NSF QCIS-FF: Columbia University Computer Science Department Proposal
NSF QCIS-FF:哥伦比亚大学计算机科学系提案
基本信息
- 批准号:1926524
- 负责人:
- 金额:$ 75万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-05-01 至 2024-04-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
All of us are aware that the computing revolution has changed our world in profound ways over the past several decades, but many desired applications of computing -- of great potential importance and utility -- are simply beyond the capabilities of today's computer systems. This is because for many central problems in computer science, the fastest known computer programs that can solve these problems have running time or storage requirements that scale too quickly with increasing input sizes (exponentially as opposed to linearly or polynomially); even worse, it is widely believed that faster programs for today's conventional computer systems simply cannot exist. Quantum computing offers the possibility of a new and dramatically different approach. The key premise is that by harnessing quantum mechanical properties, in certain settings it is possible for a quantum computer to simultaneously process an exponentially large number of superimposed states, and thus (in some sense) to perform parallel operations over an enormous search space in a way that is simply impossible for classical computers. Motivated by this exciting possibility, the School of Engineering and Applied Sciences at Columbia University (SEAS) has launched the "Columbia Quantum Initiative," an ambitious multi-year plan to develop a center of excellence at Columbia University spanning multiple aspects of quantum science and technology, leveraging the long-standing world-class research and educational programs at Columbia University on closely related topics. A central goal of the Quantum Initiative is to recruit a half dozen or more tenured/tenure-track faculty over the next few years whose primary research interests are in the areas of quantum computing, communication, sensing, materials, devices and technologies, and a near-term goal of the Quantum Initiative is to hire two tenured/tenure-track faculty members in the area of quantum computing. As part of the Quantum Initiative, this award will enable the creation of a tenured/tenure-track position for a faculty member with research specialization in quantum computing, either in the Computer Science Department or as an interdisciplinary joint hire between Computer Science and a second department such as Applied Physics/Applied Mathematics, Electrical Engineering, or Industrial Engineering and Operations Research. A core Computer Science hire is necessary to provide a solid base of expertise in key computational aspects of quantum technology such as quantum algorithms, quantum programming methodologies, and quantum computer architectures, while an interdisciplinary hire will play a key bridging role in ensuring that the