D-Index & Metrics Best Publications

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 34 Citations 5,146 150 World Ranking 8126 National Ranking 3776

Overview

What is she best known for?

The fields of study Aarti Singh is best known for:

  • Gene
  • Enzyme
  • Mitochondrion

Her Nanotechnology study frequently draws parallels with other fields, such as Thin film and Atomic layer deposition. Her Thin film study typically links adjacent topics like Nanotechnology. Among her Pure mathematics studies, you can observe a synthesis of other disciplines of science such as Quantum mechanics and Combinatorics. In her research, she performs multidisciplinary study on Quantum mechanics and Pure mathematics. She integrates Combinatorics with Discrete mathematics in her study. Aarti Singh brings together Discrete mathematics and Statistics to produce work in her papers. Aarti Singh integrates Statistics with Algorithm in her research. Many of her studies involve connections with topics such as Parameterized complexity and Algorithm. With her scientific publications, her incorporates both Artificial intelligence and Artificial neural network.

Her most cited work include:

  • Incipient Ferroelectricity in Al-Doped HfO2 Thin Films (557 citations)
  • Gradient Descent Provably Optimizes Over-parameterized Neural Networks (333 citations)
  • Gradient Descent Provably Optimizes Over-parameterized Neural Networks (211 citations)

What are the main themes of her work throughout her whole career to date

Aarti Singh applies her multidisciplinary studies on Artificial intelligence and Algorithm in her research. She undertakes multidisciplinary investigations into Algorithm and Artificial intelligence in her work. Aarti Singh integrates many fields, such as Statistics and Mathematical optimization, in her works. In her research, she undertakes multidisciplinary study on Mathematical optimization and Statistics.

Aarti Singh most often published in these fields:

  • Artificial intelligence (31.03%)
  • Algorithm (29.31%)
  • Statistics (25.86%)

What were the highlights of her more recent work (between 2019-2021)?

  • Cell biology (40.00%)
  • Statistics (30.00%)
  • Immunology (20.00%)

In recent works Aarti Singh was focusing on the following fields of study:

In her study, Minimax is inextricably linked to Mathematical optimization, which falls within the broad field of Minification. Minimax and Mathematical optimization are frequently intertwined in her study. Her work in Test (biology) addresses issues such as Paleontology, which are connected to fields such as Context (archaeology). Her research links Paleontology with Context (archaeology). Cell biology and Microbiology are two areas of study in which Aarti Singh engages in interdisciplinary research. Her work blends Microbiology and Immunology studies together. Many of her studies on Immunology apply to Translocator protein as well. Her work in Translocator protein is not limited to one particular discipline; it also encompasses Neuroinflammation. She integrates many fields in her works, including Neuroinflammation and Inflammation.

Between 2019 and 2021, her most popular works were:

  • Mitochondria form contact sites with the nucleus to couple prosurvival retrograde response (52 citations)
  • Classification accuracy as a proxy for two-sample testing (33 citations)
  • Pharmacological advances in mitochondrial therapy (29 citations)

In her most recent research, the most cited works focused on:

  • Gene
  • Mitochondrion
  • Statistics

Aarti Singh integrates Mitochondrion and Translocator protein in her research. Translocator protein is closely attributed to Inflammation in her study. She incorporates Inflammation and Apoptosis in her research. Aarti Singh performs multidisciplinary studies into Apoptosis and Mitochondrion in her work. Her Cell biology study frequently links to related topics such as Mediator. Aarti Singh carries out multidisciplinary research, doing studies in Mediator and Gene. Her Gene study frequently links to other fields, such as Retrograde signaling. As part of her studies on Retrograde signaling, she frequently links adjacent subjects like Biochemistry. Her Biochemistry study frequently draws connections between related disciplines such as Transcription factor.

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.

Best Publications

Gradient Descent Provably Optimizes Over-parameterized Neural Networks

Simon S. Du;Xiyu Zhai;Barnabas Poczos;Aarti Singh.
international conference on learning representations (2018)

633 Citations

Unlabeled data: Now it helps, now it doesn't

Aarti Singh;Robert Nowak;Xiaojin Zhu.
neural information processing systems (2008)

265 Citations

Decentralized compression and predistribution via randomized gossiping

Michael Rabbat;Jarvis Haupt;Aarti Singh;Robert Nowak.
information processing in sensor networks (2006)

213 Citations

Confidence sets for persistence diagrams

Brittany Therese Fasy;Fabrizio Lecci;Alessandro Rinaldo;Larry Wasserman.
Annals of Statistics (2014)

209 Citations

Data Poisoning Attacks on Factorization-Based Collaborative Filtering

Bo Li;Yining Wang;Aarti Singh;Yevgeniy Vorobeychik.
neural information processing systems (2016)

179 Citations

Gradient Descent Can Take Exponential Time to Escape Saddle Points

Simon S. Du;Chi Jin;Jason D. Lee;Michael I. Jordan.
neural information processing systems (2017)

178 Citations

Multi-Manifold Semi-Supervised Learning

Andrew B. Goldberg;Xiaojin Zhu;Aarti Singh;Zhiting Xu.
international conference on artificial intelligence and statistics (2009)

171 Citations

Gradient Descent Learns One-hidden-layer CNN: Don’t be Afraid of Spurious Local Minima

Simon S. Du;Jason D. Lee;Yuandong Tian;Barnabas Poczos.
international conference on machine learning (2018)

164 Citations

Low-Rank Matrix and Tensor Completion via Adaptive Sampling

Akshay Krishnamurthy;Aarti Singh.
neural information processing systems (2013)

145 Citations

On the decreasing power of kernel and distance based nonparametric hypothesis tests in high dimensions

Aaditya Ramdas;Sashank J. Reddi;Barnabás Póczos;Aarti Singh.
national conference on artificial intelligence (2015)

136 Citations

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