Early Lessons Learned in Applying Big        Data To TV Advertising         IAB ITV for Agencies Day      Dave Morgan, CEO...
About Us        Who We Are    We are a New York based start-up. We are venture backed by Avalon                      Ventu...
Why Did We Leave The Web?                   Television remains the dominant consumer medium(a) Nielsen US TV Viewing Audic...
TV Spend Is IncreasingSource: MAGNAGLOBAL                              4
Audience Is FragmentingSource: Nielsen via TVbythenumbers.com                                         5
Campaign Reach Is Declining                             Impossible for measurement and planning tools to keep paceSource: ...
Big Data           Highly Confidential
Big Data Is Driving Growth      “We are on the cusp of a tremendous wave of     innovation, productivity and growth, as we...
Size Is Relative              1 byte x 1000 = 1 kilobyte                …x 1000 = 1 megabyte                …x 1000 = 1 gi...
Size Is Relative                                          Telegram = 100 bytesData © 1997-2011, James S. Huggins http://ww...
Size Is Relative                           Page of an Encyclopedia = 100 kilobytesData © 1997-2011, James S. Huggins http:...
Size Is Relative                         Pickup truck bed full of paper = 1 gigabyteData © 1997-2011, James S. Huggins htt...
Size Is Relative        Entire print collection of the Library of Congress = 10 terabytesData © 1997-2011, James S. Huggin...
Size Is Relative                    All hard drives produced in 1995 = 20 petabytesData © 1997-2011, James S. Huggins http...
Size Is Relative                              All printed material = 200 petabytesData © 1997-2011, James S. Huggins http:...
But Big Data Is More Than Size                                                BIG DATA                  What        Why di...
Accelerating The Push To Big Data Hadoop, cloud computing, Facebook, Yahoo,quants, Bittorrent, machine learning, Stanford,...
What Can It Mean For TV Advertising?       Big data drove the rise of web & search advertising     • Accumulation of high ...
Post Modern Architecture    Have we reached the limits of classic data storage architecture?Data Warehouses               ...
Our Idea of Big Data                   Bringing the data set together in a single platform                                ...
Rethinking Media Data Architecture  Applying big data to television required us to rethink what our                 techni...
Some Wrinkles In The Matrix                              22
The People We Needed          A different approach required different skill sets   • New core skills for everyone in the c...
10 Lessons We’ve Learned                    Highly Confidential
Some Things To Know, First• Live viewing unless otherwise noted   • Time shifting lessons is a whole other presentation   ...
60% of TV Viewers Watch       90% of TV                   Highly Confidential
Where The Other 40% Are                                                                    TCM                   13.6     ...
Where The Other 40% Are  To capture light viewers, media planning and measurement  tools must quickly apply new methods to...
Quality Control Is A Full       Time Job                     Highly Confidential
When Data Goes Missing                             Automation of error                             checking/quality contro...
More Data Really Is Better                     Highly Confidential
Disambiguation: The Madonna Problem                      OR        Pop Icon?                     Religious icon?          ...
The Revolution of Simple Methods                                                                             More data bea...
Packaging Reach  Very large data sets better predict TV audience movements           Peter Norvig | Internet Scale Data An...
The Cost Of More Data        More data drives better results but there are costs     • All data online. All the   • All da...
The Data Isn’t Biased JustBecause It Comes From A      Set Top Box                      Highly Confidential
Applying Simple Methods At Scale                                                   High correlation of a7                 ...
And Then We Kept Going   We measured program Tune-In, Spot Tune-In, Campaign Reach,   Campaign Rating using multiple slice...
Addressability Is Here                   Highly Confidential
Closing The Loop On Program Promotion                                        Spring 2010 broadcast                        ...
Closing The Loop On Program Promotion                                        Spring 2010 broadcast                        ...
Closing The Loop Long held beliefs and rules of thumb in planning may or may                   not be supported by data  T...
Nielsen’s Ratings Are Good    (Surprisingly Good)                     Highly Confidential
Time Series: Broadcast: CBS      60 networks. High correlation between Nielsen   Hour by hour time series                 ...
Time Series: Broadcast: Fox                                     Hour by hour time series                                  ...
Time Series: Broadcast: ABC                                     Hour by hour time series                                  ...
Time Series: Cable: Investigation Discovery                                              Hour by hour time series         ...
Time Series: Cable: Golf                                     Hour by hour time series                                     ...
Time Series: Cable: Bravo                                     Hour by hour time series                                    ...
Time Series: Cable: ESPN2                                     Hour by hour time series                                    ...
