Prof Yen Shui Seng is currently the Director of KunlunWanwei 2050 Research Institute, Chief Scientist of KunlunWanwei and Skywork AI, and was formerly the Chief Scientist of Sea. He is a member of the Singapore Academy of Engineering and a Fellow of AAAI, ACM, IEEE and IAPR, and his research interests include computer vision, machine learning and multimedia analytics. To date, Prof Yen Shui-Shing has published more than 800 papers in top international journals and conferences with an H-index of 140+. Prof Yen Shuicheng's team has won more than ten awards in two core competitions, Pascal VOC and ImageNet (ILSVRC). In addition, his team has won more than ten best paper and best student paper awards, especially a grand slam at ACM Multimedia, the top conference in multimedia, including three best paper awards, two best student paper awards, and the best presentation award.

Founded the PM-Summit Global Product Manager Conference in 2009. He has rich experience and in-depth research in artificial intelligence, product innovation, and software architecture. He is also the chairman of the Global Machine Learning Technology Conference. In recent years, he focuses on the artificial intelligence methodology of large language models, and proposes the "Paradigm Shift Cube" for technological innovation, which has attracted strong attention in the industry with related research and consulting.

He has won more than 20 awards, including China's New Economic Leaders, ‘Innovative Talents’ from the Ministry of Science and Technology, China's Top Ten Software Enterprise Leaders, Modern Service Industry Leaders, and the Special Contribution Award from the China Translators' Association, etc. He is a leading figure in promoting the development of China's language service industry.

He is a director of the Chinese Information Society of China and has a PhD from the Institute of Software of the Chinese Academy of Sciences. Before that, he worked as a senior technologist in Alibaba, responsible for the new technology team. The author of "This is the Search Engine: Core Technology Explained" and "Big Data Journal: Architecture and Algorithm".

He graduated from Institute of Automation, Chinese Academy of Sciences with a PhD, and focuses on Q&A systems and large-scale language models. He has been a senior researcher in Sogou and Tencent, and has rich experience in large-scale generative modelling, leading and releasing large-scale Chinese Q&A data such as ReCO, ComQA, ChiQA, T2Ranking, as well as the Baichuan series of pre-trained models. He has published 11 papers as the first author in top international AI and natural language processing conferences such as ACL, SIGIR, AAAI, etc., and won the CIKM best paper runner up in 2021. his PhD thesis ‘Research on Key Technology of Machine Reading Comprehension’ won the Excellent Doctoral Dissertation Award of the Chinese Society of Information in China in 2019. He is an executive member of the Youth Working Committee of Chinese Language Information Society of China.

D. in Intelligent Science and Technology from Beijing University of Posts and Telecommunications (BUPT), head of the basic data research group in Beijing Zhiyuan Artificial Intelligence Research Institute (BJAIIRI), has experience in developing and open-sourcing the basic model of the Wudao-Skyhawk Aquila series (7B/34B/8x16B), and has published papers in academic conferences and journals in the field of Artificial Intelligence (AI), and is now focusing on high-quality data synthesis techniques and other research directions.

He graduated from Xiamen University with a master's degree in communication and information system, and has been working for Huawei since graduation. He has published several papers in MR and Neuro computing. As a core developer, he participated in the development of AI framework (Rise), and was responsible for the auto-differentiation of dynamic graphs and the combined dynamic and static modules. Currently, he is mainly involved in the development and optimisation of large model inference acceleration on Rise hardware, and is committed to further improving the performance of large model inference by optimising the inference framework, model algorithms, and arithmetic acceleration libraries.

