Event

Pitch Day – Pi School of AI Session nine

Pitch Day Pi School of AI Session nine

Watch the School of AI fellows present the process, hurdles and results of their challenges. Join us on 4 February at 16:00 CET via Zoom.

  • Session 9 was an intensive learning experience for the fellows with challenges ranging from 3D animation to voice classification using an accelerometer. Did they achieve their goals? Find out more about the challenges and teams and join us for the Final Pitches presentations.
Register here

The challenges

Challenge 1 - 101% - Virtual Characters

  • Challenge 1

    Virtual Characters
    In this challenge, the Pi School fellows worked on developing a system that can realistically animate 3D virtual characters using text plus some emotion metadata as input. This project initially started during the 2021 Q2 session of the School of AI. In the current session, the Pi School fellows set out to improve the previous solution by focusing on increasing the quality of the produced animations, using state-of-the-art deep learning techniques.

  • Adrian Gralak

    Fellow
    Changing the world one line of code at a time.
    Founder, inventor, trader, philanthropist.
    Member of Effective Altruism community.

  • Davide Galassetti

    Fellow
    Committed. Lifelong learner. Mechanical Engineering with a cross-disciplinary attitude, dedicated to the world of machine learning and artificial intelligence.

  • João Santos

    Coach
    João is an AI engineer with a degree in Physics Engineering from the University of Coimbra, Portugal. He is part of the Pi School team where he consults on different projects, coordinates the sessions of the School of AI, and coaches teams to achieve their goals.

Challenge 2 - Innovation Engineering - Semantic Taxonomy Builder for Enhancing Innovation Processes

  • Challenge 2

    Semantic Taxonomy Builder for Enhancing Innovation Processes
    WheesBee is an advanced retrieval engine that gives insights for decision making processes in R&D. The final goal of this challenge is to provide WheesBee with a system capable of mapping classes across content types/datasets based on their textual content and build a taxonomy across content types. The system will also be capable of predicting class labels for label-less documents based on their textual content.

  • Lucas Victorasso

    Fellow
    Machine Learning and Deep Learning enthusiast. I tackle problems practically, starting with why. I like to solve challenges in novelty ways, always having an excellent abstract discussion.

  • Ravindra Bharathi

    Fellow
    Experienced Software Engineer/Lead with expertise in Web Engineering, Deep Learning and Internet of Things. Avid learner of new technology. Passionate about applying AI to solve problems in Climate, Healthcare, Workplace Safety to name a few.

  • Cristiano De Nobili - IE 1

    Coach
    Cristiano is a Theoretical Particle Physicist with a PhD in Statistical Physics (@SISSA, Trieste). Starting from the language of Nature, in the last years, he smoothly shifted to the language of machines and Humans (NLP). Currently, Cristiano is the Lead AI Scientist @ Pi School, mainly working on AI applied to sustainability. Previously, he was a Postdoc on Deep Learning for Nanoscience Images (@CNR / MHPC) and worked as NLP Scientist (@Samsung’s Bixby project). Cristiano is a Machine Learning lecturer (he loves teaching!) as well as a TEDx Speaker (he loves to inspire!). He is not into technology for its own sake, but because he thinks it is a superpower to solve impactful challenges such as climate change. Beyond AI, Cristiano is interested in Quantum Information. By the way, he is an aviator and wannabe explorer.

Challenge 3 - Innovation Engineering - A computer-assisted query constructor for Enhancing Innovation Processes

  • Challenge 3

    A computer-assisted query constructor for Enhancing Innovation Processes
    The Pi School team worked on the improvement of WheesBee, an advanced retrieval engine that helps extract relevant information and insights for decision-making processes in R&D. In particular, the goal was to generate Solr Queries from natural language search text. Semantic search was used to generate keywords from individual concepts and also generate contextual suggestions. These suggestions are available for the users to pick from when building their own Solr Queries, giving novice users of WheesBee tools to build a powerful search.

  • Priyanshu Sinha

    Fellow
    I’m a computer scientist from IIIT-Bh.
    2 Academic Research publications on MT and IR.
    Currently working @honeywell with NLP and CV.

