Projects
In my research I try to aim at not merely develop computer science methodology and test it, but also to apply it in attempt to make scientific discoveries.
Here is a short description of the scientific projects I am working on or worked on in the past, including brief descriptions of milestones and notable discoveries made in each research project. The list does not include projects to which I am a minor contributor, so it may not be complete, but it includes the ones that I consider more significant. Most of these projects involve undergraduate and graduate students, so if you are a student (or plan to become one) and find something from this list interesting make sure to contact me.
Computer analysis of visual art
Can computers understand art? This project is an attempt to address that question. There is no evidence so far that the computer actually "understands" the art, but there are results showing that the computer analysis of the art is very similar to the way art historian analyzes it. One of the experiments showed that given the paintings only, the computer was able to organize them in a way very similar to the way art historians organizes them. The purpose of this study is not just to automate the analysis, but also to make discoveries by quantitatively analyzing the art. For instance, it showed that Van Gogh and Jackson Pollock share artistic similarities that are reflected by the mathematical analysis of the way they paint.
Publications:
Swisher, C., Shamir, L., A data science and machine learning approach to continuous analysis of Shakespeare's plays, Journal of Data Mining and Digital Humanities, accepted. CNRS/ INRA, 2023.
May, C., Shamir, L., A data science approach to movies and film director analysis, First Monday, 24(6), 2019.
Wei, F., Li, Y., Shamir, L., Computer analysis of architecture using automatic image understanding, Journal of Data Mining and Digital Humanities, jdmdh:4683. CNRS, INRA, 2019.
Burcoff, A., Shamir, L., Computer Analysis of Pablo Picasso’s Artistic Style, International Journal of Art, Culture and Design Technologies, 6(1). IGI-Global, 2017.
Shamir, L., Nissel, J., Winner, E., Distinguishing Between Abstract Art by Artists vs. Children and Animals: Comparison between Human and Machine Perception, ACM Transactions on Applied Perception, 13(3), 17, 2016.
Shamir, L., What makes a Pollock Pollock: A machine vision approach, International Journal of Art and Technology, 8(1), 1-10, 2015.
Shamir, L., Tarakhovsky, J., Computer Analysis of Art, ACM Journal on Computing and Cultural Heritage, 5(2), article 7. ACM, 2012.
Shamir, L.; Computer analysis reveals similarities between the artistic styles of Van Gogh and Pollock, Leonardo, 45(2), pp. 149-154. MIT Press, 2012.
Shamir, L., Macura, T., Orlov, N., Eckley, D. M., Goldberg, I. G.; Impressionism, expressionism, surrealism: Automated recognition of painters and schools of art, ACM Transactions on Applied Perception, 7(2), 8, 2010.
Popular media:
Art: quand la science mène l'enquête
Quebec Science, June 28, 2018.
Paolo Legrenzi “Regole e Caso”
Il Posto Delle Parole (Italy), November 26, 2017.
Eyes Without a Face, Real Life Magazine, May 10, 2017.
Adjudicado… al algoritmo!
El Pais (Spain), November 1, 2016
From technology, an artistic breakthrough,
That Artist's Magazine, July/August, 2015, page 10.
Art World Debates – Can A Computer Detect Art Forgery Or Make Paintings That Fool Even The Experts?,
Inquisitr, 3/14/2015.
Robot versus robot: The art battle,
SBS News (Australia), 3/14/2015.
Un robot pourra-t-il peindre un faux Jackson Pollock indétectable?,
Yahoo News France, 3/13/2015.
Un robot pourra-t-il peindre un faux Jackson Pollock indétectable?,
Actualities, 3/13/2015.
Robots: Experts at spotting art forgery, still don't get art,
Kill Screen Magazine, 3/13/2015.
Robot Vs. Robot,
The Atlantic, 3/12/2015.
Possible Jackson Pollock Painting Could Be Worth $160M,
NBC San Diego, 3/12/2015.
Anti-fakery program tells collectors if that Pollock is a bit fishy,
The Times, 2/19/2015.
Fake Jackson Pollock? Software will tell you,
Fox News, 2/19/2015.
A Computer Can Tell Real Jackson Pollocks From Fakes,
Smithsonian Magazine, 2/18/2015.
New Computer Program Sorts Real Jackson Pollock Paintings from Fakes,
Mental Floss, 2/16/2015.
Daily Tech News Show (starting at 18:48),
Daily Tech News Show , 2/13/2015.
Computer analysis verifies authenticity of Jackson Pollock's drip paintings,
iMagazine, 2/12/2015.
Computer analysis verifies authenticity of Jackson Pollock’s drip paintings,
Computer Magazine, 2/10/2015.
Computer algorithm can accurately identify Jackson Pollock paintings,
Arstechnica, 2/11/2015.
Computer algorithm can accurately identify Jackson Pollock paintings,
Wired, 2/11/2015.
Computer program can authenticate Jackson Pollock paintings,
Physics Today, 2/13/2015.
When is a Pollock not a Pollock? Computer analysis verifies,
Hawaii Telegraph, 2/13/2015.
Computer algorithm can accurately identify Jackson Pollock paintings,
EE Journal, 2/13/2015.
My Computer Could Paint That!,
Scientista, November 21, 2012.
Computer Programs Become Art Appreciators,
International Business Times, October 17, 2012.
Computer classifies paintings like an art critic,
NBC News, September 28, 2012.
Can computers understand art?,
Kurzweil, September 27, 2012.
Computers can understand art like humans,
Yahoo! News, Spetember 27, 2012.
Computers can understand art like humans,
Times of India, September 27, 2012.
Artificial intelligence cracks history of art,
Russia Today, september 27, 2012.
Algorithm turns computers into art experts,
Gizmag, October 3, 2012.
Computers match humans in understanding art,
R&D Magazine, September 26, 2012.
Can computers understand art?,
Engineering Edge, September 27, 2012.
Art-Interpreting Computers: The Next Jerry Saltz?,
ANIMAL New York, Spetmeber 27, 2012.
Computers match humans in understanding art,
Science Daily, September 26, 2012.
“Painting by numbers”,
The Economist, July 30, 2011 (p. 75).
“Monet or Renoir? Computers as Art Connoisseurs”,
Real Clear Science, August 1, 2011.
"Computer Analysis Identifies Similarities Between Van Gogh, Pollock" ,
CBS Detroit, August 11, 2011.
“Automating the art critic” ,
The Diagonal,
August 14, 2011.
Galaxy morphology analysis
Robotic telescopes acquire hundreds of millions of galaxy images. Due to their size it is not practical to analyze these databases manually, and automatic tools are needed. Not just that galaxies have complex morphology, the problem is even magnified by the noise and artifacts typical to such data. The purpose of this project is to develop methods for automatic galaxy image analysis, but also to apply these methods to create catalogs that can be used by others, and to mine the important discoveries that are hidden inside these huge mountains of data. Some of the work was done as part of the Zooniverse project.
Publications:
Shamir, L., Outlier galaxy images in the Dark Energy Survey and their identification with unsupervised machine learning, Astronomy and Computing, 43, 100712, 2023.
Shamir, L., Algorithmic and machine learning approaches to automatic identification of peculiar galaxies in large astronomical databases, XXXII Astronomical Data Analysis Software and Systems (ADASS), 2022. Accepted.
Goddard, H., Shamir, L., Neural network bias in analysis of galaxy photometry data, 18th IEEE International Conference on eScience, 2022.
Dhar, S., Shamir, L., Systematic biases when using deep neural networks for annotating large catalogs of astronomical images, Astronomy and Computing, 38, 100545, 2022.
Shamir, L., Outlier galaxy detection and distribution of galaxy morphology, Statistical Challenges in Modern Astronomy (SCMA VII), 2021.
Shamir, L., Automatic identification of outliers in Hubble Space Telescope galaxy images, Monthly Notices of the Royal Astronomical Society, 501(4), 5229-5238. Oxford Academic, 2021.
Goddard, H., Shamir, L., A catalog of broad morphology of Pan-STARRS galaxies based on deep learning, Astrophysical Journal Supplement Series, In Press. IOP, 2020.
Venkata Siva Kumar Margapuri, Lior Shamir, Basant Thapa, Detection of unknown galaxy types in large databases of galaxy images, 29th International Conference on Software Engineering and Data Engineering (SEDE), ISCA, 2020.
Shamir, L., Automatic detection of full ring galaxy candidates in SDSS, MNRAS, 491(3), 3767-3777, 2020.
Kuminski, E., Shamir, L., A hybrid approach to machine learning annotation of large galaxy image databases, Astronomy and Computing, 25, 257-269, 2018.
Paul, N., Virag, N., Shamir, L., A catalog of photometric redshift and the distribution of broad galaxy morphologies, Galaxies (special issue on Application of Machine-Learning Techniques in Astronomical Data Analysis), 6, 64, 2018.
Paul, N., Shamir, L., A catalog of galaxy morphology and photometric redshift, 231st American Astronomical Society Meeting, 150.04, 2018.
Timmis, I., Shamir, L., A catalog of automatically detected ring galaxy candidates in PanSTARRS, The Astrophysical Journal Supplement Series, 231(1), 2, 2017.
Shamir, L., Morphology-based query for galaxy image databases, Publications of the Astronomical Society of the Pacific, 129(972), 024003, 2017.
