School of Medicine

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  • Nicholas Haber

    Nicholas Haber

    Postdoctoral Research Fellow, Systems Medicine

    Current Research and Scholarly Interests My research lies at the intersection of medicine, artificial intelligence, and mathematics. Most of my current activities are devoted towards a collaborative project with a multidisciplinary group of researchers, aimed at developing a wearable device with automatic facial expression recognition technology for the purpose of autism therapy. Many on the autism spectrum struggle in reading facial expressions, and the standard cognitive behavioral therapy for this essentially amounts to flashcards – examples of facial expressions for memorization, without larger context. This therapy works, often, but it is a slow, painstaking process. In the creation of such a device, we look to bring this learning effort to the real world, allowing the user to practice recognizing facial expressions of their family and friends with the help of cues and hints from the software. One hypothesis is that a system which simply informs the user that the person they are talking to looks happy, surprised, or sad will lead to much more rapid development, but it could also be the case that more nuanced help, such as being able to tell when the other person is engaged or confused or nervous, will produce the most powerful learning effects. It is difficult to predict what will happen when such therapeutic tools are deployed in the home, and we are very excited to see the sort of data we will observe in upcoming studies.

    My particular contributions to this project primarily involve the core expression recognition. I design and use algorithms that learn how to recognize facial expressions from video and image data. So-called affective computing is a growing field of study with many difficulties. The art of teaching a computer to recognize the facial expressions of a person it has never seen before is very imperfect, and in a project such as this, it is imperative that recognition succeeds nearly all of the time. I thus draw on my background in mathematics and machine learning to explore new methods by which we might create more accurate recognition. Towards this, I have been working on convolutional neural network methods, and I am interested in creating novel related architectures and in exploring the properties of convnet training.

    More broadly, I see myself as a mathematician looking to bring his skills over to medicine in order to make impactful contributions to diagnosis and therapy. For instance, I have been advising an effort by researchers to develop machine learning classifiers that discern those on the autism spectrum from those with ADHD using phenotypic data. This could potentially lead to more rapid, cheaper diagnoses.

    I maintain an active interest in mathematics, both in the sorts of research I have pursued throughout my career (mathematical physics, in particularly that which pertains to the foundations of quantum theory) and in the general promotion of mathematical literacy in the sciences.

  • Jin S. Hahn, MD

    Jin S. Hahn, MD

    Professor of Neurology, of Pediatrics and, by courtesy, of Neurosurgery at the Stanford University Medical Center

    Current Research and Scholarly Interests 1. Clinical informatics and electronic health records
    2. Neonatal and fetal neurology
    3. Prenatal diagnosis neurodevelopmental anomalies
    4. Personalized Health and Wellness Records

  • Louis Halamek

    Louis Halamek

    Professor of Pediatrics (Neonatology) at the Lucile Packard Children's Hospital and, by courtesy, of Obstetrics and Gynecology at the Stanford University Medical Center

    Current Research and Scholarly Interests 1. development of hospital operations centers coupled with sophisticated simulation capabilities
    2. re-creation of near misses and adverse events
    3. optimizing human and system performance during resuscitation
    4. optimizing pattern recognition and situational awareness at the bedside
    5. evaluation and optimization of debriefing
    6. patient simulator design