Abhimanyu Lad

Search Quality at LinkedIn

San Francisco Bay Area
  1. LinkedIn
  1. Carnegie Mellon University,
  2. Yahoo!
  1. Carnegie Mellon University

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I work on search quality at LinkedIn, currently focusing on query understanding, query rewriting, and search assistance. My goal is to design and build search systems that provide highly personalized and relevant results in a transparent manner. I believe that an optimal search experience depends on the right combination of intuitive user interface design, intelligent query understanding, and personalized ranking, and I care deeply about all three.

I love to create things, tinker with them, and fail fast; I often build prototypes and web-apps using Python, HTML, and Javascript. But in production, I build robust, scalable, and maintainable systems with clean Java code.

I like to work on all aspects of product development, including conceptualization and data-driven analysis, gathering training data, feature engineering, offline prototyping, and finally, deploying to production and systematically evaluating the impact.

Specialties: information retrieval, machine learning, query log analysis, query suggestions, query understanding, search intent prediction, search ranking, search evaluation, large-scale data analytics using Hadoop and Pig.


Staff Software Engineer

– Present (6 months)San Francisco Bay Area

Query understanding and search assistance.

Senior Software Engineer

– Present (3 years 6 months)San Francisco Bay Area

- Built the search metrics infrastructure to support systematic evaluation and experimentation in the search relevance team.
- Built the query spelling suggestion service for LinkedIn's search.

Ph.D. Student

Carnegie Mellon University
(5 years 8 months)Greater Pittsburgh Area

- As part of my thesis, proposed a probabilistic framework for modeling as well as optimizing novelty and diversity in session-based retrieval (multi-query search sessions in Web search and news search)
- Topic modeling for heterogeneous data
- Multi-task active learning.

Summer Intern

(3 months)Santa Clara, CA

Analyzed the effect of non-linearity and different levels of noise in training data on various machine learning algorithms for ranking.


Providing Recommendations to Members of a Social Network

United States 13/780,116

Presenting Actionable Recommendations to Members of a Social Network

United States 13/780,198


Is it Time to Abandon Abandonment?(Link)

Human-Computer Interaction and Information Retrieval (HCIR)
October 2011

A Framework for Evaluation and Optimization of Relevance and Novelty-based Retrieval(Link)

Ph.D. Thesis, School of Computer Science, Carnegie Mellon University

Learning to Rank Relevant and Novel Documents through User Feedback(Link)

Proceedings of the 19th ACM International Conference on Information and Knowledge Management

Active Ordering of Interactive Prediction Tasks(Link)

Proceedings of the 10th SIAM International Conference on Data Mining

Modeling Expected Utility of Multi-session Information Distillation(Link)

Proceedings of the 2nd International Conference on the Theory of Information Retrieval

Volunteer Experience & Causes

Opportunities Abhimanyu is looking for:

Causes Abhimanyu cares about:

  • Human Rights
  • Disaster and Humanitarian Relief
  • Science and Technology

Organizations Abhimanyu supports:


  • Python
  • C
  • Information Retrieval
  • Machine Learning
  • Text Analytics
  • Text Classification
  • Hadoop
  • Natural Language...
  • MapReduce
  • Text Mining
  • Search
  • Statistics
  • Lucene
  • Git
  • Pig
  • Apache Pig
  • Classification
  • Algorithms
  • Data Mining
  • See 4+  See less


Carnegie Mellon University

Ph.D., Computer Science

Thesis topic: A Framework for Evaluation and Optimization of Relevance and Novelty-based Retrieval

Apeejay School


Honors & Awards

Yahoo! PhD Fellowship for 2007--2009

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