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Vijayalakshmi Srinivasan

Vijayalakshmi Srinivasan

Overview

Vijayalakshmi Srinivasan is affiliated with IBM in the United States and has contributed to research spanning both medicine and computer science. Their work intersects several specialized fields, primarily within obstetrics and gynecology as well as radiology, nuclear medicine, and imaging.

The research output of Vijayalakshmi Srinivasan includes studies focused on pregnancy-related medical conditions and advanced computational methods. Their publications cover topics such as pregnancy and preeclampsia studies, birth, development, and health, along with applications of advanced neural networks and artificial intelligence techniques.

Recent papers authored or coauthored by Vijayalakshmi Srinivasan include:

  • Multiscale and multimodal imaging of utero-placental anatomy and function in pregnancy, 2021, Placenta
  • ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training, 2021, arXiv (Cornell University)

Other prominent publications associated with their frequent coauthors reflect interdisciplinary collaboration in medicine and computer science:

  • Three-dimensional visualisation of the feto-placental vasculature in humans and rodents, 2021, Placenta
  • Feto-placental vascular structure and in silico haemodynamics: Of mice, rats, and human, 2024, Placenta

Frequent coauthors collaborating with Vijayalakshmi Srinivasan include Alys R. Clark, Swagath Venkataramani, Joanna L. James, Yutthapong Tongpob, and Caitlin S. Wyrwoll. These collaborations have contributed to multiple publications in venues such as Placenta and arXiv.

The primary publication venues where Vijayalakshmi Srinivasan has contributed are:

  • Placenta
  • arXiv (Cornell University)
  • Tropical Journal of Pathology and Microbiology
  • IEEE Micro

Their main fields of study encompass:

  • Medicine
  • Computer Science

Within these, the subfields they have addressed include:

  • Obstetrics and Gynecology
  • Radiology, Nuclear Medicine and Imaging
  • Pediatrics, Perinatology and Child Health
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Themes prevalent in their research are:

  • Pregnancy and preeclampsia studies
  • Birth, Development, and Health
  • Advanced Neural Network Applications
  • Domain Adaptation and Few-Shot Learning
  • COVID-19 diagnosis using AI
  • Reproductive System and Pregnancy
  • Gestational Diabetes Research and Management

Best Publications

  • Scalable high performance main memory system using phase-change memory technology

    Moinuddin K. Qureshi;Vijayalakshmi Srinivasan;Jude A. Rivers

  • Enhancing lifetime and security of PCM-based main memory with start-gap wear leveling

    Moinuddin K. Qureshi;John Karidis;Michele Franceschini;Vijayalakshmi Srinivasan

  • PACT: Parameterized Clipping Activation for Quantized Neural Networks

    Jungwook Choi;Zhuo Wang;Swagath Venkataramani;Pierce I.Jen Chuang

  • An Overview of the BlueGene/L Supercomputer

    N.R. Adiga;G. Almasi;G.S. Almasi;Y. Aridor

  • Microarchitectural techniques for power gating of execution units

    Zhigang Hu;Alper Buyuktosunoglu;Viji Srinivasan;Victor Zyuban

  • NDC: Analyzing the impact of 3D-stacked memory+logic devices on MapReduce workloads

    Seth H. Pugsley;Jeffrey Jestes;Huihui Zhang;Rajeev Balasubramonian

  • SAFER: Stuck-At-Fault Error Recovery for Memories

    Nak Hee Seong;Dong Hyuk Woo;Vijayalakshmi Srinivasan;Jude A. Rivers

  • A tagless coherence directory

    Jason Zebchuk;Moinuddin K. Qureshi;Vijayalakshmi Srinivasan;Andreas Moshovos

  • Two dimensional branch history table prefetching mechanism

    Philip Emma;Klaus Getzlaff;Allan Hartstein;Thomas Pflueger

  • Optimizing pipelines for power and performance

    Viji Srinivasan;David Brooks;Michael Gschwind;Pradip Bose

  • New methodology for early-stage, microarchitecture-level power-performance analysis of microprocessors

    D. Brooks;P. Bose;V. Srinivasan;M. K. Gschwind

  • Efficient scrub mechanisms for error-prone emerging memories

    Manu Awasthi;Manjunath Shevgoor;Kshitij Sudan;Bipin Rajendran

  • Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks

    Xiao Sun;Jungwook Choi;Chia Yu Chen;Naigang Wang

  • A Scalable Multi- TeraOPS Deep Learning Processor Core for AI Trainina and Inference

    Bruce Fleischer;Sunil Shukla;Matthew Ziegler;Joel Silberman

  • Accurate and Efficient 2-bit Quantized Neural Networks

    Jungwook Choi;Swagath Venkataramani;Vijayalakshmi Srinivasan;Kailash Gopalakrishnan

  • Co-designing accelerators and SoC interfaces using gem5-aladdin

    Yakun Sophia Shao;Sam Likun Xi;Vijayalakshmi Srinivasan;Gu-Yeon Wei

  • Geometric tolerancing: 2. conditional tolerances

    V. Srinivasan;R. Jayaraman

  • Approximate computing: Challenges and opportunities

    Ankur Agrawal;Jungwook Choi;Kailash Gopalakrishnan;Suyog Gupta

  • Programming with relaxed synchronization

    Lakshminarayanan Renganarayana;Vijayalakshmi Srinivasan;Ravi Nair;Daniel Prener

  • Ultra-Low Precision 4-bit Training of Deep Neural Networks

    Xiao Sun;Naigang Wang;Chia-Yu Chen;Jiamin Ni

Frequent Co-Authors

Swagath Venkataramani
Swagath Venkataramani IBM (United States)
Leland Chang
Leland Chang IBM Research - Thomas J. Watson Research Center
Alper Buyuktosunoglu
Alper Buyuktosunoglu IBM (United States)
Michael A. Guillorn
Michael A. Guillorn IBM (United States)
Moinuddin K. Qureshi
Moinuddin K. Qureshi Georgia Institute of Technology
Pradip Bose
Pradip Bose IBM (United States)
Andreas Moshovos
Andreas Moshovos University of Toronto
Luis Ceze
Luis Ceze University of Washington
Wei Wang
Wei Wang University of California, Los Angeles
David Brooks
David Brooks Harvard University

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