Shamir, L., Kuminski, E., Image-based query-by-example for Big databases of galaxy images, 229th American Astronomical Society Meeting, Dallas, TX. 2017.
Kuminski, E., Shamir, L., Computer-generated visual morphology catalog of ~3,000,000 SDSS galaxies, The Astrophysical Journal Supplement Series, 223(2), 20. IOP, 2016.
Shamir, L., Diamond, D., Wallin, J. Leveraging pattern recognition consistency estimation for crowdsourcing data analysis, IEEE Transactions on Human-Machine Systems, In Press
Schutter, A., Shamir, L., Galaxy morphology - an unsupervised machine learning approach, Astronomy and Computing, 12, 50-55, 2015.
Kuminski, E., George, J., Wallin, J., Shamir, L.; Combining human and machine learning for morphological analysis of galaxy images, Publications of the Astronomical Society of the Pacific, 126 (944), 959-967, 2014.
Shamir, L., Wallin, J.; Automatic detection and quantitative assessment of peculiar galaxy pairs in Sloan Digital Sky Survey, Monthly Notices of the Royal Astronomical Society, 443(4), 3528-3537, 2014.
Shamir, L., Holincheck, A., Wallin, J.; Automatic quantitative morphological analysis of interacting galaxies, Astronomy & Computing, 2, 67-73, 2013
Dojcsak, L., Shamir, L.; Quantitative analysis of spirality in elliptical galaxies, New Astronomy, 28, 1-8, 2014.
Shamir, L.; Automatic detection of peculiar galaxies in large datasets of galaxy images, Journal of Computational Science, Vol. 3, No. 3, p. 181-189, 2012.
Shamir, L.; Ganalyzer: A tool for automatic galaxy image analysis, The Astrophysical Journal, vol. 736, no. 2, 141. IPO, 2011.
Shamir, L.; Automatic morphological classification of galaxy images, Monthly Notices of the Royal Astronomical Society, vol. 399, no. 3, pp. 1367-1372. Wiley InterScience, 2009.
Popular media:
Featured Image: Identifying Weird Galaxies
Nova, July 31, 2017.
A.I. Is Getting Better at Spotting Galaxies, The Atlantic, May 9th, 2016
A.I. Is Getting Better at Spotting Galaxies, Gossip Newspaper, May 9th, 2016
Computers vs. Humans in Galaxy Classification,
Nova, April 27th, 2016.
Unsupervised and supervised image analysis
This project started in 2007. The initial plan was to develop a comprehensive multi-purpose image classifier, but it turned into a set of tools and methods to perform unsupervised analysis of image data. That includes tasks such as clustering and novelty detection. These tools were found to be very useful for discovery-driven research that involves image data, or mining large image databases. Most of the publications it generated are listed in the other projects, to which the methods were applied.
Publications:
Goddard, H., Shamir, L., SVMnet: Non-parametric image classification based on convolutional ensembles of support vector machines for small training sets, IEEE Access, 10, 24029-24038, 2022.
Orlov, N., Eckley, D. M., Shamir, L., Goldberg, I.; Improving class separability using extended pixel planes: a comparative study, Machine Vision and Applications, vol. 23(5), pp.1047-1058. Springer-Verlag, 2012.
Shamir, L.; Pose and illumination invariance with compound image transforms, In: Advances in Face Image Analysis - Techniques and Technologies. Zhang, Y.J. (Ed.), IGI Global Pub., p. 301-315, 2011.
ISBN: 9781615209927
Shamir, L., Unsupervised detection of outlier images using multi-order image transforms, Theory and Applications of Mathematics & Computer Science, vol. 3(1), p. 13-31, 2013.
Orlov, N., Shamir, L.*, Macura, T., Johnston, J., Eckley, D. M., Goldberg, I.; WND-CHARM: Multi-purpose image classification using compound image transforms,
Pattern Recognition Letters, vol. 29, no. 11, pp. 1684-1693. Elsevier, 2008.
Shamir, L., Orlov, N., Goldberg, I.; Evaluation of the informativeness of multi-order image transforms, International Conference on Image Processing Computer Vision and Pattern Recognition (IPCV'09/WORLDCOMP'09), p. 37-42. Las Vegas, NV. 2009.
Studying the whale language
Understanding the communication of whales is important not just for expanding our knowledge, but also for protecting this endangered species. The WhaleFM project started in November 2011, and I joined it in 2012. Using many hours of recording of the sounds whales make we try to characterize the whale language. My part in the project is the automatic quantitative analysis of the sound data. Since we have over 15,000 sound samples, and since the samples are difficult to identify, quantitative and automatic analysis is needed. We use machine learning techniques in addition to human analysis and citizen science classification. Over 800,000 citizen science participants are registered with the project.
So far our findings show that whales of the same species have similar dialects. But like some other animals, whales of the same type can have different accents at different geographical locations. These findings were validated by consequent studies.
Publications:
Shamir, L., Yerby, C., Simpson, R., von Benda-Beckmann, A., Tyack, P., Samarra, F., Miller, P., Wallin , J.; Classification of large acoustic datasets using machine learning and crowdsourcing - application to whale calls, Journal of the Acoustical Society of America, vol. 135(2), 953-962, 2014.
von Benda-Beckmann, A.M., Simpson, R., Smith, A., Shamir, L., Tyack, P.L.T.; Look who’s talking – classification of whale sounds using the Whale FM Citizen Science project, International workshop. Cetacean echolocation and outer space neutrinos: ethology and physics for an interdisciplinary approach to underwater bioacoustics and astrophysical particles detection. Erice, Sicily. October 18-21, 2013.
Popular press:
Astrophysics, Citizen Science and the Google Science Fair
Scientific American (starting around 22:00)
Lend Your Ears to Citizen Science! Help Understand Whale Communication with Whale FM
, Discovery Magazine
Could the Beatles have been Whalers?The Independent
Automatic analysis of music
The purpose of this project is to develop computational tools that can extract information from text data. That can include topic words, writing style, sentiments, etc, with a broad range of applications.
Publications:
Rosebaugh, C., Shamir, L., Data Science Approach to Compare the Lyrics of Popular Music Artists, UNISIA, 40, 1, 2022.
Tucker, E., Capps, C., Shamir, L., A data science approach to 138 years of congressional speeches, Heliyon, 6(8), E04417, 2020.
Shamir, L.,UDAT: Compound quantitative analysis of text using machine learning, Digital Scholarship in the Humanities. Oxford U. Press, fqaa007, 2020.
Napier, K., Shamir, L., Quantitative sentiment analysis of lyrics in pop music, Journal of Popular Music Studies, 30(4), 161-176, 2018.
Alluqmani, A., Shamir, L., Writing styles in different scientific disciplines: a data science approach, Scientometrics, 115(2), 1071-1085, 2018.
Popular press:
Músicas que surpreendem são as mais ouvidas
Estado de Minas (Brazil), November 3, 2019.
Iz ljubavi prema mržnji: zašto pop pjesme postaju sve depresivnije?
Elle, August 12, 2019.
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Infoseek (japan), July 7, 2019.
Pop music: Does it express more anger and sadness?
SDE (Kenya), June 1, 2019.
Is pop music getting sadder, angrier and moodier?
Beat (Australia), May 29, 2019.
Les chansons pop sont-elles de plus en plus tristes?
Radio Canada, May 29, 2019.
Nh?c pop ngày nay có ?ang tr? nên bu?n bã và gi?n d? h?n?
VN Review (Vietnam), May 16, 2019.
Les chansons pop sont de plus en plus tristes, et ce n'est pas un hasard
Slate (France), May 15, 2019.
Computer Analysis Shows Pop Music Lyrics Are Angrier, Sadder
Communications of the ACM, May 14, 2019.
Algorithmic Analysis Shows That Pop Music Is Sadder and Angrier Than Ever
Slashdot, May 14, 2019.
Is pop music really getting sadder and angrier?
BBC, May 14, 2019.
"You call that music?"
Smithsonian Magazine, May 2019 issue, page 20.
Gute Laune war gestern Wutmusiker im Pop sind Zeichen der Zeit
Mitteldeutsche Zeitung (germany), April 6, 2019.
Cómo afecta la música actual a tus emociones?
La Nota Latina April 4, 2019.
According To A Study, Music and Lyrics Become Angrier And Sadder Now
KnowledgeStall March 27, 2019.
Lirik Lagu Pop Semakin Ekspresif, Ilmiah! Ada Apa dengan Kemarahan dan Kesedihan?
Paragram (Indonesia) March 21, 2019.
Cet écran a été partagé à partir de La Presse+
La Presse (Canada) March 8, 2019.
Popular songs get sadder
NPR Michigan Radio, March 7, 2019.
Song lyrics capture sentiments of their era
Chicago Tribune, February 22, 2019.
Coffee Break: Sad and angry
The Current, February 15, 2019.
Les paroles de nos morceaux préférés sont de plus en plus tristes…
Tsugi Magazine (France), February 14, 2019.
Pourquoi écoutons-nous des chansons de plus en plus tristes ?
Le Bonbon (France), February 14, 2019.
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ClickAtLife (Greece), February 11, 2019.