Time Series: Cable: Speed                                     Hour by hour time series                                    ...
…but…        Highly Confidential
When You Look Closer                                     Hour by hour time series                                     Mar ...
High Frequency Time Series: ABC Family           Volatility in dayparts, low rated networks, demographics….             Un...
Women Are More Different      Than Men                   Highly Confidential
Gender Driven Geographic Variation Viewing by zip code among women across markets is more varied than                     ...
Gender Driven Geographic VariationPlanning tactics for female targeted campaigns should be different than                 ...
Privacy Matters                  Highly Confidential
Privacy By Design• All marketing data companies need to  care• Make consumer privacy protection part  of the business from...
Mass Reach IsIndiscriminant                 Highly Confidential
Fragmentation Effects On Frequency  Each segment was above 70% reach but the frequency distribution was nearly            ...
Fragmentation Effects On Frequency                      Fragmentation is affecting all high reach campaigns.              ...
Fragmentation Effects On Frequency   The TV advertising market can’t continue to support this                             ...
40% Of The Audience Is  Getting 85% Of The     Impressions                   Highly Confidential
Fragmentation Rears It’s Head Again                                                                                 Campai...
Fragmentation Effects on Frequency         Advertisers won’t continue to support this                                     ...
What Happens Next?                Highly Confidential
Choices• If fragmentation is causing declining campaign reach and  frequency imbalances, marketers must make choices.   • ...
Jack Smith             jack@simulmedia.com                @simulmedia                @jkellonsmith                        ...
About Our Science Team• Krishna Balasubramanian, Chief Scientist    •   Previously: Chief Scientist, Tacoda. Chief Scienti...
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Early Lessons Learned in Applying Big Data to TV Advertising presentation Presented By Dave Morgan

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Early Lessons Learned in Applying Big Data to TV Advertising presentation Presented By Dave Morgan

  1. 1. Early Lessons Learned in Applying Big Data To TV Advertising IAB ITV for Agencies Day Dave Morgan, CEO, Simulmedia
  2. 2. 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 still the most powerful advertising medium in the world. While addressability will come, we’re not waiting for it. We’ve taken a few strategies we learned from the Internet and are applying it to linear TV advertising, today. How We Do It Through partnerships with major data providers, we have assembled the world’s largest set of actionable television data. How We Make Money We sell television advertising. With inventory in over 106 million US households, we can cost-effectively extend reach into high-value target audiences across virtually any advertiser category. We use big data and science to do this. 2
  3. 3. Why Did We Leave The Web? Television remains the dominant consumer medium(a) Nielsen US TV Viewing Audicence Traditional Live-Only TV based on average monthly viewing during 1Q2011. Internet and Online Video based on average monthly consumption during July 2011. 3Video on Demand based on consumption during May 2011.
  4. 4. TV Spend Is IncreasingSource: MAGNAGLOBAL 4
  5. 5. Audience Is FragmentingSource: Nielsen via TVbythenumbers.com 5
  6. 6. Campaign Reach Is Declining Impossible for measurement and planning tools to keep paceSource: Simulmedia analysis of data from SQAD, Nielsen and TVB 6
  7. 7. Big Data Highly Confidential
  8. 8. Big Data Is Driving Growth “We are on the cusp of a tremendous wave of innovation, productivity and growth, as well as new modes of competition and value-capture – all driven by Big Data.” - McKinsey Global Institute, May 2011 “For CMOs, Big Data is a very big deal.” - Alfredo Gangotena, CMO, Mastercard, July 2011 8
  9. 9. Size Is Relative 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
  10. 10. Size Is Relative Telegram = 100 bytesData © 1997-2011, James S. Huggins http://www.jamesshuggins.com/h/tek1/how_big.htm 10
  11. 11. Size Is Relative Page of an Encyclopedia = 100 kilobytesData © 1997-2011, James S. Huggins http://www.jamesshuggins.com/h/tek1/how_big.htm 11
  12. 12. Size Is Relative Pickup truck bed full of paper = 1 gigabyteData © 1997-2011, James S. Huggins http://www.jamesshuggins.com/h/tek1/how_big.htm 12
  13. 13. Size Is Relative Entire print collection of the Library of Congress = 10 terabytesData © 1997-2011, James S. Huggins http://www.jamesshuggins.com/h/tek1/how_big.htm 13
