholar.google.com/citations?hl=en&user=wpZDx1cAAAAJ&view_op=list_works&sortby=pubdate\u003C/a> \u003C/p>\u003Cp style=\"text-align:justify;\">\u003Ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://research.com/scientists-rankings/computer-science/pl\">https://research.com/scientists-rankings/computer-science/pl\u003C/a> \u003C/p>","2025-06-09T13:36:53.318Z","2025-06-23T20:39:27.640Z","766",{"id":1441,"name":1442,"alternativeText":16,"caption":16,"width":1263,"height":1263,"formats":1443,"hash":1454,"ext":19,"mime":20,"size":1455,"url":1456,"previewUrl":16,"provider":23,"provider_metadata":16,"createdAt":1457,"updatedAt":1457},222,"AndrezejCichocki.png",{"small":1444,"thumbnail":1449},{"ext":19,"url":1445,"hash":1446,"mime":20,"name":1447,"path":16,"size":1448,"width":913,"height":913},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/small_Andrezej_Cichocki_e0cd640e51.png","small_Andrezej_Cichocki_e0cd640e51","small_AndrezejCichocki.png",281.44,{"ext":19,"url":1450,"hash":1451,"mime":20,"name":1452,"path":16,"size":1453,"width":1047,"height":1047},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/thumbnail_Andrezej_Cichocki_e0cd640e51.png","thumbnail_Andrezej_Cichocki_e0cd640e51","thumbnail_AndrezejCichocki.png",32.56,"Andrezej_Cichocki_e0cd640e51",143.52,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/Andrezej_Cichocki_e0cd640e51.png","2025-06-23T20:39:25.694Z",[],"-549","-550",{"id":1462,"session":1463},141,{"id":1464,"title":1465,"teaser":63,"body":1466,"createdAt":1467,"updatedAt":1468,"publishedAt":1469,"url_path_id":1470,"contacts":1471,"url_path":1508},138,"Building generative world models: progress and challenges","\u003Cp style=\"text-align:justify;\">\u003Cspan style=\"background-color:rgb(255,255,255);color:rgb(34,34,34);font-family:Arial, Helvetica, sans-serif;\">Equipping AI models with the ability to imagine, reason, and act in the physical world is a crucial step toward achieving Artificial General Intelligence (AGI). Generative world models, which simulate our world with high fidelity, offer a promising avenue for training and evaluating these embodied AI agents.\u003C/span>\u003Cbr>\u003Cbr>\u003Cspan style=\"background-color:rgb(255,255,255);color:rgb(34,34,34);font-family:Arial, Helvetica, sans-serif;\">In this talk, I will present recent progress in this field. I will begin by covering the latest advancements in video generation models and how they can be controlled through various actions, marking a key milestone in developing large-scale world models. Next, I will discuss how these video models can be integrated to interact with other modalities, such as audio and text. Finally, I will address the critical limitations of current video-based world models, such as ensuring long-term consistency and interactivity, and propose potential solutions to overcome these challenges.\u003C/span>\u003C/p>","2025-06-13T12:50:09.262Z","2025-06-13T12:50:11.677Z","2025-06-13T12:50:11.672Z","770",[1472],{"id":1473,"name":1474,"committee":16,"position":16,"affiliation":1475,"email":16,"biography":1476,"createdAt":1477,"updatedAt":1477,"url_path_id":1478,"contactPhoto":1479,"socialLinks":1506,"url_path":1507},568,"Ruiqi Gao","Google DeepMind","\u003Cp style=\"text-align:justify;\">\u003Cspan style=\"background-color:rgb(255,255,255);color:rgb(34,34,34);font-family:Arial, Helvetica, sans-serif;\">Ruiqi is a senior research scientist at Google Deepmind. Prior to that, she received her Ph.D. degree from UCLA in 2021 advised by Song-Chun Zhu and Ying Nian Wu. Her primary research objective is to construct powerful AI models that can comprehend, generate, and reason with multi-modal data, including natural language, images, videos and 3D. She is currently focused on the development of generative world models that not only simulate the physical world with high fidelity but also allow for complex and efficient interaction with AI agents.\u003C/span>\u003C/p>","2025-06-13T12:49:38.026Z","769",{"id":1480,"name":1481,"alternativeText":16,"caption":16,"width":1482,"height":1483,"formats":1484,"hash":1502,"ext":19,"mime":20,"size":1503,"url":1504,"previewUrl":16,"provider":23,"provider_metadata":16,"createdAt":1505,"updatedAt":1505},216,"me (1) (1).png",727,821,{"small":1485,"medium":1491,"thumbnail":1497},{"ext":19,"url":1486,"hash":1487,"mime":20,"name":1488,"path":16,"size":1489,"width":1490,"height":913},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/small_me_1_1_b909f75dfc.png","small_me_1_1_b909f75dfc","small_me (1) (1).png",490.55,443,{"ext":19,"url":1492,"hash":1493,"mime":20,"name":1494,"path":16,"size":1495,"width":1496,"height":920},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/medium_me_1_1_b909f75dfc.png","medium_me_1_1_b909f75dfc","medium_me (1) (1).png",1065.77,664,{"ext":19,"url":1498,"hash":1499,"mime":20,"name":1500,"path":16,"size":1501,"width":1464,"height":1047},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/thumbnail_me_1_1_b909f75dfc.png","thumbnail_me_1_1_b909f75dfc","thumbnail_me (1) (1).png",52.45,"me_1_1_b909f75dfc",326.75,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/ijcnn25/me_1_1_b909f75dfc.png","2025-06-13T12:49:31.125Z",[],"-552","-553",{"data":1510,"meta":1511},{"id":396,"heading":391,"createdAt":397,"updatedAt":398,"publishedAt":399,"url_path_id":400,"url_path":392,"contentType":101},{},1778852697121]