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• We're announcing a $200M ceiling contract with the U.S. Department of Defense.Through hands-on prototyping and direct collaboration across the…

  We're announcing a $200M ceiling contract with the U.S. Department of Defense.Through hands-on prototyping and direct collaboration across the…

  Liked byBen Auffarth

• On Learning from other teamsTen years ago at Bloomberg, there wasn't enough space on the engineering floor. So they put me and a colleague…

  On Learning from other teamsTen years ago at Bloomberg, there wasn't enough space on the engineering floor. So they put me and a colleague…

  Liked byBen Auffarth

• Reliability issues are important but there's much more. We discuss a lot of the failure modes and approaches to solving them. Generative AI with…

  Reliability issues are important but there's much more. We discuss a lot of the failure modes and approaches to solving them. Generative AI with…

  Shared byBen Auffarth

• Prince2 Practictioners

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  IssuedMar 2016 ExpiresMar 2021

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  Oct 2000 -Sep 20011 year

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  Teaching English, Russian, programming to children between 6 and 15 years of age

• Reputation 808: reached > 197k people

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  Jul 2017 -Jun 20181 year

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• Artificial Intelligence with Python Cookbook: Proven recipes for applying AI algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6

  Packt Publishing October 30, 2020

  With artificial intelligence (AI) systems, we can develop goal-driven agents to automate problem-solving. This involves predicting and classifying the available data and training agents to execute tasks successfully. This book will help you to solve complex AI problems using practical recipes.
  
  This book covers the following exciting features:
  * Implement data preprocessing steps and optimize model hyperparameters
  * Work with large amounts of data using distributed and parallel… Show more

  With artificial intelligence (AI) systems, we can develop goal-driven agents to automate problem-solving. This involves predicting and classifying the available data and training agents to execute tasks successfully. This book will help you to solve complex AI problems using practical recipes.
  
  This book covers the following exciting features:
  * Implement data preprocessing steps and optimize model hyperparameters
  * Work with large amounts of data using distributed and parallel computing techniques
  * Get to grips with representational learning from images using InfoGAN
  * Delve into deep probabilistic modeling with a Bayesian network
  * Create your own artwork using adversarial neural networks Show less

  See publication

• Understanding the Odour Spaces: A Step towards Solving Olfactory Stimulus-Percept Problem

  Public Library of Science (Plos ONE) 2015

  Odours are highly complex, relying on hundreds of receptors, and people are
  known to disagree in their linguistic descriptions of smells. It is partly due to these facts that,
  it is very hard to map the domain of odour molecules or their structure to that of perceptual
  representations, a problem that has been referred to as the Structure-Odour-Relationship.
  We collected a number of diverse open domain databases of odour molecules having
  unorganised perceptual descriptors… Show more

  Odours are highly complex, relying on hundreds of receptors, and people are
  known to disagree in their linguistic descriptions of smells. It is partly due to these facts that,
  it is very hard to map the domain of odour molecules or their structure to that of perceptual
  representations, a problem that has been referred to as the Structure-Odour-Relationship.
  We collected a number of diverse open domain databases of odour molecules having
  unorganised perceptual descriptors, and developed a graphical method to find the ...
  Show less

  Other authors

  See publication

• Understanding smell - the olfactory stimulus problem

  Neuroscience & Biobehavioral Reviews 2013

  The main problem with sensory processing is the difficulty in relating sensory input to physiological responses and perception. This is especially problematic at higher levels of processing, where complex cues elicit highly specific responses. In olfaction, this relationship is particularly obfuscated by the difficulty of characterizing stimulus statistics and perception. The core questions in olfaction are hence the so–called stimulus problem, which refers to the understanding of the stimulus,… Show more