Song lyrics show popular music turning sadder and angrier
WSB-TV (Channel 2, Atlanta), February 9, 2019.
Popular music is now angrier and sadder according to new study
Alternative Press, February 9, 2019.
Song lyrics show popular music turning sadder and angrier
ABC News, February 8, 2019.
Popular Music is Getting Sadder and Angrier, New Study Finds
Inside Science, February 7, 2019.
Survey: Hit Songs Have Grown Sadder, Angrier Over Past 60 Years
Inside Radio, February 6, 2019.
Music Is Getting Sadder By The Year, According To Research
The Versed, February 2, 2019.
Studi: Lirik Lagu yang Memuat Kemarahan Semakin Meningkat
iNews (Indonesia), January 30, 2019.
Die Joy-Periode ist lange zu Ende
Taz (Germany), January 30, 2019.
Öfkeli ve hüzünlü ?ark? sözleri art??ta
Mynet (Turkey), January 30, 2019.
Öfkeli ve hüzünlü ?ark? sözleri art??ta
TurkTimes (Turkey), January 30, 2019.
Pop music lyrics have gotten sadder and angrier over time
Earth.com, January 30, 2019.
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China Daily (China), January 30, 2019.
Pop Songs Have Gotten Angrier Over the Decades
Pacific Standard, January 29, 2019.
Öfkeli ve hüzünlü ?ark? sözleri art??ta
Deutsche Welle (Germany), January 29, 2019.
Lagu-lagu populer zaman sekarang sarat amarah
Beritagar (Indonesia), January 29, 2019.
Scientist say song lyrics are getting angrier (and sadder)
CNET, January 28, 2019.
Pop music became angrier, sadder over time: Study
Mumbai Mirror (India), January 28, 2019.
Scientist say song lyrics are getting angrier over time
LongRoom, January 28, 2019.
No es tu imaginación! La música pop es más triste que el resto
Tododiarios (Argentina), January 28, 2019.
Colère et tristesse hantent de plus en plus la musique que nous écoutons
Ouest-France (France), January 28, 2019.
According To A Study, Music and Lyrics Become Angrier And Sadder Now
Knowledge Stall, January 28, 2019.
Anger goes up, joy down; demand decides popular music lyrics
News Mobile (India), January 28, 2019.
No es tu imaginación! La música pop es más triste e iracunda que el resto
Life and Style (Mexico), January 28, 2019.
La música se ha vuelto más triste y agresiva
El Espectador (Colombia), January 28, 2019.
Song-Texte: Trend zur Trauer
Deutschlandfunk Nova (Germany), January 28, 2019.
A dalszövegek dühösebbek és szomorúbbak lettek az elmúlt évtizedekben
VajdasagMa (Serbia), January 28, 2019.
Analyse von Songtexten: Die Wut nimmt zu
Telepolis (Germany), January 28, 2019.
Più rabbiose le canzoni in 60 anni, ora meno gioia
Alto Adige (Italy), January 28, 2019.
El pop es cada vez más triste y rabioso, según la ciencia
El Independiente, January 28, 2019.
Anger and Sadness Are On the Rise in Popular Music Lyrics
Psychology Today, January 28, 2019.
Study finds popular music has become angrier & sadder over time due to demand
Daily Entertainment Express (India), January 28, 2019.
Popular Music became angrier, sadder over time
Millennium Post (India), January 28, 2019.
Cântecele pop devin tot mai triste de la an la an, spun studiile
Tabu (Romania), January 28, 2019.
Popular music became angrier, sadder over time: Study
The New Indian Express (India), January 28, 2019.
Songtexte immer wütender und trauriger
Press Text (Austria), January 28, 2019.
Songtexte immer wütender und trauriger
Wall Street On-Line (Germany), January 28, 2019.
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Union News (Russia), January 28, 2019.
Muzyka popularna: coraz smutniej, coraz gniewniej
Onet (Poland), January 28, 2019.
Pop music became angrier, sadder over time: Study
Pune Mirror (India), January 28, 2019.
Popular music became angrier, sadder over time: Study
Times of India (India), January 27, 2019.
Popular music became angrier, sadder over time: Study
India.com (India), January 27, 2019.
Popular music became angrier, sadder over time: Study
Business Standard, January 27, 2019.
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Sina (China), January 26, 2019.
Computer analysis shows popular music lyrics become angrier and sadder over time
New Kerala (India), January 26, 2019.
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Russia Today (Russia), January 26, 2019.
Pop songs have become angrier AND sadder! Scientists analysed lyrics from 6,000 best-selling songs from the 1950s to 2016 to make the finding
Daily Mail (UK), January 25, 2019.
Texty populárnej hudby sú stále viac a viac nahnevanejšie a smutnejšie
Zoznam (Slovakia), January 25, 2019.
Computer analysis shows popular music lyrics become angrier and sadder over time
The Siasat Daily (India), January 25, 2019.
Computer analysis shows popular music lyrics become angrier and sadder over time
ANI New (India), January 25, 2019.
Pop music lyrics becoming angrier and sadder, research suggests
E&T Magazine, January 25, 2019.
Computer analysis shows that popular music lyrics become angrier and sadder over time
ScienMag, January 24, 2019.
Computer analysis shows that popular music lyrics become angrier and sadder over time
PhysOrg, January 24, 2019.
STUDY: Music Lyrics Getting Angrier, Sadder Over Time
Energy 106 Radio, January 2, 2019.
Music Lyrics Getting Angrier, Sadder Over Time, Study Finds
CBS Philadelphia, December 28, 2018.
Music lyrics are getting angrier, sadder over time, study finds
USA Today, December 27, 2018.
Automatic analysis of music
The purpose of this project is to develop computational method to analyze music. Applications include music discovery and mining large music databases, but also studying music in a quantitative fashion. The plan is to develop tools that go beyond supervised learning (e.g., genre classification), and can perform tasks such as automatic clustering and unsupervised identification of similarities between musical styles.
Publications:
George, J., Shamir, L., Unsupervised analysis of similarities between musicians and musical genres using spectrograms, Artificial Intelligence Research, 4(2), 61-71, 2015.
George, J., Shamir, L., Computer analysis of similarities between albums in popular music, Pattern Recognition Letters, 45, 78-84, 2014.
George, J., Shamir, L., Computer-based approaches to music research, Network Detroit: Digital Humanities Theory and Practice, 2013.
Popular press:
Investigadores analizan música de Queen
El Porvenir (Mexico), November 12, 2018.
Investigadores analizan música de Queen
El Imparcial (Puerto Rico), November 11, 2018.
La ciencia demuestra la evolución en la música de Queen
Vanguardia (Mexico), November 11, 2018.
La música de Queen según la ciencia
El Universal (Mexico), November 11, 2018.
Analizan investigadores música de Queen
La Cronica (Mexico), November 11, 2018.
Cómo evolucionó la música de Queen? La ciencia te lo explica
El Universal (Mexico), November 9, 2018.
The Far Limits of Artificial Intelligence
Strategic Finance, December 1, 2017.
Did the Beijing Olympic Song Rip off Disney’s ‘Let It Go’? We Asked Science.,
Foreign Policy Magazine, August 7th, 2015.
Finally, an Algorithm to Sort Your Beatles Albums, Scientific American, August 22, 2014.
Fab 4 Math: Computer Maps Beatles' Musical Evolution, Yahoo News, August 1, 2014.
Artificial Intelligence Meets the Beatles, Discovery, July 30, 2014.
Fab 4 Math: Computer Maps Beatles' Musical Evolution, Science Live, July 29, 2014.
The Beatles Sound Evolved Over Time, Notes Artificial Intelligence Program, NHGN, July 25, 2014.
Artificial intelligence identifies the musical progression of the Beatles, ECN Magazine , July 25, 2014.
AI Identifies the Musical Progression of the Beatles, Engineering.com, July 24, 2014.
Computer charts evolution of the Beatles, Science Magazine, July 24, 2014.
Artificial intelligence identifies the musical progression of the Beatles, Business Week, July 24, 2014.
Computer May Know the Beatles Albums Better Than You Do, NBC News, July 24, 2014.
Artificial intelligence identifies the musical progression of the Beatles, Reuters, July 24, 2014.
Cosmological-scale asymmetry in galaxy spin directions
In this research I automated galaxy image analysis to separate hundreds of thousands of galaxies by their spin direction - clockwise and counterclockwise. Surprisingly, the analysis shows that the number of clockwise and counterclockwise galaxies is different, and depends on the direction of observation and redshift. It also seems that galaxies with clockwise patterns have different photometry compared to galaxies with counterclockwise patterns, and that photometry is also dependent on the direction of observation.
Publications:
Shamir, L., Large-scale asymmetry in the distribution of galaxy spin directions – analysis and reproduction, Symmetry, 15(9), 1704, 2023.
Shamir, L., Observational Link between Galaxy Rotation Physics and Anomalies in the LSS, Cosmology from Home, 2023.
McAdam, D., Shamir, L., Asymmetry Between Galaxy Apparent Magnitudes Shows a Possible Tension Between Physical Properties of Galaxies and their Rotational Velocity, Symmetry (Special Issue on Symmetry in Quantum Fields, Gravitation, and Cosmology), 15(6), 1190, 2023.
