宗教與科技 - 離合及綜觀

Time: 01/18/2019 13:30pm-16:30pm (Sat.)
Venue: ITRI International Inc. - 2870 Zanker Rd., Suite 140, San Jose, CA 95134
Registration: Sign up here

世界77億人87% 有宗教信仰,夏祖焯教授將介紹世界重要宗教的基本知識與可能的誤解,此次演講以美國主流的基督教為本。他將談論宗教的性質與科學、工程、文學、藝術、及現實政治、經濟、社會的關係;五大宗教之間的區別與消長;歷史上宗教與科技的離合、相依或對立;對科學的執著是否類似一種宗教信仰?中國是世界上唯一無「國教」的強國,這代表了什麼?為什麼不少人不信教,甚至反宗教?他們將如何面對生命及死亡?為什麼20世紀後基督教在歐洲式微卻是美國社會的支柱?今日台灣、日本及中國大陸的宗教狀況如何?演講後請各位當場發表意見,討論為何著名科學家,偉人信奉宗教,宗教與科學是否有衝突,邀請您的朋友來參加。敬請務必準時出席。

Speaker Bio:

夏祖焯教授 (Prof. Frederick Hsia, Ph.D., P.E., G.E.):

夏祖焯(夏烈),密西根(州立)大學工程博士。曾任橋樑工程師,美國聯邦政府特殊重點計劃經理等職。近年任教成功大學、清華大學,教授近代歐美、日本文學及文化,現代中文小說及散文。並任美洲中國工程師學會理事。1994年獲『國家文藝獎』。2006年獲美洲中國工程師學會「科技與人文獎」。2015年獲成大「優良教師獎」。台北建中傑出校友。除工程論文外,夏祖焯教授文學及論述文著作共10本。

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2020 Technology Trends and Predictions

Time: 01/15/2019 18:30pm-21:00pm (Wed.)
Venue: ITRI International Inc. - 2870 Zanker Rd., Suite 140, San Jose, CA 95134
Registration: Sign up here

It is the beginning of another year, 2020!

What are the technology trends that will dominate, and what are some of the technology predictions for 2020?

In this two hour seminar talk, Dr. William Kao will report on what the tech industry is predicting for 2020. This seminar will cover what major institutions such as Gartner, Forbes, Forrester Research, International Data Group are saying about this year: trends and some predictions for the year.

Some of the topics to be discussed will include AI and Machine Learning, IoT, AR/VR, Augmented Data Analytics, Quantum Computing, Robotics, 5G, Blockchain, Self Driving Vehicles, Cyber Security, etc.


Speaker Bio:
Dr. William Kao received his BSEE, MSEE and PhD from the University of Illinois Urbana-Champaign. He worked in the Semiconductor and Electronic Design Automation industries for more than 30 years holding several senior and executive engineering management positions at Texas Instruments, Xerox Corporation, Cadence Design Systems.

Dr. Kao has authored more than 40 technical papers at IEEE Journals and Conferences, and holds eight software and IC design patents. He was an Adjunct Professor at UCLA Electrical Engineering Department where he taught courses in computer aided IC design.

Dr. Kao is a Senior Member of IEEE, and was one of the founding members of IEEE-Circuits and Systems - Silicon Valley Chapter, where he was Chapter Chair in 2005 and 2006.

Dr. Kao currently teaches Renewable Energy, Clean Technology and Business Sustainability courses at the University of California Santa Cruz, Silicon Valley Extension.

Dr. Kao current interests include the subjects of Energy, Environment and Education.

He teaches and is a frequent lecturer on various Emerging Technologies including Clean Technology, Renewable Energy, Big Data, IoT, Smart Cities, Sensor Networks, Innovation, Augmented and Virtual Reality, Robotics, AI / Machine Learning, 5G and 3D Printing.

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BME Event: Precision Medical Diagnostics

Registration: https://www.eventbrite.com/e/2019-cie-sf-bme-event-precision-medical-diagnostics-tickets-72460973723

Precision Medical Diagnostics – A Cross Disciplinary Application of Advanced Optical Sensor, Microfluidic, and Biochemistry


About this Event

A journey into bridges the gap between the technology and the medical application. In the case of a precision medical diagnostic device, a fluorescence bio-marker is developed by chemists and used to identify the target gene sequence, an bio-optical sensor is developed by IC designers and used for detecting the fluorescence light generate by the bio-market, a microfluidic chip is designed by fluidic experts for mixing the sample and reagents, and an analytical software is developed for generating final results for diagnosis by medical doctors. In this talk, we will walk through the technologies involved in a precision medical diagnostic device.

