# Past Events › IDSS Special Seminars

## Using Computer Vision to Study Society: Methods and Challenges

March 11, 2019 @ 4:00 pm - 5:00 pm

32-G449 (KIva/Patel)

Abstract: Targeted socio-economic policies require an accurate understanding of a country's demographic makeup. To that end, the United States spends more than 1 billion dollars a year gathering census data such as race, gender, education, occupation and unemployment rates. Compared to the traditional method of collecting surveys across many years which is costly and labor intensive, data-driven, machine learning driven approaches are cheaper and faster--with the potential ability to detect trends in close to real time. In this work,…

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## Censored: Distraction and Diversion Inside China’s Great Firewall

November 27, 2018 @ 4:00 pm - 5:00 pm

Margaret Roberts (UC San Diego)

32-141

Abstract: As authoritarian governments around the world develop sophisticated technologies for controlling information, many observers have predicted that these controls would be ineffective because they are easily thwarted and evaded by savvy Internet users. In Censored, Margaret Roberts demonstrates that even censorship that is easy to circumvent can still be enormously effective. Taking advantage of digital data harvested from the Chinese Internet and leaks from China's Propaganda Department, this book sheds light on how and when censorship influences the Chinese…

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## Local Geometric Analysis and Applications

October 11, 2018 @ 4:00 pm - 5:00 pm

Lizhong Zheng (MIT)

32-D677

Abstract: Local geometric analysis is a method to define a coordinate system in a small neighborhood in the space of distributions over a given alphabet. It is a powerful technique since the notions of distance, projection, and inner product defined this way are useful in the optimization problems involving distributions, such as regressions. It has been used in many places in the literature such as correlation analysis, correspondence analysis. In this talk, we will go through some of the basic…

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## Text as Data in Social Science: Discovery, Measurement and Causal Inference

September 25, 2018 @ 4:00 pm - 5:00 pm

Brandon Stewart (Princeton University)

32-141

Social scientists are increasingly turning to computer-assisted text analysis as a way of understanding the digital footprints left by communities and individuals. Much of the technology that powers these approaches is borrowed from the fields of computer science and statistics; yet, social scientists have substantially different goals. We focus on the development of methods that support three core tasks: discovery, measurement and causal inference with text. We introduce the Structural Topic Model (STM), a bayesian generative model of text which…

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## Topics in Information and Inference Seminar

September 20, 2018 @ 4:00 pm - 5:00 pm

Yury Polyanskiy (MIT )

32-D677

Title: Strong data processing inequalities and information percolation Abstract: The data-processing inequality, that is, $I(U;Y) \le I(U;X)$ for a Markov chain $U \to X \to Y$, has been the method of choice for proving impossibility (converse) results in information theory and many other disciplines. A channel-dependent improvement is called the strong data-processing inequality (or SDPI). In this talk we will: a) review SDPIs; b) show how point-to-point SDPIs can be combined into an SDPI for a network; c) show recent…

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## Resource-efficient ML in 2 KB RAM for the Internet of Things

August 21, 2018 @ 2:00 pm - 3:00 pm

Prateek Jain (Microsoft Research)

E18-304

Abstract: We propose an alternative paradigm for the Internet of Things (IoT) where machine learning algorithms run locally on severely resource-constrained edge and endpoint devices without necessarily needing cloud connectivity. This enables many scenarios beyond the pale of the traditional paradigm including low-latency brain implants, precision agriculture on disconnected farms, privacy-preserving smart spectacles, etc. Towards this end, we develop novel tree and kNN based algorithm, called Bonsai and ProtoNN, for efficient prediction on IoT devices — such as those based…

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## Provably Secure Machine Learning

February 26, 2018 @ 4:00 pm - 5:00 pm

Jacob Steinhardt (Stanford)

32-G449 (Kiva/Patel)

Abstract:  The widespread use of machine learning systems creates a new class of computer security vulnerabilities where, rather than attacking the integrity of the software itself, malicious actors exploit the statistical nature of the learning algorithms. For instance, attackers can add fake data (e.g. by creating fake user accounts), or strategically manipulate inputs to the system once it is deployed. So far, attempts to defend against these attacks have focused on empirical performance against known sets of attacks. I will argue that…

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