Interview Alain Chesnais Chief Scientist

Here is a brief interview with Alain Chesnais ,Chief Scientist It is a big honor to interview such a legend in computer science, and I am grateful to both him and Mark Zohar for taking time to write these down.

Ajay-  Describe your career from your student days to being the President of ACM (Association of Computing Machinery ). How can we increase  the interest of students in STEM education, particularly in view of the shortage of data scientists.
Alain- I’m trying to sum up a career of over 35 years. This may be a bit long winded…
I started my career in CS when I was in high school in the early 70’s. I was accepted in the National Science Foundation’s Science Honors Program in 9th grade and the first course I took was a Fortran programming course at Columbia University. This was on an IBM 360 using punch cards.
The next year my high school got a donation from DEC of a PDP-8E mini computer. I ended up spending a lot of time in the machine room all through high school at a time when access to computers wasn’t all that common. I went to college in Paris and ended up at l’Ecole Normale Supérieure de Cachan in the newly created Computer Science department.
My first job after finishing my graduate studies was as a research assistant at the Centre National de la Recherche Scientifique where I focused my efforts on modelling the behaviour of distributed database systems in the presence of locking. When François Mitterand was elected president of France in 1981, he invited Nicholas Negroponte and Seymour Papert to come to France to set up the Centre Mondial Informatique. I was hired as a researcher there and continued on to become director of software development until it was closed down in 1986. I then started up my own company focusing on distributed computer graphics. We sold the company to Abvent in the early 90’s.
After that, I was hired by Thomson Digital Image to lead their rendering team. We were acquired by Wavefront Technologies in 1993 then by SGI in 1995 and merged with Alias Research. In the merged company: Alias|wavefront, I was director of engineering on the Maya project. Our team received an Oscar in 2003 for the creation of the Maya software system.
Since then I’ve worked at various companies, most recently focusing on social media and Big Data issues associated with it. Mark Zohar and I worked together at SceneCaster in 2007 where we developed a Facebook app that allowed users to create their own 3D scenes and share them with friends via Facebook without requiring a proprietary plugin. In December 2007 it was the most popular app in its category on Facebook.
Recently Mark approached me with a concept related to mining the content of public tweets to determine what was trending in real time. Using math similar to what I had developed during my graduate studies to model the performance of distributed databases in the presence of locking, we built up a real time analytics engine that ranks the content of tweets as they stream in. The math is designed to scale linearly in complexity with the volume of data that we analyze. That is the basis for what we have created for TrendSpottr.
In parallel to my professional career, I have been a very active volunteer at ACM. I started out as a member of the Paris ACM SIGGRAPH chapter in 1985 and volunteered to help do our mailings (snail mail at the time). After taking on more responsibilities with the chapter, I was elected chair of the chapter in 1991. I was first appointed to the SIGGRAPH Local Groups Steering Committee, then became ACM Director for Chapters. Later I was successively elected SIGGRAPH Vice Chair, ACM SIG Governing Board (SGB) Vice Chair for Operations, SGB Chair, ACM SIGGRAPH President, ACM Secretary/Treasurer, ACM Vice President, and finally, in 2010, I was elected ACM President. My term as ACM President has just ended on July 1st. Vint Cerf is our new President. I continue to serve on the ACM Executive Committee in my role as immediate Past President.
(Note- About ACM
ACM, the Association for Computing Machinery, is the world’s largest educational and scientific computing society, uniting computing educators, researchers and professionals to inspire dialogue, share resources and address the field’s challenges. )
Ajay- What sets Trendspotter apart from other startups out there in terms of vision in trying to achieve a more coherent experience on the web.
Alain- The Basic difference with other approaches that we are aware of is that we have developed an incremental solution that calculates the results on the fly as the data streams in. Our evaluators are based on solid mathematical foundations that have proven their usefulness over time. One way to describe what we do is to think of it as signal processing where the tweets are the signal and our evaluators are like triggers that tell you what elements of the signal have the characteristics that we are filtering for (velocity and acceleration). One key result of using this approach is that our unit cost per tweet analyzed does not go up with increased volume. Using more traditional data analysis approaches involving an implicit sort would imply a complexity of N*log(N), where N is the volume of tweets being analyzed. That would imply that the cost per tweet analyzed would go up with the volume of tweets. Our approach was designed to avoid that, so that we can maintain a cap on our unit costs of analysis, no matter what volume of data we analyze.
Ajay- What do you think is the future of big data visualization going to look like? What are some of the technologies that you are currently bullish on?
Alain- I see several trends that would have deep impact on Big Data visualization. I firmly believe that with large amounts of data, visualization is key tool for understanding both the structure and the relationships that exist between data elements. Let’s focus on some of the key things that are pushing in this direction:
  • the volume of data that is available is growing at a rate we have never seen before. Cisco has measured an 8 fold increase in the volume of IP traffic over the last 5 years and predicts that we will reach the zettabyte of data over IP in 2016
  • more of the data is becoming publicly available. This isn’t only on social networks such as Facebook and twitter, but joins a more general trend involving open research initiatives and open government programs
  • the desired time to get meaningful results is going down dramatically. In the past 5 years we have seen the half life of data on Facebook, defined as the amount of time that half of the public reactions to any given post (likes, shares., comments) take place, go from about 12 hours to under 3 hours currently
  • our access to the net is always on via mobile device. You are always connected.
  • the CPU and GPU capabilities of mobile devices is huge (an iPhone has 10 times the compute power of a Cray-1 and more graphics capabilities than early SGI workstations)
Put all of these observations together and you quickly come up with a massive opportunity to analyze data visually on the go as it happens no matter where you are. We can’t afford to have to wait for results. When something of interest occurs we need to be aware of it immediately.
Ajay- What are some of the applications we could use Trendspottr. Could we predict events like Arab Spring, or even the next viral thing.
Alain- TrendSpottr won’t predict what will happen next. What it *will* do is alert you immediately when it happens. You can think of it like a smoke detector. It doesn’t tell that a fire will take place, but it will save your life when a fire does break out.
Typical uses for TrendSpottr are
  • thought leadership by tracking content that your readership is interested in via TrendSpottr you can be seen as a thought leader on the subject by being one of the first to share trending content on a given subject. I personally do this on my Facebook page ( and have seen my klout score go up dramatically as a result
  • brand marketing to be able to know when something is trending about your brand and take advantage of it as it happens.
  • competitive analysis to see what is being said about two competing elements. For instance, searching TrendSpottr for “Obama OR Romney” gives you a very good understanding about how social networks are reacting to each politician. You can also do searches like “$aapl OR $msft OR $goog” to get a sense of what is the current buzz for certain hi tech stocks.
  • understanding your impact in real time to be able to see which of the content that you are posting is trending the most on social media so that you can highlight it on your main page. So if all of your content is hosted on common domain name (, searching for will show you the most active of your site’s content. That can easily be set up by putting a TrendSpottr widget on your front page

