PAVAN KUMAR JONNAKUTI
H. No: 5-6-1/545, SKD Nagar,
Vanasthalipuram, Hyderabad -500070, Telangana, India
: +91-8143213738
E-mail: jonnakutip@gmail.com
Summary:
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4+ years of experience in providing solutions using High Performance Computing,
Artificial Neural Networks and Data Science techniques.
Expertise in MATLAB, C#, .Net and SQL Server 2005.
Experience of developing web applications using ASP.Net MVC, JavaScript, JQuery,
HTML5, XML.
Implementation knowledge of Open Source CMS and ecommerce software’s such as
Joomla, Virtuemart, NopCommerce, Wordpress and Shopify.
Well versed with SDLC Process.
Extensive experience in engineering and management, Research and Development,
leadership. And mentoring, test and problem-solving.
Excellent Communication and Presentation Skills.
Able to share responsibilities and rewards with network team of coworkers.
Strong commitment to ongoing learning of new Research methods.
Education:
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Birla Institute of Technology and Sciences (BITS Pilani),
 MS in Software Systems
Indian Institute of Chemical Technology (IICT), Hyderabad,
 Certificate Course in Bioinformatics
Jawaharlal Nehru Technological University, Hyderabad,
 B.Tech in Computer Science & Engineering
Board of Intermediate, Hyderabad,
 12th Class in MPC
Board of Secondary Education, Hyderabad,
 S.S.C
2013-2015
2009-2010
2005-5009
2003-2005
2002-2003
Research Experience:
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Working as project SCIENTIST ‘B’ in Data & Information Management Group (DMG) at
“INCOIS” (Indian National Center for Ocean Information Services) Hyderabad, from
June 2013 to till Date.
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Worked as Junior Research Fellow in Broad Band Seismology (BBS) at “NGRI”
(National Geophysical Research Institute) Hyderabad, from April 2010 to May 2013.
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Worked as Project Assistant at “IICT” (Indian Institute of Chemical Technology)
Hyderabad, from August 2009 to March 2010.
Conference Proceedings and Contributions:
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J. Pavan Kumar, D. V. Ramana, and R. K. Chadha (2012) “A neural network model for
occurrence of seismicity in Koyna – Warna region, India”. 49th Annual Convention,
Indian Geophysical Union, Gandhinagar, Gujarat, India. October 21st – November 2nd,
2012.
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D.V.Ramana, J. Pavan Kumar and R.K. Chadha (2011) “Cross correlation between
seismicity and reservoir water level changes in the Koyna – Warna region, India“.
AGU Fall Meeting, San Francisco, CA, USA 1 5th -9th December, 2011.
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J. Pavan Kumar, D.V.Ramana and R.K.Chadha (2010) “Application of the Neural
Networks technique in forecasting the Koyna – Warna reservoir water levels”. ICON
GSECCES - 2010, BHU, Banaras, India. December 21-23, 2010.
Peer Reviewed Publications:
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D. V. Ramana , J. Pavan Kumar , Asha Chelani , R. K. Chadha, M. Shekar, R. N. Singh,
“Complexity in hydro-seismicity of the Koyna–Warna region, India”, Nat Hazards
DOI
10.1007/s11069-014-1111-x,
2014.
For
more
details
visit…
http://tinyurl.com/pecustz
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J. Pavan Kumar, D. V. Ramana, R. K. Chadha , “A Matlab based GUI Application in
Hydro seismicity of the Koyna – Warna Region, India” , International Journal of
Computer Applications (IJCA) ., 52(10):38-43, August 2012. For more details visit…
http://tinyurl.com/cyyuvlp
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J. Pavan Kumar, D. V. Ramana, R. K. Chadha, C. Singh, and M. Shekar, “The relation
between seismicity and water level changes in the Koyna–Warna region, India”, Nat.
Hazards Earth Syst. Sci., 12, 813-817, 2012. For more details visit…
http://tinyurl.com/7zhm6p7
Professional Experience:
Project: CTD Data Extraction from raw data and Gap Filling in Sea Surface
Temperature Data.