entire team of electrical engineers, applied physicists, researchers from other domains, and quantum computing researchers comprising the Quantum Initiative can work together effectively. The addition of quantum computing faculty to the intellectual ecosystem at Columbia SEAS will greatly expand educational offerings to train students at Columbia University in a range of subjects relevant to the development of quantum technologies. Such technologies have the potential to have broad, as-yet unforeseen impacts on many aspects of science and society. With its large population of outstanding students in Computer Science and associated subject areas, there is great potential for significant broader impact through education via faculty growth in quantum computing at Columbia.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
我们都知道,在过去的几十年里,计算革命以深刻的方式改变了我们的世界,但许多令人向往的计算应用--具有巨大的潜在重要性和实用性--完全超出了当今计算机系统的能力。这是因为对于计算机科学中的许多核心问题,可以解决这些问题的已知最快的计算机程序具有随着输入大小的增加而过快地扩展的运行时间或存储要求(与线性或多项式相反的指数增长);更糟糕的是,人们普遍认为,用于今天的传统计算机系统的更快的程序根本不存在。量子计算提供了一种新的、截然不同的方法的可能性。关键的前提是,通过利用量子力学特性,在某些设置下,量子计算机可以同时处理指数级大量的叠加态,从而(在某种意义上)以经典计算机根本不可能的方式在巨大的搜索空间上执行并行操作。在这种令人兴奋的可能性的激励下,哥伦比亚大学(SEA)工程与应用科学学院启动了“哥伦比亚量子计划”,这是一项雄心勃勃的多年计划,旨在利用哥伦比亚大学在密切相关主题上的长期世界级研究和教育计划,在哥伦比亚大学建立一个涵盖量子科学和技术多个方面的卓越中心。量子计划的核心目标是在未来几年招聘六名或更多终身教职/终身教职教员,他们的主要研究兴趣是量子计算、通信、传感、材料、设备和技术领域,而量子计划的近期目标是在量子计算领域招聘两名终身教职/终身教职教员。作为量子计划的一部分,该奖项将为具有量子计算研究专长的教员创造一个终身/终身教职职位,要么在计算机科学系,要么作为计算机科学和第二个系(如应用物理/应用数学、电气工程或工业工程和运筹学)之间的跨学科联合招聘。聘请一名核心计算机科学人员是必要的,以提供量子技术关键计算方面的坚实专业知识基础,如量子算法、量子编程方法和量子计算机体系结构,而跨学科聘用将在确保组成量子倡议的整个电子工程师团队、应用物理学家、其他领域的研究人员和量子计算研究人员能够有效合作方面发挥关键的桥梁作用。在哥伦比亚海洋大学的知识生态系统中增加量子计算教师将极大地扩大教育范围,以培训哥伦比亚大学的学生学习与量子技术发展相关的一系列学科。这种技术有可能对科学和社会的许多方面产生广泛的、尚未预见的影响。凭借其在计算机科学和相关学科领域的大量优秀学生,通过哥伦比亚大学量子计算方面的教师增长,通过教育产生重大的更广泛影响的潜力很大。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Rocco Servedio其他文献
Theory of Computing
计算理论
- DOI:
10.4086/toc - 发表时间:
2013 - 期刊:
- 影响因子:0
- 作者:
Alexandr Andoni;Nikhil Bansal;P. Beame;Giuseppe Italiano;Sanjeev Khanna;Ryan O’Donnell;T. Pitassi;T. Rabin;Tim Roughgarden;Clifford Stein;Rocco Servedio;Amir Abboud;Nima Anari;Ibm Srinivasan Arunachalam;T. J. Watson;Research Center;Petra Berenbrink;Aaron Bernstein;Aditya Bhaskara;Sayan Bhattacharya;Eric Blais;H. Bodlaender;Adam Bouland;Anne Broadbent;Mark Bun;Timothy Chan;Arkadev Chattopadhyay;Xue Chen;Gil Cohen;Dana Dachman;Anindya De;Shahar Dobzhinski;Zhiyi Huang;Ken;Robin Kothari;Marvin Künnemann;Tu Kaiserslautern;Rasmus Kyng;E. Zurich;Sophie Laplante;D. Lokshtanov;S. Mahabadi;Nicole Megow;Ankur Moitra;Technion Shay Moran;Google Research;Christopher Musco;Prasad Raghavendra;Alex Russell;Laura Sanità;Alex Slivkins;David Steurer;Epfl Ola Svensson;Chaitanya Swamy;Madhur Tulsiani;Christos Tzamos;Andreas Wiese;Mary Wootters;Huacheng Yu;Aaron Potechin;Aaron Sidford;Aarushi Goel;Aayush Jain;Abhiram Natarajan;Abhishek Shetty;Adam Karczmarz;Adam O’Neill;Aditi Dudeja;Aditi Laddha;Aditya Krishnan;Adrian Vladu