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School of Electrical Engineering We thrive
to be the world’s
top IT powerhouse.
We thrive to be the world’s top IT powerhouse.

Our mission is to lead innovations
in information technology, create lasting impact,
and educate next-generation leaders of the world.

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School of Electrical Engineering We thrive
to be the world’s
top IT powerhouse.
We thrive to be the world’s top IT powerhouse.

Our mission is to lead innovations
in information technology, create lasting impact,
and educate next-generation leaders of the world.

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AI in EE AI and machine learning
are a key thrust
in EE research
AI and machine learning are a key thrust in EE research

AI/machine learning  efforts are already   a big part of   ongoing
research in all 6 divisions - Computer, Communication, Signal,
Wave, Circuit and Device - of KAIST EE 

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Highlights

TITLE: EE Professor Jun-Bo Yoon’s research team developed a highly sensitive and reliable wireless Hydrogen gas sensor through phase-transition-inhibited Pd nanowires, and is selected as a supplementary cover paper.
 
 
A research team consisting of KAIST’s School of Electrical Engineering Professor Jun-Bo Yoon and Busan National University’s Professor Min-Ho Seo (KAIST Ph.D graduate) has developed a method for wireless and linear Hydrogen detection with high sensitivity, and the paper was accepted to ACS Nano 2022 (Min-Seung Jo as the first author). The research team successfully built a sensor that exhibits high sensing linearity and stable sensitivity over Hydrogen gas concentrations of 0~4% using 3-dimensional Pd nanowire structures that exhibit Pd phase-transition-inhibitions. 
*Phase-transition: physical processes of transition between a state of a medium (such as solid, liquid, and gas phase) used in chemistry and thermodynamics
 
 
The research, led by a Ph.D candidate student Min-Seung Jo as the first author, has been published in a well-known international journal ACS Nano on May 2022. (Paper: Wireless and Linear Hydrogen Detection up to 4% with High Sensitivity through Phase-Transition-Inhibited Pd Nanowires) (https://pubs.acs.org/doi/10.1021/acsnano.2c01783).
 
 
Hydrogen gas has gaining attention as the next generation environmentally friendly energy carrier due to its high combustion energy and the generation of water as the sole byproduct. However, the use of Hydrogen gas requires strict supervision as the gas is flammable and explosive at concentrations above 4% in air.
 
 
Among various Hydrogen gas sensing materials, palladium (Pd) is known to be very appealing not only for its simple mechanism of change in electrical resistance by reacting with the Hydrogen gas, but also very stable as there are no byproducts during the reaction. However, when Pd is exposed to over over 2% H2 concentration, phase transition occurs which results in limitations of concentration range for detection, delay in reaction time, and impairment of durability, and does not meet the basic requirements of being able to detect H2 concentrations of up to 4%.
 
 
To solve this issue, the research team designed and manufactured a new Pd nanostructure in which the chemical potential can be reduced that leads to a lower free energy of phase transition. The new sensor was successfully able to detect H2 concentrations of 0.1~4% with 98.9% linearity. The team also demonstrated a sensor system that wirelessly detects H2 through by incorporating the sensor with BLE (Bluetooth low energy), 3D printing, and an Android application, and it was able to reliably detect H2 leakage with a smartphone or a PC even when located 20 meters away from the sensor. This research is significant in that it was a successful attempt at building a reliable Pd-based H2 sensor that can detect H2 concentrations of over 2%, which was previously difficult to produce. In particular, it is expected that this sensor will be used for safety management in the future where Hydrogen-based clean energy is prevalent. 
 
 
Korean newpapers share this news 28th June.
 
 
[Relate  link]
 
 

(Prof. Yong Man Ro, Sangmin Lee, Sungjune Park,  from left)

 

Ph.D. Candidate Sangmin Lee and Sungjune Park (Prof. Yong Man Ro’s Lab) won the 1st place in the Ad-hoc Video Search (AVS) section of the 11th Video Browser Showdown (VBS 2022).

 

VBS is the international video retrieval competition held annually, and this year VBS 2022 is the 11th competition.