  14. 14. Size Is Relative All hard drives produced in 1995 = 20 petabytesData © 1997-2011, James S. Huggins http://www.jamesshuggins.com/h/tek1/how_big.htm 14
  15. 15. Size Is Relative All printed material = 200 petabytesData © 1997-2011, James S. Huggins http://www.jamesshuggins.com/h/tek1/how_big.htm 15
  16. 16. 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: Reporting Prediction Supports: Human Machine decisions decisions Data: Structured Unstructured Aggregated Unaggregated Human Dashboards Discovery Skills: Excel Visualization Statistics & Physics 16
  17. 17. Accelerating The Push To Big Data Hadoop, cloud computing, Facebook, Yahoo,quants, Bittorrent, machine learning, Stanford, large hadron collider, Wal-Mart, text processing, Amazon S3 & EC2, open source intelligence, NoSQL, social media, Google, commodity hardware, Hive, fraud detection, trading desks, MapReduce, natural language processing 17
  18. 18. What Can It Mean For TV Advertising? Big data drove the rise of web & search advertising • Accumulation of high volume of direct measurement of media consumption • Better predictions about consumer interests • Real time return path • Automation • 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
  19. 19. 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 Statistics: 250 tb1 compression)• AT&T: 250 tb1 • Yahoo: 22 pb4• Nielsen: 45 tb1 • Google: ???• Adidas: 13 tb1• Wal-Mart: 1 pb21 Oracle F1Q10 Earnings Call September 16, 2009 Transcript2 Stair, Principles of Information Systems, 2009, p 1813 Dhruba Borthakur, Facebook, December 2010, http://www.facebook.com/note.php?note_id=4682111939194 Simulmedia estimate 19
  20. 20. Our Idea of Big Data Bringing the data set together in a single platform Client Nielsen Set Top Boxes Program Public Ad Occurrence Proprietary Ratings • 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 (comparatively 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
  21. 21. 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 Software • Participate in the Open Source community • ELT (Extract, Load, Transform) Write Your Own • Meddle Software • Machine learning • Advanced statistical techniques Science • Experimentation 21
  22. 22. Some Wrinkles In The Matrix 22
  23. 23. The People We Needed A different approach required different skill sets • New core skills for everyone in the company • Pattern recognition • Visualization • Technology • Experimentation • Where do you find hard to find tech skills? • You don’t find them. You make them. • A dedicated Science team • Non traditional researchers (Brain imaging, bioinformatics, economic modeling, genetics) • People who watch a lot of television 23
  24. 24. 10 Lessons We’ve Learned Highly Confidential
  25. 25. Some Things To Know, First• Live viewing unless otherwise noted • Time shifting lessons is a whole other presentation • Time shifting + live viewing lessons is a whole other other presentation • Video on demand is a whole other other other presentation• We name names and provide numbers where clients and data partners permit • Client confidentiality 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… information about the graphs on each page. 25
  26. 26. 60% of TV Viewers Watch 90% of TV Highly Confidential
  27. 27. 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 Vertical: Ratio 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: Ratio 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
  28. 28. Where The Other 40% Are To capture light viewers, media planning and measurement tools must quickly apply new methods to emerging data sets 28
  29. 29. Quality Control Is A Full Time Job Highly Confidential
  30. 30. When Data Goes Missing Automation of error checking/quality control is essential Reuse the data to solve other problems Occasionally observe missing data Three choices: • Pick up the phone • Estimate missing fields • Work around the missing data Time series of SYFY network. 10645 observations from 2010.02.28 at 7:00pm Eastern to 2010.10.14 at 12:30pm Eastern 30Source: Simulmedia’s a7
  31. 31. More Data Really Is Better Highly Confidential
  32. 32. Disambiguation: The Madonna Problem OR Pop Icon? Religious icon? 32
  33. 33. The Revolution of Simple Methods More data beats better 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 betterPeter Norvig | Internet Scale Data Analysis | June 21, 2010 predict audience movement.Original graph sourced from: Banko & Brill, 2001. Mitigating the paucity-of-data problem: exploring the effectof training corpus size on classifier performance for natural language processing 33
  34. 34. Packaging Reach Very large data sets better predict TV audience movements Peter Norvig | Internet Scale Data Analysis | June 21, 2010 34
