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Simulmedia ARF Presentation - Early Lessons Learned In Applying Big Data To Television Advertising
 

Simulmedia ARF Presentation - Early Lessons Learned In Applying Big Data To Television Advertising

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Early Lessons Learned In Applying Big Data To Television Advertising. September 12, 2011 presentation by Simulmedia.

Early Lessons Learned In Applying Big Data To Television Advertising. September 12, 2011 presentation by Simulmedia.

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    Simulmedia ARF Presentation - Early Lessons Learned In Applying Big Data To Television Advertising Simulmedia ARF Presentation - Early Lessons Learned In Applying Big Data To Television Advertising Presentation Transcript

    • Early Lessons Learned in Applying Big Data To TV Advertising ARF September 12, 2011Jack Smith, Chief Product Officer, Simulmedia
    • About  Us   Who  We  Are   We  are  a  New  York  based  start-­‐up.  We  are  venture  backed  by  Avalon   Ventures,  Union  Square  Ventures  and  Time-­‐Warner.   Where  We  Have  Been   Our  35  person  team  has  veterans  of:   What  We  Believe   Television  is  sHll  the  most  powerful  adverHsing  medium  in  the  world.   While  addressability  will  come,  we’re  not  waiHng  for  it.  We’ve  taken  a  few   strategies  we  learned  from  the  Internet  and  are  applying  it  to  linear  TV   adverHsing,  today.   How  We  Do  It   Through  partnerships  with  major  data  providers,  we  have  assembled  the   world’s  largest  set  of  acHonable  television  data.       How  We  Make  Money     sell  television  adverHsing.  With  inventory  in  over  106  million  US   We   households,  we  can  cost-­‐effecHvely  extend  reach  into  high-­‐value  target     audiences  across  virtually  any  adverHser  category.  We  use  big  data  and   science  to  do  this.   2  
    • Why  Did  We  Leave  The  Web?   Television  remains  the  dominant  consumer  medium   (a)  Nielsen  US  TV  Viewing  Audicence  TradiHonal  Live-­‐Only  TV  based  on  average  monthly  viewing  during  1Q2011.    Internet  and  Online  Video  based  on  average  monthly  consumpHon  during  July  2011.     Video  on  Demand  based  on  consumpHon  during  May  2011.   3  
    • TV  Spend  Is  Increasing  Source:  MAGNAGLOBAL   4  
    • Audience  Is  FragmenEng  Source:  Nielsen  via  TVbythenumbers.com   5  
    • Campaign  Reach  Is  Declining   Impossible  for  measurement  and  planning  tools  to  keep  pace      Source:  Simulmedia  analysis  of  data  from  SQAD,  Nielsen  and  TVB   6  
    • Big  Data   Highly  ConfidenHal  
    • Big  Data  Is  Driving  Growth   “We  are  on  the  cusp  of  a  tremendous  wave  of   innova;on,  produc;vity  and  growth,  as  well  as   new  modes  of  compe;;on  and  value-­‐capture  –   all  driven  by  Big  Data.”     -­‐  McKinsey  Global  InsHtute,  May  2011 “For  CMOs,  Big  Data  is  a  very  big  deal.”   -­‐  Alfredo  Gangotena,  CMO,  Mastercard,  July  2011   8  
    • Size  Is  RelaEve   1  byte  x  1000  =  1  kilobyte   …x  1000  =  1  megabyte   …x  1000  =  1  gigabyte   …x  1000  =  1  terabyte   …x  1000  =  1  petabyte   …x  1000  =  1  exabyte       9  
