Edgar Chavez (CICESE): The Metric Approach to Reverse Searching (School Seminar)

Event details

  • When: 5th December 2017 14:00 - 15:00
  • Where: Cole 1.33a
  • Series: School Seminar Series
  • Format: Seminar

Searching for complex objects (e.g. images, faces, audio or video), is an everyday problem in computer science, motivated by many applications. Efficient algorithms are demanded for reverse searching, also known as query by content, in large repositories. Current industrial solutions are ad hoc, domain-dependant, hardware intensive and have limited scaling. However, those disparate domains can be modelled, for indexing and searching, as a metric space. This model has been championed to become a solution to generic proximity searching problems. In practice, however, the metric space approach has been limited by the amount of main memory available.

In this talk we will explore the main ideas behind this technology, present a successful example in audio indexing and retrieval. The application scales well for large amounts of audio because the representation is quite compact and the full audio streams are not needed for indexing and searching.

Speaker Bio:
Edgar Chavez received his Phd from the Center for Mathematical Research in Guanajuato, Mexico in 1999. He founded the information retrieval group at Universidad Michoacana where he worked until 2012. After a brief period in the Institute of Mathematics in UNAM, he joined the computer science department in CICESE in 2013, where he founded the data science group. His main research interest include access and retrieval of data and data representation, such as fingerprints and point clouds. In 2009 he obtained the Thompson-Reuters award for having the most cited paper in computer science in Mexico and Latin America. In 2008 he co-funded, with Gonzalo Navarro, the conference Similarity Search and Applications, which is an international reference in the area. He has published more than 100 scientific contributions, with about 3500 citations in google scholar.

PhD viva success: Adam Barwell

Congratulations to Adam Barwell, who successfully defended his thesis yesterday. Adam’s thesis was supervised by Professor Kevin Hammond. He is pictured with second supervisor Dr Christopher Brown, Internal examiner Dr Susmit Sarkar and external examiner Professor Susan Eisenbach from Imperial College, London.

One from the archives: Plans for new Computer Science building

November 2002, and plans were unveiled in the university news, for a new computer science building. Stages of the build were photographed for posterity.

Fast forward to March 2005, and the Jack Cole building was officially opened by the then First Minister, Jack McConnell. The building was named after the founder of Computer Science at St Andrews, Professor Alfred Jack Cole.

Computer Science Student Representatives 2017

Congratulations to our student representatives for 2017/8, elected by their peers last month. Our Reps are integral to the proactive communication channel between staff and the students and also chair and run the Staff-Student Consultative Committee (SSCC) held each semester within the School.

The reps are pictured outside the Jack Cole Building, after this semester’s SSCC meeting and are (from left to right)

  • Lewis Mazzei (1st year, minutes)
  • Beatrice Olivera (1st year, minutes)
  • Jamie Bell (2nd year, careers)
  • ​Gergely Flamich (School President)
  • Arnold Haidu (MSc, library)
  • Stacey Izmaylova (3rd year, social)
  • Xu Zhu (PhD, Postgrad)
  • Keno Schwalb (4th year)
  • Paul McKay (Evening)

Image courtesy of Ula Rustamova

iVoLVER receives Best Demo Jury Award at ACM ISS

The iVoLVER system, created by Gonzalo Méndez and Miguel Nacenta from the SACHI group at the School of Computer Science, University of St Andrews, received Best Demo Jury Award at the ACM Interactive Surfaces and Spaces (ACM ISS) conference last week.

ACM ISS 2017, took place in Brighton, UK and selects a different location each year, with Tokyo, Japan selected as next year’s destination. The conference is a premier venue for research that studies how people interact in smart spaces and surfaces and how to design and engineer solutions for novel interfaces.

iVoLVER is a web-based visual programming environment that enables anyone to transform visualizations that they find in-the-wild (e.g., in a poster or a newspaper) into new visualizations that are more useful for them. Congratulations to the iVoLVER team. You can try out the open source iVoLVER prototype using a browser.

An example iVoLVER interface

Best Demo Jury Award

Distinguished Lecture Series 2017: Professor Ursula Martin

On October 10th, we were delighted to welcome back Professor Ursula Martin from the University of Oxford, to deliver the semester one distinguished lecture series in the Byre Theatre. Earlier in her career Prof Martin was professor of Computer Science here, and in fact only the second female professor in the history of the University of St Andrews.

The lectures covered numerous aspects of the history of computing. A particular highlight was to hear about Ada Lovelace’s early work, on Ada Lovelace day. As a trained mathematician and computer scientist who has studied her papers in detail, Ursula has discovered new insights about Ada’s education and work with Charles Babbage. She also focussed on aspects of computing history that are often ignored, such as history of computing in countries other than the USA or UK. Another aspect was how, even today, the contribution of women in history is often ignored, which Ursula herself has been able to correct in some cases.

The well received lectures centred around what every computer scientist should know about computer history. Professor Martin is pictured at various stages throughout the lectures and with Head of School, Prof Simon Dobson, DLS Coordinator, Prof Ian Gent and Principal and Vice-Chancellor, Prof Sally Mapstone. Read more about Professor Martin and the individual lectures in what every computer scientist should know about computer history. Recordings of each lecture can be viewed at the end of this post.

Images courtesy of Ryo Yanagida.

Lecture 1- The Early History of Computing: Ada Lovelace, Charles Babbage and the early history of programming.

Lecture 2 – Case Study, Alan Turing, Grace Hopper, and the history of programming.

Lecture 3- What do historians of computing do, and why is it important for computer scientists today.

Computer Science hosts J.P. Morgan

Following on from a successful visit last year, J.P. Morgan returned to the School of Computer Science last week, to promote tech careers, internships and other student opportunities.

Staff from the company and CS students are pictured viewing project challenges and their solutions highlighted in their technology showcase whilst discussing future career openings and enjoying the complimentary pizza.

J.P. Morgan is a popular destination for our graduates demonstrated by four Alumni (Maria McParland, Nada Kartouch, Conner Somerville and Peter Cockroft) who were part of the team representing the company at the successful event.

School achieves Athena SWAN Bronze Award

Athena SWAN Bronze Award Logo

We are delighted to announced that the School of Computer Science has achieved an Athena SWAN Bronze Award, as recognition of our commitment to advancing gender equality.

Almost all teaching staff contributed to the application for the award, as well as many other staff in all categories, research students, masters students, and undergraduates. In congratulating staff, Simon Dobson as Head of School said:

It really does have all our fingerprints on it. The award reflects the fact that we’ve identified things that we wanted to change and have planned how to make them happen: from now on they’ll all just be “how things are” rather than part of an external process.

Semantics for probabilistic programming – Dr Chris Heunen

Event details

  • When: 6th October 2017 12:00
  • Where: Cole 1.33b

Statistical models in e.g. machine learning are traditionally expressed in some sort of flow charts. Writing sophisticated models succinctly is much easier in a fully fledged programming language. The programmer can then rely on generic inference algorithms instead of having to craft one for each model. Several such higher-order functional probabilistic programming languages exist, but their semantics, and hence correctness, are not clear. The problem is that the standard semantics of probability theory, given by measurable spaces, does not support function types. I will describe how to get around this.