Speaker :

Yuping Chung

SVP & Cofounder

Anitoa Systems, LLC

Menlo Park, CA

Bio

Mr. Yuping Chung is the co-founder and SVP of Anitoa Systems LLC, a 6-year medical device and bio-sensor startup. Prior to Anitoa, Yuping served as General Manager and BU Director at Faraday Technology, Microchip, Macronix, Renesas, IDT, and others. Yuping also serves as advisor for startups in medical devices, datacenter persistent memory technologies, and industrial robotics. As a volunteer, Yuping is also served as Chairman and President of CASPA (Chinese American Semiconductor Professional Association) in 2014 and now serves as a member of Board of Advisors.

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CIE-SF BME seminar: Immunosequencing

Using Machine Learning to Better Characterize and Understand the Clinical Prognostic Features and Landscape of Immunosequencing

Registration: https://www.eventbrite.com/e/cie-sf-bme-seminar-immunosequencing-tickets-73451093197

About this Event

Abstract:

In recent decades, immunotherapy has been demonstrated as a significant clinical activity in cancer studies. T cells and B cells represent a crucial component of the adaptive immune system and are thought to mediate anti-tumoral immunity. Antigen-specific recognition by T cells is via the T cell receptor (TCR) which is unique for each T cell, clonotype, while B cells can also take up antigens with their BCRs to act as antigen-presenting cells (APC). Next generation sequencing (NGS) of the TCRs/BCRs can be used as a platform to profile the T/B cell repertoire. Though there are a number of software tools available, most of them are either designed for processing repertoire data by mapping antigen receptor segments to sequencing reads and assembling the clonotypes, or only examining the diversity of the TCR/BCR repertoire, they all lack of the ability to track and examine the dynamic nature of the repertoire across serial time points (the treatment effect) or correlate with clinical characteristics and clinical outcomes. We aim to develop customized statistical methods with advanced machine learning techniques to investigate the different aspects of TCR/BCR repertoire data in a clinical context to shift into a new era of classification and pattern recognition of immunosequencing. I will present 1) a comparative study of different feature selection (chi-square filter, correlated-based filter, support vector machine wrapper SVM-RFE, random forest wrapper Boruta), classification (SVM, random forest, bagging, boosting, RBF neural nets, MLP neural nets) and clustering methods (K-means, expectation-maximization); 2) a visualization pipeline of BCR affinity maturation while solving the computational burden by social network analysis.

Speaker:

Li Zhang, Ph.D., is an Associate Professor of Department Medicine and Department of Epidemiology and Biostatistics at University of California San Francisco (UCSF). She is an associate member and principle statistician of UCSF Helen Diller Family Comprehensive Cancer Center (HDFCCC) and faculty statistician for UCSF Clinical Translational Science Institute.

She obtained her Ph.D. in Statistics from University of Florida in 2006. She received pre-doctoral training from Division of Cancer Epidemiology and Genetics at NIH/NCI. Before she joined UCSF in 2013, she was an Assistant Professor at Cleveland Clinic and Case Western Reserve University for about 7 years. She is currently the Visiting Professor or Capital Medical University in Beijing, China.

Prof. Zhang has over fifteen years of experience in applying statistical method in biomedical research including basic science studies, epidemiology studies, clinical research. Her statistical methodological research interests are cancer epidemiology and immunoinformatics. She received multiple research rewards as a principal investigator (PI) and co-Investigator (Co-I), for example, she received UCSF Global Oncology Pilot Project Award on Genome Wide Association Study (GWAS) of Esophageal Cancer in Tanzania and recently received her second UCSF Research Allocation Program Award of Using Machine Learning to Better Characterize and Understand the Clinical Prognostic Features and Landscape of Immunosequencing. She received two Prostate Cancer Foundation Challenge Awards as statistical Co-I. She has more than 80 peer-reviewed publications and has been listed on more than 20 federal or foundation grants as a statistician.

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