    • Size  Is  RelaEve   Telegram  =  100  bytes     Data  ©  1997-­‐2011,  James  S.  Huggins  hfp://www.jamesshuggins.com/h/tek1/how_big.htm   10  
    • Size  Is  RelaEve   Page  of  an  Encyclopedia  =  100  kilobytes     Data  ©  1997-­‐2011,  James  S.  Huggins  hfp://www.jamesshuggins.com/h/tek1/how_big.htm   11  
    • Size  Is  RelaEve   Pickup  truck  bed  full  of  paper  =  1  gigabyte       Data  ©  1997-­‐2011,  James  S.  Huggins  hfp://www.jamesshuggins.com/h/tek1/how_big.htm   12  
    • Size  Is  RelaEve   EnHre  print  collecHon  of  the  Library  of  Congress  =  10  terabytes     Data  ©  1997-­‐2011,  James  S.  Huggins  hfp://www.jamesshuggins.com/h/tek1/how_big.htm   13  
    • Size  Is  RelaEve   All  hard  drives  produced  in  1995  =  20  petabytes       Data  ©  1997-­‐2011,  James  S.  Huggins  hfp://www.jamesshuggins.com/h/tek1/how_big.htm   14  
    • Size  Is  RelaEve   All  printed  material  =  200  petabytes       Data  ©  1997-­‐2011,  James  S.  Huggins  hfp://www.jamesshuggins.com/h/tek1/how_big.htm   15  
    • But  Big  Data  Is  More  Than  Size       BIG  DATA         What   Why  did  it   What’s  going  to   happened?   happen?   happen  next?   Time:   Past   Future   Focus:   ReporHng   PredicHon   Supports:   Human   Machine   decisions   decisions   Data:   Structured   Unstructured   Aggregated   Unaggregated   Human   Dashboards   Discovery   Skills:   Excel   VisualizaHon     StaHsHcs  &  Physics   16  
    • AcceleraEng  The  Push  To  Big  Data   Hadoop,  cloud  compuHng,  Facebook,  Yahoo,  quants,  Biforrent,  machine  learning,  Stanford,   large  hadron  collider,  Wal-­‐Mart,  text   processing,  Amazon  S3  &  EC2,  open  source   intelligence,  NoSQL,  social  media,  Google,   commodity  hardware,  Hive,  fraud  detecHon,   trading  desks,  MapReduce,  natural  language   processing     17  
    • What  Can  It  Mean  For  TV  AdverEsing?   Big  data  drove  the  rise  of  web  &  search  adver;sing     •  AccumulaHon  of  high  volume  of  direct  measurement   of  media  consumpHon   •  Befer  predicHons  about  consumer  interests   •  Real  Hme  return  path   •  AutomaHon   •  Interim  step  for  addressability   •  More  diligence  around  consumer  privacy   •  Media  buyers  and  sellers  rethinking  their  approach  to   audience  packaging,  campaign  planning,  technology,   data  assembly  and  people   18  
    • Post  Modern  Architecture   Have  we  reached  the  limits  of  classic  data  storage  architecture?  Data  Warehouses   Data  Lakes  •  Yahoo!:  700  tb1     •  Facebook:  30  pb3  (7x  •  Australian  Bureau  of  StaHsHcs:  250  tb1   compression)  •  AT&T:  250  tb1   •  Yahoo:  22  pb4  •  Nielsen:  45  tb1   •  Google:  ???    •  Adidas:  13  tb1  •  Wal-­‐Mart:  1  pb2     1  Oracle  F1Q10  Earnings  Call  September  16,  2009  Transcript   2  Stair,  Principles  of  Informa;on  Systems,  2009,  p  181   3  Dhruba  Borthakur,  Facebook,  December  2010,  hfp://www.facebook.com/note.php?note_id=468211193919   4  Simulmedia  esHmate   19    
