The Minds of the New Machines | Research Horizons | Georgia Tech’s Research News

March 15th, 2018 Irfan Essa Posted in In The News, Machine Learning No Comments »

A nice write-up in Georgia Tech’s Research Horizons Magazine about ML@GT

Machine learning has been around for decades, but the advent of big data and more powerful computers has increased its impact significantly — ­moving machine learning beyond pattern recognition and natural language processing into a broad array of scientific disciplines. A subcategory of artificial intelligence, machine learning deals with the construction of algorithms that enable computers to learn from and react to data rather than following explicitly programmed instructions. “Machine-learning algorithms build a model based on inputs and then use that model to make other hypotheses, predictions, or decisions,” explained Irfan Essa, professor and associate dean in Georgia Tech’s College of Computing who also directs the Institute’s Center for Machine Learning.

Source: The Minds of the New Machines | Research Horizons | Georgia Tech’s Research News

AddThis Social Bookmark Button

Real-Time Captcha Technique Improves Biometric Authentication | College of Computing

February 20th, 2018 Irfan Essa Posted in Computer Vision, In The News, Machine Learning No Comments »

A short write-up on one of my recent publications.

A new login authentication approach could improve the security of current biometric techniques that rely on video or images of users’ faces. Known as Real-Time Captcha, the technique uses a unique challenge that’s easy for humans — but difficult for attackers who may be using machine learning and image generation software to spoof legitimate users. The Real-Time Captcha requires users to look into their mobile phone’s built-in camera while answering a randomly-selected question that appears within a Captcha on the screens of the devices. The response must be given within a limited period of time that’s too short for artificial intelligence or machine learning programs to respond. The Captcha would supplement image- and audio-based authentication techniques that can be spoofed by attackers who may be able to find and modify images, video and audio of users — or steal them from mobile devices.

CITATION: Erkam Uzun, Simon Pak Ho Chung, Irfan Essa and Wenke Lee, “rtCaptcha: A Real-Time CAPTCHA Based Liveness Detection System,” (Network and Distributed Systems Security (NDSS) Symposium 2018).

Source: Real-Time Captcha Technique Improves Biometric Authentication | College of Computing

AddThis Social Bookmark Button

TEDx Talk (2017) on “Bridging Human and Artificial Intelligence” at TEDxCentennialParkWomen

November 1st, 2017 Irfan Essa Posted in In The News, Interesting, Machine Learning, Presentations, Videos No Comments »

A TEDx talk that I recently did.
In this talk, the speaker takes you on a journey of how AI systems have evolved over time. DIRECTOR OF MACHINE LEARNING AT GEORGIA INSTITUTE OF TECHNOLOGY Dr. Irfan Essa is a professor in the school of Interactive Computing and the inaugural Director of Machine Learning at Georgia Tech. One of the fastest growing research areas in computing, machine learning spans many disciplines that use data to discover scientific principles, infer patterns and extract meaningful knowledge. Essa directs an interdisciplinary team studying ways machine learning connects information and actions to bring the most benefit to the most people. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at
AddThis Social Bookmark Button

Paper in IJCNN (2017) “Towards Using Visual Attributes to Infer Image Sentiment Of Social Events”

May 18th, 2017 Irfan Essa Posted in Computational Journalism, Computational Photography and Video, Computer Vision, Machine Learning, Papers, Unaiza Ahsan No Comments »


  • U. Ahsan, M. D. Choudhury, and I. Essa (2017), “Towards Using Visual Attributes to Infer Image Sentiment Of Social Events,” in Proceedings of The International Joint Conference on Neural Networks, Anchorage, Alaska, US, 2017. [PDF] [BIBTEX]
    @InProceedings{    2017-Ahsan-TUVAIISSE,
      address  = {Anchorage, Alaska, US},
      author  = {Unaiza Ahsan and Munmun De Choudhury and Irfan
      booktitle  = {Proceedings of The International Joint Conference
          on Neural Networks},
      month    = {May},
      pdf    = {},
      publisher  = {International Neural Network Society},
      title    = {Towards Using Visual Attributes to Infer Image
          Sentiment Of Social Events},
      year    = {2017}


Widespread and pervasive adoption of smartphones has led to instant sharing of photographs that capture events ranging from mundane to life-altering happenings. We propose to capture sentiment information of such social event images leveraging their visual content. Our method extracts an intermediate visual representation of social event images based on the visual attributes that occur in the images going beyond
sentiment-specific attributes. We map the top predicted attributes to sentiments and extract the dominant emotion associated with a picture of a social event. Unlike recent approaches, our method generalizes to a variety of social events and even to unseen events, which are not available at training time. We demonstrate the effectiveness of our approach on a challenging social event image dataset and our method outperforms state-of-the-art approaches for classifying complex event images into sentiments.

AddThis Social Bookmark Button

Presentation at the Machine Learning Center at GA Tech on “The New Machine Learning Center at GA Tech: Plans and Aspirations”

March 1st, 2017 Irfan Essa Posted in Machine Learning, Presentations No Comments »

Machine Learning at Georgia Tech Seminar Series

Speaker: Irfan Essa
Date/Time: March 1, 2017, 12n


The Interdisciplinary Research Center (IRC) for Machine Learning at Georgia Tech (ML@GT) was established in Summer 2016 to foster research and academic activities in and around the discipline of Machine Learning. This center aims to create a community that leverages true cross-disciplinarity across all units on campus, establishes a home for the thought leaders in the area of Machine Learning, and creates programs to train the next generation of pioneers. In this talk, I will introduce the center, describe how we got here, attempt to outline the goals of this center and lay out it’s foundational, application, and educational thrusts. The primary purpose of this talk is to solicit feedback about these technical thrusts, which will be the areas we hope to focus on in the upcoming years. I will also describe, in brief, the new Ph.D. program that has been proposed and is pending approval. We will discuss upcoming events and plans for the future.

AddThis Social Bookmark Button