Shamir, L., Relative rotational velocity of host galaxies as an explanation to the Ho constant tension, 242 AAS Meeting, 315.01, 2023.
Pavan Kumar Aluri, Paolo Cea, Pravabati Chingangbam, Ming-Chung Chu, Roger G. Clowes, Damien Hutsemékers, Joby P. Kochappan, Andrzej Krasi?ski, Alexia M. Lopez, Lang Liu, Niels C. M. Martens, C. J. A. P. Martins, Konstantinos Migkas, Eoin Ó Colgáin, Pratyush Pranav, Lior Shamir, Ashok K. Singal, M. M. Sheikh-Jabbari, Jenny Wagner, Shao-Jiang Wang, David L. Wiltshire, Shek Yeung, Lu Yin, Wen Zhao, Is the Observable Universe Consistent with the Cosmological Principle?, Classical and Quantum Gravity, 40(1), 094001, 2023.
McAdam, D., Shamir, L., Reanalysis of the spin direction distribution of Galaxy Zoo SDSS spiral galaxies, Advances in Astronomy, vol. 2023, article ID: 4114004.
Shamir, L., “Dark velocity”: Observed discrepancy between the rotational velocity of galaxies and their physical properties, 241 AAS Meeting, 403.10, 2023.
Shamir, L., Analysis of spin directions of galaxies in the DESI Legacy Survey,
Monthly Notices of the Royal Astronomical Society, 516(2), 2281-2291, 2022.
Shamir, L., Using 3D and 2D analysis for analyzing large-scale asymmetry in galaxy spin directions, Publications of the Astronomical Society of Japan, 74(5), 1114-1130, 2022.
Shamir, L., Asymmetry in galaxy spin directions - analysis of data from DES and comparison to four other sky surveys, Universe (Special Issue on the Large-scale Structure of the Universe), 8(8), 397. MDPI, 2022.
Shamir, L., Analysis of ~106 spiral galaxies from four telescopes shows large-scale patterns of asymmetry in galaxy spin directions, Advances in Astronomy, article ID: 8462363, 2022.
Shamir, L., 2022, Large-scale asymmetry in galaxy spin directions – Results from the Dark Energy Survey and comparison to four other sky surveys, 240th AAS Meeting, 202.05.
Shamir, L., Large-scale asymmetry in galaxy spin directions - analysis of galaxies with spectra in DES, SDSS, and DESI Legacy Survey, Astronomische Nachrichten, 2022.
Shamir, L., Using Machine Learning to Profile Asymmetry Between Spiral Galaxies with Opposite Spin Directions, Symmetry (Special issue on symmetry in pattern recognition), 14(5), 934.
Shamir, L., A Possible Large-scale Alignment of Galaxy Spin Directions - Analysis of 10 Datasets from SDSS, Pan-STARRS, and HST, New Astronomy, 95, 101819, 2022.
Shamir, L., New evidence and analysis of cosmological-scale asymmetry in galaxy spin directions, Journal of Astrophysics and Astronomy, 43, 24, 2022. (JCR impact factor 2020: 1.27).
Shamir, L., Asymmetry of galaxy spin directions: first results from the Dark Energy Survey, 239th AAS Meeting, 2022. (meeting was cancelled due to COVID-19).
Shamir, L., Large-scale asymmetry in galaxy spin directions: evidence from the Southern hemisphere, Publications of the Astronomical Society of Australia, 38, e037, 2021.
Shamir, L., Large-scale Asymmetry of Galaxy Spin Directions - A Comparison of 12 Datasets, 238th AAS Meeting, (virtual), 230.08, 2021.
Shamir, L., Analysis of the alignment of non-random patterns of spin directions in populations of spiral galaxies, Particles, 4(1), 11-28, 2021.
Shamir, L., Galaxy spin direction distribution in HST and SDSS show similar large-scale asymmetry, Publications of the Astronomical Society of Australia, 37, e053, 2020.
Shamir, L., Analysis methods for large-scale asymmetry of galaxy spin directions, 237th AAS Meeting, 342.18, 2021.
Shamir, L., Patterns of galaxy spin directions in SDSS and Pan-STARRS show parity violation and multipoles, Astrophysics and Space Science, 365, 136, 2020.
Shamir, L., Distribution of galaxy spin directions in Pan-STARRS and SDSS shows large-scale multipoles and redshift dependence, 236th AAS Meeting, 336.02, 2020.
Shamir, L., Large-scale asymmetry between clockwise and counterclockwise galaxies revisited, Astronomical Notes, 341(3), 324-330, 2020.
Shamir, L.,Asymmetry between galaxies with different spin patterns: A comparison between COSMOS, SDSS, and Pan-STARRS, Open Astronomy. De Gruyter, 2020
Shamir, L., Large-scale photometric asymmetry in galaxy spin patterns,
Publications of the Astronomical Society of Australia, 2017.
Shamir, L., Photometric asymmetry between clockwise and counterclockwise spiral galaxies in SDSS. Publications of the Astronomical Society of Australia, 34, e11, 2017.
Shamir, L., Colour asymmetry between galaxies with clockwise and counterclockwise handedness, Astrophysics and Space Science, 362, 33, 2017.
Shamir, L., Asymmetry between galaxies with clockwise handedness and counterclockwise handedness, The Astrophysical Journal, 823(1), 32, 2016.
Shamir, L., Color differences between clockwise and counterclockwise spiral galaxies, Galaxies, 3(1), 210-215, 2013.
Hoehn, C., Shamir, L., Characteristics of clockwise and counterclockwise spiral galaxies, Astronomische Nachrichten, 335(2), 188-191, 2014.
Shamir, L., Handedness asymmetry of spiral galaxies with z<0.3 shows cosmic parity violation and a dipole axis, Physics Letters B, 715, 25-29, 2012.
Popular media:
New Observations Suggest the Universe Is Becoming Increasingly Chaotic
Vice Magazine, June 5, 2020.
L'Univers a-t-il été en rotation?
Yahoo News (France), June 3, 2020.
Patterns found in spiral galaxies indicate universe could be far more orderly than previously believed
RT, June 2, 2020.
The entire universe may once have been spinning all over the place
New Scientist, June 2, 2020.
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Epoch Times (USA, Chinese), June 13, 2020.
Patterns Formed by Spiral Galaxies Suggest The Universe's Structure Isn't Totally Random
Science Alert, June 2, 2020.
El Universo entero podría estar en rotación
ABC (Spain), June 2, 2020.
New Universe Discovery Is Unveiled
DualDove, June 2, 2020.
Our universe could have been spinning around, new study that might change our understanding of the cosmos suggests
The Independent (UK), June 1, 2020.
Study finds that patterns formed by spiral galaxies show that the universe may have a defined structure
PhysOrg, June 1, 2020.
Our universe could have been spinning around, new study that might change our understanding of the cosmos suggests
The World News, June 1, 2020.
“Galactic 'axis of asymmetry' threatens cosmic order”,
New Scientist, August 25, 2012 (p. 7-8).
“Is God right-handed? Spiral galaxies’ rotation and isotropy”,
Astrobites, July 25, 2012.
Automatic analysis of microscopy images
Microscopes can acquire large datasets of images. The purpose of this project was to develop methods to analyze microscopy images automatically, and provide tools tht can help the "bench" biologists analyze their data and make discoveries.
Publictions:
Manning, S., Shamir, L.; CHLOE: A software tool for automatic novelty detection in microscopy image datasets, Journal of Open Research Software, 2(1), e25, 2014.
Shamir, L., Delaney, J., Orlov, N., Eckley, D. M., Goldberg, I. G.; Pattern recognition software and techniques for biological image analysis, PLoS Computational Biology, vol. 6, no. 11, p. e1000974, 2010.
Shamir, L.; Quantitative bioimage analysis using pattern recognition, Perspectives on Pattern Recognition, Monica D. Fournier (Ed.). Nova Pub., p. 225-240, 2012.
ISBN: 978-1-61209-118-1.
Orlov, N., Johnston, J., Macura, T., Shamir, L., Goldberg, I.; Computer Vision for Microscopy Applications, In: Vision Systems - Segmentation and Pattern Recognition. p. 221-242. Obinata, G., and Dutta, A. (Eds.). Vienna, Austria: ARS Pub., 2007.
ISBN: 978-3-902613-05-9
Orlov, N. V., Weeraratna, A. T., Hewitt, S. M., Coletta, C. E., Delaney, J. D., Eckley, D. M., Shamir, L., Goldberg, I.G., Automatic detection of melanoma progression by histological analysis of secondary sites, Cytometry A, Vol. 81A, No. 5, p. 364-373. Wiley, 2012.
Shamir, L., Orlov, N., Eckley, D. M., Macura, T., Goldberg, I.; IICBU-2008 - A proposed benchmark suite for biological image analysis,
Medical & Biological Engineering & Computing, vol. 46, no. 9, pp. 943-947. Springer, 2008.
Orlov, N., Chen, W., Eckely, D. M., Macura, T., Shamir, L., Jaffe, E., Goldberg, I. G.; Automatic classification of lymphoma image with transform-based global features, IEEE Transactions on Information Technology in Biomedicine, vol. 14, no. 4., p. 1003-1013, 2010.