He is now working in FTI, the R&D leader of Knowledge Enhancement for FTI Big Models, and also the leader of Intelligent Interaction Team. He mainly focuses on Agent, RAG, Alignment, RL, LLM, etc. He has published more than 30 papers in ICLR and NeurIPS, ICML and other CCF-A/B conferences and journals, and has applied for more than ten international and domestic patents, and the related research has landed on the ground in enterprise scenarios and produced large benefits. He has served as PC, Senior PC, Area Chair of the above international conferences, Forum Chair of China Conference on Data Mining (CCDM), and Executive Member of CCF Multi-Intelligence Group. He and his team have won the ‘Typical Case of AI Large Model Scenario Application’ at the Global Digital Economy Conference, the champion of NeurIPS Reinforcement Learning Competition at the International Conference on Artificial Intelligence, the ‘Outstanding Doctoral Dissertation in Multi-Intelligence Research’ at the China Computer Federation, the ‘Outstanding Doctoral Dissertation in Multi-Intelligence Research’ at Beijing Municipal Government, and the ‘Excellent Dissertation in Multi-Intelligence Research’ at Beijing Municipality. ‘D. Thesis Award for Multi-Intelligence Research of the Chinese Computer Society, Outstanding Graduate of Beijing Municipality, and Huawei Innovative Pioneer Presidential Award.

Xin Chen, Product Technical Leader of AliCloud, TONGYI Lingma, is committed to research and exploration in the direction of enterprise R&D efficiency, product quality, and DevOps. He joined Ali in 2011, and has led the big data testing team, testing tool R&D team, and R&D platform team. He has deep insights into R&D collaboration, testing, delivery, operation and maintenance. Currently, he is leading the team towards cloud native, extreme efficiency, intelligence and other areas of continuous evolution.

Joined Microsoft in 2019, Senior R&D Engineer in General Artificial Intelligence group, with main research interests in language model-based reasoning, AI intelligences and multi-intelligence systems. The related technologies developed are applied in Microsoft M365 Word and other products.

Michael is the Chair of the C++ Embedded Development Committee SG14 and the Machine Learning Committee SG19, the Chair of the C++ Language Direction Evolution Committee, the Vice President of Research and Development at Codeplay, and the Head of the Canadian Delegation to the C++ Standards Board. Michael's extensive experience in the areas of C++ Parallel Computing, High Performance Computing, and Machine Learning has led the development of the C++ Heterogeneous Programming Language (SYCL) Standard for the Development of C ++ Applications on GPUs and GPUs. He led the development of the C++ Heterogeneous Programming Language (SYCL) standard for GPU application development. He has deep research and insights into the underlying performance optimization of Tensorflow. His specific work covers parallel programming, neural networks, computer vision, autonomous driving, etc. Michael was a senior technical expert at IBM, leading the development of IBM XL C++ compiler and XL C compiler.

Baidu senior engineer, architect of Baidu Comate, 10+ years of R&D experience, successively responsible for the architectural design and implementation of code intelligence, code hosting and other products. He has in-depth research on DevOps intelligent landing, cloud native, high availability direction.

Lingxiang Wu, PhD, Associate Researcher at Institute of Automation, Chinese Academy of Sciences, and Algorithm Director at Wuhan Institute of Artificial Intelligence. He graduated from the University of Technology Sydney with a degree in Computer Vision and Natural Language Processing. He mainly researches on key technologies for AI applications, such as large-scale pre-trained models, multimodal understanding and generation. With academic and industrial background, he has participated in many large-scale projects and completed them online. His team's work on the world's first tri-modal large-scale model "Zidong Taichu" based on Rise AI won the SAIL Award at the World Conference on Artificial Intelligence in 2022. Translated with DeepL.com (free version)

He is responsible for the research and development of multilingual big models and AI Agent. He graduated from Rensselaer Polytechnic Institute with a PhD in Natural Language Processing and Signal Processing, and has published many papers in international conferences and journals such as ACL, EMNLP, NAACL, ICASSP, and IEEE Transactions on Signal Processing.

He is currently focusing on the development of multimodal fundamental models, and was formerly a supervising researcher at Microsoft Research Asia. He has published more than 30 papers in international journals and conferences in the field of artificial intelligence, such as TPAMI, CVPR, NeurIPS, etc. He serves as an associate editor of the journal Pattern Recognition, and is the chair of the ACM Multimedia conference.