  The main problem with sensory processing is the difficulty in relating sensory input to physiological responses and perception. This is especially problematic at higher levels of processing, where complex cues elicit highly specific responses. In olfaction, this relationship is particularly obfuscated by the difficulty of characterizing stimulus statistics and perception. The core questions in olfaction are hence the so–called stimulus problem, which refers to the understanding of the stimulus, and the structure–activity and structure–odor relationships, which refer to the molecular basis of smell. It is widely accepted that the recognition of odorants by receptors is governed by the detection of physico–chemical properties and that the physical space is highly complex. Not surprisingly, ideas differ about how odor stimuli should be classified and about the very nature of information that the brain extracts from odors. Even though there are many measures for smell, there is none that accurately describes all aspects of it. Here, we summarize recent developments in the understanding of olfaction. We argue that an approach to olfactory function where information processing is emphasized could contribute to a high degree to our understanding of smell as a perceptual phenomenon emerging from neural computations. Further, we argue that combined analysis of the stimulus, biology, physiology, and behavior and perception can provide new insights into olfactory function. We hope that the reader can use this review as a competent guide and overview of research activities in olfactory physiology, psychophysics, computation, and psychology. We propose avenues for research, particularly in the systematic characterization of receptive fields and of perception. Show less

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• Continuous spatial representations in the olfactory bulb may reflect perceptual categories

  2011

  In sensory processing of odors, the olfactory bulb is an important relay station,
  where odor representations are noise-filtered, sharpened, and possibly re-organized. An
  organization by perceptual qualities has been found previously in the piriform cortex,
  however several recent studies indicate that the olfactory bulb code reflects behaviorally
  relevant dimensions spatially as well as at the population level. We apply a statistical
  analysis on 2-deoxyglucose images, taken… Show more

  In sensory processing of odors, the olfactory bulb is an important relay station,
  where odor representations are noise-filtered, sharpened, and possibly re-organized. An
  organization by perceptual qualities has been found previously in the piriform cortex,
  however several recent studies indicate that the olfactory bulb code reflects behaviorally
  relevant dimensions spatially as well as at the population level. We apply a statistical
  analysis on 2-deoxyglucose images, taken over the entire bulb of glomerular layer of the ... Show less

  Other authors

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• Clustering by a genetic algorithm with biased mutation operator

  Evolutionary Computation (CEC) Jul 2010

  See publication

• Comparison of redundancy and relevance measures for feature selection in tissue classification of CT images

  Advances in Data Mining. Applications and Theoretical Aspects; Springer Berlin/Heidelberg Jul 2010

  Other authors

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• How to Build a Linux Cluster for Scientific Computing

  Technical report - university of Barcelona 2009

  A beowulf cluster is a cluster of Linux computers designed to run computing jobs in
  parallel. This article is going to give an up-to date example of assembled hardware, and
  installed programs (plus configuration). Finally there will be explanations of how to run
  processes in parallel in computing languages such as matlab and R. After the computers are
  setup we may want to transfer the configuration to other computers, a process which is
  called cloning. In the last section… Show more

  A beowulf cluster is a cluster of Linux computers designed to run computing jobs in
  parallel. This article is going to give an up-to date example of assembled hardware, and
  installed programs (plus configuration). Finally there will be explanations of how to run
  processes in parallel in computing languages such as matlab and R. After the computers are
  setup we may want to transfer the configuration to other computers, a process which is
  called cloning. In the last section, we will deal with profiling. keywords: scientific ... Show less

  See publication

• Hopfield networks in relevance and redundancy feature selection applied to classification of biomedical high-resolution micro-CT images

  Advances in Data Mining. Medical Applications, E-Commerce, Marketing, and Theoretical Aspects 2008

  We study filter–based feature selection methods for classification of biomedical
  images. For feature selection, we use two filters—a relevance filter which measures
  usefulness of individual features for target prediction, and a redundancy filter, which
  measures similarity between features. As selection method that combines relevance and
  redundancy we try out a Hopfield network. We experimentally compare selection methods,
  running unitary redundancy and relevance filters… Show more

  We study filter–based feature selection methods for classification of biomedical
  images. For feature selection, we use two filters—a relevance filter which measures
  usefulness of individual features for target prediction, and a redundancy filter, which
  measures similarity between features. As selection method that combines relevance and
  redundancy we try out a Hopfield network. We experimentally compare selection methods,
  running unitary redundancy and relevance filters, against a greedy algorithm with ... Show less

  Other authors

  See publication

• System for Automated Assistance in Correction of Programming Exercises

  Proceedings of the fifth CIDUI-V International Congress of University Teaching and Innovation 2008

