{"id":173,"date":"2023-05-23T00:29:56","date_gmt":"2023-05-22T18:59:56","guid":{"rendered":"https:\/\/aiml.3it.in\/?page_id=173"},"modified":"2026-09-23T10:16:43","modified_gmt":"2026-09-23T04:46:43","slug":"keynote_details","status":"publish","type":"page","link":"https:\/\/www.aimlsystems.org\/2026\/keynote_details\/","title":{"rendered":"Keynote Speakers"},"content":{"rendered":"

[et_pb_section fb_built=”1″ disabled_on=”on|on|on” admin_label=”Features” module_id=”about” _builder_version=”4.25.1″ _module_preset=”default” background_color=”#dbdbdb” background_image=”https:\/\/www.aimlsystems.org\/2023\/wp-content\/uploads\/2023\/05\/rm380-10.jpg” background_blend=”overlay” custom_padding=”3.9%||||false|false” use_background_color_gradient_phone=”on” background_color_gradient_stops_phone=”#001528 0%|rgba(255, 255, 255, 0) 10%|rgba(255,255,255,0) 70%|#0f0122 100%” disabled=”on” collapsed=”on” global_colors_info=”{}”][\/et_pb_section][et_pb_section fb_built=”1″ fullwidth=”on” _builder_version=”4.25.1″ _module_preset=”default” custom_margin=”-90px||||false|false” global_colors_info=”{}”][et_pb_fullwidth_code _builder_version=”4.25.1″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n

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AIMLSYSTEMS 2026<\/div>\n

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Keynote Speakers<\/h1>\n

Insights from global visionaries at the forefront of AI and Systems Engineering<\/pee> <\/p>\n

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Dr. Rajeev Rastogi<\/h2>\n

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VP and Head of Research @ Amazon India<\/span><\/h3>\n

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<\/i> Thursday, Oct 8 |<\/span> 13:30 CET<\/span> <\/div>\n

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Rajeev Rastogi is the Vice President of Machine Learning (ML) for Amazon\u2019s International Stores business. He leads the development of ML solutions in the areas of Search, Advertising, Deals, Catalog Quality, Payments, Forecasting, Question Answering, Grocery Grading, etc. Previously, he was Vice President of Yahoo! Labs Bangalore and the founding Director of the Bell Labs Research Center in Bangalore, India. Rajeev is an ACM Fellow and a Bell Labs Fellow. He has published over 125 papers, and holds over 100 patents. He currently serves on the editorial board of the CACM, and has been an Associate editor for IEEE Transactions on Knowledge and Data Engineering in the past. Rajeev received his B. Tech degree from IIT Bombay, and a PhD degree in Computer Science from the University of Texas, Austin..<\/pee> <\/div>\n

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<\/i> Keynote Title: AI Applications in E-Commerce \u2013 Solutions & Key Learnings<\/span><\/div>\n

In this talk, I will first present a brief overview of AI applications in E-Commerce across customer experience, seller experience, catalog quality, operations, ads, and payments. I will then talk about three key learnings to develop high-performance AI applications \u2013 leverage global structure, fine-tune smaller models with application-specific data, and ingest rich feature representations during fine-tuning. Finally, I will describe three pieces of work underlying our learnings \u2013 1) Asymmetric recommendations using GNNs with dual embeddings, 2) Visual Question Answering (VQA) using Vision Language Models (VLMs) fine-tuned with E-Commerce data, and 3) Joint shadow generation and relighting using bridge matching conditioned on light-geometry interaction (LGI) maps.<\/pee> <\/div>\n

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<\/i> Full Biography<\/a> <\/div>\n

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Prof. Michele Magno<\/h2>\n

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Professor & Head of Edge AI and Sensing Lab @ ETH Zurich<\/span><\/h3>\n

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<\/i> Wednesday, Oct 7 |<\/span> 09:00 CET<\/span> <\/div>\n

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Michele Magno (Fellow Member, IEEE) is Professor and Head of the Project-Based Learning Center at ETH Zurich, where he also leads the Edge AI and Sensing Lab. In 2026, he will be Head of the Edge AI and Sensing Lab at the Italian Institute of Artificial Intelligence, conducting transformative, application-oriented research contributing to innovation and industrial transformation.<\/pee> His research focuses on wireless sensor networks, wearable systems, edge machine learning, smart sensing, and autonomous robots. With over 400 publications, he has established strong partnerships with industry leaders including IBM Research, Sony, STMicroelectronics, and Ferrari.<\/pee> <\/div>\n

