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Boxin Shi
Assistant Professor & Research Professor (研究员，博士生导师) at Peking University
Professional Background
Boxin Shi is an accomplished academic and researcher in the realm of computer vision and artificial intelligence. Currently serving as a Boya Young Fellow Assistant Professor and Research Professor at Peking University, he leads the Camera Intelligence Group. His journey into academia and research began after earning his Bachelor’s degree in Information Security from the Beijing University of Posts and Telecommunications, followed by advanced studies at Peking University, where he obtained a Master’s degree in Signal and Information Processing, and later transitioned to The University of Tokyo for his Doctor of Philosophy (Ph.D.) in Information Science and Technology.
Prior to his current position at Peking University, Boxin Shi gained valuable postdoctoral research experience working with renowned institutions such as the MIT Media Lab and the Singapore University of Technology and Design (SUTD). He has also worked as a researcher at the National Institute of Advanced Industrial Science and Technology (AIST) in Japan. His diverse experiences have fostered an interdisciplinary approach to his research endeavors, integrating various aspects of technology and innovation.
Boxin has made significant contributions to the field of computer vision through his research, typically revolving around the themes of computational photography, image processing, and machine learning applications in visual media. His commitment to excellence is reflected in the accolades he has received throughout his academic career, including a Best Paper Runner-up award at the esteemed International Conference on Computational Photography in 2015.
Education and Achievements
Boxin Shi's educational background is marked by excellence, beginning with a Bachelor’s degree in Information Security from the Beijing University of Posts and Telecommunications, where he graduated with a commendable GPA of 3.83/4.0. He then pursued advanced studies at Peking University, earning a Master’s degree with an impressive GPA of 3.92/4.0 in Signal and Information Processing, focusing on Computer Vision. His academic pursuits culminated in earning a Ph.D. from The University of Tokyo in Information Science and Technology with a perfect GPA of 4.0/4.0, specializing in computer vision.
His scholarly journey is complemented by a strong research background. Before assuming his role at Peking University, he spent significant time at multiple prestigious institutions, including the National Institute of Advanced Industrial Science and Technology, Singapore University of Technology and Design, and MIT Media Lab, where he honed his skills and understanding of advanced technologies. This broad range of experiences from different cultural and academic backgrounds has significantly enriched his research perspective and collaboration efforts.
Throughout his career, Boxin Shi has been actively involved in several prestigious conferences and journals, serving in roles such as an editorial board member for the International Journal of Computer Vision (IJCV) and area chair for conferences such as CVPR (Conference on Computer Vision and Pattern Recognition) and ICCV (International Conference on Computer Vision). These roles indicate not only his expertise in the field but also his active engagement in shaping future directions of research in computer vision.
Notable Achievements
Boxin Shi's notable achievements extend beyond award recognitions. His research outputs are widely recognized in international journals and conferences, making significant contributions to the fields of image processing and machine learning. The Best Paper Runner-up award he received at the International Conference on Computational Photography showcases his innovative research contributions and the impact that his work has had in the field. He constantly explores new methodologies to enhance the understanding and applications of computer vision technologies.
In addition to his research accolades, Boxin has been pivotal in nurturing the next generation of scholars, mentoring students at Peking University and guiding them through their research journeys. His leadership of the Camera Intelligence Group not only advances the frontiers of research but also fosters a collaborative and innovative academic environment.
In summary, Boxin Shi is an inspiring figure in the academic landscape of computer vision. With a rich educational background, substantial postdoctoral research experience, and a dedication to advancing technology through research and education, Boxin continues to make impactful contributions that influence the trajectory of computer science and engineering fields.
tags':['Boya Young Fellow','Peking University','Computer Vision','Research Professor','Camera Intelligence Group','International Conference on Computational Photography','MIT Media Lab','Singapore University of Technology and Design','Nanyang Technological University','Information Securit
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Mesh makes it much easier to stay in touch with the people you care about. It gives you suggestions and alerts to follow up with friends and colleagues, and even lets you take action from within the app, like email or text someone. Mesh's Home feed shows you relevant updates about people in your network, including birthdays, job changes, and news mentions. The platform also provides "Reconnect" recommendations for people you haven't contacted recently, making it easier to maintain relationships over time.
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Mesh offers many advantages over other personal CRMs. Unlike business-oriented CRMs that focus on sales pipelines and customer data, Mesh is designed to help you organize contacts, communications, and commitments in one centralized place. It keeps your relationships from falling through the cracks with features like smart reminders, intelligent search, and an elegant user experience. Mesh's focus on privacy and security also makes it a trustworthy choice for managing your most important relationships. Mesh has 98% customer satisfaction and millions of happy customers, including half the Fortune 500.
Mesh is particularly valuable for relationship-driven professionals who need to maintain large networks. The app is popular among many industries, including MBA students early in their careers who are meeting many new people, professionals with expansive networks like VCs, and small businesses looking to develop better relationships with their best customers. Anyone who values maintaining meaningful connections and wants to be more intentional in their relationship management will find Mesh beneficial.
Mesh offers competitive pricing in the personal CRM market. Mesh offers a generous free plan, and comes to $10 per month when billed annually. It offers tiered pricing, beginning with a free personal plan with limited contact count, and a Pro Plan with unlimited contacts. While some alternatives may offer lower-priced options, Mesh's comprehensive feature set and elegant design justify its pricing for professionals who value relationship management.
Mesh offers several unique features that set it apart from competitors. Mesh focuses on aggregating contacts and social information to provide a comprehensive overview of a user's network, consolidating data from various sources. Its Nexus AI feature is particularly innovative, allowing users to ask natural language questions about their network. Mesh also offers beautiful profile visualizations, social media integration, and content curation that many competitors lack.
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