Shamir, L., Orlov, N., Eckley, D. M., Macura, T., Johnston, J., Goldberg, I.; Wndchrm - an open source utility for biological image analysis,
BMC Source Code for Biology and Medicine, vol. 3, 13. BioMed Central, 2008.
Shamir, L., Eckley, D.M., Delaney, J., Orlov, N., Goldberg, I.G.; An image informatics method for automated quantitative analysis of phenotype visual similarities, IEEE Life Science Systems and Applications Workshop (LiSSA 2009), p. 96-100, 2009.
Orlov, N., Delaney, J., Eckley, D.M., Shamir, L., Goldberg, I.; Pattern recognition for biomedical imaging and image-guided diagnosis, IEEE Life Science Systems and Applications Workshop (LiSSA 2009), p. 120-123, 2009.
Orlov, N., Eckely, D. M., Shamir, L., Goldberg, I. G.; Machine vision for classifying biological and biomedical images, Visualization, Imaging, and Image Processing (VIIP 2008), p. 192-196. Palma de Mallorca, Spain. 2008.
Orlov, N., Coletta, C., Delaney, J., Eckley, D.M., Shamir, L., Goldberg, I. G.; Pathology and pattern recognition: implementation, challenges and solutions, Pathology Informatics 2010, Boston, MA.
Published in: Journal of Pathology Informatics, 1, p. 38-39, 2010.
Delaney J.D., Eckley D.M., Shamir L., Rahimi S., Goldberg I.G.; Quantitative imaging assays using pattern recognition from images of HeLa and NIH 3T3 cells, Bioimage Informatics, Pittsburgh, PA, 2010.
Orlov, N., Coletta, C., Delaney, J., Eckley, D.M., Shamir, L., Goldberg, I. G.; Computing image similarities for quantitative morphological assays. Bioimage Informatics. Pittsburgh, PA, September 17-19, 2010.
Orlov, N., Coletta, C., Delaney, J., Eckley, D.M., Shamir, L., Goldberg, I. G.; Pattern recognition approach for classifying bioimages: A study of three cancers, Bioimage Informatics. Pittsburgh, PA, September 17-19, 2010.
Coletta, C., Delaney, J., Eckley, D. M., Orlov, N., Shamir, L., Rahimi, S., Goldberg, I., ; A worldwide volunteer computing infrastructure for bioimage data mining, Bioimage Informatics. Pittsburgh, PA, September 17-19, 2010.
Orlov N.V., Coletta C., Delaney J., Eckley D.M., Rahimi S., Shamir L., Goldberg I.G.; Global standards for grading prostate cancer, Biomedical Informatics Without Borders: Implementing Interoperability, International Symposium, p. 59-60. Bethesda, MD, 2010.
Macura, T., Johnston, J., Cordero, J., Orlov, N., Shamir, L., Goldberg, I. G.; Quantitative pattern analysis of a large tissue collection across multiple experimental variables. Third Workshop on BioImage Informatics, #1241. Santa Barbara, CA. 2008.
Eckley, D. M., Shamir, L., Macura, T., Orlov, N., Goldberg, I. G.; Automated quantitative analysis of phenotype similarities. Third Workshop on BioImage Informatics, #1242. Santa Barbara, CA. 2008.
Shamir, L., Eckley, D. M., Goldberg, I. G.; Image tiling Vs. cell segmentation - A Case Study, 47th American Society for Cell Biology Meeting (ASCB'07). p. 35. Washington, DC. 2007.
Eckley, D. M., Shamir, L., Macura, T., Orlov, N., Goldberg, I. G.; Quantiative similarity of induced phenotypes: A new method of comparing genes, 47th American Society for Cell Biology Meeting (ASCB'07). p. 35. Washington, DC. 2007.
The Astrophysics Source Code Library
In computer science, a scientific paper is no more than advertisement to the research. The research itself is the code that was written. However, researchers do not always share their code so that the code can be used by others. The Astrophysics Source Code Library aims at changing that culture. It provides tools to share code and code discoverability, and makes astronomy code indexed and citable (just like a paper). It might take time, but the change is starting to be noticed already. I have to note that I am the least important person on this group.
Selected publications:
Shamir, L., Berriman, B., Teuben, P., Nemiroff, R., Allen, A., Best Practices for a Future Open Code Policy: Experiences and Vision of the Astrophysics Source Code Library, A white paper submitted to the National Academies of Sciences, Engineering, and Medicine’s Best Practices for a Future Open Code Policy for NASA Space Science Project Committee, 2018.
Allen, A., Teuben, P., Berriman, B., DuPrie, K., Mink, J., Nemiroff, R., Ryan, PW, Schmidt, J., Shamir, L., Shortridge, K., Wallin, J., Warmels, R., "The Astrophysics Source Code Library by the numbers", 231st American Astronomical Society Meeting, 150.10, 2018.
Allen, A., DuPrie, K., Schmidt, J., Berriman, B., Hanisch, R. J., Mink, J., Nemiroff, R. J., Shamir, L., Shortridge, K., Taylor, M. B., Teuben, P. J., Wallin, J. F., Making your code citable with the Astrophysics Source Code Library, 227th American Astronomical Society Meeting, 348.01., 2016
Allen, A., Berriman, B., DuPrie, K., Hanisch, R., Mink, J., Nemiroff, R., Shamir, L., Shortridge, K., Taylor, M., Teuben, P., Wallin, J., Improving Software Citation and Credit, Astronomical Data Analysis Software & Systems (ADASS), 2015.
Allen, A., Berriman, B., DuPrie, K., Hanisch, R., Mink, J., Nemiroff, R., Shamir, L., Shortridge, K., Taylor, M., Teuben, P., Wallin, J., Astrophysics Source Code Library, ver 3.0, Astronomical Data Analysis Software & Systems (ADASS), 2015.
Allen, A., Berriman, B., DuPrie, K., Hanisch, R. J., Mink, J., Nemiroff, R. J., Schmidt, J., Shamir, L., Shortridge, K., Taylor, M. B., Teuben, P. J., Wallin, J. F.; Astrophysics Source Code Library -- Now even better!, 225th American Astronomical Society Meeting, #336.57, 2015.
Allen, A., Shamir, L., Teuben, P.; The Astrophysics Source Code Library: ascl.net, IEEE Computer, vol. 47, no. 8, p. 46-47, 2014.
Peter Teuben, Alice Allen, Bruce Berriman, Kimberly DuPrie, Robert J. Hanisch, Jessica Mink, Robert Nemiroff, Lior Shamir, Keith Shortridge, Mark Taylor, and John Wallin,Ideas for advancing code sharing (a different kind of hack day) Astronomical Data Analysis Software & Systems (ADASS) XXIII, 2013.
Alice Allen, Alberto Accomazzi, G. B. Berriman, Kimberly DuPrie, Robert J. Hanisch, Jessica D. Mink, Robert J. Nemiroff, Lior Shamir, Keith Shortridge, Mark B. Taylor, Peter J. Teuben, John F. Wallin; You've Written a Cool Astronomy Code! Now What Do You Do with It?, 223 American Astronomical Society Meeting, Washington, DC. 2014.
Alice Allen, Bruce Berriman, Kimberly DuPrie, Robert J. Hanisch, Jessica Mink, Robert Nemiroff, Lior Shamir, Keith Shortridge, Mark Taylor, Peter Teuben, and John Wallin, The Astrophysics Source Code Library: Where do we go from here? Astronomical Data Analysis Software & Systems (ADASS) XXIII, 2013.
Shamir, L., Wallin, J.F., Allen, A., Berriman, B., Teuben, P., Nemiroff, R.J., Mink, J., Hanisch, R.J., DuPrie, K., Practices in source code sharing in astrophysics, Astronomy & Computing, vol. 1, p. 54-58. Elsevier, 2013.
DuPrie, K., Allen, A., Berriman, B., Hanisch, R., Mink, J., Nemiroff, R., Shamir, L., Shortridge, K., Taylor, M., Teuben, P., Wallin, J., ASCL: Incite to Cite!, Astronomical Data Analysis Software & Systems (ADASS) XXIII, 2013.
Allen, A., Teuben, P., Nemiroff, R., Shamir, L.; Practices in Code Discoverability: Astrophysics Source Code Library, Astronomical Data Analysis Software & Systems (ADASS). Paris, France. 2012.
Teuben, P., Allen, A., Nemiroff, R., Shamir, L.; Challenges in Code Discoverability, Astronomical Data Analysis Software & Systems (ADASS). Paris, France. 2012.
Alice Allen, Peter J. Teuben, G. B. Berriman, Kim DuPrie, Robert J. Hanisch, Jessica D. Mink, Robert J. Nemiroff, Lior Shamir, John F. Wallin, Using the Astrophysics Source Code Library, 221 AAS Meeting, Long Beach, CA. 2013.
Allen, A., Nemiroff, R.J., Shamir, L., Teuben, P.J.; The Astrophysics Source Code Library: An Update, 219th American Astronomical Society Meeting, 2012.
Popular press:
Astronomy Computing Today
Portal to The Universe
"Nature"
AstroBetter
Article about this paper in ISGTW AstroBetter
Internal biometrics
Most method for biometric identification are based on external body parts such as fingerprints, faces, retina, etc' - body parts that can be seen from the outside. The purpose of this project is to show that internal body parts, imaged using radiology and medical imaging methods, can also be used to differentiate between one person and another.
