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The Perfect Conference: Using stochastic optimization to bring people together

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Brian Lange
Brian Lange

One of the most valuable parts of attending a conference is the opportunity to meet others and build your network¡ªand potentially even find new partners. The explicit goal of the Research Corporation for Scientific Advancement¡¯s (RCSA) Scialog conferences is to foster collaboration between scientists with different specialties and approaches, and, working together with Datascope, the company has been doing so in a quantitative way for the last six years. Datascope designed a survey to run before and after each conference to determine the level of familiarity between each attendee, as well as the topics they were most interested in and the other attendees they¡¯d like to discuss those topics with. After the survey, Datascope implemented and continues to adapt an optimization tool that takes the survey data along with other metadata to choose optimal large topic discussion groups and small breakout groups for the conference. Afterward, a second survey is taken to see the effects on the network of attendees. Brian Lange discusses how Datasope and RCSA arrived at the problem, the design choices made in the survey and optimization, and how the results were visualized. Along the way, Brian covers lessons learned and other problems where optimization may prove to be fruitful.

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Using stochastic optimization to bring people together The perfect conference Brian Lange @ Strata San Jos¨¦ 2017
who is this guy?
who is this guy?
who are these folks?
who are these folks?
Today
Today - Case study: our work with Research Corporation for Scienti?c Advancement (RCSA)
Today - Case study: our work with Research Corporation for Scienti?c Advancement (RCSA) - Some high level explanation of the technique we used (simulated annealing)
Today - Case study: our work with Research Corporation for Scienti?c Advancement (RCSA) - Some high level explanation of the technique we used (simulated annealing) - Our vision of the role of a data scientist, and how they interact with the people they¡¯re serving
Meet Richard* *(bad) artist¡¯s rendering
By Hbarrison (Own work) [CC BY-SA 4.0 (http://creativecommons.org/licenses/by-sa/4.0)], via Wikimedia Commons Scialog
¡°Some of the goals of Scialog conferences are to facilitate the formation of new collaborative teams, encourage sharing insights and catalyze novel lines of research.¡± Richard¡¯s needs
Scialog Conferences Solar Energy Conversion Molecules Come to Life Time Domain Astronomy 2010 2011 2012 2015 2016
What¡¯s di?erent about Scialog
What¡¯s di?erent about Scialog - big picture, cross disciplinary topics
What¡¯s di?erent about Scialog - big picture, cross disciplinary topics - small conferences (40¨C50 junior scientists, 10¨C15 senior. scientists)
What¡¯s di?erent about Scialog - big picture, cross disciplinary topics - small conferences (40¨C50 junior scientists, 10¨C15 senior. scientists) - invite only
What¡¯s di?erent about Scialog - big picture, cross disciplinary topics - small conferences (40¨C50 junior scientists, 10¨C15 senior. scientists) - invite only - unconventional format
What¡¯s di?erent about Scialog - big picture, cross disciplinary topics - small conferences (40¨C50 junior scientists, 10¨C15 senior. scientists) - invite only - unconventional format How well does this work?
Quantifying connection
Quantifying connection
Quantifying connection
Quantifying connection
Quantifying connection
The Perfect Conference: Using stochastic optimization to bring people together
The Perfect Conference: Using stochastic optimization to bring people together
before after
Scialog 2015: Molecules Come to Life
Scialog 2015: Molecules Come to Life - 118 new conversations
Scialog 2015: Molecules Come to Life - 118 new conversations - 17 new collaborations
Scialog 2015: Molecules Come to Life - 118 new conversations - 17 new collaborations - 100% growth from pre-conference
Scialog 2015: Molecules Come to Life - 118 new conversations - 17 new collaborations - 100% growth from pre-conference - 53.6% of attendees formed a new collaboration with an attendee
measure improve
Mini Breakout Groups - round 1 A D G J B E H K C F I L conference attendees
Mini Breakout Groups - round 2 A D G J B E H KC F I L
Mini Breakout Groups - round 3 A D G J B E H K C F I L
Are these good groups? A D G J B E H K C F I L
Is this a good group? DJ F L
Is this a good group? DJ F L
Is this a good group? DJ F L 1
Is this a good group? DJ F L 1 2
Is this a good group? DJ F L 4 1 2
Is this a good group? DJ F L 4 1 2 terribleness: 7
Is this a good group? J F L 4 1 2 terribleness: 7
Is this a good group? J F L 2
Is this a good group? BJ F L 2
Is this a good group? BJ F L 2 terribleness: 2
The Perfect Conference: Using stochastic optimization to bring people together
14778213400262135041705388361938994140625 possible con?gurations
14778213400262135041705388361938994140625 possible con?gurations 1.47 x 1040, or 14 duodecillion
The Perfect Conference: Using stochastic optimization to bring people together
quadrillions of years :(
terribleness slope intercept optimization
DJ F L 4 1 2 terribleness: 7 0 1.75 3.5 5.25 7 JDFL
BJ F L 2 terribleness: 2 0 1.75 3.5 5.25 7 JDFL JBFL
1 BJ C L 4 terribleness: 6 0 1.75 3.5 5.25 7 JDFL JBFL JBCL
1 BJ C E terribleness:1 0 1.75 3.5 5.25 7 JDFL JBFL JBCL JBCE
4 4 KJ C E terribleness:8 0 2 4 6 8 JDFL JBFL JBCL JBCE JKCE
slope intercept terribleness
slope intercept terribleness ?
