09/30: Fall Welcome Mixer

[shevajia]

Comment below with one speaker (and/or a paper by the speaker) whom you wish to see at our workshop.

Please make your comments by Wednesday 11:59 PM, and upvote at least five of your peers' comments on Thursday prior to the workshop. You need to use 'thumbs-up' for your reactions to count towards 'top comments,' but you can use other emojis on top of the thumbs up.

[jsoll1]

Professor Jack Soll! 🚀 🚀 🚀

He's done a lot of work examining overconfidence and using that information to better one's judgement. This kind of work blending social sciences and computational/mathematical methods and modeling is pretty relevant for our program.

[hsinkengling]

Dr. Joan Donovan

Dr. Donovan is the director of the Shorenstein Center on Media, Politics and Public Policy at Harvard's Kennedy School. I've been following her work on social media disinformation. Her team has, among other interesting projects, developed a casebook that classifies different styles of disinformation campaigns using both qualitative, quantitative, and computational means. I'm interested in the thought process behind these projects.

for more info: https://www.hks.harvard.edu/faculty/joan-donovan https://shorensteincenter.org/programs/technology-social-change/

[beilrz]

Professor James Fowler

He is a professor from UCSD, and a pioneer of computational social science . He utilize network approach extensively in his researches, and one of his most impactful research argues that obesity is not only clustered in network, but "appears to spread through social ties".

his profile: https://scholar.google.com/citations?user=zPXbwJgAAAAJ

[Thiyaghessan]

Dr. Raj Chetty / Anyone else from the Opportunity Insights Team

I have been a huge fan of his work. His team has developed beautiful visuals for tracking various aspects of intergenerational mobility across America. For example, with the opportunity atlas you can trace the roots of over 20 million Americans' socioeconomic outcomes back to the neighbourhoods in which they grew up. The team also writes papers covering other relevant topics such as unequal access to education and racial disparities in innovation.

https://opportunityinsights.org/

[nswxin]

Invite LIU CIXIN 刘慈欣 if it's possible. Yeah，he's the author of "Three-Body Problem" 《三体》, a great sci-fi writer as well as a great sociologist You may perceive his work as a collection of thought experiments. To be more specific, for instance, he's a genius at devising or forecasting varied social structures under extreme circumstances. For example, in his novel “The Wages of Humanity” 《赡养人类》，he proposed the idea of a final producer who owned almost all the fortune in the world as capitalism expanded without restrictions and regulations (we can simulate the story computationally, right?) Definitely LIU CIXIN! In addition, Si-Fi is fun!

[Lynx-jr]

I‘d possibly name Max H. Farrell from Booth. he seems to have an interesting paper called "Deep Learning for Individual Heterogeneity: An Automatic Inference Framework". https://arxiv.org/abs/2010.14694

Other than that, I hope James could invite people who own econ (or other social science) research labs/teams that might have openings suitable for MACSS students, thanks so much :)

[afchao]

In 2009, David Lazar famously wrote on the promise that a then-young computational social science held for the advancement of how we understand complex social processes such as human mobility and large-scale communication while cautioning against the field's latent dangers should its development be perverted by the preservation of old-world ideas about privacy rights and academic tribalism. It would be interesting to get him in to speak on both how these now decade-old predictions have panned out as well as how his outlook has changed (if at all) as a result.

[javad-e]

Professor Pentland from MIT Media Lab. He has been working on a number of fascinating projects in collaboration with governments and companies around the world. I particularly like his projects that focus on the applications of big data in better understanding and organizing our societies. More information about him: https://www.media.mit.edu/people/sandy/overview/ One of his speeches: https://www.youtube.com/watch?v=9r28hx8LJV0

[ValAlvernUChic]

Assoc. Prof Desmond Upton Patton from Columbia University!

His projects utilize an important mix of qualitative and computational methods to examine the relationship between youth and gang violence and social media; how and why violence, grief, and identity are expressed on social media; and the real-world impact these expressions have on well-being for low-income youth of color. Right now, he is working on an online tool for detecting aggression in social media posts. I think his focus on applying these methods for social good would lend many important insights into the potential of our course for society! His profile here!

[Raychanan]

Professor Jennifer Pan at Stanford Communication Department!

If you are interested in Chinese studies, it's very likely you have or will encounter her work! She co-authored the notable paper How Censorship in China Allows Government Criticism but Silences Collective Expression which has been cited more than 2000 times. It was also one of our required readings in the Perspectives series. Her great studies cover Chinese propaganda and social media. She graduated from Princeton University, summa cum laude, and received her Ph.D. from Harvard University’s Department of Government.