Lepton AI is committed to building an efficient and usable AI infrastructure that allows teams to focus on building and landing applications.

Tencent Angel machine learning platform expert engineer, years of focus on the field of AI engineering, is currently responsible for Angel big model one-stop platform architecture design and development work, support Tencent hybrid big model, Tencent advertising and other model R & D, application scenarios.

Senior technical expert in eBay's risk control department. He has led the design and development of eBay's real-time RCA platform for risk control, standardisation and fuzzy matching service for risk control addresses, and high-concurrency and high-availability risk control list management system, and is responsible for the design, development and maintenance of the core components of several eBay's risk control decision engines, and has a wealth of engineering experience in high-performance system architecture design. In addition, he has deep technical accumulation and rich practical experience in the integration of knowledge graph and risk control decision engine, and the application of big language model in the field of risk control.

Years of data work experience responsible for data warehouse, data platform and algorithm system architecture design and engineering implementation related to search, push and square view data; after joining Tencent, he is mainly responsible for Tencent Sky Dome Big Data Platform's platform intelligent autonomy, data governance, intelligent related system architecture design, engineering implementation and business landing work.

Graduated from Tsinghua University Wudaokou School of Finance, worked for Ping An, CCB and other financial technology enterprises, mainly engaged in big data, artificial intelligence and other areas of technology research. He has served as a technical architect of big data platform for Jingdong Technology's data team, a core architect of Jingdong Technology's Star Ocean big data platform products, and is now the head of big model technology at Jingdong Technology. He is committed to applying AI technology to enterprise practice, aiming to promote enterprise digital transformation and improve operational efficiency and decision-making quality through technological innovation.

Responsible for Dify.AI best practice exploration and architecture design. He is a 90% E person, but likes to toss around cutting-edge technologies, and believes that Code is the purest thing to do. He has rich experience in software engineering, service high availability and data processing, and has independently built a Notion-like note-based knowledge base backend service with over one million users; he has a deep understanding and practice in the RAG field, and has shared his knowledge in related fields in Vector Database Conference, A2M Conference, and Artificial Intelligence Summit for many times.

Graduated from Harbin Institute of Technology, worked for Alibaba, Microsoft and other companies. He has worked in Microsoft for more than 10 years, and was the chief development manager of Microsoft Bing Search. Currently, he is responsible for the big model technology and application implementation of Lanzhou Technology, including RAG, search, Q&A, report generation, video conference analysis and so on.

Senior Machine Learning Specialist, Algorithm Engineering Direction at DEVO, mainly responsible for the R&D work related to DEVO's algorithm platform. He is responsible for the R&D of algorithmic platform in DEVO. He has been working in DEVO from 0 to 1 to build a universal large model training and inference platform. He used to work for Tencent, Ali and other big internet companies, and joined DEVO in 2022, focusing on big model related technology, including inference acceleration and application scenarios, and has published many high quality big model related articles in DEVO's technical public number, such as: using multi-Lora to save the cost of deploying big models, KubeAI big model inference acceleration practice, and DEVO's best practice of accessing the big model platform.

Xiao Hongshu community search algorithm engineer, responsible for search recall, query analysis, pendant retrieval and other directions of technical development.

He graduated from the Institute of Computing Technology, Chinese Academy of Sciences with a master's degree, and his research areas include large-scale distributed training and code domain models. As the person in charge of the algorithms of MarsCode Code Completion, Code Completion Pro, Code Q&A and other AI functions, he is responsible for code domain model tuning, inference acceleration, and the incubation of more AI functions for developers. He has long been committed to the combination and practical implementation of soft engineering tasks and big models, and has rich practical experience in the productisation of AI capabilities such as code completion, code generation and code understanding.