Ajay- What are some of the privacy guidelines that you keep in  mind- given the fact that you collect individual information but also have government agencies as potential users.

Alain- We take privacy very seriously and anonymize all of the data that we collect. We don’t keep explicit records of the data we collected through the various incoming streams and only store the aggregate results of our analysis.
Alain Chesnais is immediate Past President of ACM, elected for the two-year term beginning July 1, 2010.Chesnais studied at l’Ecole Normale Supérieure de l’Enseignement Technique and l’Université de Paris where he earned a Maîtrise de Mathematiques, a Maitrise de Structure Mathématique de l’Informatique, and a Diplôme d’Etudes Approfondies in Compuer Science. He was a high school student at the United Nations International School in New York, where, along with preparing his International Baccalaureate with a focus on Math, Physics and Chemistry, he also studied Mandarin Chinese.Chesnais recently founded Visual Transitions, which specializes in helping companies move to HTML 5, the newest standard for structuring and presenting content on the World Wide Web. He was the CTO of from June 2007 until April 2010, and was Vice President of Product Development at Tucows Inc. from July 2005 – May 2007. He also served as director of engineering at Alias|Wavefront on the team that received an Oscar from the Academy of Motion Picture Arts and Sciences for developing the Maya 3D software package.

Prior to his election as ACM president, Chesnais was vice president from July 2008 – June 2010 as well as secretary/treasurer from July 2006 – June 2008. He also served as president of ACM SIGGRAPH from July 2002 – June 2005 and as SIG Governing Board Chair from July 2000 – June 2002.

As a French citizen now residing in Canada, he has more than 20 years of management experience in the software industry. He joined the local SIGGRAPH Chapter in Paris some 20 years ago as a volunteer and has continued his involvement with ACM in a variety of leadership capacities since then.


TrendSpottr is a real-time viral search and predictive analytics service that identifies the most timely and trending information for any topic or keyword. Our core technology analyzes real-time data streams and spots emerging trends at their earliest acceleration point — hours or days before they have become “popular” and reached mainstream awareness.

TrendSpottr serves as a predictive early warning system for news and media organizations, brands, government agencies and Fortune 500 companies and helps them to identify emerging news, events and issues that have high viral potential and market impact. TrendSpottr has partnered with HootSuite, DataSift and other leading social and big data companies.

Newer Doctrines for Newer Wars

On the Memorial Day, some thoughts on the convergence of revolutions in technology and war fare-


War – 

War is an openly declared state of organized conflict, typified by extreme aggression, societal disruption, and high mortality

1) Disrupting command and control objects is the primary stage of attack. Evading detection of your own command and control objects while retaining secure channels of communication with redundant lines of control is the primary stage of defense.