  35. 35. The Cost Of More Data More data drives better results but there are costs • All data online. All the • All data online. All the time. time. • Less expensive hardware • More expensive talent • Extremely flexible • Physicists & statisticians ain’t cheap • Hard to find programmers • Not everything meets your needs • Evolving technologies in mission critical functions 35
  36. 36. The Data Isn’t Biased JustBecause It Comes From A Set Top Box Highly Confidential
  37. 37. Applying Simple Methods At Scale High correlation of a7 measures and Nielsen estimates. Either bias is insignificant or Nielsen data and our data share the same bias. Multiple methods yield similar results Regression analysis of Nielsen Household Cume Rating against Simulmedia’s a7 cume rating. 20 Primetime Network shows withSources: Nielsen & Simulmedia’s a7 HAWAII FIVE-0. Fall 2010. 37
  38. 38. And Then We Kept Going We measured program Tune-In, Spot Tune-In, Campaign Reach, Campaign Rating using multiple slices of our data set using two different sample sets and time framesHow we sliced it Two samples• Entire a7 data set 1. Sample 1: Fall 2010: 20 Primetime• Cross correlated individual data broadcast series launches + sets contained in a7 aggregate promos 2. Sample 2: Jan 2011: 15 Primetime data set cable series premieres + promos• Aggregate cross geographies (Plus one multi-season/year (DMA to DMA) primetime broadcast premiere + promos)Observations• 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
  39. 39. Addressability Is Here Highly Confidential
  40. 40. Closing The Loop On Program Promotion Spring 2010 broadcast premiere promotion. Horizontal: Left to right moves back in time. 0 is the premiere time. Vertical: Conversion rate is measured in percent. Size ofSources: Simulmedia’s a7 the bubble represents total conversions for a given spot. 40
  41. 41. Closing The Loop On Program Promotion Spring 2010 broadcast premiere promotion. Horizontal: Left to right moves back in time. 0 is the premiere time. Vertical: Conversion rate is measured in percent. Size ofSources: Simulmedia’s a7 the bubble represents total conversions for a given spot. 41
  42. 42. 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 options for show promotion 42
  43. 43. Nielsen’s Ratings Are Good (Surprisingly Good) Highly Confidential
  44. 44. Time Series: Broadcast: CBS 60 networks. High correlation between Nielsen Hour by hour time series Mar 20 to April 8, 2011. Z large sample measurement and a7 measures score plots with Nielsen estimates in red. Simulmedia measurements in blue. Where Nielsen provided no estimate, estimates were imputed using Multiple Imputation (Rubin (1987))Sources: Nielsen & Simulmedia’s a7 44
  45. 45. Time Series: Broadcast: Fox Hour by hour time series Mar 20 to April 8, 2011. Z score plots with Nielsen estimates in red. Simulmedia measurements in blue. Where Nielsen provided no estimate, estimates were imputed using Multiple Imputation (Rubin (1987))Sources: Nielsen & Simulmedia’s a7 45
  46. 46. Time Series: Broadcast: ABC Hour by hour time series Mar 20 to April 8, 2011. Z score plots with Nielsen estimates in red. Simulmedia measurements in blue. Where Nielsen provided no estimate, estimates were imputed using Multiple Imputation (Rubin (1987))Sources: Nielsen & Simulmedia’s a7 46
  47. 47. Time Series: Cable: Investigation Discovery Hour by hour time series Mar 20 to April 8, 2011. Z score plots with Nielsen estimates in red. Simulmedia measurements in blue. Where Nielsen provided no estimate, estimates were imputed using Multiple Imputation (Rubin (1987))Sources: Nielsen & Simulmedia’s a7 47
  48. 48. Time Series: Cable: Golf Hour by hour time series Mar 20 to April 8, 2011. Z score plots with Nielsen estimates in red. Simulmedia measurements in blue. Where Nielsen provided no estimate, estimates were imputed using Multiple Imputation (Rubin (1987))Sources: Nielsen & Simulmedia’s a7 48
  49. 49. Time Series: Cable: Bravo Hour by hour time series Mar 20 to April 8, 2011. Z score plots with Nielsen estimates in red. Simulmedia measurements in blue. Where Nielsen provided no estimate, estimates were imputed using Multiple Imputation (Rubin (1987))Sources: Nielsen & Simulmedia’s a7 49
  50. 50. Time Series: Cable: ESPN2 Hour by hour time series Mar 20 to April 8, 2011. Z score plots with Nielsen estimates in red. Simulmedia measurements in blue. Where Nielsen provided no estimate, estimates were imputed using Multiple Imputation (Rubin (1987))Sources: Nielsen & Simulmedia’s a7 50
  51. 51. Time Series: Cable: Speed Hour by hour time series Mar 20 to April 8, 2011. Z score plots with Nielsen estimates in red. Simulmedia measurements in blue. Where Nielsen provided no estimate, estimates were imputed using Multiple Imputation (Rubin (1987))Sources: Nielsen & Simulmedia’s a7 51
