Can intelligence be learned? At LISP, Prof. Chin and a collection of budding (master's and PhD) computer scientists, engineers, and mathematicians are passionate about exploring, disseminating, and innovating research in machine learning, intelligent decision making systems, and signal processing in order to answer this question.
Professor Chin will be teaching a graduate level Deep Learning seminar this fall!
We will be presenting a paper on Predicting Local Field Potentials with Recurrent Neural Networks*
We're holding a machine learning research paper reading group every Friday at 9 AM this summer in MCS room 163 in the Hariri Institute! Join us!
I received my Bachelor of Science from Boston University's Computer Science department in 2014. After I graduated, I collected the data for, designed, and created the website Parkour Theory to help those interested learn and referenece terminology, and share new moves. In 2015, I returned to Boston University because I missed the joy of arduous late night studying. I am now a second year master's student in computer science again at Boston University, and a researcher at Draper. The CS department can't seem to get rid of me.
Currently, I am researching neural network architectures for improving action recognition, natural language understanding for creating labeled datasets, and unsupervised learning for constrained automatic neural network model selection.
I am currently a third-year doctoral candidate at Harvard University (SEAS) in Applied Mathematics and a Draper Laboratory fellow. I work under the joint advisory of Dr. Vahid Tarokh (Harvard) and Dr. Peter Chin (Draper). I hold a BS in Applied Math from UCLA and should be receiving my MS from Harvard (also in Applied Math!) at the end of the Fall 2016 semester. My past research has been fairly diverse, ranging from signal processing using the Empirical Wavelet Transform (UCLA, with Dr. J. Gilles), to distributed resource allocation strategies (Harvard/Draper, with Drs. N. Li & R. Mangoubi). My current and future pursuits concern computational algebraic topology: in a nutshell, identifying/characterizing lower-dimensional algebraic structure within high-dimensional data sets. Please feel free to email me at kathrynheal[at]g.harvard.edu, or visit my website at http://scholar.harvard.edu/heal.
If you're a master's or PhD student join us! You can find us in room MCS 289 or join us in the Hariri Institute for our machine learning summer reading group.