恭喜林蔚君 榮獲 INFORMS 2020 George E. Kimball Medal!
Congratulations to Prof. Grace Lin for getting the prestigious INFORMS 2020 George E. Kimball Medal!
https://www.youtube.com/watch?v=dZQ2PSbiuxQ
「運籌與管理科學學會頒發了2020年喬治·E·金博爾獎章給Grace Lin,以表彰並衷心感謝其對INFORMS以及運籌學,管理科學和分析專業的傑出貢獻。」
「For exemplary and distinguished service to INFORMS and the profession of operations research, management science, and analytics, the Institute for Operations Research and Management Sciences expresses its sincere appreciation to Grace Lin by awarding her the 2020 George E. Kimball Medal.」
Award Citation:
"Grace Lin is vice president, director of the Big Data Research Center, and chair professor in the Department of Business Administration at Asia University in Taichung, Taiwan since 2016. She recently founded the United Financial Intelligence Corp. (UFI) to support digital transformation and care quality improvement of aging care organizations. From 2011 to 2016, Dr. Lin was the founder and VP of the Data Analytics Technology and Applications (DATA) Institute and VP for the Advanced Research Institute (ARI) at the Institute for Information Industry (III). At III, she initiated key industry-government R&D programs including Smart Living and Smart Commerce Strategy Plan, Big Data Analytics, Smart Healthcare, Smart Tourism, FinTech, and Smart Agriculture. Previously, Dr. Lin worked for IBM US for more than 16 years as the Global Sense-and-Respond Value-Net Leader and CTO & director for Innovation and Emerging Solutions at IBM Global Business Services, and as a research staff member, manager, and senior manager at the IBM T.J. Watson Research Center. She was an elected member of the IBM Academy of Technology, an IBM Distinguished Engineer, and Relationship Manager for IBM's Integrated Supply Chain.
Her background and experience have positioned Dr. Lin at the intersection of technology, innovation, business consulting, and management as well as the intersection of academia and industry. Referred to by Forrester as one of the six supply chain gurus, Dr. Lin has published more than 80 technical papers, book chapters, and articles, and co-authored 10 U.S. patents and nine Taiwan patents. In 2006, she was named an INFORMS Fellow.
Dr. Lin's service to INFORMS and the profession has been substantial. She has been twice elected INFORMS VP Practice and VP International Activities, she has chaired the INFORMS Fellow Selection Committee and several INFORMS and IEEE conferences. Additionally, she has been active in the Edelman Award Competition, having served multiple years as judge and as a member of the selection committee. Active in WORMS, she has been a strong supporter of women in O.R. Passionate about bridging the gap between academia and industry, she has served on a number of university advisory boards. She has also served on National Science Foundation panels in the U.S., Canada, and Ireland, and editorial boards including Manufacturing & Service Operations Management (M&SOM), Operations Research, INFORMS Journal on Applied Analytics, and Service Science. She is a frequent keynote speaker at global conferences and industry events.
Dr Lin's awards include the INFORMS Franz Edelman Award, IBM Outstanding Technical Achievement Award, IBM Corporate Logistics Award, IBM Research Division Award, IIE Doctoral Dissertation Award, the Purdue Outstanding Industrial Engineer Award, and the Distinguished Alumni Award from the School of Science, National Tsing Hua University, Hsin-Chu, Taiwan.
For exemplary and distinguished service to INFORMS and the profession of operations research, management science, and analytics, the Institute for Operations Research and the Management Sciences expresses its sincere appreciation to Grace Lin by awarding her the 2020 George E. Kimball Medal."
同時也有10000部Youtube影片,追蹤數超過2,910的網紅コバにゃんチャンネル,也在其Youtube影片中提到,...
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【演講】2019/11/19 (二) @工四816 (智易空間),邀請到Prof. Geoffrey Li(Georgia Tech, USA)與Prof. Li-Chun Wang(NCTU, Taiwan) 演講「Deep Learning based Wireless Resource Allocation/Deep Learning in Physical Layer Communications/Machine Learning Interference Management」
IBM中心特別邀請到Prof. Geoffrey Li(Georgia Tech, USA)與Prof. Li-Chun Wang(NCTU, Taiwan)前來為我們演講,歡迎有興趣的老師與同學報名參加!