Publications:
Shamir, L., MRI based knee image for personal identification, International Journal of Biometrics, Vol. 5, No. 2, p. 113-125. Inderscience, 2013.
Shamir, L., Ling, S., Rahimi, S., Ferrucci, L., Goldberg, I.; Biometric identification using knee X-rays,
International Journal of Biometrics, vol. 1, no. 3, pp. 365-370. Inderscience, 2009.
Shamir, L.; Automatic age estimation by hand photos, Computer Science Letters, vol. 3, no. 1., 2011
Popular media
If they ask for ID, show them your knee,
NBC News, February 5, 2013.
Would you believe that this body part could be used to reveal your identity,
The Blaze, January 29, 2013.
Knobbly knees could be used for identification,
The Daily Star, January 25, 2013. page 30.
Study: MRI scans of knees can be used for biometric identification,
Wired Magazine, January 23, 2013.
Your knobbly knees really are unique: Scientists say kneecap MRI scans could replace fingerprints as ID,
The Daily Mail, January 24, 2013.
Soon, get an MRI to board flights,
Times of India, January 24, 2013.
Knee Scan Identification: MRIs May Be Better Way To ID Travelers Study Suggests,
Huffington Post, January 25, 2013.
“Knobbly ID”,
The Economist, April 2, 2009. pp. 83-84.
“The knees have it: Internal biometrics begins”,
R&D Magazine, March 26, 2009.
“Knobbly kneed ID”,
eScience News, March 26, 2009.
Validation of machine learning datasets
The idea of this on-going project is simple. I took image classification benchmarks, removed the objects they aimed at classifying from all images, and tried to classify the images without those objects (i.e., in automatic face recognition benchmarks I removed all the faces from the images, including hair ad clothes, leaving just seemingly blank background areas in the image) The results were always better than random guessing, in many cases very close to the results of the state-of-the-art produced with the original images. At first I showed that face images can be "recognized" even if the images had no face in them. That is, face recognition without faces. That went without too much of a drama. But when I did similar experiments to show that cell morphology in microscopy images can be "analyzed" without cells, it was not received so nicely by some scholars. To avoid the drama in the next round I will probably not continue with this type of research. At least I learned a few things about science that I did not know actually existed. Further work focused on audio recognition becnhmarks for accent classification, and datasets for object recognition.
Publications:
Shamir, L., Systematic biases in machine learning and their impact on astronomy research, Debating the Potential of Machine Learning in Astronomical Surveys, ML-IAP/CCA-2023.
Goddard, H., Shamir, L., Neural network bias in analysis of galaxy photometry data, 18th IEEE International Conference on eScience. IEEE, pp. 407-408, 2022.
Dhar, S., Shamir, L., Systematic biases when using deep neural networks for annotating large catalogs of astronomical images, Astronomy and Computing, 38, 100545, 2022.
Dhar, S., Shamir, L., Evaluation of the benchmark datasets for testing the efficacy of deep convolutional neural networks, Visual Informatics, 5(3), 92-101, 2021.
Model, I, Shamir, L., Comparison of dataset bias in object recognition benchmarks, IEEE Access, 3(1), 1953-1962, 2015
Bock, B., Shamir, L., Assessing the efficacy of benchmarks for automatic speech accent recognition, EAI Endorsed Transactions on Creative Technologies , 15(4), e3. EAI, 2015.
Bock, B., Shamir, L., Assessing the efficacy of benchmarks for automatic speech accent recognition, 8th International Conference on Mobile Multimedia Communications (MOBIMEDIA), Chengdu, China. ACM, 2015.
Shamir, L.; Assessing the efficacy of low-level image content descriptors for computer-based fluorescence microscopy image analysis, Journal of Microscopy, vol. 243, no. 3, pp. 284-292. Wiley-Blackwell, 2011.
Shamir, L., Evaluation of face datasets as tools for assessing the performance of face recognition methods,
International Journal of Computer Vision, vol. 79(3), pp. 225-230. Springer, 2008.
Shamir, L.; Looking for familiar faces, Science, vol. 321, p. 912, 2008.
Popular press:
The emperor's new cells, Materials Today
Quantitative profiling of aging
When I was with the National Institute on Aging I did some aging research (and if that sounds obvious to you you probably don't know the intramural research program of the NIA). In the research I first analyzed tissue changes in c. elegans (worms), and showed that the morphological changes of the tissues does not progress linearly to the age of the worm, but actually progresses in several distinct stages of mild aging, separated by short period of rapid aging. But why waste good worms when you can experiment with humans? I did an experiment with a longitudinal set of x-rays taken from human in the Baltimore Longitudinal Study of Aging (BLSA). Cross-sectional and longitudinal results show distinct stages in the aging (as reflected by the radiograph), and specifically rapid aging at around the age of 55.
Publications:
Long, J., Shamir, L., Quantitative machine learning analysis of brain MRI morphology throughout aging, Current Aging Science, 4(9), 1-7, 2016.
Lixie, E., Edgeworth, J., Shamir, L., Comprehensive analysis of large sets of age-related physiological indicators reveals rapid aging around the age of 55, Gerontology, 61(6), 526-533, 2015.
Shamir, L., Composite aging markers can be used for quantitative profiling of aging, Gerontology, 62(1), 66-68, 2015.
Shamir, L.; Quantitative measurement of human aging using computer-aided radiographic texture analysis, Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, Vol. 1, No. 3, p. 175-183, 2013
Shamir, L., Wolkow, C., Goldberg, I.; Quantitative measurement of aging using image texture entropy, Bioinformatics, vol. 25, pp. 3060-3063. Oxford University Press, 2009.
Data-driven analysis in sport
The detailed documentation in professional sport provides databases allowing data-driven analysis. Performance of teams and players under certain conditions, referee and coach decisions, and other activities related to sports can be analyzed quantitatively using statistical and computational methods.
Selected publications:
Yaldo, L., Shamir, L.,, Computational Estimation of Football Player Wages, International Journal of Computer Science in Sport, 8(2), 10-16, 2017.
Tanamati Soares, J., Shamir, L., Quantitative analysis of penalty kicks and yellow card referee decisions in soccer, American Journal of Sports Science, 4(5), 84-89, 2016.
Strange, R., Shamir, L.; Prediction of American football plays using pattern recognition, International Journal of Computer Science in Sport, 13(2), 59-64, 2014.
Popular media:
Gaji Messi Rp8,6 Miliar per Pekan Diklaim Kebesaran
CNN Indonesia, August 7, 2017.
Os salários certos para jogadores, segundo habilidades em campo
Exame (Brazil), August 7, 2017.
Penelitian Ilmuwan: Messi Dibayar Terlalu Mahal
Republika on-line, August 4, 2017.
Penelitian: Gaji Lionel Messi Terlalu Besar
Viva, August 4, 2017.
Scientists reckon Harry Kane is most underpaid player in world football
Talk Radio, August 4, 2017.
Are the World's Highest Paid Football Players Overpaid? Big Data Says Yes
Communications of the ACM, August 3, 2017.
Machine learning can identify the world’s most overpaid and underpaid soccer stars
Yahoo Sport, August 2, 2017.
The 20 most overpaid players in world football - according to science
Digital Trends, August 2, 2017.
Messi the most overpaid player
Marca (Spain),, August 2, 2017.
Machine learning can identify the world’s most overpaid and underpaid soccer stars
Digital Trends, August 1, 2017.
Lionel Messi the world's most overpaid football star, claims study
Goal.com, August 1, 2017.
Research claims Lionel Messi is the world's most overpaid player and earns twice as much as his talents merit
The Mirror (UK), August 1, 2017.
Machine learning can identify the world’s most overpaid and underpaid soccer stars
Yahoo News, August 1, 2017.
Moneyballing algorithm identifies most overpaid footballers in Europe
Tech Radar, August 1, 2017.
Moneyballing algorithm identifies most overpaid footballers in Europe
techAnerd, August 1, 2017.
Moneyballing algorithm identifies most overpaid footballers in Europe
Technology Break Mag, August 1, 2017.
Moneyballing algorithm identifies most overpaid footballers in Europe
The Baltimore Post, August 1, 2017.
Are the world's highest paid football players overpaid? Big data says yes
Science Magazine, July 31, 2017.
Are the world's highest paid football players overpaid? Big data says yes
Pop Yard, July 31, 2017.
Using AI To Find The Most Overpaid Footballers
The Huffington Post, August 1, 2017.
Now even science* says moneybags footballers are overpaid
The Register, August 1, 2017.
Are the world's highest paid football players overpaid? Big data says yes
PhysOrg, July 31, 2017.
Are the world's highest paid football players overpaid? Big data says yes
Science Daily, July 31, 2017.
Diversity in computer science and STEM
Computer science is one of the least diverse academic fields in terms of student population. In fact, while virtually all other fields are becoming more diverse over the years, computer science has been going the other way, gradually becoming less diverse. My work in that field is focused on developing computer science courses and modules at the intersection between computer science and other disciplines such as psychology, art, biology, etc' -- disciplines that are more diverse in term of student population. The development of research modules that can be used in undergraduate courses allows the students to express their culture, gender, and ethnicity through computing in udnergraduate courses.