Afrouz;J. Ameli;Ainesh Bakshi;Akihito Soeda;Akshay Krishnamurthy;Albert Cheu;A. Grilo;Alex Wein;Alexander Belov;Alexander Block;Alexander Golovnev;Alexander Poremba;Alexander Shen;Alexander Skopalik;Alexandra Henzinger;Alexandros Hollender;Ali Parviz;Alkis Kalavasis;Allen Liu;Aloni Cohen;Amartya Shankha;Biswas Amey;Bhangale Amin;Coja;Yehudayoff Amir;Zandieh Amit;Daniely Amit;Kumar Amnon;Ta;Beimel Anand;Louis Anand Natarajan;Anders Claesson;André Chailloux;André Nusser;Andrea Coladangelo;Andrea Lincoln;Andreas Björklund;Andreas Maggiori;A. Krokhin;A. Romashchenko;Andrej Risteski;Anirban Chowdhury;Anirudh Krishna;A. Mukherjee;Ankit Garg;Anna Karlin;Anthony Leverrier;Antonio Blanca;A. Antoniadis;Anupam Gupta;Anupam Prakash;A. Singh;Aravindan Vijayaraghavan;Argyrios Deligkas;Ariel Kulik;Ariel Schvartzman;Ariel Shaulker;A. Cornelissen;Arka Rai;Choudhuri Arkady;Yerukhimovich Arnab;Bhattacharyya Arthur Mehta;Artur Czumaj;A. Backurs;A. Jambulapati;Ashley Montanaro;A. Sah;A. Mantri;Aviad Rubinstein;Avishay Tal;Badih Ghazi;Bartek Blaszczyszyn;Benjamin Moseley;Benny Pinkas;Bento Natura;Bernhard Haeupler;Bill Fefferman;B. Mance;Binghui Peng;Bingkai Lin;B. Sinaimeri;Bo Waggoner;Bodo Manthey;Bohdan Kivva;Brendan Lucier Bundit;Laekhanukit Burak;Sahinoglu Cameron;Seth Chaodong Zheng;Charles Carlson;Chen;Chenghao Guo;Chenglin Fan;Chenwei Wu;Chethan Kamath;Chi Jin;J. Thaler;Jyun;Kaave Hosseini;Kaito Fujii;Kamesh Munagala;Kangning Wang;Kanstantsin Pashkovich;Karl Bringmann Karol;Wegrzycki Karteek;Sreenivasaiah Karthik;Chandrasekaran Karthik;Sankararaman Karthik;C. S. K. Green;Larsen Kasturi;Varadarajan Keita;Xagawa Kent Quanrud;Kevin Schewior;Kevin Tian;Kilian Risse;Kirankumar Shiragur;K. Pruhs;K. Efremenko;Konstantin Makarychev;Konstantin Zabarnyi;Krišj¯anis Pr¯usis;Kuan Cheng;Kuikui Liu;Kunal Marwaha;Lars Rohwedder László;Kozma László;A. Végh;L'eo Colisson;Leo de Castro;Leonid Barenboim Letong;Li;Li;L. Roditty;Lieven De;Lathauwer Lijie;Chen Lior;Eldar Lior;Rotem Luca Zanetti;Luisa Sinisclachi;Luke Postle;Luowen Qian;Lydia Zakynthinou;Mahbod Majid;Makrand Sinha;Malin Rau Manas;Jyoti Kashyop;Manolis Zampetakis;Maoyuan Song;Marc Roth;Marc Vinyals;Marcin Bieńkowski;Marcin Pilipczuk;Marco Molinaro;Marcus Michelen;Mark de Berg;M. Jerrum;Mark Sellke;Mark Zhandry;Markus Bläser;Markus Lohrey;Marshall Ball;Marthe Bonamy;Martin Fürer;Martin Hoefer;M. Kokainis;Masahiro Hachimori;Matteo Castiglioni;Matthias Englert;Matti Karppa;Max Hahn;Max Hopkins;Maximilian Probst;Gutenberg Mayank Goswami;Mehtaab Sawhney;Meike Hatzel;Meng He;Mengxiao Zhang;Meni Sadigurski;M. Parter;M. Dinitz;Michael Elkin;Michael Kapralov;Michael Kearns;James R. Lee;Sudatta Bhattacharya;Michal Koucký;Hadley Black;Deeparnab Chakrabarty;C. Seshadhri;Mahsa Derakhshan;Naveen Durvasula;Nika Haghtalab;Peter Kiss;Thatchaphol Saranurak;Soheil Behnezhad;M. Roghani;Hung Le;Shay Solomon;Václav Rozhon;Anders Martinsson;Christoph Grunau;G. Z. —. Eth;Zurich;Switzerland;Morris