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프린트페이스북

(대학원생 마일리지 적용) Special Seminar on Machine Learning (Thu., July 12, Prof. Alan Yuille and Dr. Seyoun Park at Johns Hopkins Univ.)

2018.07.12 09:06

교수님 및 학생분들께,

 

 

 

안녕하세요.

 

 

산업 및 시스템 공학과에서는 7월 12일(목)에 해외 석학을 초빙하여 컴퓨터 비전, 머신러닝에 관한 주제로

세미나를 개최합니다. Johns Hopkins 대학 CCVL (Computational Cognition, Vision and Learning) 연구

그룹의 Alan Yuille 교수와 SysE 학/석/박 졸업생인 박세연 박사가 각각 Deep networks and beyond:

vision and machine learning, Medical images and computational precision medicine를 주제로

오전/오후 세션을 진행할 예정입니다.

 

 

아래에 세미나 관련 초록 및 연사 이력을 첨부하니 확인하시고 많이 참석해 주시길 부탁 드립니다.

전문가 초청 학과 세미나가 7월 12일 오전 10시 30분과 오후 1시 30분에 산업경영학동 지하계단강의실에서

진행됩니다.

 

 

On 12th July (Thu), Prof. Alan Yuille and Dr.Seyoun Park are going to give a seminar on machine learning

and computer vision. The seminar will consist of two sessions, first of which is by Prof. Alan Yuille

with the title <Deep networks and beyond: vision and machine learning>, and the second is about

< Medical images and computational precision medicine> by Dr.Seyoun Park.

 Please check out the poster, abstracts and the attached CV to find out more information. 

 

 

1. Date and Time : Thursday, July 12th at 10:30 AM and 13:30 PM

 

2. Venue: E2 Bld., Lecture Room B105

                                                             

3. Title : Special Seminar on Machine Learning

                                                  

4. Speakers : Prof. Alan Yuille and Dr. Seyoun Park at Johns Hopkins Univ.

 

 

 

 

[Talk 1]   10:30~11:30 am                   
Prof. A. Yuille                   
- Johns Hopkins University                   
- Department of Computer Science, Department of Cognitive Science                   
- Bloomberg Distinguished Professor                   
- IEEE Fellow                   
- Author of numerous papers in top conferences such as CVPR, ICLR, NIPS, ICCV, IJCAI, AAAI, etc                   
                   
[Title] Deep Networks and Beyond: Vision and Machine Learning                    
                                          
                     [Abstract] Deep networks are very successful for computer vision applications provided there are large annotated datasets                     
                     enabling supervised learning and testing. But there remain important challenges. Firstly, "unrepresentative datasets",                     
                     where the deep networks are sensitive to adversarial attacks,  changes in context, and to rare or hazardous events.                     
                     Secondly, "limited supervised training data" which requires transfer learning to deal with few training examples and                      
                     weak supervision. Finally, "architecture design", where the goal is to automatically search over deep network architectures                     
                     or to couple deep networks with other machine learning techniques such as random forests.                     
                     This talk will address all these issues using state of the art computer vision applications.                    
                     
                                        
[Talk 2]   1:30~2:30 pm                     
Dr. Seyoun Park                     
- Johns Hopkins University                     
- Department of Radiology                   
- BS/MS/PhD in Industrial and Systems Engineering from KAIST                   
                                        
[Title] Medical Images and Computational Precision Medicine                     
                     
[Abstract] Medial images have been utilized for diagnosis and pre-/post-processing of operations. However, recent studies                    
 have been shown that medical images include rich computable low-/high-level information which may lead improved decision                     
making in various clinical applications. This talk introduces various recent research from technical aspects for computational                     
precision medicine, especially prediction of potential malignancy of lesion, survival rate, and adaptive radiation therapy, based on medical images.                    

 

 

                                                    

많은 관심과 참석부탁드립니다.

                                        

감사합니다.

 

 

 

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