 

This year’s competition was held at Vietnam Phú Quốc for two days from June 6th to 7th, with 16 finalized video search teams from around the world.

 

The Ad-hoc Video Search section is to find as exact videos for given querys out of 2.5 million videos.

Sangmin Lee and Sungjune Park won the first place by constructing a multimodal search engine based on deep learning, which effectively searches target videos through the multi-modal correspondences of visual-audio-language latent representations.

 

The core algorithm adopted in the search engine, novel visual-audio representation learning method will be presented at CVPR 2022, the top tier conference in computer vision and AI field.

 

The title of the paper is “Weakly Paired Associative Learning for Sound and Image Representations via Bimodal Associative Memory”.

 

– Competition: 11th Video Browser Showdown 2022

– Award: Best AVS (1st place winner in Ad-hoc Video Search)

– Recipient: Sangmin Lee, Sungjune Park, Yong Man Ro (Advisory Professor)

 

[Hyungtae Lim (PhD student), event officials, Prof. Hyun Myung, from the left)

Title: EE Prof. Hyun Myung’s Team wins the 2nd Prize in Academia at the Future of Construction Workshop in IEEE ICRA 2022  
 
Team QAIST (advisor: Prof. Hyun Myung) wins the 2nd prize at HILTI Challenge 2022 held in Future of Construction: Build Faster, Better, Safer – Together with Robots Workshop at 2022 IEEE International Conference on Robotics and Automation (ICRA) held in Philadelphia, USA during May 23-27, 2022.  
HILTI SLAM Challenge 2022 is  organized by HILTI Corp. in Liechtenstein, Oxford Robotics Institute in Oxford University, and Robotics and Perception Group in ETH Zürich.  
This Challenge is a competition for accurate mapping by developing simultaneous localization and mapping (SLAM) algorithms that can robustly operate even in construction environments and degeneracy environments such as narrow indoor environments that lack features. Among the  40 teams around the world, team QAIST wins the 2nd prize in the Academia. They will receive  USD 3,000 as a cash prize.  
   
Details on this good news are as follows:   
 

l  Name of Conference: 2022 IEEE International Conference on Robotics and Automation (ICRA) 

l  Name of Workshop and Date: Future of Construction: Build Faster, Better, Safer – Together with Robots Workshop, 23rd, May, 2022 

l  Prize: 2nd Prize among Academia (USD 3,000) 

l  Participants: Team QAIST (Quatro + KAIST). Hyungtae Lim, Daeboem Kim, Beomsoo Kim, Seungwon Song, Alex Junho Lee, Seungjae Lee, and Prof. Hyun Myung 

Title:  EE Professor Kim, SangHyeon’s Research Team Develops 3D Stackable Quantum Computing Readout Device

KAIST Builds 3D Stackable Quantum Computing Readout Device  Low-power, low-noise, high-speed device integrated in 3D operates at super-low temperatures and promises large-scale applications to quantum computing devices.

<(From left) EE Prof. Kim, SangHyeon, PhD candidate Jeong, Jae Yong, NanoFab PhD candidate Kim, Jongmin, and KBSI Prof. Park, Seung-Young>

 

KAIST EE Prof. Kim, SangHyeon’s research team has developed a 3D-stacked semiconductor readout device integration technology, as made public on the 16th. The team made this possible by applying the strengths of monolithic 3D integration to overcome large-scale qubit implementation based on existing quantum computing systems. Their work is a first of its kind exhibiting the 3D stackability of quantum computing readout devices after an actively pursued line of research on monolithic 3D stacking of high-speed devices following a 2021 VLSI presentation, a 2021 IEDM presentation, and a 2022 ACS Nano publication.

