What is she best known for?
The fields of study she is best known for:
- The Internet
- Microeconomics
- Statistics
Nicole Immorlica focuses on Mathematical optimization, Combinatorics, Approximation algorithm, Computer security and Common value auction.
Her research integrates issues of Optimal mechanism and Distribution in her study of Mathematical optimization.
Her work on Partition as part of her general Combinatorics study is frequently connected to K-independent hashing, thereby bridging the divide between different branches of science.
She has researched Computer security in several fields, including Resilience, Click fraud, Virtual network and Click-through rate.
Her research in Common value auction tackles topics such as Operations research which are related to areas like Secretary problem.
The study incorporates disciplines such as English auction, Keyword advertising, Advertising and Unique bid auction in addition to Forward auction.
Her most cited work include:
- Locality-sensitive hashing scheme based on p-stable distributions (2270 citations)
- Correlation clustering in general weighted graphs (216 citations)
- Matroids, secretary problems, and online mechanisms (215 citations)
What are the main themes of her work throughout her whole career to date?
Nicole Immorlica mainly focuses on Mathematical optimization, Microeconomics, Mathematical economics, Common value auction and Combinatorics.
Her Mathematical optimization study frequently links to related topics such as Scheduling.
Her work in the fields of Incentive and Externality overlaps with other areas such as Social status and Welfare.
Her work on Nash equilibrium, Best response, Repeated game and Outcome as part of general Mathematical economics study is frequently linked to Value, bridging the gap between disciplines.
Her Common value auction study combines topics in areas such as Core, Advertising, Heuristics and Mechanism design.
Her research in the fields of Partition overlaps with other disciplines such as Monotone polygon.
She most often published in these fields:
- Mathematical optimization (28.80%)
- Microeconomics (16.75%)
- Mathematical economics (15.18%)
What were the highlights of her more recent work (between 2019-2021)?
- Mathematical optimization (28.80%)
- Value (6.28%)
- Inequality (3.66%)
In recent papers she was focusing on the following fields of study:
Nicole Immorlica mostly deals with Mathematical optimization, Value, Inequality, Matching and Homophily.
Her research investigates the connection between Mathematical optimization and topics such as Feature that intersect with issues in Quality Score.
Nicole Immorlica has included themes like Stability, Risk analysis and Online algorithm in her Matching study.
Her work deals with themes such as Discrete mathematics and Leverage, which intersect with Online algorithm.
Her Homophily study integrates concerns from other disciplines, such as Disadvantage, Economic mobility, Inefficiency and Demographic economics.
Her Online advertising study deals with Common value auction intersecting with Outcome.
Between 2019 and 2021, her most popular works were:
- Maximizing Welfare with Incentive-Aware Evaluation Mechanisms. (9 citations)
- Incentivizing Exploration with Selective Data Disclosure (6 citations)
- Information Acquisition in Matching Markets: The Role of Price Discovery (4 citations)
In her most recent research, the most cited papers focused on:
- The Internet
- Microeconomics
- Statistics
Nicole Immorlica mainly investigates Mathematical optimization, Mechanism, Value, Inequality and Decision problem.
Her Mathematical optimization research focuses on Projection and how it relates to Quality Score and Incentive.
Her Mechanism research overlaps with Risk analysis, Price discovery, Information acquisition, Stability and Matching.
Her Value study spans across into subjects like Mechanism design, Optimal mechanism, Bundle, Simple and A priori and a posteriori.
Her Inequality research incorporates themes from Discrete mathematics and Leverage.
In most of her Decision problem studies, her work intersects topics such as Online algorithm.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