演講標題:Deep Learning based Wireless Resource Allocation/Deep Learning in Physical Layer Communications/Machine Learning Interference Management
演 講 者:Prof. Geoffrey Li與Prof. Li-Chun Wang
時 間:2019/11/19(二) 9:00 ~ 12:00
地 點:交大工程四館816 (智易空間)
活動報名網址:https://forms.gle/vUr3kYBDB2vvKtca6
報名方式:
費用:(費用含講義、午餐及茶水)
1.費用:(1) 校內學生免費,校外學生300元/人 (2) 業界人士與老師1500/人
2.人數:60人,依完成報名順序錄取(完成繳費者始完成報名程序)
※報名及繳費方式:
1.報名:請至報名網址填寫資料
2.繳費:
(1)親至交大工程四館813室完成繳費(前來繳費者請先致電)
(2)匯款資訊如下:
戶名: 曾紫玲(國泰世華銀行 竹科分行013)
帳號: 075506235774 (國泰世華銀行 竹科分行013)
匯款後請提供姓名、匯款時間以及匯款帳號後五碼以便對帳
※將於上課日發放課程繳費領據
聯絡方式:曾紫玲 Tel:03-5712121分機54599 Email:tzuling@nctu.edu.tw
Abstract:
1.Deep Learning based Wireless Resource Allocation
【Abstract】
Judicious resource allocation is critical to mitigating interference, improving network efficiency, and ultimately optimizing wireless network performance. The traditional wisdom is to explicitly formulate resource allocation as an optimization problem and then exploit mathematical programming to solve it to a certain level of optimality. However, as wireless networks become increasingly diverse and complex, such as high-mobility vehicular networks, the current design methodologies face significant challenges and thus call for rethinking of the traditional design philosophy. Meanwhile, deep learning represents a promising alternative due to its remarkable power to leverage data for problem solving. In this talk, I will present our research progress in deep learning based wireless resource allocation. Deep learning can help solve optimization problems for resource allocation or can be directly used for resource allocation. We will first present our research results in using deep learning to solve linear sum assignment problems (LSAP) and reduce the complexity of mixed integer non-linear programming (MINLP), and introduce graph embedding for wireless link scheduling. We will then discuss how to use deep reinforcement learning directly for wireless resource allocation with application in vehicular networks.
2.Deep Learning in Physical Layer Communications
【Abstract】
It has been demonstrated recently that deep learning (DL) has great potentials to break the bottleneck of the conventional communication systems. In this talk, we present our recent work in DL in physical layer communications. DL can improve the performance of each individual (traditional) block in the conventional communication systems or jointly optimize the whole transmitter or receiver. Therefore, we can categorize the applications of DL in physical layer communications into with and without block processing structures. For DL based communication systems with block structures, we present joint channel estimation and signal detection based on a fully connected deep neural network, model-drive DL for signal detection, and some experimental results. For those without block structures, we provide our recent endeavors in developing end-to-end learning communication systems with the help of deep reinforcement learning (DRL) and generative adversarial net (GAN). At the end of the talk, we provide some potential research topics in the area.
3.Machine Learning Interference Management
【Abstract】
In this talk, we discuss how machine learning algorithms can address the performance issues of high-capacity ultra-dense small cells in an environment with dynamical traffic patterns and time-varying channel conditions. We introduce a bi adaptive self-organizing network (Bi-SON) to exploit the power of data-driven resource management in ultra-dense small cells (UDSC). On top of the Bi-SON framework, we further develop an affinity propagation unsupervised learning algorithm to improve energy efficiency and reduce interference of the operator deployed and the plug-and-play small cells, respectively. Finally, we discuss the opportunities and challenges of reinforcement learning and deep reinforcement learning (DRL) in more decentralized, ad-hoc, and autonomous modern networks, such as Internet of things (IoT), vehicle -to-vehicle networks, and unmanned aerial vehicle (UAV) networks.
Bio:
Dr. Geoffrey Li is a Professor with the School of Electrical and Computer Engineering at Georgia Institute of Technology. He was with AT&T Labs – Research for five years before joining Georgia Tech in 2000. His general research interests include statistical signal processing and machine learning for wireless communications. In these areas, he has published around 500 referred journal and conference papers in addition to over 40 granted patents. His publications have cited by 37,000 times and he has been listed as the World’s Most Influential Scientific Mind, also known as a Highly-Cited Researcher, by Thomson Reuters almost every year since 2001. He has been an IEEE Fellow since 2006. He received 2010 IEEE ComSoc Stephen O. Rice Prize Paper Award, 2013 IEEE VTS James Evans Avant Garde Award, 2014 IEEE VTS Jack Neubauer Memorial Award, 2017 IEEE ComSoc Award for Advances in Communication, and 2017 IEEE SPS Donald G. Fink Overview Paper Award. He also won the 2015 Distinguished Faculty Achievement Award from the School of Electrical and Computer Engineering, Georgia Tech.
Li-Chun Wang (M'96 -- SM'06 -- F'11) received Ph. D. degree from the Georgia Institute of Technology, Atlanta, in 1996. From 1996 to 2000, he was with AT&T Laboratories, where he was a Senior Technical Staff Member in the Wireless Communications Research Department. Currently, he is the Chair Professor of the Department of Electrical and Computer Engineering and the Director of Big Data Research Center of of National Chiao Tung University in Taiwan. Dr. Wang was elected to the IEEE Fellow in 2011 for his contributions to cellular architectures and radio resource management in wireless networks. He was the co-recipients of IEEE Communications Society Asia-Pacific Board Best Award (2015), Y. Z. Hsu Scientific Paper Award (2013), and IEEE Jack Neubauer Best Paper Award (1997). He won the Distinguished Research Award of Ministry of Science and Technology in Taiwan twice (2012 and 2016). He is currently the associate editor of IEEE Transaction on Cognitive Communications and Networks. His current research interests are in the areas of software-defined mobile networks, heterogeneous networks, and data-driven intelligent wireless communications. He holds 23 US patents, and have published over 300 journal and conference papers, and co-edited a book, “Key Technologies for 5G Wireless Systems,” (Cambridge University Press 2017).
