Loubna Ben Allal is a Machine Learning Engineer in the Science team at Hugging Face working on LLMs for code. She is part of the core team behind the BigCode Project and has co-authored The Stack dataset, SantaCoder, and StarCoder models. Loubna holds a Master's Degree in Mathematics and Deep Learning from Ecole des Mines de Nancy and graduated from the MVA Master program at ENS Paris Saclay

Senior Manager of Strategic Alliance Ecosystem at Arm China, focusing on domestic software technology ecosystems and strategic collaborations. Possesses extensive professional experience in processor technology, software ecosystems, machine learning, and system solutions.

Bohua Chen is the head of R&D at the Shenzhen Graduate School of Peking University-Rabbit Zhan Intelligence AIGC Joint Laboratory, responsible for the ground-up R&D of big language modeling products in vertical domains. His research interests include big language modeling and reinforcement learning. The open source project Chatlaw initiated by him is dedicated to solving the problem of accessing legal aid services for ordinary people with big language modeling, which has been implemented in several scenarios.

Hai-Xu currently works as the IBM Global Lead and IBM GCG (Greater China Group) CTO (standards and open source). Hai-Xu is actively working on watsonx and generative AI at the moment. Hai-Xu has involved in the development of many China and international (ISO/IEC JTC1 SC42 (AI), SC38 (Cloud), ISO TC307 (blockchain)) standards, including AI, cloud, blockchain, Cybersecurity, IoT and big data standards. In recent years, Hai-Xu has also actively engaged in developer advocacies and open source projects promotion (AIX360, AIF360, ART360, Caikit, Kepler, Hyperledger etc.). In the past, Hai-Xu served as the lead of the IBM Global Cloud Computing Center of Excellence for Asia Pacific and participated in the implementation of many large global cloud services projects which leveraged the open cloud technologies. Hai-Xu has also served as the Chief Enterprise Architect for IBM GCG and many institutions in Australia and Singapore, and led the enterprise architecture and solution architecture development, implementation, operation and governance for many large enterprises across Asia-Pacific countries and regions (Australia, Singapore, Malaysia, Philippines, and Great China etc.).

Bosheng Ding is the founder of the open source project, PandaLLM. He is a researcher and PhD candidate at Nanyang Technological University, focusing on deep learning and natural language processing. His research focused on Large Language Models and Robust NLP Systems. Bosheng has demonstrated a strong aptitude for research, as evidenced by his publications at top AI conferences. In addition to his own research, Bosheng has also served as a reviewer for various AI conferences, including EMNLP, ACL, KDD, SIGDIAL, and WSDM. He is also an adjunct lecturer on digital marketing at the Singapore University of Social Sciences. Prior to his Ph.D. studies, Bosheng worked as a financial risk management consultant for PwC Consulting. In this role, he contributed to projects in a number of countries, including Singapore, Thailand, the Philippines, Malaysia, Austrilia, and South Africa. Bosheng’s experiences in academia and industry have equipped him with diverse skills and perspectives, making him well-suited to tackle complex problems at the intersection of technology and business.

Joseph E. Gonzalez is a Professor in the EECS department at UC Berkeley, a co-director and founding member of the UC Berkeley RISE Lab and a member of the Berkeley AI Research (BAIR Group). His research interests span machine learning and data systems.

Han Xu is the chief researcher at ModelBest. He was a postdoctoral fellow at Tsinghua University, with research directions in natural language processing, pre-trained language models, and knowledge graphs. He has published numerous papers in international conferences on natural language processing and artificial intelligence such as ACL and EMNLP. He has been cited over 5,900 times on Google Scholar. As one of the main contributors, he participated in the community building of the OpenBMB large model and the open-source of the CPM series models. During his doctoral studies, he received various honors including the National Scholarship, Tsinghua University's Jiang Nanxiang Scholarship, Tsinghua University's Department of Computer Science Zhong Shimou Scholarship, Tsinghua University's Outstanding Doctoral Dissertation, and the Microsoft Scholar Scholarship. He was selected for the CCF Excellent Doctoral Dissertation Incentive Plan for 2022, the Postdoctoral Innovative Talent Support Program, and the Tsinghua University Shuimu Scholar Program.

Kunhao Pan graduated from Beijing University of Posts and Telecommunications in 2017 with a master's degree. He is currently the head of the expert model team at IDEA-CCNL and co-leads the development of the Ziya series of large language models. His main research areas are natural language processing, large language models, retrieval algorithms, and few-shot learning. He has led the team to win the FewCLUE leaderboard championship multiple times.

Shishir Patil is a CS PhD student at UC Berkeley, where he is advised by Prof. Joseph Gonzalez and Prof. Prabal Dutta. He is interested in designing and building efficient machine-learning systems for the two extremes of computing - edge and multi-cloud. Recently, he is focused on teaching LLMs to use tools through API calls. His works include Gorilla LLM, Skyplane, and POET. He was a Research Fellow at Microsoft Research, and has interned at Amazon Science, Apple, and Google Brain.

