Raising Steam (Terry Pratchett). Mind candy. A bit later, chronologically, so a little overwritten and meandering.

No Good Men Among The Living: America, the Taliban, and the War Through Afghan Eyes (Anand Gopal). A pretty harrowing narrative of the war, how terribly the US conducted itself viz. oversight of local “allies,” and the human impact it had. Stomach-churning at times, but very engrossing. I haven’t read many books on the subject so I can’t speak to comprehensiveness, but it was very affecting for me.

A Cat, a Man, and Two Women (Junichiro Tanizaki). This was a short novella (packaged with two short stories) about a schlub who gets along with his cat more than his wife (or his ex-wife). He’s more or less a leech: unmotivated, entitled, and wasteful. But he loves his cat. Tanizaki is known for his grotesque (often erotic) fiction. This book isn’t that per se, but is fairly frank and graphic about bodily functions, the state of the litter box, and so on. Cat lovers may enjoy and be repulsed by the prose at the same time. As a non-pet person I found it slightly uncomfortable but in a way I didn’t mind. Perhaps not the best introduction to Tanizaki, but worth it for the cat lover, perhaps.

Hopscotch (Julio Cortázar). A very important work of fiction, in the Rabassa translation. Hopscotch is a novel about Horacio Oliveira, an Argentinian intellectual who starts in Paris and moves back to Argentina during the novel. The story can be read 2 ways: linearly through the first 50+ chapters, or hopscotching back and forth though the novel with the next chapter indicated at the bottom of previous chapter, like a do-not-choose-your-own-adventure. Much has been written about the novel, and it’s got some pretty amazing literary devices which feel like they must have been untranslatable. What is a bit hard is that very important events happen in a flash or are not really spelled out, and as a reader it might take you a few pages to realize that e.g. a tragedy has befallen one of the characters. It merits careful reading.

The House at Mount Char (Scott Hawkins). A pretty stunning (and stunningly violent) vaguely apocalyptic fantasy novel set in a kind of contemporary world. If you liked The Magicians and Station Eleven, you might like this book as well. There’s got to be a name for this sub-genre but I can’t figure out what it is.

Behind the Beautiful Forevers (Katherine Boo) This was recommended and lent to me by Celeste, and it’s a narrative nonfiction/investigative report of a slum in Mumbai near the airport. Although I’ve read quite a bit about economies in the slums and life therein, both from fiction and nonfiction, Boo really does a great job of telling these complex and very human stories.

Sad Little Breathing Machine (Matthea Harvey). Quirky but often cutting and a little too real poems. I picked it up on a whim and was glad I did. Like I said, I need more poetry in my life. A bit I chuckled at: “But being / matter-of-fact is like a meatpie in / the pocket. It is the way to go.”

The Lies of Locke Lamora (Scott Lynch). Mind candy of a sort: noble thieves in a gritty European-ish fantasy world that’s somewhere between Renaissance and Enlightenment in sensibility. Recommended by a friend as a good summer read, and it fit the bill quite well.

The Hundred Thousand Kingdoms (N.K. Jemisin). Since Jemisin just won the Hugo Award I figured I should read her books (not that awards make me read books, but I just bumped her up the list). Since The Fifth Season has a 58-person-long hold list at the library, I figured I would start with her earlier books. This is the first in a trilogy: a world with gods (who I kept thinking of as orbital weaponized AI satellite systems) and colonialism and all that messy stuff that good SF grapples with. Recommended for fantasy fans. I’ll read the others too, eventually.

CFP: IEEE JSTSP and T-SIPN Special Issues on Graph Signal Processing

IEEE Journal of Selected Topics in Signal Processing
IEEE Transactions on Signal and Information Processing over Networks
Special Issues on Graph Signal Processing

Numerous applications rely on the processing of high-dimensional data that resides on irregular or otherwise unordered structures which are naturally modeled as networks (such as social, economic, energy, transportation, telecommunication, sensor, and neural, to name a few). The need for new tools to process such data has led to the emergence of the field of graph signal processing, which merges algebraic and spectral graph theoretic concepts with computational harmonic analysis to process signals on structures such as graphs. This important new paradigm in signal processing research, coupled with its numerous applications in very different domains, has fueled the rapid development of an inter-disciplinary research community that has been working on theoretical aspects of graph signal processing and applications to diverse problems such as big data analysis, coding and compression of 3D point clouds, biological data processing, and brain network analysis.

The purpose of these special issues is to gather the latest advances in graph signal processing and disseminate new ideas and experiences in this emerging field to a broad audience. We encourage the submission of papers with new results, methods or applications in graph signal processing. In particular, the topics of interest include (but are not limited to):

  • Sampling and recovery of graph signals
  • Graph filter and filter bank design
  • Uncertainty principles and other fundamental limits
  • Graph signal transforms
  • Graph topology inference
  • Prediction and learning in graphs
  • Statistical graph signal processing
  • Non-linear graph signal processing
  • Applications to visual information processing
  • Applications to neuroscience and other medical fields
  • Applications to economics and social networks
  • Applications to various infrastructure networks

Submission Procedure:
Prospective authors should follow the instructions given on the IEEE JSTSP webpages and submit their manuscript with the web submission system at The decisions on whether the accepted papers will be published in IEEE JSTSP or IEEE TSIPN will depend on the respective themes of the papers and will be made by the Guest Editors.