    • Our  Idea  of  Big  Data   Bringing  the  data  set  together  in  a  single  plaMorm   Client   Nielsen   Set  Top  Boxes   Program   Public   Ad  Occurrence   Proprietary   RaHngs   • 17+  million   • 3  different   •  US census •  What ads • Business   • All  Minute   boxes   sets  of   •  Military ran? Development   Respondent   • Completely   schedule   •  Business •  Where did Indices  (BDI)   Level  Data   anonymous   data   they run? • Commercial   (AMRLD)   viewing   • Proprietary   Development   •  Live   metadata   Indices  (CDI)   •  DVR   • Regional   •  VOD   sales  data   •  Pay  channels   Our  (comparaHvely  modest)  data  set:   •  200  tb  (approx.  7x  compression)   •  113,858,592  daily  events   •  Approximately  402,301  weekly  ads   •  Double  capacity  every  6  months   …And  we  don’t  load  every  data  point  across  all  data  sets,  yet     20  
    • Rethinking  Media  Data  Architecture   Applying  big  data  to  television  required  us  to  rethink  what  our   technical  architecture  should  be   Commodity   •  No  clouds  allowed  (ISO  compliance)   Hardware   •  Expect  hardware  failure   Open  Source   •  Learn  from  those  who  have  done  it   Sosware   •  ParHcipate  in  the  Open  Source  community   •  ELT  (Extract,  Load,  Transform)   Write  Your  Own   •  Meddle   Sosware   •  Machine  learning   •  Advanced  staHsHcal  techniques   Science   •  ExperimentaHon   21  
    • Some  Wrinkles  In  The  Matrix   22  
    • The  People  We  Needed   A  different  approach  required  different  skill  sets   •  New  core  skills  for  everyone  in  the  company   •  Pafern  recogniHon   •  VisualizaHon   •  Technology   •  ExperimentaHon   •  Where  do  you  find  hard  to  find  tech  skills?   •  You  don’t  find  them.  You  make  them.   •  A  dedicated  Science  team   •  Non  tradiHonal  researchers  (Brain  imaging,  bioinformaHcs,   economic  modeling,  geneHcs)     •  People  who  watch  a  lot  of  television   23  
    • 10  Lessons  We’ve  Learned   Highly  ConfidenHal  
    • Some  Things  To  Know,  First  •  Live  viewing  unless  otherwise  noted   •  Time  shising  lessons  is  a  whole  other  presentaHon   •  Time  shising  +  live  viewing  lessons  is  a  whole  other  other  presentaHon   •  Video  on  demand  is  a  whole  other  other  other  presentaHon  •  We  name  names  and  provide  numbers  where  clients  and  data   partners  permit   •  Client  confidenHality  is  important  to  us  •  None  of  this  work  would’ve  been  possible  without  the  help  of   our  clients  and  partners   This  box  will  contain  important   Read  me…   informaHon  about  the  graphs  on   each  page.   25  
    • 60%  of  TV  Viewers  Watch   90%  of  TV   Highly  ConfidenHal  
    • Where  The  Other  40%  Are   TCM 13.6 HALLMARK 13.7 Networks with relatively fewer ADSWIM 14.0 lighter viewer NICKNITE 14.3 impressions CNBC 15.7 FOX NEWS 18.0 OXYGEN 7.4 Networks with relatively more WE 7.6 lighter viewer PLANET 7.7 VerEcal:  RaHo  of  Heavy   impressions GREEN Viewers  to  light  viewer   OVATION 7.8 impressions.     STYLE 7.8 Horizontal:  Low  rated  to   Highly  rated  networks   MTV2 7.8 Call  outs:  RaHo  is  the   SUNDANCE 7.9 number  of  Heavier   Viewer  impressions  you   IFC 7.9 Lower Higher rated would  deliver  to  reach  a   rated networks Lighter  Viewer  on  a  given   networks network   Sources:  Nielsen  &  Simulmedia’s  a7   27  
    • Where  The  Other  40%  Are   To  capture  light  viewers,  media  planning  and  measurement   tools  must  quickly  apply  new  methods  to  emerging  data  sets   28  
