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Saurabh Bawdhankar CV 100%

Personal Qualities - Detail oriented and strong interpersonal skills Good Conceptual, Analytical and Logical skills Ability to work individually as well as in group environment Design the program for various purposes Suggest innovative ideas to enhance the working of existing software Ability to adapt quickly to challenges and changing environment Extra Curricular - Building and collaborating Android Forums on XDA-Developers - Help and discuss with people on various platforms regarding Android Root, Bootloaders, Kernel, Development - Inter-colleges FIFA and Football competitions Interests - Look for problems on StackOverflow XDA Development for Android Developers Modify and try Android ROMs, Kernels Surf StackOverflow, XDA and look for new developments in free time Languages ○␣ English, Hindi, Marathi Profiles ○␣ ○␣ StackOverflow:

https://www.pdf-archive.com/2016/07/10/saurabh-bawdhankar-cv/

10/07/2016 www.pdf-archive.com

Resume 87%

StackOverflow:

https://www.pdf-archive.com/2016/12/09/resume/

09/12/2016 www.pdf-archive.com

English CV Pavlos 82%

ONLINE PRESENCE Facebook- https://www.facebook.com/pavlospt T witter - https://twitter.com/tpavlos Goog le+ - https://plus.google.com/u/0/110185423098180883078 LinkedIn - http://www.linkedin.com/pub/pavlos-petros-tournaris/44/abb/218 StackOverflow - http://stackoverflow.com/users/1470614/pavlos Github - https://github.com/pavlospt INTERESTS I am interested in Developing applications in Mobile Platforms, such as iOS and Android (where I already have 4 years of Development experience).

https://www.pdf-archive.com/2015/06/22/english-cv-pavlos/

22/06/2015 www.pdf-archive.com

Background information 78%

                                   http://www.statsoft.com/Textbook/Cluster-Analysis http://stats.stackexchange.com/questions/133656/how-to-understand-the-drawbacks-of-k-means http://stackoverflow.com/questions/15376075/cluster-analysis-in-r-determine-the-optimal-number-of-clusters http://www.machinelearning.org/proceedings/icml2007/papers/216.pdf http://webcache.googleusercontent.com/search?q=cache:http://bvmengineering.ac.in/docs/published%2520papers/ cpit/cpit/201405.pdf&gws_rd=cr&ei=H3JKVsL5A8fTmwWUz6eACA https://sites.google.com/site/vegclassmethods/statistical-analyses/classification-methods#TOC-Other-numericalapproaches https://books.google.co.nz/books?id=RQHB4_p3bJoC&pg=PA412&lpg=PA412 https://en.wikipedia.org/wiki/DBSCAN https://en.wikipedia.org/wiki/K-medoids#Algorithms http://www.ijcta.com/documents/volumes/vol3issue5/ijcta2012030521.pdf http://zdb.ru.lv/conferences/3/VTR8_II_70.pdf http://slideplayer.com/slide/3200567/ http://ijcai.org/papers13/Papers/IJCAI13-249.pdf http://www.tqmp.org/RegularArticles/vol09-1/p015/p015.pdf http://www.ijircce.com/upload/2014/january/7_A%20comparative.pdf http://www.ijraset.com/fileserve.php?FID=467 https://cran.r-project.org/web/packages/vegclust/vignettes/VegetationClassification.pdf http://www.public.iastate.edu/~maitra/stat501/lectures/ModelBasedClustering.pdf http://www.slideshare.net/petitegeek/expectation-maximization-and-gaussian-mixture-models https://cran.r-project.org/web/packages/kernlab/vignettes/kernlab.pdf http://wheatoncollege.edu/lexomics/files/2012/08/How-to-Read-a-Dendrogram-Web-Ready.pdf http://www.unesco.org/webworld/idams/advguide/Chapt7_1_5.htm http://people.stat.sc.edu/Hitchcock/chapter6_R_examples.txt http://stackoverflow.com/questions/26019584/understanding-concept-of-gaussian-mixture-models?rq=1 http://hameddaily.blogspot.co.nz/2015/03/when-not-to-use-gaussian-mixtures-model.html https://www.diva-portal.org/smash/get/diva2:198852/FULLTEXT01.pdf http://trace.tennessee.edu/cgi/viewcontent.cgi?article=2096&context=utk_gradthes http://blog.surveymethods.com/when-to-use-single-imputation-or-multiple-imputation/ https://en.wikipedia.org/wiki/Imputation_(statistics)#Single_imputation https://www.unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.44/2012/35_Japan.pdf http://machinelearningmastery.com/feature-selection-with-the-caret-r-package/ http://www.r-bloggers.com/counting-clusters/ https://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Clustering/Density-Based_Clustering http://www.osl.iu.edu/~chemuell/projects/presentations/optics-v1.pdf http://www.slideshare.net/rpiryani/optics-ordering-points-to-identify-the-clustering-structure AbacusBio Ltd.