enter: simulated annealing
simulated annealing
simulated annealing a type of stochastic optimization
simulated annealing a type of stochastic optimization stochastic optimization algorithm with randomness introduced to avoid getting caught in local minima
temperature: 100 A D G J B E H K C F I L terribleness: 350 previous terribleness: 350
temperature: 1001. make a random ¡°move¡± from current state A D G J B E H K C F I L terribleness: 350 previous terribleness: 350
temperature: 100 A D G JB E H K C F I L terribleness: 380 previous terribleness: 350 1. make a random ¡°move¡± from current state
temperature: 100 A D G JB E H K C F I L terribleness: 380 previous terribleness: 350 1. make a random ¡°move¡± from current state 2. check if the new state is better than the previous
temperature: 100 A D G JB E H K C F I L terribleness: 380 previous terribleness: 350 1. make a random ¡°move¡± from current state 2. check if the new state is better than the previous 3. If it is, keep it and skip ahead.
temperature: 100 A D G JB E H K C F I L terribleness: 380 previous terribleness: 350 1. make a random ¡°move¡± from current state 2. check if the new state is better than the previous 3. If it is, keep it and skip ahead. 4. If not, there¡¯s a chance we¡¯ll still keep it, depending on the temperature
temperature: 1001. make a random ¡°move¡± from current state 2. check if the new state is better than the previous 3. If it is, keep it and skip ahead. 4. If not, there¡¯s a chance we¡¯ll still keep it, depending on the temperature A D G JB E H K C F I L terribleness: 380 previous terribleness: 380
temperature: 1001. make a random ¡°move¡± from current state 2. check if the new state is better than the previous 3. If it is, keep it and skip ahead. 4. If not, there¡¯s a chance we¡¯ll still keep it, depending on the temperature 5. Stop when temperature is too low. Otherwise, reduce temperature and repeat. A D G JB E H K C F I L terribleness: 380 previous terribleness: 380
temperature: 901. make a random ¡°move¡± from current state 2. check if the new state is better than the previous 3. If it is, keep it and skip ahead. 4. If not, there¡¯s a chance we¡¯ll still keep it, depending on the temperature 5. Stop when temperature is too low. Otherwise, reduce temperature and repeat. A D G JB E H K C F I L terribleness: 243 previous terribleness: 380
temperature: 901. make a random ¡°move¡± from current state 2. check if the new state is better than the previous 3. If it is, keep it and skip ahead. 4. If not, there¡¯s a chance we¡¯ll still keep it, depending on the temperature 5. Stop when temperature is too low. Otherwise, reduce temperature and repeat. A D G JB E H K C F I L terribleness: 243 previous terribleness: 243
? fast forward
temperature: 31. make a random ¡°move¡± from current state 2. check if the new state is better than the previous 3. If it is, keep it and skip ahead. 4. If not, there¡¯s a chance we¡¯ll still keep it, depending on the temperature 5. Stop when temperature is too low. Otherwise, reduce temperature and repeat. A D G J B E HK C FI L terribleness: 63 previous terribleness: 63
temperature: 31. make a random ¡°move¡± from current state 2. check if the new state is better than the previous 3. If it is, keep it and skip ahead. 4. If not, there¡¯s a chance we¡¯ll still keep it, depending on the temperature 5. Stop when temperature is too low. Otherwise, reduce temperature and repeat. A D G J B E H K C FI L terribleness: 194 previous terribleness: 63
temperature: 31. make a random ¡°move¡± from current state 2. check if the new state is better than the previous 3. If it is, keep it and skip ahead. 4. If not, there¡¯s a chance we¡¯ll still keep it, depending on the temperature 5. Stop when temperature is too low. Otherwise, reduce temperature and repeat. A D G J B E HK C FI L terribleness: 63 previous terribleness: 63
The Perfect Conference: Using stochastic optimization to bring people together
The Perfect Conference: Using stochastic optimization to bring people together
Not just for groups of things
Not just for groups of things - circuit board designs
Not just for groups of things - circuit board designs - stock trading rules
Not just for groups of things - circuit board designs - stock trading rules - anything which has a de?ned state, a ¡°move¡± (aka transition to another state), and a measure of goodness
very, very tweakable
very, very tweakable - make ¡°smarter-than-random¡± moves
very, very tweakable - make ¡°smarter-than-random¡± moves - change the cooling function (reheat and cool)
very, very tweakable - make ¡°smarter-than-random¡± moves - change the cooling function (reheat and cool) - save best state so far and restart from it periodically
very, very tweakable - make ¡°smarter-than-random¡± moves - change the cooling function (reheat and cool) - save best state so far and restart from it periodically - repeat many times from di?erent random starting states