    • Quality  Control  Is  A  Full   Time  Job   Highly  ConfidenHal  
    • When  Data  Goes  Missing   AutomaHon  of  error  checking/ quality  control  is  essenHal     Reuse  the  data  to  solve  other   problems     Occasionally  observe  missing   data     Three  choices:   •  Pick  up  the  phone   •  EsHmate  missing  fields     •  Work  around  the  missing   data     Time  series  of  SYFY   network.  10645   observaEons  from   2010.02.28  at  7:00pm   Eastern  to  2010.10.14  at   12:30pm  Eastern   30  Source:  Simulmedia’s  a7  
    • More  Data  Really  Is  Befer   Highly  ConfidenHal  
    • DisambiguaEon:  The  Madonna  Problem   OR   Pop  Icon?   Religious  icon?   32  
    • The  RevoluEon  of  Simple  Methods   More  data  beats   beUer  algorithms.     The  best  performing   algorithm  underperforms   the  worst  algorithm  when   given  an  order  of   magnitude  more  data.       Simple  algorithms  at  very   large  scale  can  help  befer   Peter  Norvig  |  Internet  Scale  Data  Analysis  |  June  21,  2010   predict  audience   movement.  Original  graph  sourced  from:  Banko  &  Brill,  2001.  Mi;ga;ng  the  paucity-­‐of-­‐data  problem:  exploring  the  effect  of  training  corpus  size  on  classifier  performance  for  natural  language  processing     33  
    • Packaging  Reach   Very  large  data  sets  beUer  predict  TV  audience  movements   Peter  Norvig  |  Internet  Scale  Data  Analysis  |  June  21,  2010   34  
    • The  Cost  Of  More  Data   More  data  drives  beUer  results  but  there  are  costs       •  All  data  online.  All  the   •  All  data  online.  All  the   Hme.   Hme.   •  Less  expensive  hardware   •  More  expensive  talent   •  Extremely  flexible   •  Physicists  &  staHsHcians   ain’t  cheap   •  Hard  to  find  programmers   •  Not  everything  meets   your  needs   •  Evolving  technologies  in   mission  criHcal  funcHons   35  
    • The  Data  Isn’t  Biased  Just  Because  It  Comes  From  A   Set  Top  Box   Highly  ConfidenHal  
    • Applying  Simple  Methods  At  Scale   High  correlaHon  of  a7   measures  and  Nielsen   esHmates.     Either  bias  is  insignificant  or   Nielsen  data  and  our  data   share  the  same  bias.     MulHple  methods  yield   similar  results     Regression  analysis  of   Nielsen  Household  Cume   RaEng  against   Simulmedia’s  a7  cume   raEng.  20  PrimeEme   Network  shows  with  Sources:  Nielsen  &  Simulmedia’s  a7   HAWAII  FIVE-­‐0.  Fall  2010.   37  
    • And  Then  We  Kept  Going   We  measured  program  Tune-­‐In,  Spot  Tune-­‐In,  Campaign  Reach,   Campaign  Ra;ng  using  mul;ple  slices  of  our  data  set  using  two   different  sample  sets  and  ;me  frames  How  we  sliced  it   Two  samples  •  EnHre  a7  data  set     1.  Sample  1:  Fall  2010:  20  PrimeHme  •  Cross  correlated  individual  data   broadcast  series  launches  +   sets  contained  in  a7  aggregate   promos   2.  Sample  2:  Jan  2011:  15  PrimeHme   data  set     cable  series  premieres  +  promos  •  Aggregate  cross  geographies   (Plus  one  mulH-­‐season/year   (DMA  to  DMA)   primeHme  broadcast  premiere  +   promos)  ObservaEons  •  Sample  1  average  r2>0.85   •  Hand  selected  programs    •  Sample  2  average  r2>0.93   •  Mix  of  genres     •  Mix  of  new  vs.  returning  shows   38  