Yau — Massachusetts;Noah Golowich;Dhruv Rohatgi — Massachusetts;Qinghua Liu;Praneeth Netrapalli;Csaba Szepesvári;Debarati Das;Jacob Gilbert;Mohammadtaghi Hajiaghayi;Tomasz Kociumaka;B. Saha;K. Bringmann;Nick Fischer — Weizmann;Ce Jin;Yinzhan Xu — Massachusetts;Virginia Vassilevska Williams;Yinzhan Xu;Josh Alman;Kevin Rao;Hamed Hatami;—. XiangMeng;McGill University;Edith Cohen;Xin Lyu;Tamás Jelani Nelson;Uri Stemmer — Google;Research;Daniel Alabi;Pravesh K. Kothari;Pranay Tankala;Prayaag Venkat;Fred Zhang;Samuel B. Hopkins;Gautam Kamath;Shyam Narayanan — Massachusetts;Marco Gaboardi;R. Impagliazzo;Rex Lei;Satchit Sivakumar;Jessica Sorrell;T. Korhonen;Marco Bressan;Matthias Lanzinger;Huck Bennett;Mahdi Cheraghchi;V. Guruswami;João Ribeiro;Jan Dreier;Nikolas Mählmann;Sebastian Siebertz — TU Wien;The Randomized k ;Conjecture Is;False;Sébastien Bubeck;Christian Coester;Yuval Rabani — Microsoft;Wei;Ethan Mook;Daniel Wichs;Joshua Brakensiek;Sai Sandeep — Stanford;University;Lorenzo Ciardo;Stanislav Živný;Amey Bhangale;Subhash Khot;Dor Minzer;David Ellis;Guy Kindler;Noam Lifshitz;Ronen Eldan;Dan Mikulincer;George Christodoulou;E. Koutsoupias;Annamária Kovács;José Correa;Andrés Cristi;Xi Chen;Matheus Venturyne;Xavier Ferreira;David C. Parkes;Yang Cai;Jinzhao Wu;Zhengyang Liu;Zeyu Ren;Zihe Wang;Ravishankar Krishnaswamy;Shi Li;Varun Suriyanarayana - 通讯作者:
Varun Suriyanarayana
Rocco Servedio的其他文献
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{{ truncateString('Rocco Servedio', 18)}}的其他基金
Collaborative Research: AF: Medium: Continuous Concrete Complexity
合作研究:AF:中:连续混凝土复杂性
- 批准号:
2211238 - 财政年份:2022
- 资助金额:
$ 75万 - 项目类别:
Continuing Grant
AF: Medium: The Trace Reconstruction Problem
AF:中:迹线重建问题
- 批准号:
2106429 - 财政年份:2021
- 资助金额:
$ 75万 - 项目类别:
Continuing Grant
Student Travel Grant for 2019 Conference on Computational Complexity (CCC)
2019 年计算复杂性会议 (CCC) 学生旅费补助
- 批准号:
1919026 - 财政年份:2019
- 资助金额:
$ 75万 - 项目类别:
Standard Grant
BIGDATA: F: Big Data Analysis via Non-Standard Property Testing
BIGDATA:F:通过非标准属性测试进行大数据分析
- 批准号:
1838154 - 财政年份:2019
- 资助金额:
$ 75万 - 项目类别:
Standard Grant
AF: Small: Collaborative Research: Boolean Function Analysis Meets Stochastic Design
AF:小型:协作研究:布尔函数分析与随机设计的结合
- 批准号:
1814873 - 财政年份:2018
- 资助金额:
$ 75万 - 项目类别:
Standard Grant
Student Travel Support for CCC 2018
CCC 2018 学生旅行支持
- 批准号:
1822097 - 财政年份:2018
- 资助金额:
$ 75万 - 项目类别:
Standard Grant
AF: Medium: Collaborative Research: Circuit Lower Bounds via Projections
AF:中:协作研究:通过投影确定电路下界
- 批准号:
1563155 - 财政年份:2016
- 资助金额:
$ 75万 - 项目类别:
Continuing Grant
AF: Small: Linear and Polynomial Threshold Functions: Structural Analysis and Algorithmic Applications
AF:小:线性和多项式阈值函数:结构分析和算法应用
- 批准号:
1420349 - 财政年份:2014
- 资助金额:
$ 75万 - 项目类别:
Standard Grant
AF: Small: Learning and Testing Classes of Distributions
AF:小:学习和测试分布类
- 批准号:
1319788 - 财政年份:2013
- 资助金额:
$ 75万 - 项目类别:
Standard Grant
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