They are responsible for selecting awardees that have shown dedication, commitment, leadership and continuous contribution to the field of Textile Conservation.The four committee members in 2023-2024 are Sarah Scaturro, Obie Linn, Kathleen Kiefer, and Kaelyn Garcia. The committee comprises four TSG members, three voting members and an alternate.The review committee may consider an nomination for up to three years.Members currently serving on AIC Boards/Committees may not receive awards during their tenure.Nominators must be an Associate, a Professional Associate or a Fellow member.The review committee takes into consideration the nominee's dedication to the organization over their years of membership and considers those not Professional Associates or Fellows based on their individual merit. Nominees should be or have been a Professional Associate or a Fellow member of the American Institute for the Conservation.Submit the completed nomination form by January 15.Additional co-sponsors are welcome but not required. List the name and email address of one additional co-sponsor who also provides a written statement testifying to the nominee's commitment to the field.Write a statement on the nominee’s commitment to the field in areas such as research, analysis, conservation, teaching, and support of the textile conservation community.This award recognizes outstanding contributions to the field of textile conservation by an individual or entity that has promoted, defended, and worked in support of the importance of textiles and their need for preservation. His current research focuses on hybrid systems which integrate data- and knowledge driven models and techniques and on quantum computing for optimization problems in AI.Textile Specialty Group Achievement Award He has (co)authored more than 200 papers on data mining, pattern recognition and machine learning several of which received awards. Biography:Ĭhristian Bauckhage is a professor for intelligent learning systems at the University of Bonn, lead scientist for machine learning at Fraunhofer IAIS, and one of the directors of the Lamarr Institute for ML and AI.Īfter obtaining his PhD in computer science from Bielefeld University, he worked as a PostDoc at the Center for Vision Research in Toronto and as a senior research scientist at Deutsche Telekom Laboratories in Berlin before being appointed in Bonn in 2008. Third, we emphasize that there is still hope for quantum AI and show how quantum algorithms can accelerate Bayesian network inference. Second, we point out the sobering fact that most promises as to quantum supremacy for ML and AI are and will likely remain severely exaggerated. Our goals with this presentation are threefold: First, we provide an ever so brief introduction to quantum computing and its expected benefits. It is particularly noticeable that the quantum computing community has jumped on the machine learning (ML) bandwagon and is promising quantum advantages for artificial intelligence (AI). Over the past decade there has been encouraging progress on building quantum computers so that hopes regarding the practical applications and disruptive potential of quantum computing are rising.
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