    • Addressability  Is  Here   Highly  ConfidenHal  
    • Closing  The  Loop  On  Program  PromoEon   Spring  2010  broadcast   premiere  promoEon.   Horizontal:  Leb  to  right  moves   back  in  Eme.  0  is  the  premiere   Eme.  VerEcal:  Conversion  rate   is  measured  in  percent.  Size  of  Sources:    Simulmedia’s  a7   the  bubble  represents  total   conversions  for  a  given  spot.   40  
    • Closing  The  Loop  On  Program  PromoEon   Spring  2010  broadcast   premiere  promoEon.   Horizontal:  Leb  to  right  moves   back  in  Eme.  0  is  the  premiere   Eme.  VerEcal:  Conversion  rate   is  measured  in  percent.  Size  of  Sources:    Simulmedia’s  a7   the  bubble  represents  total   conversions  for  a  given  spot.   41  
    • Closing  The  Loop   Long  held  beliefs  and  rules  of  thumb  in  planning  may  or  may   not  be  supported  by  data     TV  marketers  now  have  more  opHons  for  show  promoHon   42  
    • Nielsen’s  RaHngs  Are  Good   (Surprisingly  Good)   Highly  ConfidenHal  
    • Time  Series:  Broadcast:  CBS  60  networks.  High  correla;on  between  Nielsen  large   Hour  by  hour  Hme  series   Mar  20  to  April  8,  2011.  Z   sample  measurement  and  a7  measures   score  plots  with  Nielsen   esHmates  in  red.   Simulmedia   measurements  in  blue.   Where  Nielsen  provided   no  esHmate,  esHmates   were  imputed  using   MulHple  ImputaHon   (Rubin  (1987))    Sources:  Nielsen  &  Simulmedia’s  a7   44  
    • Time  Series:  Broadcast:  Fox   Hour  by  hour  Hme  series   Mar  20  to  April  8,  2011.  Z   score  plots  with  Nielsen   esHmates  in  red.   Simulmedia   measurements  in  blue.   Where  Nielsen  provided   no  esHmate,  esHmates   were  imputed  using   MulHple  ImputaHon   (Rubin  (1987))    Sources:  Nielsen  &  Simulmedia’s  a7   45  
    • Time  Series:  Broadcast:  ABC   Hour  by  hour  Hme  series   Mar  20  to  April  8,  2011.  Z   score  plots  with  Nielsen   esHmates  in  red.   Simulmedia   measurements  in  blue.   Where  Nielsen  provided   no  esHmate,  esHmates   were  imputed  using   MulHple  ImputaHon   (Rubin  (1987))    Sources:  Nielsen  &  Simulmedia’s  a7   46  
    • Time  Series:  Cable:  InvesEgaEon  Discovery   Hour  by  hour  Hme  series   Mar  20  to  April  8,  2011.  Z   score  plots  with  Nielsen   esHmates  in  red.   Simulmedia   measurements  in  blue.   Where  Nielsen  provided   no  esHmate,  esHmates   were  imputed  using   MulHple  ImputaHon   (Rubin  (1987))    Sources:  Nielsen  &  Simulmedia’s  a7   47  
    • Time  Series:  Cable:  Golf   Hour  by  hour  Hme  series   Mar  20  to  April  8,  2011.  Z   score  plots  with  Nielsen   esHmates  in  red.   Simulmedia   measurements  in  blue.   Where  Nielsen  provided   no  esHmate,  esHmates   were  imputed  using   MulHple  ImputaHon   (Rubin  (1987))    Sources:  Nielsen  &  Simulmedia’s  a7   48  
    • Time  Series:  Cable:  Bravo   Hour  by  hour  Hme  series   Mar  20  to  April  8,  2011.  Z   score  plots  with  Nielsen   esHmates  in  red.   Simulmedia   measurements  in  blue.   Where  Nielsen  provided   no  esHmate,  esHmates   were  imputed  using   MulHple  ImputaHon   (Rubin  (1987))    Sources:  Nielsen  &  Simulmedia’s  a7   49  
