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Deepak Agarwal

Deepak Agarwal

D-Index & Metrics

Computer Science

D-Index
36
Citations
5409
World Ranking
11276
National Ranking
4656

Overview

Deepak Agarwal is a researcher primarily affiliated with Pinterest in the United States. Their work focuses extensively on the field of Medicine with a particular emphasis on Pulmonary and Respiratory Medicine, Urology, Ophthalmology, Radiology, Nuclear Medicine and Imaging, and Rheumatology as prominent subfields of study.

The main topics covered in Agarwal's research include:

  • Urinary Bladder and Prostate Research
  • Kidney Stones and Urolithiasis Treatments
  • Prostate Cancer Diagnosis and Treatment
  • Pediatric Urology and Nephrology Studies
  • Pelvic Floor Disorders Treatments
  • Retinal and Macular Surgery
  • Hormonal and Reproductive Studies

Frequent co-authors who have collaborated with Agarwal include:

  • Amy E. Krambeck
  • Tim Large
  • Marcelino Rivera
  • Mark Assmus
  • Matthew Lee

Agarwal's publications appear most often in the following journals:

  • The Journal of Urology
  • Journal of Endourology
  • Urology
  • World Journal of Urology
  • Journal of Energy Resources Technology

Notable recent research papers authored by Deepak Agarwal include:

  • Same Day Discharge is a Successful Approach for the Majority of Patients Undergoing Holmium Laser Enucleation of the Prostate (2021) published in European Urology Focus
  • Catheter Removal on the Same Day of Holmium Laser Enucleation of the Prostate: Outcomes of a Pilot Study (2020) published in Urology

Other related papers in the domain, while not directly authored by Agarwal but relevant to the field and timeframe, include studies focusing on large gland prostate procedures and advancements in laser enucleation technology, appearing in journals such as Journal of Endourology and European Urology Focus.

Best Publications

  • Regression-based latent factor models

    Deepak Agarwal;Bee-Chung Chen

  • fLDA: matrix factorization through latent dirichlet allocation

    Deepak Agarwal;Bee-Chung Chen

  • Multi-armed bandit problems with dependent arms

    Sandeep Pandey;Deepayan Chakrabarti;Deepak Agarwal

  • Amazon Redshift and the Case for Simpler Data Warehouses

    Unknown

  • Contextual advertising by combining relevance with click feedback

    Deepayan Chakrabarti;Deepak Agarwal;Vanja Josifovski

  • Response prediction using collaborative filtering with hierarchies and side-information

    Aditya Krishna Menon;Krishna-Prasad Chitrapura;Sachin Garg;Deepak Agarwal

  • Spatio-temporal models for estimating click-through rate

    Deepak Agarwal;Bee-Chung Chen;Pradheep Elango

  • Explore/Exploit Schemes for Web Content Optimization

    Deepak Agarwal;Bee-Chung Chen;Pradheep Elango

  • Online Models for Content Optimization

    Deepak Agarwal;Bee-chung Chen;Pradheep Elango;Nitin Motgi

  • Bandits for Taxonomies: A Model-based Approach.

    Sandeep Pandey;Deepak Agarwal;Deepayan Chakrabarti;Vanja Josifovski

  • Automatic ad format selection via contextual bandits

    Liang Tang;Romer Rosales;Ajit Singh;Deepak Agarwal

  • Predictive discrete latent factor models for large scale dyadic data

    Deepak Agarwal;Srujana Merugu

  • Estimating rates of rare events with multiple hierarchies through scalable log-linear models

    Deepak Agarwal;Rahul Agrawal;Rajiv Khanna;Nagaraj Kota

  • A spatio-temporal approach to collaborative filtering

    Zhengdong Lu;Deepak Agarwal;Inderjit S. Dhillon

  • Spatial scan statistics: approximations and performance study

    Deepak Agarwal;Andrew McGregor;Jeff M. Phillips;Suresh Venkatasubramanian

  • Localized factor models for multi-context recommendation

    Deepak Agarwal;Bee-Chung Chen;Bo Long

  • LASER: a scalable response prediction platform for online advertising

    Deepak Agarwal;Bo Long;Jonathan Traupman;Doris Xin

  • Fast online learning through offline initialization for time-sensitive recommendation

    Deepak Agarwal;Bee-Chung Chen;Pradheep Elango

  • Click shaping to optimize multiple objectives

    Deepak Agarwal;Bee-Chung Chen;Pradheep Elango;Xuanhui Wang

  • GLMix: Generalized Linear Mixed Models For Large-Scale Response Prediction

    XianXing Zhang;Yitong Zhou;Yiming Ma;Bee-Chung Chen

  • Estimating rates of rare events at multiple resolutions

    Deepak Agarwal;Andrei Zary Broder;Deepayan Chakrabarti;Dejan Diklic

  • Budget pacing for targeted online advertisements at LinkedIn

    Deepak Agarwal;Souvik Ghosh;Kai Wei;Siyu You

Frequent Co-Authors

Deepayan Chakrabarti
Deepayan Chakrabarti The University of Texas at Austin
Raghu Ramakrishnan
Raghu Ramakrishnan Microsoft (United States)
Andrei Z. Broder
Andrei Z. Broder Google (United States)
Evgeniy Gabrilovich
Evgeniy Gabrilovich Google (United States)
Alexander J. Smola
Alexander J. Smola Amazon (United States)
Dimitrios Gunopulos
Dimitrios Gunopulos National and Kapodistrian University of Athens
Xuanhui Wang
Xuanhui Wang Google (United States)
Qiang Yang
Qiang Yang Hong Kong University of Science and Technology
Jayavel Shanmugasundaram
Jayavel Shanmugasundaram Yahoo (United States)

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