Head of Machine Learning for News Feed Personalization @ LinkedIn
Senior Scientist and Manager, Machine Learning, Advertising Optimization and Analytics @ Quantcast
B.S., Computer Science, with minor in Economics @
Data scientist and engineer with a proven ability to hire, develop and lead top-notch data science teams driving business impact by solving non-routine problems. I enjoy advising high-growth companies on building data products by leveraging best in class machine learning methodologies, and building data teams when the costs of making wrong hires are extremely high and the
Data scientist and engineer with a proven ability to hire, develop and lead top-notch data science teams driving business impact by solving non-routine problems. I enjoy advising high-growth companies on building data products by leveraging best in class machine learning methodologies, and building data teams when the costs of making wrong hires are extremely high and the payoffs from the right hires are outstanding.
PhD in Computer Science. Expert in machine learning, statistically sound experiment design, and data analytics for product optimization and innovation. Published in top-tier machine learning and bioinformatics journals. Inventor of 16 US patents. Self-starter with an excellent ability to collaborate cross-functionally with executive and senior leaders from Product, Engineering, Legal and Commercial groups in a fast-paced environment. Solid communication and supervisory skills.
• "Building and deploying effective data science teams," Predictive Analytics & Business Insights 2014
Engineering Manager, Machine Learning and Feed Personalization @ Leading a team of modeling scientists and engineers personalizing LinkedIn Feed - making it relevant and engaging for over 400 million LinkedIn members. Helping members connect with relevant professional opportunities and knowledge. Driving hundreds of millions in annual revenue.
Large-scale statistical inference and machine learning.
Inventor of 13 US patents.
Hiring data scientists and engineers to tackle some of the most challenging problems in the industry.
Previously, I led a team of data scientists building data products and recommendation systems that help millions of LinkedIn's members grow their careers. We took data products from concepts to production deployments on LinkedIn.com by developing novel statistical methodologies and leveraging technologies such as Kafka, real-time key-value stores, PIG, Scalding, R and Python.
Data products we built for Education and Consumer segments received broad coverage in the media with over 30 publications including The New York Times, The Wall Street Journal, Forbes, Inside HigherEd, Fast Company, and TechCrunch. From 2013 to Present (2 years) Lead Modeling Scientist, Advertising Optimization and Analytics @ Lead a team of data scientists developing innovative products for customer value modeling, conversion prediction, inventory pricing, fraud detection, and data driven decision support systems for online advertising using methods from statistics, optimization and computer science. The team drove double-digit increases in performance of Quantcast's Advertising product.
Collaborated with executive and senior members of Product, Engineering, Commercial and Legal groups on identifying and translating business problems into technical projects. Provided guidance substantiated by empirical results on improving existing product offerings and introducing new impactful products.
Determined project scope and managed execution across multiple areas while providing technical guidance to individual team members on experiment design, statistical modeling and software implementation.
Lead efforts to hire data scientists by designing and conducting technical and interpersonal interviews. Coached team members on effective interviewing strategies. Selected top talent for the team by interviewing over 150 data science candidates.
Built massive-scale predictive models using feature selection, regression, optimization and MapReduce. Implemented ultra low-latency pricing algorithms for Real Time Bidding (RTB) using Java.
First place for Best New Modeling Idea in a company-wide competition of over 70 participants.
Inventor of 4 US patents. From July 2011 to December 2013 (2 years 6 months) Senior Data Scientist, Center for Health Informatics and Bioinformatics @ Designed and conducted large-scale computational experiments and led implementation of causal discovery and machine learning algorithms. Completed over 10 different research projects with over 60 high-throughput datasets from a wide range of sources such as gene expression microarrays, micro RNA arrays and genome-wide SNP arrays.
- Built predictive models in high-dimensional data using state-of-the-art methods for feature selection (e.g. statistical hypothesis testing, graphical models), classification and regression (e.g. Support Vector Machine)
- Implemented complex machine learning algorithms and optimizing existing implementations
- Participated in development of novel methods for causal feature selection
- Coauthored 7 research publications in leading biomedical and computer science journals such as Cell Host & Microbe, Genomics and Journal of Machine Learning Research (JMLR)
- Published a book chapter From September 2009 to June 2011 (1 year 10 months) Research Intern, Medical Imaging and Visualization @ Lead an investigation of approaches to automatic detection and segmentation of ischemic patterns from cardiac perfusion magnetic resonance imaging (MRI) time series by clustering in spatial and temporal domains. Implemented graph and kernel-based clustering algorithms. Conducted a benchmarking evaluation of 10+ clustering methods/variants for segmentation of ventricular blood pools and the myocardium. From May 2009 to August 2009 (4 months) Graduate Research Fellow, Department of Computer Science @ Developed novel methods for clustering data by gradient-based combinatorial optimization of objective functions involving higher-order statistical moments. Designed and implemented highly-scalable clustering algorithms for applications on financial time series and text datasets. Coauthored a National Science Foundation research grant proposal. From September 2003 to August 2009 (6 years) Research Intern, Analysis & Control Technology @ Developed Bayesian Networks for diagnosis and prediction of faults in semiconductor manufacturing process. Implemented inference engine in C++ using SMILE library. From June 2006 to August 2006 (3 months) Research Intern, Machine Learning @ Developed approaches to hand written character recognition using Vicinal Support Vector Machines with transformation-invariant kernel functions. Analytically derived Lie transformations-based kernel functions. Evaluated performance of the analytic kernel functions versus those based on explicit image perturbations and numerical integration. Developed prototype implementations in C++. From June 2005 to August 2005 (3 months)
Ph.D., Computer Science @ Rutgers University-New Brunswick From 2003 to 2009 B.S., Computer Science, with minor in Economics @ Rutgers University-New Brunswick From 1999 to 2002
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