  In university programming classes often hundreds of students participate having to
  solve each hundreds of programming assignments a situation which puts instructors to the
  difficult task of validating hundreds of programming assignments. We present a framework
  that can help instructors and students in organization and validation of program code. Our
  “System for Automated Assistance in Correction of Programming Exercises “(short: SAC) is a
  web-platform for test-driven… Show more

  In university programming classes often hundreds of students participate having to
  solve each hundreds of programming assignments a situation which puts instructors to the
  difficult task of validating hundreds of programming assignments. We present a framework
  that can help instructors and students in organization and validation of program code. Our
  “System for Automated Assistance in Correction of Programming Exercises “(short: SAC) is a
  web-platform for test-driven development and automated validation. The web-platform is ... Show less

  Other authors

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• Spectral graph clustering

  Technical report - University of Barcelona 2007

  Spectral clustering is a powerful technique in data analysis that has found
  increasing support and application in many areas. This report is geared to give an
  introduction to its methods, presenting the most common algorithms, discussing advantages
  and disadvantages of each, rather than endorsing one of them as the best, because,
  arguably, there is no black-box algorithm, which performs equally well for any data. We
  present results from previous studies and conclude that… Show more

  Spectral clustering is a powerful technique in data analysis that has found
  increasing support and application in many areas. This report is geared to give an
  introduction to its methods, presenting the most common algorithms, discussing advantages
  and disadvantages of each, rather than endorsing one of them as the best, because,
  arguably, there is no black-box algorithm, which performs equally well for any data. We
  present results from previous studies and conclude that methods based on Ncut and ... Show less

  See publication

• Marie Curie Fellowship

  European Union

  Dec 2012

• FI (Formació de personal investigador)

  Federal state government of Catalonia.

  Feb 2009

• Monbukagakusho

  Japanese government

  Oct 2004

• Deutscher Akademischer Auslandsdienst

  DAAD

  Oct 2002

• English

  Professional working proficiency

• Spanish

  Professional working proficiency

• Catalan

  Full professional proficiency

• Russian

  Limited working proficiency

• German

  Native or bilingual proficiency

• French

  Limited working proficiency

• Italian

  Elementary proficiency

• Toastmasters international - data science speakers' club

  President; VPE

  Sep 2015 - Present

• Amol Dixit

  “I had the pleasure of working with Ben for a few short months. During this time, I delved deeply into his codebase and ways of working.I was genuinely impressed by the architecture and design of his work. The code was not only exceptionally well-written but also followed best practices in software engineering and employed state of the art data science techniques at the same time. It was modular, functionalized, and scalable, with a sophisticated structure that ensured smooth transitions across different scenarios without a significant increase in complexity.It made a challenging project feel far more approachable and has set a standard for how I view well-architected data science workflows.I highly recommend Ben to anyone looking for a skilled, thoughtful, and innovative professional for any DS product oriented team.”

• Linh Nguyen

  “Ben has always been approachable and supportive, taking time to help me thoroughly understand issues, analyze data, and effectively resolve bugs. He invested significant effort in conducting thorough peer reviews of my code, offering constructive feedback that not only improved the quality of the codebase but also helped me grow as a data scientist. Ben possesses outstanding knowledge of machine learning, coupled with great programming skills. He utilized a wide range of libraries and tools to improve the robustness, efficiency and scalability of our codebase. Ben's technical acumen and dedication to mentoring makes him a valuable asset to any team.”

More activity by Ben

• Nearly a month back now we had our #pydatalondon2025 conference, a blast as always and well covered by others already.I had the pleasure of getting…

  Nearly a month back now we had our #pydatalondon2025 conference, a blast as always and well covered by others already.I had the pleasure of getting…

  Liked byBen Auffarth

• If you want to get ahead with #AI development Shantanu Ladhwe has you covered. I feel honoured my book about development with LLMs in LangChain (with…

  If you want to get ahead with #AI development Shantanu Ladhwe has you covered. I feel honoured my book about development with LLMs in LangChain (with…

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• "Challenging GBDT" — or just challenging credibility?📄 Booking.com's latest paper, “Challenging Gradient Boosted Decision Trees with Tabular…

  "Challenging GBDT" — or just challenging credibility?📄 Booking.com's latest paper, “Challenging Gradient Boosted Decision Trees with Tabular…

  Liked byBen Auffarth