  • Daniele Pace

    Fellow
    Lifelong learner. Bachelor's degree @PoliTOnews. Computer Engineering master's degree with flying colours @AaltoUniversity.
    Currently working at Manteia on NLP.

  • Cristiano De Nobili - IE 2

    Coach
    Cristiano is a Theoretical Particle Physicist with a PhD in Statistical Physics (@SISSA, Trieste). Starting from the language of Nature, in the last years, he smoothly shifted to the language of machines and Humans (NLP). Currently, Cristiano is the Lead AI Scientist @ Pi School, mainly working on AI applied to sustainability. Previously, he was a Postdoc on Deep Learning for Nanoscience Images (@CNR / MHPC) and worked as NLP Scientist (@Samsung’s Bixby project). Cristiano is a Machine Learning lecturer (he loves teaching!) as well as a TEDx Speaker (he loves to inspire!). He is not into technology for its own sake, but because he thinks it is a superpower to solve impactful challenges such as climate change. Beyond AI, he is an aviator and wannabe explorer.

Do you have a challenge that AI can solve?

Guest speaker

  • Andrea Cosentini

    Head of Data Science & AI at Intesa Sanpaolo

    Andrea has a degree in Physics and a PhD in Mathematical and Statistical Methods for Economics.
    He started as a quantitative analyst and worked his way to a derivatives trader for investment banks. He later became CEO of Cgnal, a company focused on the development of machine learning algorithms for big data projects that arise from business objectives. Three years ago. He joined the team at Intesa Sanpaolo as Head of Data Science & AI.
    In 2020, Intesa San Paolo proposed the challenge ‘Identifying companies mentioned in financial news' as part of Session Q2 2020 of the School of AI.

Pi School of AI Mentors

  • Alberto Danese

    Head of Data Science at Nexi

    Alberto Danese is the Head of Data Science at Nexi, one of the largest European players in digital payments.
    Passionate about data science and machine learning, his main focus is delivering tangible business results through the use of advanced algorithms applied to real-world problems.
    He spent countless hours on Kaggle, becoming the only Competitions Grandmaster in Italy, and has since shared his experience as a speaker in several AI events, including AWS re:Invent, Codemotion and various Kaggle events.

  • Simone Scardapane

    Assistant professor at Università la Sapienza

    Simone Scardapane is currently an Assistant Professor at the Sapienza University of Rome, Italy. He works on deep learning applied to audio, video, and graphs and their application in distributed and decentralized environments. He has authored more than 70 articles in machine and deep learning. Dr Scardapane is a member and chair of several technical committees and task forces. He has a continuing interest in the no-profit dissemination and promotion of machine learning in Italy.

  • Timo Bolkart

    Research Scientist at PerceivingSys, at MPI_IS, Visiting Academic at Amazon

    Timo Bolkart received a Diplom-Mathematik (FH) in 2008 and an M.Sc. in Mathematics in 2010 from the University of Applied Science in Stuttgart, Germany. From 2010 to 2012, he worked as a software engineer at the RIB Software AG in Stuttgart. From 2012 to 2016 he was a research associate at the Cluster of Excellence of the Saarland University, Germany, where he received a PhD in Computer Science in 2016. In 2016 he joined the Perceiving Systems Department of the Max Planck Institute for Intelligent Systems in Tuebingen, Germany, as a post-doctoral researcher. Since 2018, he is a research scientist at the Max Planck Institute for Intelligent Systems, and since 2020 a part-time Visiting Academic at Amazon.

  • Gabriele Sarti

    PhD Student in Natural Language Processing

    Gabriele is a PhD student at the Computational Linguistics Group of the University of Groningen. His research focuses on interpretability for NLP models, in particular to the benefit of end-users and by leveraging human behavioural signals.
    Previously, he was a research scientist at Aindo, a student in the Data Science MSc at the University of Trieste & SISSA, and a founding member of the AI Student Society. His master’s thesis with the ItaliaNLP Lab in Pisa was about the study of linguistic complexity using gaze recordings and neural language models. He is also passionate about the social applications of machine learning, ethical AI, and open source collaboration.

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