Publications:
Chung, CJ, Shamir, L., Introducing Machine Learning with Scratch and Robots as a Pilot Program for K-12 Computer Science Education, Third International Conference on Future Learning (ICFL), December 17-19, 2020.
Shamir, L., Eliminating self-selection: Using data science for authentic undergraduate research in a first-year introductory course, Thirty Fourth Conference on Artificial Intelligence (AAAI) Workshop on on Artificial Intelligence Diversity, Belonging, Equity and Inclusion (AIDBEI), p. 10-16, New York, NY, 2020.
Pre-print
Shamir, L., Delogu, F., Weinstein, M., Moore, H.P., Culturally-responsive computational science through research experience in core-curriculum courses, In: Culturally Responsive Strategies for Reforming STEM Higher Education: Turning the TIDES on Inequity. Mack. K. M., Winter, K., Soto, M. (Eds.), Emerald, 2019.
Shamir, L., Delogu, F., Zwiesler-Vollick, J., Inclusion of non-traditional students through college-wide course-based research experience, Transforming STEM Higher Education. Atlanta, GA. 2018.
Shamir, L., Culturally responsive data and computational science through course-based research experience, Transforming STEM Higher Education. Atlanta, GA. 2018.
Shamir, L., Classroom-based research experience at the institutional level, CUR Biennial Conference 2018, Arlington, VA, 2018.
Shamir, L., Authentic computer science undergraduate research experience through computational science and research ownership. Journal of Computational Science Education, 8(2), 10-16, 2017.
Shamir, L., Delogu, F., Weinstein, M., Shamir, M., Moore, H.P.; Using computing and data science course-based research to increase equity and culturally-sensitivity in STEAM, Equity Within the Classroom Conference, Rochester, MI, 2016
Shamir, L., Delogu, F., Culturally sensitive undergraduate course-based research experience as pathway to computer science, 8th Conference on Understanding Interventions, p. 25-26, February 2016. Philadelphia, PA,.
Delogu, F., Shamir, L., Weinstein, M., Moore, H.P., Culturally sensitive undergraduate course-based research experience as pathway to computer science, 8th Conference on Understanding Interventions, p. 33, Philadelphia, PA, 2016.
Shamir, L., Delogu, F., Weinstein, M., Moore, H.P.; Teaching freshmen-level art history through computer science research, Crossing Boundaries: Transforming STEM Education, Seattle, WA. 11/13/2015.
Delogu, F., Shamir, L., Moore, H.P., Weinstein, M.; Using computer science research for student engagement in introductory level psychology courses, Crossing Boundaries: Transforming STEM Education, Seattle, WA. 11/13/2015.
Shamir, L., Weinstein, L., Moore, H.P., Delogu, F., Stein, G., Falberg, D., Teaching Diversity in Computing through Music, Literature, Art, and Sport, Network Detroit: Digital Humanities Theory and Practice, 2015.
Shamir, L., Delogu, F., Weinstein, M., Shamir, M., Moore, H.P.; A culturally responsive approach to STEM education: Course-based research experience in computer science, Equity Within the Classroom Conference, p. 5, Grand Rapids, MI, 2015.
Popular media:
$1 million grant helping Lawrence Technological University students express cultural identity, Daily Tribune, June 13, 2017
$1 million grant helping Lawrence Technological University students express cultural identity, Oakland Press, June 13, 2017
LTU Wins Grant To Boost STEM Education From Howard Hughes Medical Institute, Automation Alley, June 8, 2017
LTU Wins Grant To Boost STEM Education From Howard Hughes Medical Institute, MI Tech News, June 6, 2017
Large Synoptic Survey Telescope
LSST will be the most power astronomical imaging device ever built, and will provide data for decades. This multi-billion dollar venture will be astronomy's flagship ground-based project for the next decade, also generating the world's largest public database. I am fortunate to be part of this project. My role in the project will be developing algorithms that will analyze the huge data and turn them into knowledge.
Ancient lives
This project hasn't started yet. In this project we will analyze the Oxford collection of Greek papyri, by far the world's largest of its kind. Using computational methods we will try to learn new things about the lives of the ancient ones.
Human perception-based analysis of color
I published this algorithm when I was a graduate student, and then I forgot about it. Until one morning I received an anonymous email saying that a certain researcher plagiarized my paper. I looked into it and realized that the email was wrong! It's not one person, but more than 30 papers authored by more than 70 different authors plagiarized the same conference paper. Copy-paste verbatim plagiarism. I wonder if that makes it the paper as the world's most plagiarized paper.
Publications:
Shamir, L.; A proposed stereo matching algorithm for noisy sets of color images,
Computers & Geosciences, vol. 33, pp. 1052-1063. Elsevier, 2007.
Shamir, L.; Human perception-based color segmentation using Fuzzy Logic,
International Conference on Image Processing, Computer Vision and Pattern Recognition (IPCV'06/WORLDCOMP'06), vol. II, p. 496-505. Las Vegas, NV. 2006.
Popular media:
“Journal Editors' Reactions to Word of Plagiarism? Largely Silence”,
The Chronicle of Higher Education, November 20th, 2011.
“Nagpur scientists concede plagiarism”,
Nature India, September 30, 2011.
Research-based courses and culturaly-responsive teaching in STEM
Research experience is one of the most effective interventions in STEM education. The purpose of this project is to use research experience for undergraduate education, exposing students to authentic research. The unique approach is based on student ownership of the research, so that students can choose their own topics of interest, and lead them to authentic scientific discoveries and prestigious peer-reviewed publications.
Shamir, L., Inclusion in data science: designing a new discipline for inclusion, Transforming STEM Higher Education, Atlanta, GA. 2018, accepted.
Shamir, L., Delogu, F., Zwiesler-Vollick, J., Inclusion of non-traditional students through college-wide course-based research experience, Transforming STEM Higher Education. Atlanta, GA. 2018, accepted.
Shamir, L., Culturally responsive data and computational science through course-based research experience, Transforming STEM Higher Education. Atlanta, GA. 2018, accepted.
Shamir, L., Delogu, F., Weinstein, M., Moore, H.P., Culturally-responsive computational science through research experience in core-curriculum courses, In: Culturally Responsive Strategies for Reforming STEM Higher Education: Turning the TIDES on Inequity. Mack. K (Ed.), Emerald, 2019.
Weinstein, M., Shamir, L., LaTham-Salaberrios, X., College-Wide Course-Based-Research-Experience Intervention as a Pathway towards Inclusive Excellence, Network Detroit: Digital Humanities, 2018.
Shamir, L., Classroom-based research experience at the institutional level, CUR Biennial Conference 2018
Shamir, L., Authentic computer science undergraduate research experience through computational science and research ownership. Journal of Computational Science Education, 8(2), 10-16, 2017.
Shamir, L., Delogu, F., Weinstein, M., Shamir, M., Moore, H.P., Using computing and data science course-based research to increase equity and culturally-sensitivity in STEAM, Equity Within the Classroom Conference, Rochester, MI, 2017
Shamir, L., Delogu, F., Culturally sensitive undergraduate course-based research experience as pathway to computer science, 8th Conference on Understanding Interventions, p.25-26, Philadelphia, PA, 2016.
Delogu, F., Shamir, L., Weinstein, M., Moore, H.P., Culturally sensitive undergraduate course-based research experience as pathway to computer science, 8th Conference on Understanding Interventions, p. 33, Philadelphia, PA, 2016.
Delogu, F., Shamir, L., Moore, H.P., Weinstein, M.; Using computer science research for student engagement in introductory level psychology courses, Crossing Boundaries: Transforming STEM Education, Seattle, WA. 11/13/2015.
Shamir, L., Weinstein, L., Moore, H.P., Delogu, F., Stein, G., Falberg, D., Teaching Diversity in Computing through Music, Literature, Art, and Sport, Network Detroit: Digital Humanities Theory and Practice, 2015.
Shamir, L., Delogu, F., Weinstein, M., Shamir, M., Moore, H.P.; A culturally responsive approach to STEM education: Course-based research experience in computer science, Equity Within the Classroom Conference, p. 5, Grand Rapids, MI, 2015.
Popular media:
Laying Down the New Tech Hiring Pipeline,
Dice.com, October 13th, 2016.
Computer-based diagnostics of joint diseases
In this project I used computers to diagnose joint diseases, especially osteoarthritis. OA is the most prevalent disease in the industrialized world. There is no cure to it, but it can be delayed if diagnosed early. In this study I automated and profiled the progression of OA, and results also show that the computer analysis can predict OA ~20 years before it becomes symptomatic.
Publications:
Shamir, L., Felson, D., Ferrucci, L., Goldberg, I; Assessment of Osteoarthritis Initiative-Kellgren and Lawrence scoring projects quality using computer analysis, Journal of Musculoskeletal Research, vol. 13, no. 4, p. 197-201, 2011.
Shamir, L., Ling, S., Scott, W., Hochberg, M., Ferrucci, L., Goldberg, I.; Early detection of radiographic knee osteoarthritis using computer-aided analysis, Osteoarthritis and Cartilage, vol. 17, pp. 1307-1312. Elsevier, 2009.
