Ranak Roy Chowdhury, Applied Scientist @ AWS Supply Chain

Ranak Roy Chowdhury is an Applied Scientist at AWS Supply Chain Team. He received his Ph.D. degree from University of California San Diego. His research interest is time-series data and its applications in supply chain, wearable motion, healthcare, and speech. He has received several awards, including the Qualcomm Innovation Fellowship and Halıcıoğlu Data Science Institute Graduate Prize Fellowship. He was an invited keynote speaker in KDD 2023 Workshop on Machine Learning in Finance.

Yan Liu, Professor @ University of Southern California, Scholar @ Amazon

Yan Liu is a Professor in the Computer Science Department and the Director of the Machine Learning Center at the University of Southern California. She is an Amazon Scholar working with AWS Supply Chain Team. She received her Ph.D. degree from Carnegie Mellon University. Her research interest is machine learning and its applications to climate science, health care, and sustainability. She has received several awards, including NSF CAREER Award, Okawa Foundation Research Award, New Voices of Academies of Science, Engineering, and Medicine, Best Paper Award in SIAM Data Mining Conference. She serves as general chair for KDD 2020 and ICLR 2023, and program chairs for WSDM 2018, SDM 2020, KDD 2022 and ICLR 2022.

Caiming Xiong, Senior VP of AI Research and Applied AI @ Salesforce

Caiming Xiong is the Senior Vice President of AI Research and Applied AI at Salesforce, leading transformative AI innovation across enterprise applications. He has built and scaled AI research and applied AI teams, driving cutting-edge advancements in NLP, conversational AI, time-series analysis, speech recognition, computer vision, and recommendation systems. His leadership has been instrumental in integrating AI solutions into Sales, Service, Commerce, Marketing, and Availability Clouds in CRM, ensuring AI is embedded at every level of enterprise decision-making. With a strong vision for deep learning and machine learning in enterprise AI, he has spearheaded the development of AI platforms that power B2B, B2C, and internal operations at scale. Committed to both research and real-world impact, he has published in top-tier AI conferences while bridging breakthrough AI innovations with practical applications. Through his work, he is shaping the future of enterprise AI, redefining customer engagement, automation, and business intelligence.

Huiming Qu, VP of Data Science @ The Home Depot

Huiming Qu is Vice President of Data Science at The Home Depot. She is a seasoned data science executive with over 15 years of experience in building high-performing data science organizations and developing scalable AI/ML solutions that drive significant business outcomes. As the leader of AI strategy and the data science platform at The Home Depot, she spearheads the development of AI/ML solutions that enhance customer experience and operational efficiency across e-commerce, marketing, delivery, and contact centers, driving innovations in search, recommendations, personalization, content creation, contact center automation and multi-modal conversational shopping experiences. With deep expertise in data science, engineering, and product leadership, Huiming has a proven track record of driving billion-dollar contributions through scalable AI solutions and strategic innovation. Before joining The Home Depot, Huiming has years of experience developing innovative AI/ML solutions at IBM’s Watson Research Lab, Distillery, and American Express. She earned a Ph.D. in computer science from the University of Pittsburgh, holds 20 issued patents, and has published over 15 academic papers on data management, machine learning, and optimization.

Qingsong Wen, Head of AI Research & Chief Scientist @ Squirrel AI Learning

Qingsong Wen is the Head of AI Research & Chief Scientist at Squirrel AI Learning. Before that, he worked at Alibaba, Qualcomm, Marvell, etc., and received his M.S. and Ph.D. degrees in Electrical and Computer Engineering from Georgia Institute of Technology, USA. His research interests include machine learning and data mining, especially AI for Time Series, AI for Education, LLM, and GenAI. He has published over 120 top-ranked AI conference and journal papers, had multiple Oral/Spotlight Papers at NeurIPS, ICML, and ICLR, had multiple Most Influential Papers at IJCAI, received multiple IAAI Deployed Application Awards at AAAI, and won First Place of SP Grand Challenge at ICASSP. He has served as Organizer/Co-Chair of multiple workshops at KDD, ICDM, SDM, AAAI, IJCAI, CAI, and CIKM. He also serves as Associate Editor for Neurocomputing, Associate Editor for IEEE Signal Processing Letters, Guest Editor for Applied Energy, and Guest Editor for IEEE Internet of Things Journal. In addition, he regularly serves as Area Chair of the AI conferences including NeurIPS, ICML, KDD, IJCAI, ICASSP, etc.