https://www.pdf-archive.com/2016/02/05/background-information/

05/02/2016 www.pdf-archive.com

Tournament Results Overview 68%

General P2 Tips Error Handling Table Design SQL W3Schools - SQL tutorial SQL Practice on SQLZoo.net Relational Databases is Difficult Visual Explanation of Joins SQL Views on W3Schools PostgreSQL PostgreSQL Docs Vagrant Udacity Forum - Vagrant Vagrant Docs Swiss Pairing SwissPairing Hints Swiss Pairing Topics in Forum README README Templates How to Write Good READMEs - StackOverflow Can't find the answer?

https://www.pdf-archive.com/2016/07/14/tournament-results-overview/

14/07/2016 www.pdf-archive.com

cvfr2014 61%

62 100 rue de Laseppe 33000 Bordeaux https://gist.github.com/Tronix117 http://stackoverflow.com/users/1343924/tronix117 http://www.linkedin.com/in/jeremytrufier 

https://www.pdf-archive.com/2014/06/05/cvfr2014/

05/06/2014 www.pdf-archive.com

NetworkPaper (1) 52%

  Another  reason  we   chose  Stack  Overflow  is  because  they  provide  a  good  API  that  we can  use  and their  data has a timestamp that we  can adopt  to explore and characterize the  evolution   and the similarity of the post tags.  StackOverflow provides their  data  in JSON  format.  Each  JSON  object  provides  various  information  about   the   attributes  of  each  post.  Out  of  all  the  attributes,  we  filtered  the  once  that  include  information  about the creation  date  of  the post and  the  tags  the  users   submitted  when  creating  each  post.  Out  of  the  10GB  of  initial  data,  our  parsing  algorithm  filtered  the  dataset  down  to  168MB. The  total   length  of  the  set  of  tags  used  in  all  posts  exceeded  30,000  tags.  We  chose  to  narrow  down  our target to the 1,000 most encountered tags.     Results and Discussion  We   constructed  a  visualization  that  accurately  describes the relationship between technology tags  based  on  the  frequency  two  tags  are  seen  together.  Our  network   contained  1000  nodes  and  6260 edges. The  average  shortest  path length was  2.6  for  unweighted  and  6.3   for  weighted  computations.  That  means  that  given  2  random  tags  as a  source  and a destination, it would take on  average 6 to 7 path  lengths  to reach the destination  given  a  weighted  and  directional  environment  but  only  2­3  path  lengths  given  a  non  weighted,   directional environment .    That  translates  into  a   network  that  is  tightly  connected yet  directionally separate.  We found the  reciprocity  of  the  network  to   be  0.2,  meaning  that  given  a  directional  connection of two  nodes,  there’s   a  20%  chance  the  connection  is   a  two­way  connection (both nodes point at each other.)   With that in mind,  we can assert  that the hub nodes  are connected to many technologies that are highly  dependent  on  them  and   that  establishes  a  clear  community  unfolding  within  the  network.  We   identified  9  key  communities  structured  around the  following hub nodes:     Javascript,  Java,  Android, Python, C++/Linux,   C#,  php/SQL, iOS and misc​ .  Conclusion  Given  our  findings,  we  have  created  an  intuitive  visualization  of  technologies  and  their  weighted,  directional  interaction. Stack  Overflow proved to be  a  promising  platform  for monitoring  relationships of  technologically  relevant   tags.  The  visualization  of  our graph can be found at ​ http://bit.ly/1TbZAyz  References     ​ 1.  Blondel,  V.  D.,  Guillaume,  J.,  Lambiotte,  R.,  &  Lefebvre,  E.  (2008).  Fast  unfolding  of  communities  in  large  networks.  ​ J.  Stat.  Mech.  Journal  of  Statistical  Mechanics:  Theory  and  Experiment,  ​ 2008​ (10).  doi:10.1088/1742­5468/2008/10/p10008        

https://www.pdf-archive.com/2017/02/09/networkpaper-1/

09/02/2017 www.pdf-archive.com

UROP Logbook (1) 46%

http://www.lfd.uci.edu/~gohlke/pythonlibs) MATHEMATICS Linear Algebra https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab Multivariable Gaussian Distribution (I only understood the first half) http://videolectures.net/gpip06_mackay_gpb/ Discrete Fourier Transform https://www.youtube.com/watch?v=mkGsMWi_j4Q http://www.dspguide.com/pdfbook.htm 4.1 Wikipedia summaries https://en.wikipedia.org/wiki/Pitch_detection_algorithm https://en.wikipedia.org/wiki/Autocorrelation http://stackoverflow.com/questions/11553047/frequency-pitch-detection-for-dummies 2 5 Book Exercises We have completed some exercises as stipulated by the book:

https://www.pdf-archive.com/2017/04/07/urop-logbook-1/

07/04/2017 www.pdf-archive.com

AppCoda-WhatamIFlyingOn 46%

You will find lots of answers on StackOverflow.

https://www.pdf-archive.com/2017/07/02/appcoda-whatamiflyingon/

02/07/2017 www.pdf-archive.com

KMeansRE 32%

In my understanding, this method does NOT require ANY assumptions, i.e., give me a data set and a pre-specified number of clusters, k, then I just apply this algorithm which minimize the SSE, the within cluster square error.” – David Robinson, Data Scientist at StackOverflow.

https://www.pdf-archive.com/2016/12/13/kmeansre/

13/12/2016 www.pdf-archive.com