Lingyun Tan is the Shanghai R&D center GM at Ysemi Computing, managing the engineering team on the hardware design and validation, for Arm based cloud native CPU. He has 20+ years’ experience on silicon, platform/system engineering activities. Prior to Ysemi, he was the Chief Engineer at AMD and the platform leader, managed APU and GPU engineering and project teams.

Chief of Artificial Intelligence at Luchen Technology

Vivian Hu is a Product Manager at Second State and a columnist at InfoQ. She is a founding member of the WasmEdge project. She organizes Rust and WebAssembly community events in Asia.

Hao Wang received his Ph.D. in Physics from Peking University in 2018. His research interests include deep reinforcement learning, natural language processing, and cognitive intelligence. He has published more than 10 top-tier papers and holds several patents in both China and the United States in these domains. Currently, he is the head of the RL algorithm team at IDEA-CCNL, where he co-leads the development of the 'Ziya' series of general artificial intelligence models.

Wilson Wang is a researcher at ByteDance's North American branch. He earned his Ph.D. from the State University of New York in 2013. His research primarily focuses on areas such as VM (Virtual Machines), operating systems, distributed systems, network protocols, and network virtualization. Currently, he is concentrating on the applications of WebAssembly in distributed computing engines, heterogeneous computing, and AI-related fields.

Dify.AI co-founder & VP of Product, with over 10 years of experience in product architecture and commercialization; Previously deeply involved in the online education and enterprise service sectors, excelling at PMF (Product-Market Fit) validation and driving business growth from a scale of 1 to 10; Currently focused on the LLMs Infra domain, dedicated to liberating human creativity through LLMs-related technologies and products.

Yang You serves as a Presidential Young Professor at the National University of Singapore (NUS), joining the ranks of exceptional early-career talents. He earned his Ph.D. in Computer Science from UC Berkeley under the guidance of Prof. James Demmel, a notable figure in the Computer Science Division and the EECS Department. Yang's research delves into Parallel/Distributed Algorithms, High Performance Computing, and Machine Learning, primarily focusing on amplifying deep neural network training on extensive systems. In 2017, his team set a new benchmark by achieving the world's fastest ImageNet training speed, gaining significant media attention. Two years later, they surpassed the world record for BERT training speed, influencing the training techniques adopted by tech behemoths like Google, Microsoft, and NVIDIA. Yang's pioneering LARS and LAMB optimizers feature in the industry-standard MLPerf benchmark. Over the years, he has clinched numerous awards, such as the IPDPS 2015 Best Paper Award, ICPP 2018 Best Paper Award, and the prestigious AAAI 2023 Distinguished Paper Award. Recognized as a Siebel Scholar and recipient of the Lotfi A. Zadeh Prize, Yang was also UC Berkeley's nominee for the ACM Doctoral Dissertation Award. His influence and achievements secured him a spot in the Forbes 30 Under 30 Asia list in 2021 and earned him the IEEE CS TCHPC Early Career Researchers Award.

Michael is the maintainer of WasmEdge Runtime, a cloud native WebAssembly sandbox project under CNCF. He is the author of six books on software engineering.

Bruce Zhang is the system architect of Alibaba Cloud, responsible for ECS ARM product line and cloud native Application performance. Main work is to build Cloud product competitiveness, performance optimization, ecosystem construction. And for the large-scale application of Arm ECS in the group and the cloud PaaS products.

Tianjun Zhang is a PhD student at UC Berkeley, advised by Prof. Joseph E. Gonzalez. He has been in active collaboration with Prof. Pieter Abbeel and Prof. Sergey Levine. He mainly works on the Reinforcement Learning and Large Language Model backed agents. His has published more than 15 papers on the top-tier machine learning conferences (NeurIPS, ICML and ICLR), including two oral presentations. He co-leads the Gorilla project, which aims to build LLMs connecting with external tool usage through API calls.

Yanzhi Wang is an associate professor and faculty fellow at Dept. of ECE at Northeastern University, Boston, MA. He received the B.S. degree from Tsinghua University in 2009, and Ph.D. from the University of Southern California in 2014. His research interests focus on model compression and platform-specific acceleration of deep learning applications. His work has been published broadly in top conference and journal venues (e.g., DAC, ICCAD, ASPLOS, ISCA, MICRO, HPCA, PLDI, ICS, PACT, ISSCC, AAAI, ICML, NeurIPS, CVPR, ICLR, IJCAI, ECCV, ICDM, ACM MM, FPGA, LCTES, CCS, VLDB, PACT, ICDCS, RTAS, Infocom, C-ACM, JSSC, TComputer, TCAS-I, TCAD, TCAS-I, JSAC, TNNLS, etc.), and has been cited around 16,000 times. He has received six Best Paper and Top Paper Awards and one Communications of the ACM cover featured article. He has another 13 Best Paper Nominations and four Popular Paper Awards. He has received the IEEE TC-SDM Early Career Award, APSIPA Distinguished Leader Award, Massachusetts Acorn Innovation Award, Martin Essigmann Excellence in Teaching Award, Massachusetts Acorn Innovation Award, Ming Hsieh Scholar Award, YIP Award and other research awards from Google, MathWorks, etc.