Deblina Bhattacharjee

ARTIFICIAL INTELLIGENCE RESEARCHER

About

I am a researcher working at the Samsung Postech Intelligent Media Research Centre in South Korea. In my research at the Intelligent Media Research Centre, I develop deep learning algorithms for visual microphone and visual vibrometry techniques based on image and signal processing. I am affiliated to POSTECH, Pohang University of Science and Technology, South Korea and Samsung R&D.

To put it simply, I develop intelligent data driven models such that the resulting algorithm can understand imperceptible motions (invisible to a human eye) from just a video. This motion signal is visible to the computer as the algorithm magnifies the natural movement in every pixel of the video content. It then can be used in two ways a)retrieve sound (aka visible sound) from that motion as done by MIT CSAIL -VISUAL MICROPHONE b) analyse the movement of structures to make behavioral predictions of such structures during calamities.

Also, in my previous work, I have worked on an evolutionary learning algorithm based on biological plant intelligence to solve optimization problems. The algorithm is interesting as it is motivated from something separate from neural nets and the concept of a human brain. It is based on the intelligence of biological plants in non-stationary environments that follows the universal Fibonacci series and Golden Ratio. The developed evolutionary machine learning algorithm was applied to medical imaging and terrestrial image processing problems.

During this time, I was a machine learning researcher at Connected Computing and Media Processing Lab, Kyungpook National University, South Korea.

I have completed my MS in Artificial Intelligence from Kyungpook National University, specializing in optimization and evolutionary machine learning. My supervisor was Prof. Anand Paul. A core part of my MS was working in collaboration with the National Research Foundation and the Brain Korea 21 Plus Program, funded by the Ministry of Education, South Korea and the IITP International Industry Convergence Project, Daegu South Korea.

I am passionate about learning, teaching and sharing my ideas about artificial intelligence and machine learning, both from an academic and industrial standpoint.

Before MS, I worked for Teach for India and EduCare (a non-profit initiative for teaching children and empowering girls). I was based in Bangalore, India at the time of my outreach programs with Centre for Social Action which I undertook during my undergrad degree (B.Tech) from Christ University, India. Until recently, I am actively supporting and volunteering as a mentor at India STEM Foundation where I host workshops and mentor teams for International Robotics Olympiad.

News

  • I am honoured to take part in the podcast Solving an Optimization Problem with a Custom Built Algorithm at SuperdataScience, March 2017 in Australia.

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Publications

Image Analysis using a novel learning algorithm based on Plant Intelligence

Deblina Bhattacharjee
NIPS / WiML 2017 workshop, Long Beach, California, USA
Abstract content goes here...

An Immersive Learning Model Using Evolutionary Learning

Deblina Bhattacharjee, Anand Paul, J.H. Kim and P. Karthigaikumar
Elsevier Computers and Electrical Engineering (CAEE) Journal (2017)- accepted. To be published.
In this article, we have proposed an educational model using virtual reality on a mobile platform by personalizing the simulated environments as per user actions. We have also proposed an evolutionary learning algorithm based on which the user learning path is designed and the corresponding simulated learning environment is modified. The main objective of this study is to create a personalized learning path for each student as per their calibre and make the learning immersive and retainable using virtual reality. Our proposed model emulates the innate natural learning process in humans and uses that to customize the virtual simulations of the lessons by applying the evolutionary learning technique. A quasi-experimental study is conducted by taking different case studies to establish the effectiveness of our learning model. The results show that our learning model is immersive and gives long term retention while enhancing creativity through reinforced customization of the simulations.