  52. 52. …but… Highly Confidential
  53. 53. When You Look Closer Hour by hour time series Mar 20 to April 8, 2011. Z score plots with Nielsen estimates in red. Simulmedia measurements in blue. Where Nielsen provided no estimate, estimates were imputed using Multiple Imputation (Rubin (1987))Sources: Nielsen & Simulmedia’s a7 53
  54. 54. High Frequency Time Series: ABC Family Volatility 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) 54Sources: Nielsen & Simulmedia’s a7
  55. 55. Women Are More Different Than Men Highly Confidential
  56. 56. Gender Driven Geographic Variation Viewing by zip code among women across markets is more varied than men in the same zip codes Women 18-54 Men 18-54 Fraction of view time for ages 18-54 as fraction of view time for all TV viewers. Week 2 vs. the same fraction for week 1 (last two weeks in January). Three markets: Philadelphia (blue) Atlanta (red) and Chicago (green) EachSource: Simulmedia’s a7 point represents a zip code in one of these markets. 56
  57. 57. Gender Driven Geographic VariationPlanning tactics for female targeted campaigns should be different than male target campaigns PS…Also a good case for geo based creative versioning 57
  58. 58. Privacy Matters Highly Confidential
  59. 59. Privacy By Design• All marketing data companies need to care• Make consumer privacy protection part of the business from the beginning • Anonymous, aggregated data only • No personal data or data that can be related to particular individuals or devices • Broad marketing segmentations, not profiling • No sensitive data Don’t be creepy 59
  60. 60. Mass Reach IsIndiscriminant Highly Confidential
  61. 61. Fragmentation Effects On Frequency Each segment was above 70% reach but the frequency distribution was nearly identical Percent of audience reached for major animated motion picture campaign 2011. Two weeks prior to release. Each stacked bar is a different audience segment. Each colorSource: Nielsen & Simulmedia’s a7 with the stacked bar represents the frequency of ad view 61 for each segment.
  62. 62. Fragmentation Effects On Frequency Fragmentation is affecting all high reach campaigns. Percent of audience reached for insurance advertisers September to October 2010. Approximately 8000 ads. Each stacked bar is a different audience segment. EachSource: Nielsen & Simulmedia’s a7 color with the stacked bar represents the frequency of ad 62 view for each segment.
  63. 63. Fragmentation Effects On Frequency The TV advertising market can’t continue to support this 63
  64. 64. 40% Of The Audience Is Getting 85% Of The Impressions Highly Confidential
  65. 65. Fragmentation Rears It’s Head Again Campaign impressions increasingly concentrated against 0.0 0.0% heavy viewers. 1.4 3.6% TotalUS Television 4.3 10.8% Audience Percent of audience 23.0% reached for a different 9.1 major animated motion picture campaign 2011. Two weeks prior to release. The stacked bar 24.8 62.6% represents quintiles. Blue labels are average frequency per Average Frequency % of Total Impressions respective quintile. Red Per Quintile Per Quintile labels are % of total campaign impressions Source: Nielsen & Simulmedia’s a7 by respective quintile. 65
  66. 66. Fragmentation Effects on Frequency Advertisers won’t continue to support this 66
  67. 67. What Happens Next? Highly Confidential
  68. 68. Choices• If fragmentation 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
  69. 69. Jack Smith jack@simulmedia.com @simulmedia @jkellonsmith 69
  70. 70. About Our Science Team• Krishna Balasubramanian, Chief Scientist • Previously: Chief Scientist, Tacoda. Chief Scientist, Real Media. • Doctoral Candidate, Physics. (Condensed Matter Physics) The Ohio State University • MS, Computer & Information Systems. The Ohio State University • MSc, Physics. Indian Institute of Technology, Kanpur• Yuliya Torosjan, Scientist • Previously: Clinical Research (Brain Imaging), Mount Sinai College of Medicine • MA, Statistics. Columbia University • BSE, Computer Science & Engineering. University of Pennsylvania • BA, Psychology. University of Pennsylvania• Mario Morales, Scientist • Previously: Lecturer, Bioinformatics, New York University. Senior Consultant, Weiser LLP. • MS, Statistics. Hunter College • MS, Bioinformatics. New York University• Dr. Sidd Mukherjee, Scientist • Previously, Visiting Scholar (Atomic Scattering experiments), The Ohio State University • Post doctoral research, Heat capacity of Helium-4. Pennsylvania State University • PhD, Physics. (Thesis: Measurements of Diffuse and Specular Scattering of 4He Atoms from 4He Films), Ohio State University • MS, Computer &Information Systems. The Ohio State University • BSc, Physics & Mathematics. University of Bombay 70

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