(*monolithic 3D integration: dubbed the ultimate 3D integration tech, wherein after the lower-layer devices, the upper layer’s thin film is created and stacking proceeds sequentially so as to maximize the upper-lower device alignment)

KAIST EE Prof. Kim, SangHyeon Kim’s research team member Jeong, Jae Yong led the work as first author, collaborating with NanoFab PhD candidate Kim, Jongmin and KBSI Prof. Park, Seung-Young. Their joint work has been presented at the “semiconductor Olympics”, Symposium on VLSI Technology. (Paper: 3D stackable cryogenic InGaAs HEMTs for heterogeneous and monolithic 3D integrated highly scalable quantum computing system)

A qubit is capable of processing twice the amount computation compared with that of a bit. Number of qubits increasing linearly results in exponential speedup of their computation. Thus, developing large-scale quantum computing is of utmost importance. IBM, for instance, presented Eagle containing 127 qubits, and the IBM roadmap outlines development of a 4,000-qubit quantum computer by 2025 and one with 10,000-qubits or more in 10 years.

Designing such large-scale quantum computers with many qubits requires implementing devices for qubit control/readout. The research team has not only proposed and implemented 3D-stacked control/readout devices but also achieved world-best cutoff frequency characteristics at cryogenic settings despite the 3D stacking.

This work has been supported by the National Research Foundation of Korea, the System Semiconductor Development Program funded by Gyeonggi-do, and the Korea Basic Science Institute.

 EE Professors Kim, Changick and Jeong, Jae-Woong Awarded on 2022 KAIST Research Day.

[From left, Prof. Kim, Changick, Prof. Jeong, Jae-Woong]

 

Professor Kim, Changick has been recognized with the Transdisciplinary Research Prize for his contributions to computer vision- and artificial intelligence-based monitoring  technology of anthropocene effects on the planet. Anthropocene is a scientific concept referring to the recent geological epoch distinct from previous ones, marked by unprecedented transformations in the planet’s system from human activities since the Industrial Revolution. Prof. Kim has been conducting research with satellite images, computer modeling, and deep learning tools on monitoring the compromised states of planet Earth, such as climate change and sea level rises. In addition, as part of AI-based digital study of ecology, he has cooperated closely with anthropogeography and ecology experts to detect endangered species in the DMZ; he has developed a deep network capable of counting and classifying endangered species, such as the red-crowned cranes, the white-naped cranes, and the white-fronted geese. This study is meaningful in automating and maintaining the monitoring process of endangered species in the DMZ and Cheorwon.

 

Professor Jeong, Jae-Woong has been awarded the KAIST Scholastic Award for proposing a new direction in the automated treatment of brain diseases and cognitive research by developing for the first time an IoT (Internet of Things) based wireless remote control system for neural circuits in the brain. The proposed direction sets a vision for one of humanity’s most difficult challenges: overcoming brain diseases. Prof. Jeong has also led the field of research in wirelessly rechargeable soft subdermal implantable devices. These works have been published in 2021 in top journals of medical engineering: Nature Biomedical Engineering and Nature Communications. Said studies were led by Prof. Jeong’s team, with international collaborators in Washington University in School of Medicine, attracting over 60 press reports across the world.

 

[Prof. Yoon, Young-Gyu, Kim, Jeewon (PH.D candidate), Prof. Chang, Jae-Byum From Left: ]

KAIST EE Professor Yoon, Young-Gyu’s and Materials Science and Engineering Professor Chang, Jae-Byum’s team conducted a joint research published as PICASSO, a simultaneous detector of multiple markers capable of capturing five times as many protein markers in comparison with existing techniques.

Recent studies have shown that protein markers in the cancer tissues manifest differently across cancer patients. Related research findings indicate that such a difference determines the cancer progress as well as reactivity to anti-cancer drugs. This is why detecting multiple markers, also known as multiplexed imaging, from cancer tissues is deemed essential.

The research team’s development, PICASSO, is capable of detecting 15 – and at most 20 – protein markers at once via fluorescence imaging. This development was made possible by utilizing fluorophores exhibiting similar emission spectra simultaneously and accurately isolating each type of the fluorophores with blind source separation. Said technique does not require specialized reagent or expensive equipment and is thus considered a promising method of better diagnosis of cancer and drug development as well as protein marker discovery.

