CSRankings: Computer Science Rankings

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This ranking is designed to identify institutions and faculty actively engaged in research across a number of areas of computer science, based on the number of publications by faculty that have appeared at the most selective conferences in each area of computer science (see the FAQ for more details).

We gratefully acknowledge the generous support of our sponsors, including Stony Brook University . Sponsor CSrankings

Prominent mentions of CSrankings: CMU ( 1 , 2 ) | Edinburgh | Michigan | Rutgers | Technion | Yann LeCun | John Regehr | Charles Sutton

CSRankings by Emery Berger is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License . Based on a work at https://github.com/emeryberger/CSrankings . Follow @csrankings for updates. Copyright 2017-2023 © Emery Berger

computer science open rankings

Rankings are an ideology . Each is biased in its own way. So choose and combine existing rankings to generate your preferred meta ranking for computer science programs in the United States and Canada. Ranking sources represent: reputation ( U.S. News * U.S. News provides data for universities in the United States and Maclean's provides data for universities in Canada ), faculty publications ( csrankings.org * The rankings shown under the csrankings.org column may differ from their website because of different subfields and implementation of geometric mean count. ), academic placement ( placement rank ), and recognition ( best paper awards ). Use this to find the best computer science program for you in artificial intelligence, systems, or theory.

by Alice Marbach, Jeff Huang, Long Do, Shaun Wallace, and others who contributed the original ranking sources.

ranking source

A ranking based on reputation of graduate programs, compiled from the survey responses of academics to produce a peer assessment score per university. Click here and here for more information.

This ranking measures research publications by professors across the field of computer science. Publications occurring in selected conferences in the year 2010 or later are counted based on the proportion of authors that are professors at that institution. The numbers we report under the csrankings column might differ from csrankings.org because while we use the same formula (geometric mean count) we have a different implementation and different subfields. Click here for more information.

Ranking based on placement of students from an institution into faculty positions. The data comes from a publicly-editable dataset of professor profiles . The university where a professor got their bachelors or doctorate is treated as the source node in the Pagerank algorithm, where the score is generated from. See the original scores for the PageRank calculations.

This ranking measures papers from 30 top computer science conferences recognized as "best papers". Points are assigned to schools based on author affiliation and their position in the author list based on a decreasing exponential scale. See the best papers page for the full listing.

View the latest institution tables

View the latest country/territory tables

Google Scholar reveals its most influential papers for 2020

Artificial intelligence papers amass citations more than any other research topic.

computer science paper ranking

Chinese Go player Ke Jie (L) attends a press conference after his second match against Google's artificial intelligence programme AlphaGo on day two of Future of Go Summit in Wuzhen on May 25, 2017 in Jiangxi, Zhejiang Province of China. Credit: VCG / Contributor / Getty

13 July 2020

computer science paper ranking

VCG / Contributor / Getty

Chinese Go player Ke Jie (L) attends a press conference after his second match against Google's artificial intelligence programme AlphaGo on day two of Future of Go Summit in Wuzhen on May 25, 2017 in Jiangxi, Zhejiang Province of China.

Google Scholar has released its annual ranking of most highly cited publications. Artificial intelligence (AI) research dominates once again , accumulating huge numbers of citations over the past year.

Computer vision research in particular attracts a high number citations over a short period of time. Many of the most highly cited papers in this ranking are centred on object detection and image recognition – research that is crucial for technologies such as self-driving cars and surveillance.

The high citations numbers for AI-related papers mirror the increasing importance governments around the world are placing on the technologies they underpin.

In February , the United States government announced its commitment to double research and development spending in non-defense AI and quantum information science by 2022.

In April, the European Commission announced that it is increasing its annual investments in AI by 70% under the research and innovation programme, Horizon 2020.

Google Scholar is the largest database in the world of its kind, tracking citation information for almost 400 million academic papers and other scholarly literature.

The 2020 Google Scholar Metrics ranking , which is freely accessible online, tracks papers published between 2015 and 2019, and includes citations from all articles that were indexed in Google Scholar as of June 2020.

The most highly-cited paper of all, "Deep Residual Learning for Image Recognition", published in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , was written by a team from Microsoft in 2016. It has made a huge leap from 25,256 citations in 2019 to 49,301 citations in 2020.

“Deep learning”, a seminal review of the potential of AI technologies that was published in Nature in 2015, has had an increase in citations from 16,750 in 2019 to 27,375 in 2020.

It is the most highly-cited paper in the listing Nature , which is ranked by Google Scholar as the most influential journal , based on a measure called the h5-index, which is the h-index for articles published in the last five years .

Three of the top five papers listed by Google Scholar for Nature are related to AI. Two are genetics papers. Citations counts for the AI papers are significantly higher.

For example, the AI paper, "Deep learning", with the highest number of citations for Nature , has 27,375. The paper, “Analysis of protein-coding genetic variation in 60,706 humans”, is the highest ranked non-AI-related paper published in Nature , and has 6,387 citations.

Of the 100 top-ranked journals in 2020, six are AI conference publications. Their papers tend to amass citations much faster than papers in influential journals such as The New England Journal of Medicine , Nature , and Science .

Such rapid accumulation of citations may be in part explained by the fact that at these annual conferences that can attract thousands of attendees from around the world, new software, which is often open source, is shared and later built upon by the community.

Below is our 2020 selection of Google Scholar’s most highly-cited articles published by the world's most influential journals.

See our 2019 coverage for a selection that includes the high-performers mentioned above.

1. “ Adam: A Method for Stochastic Optimization ” (2015) International Conference on Learning Representations 47,774 citations

Adam is a popular optimization algorithm for deep learning – a subset of machine learning that uses artificial neural networks inspired by the human brain to imitate how the brain develops certain types of knowledge.

Adam was introduced in this paper at the 2014 International Conference on Learning Representations (ICLR) by Diederik P. Kingma, today a machine learning researcher at Google, and Jimmy Ba from the Machine Learning Group at the University of Toronto, Canada. Adam has since been widely used in deep learning applications in computer vision and natural language processing

The ICLR, one of the most prestigious conferences on machine learning, is an important platform for researchers whose papers are accepted. In May 2020, the conference drew 5,600 participants from nearly 90 countries to its virtual sessions – more than double the turnout in 2019 , at 2,700 physical attendees.

2. “ Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks ” (2015) Neural Information Processing Systems 19,507 citations

Presented at the 2015 Neural Information Processing Systems annual meeting in Canada, this paper describes what has now become the most widely-used version of an object detection algorithm called R-CNN .

Object detection is a major part of computer vision research, used to identify objects such as humans, cars, and buildings in images and videos.

The lead author, Shaoqing Ren, is also a co-author on Google’s most-cited paper for 2020, "Deep Residual Learning for Image Recognition", which has amassed almost 50,000 citations. Read more about it here .

That paper was co-authored by Ross Girshick, one of the invertors of R-CNN and now a research scientist at Facebook AI.

In the same week that “Faster R-CNN” was presented by Ren and his colleagues, Girshick presented a paper on “Fast R-CNN”, another version of R-CNN, at a different conference. That paper, presented at the 2015 IEEE International Conference on Computer Vision in Chile, has amassed more than 10,000 citations .

3. “ Human-level control through deep reinforcement learning ” (2015) Nature 10,394 Citations

After “Deep learning” (mentioned above), which is Nature ’s most highly cited paper in the Google Scholar Metrics ranking, this paper is the journal’s second-most cited paper for 2020.

It centres on reinforcement learning – how machine learning models are trained to make a series of decisions by interacting with their environments.