Duration: June 2013 – Till Date
Technology: Matlab, SQL, Neural Networks, Big Data.
 Project Scientist ‘B’ (Data Scientist)
Once raw Sea-Bird CTD data has been acquired there are a number of
processing modules which may be run to convert the raw binary data into the desired
finished product. The raw data can be converted into engineering units, corrected
for the physical properties and location of the sensors, filtered for outliers, averaged
into one meter depth bins, and converted to ASCII. Additional oceanographic
parameters such as salinity and density may also be computed from the raw data.
Developed a MATLAB based GUI automation tool to process, analyze and visualize
the raw data. The goal is to provide a relatively seamless, automated transition from
shipboard acquisition of raw data to processed data in the CTD database.
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Designing and developing Algorithms and implementation using the
C#.net language
Designing and developing user interfaces using C#.net and MATLAB
Preparation of data through sequential manner i.e. Processing, Analysis
and Visualization.
Project: FORECASTING THE SEISMICITY OF KOYNA – WARNA
REGION, INDIA USING THE ERROR CORRECTION MODEL
Duration: October 2012 – Till Date.
Technology: MATLAB, EVIEWS 7, Cointegration.
 JRF( Research Scholar)
For the Earth scientist’s prediction/forecasting the seismicity is a challenging
problem. In recent years some works have been carried out in these lines by using
different techniques. In this work we study the unit root tests for the Koyna, Warna
reservoirs water level data and for the seismicity of the region. We developed the
error correction model to forecast the seismicity by using Co-integration analysis.
The results show that the models are suitable to forecast the seismicity of the Koyna
– Warna region.
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Designing and developing Algorithms and implementation using the
C#.net language
 Designing and developing user interfaces using C#.net and MATLAB
 Data Analysis and Sample Data creation.
 Creation of the database scripts and Stored Procs.
Project: A NEURAL NETWORK MODEL FOR OCCURRENCE OF
SEISMICITY IN KOYNA – WARNA REGION, INDIA
Duration: April 2010 – Dec2012
Technology: C#.net, SQL Server 2005, Neural Networks, Matlab.
 JRF(Research Scholar)
Earthquake forecasting has become an emerging science, which has been applied
in different areas of the world to monitor seismic activities. The application of the
artificial neural network has been proposed to predicate the seismicity of the Koyna
– Warna region, India. The neural network technique is widely used, since it is having
the capability of capturing the non-linear relationship. In this work we study past
earthquakes in the region to give better forecasting the earthquakes. Here, we
developed the scheme based on feed forward neural network model with hidden
layers. The model consists of neural network training from the known input and
testing the data. The model results reveal the application of the ANN quite useful in
forecasting the seismicity of the region.
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Designing and developing Algorithms and implementation using the
C#.net language
Designing and developing user interfaces using C#.net and MATLAB
Data Analysis and Sample Data creation.
Project: ACLSD (Accelerating classification of large seismic data sets on
GPGPUs).
Duration: Jan 2012 – Oct 2012.
Technology: High Performance Computing, CUDA, GPU, C, Matlab.
 JRF(Research Scholar)
Parallel computing platforms provide researchers the means to apply
computationally demanding algorithms to large sets of data. Although these
algorithms can be applied in a sequential computing system, the use of parallelization
techniques allows the substantial reduction of the execution time. One such case is
the classification of huge seismic data using GPU computing technique. Herein is
proposed a platform for mass classification of seismograms, using data parallelism
for reducing computational time. We developed the algorithm to automatic pick up
of P and S phases based on the traditional methods like noise reduction by using the
filters and then apply the STA/LTA ratio through General- Purpose computing
on GPUs (GPGPU) and with CUDA support, Further this data is used in
classification of the event whether local or regional or teleseismic. These phases are
compared with the manual pickups in 10 broad band seismic stations in Southern
India. The differences in the both methods are in considerable limits and also
applicable in getting the appropriate locations.
The main advantages of employing GPU computing for data classification are:
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Speeding up evolutionary process by parallel hardware fitness evaluation.
Discovering parallel algorithms automatically; and
With this approach we can achieve good generalization performance and
which is comparable to other existing classification algorithms.