A Leukocyte Detection technique in Blood Smear Images using Plant Growth Simulation Algorithm

Deblina Bhattacharjee and Anand Paul, Kyungpook National University
AAAI 2017: 31st Association for the Advancement of Artificial Intelligence, San Francisco,USA, February 03~09 2017
For quite some time, the analysis of leukocyte images has drawn significant attention from the fields of medicine and computer vision alike where various techniques have been used to automate the manual analysis and classification of such images. Analysing such samples manually for detecting leukocytes is time-consuming and prone to error as the cells have different morphological features. Therefore, in order to automate and optimize the process, the nature-inspired Plant Growth Simulation Algorithm (PGSA) has been applied in this paper. An automated detection technique of white blood cells embedded in obscured, stained and smeared images of blood samples has been presented in this paper which is based on a random bionic algorithm and makes use of a fitness function that measures the similarity of the generated candidate solution to an actual leukocyte. As the proposed algorithm proceeds the set of candidate solutions evolves, guaranteeing their fit with the actual leukocytes outlined in the edge map of the image. The experimental results of the stained images and the empirical results reported validate the higher precision and sensitivity of the proposed method than the existing methods. Further, the proposed method reduces the feasible sets of candidate points in each iteration, thereby decreasing the required run time of load flow, objective function evaluation, thus reaching the goal state in minimum time and within the desired constraints.

A Hybrid Search Optimization Technique Based on Evolutionary Learning in Plants

Deblina Bhattacharjee and Anand Paul
Springer LNCS and Proceedings of the Seventh International Conference on Swarm Intelligence (ICSI), Bali, Indonesia, June 24~30 2016
In this article, we have proposed a search optimization algorithm based on the natural intelligence of biological plants, which has been modelled using a three tier architecture comprising Plant Growth Simulation Algorithm (PGSA), Evolutionary Learning and Reinforcement Learning in each tier respectively. The method combines the heuristic based PGSA along with Evolutionary Learning with an underlying Reinforcement Learning technique where natural selection is used as a feedback. This enables us to achieve a highly optimized algorithm for search that simulates the evolutionary techniques in nature. The proposed method reduces the feasible sets of growth points in each iteration, thereby reducing the required run times of load flow, objective function evaluation, thus reaching the goal state in minimum time and within the desired constraints.

An object localization optimization technique in medical images using plant growth simulation algorithm

Deblina Bhattacharjee, Anand Paul, J. H. Kim and M. Kim
Springer Plus Journal (2016) Volume 5, Number 1784, pages: 1-20
The analysis of leukocyte images has drawn interest from fields of both medicine and computer vision for quite some time where different techniques have been applied to automate the process of manual analysis and classification of such images. Manual analysis of blood samples to identify leukocytes is time-consuming and susceptible to error due to the different morphological features of the cells. In this article, the nature-inspired plant growth simulation algorithm has been applied to optimize the image processing technique of object localization of medical images of leukocytes. This paper presents a random bionic algorithm for the automated detection of white blood cells embedded in cluttered smear and stained images of blood samples that uses a fitness function that matches the resemblances of the generated candidate solution to an actual leukocyte. The set of candidate solutions evolves via successive iterations as the proposed algorithm proceeds, guaranteeing their fit with the actual leukocytes outlined in the edge map of the image. The higher precision and sensitivity of the proposed scheme from the existing methods is validated with the experimental results of blood cell images. The proposed method reduces the feasible sets of growth points in each iteration, thereby reducing the required run time of load flow, objective function evaluation, thus reaching the goal state in minimum time and within the desired constraints.

Autonomous Terrestrial Image Segmentation and Sensor Node Localization for Disaster Management using Plant Growth Simulation Algorithm

Deblina Bhattacharjee, Anand Paul, WH Hong, HC Seo, S Karthik
preprints.org
The use of unmanned aerial vehicle (UAV) during emergency response of a disaster has been widespread in recent years and the terrain images captured by the cameras on board these vehicles are significant sources of information for such disaster monitoring operations. Thus, analyzing such images are important for assessing the terrain of interest during such emergency response operations. Further, these UAVs are mainly used in disaster monitoring systems for the automated deployment of sensor nodes in real time. Therefore, deploying and localizing the wireless sensor nodes optimally, only in the regions of interest that are identified by segmenting the images captured by UAVs, hold paramount significance thereby effecting their performance. In this paper, the highly effective nature-inspired Plant Growth Simulation Algorithm (PGSA) has been applied for the segmentation of such terrestrial images and also for the localization of the deployed sensor nodes. The problem is formulated as a multi-dimensional optimization problem and PGSA has been used to solve it. Furthermore, the proposed method has been compared to other existing evolutionary methods and simulation results show that PGSA gives better performance with respect to both speed and accuracy unlike other techniques in literature.