The paper was authored by a team from Google DeepMind, a London-based organization acquired by Google in 2014 that has developed AI technologies for the diagnosis of eye diseases, energy conservation, and to predict the complex 3D structures of proteins.

4. “ Attention Is All You Need ” (2017) Neural Information Processing Systems 9,885 citations

Authored by researchers at Google Brain and Google Research, this paper proposed a new deep learning model called the Transformer.

Designed to process sequential data such as natural language, Transformer is used by translation, text summarization, and voice recognition technologies, and other applications that use sequence analysis such as DNA, RNA, and peptide sequencing. It’s been used, for example, to generate entire Wikipedia articles .

Earlier this year, researchers at Google predicted that Transformer could be used for applications beyond text, including to generate music and images.

The paper was part of the proceedings from the 2017 Neural Information Processing Systems conference held in Long Beach, California.

5. “ The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) ” (2016) JAMA 8,576 citations

The first formal revision of the definitions of sepsis and septic shock in 15 years, this paper describes a condition that’s estimated to affect more than 30 million people worldwide every year.

Led by the European Society of Intensive Care Medicine and the Society of Critical Care Medicine, the study convened a task force of 19 critical care, infectious disease, surgical, and pulmonary specialists in 2014 to provide a more consistent and reproducible picture of sepsis incidence and patient outcomes.

The paper, led by Mervyn Singer, professor of intensive care medicine at University College London, is by far the most highly cited paper in JAMA . The second-most highly cited paper , on opioids, has 3,679 citations, according to Google Scholar.

6. “ limma powers differential expression analyses for RNA-sequencing and microarray studies ” (2015) Nucleic Acids Research 8,328 citations

limma is a widely used, open source analysis tool for gene expression experiments, and has been available for more than a decade. A large part of its appeal is the ease at which new functionality and refinements can be added as new applications arise.

This paper, led by Matthew Ritchie from the Molecular Medicine Division of the Walter and Eliza Hall Institute of Medical Research in Melbourne, Australia, is presented as a review of the “philosophy and design” of the limma package, looking at its recent and historical features and enhancements.

The journal, Nucleic Acids Research , while ranked outside the top 10 of Google Scholar’s most influential journals , has more papers with 3,000+ citations each than The Lancet (ranked 4th).

7. “ Mastering the game of Go with deep neural networks and tree search ” (2016) Nature 8,209 citations

Viewed as one of the most challenging classic games to master, Go is a 2,500-year-old game that will put any player – living or otherwise – through their paces.

In 2016, a computer program called AlphaGo defeated the world Go champion, Lee Sedo , in what would be hailed as a major milestone for AI technology. AlphaGo was the brainchild of computer scientist David Silver when he was a PhD student at the University of Alberta in Canada.

This paper, co-led by David Silver and Aja Huang, today both research scientists at Google DeepMind, describes the technology that underpins AlphaGo. It is the third-most highly cited in Nature , according to Google Scholar.

In 2017, the team introduced AlphaGo Zero , which improves on previous iterations by using a single neural network , rather than two, to evaluate which sequence of moves is the most likely to win. That paper is the eighth-most cited in Nature .

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World University Rankings 2022 by subject: computer science

The computer science subject table uses the same trusted and rigorous performance indicators as the  Times Higher Education  World University Rankings 2022, but the methodology has been recalibrated to suit the discipline.

This year’s table includes 891 universities, up from 827 last year.

View the World University Rankings 2022 by subject: computer science methodology

The University of Oxford leads the table for the fourth consecutive year, while Princeton University joins the top 10 after climbing one place to 10th.

There are also some eye-catching improvements for the US just outside the top 10: UCLA rises five places to 11th, Caltech rises 12 places to 13th and Washington rises five places to 16th, largely because of improvements in their teaching and research scores.

Australia has a new number one university for computer science: the University of Melbourne, which ranks 51st overall.

Three institutions rank in the top 100 in their debut year; Institut Polytechnique de Paris is 48th, Université Paris-Saclay is 76th and National Yang Ming Chiao Tung University in Taiwan is joint 88th.

Read our analysis of the computer science subject rankings 2022 results

View the full results of the overall World University Rankings 2022

To raise your university’s global profile with  Times Higher Education , contact [email protected]

To unlock the data behind THE ’s rankings and access a range of analytical and benchmarking tools,  click here

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This ranking is designed to identify institutions and faculty actively engaged in research across a number of areas of computer science, based on the number of publications by faculty that have appeared at the most selective conferences in each area of computer science (see the FAQ for more details).

CSRankings by Emery Berger is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License . Based on a work at http://github.com/emeryberger/CSrankings . Copyright 2017-2018 © Emery Berger

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CORE Rankings Portal





ADVISORY COMMITTEE CHAIR : Michael Winikoff ( [email protected] )


 Alan Fekete

 Stephen MacDonell

 Lin Padgham

 Stefanie Zollmann



Alistair Moffat

Jenny Zhang

Ben Rubinstein

John Grundy

Maria Garcia De La Banda

Dali Kaafar

Shazia Sadiq


These committees are organised according to the 2020 Australasian Field of Research (FoR) codes

460 1 Applied computing (being considered by other committees as relevant)

4602 Artificial Intelligence

Michael Winikoff (Chair) Victoria University of Wellington

Noa Agmon Bar-Ilan University

Longbing Cao  University of Technology, Sydney

Christian Blum Artificial Intelligence Research Institute (IIIA), Spanish National Research Council (CSIC), Spain

Eneko Agirre University of the Basque Country, Spain

Sergio Greco  Università della Calabria, Italy

Reza Haffari  Monash University, Australia

4603 Computer vision and multimedia computation

Helen Huang (Chair) University of Queensland

Jianfei Cai Monash University

Xian-sheng Hua Alibaba DAMO Academy

Tatsuya Harada The University of Tokyo

Cheng Xu University of Sydney, Australia

Qianru Sun Singapore

Nicu Sebe  Italy

4604 Cybersecurity and privacy

Vincent Gramoli (Chair)

Daniel Gruss, Austria

Ivan Visconti  Italy

Juan Caballero  Spain

Davide Balzarotti  F rance

Tob y Murray  University of Melbourne

Saurabh Bagchi USA

4605 Data management and data science

Shazia Sadiq (Chair) The University of Queensland

Gillian Dobbie University of Auckland

Hui Ma Victoria University of Wellingto n

Laura Dietz  USA

Ernest Teniente  Spain

Wenjie Zhang  University of NSW

Zhifeng Bao  RMIT Melbourne

4606 Distributed computing and systems software

David Eyers (Chair) University of Otago, New Zealand

Xiao Liu  Deakin University

William Fornaciari  italy

Abusayeed Saifulla  USA

Jan Rellermeye  Germany

4607 & 4608 (combined) Graphics, augmented reality and games  plus Human centred computing

Stefanie Zellmann (Chair) University of Otago, New Zealand

Karan Singh  Canada

Maud Marchal  France

Phoebe Toups Dugas  Monash University and USA

Paweł W. Woźniak  Sweden/Poland

Denis Kalkofen University of Graz

Judy Kay University of Sydney

4611 Machine learning

James Bailey (chair) University of M elbourne

Lina Yao  Data61/UNSW

Sunil Gupta  Deakin University

Jilles Vreeken  Germany

Tongliang Liu University of Sydney

Geoff Webb Monash University, Melbourne

Ling Chen Univerity of Technnology Sydney

4612 Software (and its) engineering

Stephen Macdonell (Chair) Auckland University of Technology and the University of Otago

