Lo ch an  R aj T  D  
Data  S cie n tis t 
Data  S cie n tis t  w it h  3 + ye ars   o f  stro ng  e xp erie n ce  d eliv e rin g  h ig h-q ualit y   B 2B  a n d  B 2C  M L m odels .
Exp erie n ce  in  d eve lo pin g  m ach in e le arn in g  m odels  a n d  c o m pute r  v is io n a p plic a tio ns a n d  s o lu tio ns to
so lv e  r e al- w orld  b usin ess p ro ble m s 
lo ch an d eva ra ja 0 817@ gm ail .c o m  
8150888972 
Ben g alu ru  
lin ke d in .c o m /in /lo ch an ra jt d  
WORK  E X PER IE N CE 
Data  S cie n tis t 
Hew le tt-P ack ard  E n te rp ris e  
12/2 022 -  P re se n t
, 
  Ben galu ru  
En han ce d  a ir p o rt  s e cu rit y  a n d  p asse n g er s c re en in g  a ccu ra cy
by 9 9%  u sin g  a  Y O LO v7 -b ase d  co lo r  d ete ctio n m odel  fo r
eﬃ cie n t  to p  an d   bo tto m  clo th in g   dete ctio n,  k-m ean s
clu ste rin g   fo r  do m in an t  co lo r  id en tiﬁ ca tio n,  an d   th e
delt a _e _cm c m eth o d f o r p re cis e  c o lo r d ete ctio n.  In te g ra te d
wit h  t h e S A FR  p la tfo rm  f o r c o m pre h en siv e  p eo ple -t ra ck in g  
Part n ere d  w it h  c ro ss-f u nctio nal  t e am s ( R eg io n a n d  P ro duct)
to  d eﬁ ne c lie n t r e q uir e m en ts  a n d  d eve lo p P O Cs le ve ra g in g
Mach in e L e arn in g  m odels   a n d  te ch n iq ues  to  a d dre ss  th eir
busin ess c h alle n g es 
Le d   th e  deve lo pm en t  an d   im ple m en ta tio n  of  a  m ed ic a l
do m ain   PO C  fo r  tu m or  cla ssiﬁ ca tio n.  A ch ie ve d   a  99.8 %
accu ra cy   ra te  w it h  a  n ove l  im ag e p ro ce ssin g  m odel  b uil t
usin g  V is io n T ra n sfo rm ers  
Sp earh ead ed  a  P O C fo r  fra u d  tra n sa ctio n d ete ctio n at  a
le ad in g  In d o nesia n  b an k.  L e ve ra g ed  O CR  to ols   lik e  T a b ula
an d   PyP D F2   to   extra ct  stru ctu re d   data   fro m  acco unt
sta te m en ts , e n ab lin g  r e al- t im e f ra u d  i d en tiﬁ ca tio n 
Exp erie n ce   in   m odel  m onit o rin g ,  tra ck in g ,  an d   sto rin g
diﬀ ere n t  model  mult ip le -r u n  re su lt s   wit h   diﬀ ere n t
para m ete rs  o n t h e G CP c lo ud  u sin g  M LF lo w  
So ft w are  E ng in eer-L 2  
Pace  W is d o m  S o lu tio ns P vt L td  
03/2 020 -  1 1/2 022
, 
  Ben galu ru  
Constru cte d  a  t a rg ete d  c u sto m er c la ssiﬁ ca tio n m odel  u sin g
XG Boost  to   pro vid e  actio nab le   busin ess  in sig hts   to   th e
sta ke h o ld ers  f o r m ark e tin g  a n d  p ro m otin g  t h eir  m utu al f u nd
an d  i n su ra n ce  p la tfo rm  
Deve lo ped  a  P O C f o r a  t o uris m -s m art  it in era ry  t o  o ne o f t h e
to p  10  re n te d   bik e   co m pan ie s  usin g   Fo liu m  to   cre ate
in te ra ctiv e  g eo gra p hic  v is u aliz a tio ns 
Vis u aliz in g   th e  cu sto m er  po rt fo lio   pag e  w it h   diﬀ ere n t
gra p hs a n d  c h art s  u sin g  S e ab o rn  a n d  M atp lo tlib  
ED UCATIO N 
Post G ra d uatio n D ip lo m a i n  D ata  S cie n ce  
IIIT -B an g alo re  
12/2 020 -  0 1/2 022
, 
  Ben galu ru  
Deep  L e arn in g  
BE i n  E le ctro nic s a n d  I n stru m en ta tio n 
Maln ad  C ollg e o f E n g in eerin g  
2015 -  2 019
, 
  SK IL L S  
Pyth o n 
Mach in e L e arn in g  A lg o rit h m s 
Art iﬁ cia l I n te llig en ce  
Com pute r V is io n 
Deep  L e arn in g  
Im ag e P ro ce ssin g  
SQ L 
Kera s 
ED A 
Open C V  
Scik it -L e arn  
Te n so rF lo w  
GCP 
MLF lo w  
Dock e r 
NLP  
Data  V is u aliz a tio n -  S e ab o rn , M atp lo tL ib  
Deb ug gin g  
Decis io n-M akin g  
PER SO NAL P R O JE C TS 
MRI I M AG E P R O CESSIN G
 ( 1 2/2 021 -  0 2/2 022)
 
Buil t  G AN  m odel u sin g  G en era to rs  a n d  D is c rim in ato rs  f o r e xtra ctin g
mult ip le  i m ag es f ro m  t h e o rig in al M RI i m ag e 
Pro je ct  c y cle  in clu d es  D ata  P ro ce ssin g ,  M odel  B uil d in g ,  H an d lin g
Lo ss a n d  M odel T ra in in g  
ACH IE V EM EN TS 
Best P ro je ct A w ard  
HPE. &bull;  F o r b uil d in g C om pu te r V is io n b ase d  m odel 
Sta r P erfo rm er O f T h e Q uart e r 
Pace  W is d o m  S o lu tio ns P vt L td  &bull;  2 021-2 022 
LA N GUAG ES 
En g lis h  
Fu ll P ro fe ssio nal P ro ﬁ cie n cy  
Kan nad a 
Nativ e  o r B il in gual P ro ﬁ cie n cy  
IN TER EST S 
Te ach in g  
Pho to gra p hy 
Ach ie ve m en ts /T a sk s 
Ach ie ve m en ts /T a sk s 
Cours e s