Jyotikrishna Dass
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Research Scientist E-mail: jdass [@] rice [DOT] edu |
Biography
I am currently a Research Scientist in The Rice University Centre for Transforming Data to Knowledge (Rice D2K Lab). My research broadly focuses on integrating ideas from machine learning, parallel and distributed computing, and hardware design to develop algorithms and architecture for distributed edge, and federated machine learning. As a member of the leadership team at D2K, I am responsible for leading the development of D2K policies and procedures. I also build relationships with industrial, healthcare, and community partners for D2K capstone program. In addition, I oversee the management of administrative functions in the center and direct the day-to-day financial, research and academic administration.Previously, I was a Postdoctoral Research Associate at Rice University. I completed my Ph.D. in Computer Engineering at the Department of Computer Science & Engineering, Texas A&M University. I am also passionate about teaching and mentoring which involves sharing knowledge and exchanging ideas with students. At Texas A&M, I had been appointed as Graduate Assistant Lecturer in Fall 2018 and Fall 2020 and was awarded Graduate Teaching Fellowship in Spring 2020. I am also the recipient of the Teaching Assistant Excellence Award in Spring 2018. Before starting my Ph.D., I completed my Bachelor of Technology program from the Indian Institute of Technology (IIT Guwahati) majoring in Electronics and Communication Engineering with a minor in Computer Science and Engineering.
News
[Oct,2023] Our work on “NetDistiller: Empowering Tiny Deep Learning via In-Situ Distillation” has been accepted for publication in IEEE Micro Special Issue on tinyML
[Oct,2023] I am serving as a reviewer for ICLR 2024
[Mar,2023] I am serving as Local Chair for the 11th IEEE International Conference on Healthcare Informatics (ICHI 2023) to be held from June 26th-June 29th in Houston,TX
[Feb,2023] I presented our work “ViTALiTY” at IEEE HPCA in Montréal,CA
[Oct,2022] Our work on “ViTALiTy: Unifying Low-rank and Sparse Approximation for Vision Transformer Acceleration with a Linear Taylor Attention” has been accepted for publication in IEEE HPCA 2023 (25%)
[Oct,2022] I am serving as a reviewer for ICLR 2023
[Oct,2022] I attended the META Communication and Networking Faculty Summit as an awardee of META:Network for AI RFP
[Aug,2022] I joined The Rice University Data to Knowledge (Rice D2K) Lab as a Research Scientist
[Aug,2022] Our proposal “SHF:Medium:DILSE:Codesigning Decentralized Incremental Learning System via Streaming Data Summarization on Edge” has been accepted for NSF CISE Core Programs grant ($1,200,000)
[Aug,2022] Our proposal “MILES: Multi-device Incremental Learning on Edge via Summarization” has been accepted for META:Network for AI funding ($50,000)
[Jul,2022] Our proposal “Tutorial on Automated Tools for Fast Development of Deep Learning Networks and Accelerators” has been accepted for MICRO 2022, Chicago, IL
[Jul,2022] I chaired the research session on “Machine Learning for Resource Management: From Edge to Cloud” at DAC 2022 in San Francisco, CA
[Mar,2022] Our proposal “Workshop on Automated AI Tools for Computing and Communication” has been accepted for Creative Ventures Fund: Conference and Workshop Development ($10,000) from Rice University
[Sep,2021] I moved to Houston and joined Rice University as a Postdoctoral Associate
[Aug,2021] I graduated from Texas A&M University with a PhD degree in Computer Engineering
[June,2021] I defended my PhD disertation on “Efficient and Scalable Machine Learning for Distributed Edge Intelligence”
[May,2021] Our work on “Householder Sketch for Accurate and Accelerated Least-Mean-Squares Solvers” has been accepted at ICML 2021
[May,2021] I will be joining Dr. Yingyan Lin's lab at Rice University as a postdoc in Fall 2021
[Apr,2021] I will serve as invited reviewer in the Program Committee for NeurIPS 2021
[Dec,2020] I will serve as invited reviewer for ICML 2021
[Aug,2020] Teaching CSCE 312: Computer Organization as Graduate Assistant Lecturer for Fall 2020 | Syllabus
[May,2020] Our work on distributed training of SVM on multiple-FPGA system has been accepted for publication in IEEE Transactions on Computers, Impact factor: 3.131 with acceptance rate 21% in Special Issue on Machine-Learning Architectures and Accelerators
[Apr,2020] As a part of community service during COVID-19,
- Organized a free and synchronous online educational inititiative ShiP.py: Learning to Py while Shelter-in-Place with a team of undergraduate and PhD student volunteers | Course Playlist
- Organize and co-instructed a free online course Stay Home and Learn AI with a team of volunteers comprising professors, industry professionals, and students working in data science, machine learning, and deep learning | My SHALA Lectures | Course Playlist
[Feb,2020] Wrote my first blog inspired by Yoshua Bengio and Carl-Johann SIMON-GABRIEL | Decentralizing Academic Conferences for a Better Climate