Filippo Lanubile  Italy

Luciano Baresi Italy

Kelly Blincoe  New Zealand

Ralf Reussner Germany

Silvia Abrahao Spain

Xing Zhenchang  Data61/ANU

4613 Theory of computation

Siu-Wing Chen (Chair) U niversity of Hong Kong

Giuseppe Liotta  Italy

Troy Lee  University of Technology, Sydney

Seokhee Hong  University of Sydney

Xiaoming Sun  China

Marek Chobak  USA

Thomas Place  France

Anthony Widjaja Lin  Germany


2021 Conference ranking committees (organised by research codes)

Toby Walsh UNSW Sydney

Bing XUE Victoria University of Wellington

Yuan-Fang Li Monash University

Serena Villata Université Côte d’Azur, CNRS, Inria, I3S, France

Thanh-Toan Do Monash University

Dong Xu University of Sydney

Dali Kaafar (Chair) Macquarie university

Yuval Yarom Uni of Adelaide

Haya Shulman Fraunhofer Institute for Secure Information Technology SIT

Ryan Ko University of Queensland

Seyit Camtepe CSIRO Data61

Willy Susilo University of Wollongong

Joseph Liu Monash University

Catuscia Palamidessi Inria and E cole Polytechnique of Paris

Hui Ma Victoria University of Wellington

Uwe Roehm The University of Sydney

Aamir Cheema Monash University


Claudia Hauff Delft University of Technology

Hua Wang Victoria University

Weifa Liang (Chair) Australian National University

Albert Zomaya The University of Sydney

Wen Hu UNSW, Sydney

Winston Seah Victoria University of Wellington

Lorenzo Alvisi Cornell, USA

Yuanyuan Yang Stony Brook University, USA

Mark Billing hurst (Chair) University of South Australia

Vassilis Kostakos UoM

Peter Brusilovsky University of Pittsburgh

Danielle Lottridge University of Auckland

Stefanie Zollmann University of Otago

Tobias Langlotz University of Otago

Geoff Webb (chair) Monash University

Chang Xu The University of Sydney

ben Rubinstein The University of Melbourne

Peter Flach University of Bristol

John Grundy (Chair) Monash University

Maria Spichkova RMIT University

Stephen MacDonell Auckland University of Technology and the University of Otago

Antonio Vallecillo Universidad de Málaga

Hongyu Zhang The University of Newcastle

Serge Gaspers (Chair) UNSW

Tony Wirth U Melbourne

Clément Canonne U Sydney

Christophe Paul LIRMM Montpellier

Joanna Ochremiak LaBRI, Bordeaux

Annabelle McIver Macquarie U

2019/20 Journal Rankings

Lin Padgham (Chair 0801)

Helen Huang (University of Queensland, Australia)

Karin Verspoor (University of Melbourne, Australia)

Peter Stuckey (Monash University, Australia)

Leszek Rutkowski (Czestochowa University of Technology, Poland)

Neil Dodgson (Victoria University of Wellington, New Zealand)

Ferdous Sohel (Murdoch University, Australia)

David Dowe (Monash University, Australia)

Michael Winikoff (Chair 0802) (Victoria University of Wellington, New Zealand)

Emanuela Merelli (University of Camerino, Italy)

Tony Wirth (University of Melbourne, Australia)

Seok-Hee Hong (University of Sydney, Australia)

Sebastian Link (University of Auckland) withdrew partway due to some disagreements regarding the process.

Joachim Gudmundsson (University of Melbourne) withdrew partway due to some disagreements regarding the process.

John Grundy (Chair 0803) (Monash University, Australia)

Stephen MacDonell (University of Otago, New Zealand)

Philippe Kruchten (University of British Columbia, Canada)

James Noble (Victoria University of Wellington, New Zealand)

Maria Garcia de la Banda (Monash University, Australia)

Tim Menzies (N. Carolina State university, USA

Maria Spichkova (RMIT University, Australia)

Burak Turhan (Monash University, Australia)

Alan Fekete (Chair 0805) (University of Sydney, Australia)

Albert Zomaya (University of Sydney, Australia)

Ezio Bartocci (Technical University of Wien, Austria)

Salil Kanhere (University of NSW, Australia)

Winston Seah (Victoria University of Wellington, New Zealand)

Wen Hu (University of NSW, Australia)

Weifa Liang (Australian National University, Australia)

Claudia Szabo p(University of Adelaide, Australia)

Guangyan Huang (Deakin University, Australia)

Alistair Moffat (Chair 0806) (University of Melbourne, Australia)

Judy Kay (University of Sydney, Australia)

Margot Brereton (Queensland University of Technology, Australia)

Vassilis Kostakos (University of Melbourne, Australia)

Richi Nayak (Queensland University of Technology, Australia)

Monique Snoeck (KU Leuven, Belgium)

Yan Wang (Macquarie University, Australia)

Frada Burstein (Monash University, Australia)

Stephen Viller (University of Queensland, Australia)

2018 Conference Rankings

Michael Winikoff (University of Otago)

Neil Dodgson (Victoria University, NZ)

Yuefeng Li (Queensland University of Technology)

Joarder Kamruzzaman (Federation University)

Karin Verspoor (University of Melbourne)

Franck Cassez (Macquarie University)

Tony Wirth (University of melbourne)

Robert van Glabbeek (Data61)

Serge Gaspers (University of NSW)

Joachim Gudmundsson (University of Sydney)

Stephen MacDonell (Otago University)

Wen Hu (University of NSW)

Ali Babar (University of Adelaide)

Thomas Kuehne (Victoria University, NZ)

Antony Tang (Swinburne University)

Tony Hosking (Australian National University)

Young Choon Lee (Macquarie University)

Albert Zomaya (University of Sydney)

Matthew Luckie (Waikato University)

Jingling Xue (University of New South Wales)

Andy Cockburn (University of Canterbury)

Judy Kay (University of Sydney)

Ying Zhang (University of Technology, Sydney)

Peter Bernus (Griffith University)

Frada Burstein (Monash University)

Kristina Falkner (University of Adelaide) was also appointed to assist with rankings of any CS Ed conferences, but none were submitted.

2017 Conference Rankings

Conference Details

The CORE Conference Ranking provides assessments of major conferences in the computing disciplines.The rankings are managed by the CORE Executive Committee, with periodic rounds for submission of requests for addition or reranking of conferences. Decisions are made by academic committees based on objective data requested as part of the submission process. To be informed of when a submission round is opened, please join the rankings email list at Mailing Lists .

Conferences are assigned to one of the following categories:

A* - flagship conference, a leading venue in a discipline area

A  - excellent conference, and highly respected in a discipline area

B  - good to very good conference, and well regarded in a discipline area

C  - other ranked conference venues that meet basic standards for peer reviewed venues.

Australasian - A conference for which the audience is primarily Australians and New Zealanders (these may be Australasian B or Australasian C)

Unranked - A conference for which no ranking decision has been made

National - A conference which is run primarily in a single country, usually with Chairs from that country, and which is not sufficiently well known to be ranked. (Papers and PC may be international)

Regional - Similar to National but may cover a region crossing national borders.

Conference rankings are determined by a mix of indicators, including citation rates, paper submission and acceptance rates, and the visibility and research track record of the key people hosting the conference and managing its technical program. A more detailed statement categorizing the ranks A*, A, B, and C can be found below.

Description of Conference Ranks

Guidelines for reporting rankings  

Usage of CORE rankings

2021 Rankings Process

Data used in Rankings and responses to common concerns

2020 Conference database update

Spreadsheet of 2020 changes

2018 Rankings Process and Summary

2017 Ranking Process

2014 Ranking Process

2014 Summary of Rank changes

2013 Ranking Process

History of CORE Rankings

The CORE Journal Rankings were discontinued in 2022 due to limited resources.