EE PH.D student Kim, Jeewon and Materials Science and Engineering student Seo, Junyoung, and alumnus Sim, Yeonbo have led the research as first authors, and their paper was published in Nature Communications, book 13, May as “PICASSO allows ultra-multiplexed fluorescence imaging of spatially overlapping proteins without reference spectra measurement”.

 

< Readmission Procedures for Fall 2022 >

Acceptance of application : To be accepted by mail or in person

【Applicant’s applications must arrive by June 15, 2022.】

 Address : ㅇㅇ Department Office, KAIST, 291, Yuseong-gu, Daejeon, 34141

 

Advisor’s Opinion

 Applicants should contact the advisor, and have a face-to-face or non-face-to-face interview with him/her

 The Advisor’s Opinion should be prepared by the advisor and submitted to the department office directly.

 

Signature of advisor/department head

 Applicants must submit the application to the department office after receiving the signatures of the advisor and the department head.

 

Application for Department Transfer

 Complete the application form including the signature of applicant and guarantor.

 Applicants should have an interview with both the current and prospective advisors.

 The application form should be submitted to the respective department offices after obtaining the signatures of the advisors and department heads of both the current department and prospective department the applicant is applying to.

 The current and prospective advisors fill out the Advisor’s Opinion and submit it directly to the respective department offices.

 

Method of Review by Department/Division : face-to-face or non-face-to-face interview

 When conducting an interview by department, please observe the self-responsible quarantine system under the responsibility of the department head

Method of Review by Dean of Student Affairs and Policy (applicable to undergraduate students only) : face-to-face or non-face-to-face interview

 

 

Refer to the instructions below on application for readmission in Fall 2022 for dropouts and expelled students.

 

Eligibility : Dropouts and expelled students of Undergraduate or Graduate programs

 

Deadlines

❍ Submission by students : submit to the department/division office by Wednesday, June 15, 2022

❍ Evaluation by Departments/Divisions : Thursday, June 16 ~ Thursday, June 30, 2022

❍ Submission of Departments/Divisions evaluation result to Academic Registrar’s Team : Thursday, June 30, 2022

 

Conditions for Readmission

❍ Students who belong to the following categories are not eligible for readmission

– Those who are expelled for exceeding enrollment duration limit (Up to students enrolled in 2008)

– Those who are expelled for exceeding enrollment duration limit or who are expelled or voluntarily withdraw after getting enrollment duration limit extended (Starting from student enrolled in 2009)

❍ Readmission is possible after 2 semesters including the semester in which dropping out or expulsion occurred.

 

Readmission Application and Review Process

Application for Readmission Department/

Division

Review

Review by Dean of Student Affairs & Policy

(undergraduate students)

Review by Academic and Research Review Committee Approval by Provost

 

Readmission with changing of major

Application for Readmission Prepare two copies of each required document, and submit to both the current department and the new department
※ In addition to the application for readmission, Future study plan, opinion of advisor on readmission, and Academic transcript, applicants seeking readmission to a different department must submit an application for department transfer, and undergraduate applicants who have not declared major but decided his/her department must submit a declaration of major(undergraduate).
󰀻
Department/

Division

Review

Review by both the Current and new departments
※ Students who have not declared major shall go through the new department/division review only
󰀻
Review by Dean of Student Affairs & Policy (undergraduate students)
󰀻
Review by Academic and Research Review Committee
󰀻
Approval by Provost

 

Recommendation for Readmission by Department

❍ The candidate is evaluated on academic capacity, remaining number of semesters, and possibility of graduation based on the advisor’s opinion on readmission and future study plan by a three-member review committee consisting of the advisor, department head, and department professor or a department-related committee.

❍ For undergraduate students, the Dean of Student Affairs & Policy will interview the recommended candidate and results will be announced through the Academic and Research Review Committee.