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<\/i> Keynote Title: From Wearables to Robotics: Energy-Efficient Sensing and Edge AI for Real-World Intelligent Systems<\/span><\/div>\n

Recent advances in sensing technologies and edge artificial intelligence are enabling a new generation of intelligent systems capable of operating directly in the physical world with low latency, reduced energy consumption, and increased autonomy. This talk presents advances in sensing and edge AI technologies for intelligent embedded systems, ranging from wearable devices to autonomous robots, highlighting hardware-software co-design approaches that enable real-time AI directly on battery-operated platforms.<\/pee> <\/div>\n

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<\/i> Full Biography<\/a> <\/div>\n

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\"Cesare <\/p>\n
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Cesare Alippi<\/h2>\n

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Professor@Politecnico di Milano (Italy) & Universit\u00e0 della Svizzera italiana (Switzerland)<\/span><\/h3>\n

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<\/i> Wednesday, Oct 7 |<\/span> 13:30 CET<\/span> <\/div>\n

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CESARE ALIPPI is Professor with the Politecnico di Milano (Italy) and Professor with theUniversit\u00e0 della Svizzera italiana (Switzerland); he is visiting Professor at the GuandongUniversity of Technology (China) and Consultant Professor at the Northwestern Polytechnic ofXi\u2019An (China).He has been a visiting researcher\/professor at UCL (UK), MIT (USA), ESPCI (F), CASIA (RC),A*STAR (SIN), U.Kobe (Japan).Alippi is an\u00a0IEEE Fellow,\u00a0ELLIS Fellow, INNS Fellow\u00a0and\u00a0AAIA Fellow, Past Board of Governorsmember of the\u00a0International Neural Network Society, Past member of the Administrative Committeeof the\u00a0IEEE Computational Intelligence Society (CIS), Past Board of Directors member ofthe\u00a0European Neural Network Society, Past Vice-President education of the\u00a0IEEE ComputationalIntelligence Society, Associate Editor of\u00a0Proceedings of IEEE\u00a0and other journals, Past Associate editorof the IEEE Computational Intelligence Magazine, the\u00a0IEEE-Transactions on Instrumentation andMeasurements,\u00a0IEEE-Transactions on Neural Networks, IEEE-Transactions on Emerging Topics inComputational Intelligence\u00a0and member and chair of many IEEE committees.In 2024 he received the IEEE CIS Enrique Ruspini Meritorious Service Award, the 2018\u00a0IEEECIS Outstanding Computational Intelligence Magazine paper award, the 2016\u00a0Gabor Award\u00a0from theInternational Neural Networks Society and the\u00a0Outstanding Transactions on Neural Networks andLearning Systems Paper Award\u00a0from the\u00a0IEEE Computational Intelligence Society; in 2013 the IBMFaculty award; in 2004 the IEEE Instrumentation and Measurement Society Young EngineerAward.Current research activity addresses\u00a0graph-based learning,\u00a0adaptation and learning in non-stationaryenvironments\u00a0and Intelligence for embedded, cyber-physical systems\u00a0and IoT. For the graph-based learningresearch please refer to\u00a0http:\/\/gmlg.chHe holds 8 patents, has published one monograph book (translated in Chinese), 7 edited booksand more than 250 papers in international journals and conference proceedings.<\/pee> <\/div>\n

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<\/i> Keynote Title: Graph Deep Learning for Irregular Spatiotemporal Data<\/span><\/div>\n

Irregular spatiotemporal data consist of observations collected asynchronously acrossdifferent times and spatial locations. Unlike regular data, their inherent irregularity makestraditional methods designed for discrete-time sequences and Euclidean spaces partlyineffective.In this talk, we explore how graph deep learning leverages the existence of relationaldependencies to tackle both interpolation (imputation) and extrapolation (prediction)challenges. We will cover techniques for reconstructing missing data from a limited set ofobservations by enforcing spatiotemporal consistency, as well as introduce forecastingmethods for predicting future values from sparse inputs. Finally, we will see how virtualsensors readings can be inferred from the graph structure provided some exogenousinformation is available.<\/pee> <\/div>\n

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<\/i> Full Biography<\/a> <\/div>\n

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