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<演講公告>Design Techniques for Low-Power Successive-Approximation-Register Analog-to-Digital Converters,歡迎自由到場聽講^^
講者 : Professor Shiuh-hua Wood Chiang(江旭樺教授), Brigham Young University
時間:107年8月14日(二) 上午10:00-11:30, 8月15日(三) 上午10:00-11:30
地點:交通大學工程四館四樓 AaPaTo Space(四樓中庭電梯旁)
【Title】
Design Techniques for Low-Power Successive-Approximation-Register Analog-to-Digital Converters
【Abstract】
This two-part course consists of recent lessons on analog/mixed-signal techniques for successive-approximation-register (SAR) analog-to-digital converters (ADC). In the first part, I will share two recent works on the building blocks for low-power ADCs. Specifically, I will discuss the design considerations and results of low-power charge-steering amplifiers and high-precision capacitor mismatch measurement. In the second part, I will discuss the fundamentals of the SAR ADC and present recent design examples from my group. The discussions will cover several innovative circuit and architectural-level techniques to achieve high power efficiencies in SAR.
【Bio】
Shiuh-hua Wood Chiang received the B.S. degree in computer engineering from the University of Waterloo in 2007, the M.S. degree in electrical engineering from the University of California, Irvine in 2009, and the Ph.D. degree in electrical engineering from the University of California, Los Angeles in 2013. He was a postdoctoral scholar in the Communication Circuits Laboratory at the University of California, Los Angeles, in 2013. He was a Senior Engineer at Qualcomm from 2013 to 2014. He joined Brigham Young University in 2014 as an Assistant Professor. He is currently the director of the Micropower Circuits Laboratory. His research interests include RF/analog/mixed-signal integrated circuits. He is currently serving as the Vice-Chair of the IEEE Solid-State Circuits Society (SSCS) Utah Chapter and he is a member of the Editorial Review Board for IEEE Solid-State Circuits Letters (SSCL).
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ieee senior member 在 IEEE Taipei Section 台北分會- Home | Facebook 的推薦與評價
近半年台北分會申請的七位Senior Member 全數通過! ✍️ 晉升為IEEE 資深會員(Senior Member)的門檻並不高,十年工作經驗或PhD + 工作滿五年即可提出申請,僅需要 ... ... <看更多>
ieee senior member 在 Re: [請益] 什麼是IEEE Fellow? - 精華區Master_D 的推薦與評價
一般人如果加入 IEEE 成為會員, 可以有兩條成長路線 (很像培養遊戲)
起點 -> Student member --
| 得具學生身份, |
| 會員費便宜 |
| |
------------------> Member ---------> Senior Member --> Fellow
有繳會員費即可 在專業上有重要影響力 必須是
不限身份 必須經過遴選程序 Senior Member
必須加入為會員超過5年
必須經過遴選程序
基本上隨著經驗值的上升, 玩家可以到教會 (IEEE) 申請轉職 (升會員等級)
很簡單吧
--
I-Hsuan Huang
Yuan Ze University
Dept. of Computer Science and Engineering
E-Mail:[email protected]
Web: https://syslab.cse.yzu.edu.tw/~ihhuang/
> -------------------------------------------------------------------------- <
作者: alert3210 (alert) 站內: Master_D
標題: Re: [請益] 什麼是IEEE Fellow?
時間: Wed Apr 27 20:59:33 2005
※ 引述《femto (大屁喵是最愛)》之銘言:
: 那那我也問個問題,
: 我最近找國外的老師
: 看到大部份的人都是XX fellow和某journal editor
: 請問成為fellow或editor就真的有特別的成就嗎???
: 因為幾乎人人都有, 而且說真的期刊那麼多也需要很多editor
: 那到底找老師時有什麼方法只到他是真的很有學術成就呢????
: 多謝囉
:
一般來說,如果以頭銜來看的話,大概可以這樣看,
1.諾貝爾獎(通常一流學校約有十來位)
2.美國國家科學院院士(National Academy of Sciences )
(通常一流學校約有百來位,台灣現有四位)
如果是journal的話,要看不同的領域,以物理來說,最有名的就是PRL了,
如果不太清楚的話,可以上網查journal的IF值(Impact Factor)
值越高代表這個期刊被引用的次數越多,通常就比較好。
不過,通常IF值很高的,大都是跨領域的journal(如Science Nature......)
不過,若能當上那些期刊的editor,也都很不容易。
參考看看~
... <看更多>