Anna Choromanska, Department of Electrical and Computer Engineering at NYU Tandon School of Engineering
Professor Anna Choromanska did her Post-Doctoral studies in the Computer Science Department at Courant Institute of Mathematical Sciences in NYU and joined the Department of Electrical and Computer Engineering at NYU Tandon School of Engineering in Spring 2017 as an Assistant Professor. She is affiliated with the NYU Center for Data Science.
Prof. Choromanska’s research interests focus on machine learning both theoretical and applicable to the variety of real-life phenomena. Currently, her main research projects focus on numerical optimization, deep learning, large data analysis, and learning from data streams. Prof. Choromanska also works on machine learning for robotics and autonomous systems. She collaborates with NVIDIA (New Jersey lab) on the autonomous car driving project.
Prof. Choromanska was a recipient of The Fu Foundation School of Engineering and Applied Science Presidential Fellowship at Columbia University in the City of New York. She co-authored several international conference papers and refereed journal publications, as well as book chapters. The results her works are used in production by Facebook (training production vision systems and entry to COCO competition) and Baidu, and in product development by NVIDIA. She is also a contributor to the open source fast out-of-core learning system Vowpal Wabbit (aka VW). Prof. Choromanska gave over 50 invited and conference talks and serves as a book editor (MIT Press volume), organizer of top machine learning events (workshops at conferences such as the International Conference on Neural Information Processing Systems), and a reviewer and area chair for several top machine learning conferences and journals.
Kavya Kopparapu is a freshman at Harvard University and the Founder/CEO of GirlsComputingLeague
Kavya Kopparapu is a freshman at Harvard University and the Founder/CEO of GirlsComputingLeague, a nonprofit group she established to help close the gender gap in computer science. GirlsComputingLeague has been recognized by White House, raised over $50,000 for computer science programs, and impacted over 4,000 students in the DC Metro Area.
Kavya passionately engages in research at the intersection of medicine and computer science, working on projects that have been recognized by IEEE Spectrum, Tech Crunch, and NVIDIA. In this line, she was recognized as a 2017 WebMD Health Hero, as finalist in the 2018 Regeneron Science Talent Search “Junior Nobel Prizes”, and as a 2018 US Presidential Scholar.
Kavya is an avid public speaker, having given a TEDx Talk, spoken at the Smithsonian, NASA Kennedy Space Center, World Bank Group, and several Artificial Intelligence Conferences. Kavya is collaborating with the University of Montreal as a signatory of the Montreal Declaration for the Ethical Use of Artificial Intelligence.
GlioVision: A Platform for the Automatic Assessment of Glioblastoma Tumor Features, Molecular Identity, and Gene Methylation from Histopathological Images Using Deep Learning:
Glioblastoma Multiforme (GBM) is one of the most aggressive types of brain cancer, the most common malignant brain tumor amongst adults, and has a mean survival time of 12 months post-diagnosis. While many researchers focused on the diagnosis of GBM, one overlooked area is the post-diagnosis treatment determination. The current treatment pipeline takes several weeks with costly manual tumor feature segmentation, survivorship estimations, and a genetic panel to determine molecular subtype and MGMT promoter methylation, each of which are implicated in chemotherapy effectiveness.
Despite the need for an accurate and data-driven approach to extracting tumor information from a single brain biopsy image (WSI) after diagnosis, a solution does not currently exist. This study presents GlioVision, an unprecedented assessment platform that uses state-of-the-art neural networks and a dataset of over 250 WSIs to extract relevant histopathological and genetic information. The networks exhibited 86%, 94%, and 96% testing accuracy for feature segmentation, subtype classification, and MGMT methylation prediction, respectively.
To understand the basis of this prediction, 94 textural features were extracted and correlated to the gene expression of differentially expressed genes in high/low glioblastoma survivorship. Not only were 23 novel genetic associations discovered, but a prediction model of glioblastoma survivorship utilizing the textural features was also constructed with high accuracy.
GlioVision is high speed, low cost, and ready to implement with significantly improved accuracy over the current gold standard. The patent-pending GlioVision system pioneers the segmentation of tumor features and prediction of molecular subtype and gene methylation status from a WSI.
Neel Sundaresan, Partner Director, Microsoft Cloud and AI Division
Neel Sundaresan is a Partner Director at Microsoft Cloud and AI Division where he leads projects in infusing AI into software development. Prior to Microsoft he was the head of eBay Research Labs and eBay Data Labs. He was also a research manager at IBM research and was a founding CTO of a CRM company. He has a PhD in computer Science from Indiana University Bloomington and a degree in Computer science and in mathematics from IIT Mumbai. He has over 100 technical publications and over 160 issued patents. He is a frequent speaker at national and international conferences.
Teaching a Machine to Code:
There has been extensive work in machine learning in speech, vision, text, and machine translation. One new area that is gaining a lot of interest is program synthesis. With the availability of vast amount of open source code in a wide variety of languages from sources like github and also associated textual information from platforms like stackoverflow, the field is ripe for using the latest advances in machine learning techniques for automation in software engineering in general and program synthesis in particular. While programs are highly structured as they obey the syntactic and semantic structures imposed by the compilers the intent behind the code is only human decipherable. By using associated comments, and PR reviews along with the signatures from code structures machine learning techniques can be applied to implement code completion and also assist in other software development processes like automated testing, risk analysis, and review processes. We will discuss how we have used machine learning and, in some cases, deep learning methods to assist in this automation. More specifically I will talk about deep learning techniques for understanding programs beyond semantics for identifying intent and idioms and match them with comments so as to automate and create assistive tools to the software developer during editing, building, PR, CI/CD workflows of software deployment. Mundane tasks during software development can be automated while more complex tasks can be alleviated with assistive AI solutions. We will walk through examples, experiences on what worked and what didn’t work in deploying such models to production.