 

Required Documents for Readmission

❍ Application for Readmission (see attachment)_to be submitted by applicant

❍ Future Study Plan (see attachment)_to be submitted by applicant

❍ Opinion of Advisor on Readmission (see attachment)_to be submitted by applicant

❍ Academic Transcript_to be submitted by applicant

❍ Application for department transfer_to be submitted by applicant (Change a major)

❍ Declaration of major(undergraduate)_to be submitted by applicant (Declared a major)

❍ Recommendation for Readmission (see attachment)_to be submitted by department

Students applying for readmission to a different department shall prepare two copies each, and submit to both the current and new departments

 

Others

❍ Previously earned credits will be automatically recognized without any additional procedure.

❍ Requirements for graduation will be unchanged from the date of initial admission.

❍ The number of enrolled semesters will be counted from the date of initial admission.

❍ Students readmitted after expulsion will be expelled upon receiving an academic warning.

❍ Students expelled due to failing the qualifying exam must pass the exam within 1 year(including the period of taking a leave of absence except maximum 1 semester) of readmission, or will face expulsion again if requirements are not met.

❍ Students can be readmitted only once and will be accepted to the original academic year or lower.

❍ Tuition for readmitted students shall follow the rules on ″Imposition of Tuition fees″.

 

【Attachment】1. Application for Readmission

  1. Future Study Plan
  2. Opinion of(Prospective)Advisor on Readmission
  3. Recommendation for Readmission
  4. Application for department transfer
  5. Declaration of major(undergraduate)

▶ Contact : Academic Registrar’s Team.(Ext 2361 /  registrar@kaist.ac.kr)

 

  1. 5.

Associate Vice President of Academic Affairs

연구성과도_캡처.PNG

EE Prof. Myoungsoo Jung’s research team develops the world’s first computational storage/SSD accelerator capable of graph machine learning, surpassing NVIDIA GPU performance by 7 times

Our department’s Professor Myounsoo Jung’s research team has developed the world’s first computational SSD (CSSD) based accelerator to speed up the graph neural network (GNN)

 

The research team has developed the ‘holistic graph neural network technology (HolisticGNN)’, which directly accelerates graph neural network (GNN) near storage/SSD device where the actual graph data exist. HolisticGNN outperforms GNN inference services compared to high-performance NVIDIA GPUs by 7x while reducing energy consumption by 33x.

 

Among the proposed research results, especially noteworthy is that HolisticGNN provides a software framework that allows users to easily program various GNN models and hardware logic/RTLs for neural network acceleration that users can freely customize. The research team implemented the proposed HolisticGNN on their FPGA-based computational SSD prototype and verified the effectiveness of HolisticGNN.

 

HolisticGNN not only services high-speed GNN inference near storage/SSD for large-scale graphs, but also processes GNN preprocessing such as graph transformation and graph sampling near non-volatile memory by

securing a computational SSD acceleration system optimized for energy saving. This is expected to replace existing high-performance acceleration systems for a wide range of practical applications such as super-large recommendation systems, traffic prediction systems, and drug development.

 

The KAIST Ph.D. Candidates (Miryeong Kwon, Donghyun Gouk, and Sangwon Lee) participate in this research, and the paper (Hardware/Software Co-Programmable Framework for Computational SSDs to Accelerate Deep Learning Service on Large-Scale Graphs) will be reported in February at ‘USENIX Conference on File and Storage Technologies, (FAST) 2022’.

 

The research was supported by the Samsung Science & Technology Foundation. More information on this paper can be found at http://camelab.org, and this result has been reported by domestic media as follow.

[Link]

KAIST

https://news.kaist.ac.kr/news/html/news/?mode=V&mng_no=18410

Naver website 

https://search.naver.com/search.naver?where=news&sm=tab_tnw&query=%EA%B7%B8%EB%9E%98%ED%94%84&sort=0&photo=0&field=0&pd=0&ds=&de=&mynews=0&office_type=0&office_section_code=0&news_office_checked=&related=1&docid=4210005835208&nso=so:r,p:all,a:all 

Copyright ⓒ 2015 KAIST Electrical Engineering. All rights reserved. Made by PRESSCAT

Copyright ⓒ 2015 KAIST Electrical Engineering. All rights reserved. Made by PRESSCAT

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Copyright ⓒ 2015 KAIST Electrical
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