Currently, he is mainly responsible for the RAG algorithm architecture of Tongyi Hundred Refine products and the offline algorithm technology of RAG of other products in Tongyi Labs. 7 years of experience in NLP & AI algorithm research and development as well as landing. He has published many papers in top conferences such as ACL, EMNLP, NAACL, SIGIR, etc. His research area involves NLP traditional tasks, multimodal pre-training, RAG, and he has proposed the industry's first geographic multimodal pre-training model, MGeo, which has been downloaded more than a million times. Currently, he is committed to the construction of process-oriented and modularised landable RAG technology solutions, and has open-sourced the CQDA RAG dataset as well as the CoFE-RAG full-link RAG evaluation framework.

He is a special master tutor of School of Software and Microelectronics of Peking University, an overseas high-level talent in Shenzhen, and a science and technology expert of Shenzhen Science and Innovation Commission. His research interests include recommendation systems, data mining, and natural language processing. The representative research result ‘Time Mobility’ is widely used in industrial practice, and has been promoted in Tencent Advertising, Microsoft Bing, Alibaba Tmall and other enterprises. He has published more than ten papers in top international conferences and has been cited more than one thousand times, and has undertaken several provincial and national projects.

Recipient of the National High-level Talent Programme. He has conducted research at National University of Singapore, University of Illinois at Urbana-Champaign, and University of Technology Sydney. Selected as MIT TR35 China, Baidu Global High Potential Chinese Young Scholar, The Australian Top 40 Rising Star; awarded the Leading Science and Technology Award of the World Internet Conference (2023), the First Prize of Natural Science Award of Higher Education Institutions of the Ministry of Education (2022), the First Prize of Science and Technology Award of the Chinese Society of Graphics and Graphics (2019), the Australian Research Council Young Research Award (2019), IBM C3SR Best Research Award (2019), ImageNet Target Detection Champion of the Computer Vision World Cup (2014), and several CVPR competitions; published more than 100 papers in top journals/conferences, such as TPAMI, CVPR, etc., with more than 22000 Google citations. His current main research interests include visual perception for imperfect data, multimodal data analysis, and generative artificial intelligence.

He is a Senior Algorithm Scientist at Jingdong Exploration Research Institute (JERI), focusing on Embodied Intelligence and Indoor Service Robotics applications, and has published several papers in ICRA and IROS. He has a PhD in Communication and Information System from Beijing Jiaotong University, won the Beijing Yikylin Outstanding Talent Award, and the Technology Star Award from the Jingdong Exploration Research Institute. He has constructed the ‘Big Model + Robot’ embodied system, which has provided technical support for the relevant key projects of the Jingdong Group, and is in charge of the application of the Big Model in the development of the project. Currently, he focuses on building a dual-arm mobile robot platform based on multimodal large models, focusing on the improvement of upper cognitive ability and lower operating skills.

Graduated from Zhejiang University with a master degree, his research area focuses on full-stack optimisation of deep learning algorithms, covering model optimisation and N-card inference optimisation. In ByteDance ByteNN team, he has been responsible for the construction of server-side sparse acceleration/LLM inference optimisation capability, and supported the inference optimisation of beanbag vision multimodal large model project. Currently, he is responsible for model optimisation in ByteNN team, and is committed to reducing the cost of cloud-based reasoning for LLM/SD models through the collaborative optimisation at the reasoning engine and model level, and further promoting the end-side landing of AIGC models.

Graduated from Beijing University of Posts and Telecommunications, majoring in communication engineering, he has 16 years of experience in the engineering application of advanced technologies, and has led the design and development of large-scale software architecture in the fields of video communication, computer vision, CV heterogeneous reasoning framework, and large language model application. He is now focusing on the research and development of AI application products, and has led the team to develop and launch the ‘Code Raccoon’ and ‘Office Raccoon’ series of AI Native productivity tools, providing programming assistance services for hundreds of thousands of developers.