    • Time  Series:  Cable:  ESPN2   Hour  by  hour  Hme  series   Mar  20  to  April  8,  2011.  Z   score  plots  with  Nielsen   esHmates  in  red.   Simulmedia   measurements  in  blue.   Where  Nielsen  provided   no  esHmate,  esHmates   were  imputed  using   MulHple  ImputaHon   (Rubin  (1987))    Sources:  Nielsen  &  Simulmedia’s  a7   50  
    • Time  Series:  Cable:  Speed   Hour  by  hour  Hme  series   Mar  20  to  April  8,  2011.  Z   score  plots  with  Nielsen   esHmates  in  red.   Simulmedia   measurements  in  blue.   Where  Nielsen  provided   no  esHmate,  esHmates   were  imputed  using   MulHple  ImputaHon   (Rubin  (1987))    Sources:  Nielsen  &  Simulmedia’s  a7   51  
    • …but…   Highly  ConfidenHal  
    • When  You  Look  Closer   Hour  by  hour  Hme  series   Mar  20  to  April  8,  2011.  Z   score  plots  with  Nielsen   esHmates  in  red.   Simulmedia   measurements  in  blue.   Where  Nielsen  provided   no  esHmate,  esHmates   were  imputed  using   MulHple  ImputaHon   (Rubin  (1987))    Sources:  Nielsen  &  Simulmedia’s  a7   53  
    • High  Frequency  Time  Series:  ABC  Family   Vola;lity  in  dayparts,  low  rated  networks,  demographics….       Unrated  networks  “don’t  exist.”  Did  NOT  look  at  local.   a7 Nielsen Sample  graph  from  High  Frequency   (Second  and  Minute  level)  Time  Series   Analysis  of  45  networks  on  January  19th   2011.     Simulmedia  a7  Sample  (Second  by  Second   to  Minute)     Nielsen  Sample    (Minute  by  Minute)       54  Sources:  Nielsen  &  Simulmedia’s  a7  
    • Women  Are  More  Different   Than  Men   Highly  ConfidenHal  
    • Gender  Driven  Geographic  VariaEon   Viewing  by  zip  code  among  women  across  markets  is  more  varied  than   men  in  the  same  zip  codes   Women  18-­‐54   Men  18-­‐54   FracHon  of  view  Hme  for  ages  18-­‐54  as  fracHon  of  view   Hme  for  all  TV  viewers.  Week  2  vs.  the  same  fracHon  for   week  1  (last  two  weeks  in  January).  Three  markets:   Philadelphia  (blue)  Atlanta  (red)  and  Chicago  (green)  Each   Source:  Simulmedia’s  a7   point  represents  a  zip  code  in  one  of  these  markets.     56  
    • Gender  Driven  Geographic  VariaEon   Planning  tac;cs  for  female  targeted  campaigns  should  be  different  than   male  target  campaigns   PS…Also  a  good  case  for  geo  based  crea;ve  versioning   57  
    • Privacy  Mafers   Highly  ConfidenHal  
    • Privacy  By  Design  •  All  markeHng  data  companies  need  to   care  •  Make  consumer  privacy  protecHon   part  of  the  business  from  the   beginning     •  Anonymous,  aggregated  data  only   •  No  personal  data  or  data  that  can   be  related  to  parHcular  individuals   or  devices   •  Broad  markeHng  segmentaHons,   not  profiling   •  No  sensiHve  data   Don’t  be  creepy     59
    • Mass  Reach  Is  Indiscriminant   Highly  ConfidenHal  
    • FragmentaEon  Effects  On  Frequency   Each  segment  was  above  70%  reach  but  the  frequency  distribu;on  was  nearly   iden;cal   Percent  of  audience  reached  for  major  animated  moHon   picture  campaign  2011.  Two  weeks  prior  to  release.    Each   stacked  bar  is  a  different  audience  segment.  Each  color   Source:  Nielsen  &  Simulmedia’s  a7   with  the  stacked  bar  represents  the  frequency  of  ad  view   for  each  segment.     61  