Journal Rankings History

Links to other lists or ranking systems

Disclaimer: These links are provided as people may find them useful. CORE does not take any responsibility for the content at these links. Nor does their publication on this page imply that CORE agrees with the information provided there.

GII-GRIN-SCIE (GGS) Conference Rating: http://gii-grin-scie-rating.scie.es/ratingSearch.jsf

Particular thanks to the developers of this ranking for collaboration and sharing of data.

Arnetminer: https://www.aminer.cn

Microsoft Academic Search: http://academic.research.microsoft.com/

ACPHIS list: http://www.acphis.org.au/index.php/is-journal-ranking/rank-order

Beall's list (Potential, possible, or probable predatory scholarly open-access publishers):  http://scholarlyoa.com/

Springer's Linked Open Data list: http://lod.springer.com/wiki/bin/view/Linked+Open+Data/About

A ranking of Information Systems type journals which like CORE uses a mix of data and human judgement: http://charteredabs.org/academic-journal-guide-2015/

CORE Rankings Mailing List

For updates and future calls for ranking updates, please join the rankings mail list at: Mailing Lists

computer science paper ranking

Best Undergraduate Computer Science Programs Rankings

Top academics and officials at computer science programs rated the overall quality of undergraduate programs with which they were familiar on a 1-5 scale. A school’s undergraduate computer science rank is solely determined by its average of scores received from these surveys. To be included in this standalone peer assessment survey and ranked, a program must either have been accredited by ABET or have recently awarded 20 or more bachelor's degrees in computer science. Read the methodology »

To unlock full rankings, SAT/ACT scores and more, sign up for the U.S. News College Compass !

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computer science paper ranking

Massachusetts Institute of Technology

Cambridge, MA

  • #1 in Computer Science

Though the Massachusetts Institute of Technology may be best known for its math, science and engineering education, this private research university also offers architecture, humanities, management and social science programs. The school is located in Cambridge, Massachusetts, just across the Charles River from downtown Boston.

(fall 2022)

SAT, GPA and More

computer science paper ranking

Carnegie Mellon University

Pittsburgh, PA

  • #2 in Computer Science  (tie)

Carnegie Mellon University, a private institution in Pittsburgh, is the country’s only school founded by industrialist and philanthropist Andrew Carnegie. The school specializes in academic areas including engineering, business, computer science and fine arts.

computer science paper ranking

Stanford University

Stanford, CA

The sunny campus of Stanford University is located in California’s Bay Area, about 30 miles from San Francisco. The private institution stresses a multidisciplinary combination of teaching, learning, and research, and students have many opportunities to get involved in research projects.

computer science paper ranking

University of California, Berkeley

Berkeley, CA

The University of California, Berkeley overlooks the San Francisco Bay in Berkeley, Calif. Students at this public school have more than 1,000 groups to get involved in, including more than 60 fraternity and sorority chapters.


computer science paper ranking

University of Illinois Urbana-Champaign

Champaign, IL

  • #5 in Computer Science

The University of Illinois is located in the twin cities of Urbana and Champaign in east-central Illinois, only a few hours from Chicago, Indianapolis and St. Louis. The school's Fighting Illini participate in more than 20 NCAA Division I varsity sports and are part of the Big Ten Conference. The university boasts one of the largest Greek systems in the country, and almost a quarter of the student body is involved. It’s not hard to find something to do on campus with more than 1,600 student organizations, including professional, political and philanthropic clubs. All freshmen are required to live on campus.

computer science paper ranking

California Institute of Technology

Pasadena, CA

  • #6 in Computer Science  (tie)

The California Institute of Technology focuses on science and engineering education and has a low student-to-faculty ratio of 3:1. This private institution in Pasadena, California, is actively involved in research projects with grants from NASA, the National Science Foundation and the U.S. Department of Health and Human Services.

computer science paper ranking

Cornell University

Cornell University, a private school in Ithaca, New York, has 14 colleges and schools. Each admits its own students, though every graduate receives a degree from Cornell University. The university has more than 1,000 student organizations on campus.

computer science paper ranking

Georgia Institute of Technology

Atlanta, GA

Georgia Tech, located in the heart of Atlanta, offers a wide range of student activities. The Georgia Tech Yellow Jackets, an NCAA Division I team, compete in the Atlantic Coast Conference and have a fierce rivalry with the University of Georgia. Since 1961, the football team has been led onto the field at home games by the Ramblin' Wreck, a restored 1930 Model A Ford Sport Coupe. Georgia Tech has a small but vibrant Greek community. Freshmen are offered housing, but aren't required to live on campus. In addition to its campuses in Atlanta and Savannah, Georgia Tech has campuses in France, Ireland, Costa Rica, Singapore and China.

computer science paper ranking

Princeton University

Princeton, NJ

The ivy-covered campus of Princeton University, a private institution, is located in the quiet town of Princeton, New Jersey. Princeton was the first university to offer a "no loan" policy to financially needy students, giving grants instead of loans to accepted students who need help paying tuition.

computer science paper ranking

University of Washington

Seattle, WA

  • #10 in Computer Science

Located north of downtown Seattle, the University of Washington is one of the oldest public universities on the West Coast. It is also a cutting-edge research institution, receiving significant yearly federal funding, and hosting an annual undergraduate research symposium for students to present their work to the community. The university has a highly ranked School of Medicine, College of Engineering and Michael G. Foster School of Business. Known as a commuter school, the university does not require freshmen to live on campus, but it encourages students who do to conserve energy and recycle. Students can join one of the 950-plus student organizations on campus, including about 70 sororities and fraternities. Nearly three-fourths of UW graduates remain in the state post-graduation.

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Abilene Christian University

Abilene, TX

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Abilene Christian University is a Texas institution affiliated with the Churches of Christ. Students are required to attend daily chapel sessions and take Bible courses.

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Alabama A&M University

Founded in 1875, Alabama A&M University is a public institution.

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Alabama State University

Montgomery, AL

Alabama State University, located in Montgomery, was founded by freed slaves. Notable alumni from ASU include Fred Gray, the attorney who defended Rosa Parks during the Montgomery Bus Boycott.

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Allegheny College

Meadville, PA

At Allegheny College in northwest Pennsylvania, students can choose from about 30 majors and must complete and present a capstone project their senior year. William McKinley, the 25th president of the United States, attended Allegheny College in the 1800s, but legend has it that he was expelled for shoving a cow into the school’s bell tower.

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American University

Washington, DC

Students at American University benefit from the school's location in the political hub of the nation. Washington, D.C., is a playground for the politically and socially oriented with its countless museums, restaurants, clubs and year-round events. The university, located in a suburban pocket of northwest Washington, is close to a stop on the D.C.-area Metrorail transit system. Rides downtown take about 15 minutes. Students at American have been rated among the most politically active in the nation, and there are more than 270 student clubs and organizations on campus. The school also has a sizable Greek system, with more than 25 fraternities and sororities.

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Amherst College

Amherst, MA

Amherst College, a private school in Amherst, Massachusetts, is known for its rigorous academic climate. Because Amherst is a member of the Five Colleges consortium, students can also take courses at Smith College, Mount Holyoke College, Hampshire College and the University of Massachusetts—Amherst.

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Appalachian State University

Appalachian State University is a public school tucked in the Blue Ridge Mountains of North Carolina. Students at Appalachian have many programs, sports, and clubs to choose from, including the Appalachian Popular Programming Society, which plans campus events. 

