جست‌وجوی استاد و پژوهشگر

Sinho Chewiمشاهده پروفایل

استادیار

• Optimal Transport
• Log-concave Sampling
• Machine Learning Theory
• +۳ مورد دیگر

Sinho Chewi is an Assistant Professor of Statistics and Data Science at Yale University, where he conducts research at the intersection of mathematics, statistics, and machine learning. His work focuses on theoretical aspects of computational statistics, particularly leveraging optimal transport theory for solving complex problems in sampling and inference. Chewi earned his B.S. in Engineering Mathematics and Statistics from the University of California, Berkeley in 2018, followed by a PhD in Mathematics and Statistics from the Massachusetts Institute of Technology in 2023 under the supervision of Philippe Rigollet. Prior to joining Yale, he was a postdoctoral researcher at the Institute for Advanced Study during the 2023-2024 academic year. His research interests span optimal transport theory , log-concave sampling , variational inference , and theoretical foundations of machine learning . Chewi is currently authoring a comprehensive book on the complexity of log-concave sampling, building on his extensive publication record. His work bridges theoretical mathematics with practical applications in statistical computing and artificial intelligence. Analysis of Chewi's publication record reveals a strong focus on developing rigorous mathematical frameworks for sampling algorithms, with particular emphasis on complexity analysis, convergence guarantees, and connections between functional inequalities and optimization. His recent work extends into diffusion models, neural network theory, and parallel sampling algorithms, demonstrating both depth in core statistical theory and breadth across machine learning applications. ICLR 2023 (Notable Top 5%) ALT 2023 (Best Student Paper) NeurIPS 2021 (Spotlight) ICLR 2025 DeLTA Workshop (Best Short Paper Award) Chewi has received significant research support through his postdoctoral position at the Institute for Advanced Study and collaborations with leading researchers at institutions including MIT, NYU, and Microsoft Research. His teaching portfolio includes advanced courses in optimization techniques and sampling methods, reflecting his expertise in theoretical foundations of machine learning algorithms.

Kawashima Ryotaمشاهده پروفایل

دانشیار

• Network Function Virtualization
• Cloud-Native Networking
• High-Performance Computing
• +۳ مورد دیگر

Kawashima Ryota is an Associate Professor at Chubu University in the Department of Information Engineering, Network Field. He holds a PhD in Information Science from The Graduate University for Advanced Studies (2010) and a Master's in Software Information Science from Iwate Prefectural University (2007). Prior to his academic career, he worked as an R&D Engineer at Stratosphere, Inc. and ACCESS CO., LTD. His research focuses on: High-performance cloud-native networking and NFV infrastructure optimization CPU cache efficiency and I/O parallelization in virtualized environments Transparent acceleration techniques for distributed databases and key-value stores Low-latency solutions for beyond-5G network architectures Recent publications (2020-2024) demonstrate strong emphasis on: Optimizing cloud-native network functions for terabit-class throughput Reducing virtualization overhead through hardware-aware resource allocation Developing proxy-based acceleration layers for NewSQL and distributed KVS Addressing latency/jitter challenges in next-generation networks Awards & Honors: IEEE FNWF'24 Best Paper Award (2024) IEICE ICM Research Award (2023) IEEE NFV-SDN Best Paper Award (2018) IEICE Communications Society Best Paper Award (2017) Research Leadership: Secured competitive grants including JSPS Kakenhi (¥16.9M direct funding) for terabit-class cloud networking research. Mentored award-winning students including IEEE Nagoya Section Excellent Student Award recipient Yuki Taguchi. Active in industry collaborations with Bosco Technologies on NFV efficiency.

• Computational Linguistics
• Natural Language Processing
• Czech Language Processing
• +۴ مورد دیگر

Zuzana Nevěřílová serves as an Assistant Professor at Masaryk University's Department of Czech Language within the Faculty of Arts. She also maintains external cooperation with the Department of Machine Learning and Data Processing at the Faculty of Informatics, demonstrating interdisciplinary engagement between linguistic theory and computational approaches. Her research interests span computational linguistics with particular emphasis on Czech language processing, natural language processing methodologies, and development of language resources. Her work bridges theoretical linguistics with practical computational applications, focusing on areas such as named entity recognition, text analysis, word embeddings, and language resource development. Dr. Nevěřílová's recent publication trends (2021-2025) reveal strong focus on Czech language technologies, including named entity processing in parallel corpora, efficient language models, and educational applications of NLP. Her work demonstrates consistent contributions to Slavonic language processing communities through workshops like RASLAN and conferences like TSD. Her scientific contributions include extensive publications in proceedings of international conferences and workshops, software development for language resources, and educational materials for digital humanities. She has supervised various projects related to language technology and has contributed to significant resources like the New Encyclopedic Dictionary of Czech and VerbaLex lexical database. Her research has practical applications in language services, educational technology, and digital humanities. Dr. Nevěřílová works within the Institute of the Czech Language at Masaryk University, contributing to research teams focused on Czech language processing, corpus development, and computational lexicography.