中嶋研究室 / Nakajima Lab​

 

東京大学大学院 情報理工学系研究科 情報理工学教育研究センター 次世代知能科学研究部門
東京大学 連携研究機構 次世代知能科学研究センター(AIセンター)(兼務)
東京大学大学院 情報理工学系研究科 先端人工知能学教育寄付講座(兼務)
東京大学大学院 情報理工学系研究科 創造情報学専攻(兼担)

Next Generation Artificial Intelligence Research Center (AI Center),
The University of Tokyo

Chair for Frontier AI Education, Graduate School of Information Science and Technology,

The University of Tokyo

Department of Creative Informatics, Graduate School of Information Science and Technology,
The University of Tokyo

東京都文京区本郷7-3-1

7-3-1 Hongo, Bunkyo-ku, 113-8656 Tokyo, Japan

Email: k_nakajima[at]mech.t.u-tokyo.ac.jp

Information,

Dynamics,

Computation,...

 

Soft Robotics,

Physical Reservoir Computing,...

中嶋研究室へようこそ!

 

東京大学大学院情報理工学系研究科創造情報学専攻の研究室として発足しました。
中嶋研究室では、特任研究員、大学院生(博士課程・修士課程)を募集しています。
興味のある方は、メールにてご連絡ください。

 


Welcome to Nakajima Lab!

 

Our lab is diverse and interdisciplinary. 
Prospective students/postdocs are encouraged to email k_nakajima@mech.t.u-tokyo.ac.jp for more details.

Reservoir Computing Seminarを開催しています

 

2021年度のReservoir Computing Seminarを4月15日より開催します。
Reservoir Computingを中心に、
ソフトロボティクス、カオス力学系、スピントロニクス、量子機械学習などの
研究発表やジャーナルクラブを行います。
参加を希望される方は、メールにてご連絡ください。
現在は、zoomによる参加となります。

2021/06/17

Press release "Vortex, the Key to Information Processing Capability: Virtual Physical Reservoir Computing" from JST and Kanazawa University

 

journal info:

K. Goto, K. Nakajima, H. Notsu,

Twin vortex computer in fluid flow,

New Journal of Physics 23: 063051, 2021.
https://doi.org/10.1088/1367-2630/ac024d

JST
Japanese: https://www.jst.go.jp/pr/announce/20210617/index.html

Kanazawa University
Japanese: https://www.kanazawa-u.ac.jp/press
English: https://www.kanazawa-u.ac.jp/latest-research/93394

2021/07/01
Invited talk at 2021 Virtual Research Seminar Series on Complex Active and Adaptive Material Systems

Session "Information Processing in Materials"

info: https://sites.google.com/afwerx.af.mil/adaptivematerialsworkshop/home
session chair: Dr. Jiangying Zhou, DARPA/DSO
date: July 1st, 2021
time: 12:45 – 1:00 EDT
talk: Physical Reservoir Computing: Exploiting Physical Dynamics as a Computational Resource

2021/07/20

Invited talk at 2021 IEEE Summer Topicals Meeting Series (SUM2021)

session: Advanced Computing
date: 9:00am - 10:30am, Tuesday, 20 July 2021
info: https://www.ieee-sum.org/
talk: Physical reservoir computing: exploiting physical dynamics as a computational resource

2021/07/23

Invited talk at Deep Learning in Unconventional Neuromorphic Hardware (Satellite Workshop of IJCNN2021)

dates: July 23, 2021

time: 12:30 (UTC+1)
website: https://events.femto-st.fr/DLUNH/en

scope of the workshop:
The importance and impact of Deep Learning and Deep Neural Network methodologies are by now widely accepted. This exceptional success is nowadays associated with special purpose hardware acceleration technologies (e.g., GPUs, TPUs), based primarily on conventional electronic hardware. However, the observed architecture of biological neural systems fundamentally differs from von Neumann processors. Following a biological inspiration, unconventional neuromorphic hardware using photonics, 3D integration, spin-tronic, in-memory substrates and architectures attract increasing attention as a way to implement Deep Learning algorithms. The common objective is to leverage substrate or architecture inherent advantages in terms of speed, power consumption, latency, and scalability.

 

A significant effort to increase synergy between neuromorphic computing substrate developments and Deep Learning concepts is needed. This includes developing high-performance Deep Neural Networks topologies amenable to neuromorphic implementations, finding solutions to manage the intrinsic physical noise for neuromorphic computation, and exploring learning solutions alternative to Backpropagation. Simultaneously, inherent properties of neuromorphic substrates motivate novel Deep Learning models and algorithms with intriguing possibilities, for example leveraging intrinsic continuous dynamics offered by photonics.

organizing committee:
Brunner, Daniel (FEMTO-ST, CNRS)
Gallicchio, Claudio (University of Pisa)
Soriano, Miguel C. (IFISC, CSIC-UIB)

 

program committee:
Estébanez, Irene (IFISC, CSIC-UIB)
Porte, Xavier (FEMTO-ST, UBFC)
Semenova, Nadezhda (FEMTO-ST, SSU)

 

invited speakers:
Kohei Nakajima (University of Tokyo): "Physical reservoir computing"
Julie Grollier (CNRS/Thales lab): "Equilibrium Propagation for Intrinsically Learning Hardware"
Sylvain Gigan (Sorbonne Université / Ecole Normale Supérieure): "An optical random machine for inference and training"

twin_vortex.jpg