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Arizona State University

Arizona State University’s Tempe campus offers more than 200 research-based programs in the arts, business, engineering and more. The campus is located just outside of Phoenix, in the suburb of Tempe, Arizona.

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Arkansas Tech University

Russellville, AR

Founded in 1909, Arkansas Tech University is a public institution.

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Auburn, Alabama, has been ranked one of the best places to live, and life at Auburn University can be similarly enjoyable for students. Football is a particularly big attraction in the fall, as fans support the Auburn Tigers with the help of school mascot Aubie the Tiger. The Tiger sports teams compete in the NCAA Division I Southeastern Conference, and the influx of football fans makes Auburn the fifth-largest city in the state on game days. Pep rallies are held in the downtown Toomer's Corner, and the area is covered in toilet paper by fans after every big victory. There are more than 500 student organizations on campus, and about 6,000 students are involved in the school's Greek system. Auburn freshmen ease into the college transition through Camp War Eagle, a two-day, overnight summer orientation program. Transition help continues on Hey Day, an annual effort to get students to wear name tags and say hello to one another. Freshmen do not have to live on campus; in fact, a dorm room isn't even guaranteed. Because of space restraints, first-year students are awarded on-campus living assignments on a first-come, first-served basis.

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Computer Science > Machine Learning

Title: beyond scaling laws: understanding transformer performance with associative memory.

Abstract: Increasing the size of a Transformer model does not always lead to enhanced performance. This phenomenon cannot be explained by the empirical scaling laws. Furthermore, improved generalization ability occurs as the model memorizes the training samples. We present a theoretical framework that sheds light on the memorization process and performance dynamics of transformer-based language models. We model the behavior of Transformers with associative memories using Hopfield networks, such that each transformer block effectively conducts an approximate nearest-neighbor search. Based on this, we design an energy function analogous to that in the modern continuous Hopfield network which provides an insightful explanation for the attention mechanism. Using the majorization-minimization technique, we construct a global energy function that captures the layered architecture of the Transformer. Under specific conditions, we show that the minimum achievable cross-entropy loss is bounded from below by a constant approximately equal to 1. We substantiate our theoretical results by conducting experiments with GPT-2 on various data sizes, as well as training vanilla Transformers on a dataset of 2M tokens.

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Natural language boosts LLM performance in coding, planning, and robotics

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Three boxes demonstrate different tasks assisted by natural language. One is a rectangle showing colorful lines of code with a white speech bubble highlighting an abstraction; another is a pale 3D kitchen, and another is a robotic quadruped dropping a can into a trash bin.

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Large language models (LLMs) are becoming increasingly useful for programming and robotics tasks, but for more complicated reasoning problems, the gap between these systems and humans looms large. Without the ability to learn new concepts like humans do, these systems fail to form good abstractions — essentially, high-level representations of complex concepts that skip less-important details — and thus sputter when asked to do more sophisticated tasks. Luckily, MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers have found a treasure trove of abstractions within natural language. In three papers to be presented at the International Conference on Learning Representations this month, the group shows how our everyday words are a rich source of context for language models, helping them build better overarching representations for code synthesis, AI planning, and robotic navigation and manipulation. The three separate frameworks build libraries of abstractions for their given task: LILO (library induction from language observations) can synthesize, compress, and document code; Ada (action domain acquisition) explores sequential decision-making for artificial intelligence agents; and LGA (language-guided abstraction) helps robots better understand their environments to develop more feasible plans. Each system is a neurosymbolic method, a type of AI that blends human-like neural networks and program-like logical components. LILO: A neurosymbolic framework that codes Large language models can be used to quickly write solutions to small-scale coding tasks, but cannot yet architect entire software libraries like the ones written by human software engineers. To take their software development capabilities further, AI models need to refactor (cut down and combine) code into libraries of succinct, readable, and reusable programs. Refactoring tools like the previously developed MIT-led Stitch algorithm can automatically identify abstractions, so, in a nod to the Disney movie “Lilo & Stitch,” CSAIL researchers combined these algorithmic refactoring approaches with LLMs. Their neurosymbolic method LILO uses a standard LLM to write code, then pairs it with Stitch to find abstractions that are comprehensively documented in a library. LILO’s unique emphasis on natural language allows the system to do tasks that require human-like commonsense knowledge, such as identifying and removing all vowels from a string of code and drawing a snowflake. In both cases, the CSAIL system outperformed standalone LLMs, as well as a previous library learning algorithm from MIT called DreamCoder, indicating its ability to build a deeper understanding of the words within prompts. These encouraging results point to how LILO could assist with things like writing programs to manipulate documents like Excel spreadsheets, helping AI answer questions about visuals, and drawing 2D graphics.

“Language models prefer to work with functions that are named in natural language,” says Gabe Grand SM '23, an MIT PhD student in electrical engineering and computer science, CSAIL affiliate, and lead author on the research. “Our work creates more straightforward abstractions for language models and assigns natural language names and documentation to each one, leading to more interpretable code for programmers and improved system performance.”

When prompted on a programming task, LILO first uses an LLM to quickly propose solutions based on data it was trained on, and then the system slowly searches more exhaustively for outside solutions. Next, Stitch efficiently identifies common structures within the code and pulls out useful abstractions. These are then automatically named and documented by LILO, resulting in simplified programs that can be used by the system to solve more complex tasks.

The MIT framework writes programs in domain-specific programming languages, like Logo, a language developed at MIT in the 1970s to teach children about programming. Scaling up automated refactoring algorithms to handle more general programming languages like Python will be a focus for future research. Still, their work represents a step forward for how language models can facilitate increasingly elaborate coding activities. Ada: Natural language guides AI task planning Just like in programming, AI models that automate multi-step tasks in households and command-based video games lack abstractions. Imagine you’re cooking breakfast and ask your roommate to bring a hot egg to the table — they’ll intuitively abstract their background knowledge about cooking in your kitchen into a sequence of actions. In contrast, an LLM trained on similar information will still struggle to reason about what they need to build a flexible plan. Named after the famed mathematician Ada Lovelace, who many consider the world’s first programmer, the CSAIL-led “Ada” framework makes headway on this issue by developing libraries of useful plans for virtual kitchen chores and gaming. The method trains on potential tasks and their natural language descriptions, then a language model proposes action abstractions from this dataset. A human operator scores and filters the best plans into a library, so that the best possible actions can be implemented into hierarchical plans for different tasks. “Traditionally, large language models have struggled with more complex tasks because of problems like reasoning about abstractions,” says Ada lead researcher Lio Wong, an MIT graduate student in brain and cognitive sciences, CSAIL affiliate, and LILO coauthor. “But we can combine the tools that software engineers and roboticists use with LLMs to solve hard problems, such as decision-making in virtual environments.”

When the researchers incorporated the widely-used large language model GPT-4 into Ada, the system completed more tasks in a kitchen simulator and Mini Minecraft than the AI decision-making baseline “Code as Policies.” Ada used the background information hidden within natural language to understand how to place chilled wine in a cabinet and craft a bed. The results indicated a staggering 59 and 89 percent task accuracy improvement, respectively. With this success, the researchers hope to generalize their work to real-world homes, with the hopes that Ada could assist with other household tasks and aid multiple robots in a kitchen. For now, its key limitation is that it uses a generic LLM, so the CSAIL team wants to apply a more powerful, fine-tuned language model that could assist with more extensive planning. Wong and her colleagues are also considering combining Ada with a robotic manipulation framework fresh out of CSAIL: LGA (language-guided abstraction). Language-guided abstraction: Representations for robotic tasks Andi Peng SM ’23, an MIT graduate student in electrical engineering and computer science and CSAIL affiliate, and her coauthors designed a method to help machines interpret their surroundings more like humans, cutting out unnecessary details in a complex environment like a factory or kitchen. Just like LILO and Ada, LGA has a novel focus on how natural language leads us to those better abstractions. In these more unstructured environments, a robot will need some common sense about what it’s tasked with, even with basic training beforehand. Ask a robot to hand you a bowl, for instance, and the machine will need a general understanding of which features are important within its surroundings. From there, it can reason about how to give you the item you want. 