Evolutionary Reinforcement Learning based Search Optimization

Deblina Bhattacharjee
SAC 2016: Proceedings of the 31st Annual ACM Symposium on Applied Computing, Pisa, Italy, April 4~8 2016. Publisher: ACM
Nature has always inspired researchers to find the best solutions to the toughest of problems. In this article, we proposed a search optimization algorithm based on a refined Plant Growth Simulation Algorithm (PGSA) that uses reinforcement learning. The method combines the heuristic based PGSA with reinforcement learning techniques where natural selection is used as a feedback, thus combining evolutionary algorithms with learning. This enables us to achieve a highly optimized algorithm for growth point search that simulates the evolutionary techniques seen in nature. The proposed method reduces the feasible sets of growth points in each iteration, thereby reducing the required run times of load flow, objective function evaluation and morphactin concentration calculation.

Adaptive Transcursive Algorithm for Depth Estimation in Deep Learning Networks

Uthra Kunathur Thikshaja, Anand Paul, Seungmin Rho and Deblina Bhattacharjee
2016 International Conference on Platform Technology and Service (PlatCon), Jeju, South Korea, Feb 15~17 2016.
Estimation of depth in a Neural Network (NN) or Artificial Neural Network (ANN) is an integral as well as complicated process. In this article, we propose a way of using the transformation of functions combined with recursive nature to have an adaptive, transcursive algorithm to represent the backpropagation concept used in deep learning for a Multilayer Perceptron Network. Each function can be used to represent a hidden layer used in the neural network and they can be made to handle a complex part of the processing. Whenever an undesirable output occurs, we transform (modify) the functions until a desirable output is obtained. We have an algorithm that uses the transcursive model to create an interpretation of the concept of deep learning using multilayer perceptron network (MPN).

Talks

  • Invited Speaker about Future Trends in Deep Learning, at International Conference on Data Science and Big Data Analytics, on May 24-25, 2018 in Toronto, Canada
  • Poster Presentation at WiML (Women in Machine Learning) workshop, Long Beach, California, USA, 2017
  • Advances in Deep Learning, CiTE, Samsung Intelligent Media Research Centre,Postech, South Korea, 2017
  • Oral presentation at AAAI about Plant Intelligence and how it can be used to optimize Machine Learning, San Francisco, USA, 2017
  • Oral presentation at ICSI, Bali, Indonesia, 2016
  • Oral presentation and poster presentation at ACM SAC, Italy, 2016
  • Talk at Platcon, Jeju, South Korea, 2017
  • Departmental Talk on Vision, Deep Learning and Optimization, Kyungpook National University 2016
  • Oral presentation at Computer Science and Engineering department, Kyungpook National University, 2015
  • Oral presentation at the project grant proposal meetings, Daegu, South Korea, 2015-2017

Academic Service

Reviewer of Machine Learning, Signal Processing and IoT journals and conferences:

  • Reviewer of Women in Machine Learning (2017)
  • Reviewer of IEEE Transactions on Signal and Information Processing (top journal in Machine Learning and Signal Processing) [invited]
  • Reviewer of Elsevier Computers And Electrical Engineering
  • Reviewer of ACM Transactions on Embedded Computing Systems
  • Reviewer of IEEE Intelligent Transport Systems [invited]
  • Reviewer of Thomas and Francis Behaviour and Information Technology
  • Reviewer for Springer Plus Journal
  • Reviewer of IEEE Transactions on Emerging Topics in Computing
  • Reviewer of MDPI Sensors
  • Reviewer of IEEE ICIT (International Conference on Industrial Technology) [invited]
  • Reviewer of Springer Cluster Computing- The Journal of Networks, Software Tools and Applications
  • Reviewer of ACM SAC 2016, 2017