The intersection of political communication and authoritarian politics is the flashpoint for conflict in numerous places around the world. Her research resides at this intersection, showing how authoritarian governments try to control society, how the public responds, and when and why each is successful.

Pan uses experimental and computational methods with large-scale datasets on political activity in China and other authoritarian regimes to answer questions about how autocrats perpetuate their rule. Her work on censorship and propaganda shows how authoritarian governments try to control the information environment in the digital age. How preferences and behaviors are shaped as a result.

Here is one of her public talks: https://www.youtube.com/watch?v=slbP8GUSAdU

[JoeHelbing]

Eliot Higgins, founder of Bellingcat. https://www.bellingcat.com/

The Open Source Intelligence (OSINT) methods he developed and disseminated are interesting in their own right, but the organization he has assembled and the subsequent human rights work it has done is pretty incredible. They do a lot of work with Geospatial imagery now, and have published a lot of their tools online. Some of which might be of interest in social science work.

[yehfanju]

Assoc. Prof. Sandra Wachter from Oxford Internet Institute. Dr. Wachter specialized in bias in algorithms, data privacy and protection, governmental surveillance, and online human rights. She works closely with interdisciplinary scholars and cross-sector stakeholders to address internet governance and ethical designs issues. Would really want to learn from her experiences from her amazing projects. Read more at: How a paper by three Oxford academics influenced AWS bias and explainability software

[helyap]

Professor T.L. Taylor, MIT Comparative Media Studies

Professor Taylor is a qualitative sociologist that studies internet culture and the interrelations between culture and tech in online environments such as video games. Especially when considering the richness of social connections in video games (and of course online communities broadly), the evolution of video game platforms, and also byproducts of video games (live streaming, e-sports, chat platforms) that all foster novel social interactions, Professor Taylor’s work inspires questions about society and culture and how to study them in virtual and complex settings like video game platforms.

Her Google Scholar page: https://scholar.google.com/citations?user=GakpYnoAAAAJ&hl=en

[yhchou0904]

Professor Vincent Conitzer from Duke University. Professor Conitzer's research greatly focuses on AI and game theory. He applies computational methods to social choice mechanisms, designing algorithms for agents in a market or a game. He also cares about AI and ethics, and I think this is also a topic we need to be aware of when we using data to do social science research. Here is his google scholar page.

[y8script]

Guangyu Robert Yang who is a new assistant professor from MIT. His research focuses on building fancy neural network models to understand the nature of brain. One recent study built up a single neural network which can flexibly perform many different cognitive tasks, and the selectivity of this network is similar to actual neuron activity. It would be fun to hear about his recent advances on brain neural network models.

[isaduan]

Professor Duncan J. Watts at UPenn. He is a computational social scientist interested in social and organizational networks and web-based experiments. Before coming to Penn, Watts was a principal researcher at Microsoft Research (MSR) and a founding member of the MSR-NYC lab. He directs the newly launched Computational Social Science Lab at UPenn so it would be great to explore opportunities for collaborations! I in particular love his paper "Common Sense and Sociological Explanations" which prompts me to reconsider what social sciences and explanations are all about!

[MkramerPsych]

I would recommend reaching out to Dr. Alexander Todorov at Booth. He has a long-standing line of research regarding judgments based on facial traits, but his more recent research suggests a more general approach to decision-making. His established background as a social neuroscience researcher combined with his integration of neuroscience into a business environment makes him ideally suited to present to our program.

[erweinstein]

Michael I. Jordan (aka, "Michael Jordan the Statistician, not Michael Jordan the basketball player") from Berkeley. Like the athlete with a similar name, he is a legend in his field (machine learning); his work going back to the early 1990s is foundational to how researchers and practitioners use (and think about how to use) various types of neural networks for AI and learning. He was also a vocal (and early) advocate for Open Access science, and he has argued (in non-technical talks) that applications of machine learning will increasingly hit real-world bottlenecks due to insufficient attention being given to social-scientific concerns. E.g., if an app gives me top restaurant recommendations in a given city, won't the recommendations need to change if 1000, or 10000, or 100000 people in that same city are all using that same app? (Because no matter how high they rank, we all don't want to, and/or can't, go to the same 10 restaurants.) Jordan's 2003 paper "Latent Dirichlet Allocation" with David M. Blei and Andrew Y. Ng has over 39000 citations, but my favorite works of his are his recent papers on recommender systems, such as "The Stereotyping Problem in Collaboratively Filtered Recommender Systems".