In LGA’s case, humans first provide a pre-trained language model with a general task description using natural language, like “bring me my hat.” Then, the model translates this information into abstractions about the essential elements needed to perform this task. Finally, an imitation policy trained on a few demonstrations can implement these abstractions to guide a robot to grab the desired item. Previous work required a person to take extensive notes on different manipulation tasks to pre-train a robot, which can be expensive. Remarkably, LGA guides language models to produce abstractions similar to those of a human annotator, but in less time. To illustrate this, LGA developed robotic policies to help Boston Dynamics’ Spot quadruped pick up fruits and throw drinks in a recycling bin. These experiments show how the MIT-developed method can scan the world and develop effective plans in unstructured environments, potentially guiding autonomous vehicles on the road and robots working in factories and kitchens.

“In robotics, a truth we often disregard is how much we need to refine our data to make a robot useful in the real world,” says Peng. “Beyond simply memorizing what’s in an image for training robots to perform tasks, we wanted to leverage computer vision and captioning models in conjunction with language. By producing text captions from what a robot sees, we show that language models can essentially build important world knowledge for a robot.” The challenge for LGA is that some behaviors can’t be explained in language, making certain tasks underspecified. To expand how they represent features in an environment, Peng and her colleagues are considering incorporating multimodal visualization interfaces into their work. In the meantime, LGA provides a way for robots to gain a better feel for their surroundings when giving humans a helping hand. 

An “exciting frontier” in AI

“Library learning represents one of the most exciting frontiers in artificial intelligence, offering a path towards discovering and reasoning over compositional abstractions,” says assistant professor at the University of Wisconsin-Madison Robert Hawkins, who was not involved with the papers. Hawkins notes that previous techniques exploring this subject have been “too computationally expensive to use at scale” and have an issue with the lambdas, or keywords used to describe new functions in many languages, that they generate. “They tend to produce opaque 'lambda salads,' big piles of hard-to-interpret functions. These recent papers demonstrate a compelling way forward by placing large language models in an interactive loop with symbolic search, compression, and planning algorithms. This work enables the rapid acquisition of more interpretable and adaptive libraries for the task at hand.” By building libraries of high-quality code abstractions using natural language, the three neurosymbolic methods make it easier for language models to tackle more elaborate problems and environments in the future. This deeper understanding of the precise keywords within a prompt presents a path forward in developing more human-like AI models. MIT CSAIL members are senior authors for each paper: Joshua Tenenbaum, a professor of brain and cognitive sciences, for both LILO and Ada; Julie Shah, head of the Department of Aeronautics and Astronautics, for LGA; and Jacob Andreas, associate professor of electrical engineering and computer science, for all three. The additional MIT authors are all PhD students: Maddy Bowers and Theo X. Olausson for LILO, Jiayuan Mao and Pratyusha Sharma for Ada, and Belinda Z. Li for LGA. Muxin Liu of Harvey Mudd College was a coauthor on LILO; Zachary Siegel of Princeton University, Jaihai Feng of the University of California at Berkeley, and Noa Korneev of Microsoft were coauthors on Ada; and Ilia Sucholutsky, Theodore R. Sumers, and Thomas L. Griffiths of Princeton were coauthors on LGA.  LILO and Ada were supported, in part, by ​​MIT Quest for Intelligence, the MIT-IBM Watson AI Lab, Intel, U.S. Air Force Office of Scientific Research, the U.S. Defense Advanced Research Projects Agency, and the U.S. Office of Naval Research, with the latter project also receiving funding from the Center for Brains, Minds and Machines. LGA received funding from the U.S. National Science Foundation, Open Philanthropy, the Natural Sciences and Engineering Research Council of Canada, and the U.S. Department of Defense.

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CVPR Technical Program Features Presentations on the Latest AI and Computer Vision Research

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LOS ALAMITOS, Calif., 16 May 2024 – Co-sponsored by the IEEE Computer Society (CS) and the Computer Vision Foundation (CVF), the 2024 Computer Vision and Pattern Recognition (CVPR) Conference is the preeminent event for research and development (R&D) in the hot topic areas of computer vision, artificial intelligence (AI), machine learning (ML), augmented, virtual and mixed reality (AR/VR/MR), deep learning, and related fields. Over the past decade, these areas have seen significant growth, and the emphasis on this sector by the science and engineering community has fueled an increasingly competitive technical program.

This year, the CVPR Program Committee received 11,532 paper submissions—a 26% increase over 2023—but only 2,719 were accepted, resulting in an acceptance rate of just 23.6%. Of those accepted papers, only 3.3% were slotted for oral presentations based on nominations from the area chairs and senior area chairs overseeing the program.

“CVPR is not only the premiere conference in computer vision, but it’s also among the highest-impact publication venues in all of science,” said David Crandall, Professor of Computer Science at Indiana University, Bloomington, Ind., U.S.A., and CVPR 2024 Program Co-Chair. “Having one’s paper accepted to CVPR is already a major achievement, and then having it selected as an oral presentation is a very rare honor that reflects its high quality and potential impact.”

Taking place 17-21 June at the Seattle Convention Center in Seattle, Wash., U.S.A., CVPR offers oral presentations that speak to both fundamental and applied research in areas as diverse as healthcare applications, robotics, consumer electronics, autonomous vehicles, and more. Examples include:

  • Pathology: Transcriptomics-guided Slide Representation Learning in Computational Pathology *– Training computer systems for pathology requires a multi-modal approach for efficiency and accuracy. New work from a multi-disciplinary team at Harvard University (Cambridge, Mass., U.S.A.), the Massachusetts Institute of Technology (MIT; Cambridge, Mass., U.S.A.), Emory University (Atlanta, Ga., U.S.A.) and others employs modality-specific encoders, and when applied on liver, breast, and lung samples from two different species, they demonstrated significantly better performance when compared to current baselines.
  • Robotics: SceneFun3D: Fine-Grained Functionality and Affordance Understanding in 3D Scenes – Creating realistic interactions in 3D scenes has been troublesome from a technology perspective because it has been difficult to manipulate objects in the scene context. Research from ETH Zürich (Zürich, Switzerland), Google (Mountainview, Calif., U.S.A.), Technical University of Munich (TUM; Munich, Germany), and Microsoft (Redmond, Wash., U.S.A.) has begun bridging that divide by creating a large-scale dataset with more than 14.8k highly accurate interaction annotations for 710 high-resolution real-world 3D indoor scenes. This work, as the paper concludes, has the potential to “stimulate advancements in embodied AI, robotics, and realistic human-scene interaction modeling.”
  • Virtual Reality: URHand: Universal Relightable Hands – Teams from Codec Avatars Lab at Meta (Menlo Park, Calif., U.S.A.) and Nanyang Technological University (Singapore) unveil a hand model that generalizes to novel viewpoints, poses, identities, and illuminations, which enables quick personalization from a phone scan. The resulting images make for a more realistic experience of reaching, grabbing, and interacting in a virtual environment.
  • Human Avatars: Semantic Human Mesh Reconstruction with Textures – Working to create realistic human models, teams at Nanjing University (Nanjing, China) and Texas A&M University (College Station, Texas, U.S.A.) designed a method of 3-D human mesh reconstruction that is capable of producing high-fidelity and robust semantic renderings that outperform state-of-the-art methods. The paper concludes, “This approach bridges existing monocular reconstruction work and downstream industrial applications, and we believe it can promote the development of human avatars.”
  • Text-to-Image Systems: Ranni: Taming Text-to-Image Diffusion for Accurate Instruction – Existing text-to-image models can misinterpret more difficult prompts, but now, new research from Alibaba Group (Hangzhou, Zhejiang, China) and Ant Group (Hangzhou, Zhejiang, China) has made strides in addressing that issue via a middleware layer. This approach, which they have dubbed Ranni, supports the text-to-image generator in better following instructions. As the paper sums up, “Ranni shows potential as a flexible chat-based image creation system, where any existing diffusion model can be incorporated as the generator for interactive generation.”
  • Autonomous Driving: Producing and Leveraging Online Map Uncertainty in Trajectory Prediction – To enable autonomous driving, vehicles must be pre-trained on the geographic region and potential pitfalls. High-definition (HD) maps have become a standard part of a vehicle’s technology stack, but current approaches to those maps are siloed in their programming. Now, work from a research team from the University of Toronto (Toronto, Ontario, Canada), Vector Institute (Toronto, Ontario, Canada), NVIDIA Research (Santa Clara, Calif., U.S.A.), and Stanford University (Palo Alto, Calif., U.S.A.) enhances current methodologies by incorporating uncertainty, resulting in up to 50% faster training convergence and up to 15% better prediction performance.

“As the field’s leading event, CVPR introduces the latest research in all areas of computer vision,” said Crandall. “In addition to the oral paper presentations, there will be thousands of posters, dozens of workshops and tutorials, several keynotes and panels, and countless opportunities for learning and networking. You really have to attend the conference to get the full scope of what’s next for computer vision and AI technology.”

Digital copies of all final technical papers* will be available on the conference website by the week of 10 June to allow attendees to prepare their schedules. To register for CVPR 2024 as a member of the press and/or request more on a specific paper, visit https://cvpr.thecvf.com/Conferences/2024/MediaPass or email [email protected]. For more information on the conference, visit https://cvpr.thecvf.com/ .

*Papers linked in this press release refer to pre-print publications. Final, citable papers will be available just prior to the conference.

About the CVPR 2024 The Computer Vision and Pattern Recognition Conference (CVPR) is the preeminent computer vision event for new research in support of artificial intelligence (AI), machine learning (ML), augmented, virtual and mixed reality (AR/VR/MR), deep learning, and much more. Sponsored by the IEEE Computer Society (CS) and the Computer Vision Foundation (CVF), CVPR delivers the important advances in all areas of computer vision and pattern recognition and the various fields and industries they impact. With a first-in-class technical program, including tutorials and workshops, a leading-edge expo, and robust networking opportunities, CVPR, which is annually attended by more than 10,000 scientists and engineers, creates a one-of-a-kind opportunity for networking, recruiting, inspiration, and motivation.

CVPR 2024 takes place 17-21 June at the Seattle Convention Center in Seattle, Wash., U.S.A., and participants may also access sessions virtually. For more information about CVPR 2024, visit cvpr.thecvf.com .

About the Computer Vision Foundation The Computer Vision Foundation (CVF) is a non-profit organization whose purpose is to foster and support research on all aspects of computer vision. Together with the IEEE Computer Society, it co-sponsors the two largest computer vision conferences, CVPR and the International Conference on Computer Vision (ICCV). Visit thecvf.com for more information.

About the IEEE Computer Society Engaging computer engineers, scientists, academia, and industry professionals from all areas and levels of computing, the IEEE Computer Society (CS) serves as the world’s largest and most established professional organization of its type. IEEE CS sets the standard for the education and engagement that fuels continued global technological advancement. Through conferences, publications, and programs that inspire dialogue, debate, and collaboration, IEEE CS empowers, shapes, and guides the future of not only its 375,000+ community members, but the greater industry, enabling new opportunities to better serve our world. Visit computer.org for more information.

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'World's purest silicon' could lead to 1st million-qubit quantum computing chips

Scientists engineer the 'purest ever silicon' to build reliable qubits that can be manufactured to the size of a pinhead on a chip and power million-qubit quantum computers in the future.

Abundance of Camera CMOS on Silicon Wafer

Scientists have created an enhanced, ultra-pure form of silicon that could one day be the foundation for highly reliable "silicon-spin qubits" in powerful quantum computers.

While the bits in classical computers encode data as either 1 or 0, qubits in quantum computers can be a superposition of these two states — meaning they can achieve a quantum state known as "coherence" and occupy both 1 and 0 in parallel while processing calculations. 

These machines could potentially be more powerful than the world's fastest supercomputers but would need around a million qubits to achieve this, the scientists said. The largest quantum computer today has roughly 1,000 qubits . 

But a key challenge with quantum computing is that qubits are "noisy," meaning they are highly prone to interference, such as temperature changes, and need to be cooled to near absolute zero . Otherwise, they easily lose information and fail midway through operations. 

This means that even if we had a quantum computer with millions of qubits, many of those would be redundant even with error-correction technologies , making the machine extremely inefficient.

Tapping into silicon quantum computing

Qubits are normally made from superconducting metals such as tantalum and niobium because they possess near-infinite conductivity and near-infinite resistance.

But in a new study, published May 7 in the journal Nature Communications Materials , researchers proposed using a new, pure form of silicon — the semiconductor material used in conventional computers — as the basis for a qubit that is far more scalable than existing technologies.

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Related: Quantum computing breakthrough could happen with just hundreds, not millions, of qubits using new error-correction system

Building qubits from semiconducting materials like silicon, gallium or germanium has advantages over superconducting metal qubits, according to the quantum computing company QuEra . The coherence times are relatively long, they are cheap to make, they operate at higher temperatures and they are extremely tiny — meaning a single chip can hold huge numbers of qubits. But impurities in semiconducting materials cause decoherence during computations, which makes them unreliable.

In the new study, the scientists proposed making a qubit out of silicon-28 (Si-28), which they described as the "world's purest silicon," after stripping away the impurities found in natural silicon. These silicon-based qubits would be less prone to failure, they said, and could be fabricated to the size of a pinhead.

Lead author prepares a silicon chip for enrichment in the lab

Natural silicon is normally made up of three isotopes, or atoms of different masses — Si-28, Si-29 and Si-30. Natural silicon works well in conventional computing due to its metalloid properties, but problems arise when using it in quantum computing. 

Si-29 in particular, which makes up 5% of natural silicon, causes a "nuclear flip-flopping effect" that leads to decoherence and the loss of information. In the study, the scientists got around this by developing a new method to engineer silicon without Si-29 and Si-30 atoms.

Cheaper, more scalable quantum computing

"What we've been able to do is effectively create a critical 'brick' needed to construct a silicon-based quantum computer," lead study author Richard Curry , professor of advanced electronic materials at the University of Manchester, said in a statement. "It’s a crucial step to making a technology that has the potential to be transformative for humankind feasible."

