We are hiring:* Applied ML (lead and mid-level)* Front-end* DesignIf you are interested, DM me!If you know someone who might be interested, please send them my way or connect us.

The MS Analytics Advisory Board is a select group of analytics executives and professionals who help ensure that the content of the degree meets the needs of business and industry, and that the curriculum gives our students the skills and knowledge they need to excel as analytics professionals.

Lead the Knowledge Graph Foundation team to help build the best Professional Knowledge Graph in the world.This is a diverse team of AI/ML Engineers, Fullstack Engineers, Linguists, Taxonomists who every day improve our core mission of:1. Content understanding: For any piece of content, extract the entities, concepts, relations and map them to our ecosystem of members, companies, courses and more.2. Knowledge Graph Platform and Tooling: Expand, curate and upgrade our knowledge graph to make sure it is fresh and of the highest quality.3. Taxonomy: Expand and curate the core taxonomies that power the Economic Graph, the Knowledge Graph and all LI products (skills, industries, titles, geo, and more). (e.g., see https://engineering.linkedin.com/blog/2022/building-linkedin-s-skills-graph-to-power-a-skills-first-world)4. Labeling and annotation: Efficiently label and annotate data sets that are used to train and evaluate our models.5. Value assessment: Measure and evaluate the value of the data that makes up the knowledge graph.

I lead the Data Science Research and Productivity team (DSRP).Our mission is to push the boundaries on what we can do with our data, building and incubating new capabilities for insights and enabling data-driven decisions.Areas of focus:* Applied research: We conduct cutting edge research in multiple areas including responsible data use, explainable AI/ML, computational social science, experimentation, differential privacy and time series/forecasting. We take on both short-term and long-term challenges and get our work into production as soon as we can.* Productivity: We build the data tools that support the broader data org and beyond, making sure all capabilities are scalable and easy to use for all of LinkedIn.* Standardization: How to we measure utility of our taxonomies, where do they need to improve, how do we validate them and the system by which they are generated and maintained.* Incubation and Expansion: We look for new areas where data science can make impact. One such area is supporting infrastructure teams to help them plan and maintain LinkedIn’s infrastructure. Others include BizOps, Finance, and beyond.

Mission: Match every developer to the right job

I am heading the data and analytics team at Branch Metrics.I have a world-class team that can handle big data from data ingestion and storage (data infrastructure), to efficient data modeling and data pipelines (data science and data engineering), to getting business insights to help us and our customers in their decision-making (data analytics).We manage billions of records per day and turn them into valuable data assets for our partners and ourselves. We are only getting started!

I manage a team of machine learning experts to tackle some of the hard machine learning problems facing product teams. We help spread the adoption of ML, develop ML methodologies and algorithms as needed, and identify horizontal opportunities.

I lead a team within Facebook Data Science, focusing specifically on user modeling. We are hard at work making sense of all the data, making it actionable, aiding decision-making and improving the product.

Theme for SIGKDD-2014: Data Science for Social Good.Started in 1989, KDD is the oldest & largest data mining conference worldwide. We pioneered “Big Data”, “Data Science”, and “Predictive Analytics” solutions before these names existed – some of the first & most highly cited research papers on these topics were published in our conference. Other notable innovations that originated in our conference include crowd sourcing; Large scale data mining competitions with over 10,000 participants, personalized advertising eg. on Google, graph mining algorithms that power Facebook & LinkedIn, and recommender systems used by Netflix, Amazon etc. After 25 years and an explosive growth in this industry, we are still the home for the latest cutting-edge research in these topics.

Research Professor in the Computer Science Department at the Viterbi School of Engineering, USC. Focus on applying machine learning and data mining to big data problems, particularly focused on social media, user modeling and information filtering.

I teach machine learning at USC. I intend to teach a seminar advanced topics in machine learning on a semi-regular basis.

Conduct and lead research in social media, social networks, information analytics and personalized information management.

Conduct and lead research in social media, social networks, information analytics and personalized information management.

Building and leading a world-class research team in information extraction, integration and analysis. Focus is to conduct core academic research and push its transition into the Fetch product line as well as to interact with customers to pursue research that is mutually beneficial. Continue pursuing research that I focused on as a principal scientist.

Direct research in machine learning, network learning, relational learning. Grow a research team and direct long-term research plans. Primary domain focus is web-based information filtering/extraction/personalization. General research problems include record linkage, classifying web-pages, personalized information filtering, alerting, personal information assistants/agents.

Worked on baseline methods within Network Learning, such as the Relational Neighbor classifier (RN), to which relational learners should be compared when assessing how well they have extracted a useful model from the given relational structure.

Performed research in machine learning with my advisor and colleagues in the machine learning research group. Research spanned developing a framework for ranking of information based on user interest and multiple information sources, developing the Information Valet framework, to work with multiple wireless devices and multiple information sources. The EmailValet was the first instantiation of this work. The EmailValet learns to predict whether to forward a new email message to a user's pager based on past email reading behavior of the user on the pager. My research also explored core text classification question such as how to represent numerical attributes in a way that standard text classification algorithms can make the most use of.

Chief Architect and Designer for an agent framework for the web as well as an event- and messaging- driven communication model. The agent framework, available as part of the SmartCode product, and built entirely in Java, uses an event- and messaging- driven model and include work on distributed computing using the HTTP, FTP and SMTP protocol levels. This framework empowers applications to track resources easily and transparently with minimum amount of cpu and network traffic. No spidering is involved unless strictly necessary.

Chief Architect and Designer for a prototype web-agent framework. Did initial performance experiments for proof of concept. Started on the design of the next generation of the framework, which realized a commercial release at Information Architects.

Taught core data structures and programming fundamentals to both graduates and undergraduates in Computer Science.

Developed and maintained a beta-release of an Inter-Process-Communication (IPC) package between Unix and MacIntoshes using the AppleEvent(AE) protocol. The package was developed using the MPW and ThinkC environments on the MacIntosh. Compared three different environments: Prograph, SmallTalk, and SmallTalk Agents(beta-tested) and advised on which environment would be better suited for the research-group. Particular attention was made to ease-of-use and extensiveness of libraries for Graphics and Math.