[pranathiiyer]

Professor Rayid Ghani at CMU. His research is primarily focused around fairness of data science models that are used to solve problems in public policy. He has extensively worked on machine learning solutions for policy, and has looked at how accuracy of an algorithm need not be sacrificed to ensure fairness of a policy. His works also talk about approaches for data scientists in big data and social science. I believe his understanding of the subject could provide meaningful learnings for students of computational social science. Before moving to CMU, he was heading the Centre for data science and public policy at Harris!

[LuZhang0128]

Professor Daniel McFarland from Stanford University. His research interests are broad which lead to studies of big data and methodological advances in social networks, language modeling, and the study of innovation. He is a professor of Sociology and Organizational Behavior (under Business School), and the Director of the Center for Computational Social Science at Stanford. He received his PhD in Sociology (1999) at UChicago. I was fascinated when reading his paper “Sociology in the Era of Big Data”, which inspired me a lot back then in computational social science.

[tangn121]

Professor Jonathan Nagler from NYU. Social media has transformed politics around the world and the way we receive and engage with information. I think it would be interesting if we could use social media data to study politics in new ways and explores how social media affects public opinion and political behavior. Such methods could also be used in other social science area.

Jonathan Nagler is Professor of Politics and affiliated faculty at the Center of Data Science at New York University. He is a co-Director of the NYU Social Media and Political Participation Laboratory. Professor Nagler has been at the forefront of computational social science for many years, and pioneered innovative methods for analysis of discrete choice problems. Nagler has produced recent papers on the nature of online ideological media consumption of individuals, the amount of hate speech on Twitter, the impact of exposure to online information on knowledge of politics and political attitudes, and the impact of media coverage of the economy on economic perceptions. Several of these papers have combined survey data with social media consumption in novel ways.

[ginxzheng]

Kindly invite Professor Melissa Dell from Harvard University, if possible. Her research explains economic development, especially through historical institutions. She has also investigated the effect of conflict on the labor market and political outcomes.

In particular, I would like to learn more about the Layout Parser developed by her team, which is a very useful toolkit based on deep learning analysis.

[yjhuang99]

Professor Melissa Dell from Harvard University. Her research mainly focuses on economic growth and political economy. She has examined the factors leading to the persistence of poverty and prosperity in the long run, the effects of trade-induced job loss on crime, the impacts of U.S. foreign intervention, and the effects of weather on economic growth. Most importantly, she is recently very involved in developing deep learning powered methods for curating social science data at scale, including her newly released (April 2021) Layout Parser package, which is a unified toolkit for deep learning based document image analysis. Thus, I believe she is a desirable visitor to the computational social science workshop.

Professor Dell is the Andrew E. Furer Professor of Economics at Harvard University. She is the 2020 recipient of the John Bates Clark Medal, awarded each year to an American economist under the age of forty who is judged to have made the most significant contribution to economic thought and knowledge. In 2018, The Economist named her one of the decade’s eight best young economists, and in 2014 she was named by the IMF as the youngest of 25 economists under the age of 45 shaping thought about the global economy.

[william-wei-zhu]

Professor Jack Soll! 🚀 🚀 🚀

He's done a lot of work examining overconfidence and using that information to better one's judgement. This kind of work blending social sciences and computational/mathematical methods and modeling is pretty relevant for our program.

Highly recommend Professor Jack Soll as well!

[jiehanL]

Professor Margaret E. Roberts from UCSD. Her research lies in the intersection of political methodology and the politics of information, with a specific focus on methods of automated content analysis and the politics of censorship in China. Her most recent publication on NLP and text as data provides a new framework in causal inference and social science studies in general. https://arxiv.org/abs/2109.00725 https://www.annualreviews.org/doi/abs/10.1146/annurev-polisci-053119-015921

[NikkiTing]

Professor Ricardo Hausmann of the Harvard Kennedy School. He’s the founder and director of Harvard’s Growth Lab and has done a lot of work on economic complexity, including using network science to visualize and analyze “The Product Space.” I suggest checking out the Growth Lab’s Atlas of Economic Complexity to see some of their work.

[GabeNicholson]

Professor Duncan J. Watts at UPenn. He is a computational social scientist interested in social and organizational networks and web-based experiments. Before coming to Penn, Watts was a principal researcher at Microsoft Research (MSR) and a founding member of the MSR-NYC lab. He directs the newly launched Computational Social Science Lab at UPenn so it would be great to explore opportunities for collaborations! I in particular love his paper "Common Sense and Sociological Explanations" which prompts me to reconsider what social sciences and explanations are all about!

I agree with the recommendation for Duncan Watts. He's a highly qualified computational social scientist and I particularly enjoyed his book, "Everything is Obvious."

[william-wei-zhu]

Professor Amir Goldberg and Professor Sameer Srivastava who together direct the computational culture lab at Stanford and Berkeley.

