data+mining
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Yuji Roh Awarded 2022 Microsoft Research PhD Fellowship
KAIST PhD candidate Yuji Roh of the School of Electrical Engineering (advisor: Prof. Steven Euijong Whang) was selected as a recipient of the 2022 Microsoft Research PhD Fellowship.
< KAIST PhD candidate Yuji Roh (advisor: Prof. Steven Euijong Whang) >
The Microsoft Research PhD Fellowship is a scholarship program that recognizes outstanding graduate students for their exceptional and innovative research in areas relevant to computer science and related fields. This year, 36 people from around the world received the fellowship, and Yuji Roh from KAIST EE is the only recipient from universities in Korea. Each selected fellow will receive a $10,000 scholarship and an opportunity to intern at Microsoft under the guidance of an experienced researcher.
Yuji Roh was named a fellow in the field of “Machine Learning” for her outstanding achievements in Trustworthy AI. Her research highlights include designing a state-of-the-art fair training framework using batch selection and developing novel algorithms for both fair and robust training. Her works have been presented at the top machine learning conferences ICML, ICLR, and NeurIPS among others. She also co-presented a tutorial on Trustworthy AI at the top data mining conference ACM SIGKDD. She is currently interning at the NVIDIA Research AI Algorithms Group developing large-scale real-world fair AI frameworks.
The list of fellowship recipients and the interview videos are displayed on the Microsoft webpage and Youtube.
The list of recipients: https://www.microsoft.com/en-us/research/academic-program/phd-fellowship/2022-recipients/
Interview (Global): https://www.youtube.com/watch?v=T4Q-XwOOoJc
Interview (Asia): https://www.youtube.com/watch?v=qwq3R1XU8UE
[Highlighted research achievements by Yuji Roh: Fair batch selection framework]
[Highlighted research achievements by Yuji Roh: Fair and robust training framework]
2022.10.28 View 8587 -
Workshop on Techniques in Prediction Analysis for the Industry
There has been growing interest in the value and the application of “big data” in recent years. To meet this interest, a workshop was held to discuss the possibility and the future of prediction analysis, which is the next big step in data mining after big data.
On February 25 in COEX, Seoul, the Department of Knowledge Service Engineering at KAIST held the 4th knowledge service workshop on “Techniques in Prediction Analysis for the Industry.”
Predication analysis is a technique that can predict the future based on the understanding of the past and the present through analyzing “big data.” If “big data” is fuel in figurative sense, the prediction analysis serves as the engine.
The Department seeks to help those companies interested in data mining by introducing fundamentals and some application examples to the executives of companies who are interested in implementation of the technique.
The lecture was delivered by six professors from the Department of Knowledge Service Engineering and the Department of Industrial and Systems Engineering at KAIST.
Thomas Miller, the author of Modeling Techniques in Predictive Analytics, covered the contents of his book at the event. Professor Moon-Yong Yi, Chair of the Department of Knowledge Service Engineering, said, “This conference will be important to companies that are considering the implementation of the prediction analysis as well as to students who are interested in the field.”
2016.02.22 View 4606 -
Professor Kyu-Young Whang receives the PAKDD Distinguished Contributions Award
Professor Kyu-Young Whag
Dr. Kyu-Young Whang, Distinguished Professor from the Department of Computer Science, KAIST, has received the 2014 Distinguished Contributions Award from the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD). PAKDD is the leading academic international conference on data mining held in Asia/Pacific. This year’s international conference was held from 13th to 15th May at Tainan, Taiwan.
As a life member of the PAKDD Steering Committee, Professor Whang worked for the development of the data mining field in the Asia-Pacific region, and his contribution to the international database and data-mining field has been widely recognized.
The PAKDD Distinguished Contributions Award has been awarded to a total of six people until now, including Professor Whang, and he is the first Korean to receive this award.
Professor Whang has also a history of receiving the Outstanding Contributions Award in 2011 from the Database Systems for Advanced Applications (DASFAA), the prestigious database academic conference in the Asia-Pacific region. The database and data mining field in the region was barren 20 years ago, but through the efforts and contributions of many researchers, including Professor Whang, it has now leapt to the level of being the equal of North American and European researchers.
In fact, three academic organizations in the current international database field are led by professors in the Asia-Pacific region. The IEEE ICDE (Institute of Electrical and Electronics Engineers Technical Committee on Data Engineering) is led by Professor Whang; the VLDB (Very Large Data Base) Endowment by Professor Beng Chin Ooi from National University of Singapore (NUS); and the ACM SIGMOD (Association for Computing Machinery Special Interest Group on Management of Data) by Professor Don Kossmann from ETH Zurich.
2014.05.26 View 8026 -
Is it possible to identify rumors on SNS?
Rumors
sporadically spread with people with fewer followers in the centerResearched
over 100 rumors in the US from 2006 to 2009 Is it
possible to filter information on SNS such as Twitter and Facebook? A research
team led by Professor Mee-Young Cha from the Department of Cultural Technology
Graduate School at KAIST, Professor Kyo-Min Jung of Seoul National University,
Doctor Wei Chen and Yajun Wang of Microsoft Asia, has developed a technology
that can accurately filter out information on Twitter to 90% accuracy. The
research not only deduced a new mathematical model, network structure, and
linguistic characteristics on rumors from SNS data, but is also expected to
enhance the effort to make secure technology to regulate Internet rumors. The team
analysed the characteristics of rumors in over 100 widespread cases in the US
from 2006 to 2009 on Twitter. The team gathered data, which included a range of
areas such as politics, IT, health and celebrity gossips, and their analysis
could identify rumors to 90% accuracy. The filtering was more accurate in
rumors that included slanders or insults. The
research team identified three characteristics of the spread of rumors. Firstly,
rumors spread continuously. Normal news spreads widely once and is mentioned
rarely again on media, but rumors tend to continue for years. Secondly,
rumors spread through sporadic participation of random users with no
connections. Rumors start from people with fewer followers and spread to the
more popular. This phenomenon is often observed in rumors concerning
celebrities or politicians. Lastly,
rumors have unique linguistic characteristics. Rumors frequently include words
(such as “it may be true,” “although not certain, I think,” “although I cannot
fully remember”) related to psychological processes that question, deny, or
infer the reliability of the information. Professor
Cha said, “This research deduced not only a statistical and mathematical model
but also is an integrated research on social psychological theory on the
characteristics of rumors that attract great attention from the society based
on ample data.” The results
were made public in IEEE International Conference on Data Mining last December
in Texas, USA.
2014.02.03 View 9447