School of Medicine
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Maria Karoliina Haanpaeae
Visiting Instructor, Medicine - Med/Oncology
Bio Maria Haanpaa, M.D., PhD worked as a Postdoctoral Fellow at Stanford University 2015-2018 in Dr. James Ford laboratory in Oncology, Cancer genetics. Now she is a clinical instructor since August 2018. Dr. Haanpaa is currently doing research in hereditary cancer syndromes with her special interest are dysmorphology and childhood syndromes which predispose to cancer. Dr. Haanpaa received M.D. and PhD degrees at the University of Oulu, Finland. She completed training as a Clinical Geneticist in 2015 from Turku University, during which she also worked at the Manchester Centre for Genomic Medicine, UK. She works as a clinician (Medical Genetics) in Finland and does her research at Stanford.
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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.