Shamir, L., Rahimi, S., Orlov, N., Ferrucci, L., Goldberg, I.; Progression analysis and stage discovery in continuous physiological processes using image computing, EURASIP Journal on Bioinformatics and Systems Biology (Special Issue on Computational Approaches to Assist Disease Mechanism Discovery), vol. 2010, 107036. Hindawi Pub., 2010.
Shamir, L., Ling, S.M., Scott, W., Boss, A., Orlov, N., Macura, T., Eckley, D.M, Ferrucci, L., Goldberg, I.G.; Knee X-ray image analysis method for automated detection of Osteoarthritis,
IEEE Transactions on Biomedical Engineering, vol. 56, no. 2, pp. 407-415, 2009.
Shamir, L., Ling, S.M., Scott, W., Hochberg, M., Delaney, J., Eckley, D.M., Orlov, N., Ferrucci, L., Goldberg, I.; Early detection of osteoarthritis using computer-aided radiography, Osteoarthritis Biomarker Workshop, p. 23. Bethesda, MD. 2009.
Popular media:
“Painful predictions - Early detection of arthritis”,
The Economist, June 4, 2009. p. 78.
"New way to read X-rays may predict osteoarthritis”,
Arthritis Today, June 10, 2009.
“Early Detection of Arthritis: Painful Predictions”,
TecTrends, June 6, 2009.
Correlation in image and multimedia data
Correlation is one of the most common tools for discovery from data, but the methodology for correlation in image data is limited. The goal of this project is to develop correlation methods that can be applied to sequences of images. Just like correlating two numerical variables, the idea here is to find the correlation between two variables, such that one is numerical and the other is an image. Another goal is to be able to idneityf correlations between two image variables.
Publications:
Shamir, L., Automatic identification of correlation between image sequences, International Journal of Data Science and Analytics, accepted. Springer, 2023.
Schwartz, E., Shamir, L.; Correlation between brain MRI and continuous physiological and environmental traits using 2D global descriptors and multi-order image transforms, Journal of Medical Imaging and Health Informatics, vol. 3, p. 12-16, 2013.
Shamir, L.; A computer analysis method for correlating knee x-rays with continuous indicators, International Journal of Computer Assisted Radiology and Surgery. vol. 6, no. 5, pp. 699-704. Springer, 2011.
Analysis of panoramic astronomical images
This was my first scientific project. In this project I automated the analysis of panoramic astronomical images. Tasks included star detection, cosmic ray hit rejection, sky opacity analysis, meteor science, etc'. The algorithms that I developed in that project were used in many of the world's premier observatories such as Mauna Kea (Hawaii), Cerro Pachon (Chile), Canary Islands, SALT (South Africa), and many more.
Publications:
Shamir, L.; Astronomical Image Processing. VDM-Verlag Pub., Berlin, Germany. 2008.
ISBN: 978-3-8364-6630-1.
Nemiroff, R.J., Shamir, L., Pereira, W., The History of the CONCAM Project: All Sky Monitors in the Digital Age, 231st American Astronomical Society Meeting, 143.05, 2018.
Shamir, L.; Soft computing in astronomy. Also published in: Encyclopedia of Computer Science, Robert T. Abrams (Ed.). Nova Pub., p. 227-260, 2012.
ISBN: 978-1-61324-635-1.
Shamir, L.; Soft Computing in Astronomy. In: Soft Computing - New Research. . Giordano, A.J. and Costa, G.E. (Eds.), p. 231-264. Nova Pub., 2008.
ISBN: 978-1-60456-883-7
Shamir, L., Nemiroff, R.J.; Frequency limits on naked-eye optical transients lasting from minutes to years, The Astronomical Journal, vol. 138, p. 956-962. IOP, 2009.
Shamir, L., Nemiroff, R. J.; Astronomical pipeline processing using fuzzy logic,
Applied Soft Computing, vol. 8, pp. 79-87. Elsevier, 2008.
Shamir, L., Nemiroff, R. J.;
OT 060420: A seemingly optical transient recorded by all-sky cameras,
Publications of the Astronomical Society of the Pacific, vol. 118, no. 846, pp. 1180-1185. University of Chicago Press, 2006.
Shamir, L., Nemiroff, R. J., Torrey, D. O., Pereira, W. E.;
Software design for panoramic astronomical pipeline processing,
Monthly Notices of the Royal Astronomical Society, vol. 366(2), pp. 353-357. Blackwell Publishing, 2006.
Shamir, L.;
Transient detection using panoramic all-sky cameras,
Astrophysics & Space Science, vol. 305, pp. 165-168. Springer, 2006.
Shamir, L., Nemiroff, R. J.;
PHOTZIP: A lossy FITS image compression algorithm that protects user-defined levels of photometric integrity,
The Astronomical Journal, vol. 129(1), pp. 539-546. University of Chicago Press, 2005.
Shamir, L., Nemiroff, R. J.;
All-sky relative opacity mapping using nighttime panoramic images,
Publications of the Astronomical Society of the Pacific, vol. 117, no. 835, pp. 972-977. University of Chicago Press, 2005.
Shamir, L., Nemiroff, R. J.;
A Fuzzy Logic based algorithm for finding astronomical objects in wide-angle frames,
Publications of the Astronomical Society of Australia, vol. 22(2), pp. 111-117. CSIRO Publishing, 2005.
Shamir, L.;
A Fuzzy Logic-based algorithm for cosmic ray hit rejection from single images,
Astronomical Notes (Astronomische Nachrichten), vol. 326(6), pp. 428-431. Wiley InterScience, 2005.
Shamir, L.;
Analysis of meteor trails using the Night Sky Live network of panoramic CCD cameras,
WGN - Journal of the International Meteor Organization, vol. 33(3), pp. 75-80. IMO, 2005.
Shamir, L., Nemiroff, R. J.;
Using Fuzzy Logic for automatic analysis of astronomical pipelines, Second International IEEE Conference on Fuzzy Systems and Knowledge Discovery (FSKD'05),
Lecture Notes in Artificial Intelligence vol. 3614, pp. 634-638. Changsha, China. 2005.
Brosch, N., Nemiroff, R. J., Shamir, L.; Panoramic camera systems for meteor tracking and meteorite recovery, XXVI IAU General Assembly, p. 123. Prague, Czech Republic. 2006.
Shamir, L., Nemiroff, R. J.; Cosmic ray hit rejection using Fuzzy Logic modeling,
207th American Astronomical Society Meeting, 29.05. Washington, DC. 2006. Bulletin of the American Astronomical Society, vol. 37, no. 4, p. 1320, 2006.
Popular media:
When is the Taurid meteor shower, where’s the best place in the UK to watch it and when does it peak?
The Sun, November 3, 2017.
Asteroids that could wipe out an entire CONTINENT are hidden by the Taurids meteor shower, astronomers claim, The Sun, May 25, 2017
"When is the Taurids Meteor Shower, where’s the best place in the UK to watch and when does it peak?",
The Sun, October 27, 2016
Taurids Meteor Shower 2014: Peak Dates and Times, Where to Look, Epoch Times, October 28, 2014.
“A Taurid Meteor Fireball”,
NASA Astronomy Picture of the Day, November 15, 2005.
Other projects
Here are other small-scale projects that I find interesting:
cosmic weather and infant mortality.
Correlation between infant mortality rate and cosmic weather
Here I found a correlation between infant mortality rate and the solar cycle. My (rather weak) hypothesis is that the solar cycle affects the cosmic ray flux such that when the sun is more active it provides a better shield against galactic cosmic radiation (that part is actually not a hypothesis). So when the cosmic ray flux at sea level decreases, fetus is less likely to be hit by a cosmic ray (more ionizing and much more harmful than any other type of radiation, x-rays for example), and therefore infant mortality should be lower in the year after, as the correlation indeed shows. I guess I will have to follow-up and every few years check if the correlation increases. So far it has.
Paper:Shamir, L.; Does cosmic weather affect infant mortality rate?, J. of Environmental Health, vol. 73, no. 1, p. 20-24, 2010.
Bluebird nesting:
That project was done with Sarah Svatora, an excellent undergraduate student that I was fortunate to work with. Since she noticed that some nesting boxes that aim at serving eastern bluebirds nesting purposes were not used by the birds, we decided to develop a computer method that can identify features around the area where the box is placed that make the box more appealing to the birds. We found that birds are attracted to certain patterns of texture directionality, and our algorithm was able to predict whether a nesting box will be used or not. In one semester we did the research, produced the results, and submitted the paper.
Paper: Svatora, S., Shamir, L., Improving Eastern Bluebird nest box performance using computer analysis of satellite images, Computational Ecology and Software, vol. 2, no. 2, p. 96-102. IAEES, 2012.
Dynamic reuse of subroutine results:
That was my final project when I took computer architecture as a student. Each student was asked to come up with a project, and that is what I came up with. The idea is that the CPU has no reason to execute a subroutine (function) if it has already executed in the recent past with the same parameters. In that case the results from the previous run can be used without calling the function. In some cases the improvement was dramatic, so I published it as a paper, but I don't think it was noticed (was cited just once since it was published).
Paper: Shamir, L.;
Dynamic reuse of subroutine results, Journal of Systems Architecture, vol. 52, no. 10, pp. 603-608. Elsevier, 2006.