2) Pre emptive strikes are in. Reactive all out wars are out. Countries will no longer “declare war” before going to war. They already dont.

3) Commando /Special Forces/Terror strikes /Guerrilla warfare weapons, tactics and technology will have a big demand. So will be specialist trainers.

4) Improving the predictability of your own detect and destroy mechanisms, and disrupting the predictability of enemy detect and react mechanisms will be hugely in- even more than commissioning one more submarine and one more aircraft type.

5) Countries will revert to ancient tribal paradigms in fast shifting alliances for economics as well as geo politics. Very stupidly religion can be  factor in warfare even in the 21 st century.


6) Number of Kills per Weapons fired will converge to a constant .  Risks of secondary collateral damage will need to have a higher weight-age because they spur more retal attacks. Fewer prisoner of wars, higher KIA/ MIA ratio.

7) Fewer civilian casualties than all previous wars. This includes fewer civilian casualties even in nuclear war than previous nuclear scenarios.

8) War is a business. It will not be allowed to disrupt global supply chains for more than 2-3 weeks (or inventory replenishment of critical goods and /or services). commodities will lead to wars explicitly, especially since nuclear energy is discredited and carbon energy is diminishing. Expect synchronization with financial derivatives activity. War futures anyone.

9) The Geneva Convention is overdue for an update. Call it Geneva Convention 3.0 United Nations will remain critical to preventing or hastening global conflicts (remember the league of extra ordinary nations .)

10) Economic weapons, climate changing weapons, and sky weapons will emerge. Expect newer kinds of gun powder to be invented. Cyber weapons and hackers will be in demand . Thats the only bright spot.

Happy Memorial Day.


Enjoy that freedom to eat an barbecue- it was paid for in more blood than you will ever care to know.


Weather Modifying Weapons

OSTM/Jason-2's predecessor TOPEX/Poseidon caug...
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This is part of a continuing series of theoretical weapons. The weapons are theoretical as the United Nations has already banned the weapons (but not banned the building of research of defense from these weapons).

Possible applications of weather modifying weapons.

1) Use surface modifiers on oceans including but not limited to submerged nuclear heaters, airborne solar powered  lasers, surface spreaders like oil slicks. This will help modify the temperature of the ocean in certain critical areas  at critical times, influencing weather esp winds that bring rains.

Example- Modifying or Enhancing El Nino to influence rain to specific countries.

2) Use of air borne or aircraft borne lasers to start forest fires

3) Use of lasers to enhance the rate of melting of strategic glaciers.

4) Modify and interfere with the timing of an active volcano to prevent big rupture, rather to go for controlled releases.

5) Use of harmonics to influence seismic wave activity in geological reasons.

Amazing Data Visualization- UN Counter Terrorism

Here is an amazing organigram depicting the organization of United Nations Task force of Countering Terrorism


so now you know whom to call at 3 am, in case of airline bombing terrorists or just bad guys in general.


Assumptions on Guns

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While sitting in Delhi, India- I sometimes notice that there is one big new worthy gun related incident in the United States every six months (latest incident Gabrielle giffords incident) and the mythical NRA (which seems just as powerful as equally mythical Jewish American or Cuban American lobby ) . As someone who once trained to fire guns (.22 and SLR -rifles actually), comes from a gun friendly culture (namely Punjabi-North Indian), my dad carried a gun sometimes as a police officer during his 30 plus years of service, I dont really like guns (except when they are in a movie). My 3 yr old son likes guns a lot (for some peculiar genetic reason even though we are careful not to show him any violent TV or movie at all).

So to settle the whole guns are good- guns are bad thing I turned to the one resource -Internet

Here are some findings-

1) A lot of hard statistical data on guns is biased by the perspective of the writer- it reminds me of the old saying Lies, True lies and Statistics.

2) There is not a lot of hard data in terms of a universal research which can be quoted- unlike say lung cancer is caused by cigarettes- no broad research which can be definitive in this regards.

3) American , European and Asian attitudes on guns actually seem a function of historical availability , historic crime rates and cultural propensity for guns.

Switzerland and United States are two extreme outlier examples on gun causing violence causal statistics.

4) Lot of old and outdated data quoted selectively.

It seems you can fudge data about guns in the following ways-

1) Use relative per capita numbers vis a vis aggregate numbers

2) Compare and contrast gun numbers with crime numbers selectively

3) Remove drill down of type of firearm- like hand guns, rifles, automatic, semi automatic

Maybe I am being simplistic-but I found it easier to list credible data sources on guns than to summarize all assumptions on guns. Are guns good or bad- i dont know -it depends? Any research you can quote is welcome.