— Future quantum computers could use bizarre 'error-free' qubit design built on forgotten research from the 1990s

— World's 1st fault-tolerant quantum computer launching this year ahead of a 10,000-qubit machine in 2026

— Error-corrected qubits 800 times more reliable after breakthrough, paving the way for 'next level' of quantum computing

Components for silicon-based quantum computers could in theory be built using the same methods used to manufacture classical electronic chips, which can fit billions of transistors onto a tiny circuit board, the scientists said. Silicon qubits, or silicon-spin qubits, are nothing new, but the quality of the silicon has never been as pure, they added, which is determined based on microscopy testing.

Silicon-based qubits could also be manufactured far more easily than other kinds of qubit because of existing chip fabrication methods. And, therefore, quantum computers that use them can be scaled to the million-qubit region much more quickly than competing methods, the researchers said.

"Now that we can produce extremely pure silicon-28, our next step will be to demonstrate that we can sustain quantum coherence for many qubits simultaneously," project co-supervisor David Jamieson , professor of physics at the University of Melbourne, said in the statement. "A reliable quantum computer with just 30 qubits would exceed the power of today's supercomputers for some applications."

Keumars Afifi-Sabet

Keumars is the technology editor at Live Science. He has written for a variety of publications including ITPro, The Week Digital, ComputerActive, The Independent, The Observer, Metro and TechRadar Pro. He has worked as a technology journalist for more than five years, having previously held the role of features editor with ITPro. He is an NCTJ-qualified journalist and has a degree in biomedical sciences from Queen Mary, University of London. He's also registered as a foundational chartered manager with the Chartered Management Institute (CMI), having qualified as a Level 3 Team leader with distinction in 2023.

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NeurIPS 2024

Conference Dates: (In person) 9 December - 15 December, 2024

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Call For Papers 

Abstract submission deadline: May 15, 2024

Author notification: Sep 25, 2024

Camera-ready, poster, and video submission: Oct 30, 2024 AOE

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The site will start accepting submissions on Apr 22, 2024 

Subscribe to these and other dates on the 2024 dates page .

The Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024) is an interdisciplinary conference that brings together researchers in machine learning, neuroscience, statistics, optimization, computer vision, natural language processing, life sciences, natural sciences, social sciences, and other adjacent fields. We invite submissions presenting new and original research on topics including but not limited to the following:

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Machine learning is a rapidly evolving field, and so we welcome interdisciplinary submissions that do not fit neatly into existing categories.

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Formatting instructions:   All submissions must be in PDF format, and in a single PDF file include, in this order:

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Other supplementary materials such as data and code can be uploaded as a ZIP file

The main text of a submitted paper is limited to nine content pages , including all figures and tables. Additional pages containing references don’t count as content pages. If your submission is accepted, you will be allowed an additional content page for the camera-ready version.

The main text and references may be followed by technical appendices, for which there is no page limit.

The maximum file size for a full submission, which includes technical appendices, is 50MB.

Authors are encouraged to submit a separate ZIP file that contains further supplementary material like data or source code, when applicable.

You must format your submission using the NeurIPS 2024 LaTeX style file which includes a “preprint” option for non-anonymous preprints posted online. Submissions that violate the NeurIPS style (e.g., by decreasing margins or font sizes) or page limits may be rejected without further review. Papers may be rejected without consideration of their merits if they fail to meet the submission requirements, as described in this document. 

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Please join the NeurIPS 2024 Checklist Assistant Study that will provide you with free verification of your checklist performed by an LLM here . Please see details in our  blog

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We encourage authors to upload their code and data as part of their supplementary material in order to help reviewers assess the quality of the work. Check the policy as well as code submission guidelines and templates for further details.

Use of Large Language Models (LLMs): We welcome authors to use any tool that is suitable for preparing high-quality papers and research. However, we ask authors to keep in mind two important criteria. First, we expect papers to fully describe their methodology, and any tool that is important to that methodology, including the use of LLMs, should be described also. For example, authors should mention tools (including LLMs) that were used for data processing or filtering, visualization, facilitating or running experiments, and proving theorems. It may also be advisable to describe the use of LLMs in implementing the method (if this corresponds to an important, original, or non-standard component of the approach). Second, authors are responsible for the entire content of the paper, including all text and figures, so while authors are welcome to use any tool they wish for writing the paper, they must ensure that all text is correct and original.

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OpenReview: We are using OpenReview to manage submissions. The reviews and author responses will not be public initially (but may be made public later, see below). As in previous years, submissions under review will be visible only to their assigned program committee. We will not be soliciting comments from the general public during the reviewing process. Anyone who plans to submit a paper as an author or a co-author will need to create (or update) their OpenReview profile by the full paper submission deadline. Your OpenReview profile can be edited by logging in and clicking on your name in https://openreview.net/ . This takes you to a URL "https://openreview.net/profile?id=~[Firstname]_[Lastname][n]" where the last part is your profile name, e.g., ~Wei_Zhang1. The OpenReview profiles must be up to date, with all publications by the authors, and their current affiliations. The easiest way to import publications is through DBLP but it is not required, see FAQ . Submissions without updated OpenReview profiles will be desk rejected. The information entered in the profile is critical for ensuring that conflicts of interest and reviewer matching are handled properly. Because of the rapid growth of NeurIPS, we request that all authors help with reviewing papers, if asked to do so. We need everyone’s help in maintaining the high scientific quality of NeurIPS.  

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Jamshidi earns recognition for most influential paper

Pooyan Jamshidi

When someone in academia publishes a research paper, one of the goals is to have the paper cited by other professors and researchers. A paper published 10 years ago by Computer Science and Engineering Assistant Professor Pooyan Jamshidi was recently recognized for its significant impact.

Jamshidi received the Most Influential Paper Award in April at the 19th International Conference on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) in Lisbon, Portugal. Jamshidi’s paper, “ Autonomic Resource Provision for Cloud-based Software ,” was submitted, accepted and published just prior to earning his Ph.D. from Dublin City University in Ireland in 2014. It was presented at the 2014 SEAMS Conference in India.

For the most influential paper award, a select committee considers conference publications published approximately 10 years previously and selects those that have made the most impact according to several criteria, including the number of citations, practical applications and industry adoption, and influence on subsequent research. The most influential award is selected from this short list.

“I wanted to publish the most important part of my Ph.D. research at SEAMS because it was a special community, and their work was close to mine,” Jamshidi says. “Receiving this award is important because this was my first paper with the community. I kept publishing with SEAMS and remained engaged.” 

The paper’s title referred to a groundbreaking approach to fundamentally transform how resources are managed and allocated in cloud environments. The key innovation was to enable multiple tenants to describe their adaptation rules for cloud and multi-cloud resource provisioning using a specific language that enables the incorporation of reasoning, inference and resolution of conflicting adaptation rules.

Since the paper was published, it has received 188 citations according to Google Scholar . In addition, the autonomic resource provision technique has been integrated with Microsoft Azure and OpenStack . The concepts and methods introduced in the paper have also led to follow-up research in cloud autoscaling, Edge-and-Internet of Things resource scaling, and networking and autonomous driving.

The paper has impacted the field of software engineering, especially in the context of adaptive and self-managing systems in the cloud, research, industry practices and the broader technological landscape.

While Jamshidi admits that autonomous autoscaling system for cloud-based software is not as a hot topic as it was when his paper was published, it is still a relevant research area that is leading to new ideas, methods, and approaches.

“The most exciting direction in cloud auto-scaling and resource provisioning overall is sustainability-aware approaches to enable sustainable computer usage for modern applications, such as AI systems,” Jamshidi says. “We plan to continue this line of research. For example, thanks to funds provided by the National Science Foundation and collaborators from Carnegie Mellon University and Rochester Institute of Technology, we are investigating software-driven sustainability.” 

Challenge the conventional. Create the exceptional. No Limits.


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