very, very tweakable - make ¡°smarter-than-random¡± moves - change the cooling function (reheat and cool) - save best state so far and restart from it periodically - repeat many times from di?erent random starting states - etc¡­
DATA SCIENCE PITFALL WARNING very, very tweakable Icons by Andrea Novoa, Chris Kerr, and Ananth from The Noun Project - make ¡°smarter-than-random¡± moves - change the cooling function (reheat and cool) - save best state so far and restart from it periodically - repeat many times from di?erent random starting states - etc¡­
let¡¯s talk more about terribleness
client needs
client needs - minimize how ¡°connected¡± the people in each group are
client needs - minimize how ¡°connected¡± the people in each group are - don¡¯t want people to be in the same group with somebody twice
client needs - minimize how ¡°connected¡± the people in each group are - don¡¯t want people to be in the same group with somebody twice - etc¡­
client needs - minimize how ¡°connected¡± the people in each group are - don¡¯t want people to be in the same group with somebody twice - etc¡­ math/code
generate ideas build prototypeevaluation iterative problem solving anticipate re?ning your solution ¡°what does Richard need?¡±
generate ideas build prototypeevaluation iterative problem solving anticipate re?ning your solution ¡°these could be better¡±
generate ideas build prototypeevaluation iterative problem solving anticipate re?ning your solution ¡°let¡¯s tweak the optimization technique¡±
generate ideas build prototypeevaluation iterative problem solving anticipate re?ning your solution ¡°much better, what do you think Richard?¡±
generate ideas build prototypeevaluation iterative problem solving anticipate re?ning your solution ¡°pretty good, but I don¡¯t like ______¡±
generate ideas build prototypeevaluation iterative problem solving anticipate re?ning your solution ¡°cool, let¡¯s tweak the objective function¡±
generate ideas build prototypeevaluation iterative problem solving anticipate re?ning your solution ¡°these look great!¡±
generate ideas build prototypeevaluation iterative problem solving ¡°these are great!¡± also an optimization algorithm of sorts¡­
so, did it work?
Scialog 2015: Molecules Come to Life
Scialog 2015: Molecules Come to Life - In 30 out of 39 group discussions (77%), none of the people in those groups had even heard of each other before
Scialog 2015: Molecules Come to Life - In 30 out of 39 group discussions (77%), none of the people in those groups had even heard of each other before - In all group discussions, no one had spoken or collaborated with anyone else in their group
Scialog 2015: Molecules Come to Life - In 30 out of 39 group discussions (77%), none of the people in those groups had even heard of each other before - In all group discussions, no one had spoken or collaborated with anyone else in their group - On average, a group had 2.6 di?erent disciplines (physics, biology, etc)
Scialog 2015: Molecules Come to Life - In 30 out of 39 group discussions (77%), none of the people in those groups had even heard of each other before - In all group discussions, no one had spoken or collaborated with anyone else in their group - On average, a group had 2.6 di?erent disciplines (physics, biology, etc) - Each group had at least one theorist and one experimentalist
Scialog 2015: Molecules Come to Life __________ is important to the success of Scialog 0 7.5 15 22.5 30 Very Much Disagree Neutral Very Much Agree Mini Breakouts Regular Discussions
Scialog 2015: Molecules Come to Life ¡°We received 20 collaborative proposals, the most ever at a Scialog.¡±
further iterations
A D G J B E HK C FI L Tissue mechanics and evolution The physics of genetic networks In vivo mechanics Topic groups/preferences
Topic A Topic B Topic C Topic groups/preferences
Desired connections
Nominated topic experts
how might this work at a bigger conference? - survey including EVERYONE not tenable - use latent sources like social media - use RFID or other hardware to infer connections at the conference - use a survey with a representative sample and similarity metrics to project preferences
how might this work at a bigger conference? - di?erent format - separate opt-in discussion track - rather than groups, make email introductions
The Perfect Conference: Using stochastic optimization to bring people together
what to learn from this
optimization isn¡¯t just for straightforward mathematical problems
data science isn¡¯t just for straightforward mathematical problems
a great data scientist acts as a translator
iteration drives improvement on both methods and data collection
if you can measure it, you have the data to actively improve it
.co@bjlange thanks! questions? brian [at] datascope.co

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The Perfect Conference: Using stochastic optimization to bring people together