[kuitaiw]

James Harris Simons .He is the founder of Renaissance Technologies, a quantitative hedge fund in New York. He and his hedge fund use computer-based models to predict price changes in financial instruments. These models are based on analyzing as much data as can be collected and looking for non-random movements to predict.

[sdbaier]

Professor Jorge Guzman (Columbia Business School)

In his research on regional entrepreneurship and entrepreneurial ecosystems, he leverages large-scale data on business registration, predictive analytics, and GIS to measure, map, and forecast the quality of entrepreneurship across time and space.

His research is interdisciplinary in terms of both approach and content (and thus hopefully of relevance for some of us) – he is drawing on methodological tools from geography, sociology, economics, and computer science to study entrepreneurship and innovation. The combination of novel methodology and vast datasets leads to research that is distinctly computational in the sense that it would be impossible to arrive at similar findings with more traditional approaches. If this sounds interesting, I encourage you to play around with his interactive map on the quantity and quality of entrepreneurship in the US. For an overview of his approach and a deep dive into California, I have attached his initial Science paper.

Startup Cartography Project: http://maps.startupcartography.com/usa/#3.68/38/-105 Where is Silicon Valley: https://www.science.org/doi/abs/10.1126/science.aaa0201 Faculty Profile: https://www8.gsb.columbia.edu/cbs-directory/detail/jag2367

[FranciscoRMendes]

Professor Jorge Guzman (Columbia Business School)

In his research on regional entrepreneurship and entrepreneurial ecosystems, he leverages large-scale data on business registration, predictive analytics, and GIS to measure, map, and forecast the quality of entrepreneurship across time and space.

His research is interdisciplinary in terms of both approach and content (and thus hopefully of relevance for some of us) – he is drawing on methodological tools from geography, sociology, economics, and computer science to study entrepreneurship and innovation. The combination of novel methodology and vast datasets leads to research that is distinctly computational in the sense that it would be impossible to arrive at similar findings with more traditional approaches. If this sounds interesting, I encourage you to play around with his interactive map on the quantity and quality of entrepreneurship in the US. For an overview of his approach and a deep dive into California, I have attached his initial Science paper.

Startup Cartography Project: http://maps.startupcartography.com/usa/#3.68/38/-105 Where is Silicon Valley: https://www.science.org/doi/abs/10.1126/science.aaa0201 Faculty Profile: https://www8.gsb.columbia.edu/cbs-directory/detail/jag2367

I agree with this recommendation and would like to see Jorge as well.

[FranciscoRMendes]

Michael I. Jordan (aka, "Michael Jordan the Statistician, not Michael Jordan the basketball player") from Berkeley. Like the athlete with a similar name, he is a legend in his field (machine learning); his work going back to the early 1990s is foundational to how researchers and practitioners use (and think about how to use) various types of neural networks for AI and learning. He was also a vocal (and early) advocate for Open Access science, and he has argued (in non-technical talks) that applications of machine learning will increasingly hit real-world bottlenecks due to insufficient attention being given to social-scientific concerns. E.g., if an app gives me top restaurant recommendations in a given city, won't the recommendations need to change if 1000, or 10000, or 100000 people in that same city are all using that same app? (Because no matter how high they rank, we all don't want to, and/or can't, go to the same 10 restaurants.) Jordan's 2003 paper "Latent Dirichlet Allocation" with David M. Blei and Andrew Y. Ng has over 39000 citations, but my favorite works of his are his recent papers on recommender systems, such as "The Stereotyping Problem in Collaboratively Filtered Recommender Systems".

I second this vote as well, esp his paper : A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification

[zoeyjiao1104]

Professor Jennifer Pan from Stanford University. She uses computational methods with large-scale datasets on political activity in China and other authoritarian regimes to answer questions about how autocrats perpetuate their rule. I read her paper 'CASM: A Deep-learning Approach for Identifying Collective Action Events with Text and Image Data from Social Media' for which she employs protest event analysis and neural networks on text data and builds a classifier to identify social media posts about collective action. In particular, she draws on Chinese social media data and identifies more than 100,000 collective action events. It would be intriguing for people who are interested in computational linguistics and political sciences.

[a-bosko]

I recommend inviting Dr. Ceren Budak, an Assistant Professor at the University of Michigan School of Information. Before her career at the University of Michigan, Dr. Budak worked as a researcher in the Microsoft Research Lab in New York.

Some of her research includes topics such as fake news, online protests, political subreddits, and Wikipedia pages. Overall, I think Dr. Budak would be a wonderful addition to our workshop!