Currently, he is leading the vision team of the institute to carry out cutting-edge exploration and industrial landing work in the direction of multimodal macromodels, vision AIGC, cross-modal graphic learning, open-world target detection, open-word list video analytics, AIoT, and so on.

Engaged in machine learning research with special focus on large-scale machine learning, statistical machine learning theory, etc. He has published more than 40 papers in high-level journals and conferences, including IEEE TPAMI, ICML, NeurIPS, ICLR, AAAI, IJCAI, etc., of which more than 20 academic papers have been published by first author/sole corresponding author in Recommended Class A of the Chinese Computer Society. He was awarded the best paper in the Doctoral Forum of the 2014 Asia-Pacific Conference on Knowledge Discovery and Data Mining (PAKDD), and supervised a student to win the best student paper in the 2021 Pacific Rim International Conference on Artificial Intelligence (PRICAI). As a project leader, he has presided over the surface and youth projects of National Natural Science Foundation of China, the surface project of Beijing Natural Science Foundation, and the Basic Frontier Science Research Programme of Chinese Academy of Sciences. He was awarded the ‘Outstanding Scholar’ of Renmin University of China in 2020, the ‘Youth Innovation Promotion Society’ of the Chinese Academy of Sciences in 2019, the ‘Introduced Outstanding Youth’ of the Institute of Information Engineering of the Chinese Academy of Sciences in 2016, and the ‘Outstanding Youth’ of the Chinese Academy of Sciences in 2012. He has been awarded ‘Outstanding Scholar’ by Renmin University of China in 2020, ‘Young Innovation Promotion Society’ by Chinese Academy of Sciences in 2019, ‘Outstanding Young Person Introduced’ by Institute of Information Engineering, Chinese Academy of Sciences in 2016, and ‘Academic Newcomer Award for Doctoral Students’ in 2012.

TuGraph Maintainer, author of DB-GPT GraphRAG, author of ‘Do-It-Yourself Compilation System’, responsible for TuGraph's open source technology evolution, community operation and commercialisation. He has worked in Tencent, AliCloud and Ant, engaged in product design and technology construction related to big data platform, cloud database and graph computing. He has rich development and practice experience in distributed computing, data security management, data middleware architecture, open source evangelism and other fields.

Has nearly 10 years of experience in applying machine learning and artificial intelligence in healthcare and retail. Worked for General Electric, Fosun and other well-known companies. Long-term focus on medical imaging, natural language processing, and data science, including training/inference frameworks, data mining and analysis. Founded the AI product team in July 2024, and is responsible for the development of application products based on large models, such as RAG & Agent.

He graduated from the University of Texas with a master's degree in software engineering in 2017, and has been engaged in NLP-related research since graduation, focusing on model training and application landing. He joined Smart Spectrum AI in 2023, and is responsible for CodeGeex code model, Embedding model training, and led the landing of CodeGeeX project-level intelligent Q&A, connected search and other functions. Currently, he is in charge of cutting-edge algorithm development in CodeGeeX team, aiming to promote the innovation and application of big model technology in the code field.

Worked in the Chinese Academy of Sciences, presided over the development of the whole industry matter atlas, 360 encyclopaedic atlas, knowledge graph platform, 360 version of the analytical model and other projects, 360 brain big model former core members, applied for more than ten invention patents, several papers, more than 70 foreign open source projects. In recent years, he has won a number of championships and runners-up in OGB-Wikikg2 entity linking, ICPR multi-line mathematical expression recognition, CCKS multi-modal entity alignment, and explainable case matching, etc. He has founded the NLP technology community of Lao Liu, which has wide influence.

Graduated from Tianjin University, majoring in Image Processing and Pattern Recognition, he has nearly 10 years of experience in applying machine learning in the office field, and is deeply engaged in the fields of big data, user image, computational advertisement, search recommendation, and computer vision, etc. He is currently the director of algorithms in Kingsoft Office, and is responsible for the development of products based on the application of the big model, and focuses mainly on the application of Agent, RAG, and LLM, which is committed to providing users with an AI office experience beyond expectations. AI office experience that exceeds expectations.