Data Sources on Guns and Firearms and Crime-



* As of 2009, the United States has a population of 307 million people.[5]

* Based on production data from firearm manufacturers,[6] there are roughly 300 million firearms owned by civilians in the United States as of 2010. Of these, about 100 million are handguns.[7]

* Based upon surveys, the following are estimates of private firearm ownership in the U.S. as of 2010:

Households With a Gun Adults Owning a Gun Adults Owning a Handgun
Percentage 40-45% 30-34% 17-19%
Number 47-53 million 70-80 million 40-45 million


* A 2005 nationwide Gallup poll of 1,012 adults found the following levels of firearm ownership:

Category Percentage Owning 

a Firearm

Households 42%
Individuals 30%
Male 47%
Female 13%
White 33%
Nonwhite 18%
Republican 41%
Independent 27%
Democrat 23%


* In the same poll, gun owners stated they own firearms for the following reasons:

Protection Against Crime 67%
Target Shooting 66%
Hunting 41%


VIEW DATA: Totals Per capita
Definition Source Printable version
Bar Graph Pie Chart Map

Showing latest available data.

Rank Countries Amount
# 1 South Africa: 31,918
# 2 Colombia: 21,898
# 3 Thailand: 20,032
# 4 United States: 9,369
# 5 Philippines: 7,708
# 6 Mexico: 2,606
# 7 Slovakia: 2,356
# 8 El Salvador: 1,441
# 9 Zimbabwe: 598
# 10 Peru: 442
# 11 Germany: 269
# 12 Czech Republic: 181
# 13 Ukraine: 173
# 14 Canada: 144
# 15 Albania: 135
# 16 Costa Rica: 131
# 17 Azerbaijan: 120
# 18 Poland: 111
# 19 Uruguay: 109
# 20 Spain: 97
# 21 Portugal: 90
# 22 Croatia: 76
# 23 Switzerland: 68
# 24 Bulgaria: 63
# 25 Australia: 59
# 26 Sweden: 58
# 27 Bolivia: 52
# 28 Japan: 47
# 29 Slovenia: 39
= 30 Hungary: 38
= 30 Belarus: 38
# 32 Latvia: 28
# 33 Burma: 27
# 34 Macedonia, The Former Yugoslav Republic of: 26
# 35 Austria: 25
# 36 Estonia: 21
# 37 Moldova: 20
# 38 Lithuania: 16
= 39 United Kingdom: 14
= 39 Denmark: 14
# 41 Ireland: 12
# 42 New Zealand: 10
# 43 Chile: 9
# 44 Cyprus: 4
# 45 Morocco: 1
= 46 Iceland: 0
= 46 Luxembourg: 0
= 46 Oman: 0
Total: 100,693
Weighted average: 2,097.8

DEFINITION: Total recorded intentional homicides committed with a firearm. Crime statistics are often better indicators of prevalence of law enforcement and willingness to report crime, than actual prevalence.

SOURCE: The Eighth United Nations Survey on Crime Trends and the Operations of Criminal Justice Systems (2002) (United Nations Office on Drugs and Crime, Centre for International Crime Prevention)


Bureau of Justice Statistics


or the brand new website (till 2009) on which I CANNOT get gun crime but can get total

Estimated  murder rate *
Year United States-Total

1960 5.1
1961 4.8
1962 4.6
1963 4.6
1964 4.9
1965 5.1
1966 5.6
1967 6.2
1968 6.9
1969 7.3
1970 7.9
1971 8.6
1972 9.0
1973 9.4
1974 9.8
1975 9.6
1976 8.7
1977 8.8
1978 9.0
1979 9.8
1980 10.2
1981 9.8
1982 9.1
1983 8.3
1984 7.9
1985 8.0
1986 8.6
1987 8.3
1988 8.5
1989 8.7
1990 9.4
1991 9.8
1992 9.3
1993 9.5
1994 9.0
1995 8.2
1996 7.4
1997 6.8
1998 6.3
1999 5.7
2000 5.5
2001 5.6
2002 5.6
2003 5.7
2004 5.5
2005 5.6
2006 5.7
2007 5.6
2008 5.4
2009 5.0
Notes: National or state offense totals are based on data from all reporting agencies and estimates for unreported areas.
* Rates are the number of reported offenses per 100,000 population
  • United States-Total –
    • The 168 murder and nonnegligent homicides that occurred as a result of the bombing of the Alfred P. Murrah Federal Building in Oklahoma City in 1995 are included in the national estimate.
    • The 2,823 murder and nonnegligent homicides that occurred as a result of the events of September 11, 2001, are not included in the national estimates.


  • Sources: 

    FBI, Uniform Crime Reports as prepared by the National Archive of Criminal Justice Data

    4) united nation statistics of 2002  were too old in my opinion.
    wikipedia seems too broad based to qualify as a research article but is easily accessible
    to actually buy a gun or see guns available for purchase in United States see