[chrismaurice0]

For our workshop, I recommend we bring in Clifford Stein, the Director of Columbia University's Data Science institute. jThis group has a number of "Focus areas" but the one I want to focus on is climate. I hope a discussion with Mr. Stein can provide us all insights into how data science, big data, and large scale computing can be used to combat the negative effects of of climate change.

[wanxii]

David C. Parkes from Harvard.

Lots of cool works combining algorithms and economics. Definitely check out this interesting paper using Deep Reinforcement Learning to build AI-economist to design optimal tax policy.

[xxicheng]

I recommend Prof. Xi Song from UPenn. Not only because we share the same first name :), but her work in social mobility is fascinating!

[borlasekn]

For our workshop, I think that Dan Goldstein, from Microsoft Research Lab's Computational Social Science team. Goldstein is the Senior Principal Research Manager, but I think having other individuals from his team could be just as informative. I think it could be really valuable to learn how industry professionals are using Computational Social Science and what sorts of phenomena they are hoping to explain or predict.

[Yutong0828]

Gabriella Harari, an Assistant Professor in the Department of Communication at Stanford University. Her research examines how personality is expressed in physical and digital contexts in everyday life. More specifically, she examines what digital media technologies, and smartphones in particular, reveal about people’s behavioral patterns and psychological states. I recommend her because she adopts an interdisciplinary approach to study the individual differences in socializing behaviors (e.g., texting, calling, using social media apps, in-person conversations), and links these behavioral patterns to psychological characteristics (e.g., personality traits) and life outcomes (e.g., well-being). She is also studying whether smartphone-based self-tracking of thoughts, feelings, and behaviors can promote self-insight and positive changes in behavior.

[LFShan]

I would recommend Nate Silver, who is the founder of FiveThirtyEight website. He is a statistician and analyst and alumni of UChicago.

[yierrr]

Professor Sendhil Mullainathan at Booth who studies algorithms and behaviors like decision-making, such as this paper:https://sendhil.org/wp-content/uploads/2019/08/Working-Paper-19.pdf

[robertorg]

Xu Yiqing from Stanford University. His research lays in the interception of political methodology and Chinese politics. For example why policy preferences in authoritarian states matter, the ideological spectrum in china, or how career tracks matter for promotion inside the CCP. https://yiqingxu.org/research/#Peer%20Reviewed%20Articles

[Sirius2713]

Professor Susan Athey from Stanford. Professor Athey is The Economics of Technology professor at Stanford GSB. She is also the associate director of Stanford Institute for Human-Centered Artificial Intelligence. She has made amazing contributions to apply machine learning into econometrics research, especially causal inference.

[ttsujikawa]

I would recommend Prof.Jay R. Ritter from U of Florida who has received BA, MA, and Ph.D. from Uchicago. Not only his research on Initial Public Offering is very fascinating, but IPO market is also experiencing drastic changes so that we could hear some valuable insights on that from his presentation.

[NaiyuJ]

I would recommend Professor Katherine Milkman at The Wharton School of the University of Pennsylvania. She employs big data and field experiments to study how people can make optimal choices and how to improve human decision-making. Professor Milkman made lots of wonderful speeches and was voted Wharton’s “Iron Prof” by MBA students for a PechaKucha-style presentation of her research topics. One thing I love the most is that her papers are continuing to reach beyond academia, providing insights into many real-world problems.

[Jasmine97Huang]

I would love to see Dr. Mark Esposito from Harvard University present. He is a thought leader in The Fourth Industrial Revolution, the changes and opportunities that technology will bring to a variety of industries. He also wrote a book The AI Republic: The Nexus between Humans and Intelligent Automation". It would be really interesting to hear more about the applications and impacts of AI in real world.

[sabinahartnett]

As someone interested in media consumption and natural language processing, I would be excited to invite Dr. Thomas Davidson from Rutgers University and more about his work on automated hate-speech detection and his analysis of the role of social media in right-wing group organization.

[jinfei1125]

Last year's Michal Kosinski is really a surprise!! He is amazing. This year I would be surprised by prof David Lazer. Am I being too greedy? And most importantly, thank you James to kick off our new academic year! Cheers!

[hazelchc]

I would recommend Professor Matthew Salganik from Princeton University as our guest speaker. His research interests include social networks and computational social science. His paper "Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market" is absolutely amazing!

He is also the author of Bit by Bit: Social Research in the Digital Age (our assigned reading in MACS 30000)!

[lguo7]

I will recommend Professor Cecilia Mascolo from University of Cambridge, I like her projects on mobile systems which are related to our daily life. For example, recently she studies Mobile Health and utilizes the data from mobile and wearable device sensors to machine learning for mobile health analytics.