What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
The scientist’s investigation covers issues in Artificial intelligence, Data mining, Machine learning, Theoretical computer science and Algorithm.
His studies deal with areas such as Construct, Wireless sensor network, Unobservable and Computer vision as well as Artificial intelligence.
His Data mining study incorporates themes from Data publishing, Statistic, STREAMS and Pattern recognition.
His study in the field of Feature selection also crosses realms of Audit.
His Theoretical computer science research integrates issues from Data security, t-closeness, Data anonymization, Information privacy and Equivalence class.
His Data security research includes themes of k-anonymity, Closeness, Earth mover's distance, Probability measure and Distance measures.
His most cited work include:
- t-Closeness: Privacy Beyond k-Anonymity and l-Diversity (2297 citations)
- Certifying and Removing Disparate Impact (742 citations)
- Proximity Search in Databases (207 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Theoretical computer science, Artificial intelligence, Data mining, Algorithm and Discrete mathematics.
His Theoretical computer science research is multidisciplinary, incorporating elements of Correctness, Mathematical proof, Key and Computation.
His research in Artificial intelligence tackles topics such as Machine learning which are related to areas like Variety.
His Data mining study focuses on Data stream mining in particular.
His studies in Algorithm integrate themes in fields like Multiple kernel learning and Graphics hardware.
His Discrete mathematics research incorporates elements of Embedding and Combinatorics.
He most often published in these fields:
- Theoretical computer science (19.79%)
- Artificial intelligence (19.27%)
- Data mining (15.62%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (19.27%)
- Pipeline (3.12%)
- Machine learning (10.42%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Pipeline, Machine learning, Data mining and Theoretical computer science are his primary areas of study.
His study in Artificial intelligence focuses on Deep learning in particular.
The study incorporates disciplines such as Variety and Benchmark in addition to Machine learning.
His study focuses on the intersection of Data mining and fields such as Classifier with connections in the field of Proxy.
He has included themes like Representativeness heuristic, Mathematical proof and Approximation algorithm in his Theoretical computer science study.
His work carried out in the field of Representation brings together such families of science as Allocative efficiency and Data science.
Between 2017 and 2021, his most popular works were:
- Fairness and Abstraction in Sociotechnical Systems (182 citations)
- A comparative study of fairness-enhancing interventions in machine learning (158 citations)
- Auditing black-box models for indirect influence (86 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Suresh Venkatasubramanian mainly investigates Machine learning, Variety, Artificial intelligence, Psychological intervention and Affect.
In the subject of general Machine learning, his work in Feature selection is often linked to Harm and Audit, thereby combining diverse domains of study.
His Variety research includes elements of Feature, Deep learning, Data set and Black box.
Suresh Venkatasubramanian combines Artificial intelligence and Pipeline in his studies.
Psychological intervention combines with fields such as Benchmark, Fairness measure, Data science and Preprocessor in his work.
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