About me

Research Assistant and Graduate Student in the Artificial Intelligence department of Stanford University, I am conducing my research under the supervision of Sebastian Thrun and Silvio Savarese (Computer Science Department, Computational Vision and Geometry Lab).

Working in the fields of artificial intelligence, machine learning, and deep learning, I'm interested in broadly applying them to domains such as robotics, healthcare, and medicine.


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Contact information

Email arobicqu@stanford.edu
Address Gates Computer Science, #124A, 353 Serra Mall, Stanford, CA 94305
Labs

Thrun Lab, CVGL, Crossing Minds

Links

LinkedIn, GitHub, About Me, Angel List, CrunchBase

Citations

Google Scholar, Semantic Scholar, Dblp, Research Gate


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Projects


  • Ultrasound and AI for cancer detection and tracking

    Many internal cancers go undetected due to a lack of symptoms in early stages. Pancreatic cancer, for instance, is largely asymptomatic until it is terminal. Using AI we are developing techniques for the 3D reconstruction, detection, and tracking of internal growths over time, in the hopes of providing an at-home method for the monitoring of cancers.


  • Crossing Minds

    Crossing Minds applies next-generation artificial intelligence to enrich the human experience. Using predictive models and cultural taste correlations, Crossing Minds builds products and services that appreciate and adapt to every individual.

    Crossing Minds first product, Hai, is a platform of cultural discovery, Hai generates custom, personalized recommendations in categories ranging from music, books, movies, tv shows, podcasts and videos games - with further channels in development such as restaurants, travel activities, alcohol and more. Hai was designed to feel like a game: intuitive and playful. As users rate and archive items for their library, or add third-party accounts to improve their profile, Hai opens more mediums of discovery and introduces advanced features like chat and voice interaction. In understanding the full spectrum of a user’s taste, Hai’s suggestions speak to the heart of a person’s preferences, and can adapt to mood and situation.




  • Jackrabbot

    Our work at the CVGL is making practical a new generation of autonomous agents that can operate safely alongside humans in dynamic crowded environments such as terminals, malls, or campuses. This enhanced level of proficiency opens up a broad new range of applications where robots can replace or augment human efforts. One class of tasks now susceptible to automation is the delivery of small items – such as purchased goods, mail, food, tools and documents – via spaces normally reserved for pedestrians.

    In this project, we are exploring this opportunity by developing a demonstration platform to make deliveries locally within the Stanford campus. The Stanford “Jackrabbot”, which takes it name from the nimble yet shy Jackrabbit, is a self-navigating automated electric delivery cart capable of carrying small payloads. In contrast to autonomous cars, which operate on streets and highways, the Jackrabbot is designed to operate in pedestrian spaces, at a maximum speed of five miles per hour.

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    Updates

    Jan 2017: New Template Jupyter Notebook Dark - Atom like. Available on Github .

    Dec 2016: Interview on Jackrabbot for VICE - Cyberwar: THE RACE FOR ARTIFICIAL INTELLIGENCE.

    Oct 2016: We will be giving a demo at the 3DV2016 conference of the new functionalities of Jackrabbot, on October 28th, 10.00AM-5.00PM, in McCaw Hall.

    Sept 2016: Eccv16 in Amsterdam. Poster Presentation of our paper Learning Social Etiquette: Human Trajectory Understanding In Crowded Scenes.

    Jul 2016: Stanford Artificial Intelligence Laboratory's Outreach Summer program 2016 (SAILORS) is starting this Monday July 11th and we are expecting 24 bright students that will make our halls lively and buzzing for two full weeks.

    Jul 2016: Jackrabbot on BBC .

    Jun 2016: Jackrabbot on Stanford News, Tech crunch and abc .


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    Selected Publications

    • Learning Social Etiquette: Human Trajectory Prediction
      Alexandre Robicquet, Amir Sadeghian, Alexandre Alahi, Silvio Savarese.
      European Conference on Computer Vision (ECCV), 2016.
      [pdf | project page]

    • Social LSTM: Human Trajectory Prediction in Crowded Spaces
      Alexandre Alahi*, Kratarth Goel*, Vignesh Ramanathan, Alexandre Robicquet, Li Fei-Fei, Silvio Savarese.
      IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. (* equal contribution)
      (Spotlight oral)

      [pdf | project page]

    • Forecasting Social Navigation in Crowded Complex Scenes
      Alexandre Robicquet, Alexandre Alahi, Amir Sadeghian, Bryan Anenberg, John Doherty, Eli Wu, Silvio Savarese.
      ArXiv. Cornell Univeristy Library.
      [pdf | project page]

    • Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration
      Emile Contal, David Buffoni, Alexandre Robicquet, Nicolas Vayatis.
      ECML.
      [pdf]


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    Older Work


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