Member of organizations:

  • Member of Association for the Advancement of Artificial Intelligence (AAAI)
  • Member of International Machine Learning Society (IMLS)
  • Member of Computer Society of India
  • Member of Association for Computing Machinery

Certificates

  • TensorFlow BootCamp 2017 (Online)
  • Computer Vision A-Z: superdatascience.com 2017 (Online)
  • Deep Learning A-Z: superdatascience.com 2017 (Online)
  • Artificial Intelligence A-Z: superdatascience.com 2017 (Online)
  • Machine Learning A-Z: superdatascience.com 2016 (Online)
  • Microsoft Student Program: Hackathon via Microsoft 2015 (Christ University Campus)
  • R Programming and Data Analysis, Johns Hopkins University, USA via coursera.org 2014 (Online)
  • SAP HANA Cloud Platform (Advanced) via openSAP 2014 (Online)
  • MAS.S69x: Big Data and Social Physics, MIT, USA via edX.org 2014 (Online)
  • CS 6.001: Certificate in Introduction to computer Science and Programming using Python, MIT, USA via edX.org 2014 (Online)
  • SAP 1.0 Certification, from Christ University and Waldorf, Germany 2013. (Christ University Campus)
  • Stat 2.3X: Introduction to Statistics: Inference. University of California, Berkeley via edX.org 2013(Online)
  • CS169: Software as a Service. University of California, Berkeley, USA via edX.org 2013 (Online)
  • Stat 2.2 X: Introduction to Statistics: Probability. University of California, Berkeley via edX.org2013 (Online)

Awards

  • Women in Machine Learning (WIML) Grant, 2017
  • ACM SAC SRC best student paper nomination -top 5 globally, 2016
  • ACM SIGAPP Travel Award, Italy, 2016
  • KNU International Student Ambassador 2017- present
  • Brain Korea 21 Plus grant for research, Kyungpook National University, awarded to top 1% of the applicants in Department of Computer Science Engineering 2015-2017
  • Awarded full merit scholarship by Kyungpook National University, 2015-2017 (4 Semesters)
  • Christ University Merit Scholarship - all 8 semesters, 2011-2015. Dean's List
  • MS Artificial Intelligence, Offer of Study, from New York University, 2015
  • MBA Business Analytics, Offer of Study, from University of Tampa, Florida, USA, 2015
  • BS Computer Science Engineering (transfer), Offer of Study, from University of Rochester, USA 2013
  • Best Overall Performer of the Year 2012 of all undergraduate and postgraduate students, Christ University
  • Runner-up International Science Debate Competition by Quanta, November 2009
  • Won Gold medal and ranked 1st in National Cyber Olympiad in India, 2005
  • Awarded Distinction in Macmillan International Assessment, University of New South Wales, Australia, 2004-2006

Outreach

  • September 2017

    Sponsored and Mentored a team of middle school children for the First Lego League in India. Click to know more.

  • June, 2017

    Volunteered as a Mentor at the Robo Siksha Kendra (the Non-profit Robotics school of India STEM Foundation) event for encouraging kids specially girls in STEM and AI Click to know more

  • May, 2017

    Organized a preparation workshop on Computer Science for International Robotics Competition at India STEM Foundation. Attended by 350 children aged between 6-12 years. Mentored an all-girls team for the International Robotics Competition.

Works in Progress

  • Deep Neural Networks in Visual Vibrometry: Estimating Material Properties from Small Motions in Video, Samsung Postech Intelligent Media Research Center and MIT Link
  • Extending the work of Abe Davis at MIT CSAIL - The Visual Microphone: Passive Recovery of Sound from Video at Samsung Postech Intelligent Media Research Center
  • Refining the Plant Intelligence Learning Algorithm Link