    • FragmentaEon  Effects  On  Frequency   Fragmenta;on  is  affec;ng  all  high  reach  campaigns.   Percent  of  audience  reached  for  insurance  adverHsers   September  to  October  2010.  Approximately  8000  ads.   Each  stacked  bar  is  a  different  audience  segment.  Each   Source:  Nielsen  &  Simulmedia’s  a7   color  with  the  stacked  bar  represents  the  frequency  of  ad   view  for  each  segment.     62  
    • FragmentaEon  Effects  On  Frequency   The  TV  adverHsing  market  can’t  conHnue  to  support  this   63  
    • 40%  Of  The  Audience  Is   Geyng  85%  Of  The   Impressions   Highly  ConfidenHal  
    • FragmentaEon  Rears  It’s  Head  Again     Campaign  impressions   increasingly  concentrated  against   0.0     0.0%     heavy  viewers.   1.4     3.6%     Total    US  Television   4.3     10.8%     Audience   Percent  of  audience   reached  for  a  different   9.1     23.0%     major  animated  moHon   picture  campaign  2011.   Two  weeks  prior  to   release.  The  stacked  bar   24.8     62.6%     represents  quinHles.   Blue  labels  are  average   frequency  per   Average  Frequency     %  of  Total  Impressions     respecHve  quinHle.  Red   Per  QuinEle   Per  QuinEle   labels  are  %  of  total   campaign  impressions   Source:  Nielsen  &  Simulmedia’s  a7   by  respecHve  quinHle.   65  
    • FragmentaEon  Effects  on  Frequency   AdverHsers  won’t  conHnue  to  support  this   66  
    • What  Happens  Next?   Highly  ConfidenHal  
    • Choices  •  If  fragmentaHon  is  causing  declining  campaign  reach  and   frequency  imbalances,  marketers  must  make  choices.   •  Reduce  reach   •  Do  nothing   •  Use  other  channels   •  Stabilize  or  improve  reach   •  Re-­‐aggregate  audiences  using  big  data         What  do  you  think?     68  
    • Jack Smith jack@simulmedia.com @simulmedia   @jkellonsmith   69  
    • About  Our  Science  Team  •  Krishna  Balasubramanian,  Chief  ScienHst   •  Previously:  Chief  ScienHst,  Tacoda.  Chief  ScienHst,  Real  Media.   •  Doctoral  Candidate,  Physics.  (Condensed  Mafer  Physics)  The  Ohio  State  University   •  MS,  Computer  &  InformaHon  Systems.  The  Ohio  State  University   •  MSc,  Physics.  Indian  Ins;tute  of  Technology,  Kanpur  •  Yuliya  Torosjan,  ScienHst   •  Previously:  Clinical  Research  (Brain  Imaging),  Mount  Sinai  College  of  Medicine   •  MA,  StaHsHcs.  Columbia  University   •  BSE,  Computer  Science  &  Engineering.  University  of  Pennsylvania   •  BA,  Psychology.  University  of  Pennsylvania  •  Mario  Morales,  ScienHst   •  Previously:  Lecturer,  BioinformaHcs,  New  York  University.  Senior  Consultant,  Weiser  LLP.   •  MS,  StaHsHcs.  Hunter  College   •  MS,  BioinformaHcs.  New  York  University  •  Dr.  Sidd  Mukherjee,  ScienHst   •  Previously,  VisiHng  Scholar  (Atomic  Scafering  experiments),  The  Ohio  State  University   •  Post  doctoral  research,  Heat  capacity  of  Helium-­‐4.  Pennsylvania  State  University   •  PhD,  Physics.  (Thesis:  Measurements  of  Diffuse  and  Specular  Scafering  of  4He  Atoms  from   4He  Films),  Ohio  State  University   •  MS,  Computer  &InformaHon  Systems.  The  Ohio  State  University   •  BSc,  Physics  &  MathemaHcs.  University  of  Bombay   70