Chen-Yu Lee, Senior Staff Research Scientist and Manager @ Google

Chen-Yu Lee is a Senior Staff Research Scientist and Manager at Google Cloud AI Research, working at the intersection of large AI models and high-value enterprise AI challenges across various tasks and modalities. Before joining Google, he spent two years at Apple, contributing to the Technology Development Group's inaugural research paper at CVPR and launching key features in ARKit (now Vision Pro). He earned his Ph.D. in Computer Science from the University of California, San Diego. He has an extensive publication record, with numerous papers in top-tier conferences and journals, reflecting his significant impact on the field.

Vincent Quenneville-Bélair, Senior Applied Scientist @ Amazon

Vincent Quenneville-Bélair is a Senior Applied Scientist at Amazon’s Supply Chain Optimization Technologies (SCOT), where he develops large-scale forecasting and optimization models that power Amazon’s global supply chain. His work spans probabilistic forecasting, demand planning, and scalable algorithms for decision-making under uncertainty. Before joining Amazon, Vincent completed his Ph.D. in Economics at Columbia University, where his research focused on computational methods for high-dimensional data and dynamic decision processes. He has also contributed to advancing machine learning approaches for time-series forecasting, reinforcement learning, and operations research applications.

Naren Agrawal, Professor @ Santa Clara University

Naren Agrawal is the Benjamin and Mae Swig Professor at the Leavey School of Business, Santa Clara University, specializing in supply chain management, operations management, and business analytics. He earned his Ph.D. in Operations and Information Management from The Wharton School, University of Pennsylvania, and has since established himself as a leading academic in his field. His research, widely published in top-tier journals such as Management Science and Manufacturing & Service Operations Management, focuses on retail supply chains, inventory optimization, and digital advertising operations. He is also the co-editor of the influential book ``Retail Supply Chain Management: Quantitative Models and Empirical Studies'' (Springer), which provides key insights into modern retail operations. Throughout his career, Dr. Agrawal has held several leadership positions, including Interim Dean (2020–2021) and Associate Dean of Faculty (2010–2015) at Santa Clara University. A Fulbright Fellow and recipient of multiple teaching and research awards, he has worked extensively with industry leaders to apply AI and machine learning in supply chain resilience. Beyond academia, he serves on the board of Give2Asia, promoting philanthropic initiatives across the region.

Vinayak Deshpande, Professor @ University of North Carolina

Vinayak Deshpande is the Mann Family Distinguished Professor and Chair of Operations at UNC’s Kenan-Flagler Business School. He holds a Ph.D. in Operations Management from Wharton, where his dissertation on U.S. Navy and DLA supply chains won the Dantzig Dissertation Award. He also earned an M.S. in Operations Research from Columbia and a B.Tech. in Mechanical Engineering from IIT Bombay. His research focuses on supply chain, spare parts, inventory, sustainable operations, and healthcare, with applications across defense, aviation, hi-tech, retail, airlines, and healthcare. His work has appeared in leading journals including Management Science, Operations Research, POMS, and M&SOM. He has led impactful projects with organizations such as the U.S. Coast Guard, Alibaba, JD.com, and healthcare systems, earning recognition as an Edelman Laureate, AGIFORS Best Contribution awardee, and finalist for several INFORMS awards. He is past president of the POMS Supply Chain College and co-founder of AD3 Analytics, which helps companies build agile, data-driven supply chains.

