Christopher White has served as the President at NEC Laboratories America, Inc. since March 2020, where he leads a team of world-class researchers focusing on diverse topics from sensing to networking to machine learning-based understanding.
Chris has extensive expertise in scientific computing, hierarchical simulation techniques, quantum chemistry, optical networks, optical devices, and acoustic scattering. His research interests include the development of computational models and methods for the simulation and control of interesting physical and digital systems. This has included work in areas ranging from linear scaling quantum chemistry simulations to the design of new optical devices, to the global control of transparent optical mesh networks and to understanding and facilitating the propagation of ideas in organizations. In addition to the management of a team of world-class researchers, his current work focuses on the creation of assisted thinking tools that leverage structural similarity in data with the goal of augmenting human intelligence.
Prior to his role at NEC Laboratories America, Inc., he spent 22 years working at Nokia Bell Labs where he led the Algorithms, Analytics, Augmented Intelligence and Devices (AAAID) research lab.
Chris has a Ph.D. in Theoretical Quantum Chemistry from the University of California, Berkeley, and a B.S. and M.S. in Chemistry from Carnegie Mellon University, with a concentration in Computer Science.
Beyond One Model Fits All: A Survey of Domain Specialization for Large Language Models Large language models (LLMs) have significantly advanced the field of natural language processing (NLP), providing a highly useful, task agnostic foundation for a wide range of applications. The great promise of LLMs as general task solvers motivated people to extend their functionality largely beyond just a chatbot, and use it as an assistant or even replacement for domain experts and tools in specific domains such as healthcare, finance, and education. However, directly applying LLMs to solve sophisticated problems in specific domains meets many hurdles, caused by the heterogeneity of domain data, the sophistication of domain knowledge, the uniqueness of domain objectives, and the diversity of the constraints (e.g., various social norms, cultural conformity, religious beliefs, and ethical standards in the domain applications). To fill such a gap, explosively increase research, and practices have been conducted in very recent years on the domain specialization of LLMs, which, however, calls for a comprehensive and systematic review to better summarizes and guide this promising domain. In this survey paper, first, we propose a systematic taxonomy that categorizes the LLM domain specialization techniques based on the accessibility to LLMs and summarizes the framework for all the subcategories as well as their relations and differences to each other. We also present a comprehensive taxonomy of critical application domains that can benefit from specialized LLMs, discussing their practical significance and open challenges. Furthermore, we offer insights into the current research status and future trends in this area.