He was the Algorithm Director of Big Digital Meditech Co., Ltd. and the System Architect of Alibaba Data Division, and has been deeply involved in the application and development management of Big Data and Artificial Intelligence in the industrial world for many years. He has rich practical experience in the application and development of big language models in serious medical scenarios.

Bian Zhengda studied under Prof You Yang at the National University of Singapore and is one of the top talents in the field of supercomputing in the world. He has 7 years of experience in high-performance AI systems and participated in the development of Colossal-AI system as a core developer. He has presented a paper at SC, the world's top conference on supercomputing, demonstrating his excellent ability in the field of AI computation and system optimisation. Zhengda Bian's commitment to advancing the technology of high-performance AI and improving the efficiency of AI model training and inference by optimising the use of computational resources has had a far-reaching impact on the international scene.

He is responsible for the research work in the field of large language model retrieval enhancement.He graduated from the School of Artificial Intelligence, Renmin University of China (RUC) in 2024 with a PhD degree, and his research interests are in natural language processing and information retrieval.He has published several papers in international conferences such as ACL, EMNLP, SIGIR, theWebConf, etc.He was nominated for the best paper in theWebConf 2023.

D. degree from the Institute of Computing Technology, Chinese Academy of Sciences. His research interests include compilation technology, parallel computing and heterogeneous computing. Dr Chen has been engaged in the R&D of programming and compilation technologies for domestic chips for many years. He was the architect of Huawei's compiler and programming language lab, and led the development of many compiler products for domestic chips. He has led the development of many compiler products for localised chips. His technologies cover a wide range of fields, including high-performance servers, wireless communications, and AI acceleration chips, and have been widely used in the market. Based on years of experience in chip infrastructure software construction, in 2023, he co-founded CKGH Technology Co., Ltd, which is dedicated to solving the difficulties faced by domestic AI arithmetic power landing application. Through full-system optimisation, compilation technology and tool support, CKGH provides large model inference and fine-tuning system tools to help accelerate the landing of domestic AI arithmetic.

Responsible for the whole process of aiXcoder big model development, including data acquisition and cleaning, big model construction and training, model inference optimisation and service, model evaluation, etc. Concerned about how to get the base code big model more in line with the actual development scenario of software engineering through large-scale distributed training; concerned about building a human-aligned training method that can match the software development process, tools, and behaviours; concerned about how to do domain-based incremental training on private code and minimize the disaster forgetting and other issues.

Dr Nan Wang, co-founder and CTO of Jina AI, graduated with a PhD in Computational Neuroscience from the University of Bochum, Germany. He then worked as a senior algorithm engineer at Zalando, a leading European e-commerce company, and Tencent, where he was responsible for the search and recommendation business, and accumulated rich experience in model design, implementation, and deployment in these areas. Since founding Jina AI in 2020, as co-founder and CTO, Dr Nan Wang led the team to develop and open source the neural search framework, jina. As a TAC member of the Linux Foundation AI&Data Fund, he facilitated DocArray's graduation from the Linux Foundation AI&DATA. Dr Nan Wang has organised the development and open-sourcing of several textual and multimodal vector models, which have accumulated over 10 million downloads worldwide. Dr Nan Wang is passionate about the practical application of AI technology in the search field and actively promotes the open source development of AI technology. His outstanding contribution in the field of AI technology has earned him the title of 33 Chinese Open Source Pioneers in 2023.

Master of Tsinghua University, many years of big data and AI full-stack development experience, was responsible for several multi-million products development and delivery. He is one of the core developers of Ktransformers, the industry's first high-performance inference framework that locally supports hundreds of billions of models on a single GPU, and supports 1M ultra-long contextual inference.