Ayushi Goel, Data Scientist @ Amazon

Ayushi Goel is a Data Scientist at Amazon, working in the ATS Science and Tech (SnT) team, and is currently pursuing a master’s degree at the University of California, Berkeley. Her research interests span natural language processing, large language models, and the intersection of data engineering and AI. She is passionate about leveraging LLMs to automate and enhance data analysis workflows, with a particular focus on real-world applications in supply chain and enterprise data ecosystems.

Jhalak Rawat, Global Supply Manager @ Apple

Jhalak Rawat is a seasoned supply chain and operations professional, currently serving as a Senior Manager at Apple. Prior to this, she honed her expertise at Dell Technologies, where she played a pivotal role in global supply management, focusing on optimizing procurement strategies and enhancing supplier relationships. She holds a Master's degree in Industrial Engineering and Operations Research from the University of California, Berkeley, and a Bachelor's degree in Mechanical Engineering from the Indian Institute of Technology, Delhi.

Olivier Sprangers, Applied Scientist @ Nixtla

Olivier Sprangers is an Applied Scientist at Nixtla, where he focuses on advancing time series forecasting methodologies. He earned his Ph.D. from the University of Amsterdam. His research interests include probabilistic forecasting, hierarchical forecasting, and the development of parameter-efficient deep learning models. Olivier has contributed to several publications in esteemed journals and conferences, reflecting his commitment to enhancing forecasting techniques in large-scale applications. 

David Simchi-Levi, Professor @ Massachusetts Institute of Technology

David Simchi-Levi is the William Barton Rogers Professor at the Massachusetts Institute of Technology (MIT), where he leads the MIT Data Science Lab. A distinguished authority in supply chain management, operations research, and business analytics, he has founded multiple companies, including LogicTools, which was integrated into IBM in 2009, OPS Rules, acquired by Accenture in 2016, and Opalytics, also acquired by Accenture in 2018. His pioneering Risk Exposure Index (REI) methodology has been instrumental in identifying and mitigating disruptions in global supply chains, earning him the 2014 INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice. In 2020, he was honored with the INFORMS Impact Prize for his significant contributions to the field. His insights have been featured in prominent publications, including the Harvard Business Review, where he co-authored articles predicting global supply chain disruptions. Professor Simchi-Levi's extensive media appearances and numerous prestigious awards underscore his profound impact on both academia and industry.

Sercan Ö. Arik, Senior Staff Research Scientist Manager @ Google

Sercan Ö. Arik is a Senior Staff Research Scientist and Manager at Google Cloud AI Research, where he leads initiatives to democratize artificial intelligence across sectors such as healthcare, finance, technology, retail, media, and manufacturing. His work emphasizes enhancing AI performance, interpretability, trustworthiness, data efficiency, robustness, and reliability. Prior to joining Google, he was a Research Scientist at Baidu's Silicon Valley AI Lab. Dr. Arik earned his Ph.D. in Electrical Engineering from Stanford University and has co-authored over 100 journal and conference publications and patents. His research contributions have significantly influenced the development of major Google Cloud products, yielding substantial business impact. 

Alexis Roos, Sr Manager of Applied Science and ML @ AWS Supply Chain

Alexis Roos is a Senior Manager of Applied Science and ML at AWS Supply Chain Team. With over 25 years in the tech industry and more than a decade in AI, he is a seasoned AI leader and computer scientist. He has initiated and led over a dozen AI teams, comprising hundreds of PhD researchers, data scientists, and machine learning engineers, and has launched innovative, high-quality AI products across diverse industries in both B2B and consumer sectors. His applications span supply chain, fashion-tech, marketing and sales intelligence, fraud detection, chatbots, computer vision, general AI applications, recommendation systems, and knowledge graphs. Prior to AWS, he was a Senior Manager & Director of Machine Learning at Twitter and Salesforce.