Schedule (all deadlines are firm):

Manuscript due: Nov 1, 2016
First Review Completed: Jan 1, 2017
Revised manuscript due: Mar 1, 2017
Second Review Completed: May 1, 2017
Final manuscript due: June 1, 2017
Publication date: September 2017

Guest Editors:

  • Pier-Luigi Dragotti, Imperial College, London (
  • Pascal Frossard, EPFL, Lausanne (
  • Antonio Ortega, USC, Los Angeles (
  • Michael Rabbat, McGill University, Montreal (
  • Alejandro Ribeiro, UPenn, Philadelphia (

Postdoctoral Position at Rutgers with… me!

I keep posting ads for postdocs with other people but this is actually to work with little old me!

Postdoctoral Position
Department of Electrical and Computer Engineering
Rutgers, The State University of New Jersey

The Department of Electrical and Computer Engineering (ECE) at Rutgers University is seeking a dynamic and motivated Postdoctoral Fellow to work on developing distributed machine learning algorithms that work on complex neuroimaging data. This work is in collaboration with the Mind Research Network in Albuquerque, New Mexico under NIH Grant 1R01DA040487-01A1.

Candidates with a Ph.D. in Electrical Engineering, Computer Science, Statistics or related areas with experience in one of

  • distributed signal processing or optimization
  • image processing with applications in biomedical imaging
  • machine learning theory (but with a desire to interface with practice)
  • privacy-preserving algorithms (preferably differential privacy)

are welcome to apply. Strong and self-motivated candidates should also have

  • a strong mathematical background: this project is about translating theory to practice, so a solid understanding of mathematical formalizations is crucial;
  • good communication skills: this is an interdisciplinary project with many collaborators

The Fellow will receive valuable experience in translational research as well as career mentoring, opportunities to collaborate with others outside the project within the ECE Department, DIMACS, and other institutions.

The initial appointment is for 1 year but can be renewed subject to approval. Salary and compensation is at the standard NIH scale for postdocs.

To apply, please email the following to Prof. Anand D. Sarwate (

  • Curriculum Vitae
  • Contact information for 3 references
  • A brief statement (less than a page!) addressing the qualifications above and why the position is appealing.
  • Standard forms: Equal Employment Opportunity Data Form [PDF] Voluntary Self-Identification of Disability Form [PDF] Invitation to Covered Veterans to Self-Identify [PDF].

    Applications are open until the position is filled. Start date is flexible but sometime in Fall 2016 is preferable.

    Rutgers, The State University of New Jersey, is an Equal Opportunity / Affirmative Action Employer. Qualified applicants will be considered for employment without regard to race, creed, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability status, genetic information, protected veteran status, military service or any other category protected by law. As an institution, we value diversity of background and opinion, and prohibit discrimination or harassment on the basis of any legally protected class in the areas of hiring, recruitment, promotion, transfer, demotion, training, compensation, pay, fringe benefits, layoff, termination or any other terms and conditions of employment.

IHP “Nexus” Workshop on Privacy and Security: Days 4-5

Wrapping this up, finally. Maybe conference blogging has to go by the wayside for me… my notes got a bit sketchier so I’ll just do a short rundown of the topics.

Days 4-5 were a series of “short” talks by Moni Naor, Kobbi Nissim, Lalitha Sankar, Sewoong Oh, Delaram Kahrobaie, Joerg Kliewer, Jon Ullman, and Sasho Nikolov on a rather eclectic mix of topics.

Moni’s talk was on secret sharing in an online setting — parties arrive one by one and the qualified sets (who can decode the secret) is revealed by all parties. The shares have to be generated online as well. Since the access structure is evolving, what kinds of systems can we support? As I understood it, the idea is to use something similar to threshold scheme and a “doubling trick”-like argument by dividing the users/parties into generations. It’s a bit out of area for me so I had a hard time keeping up with the connections to other problems. Kobbi talked about reconstruction attacks based on observing traffic from outsourced database systems. A user wants to get the records but the server shouldn’t be able to reconstruct: it knows how many records were returned from a query and knows if the same record was sent on subsequent queries — this is a sort of access pattern leakage. He presented attacks based on this information and also based on just knowing the volume (e.g. total size of response) from the queries.

Lalitha talked about mutual information privacy, which was quite a bit different than the differential privacy models from the CS side, but more in line with Ye Wang’s talk earlier in the week. Although she didn’t get to spend as much time on it, the work on interactive communication and privacy might have been interesting to folks earlier in the workshop studying communication complexity. In general, the connection between communication complexity problems and MPC, for example, are elusive to me (probably from lack of trying).

Sewoong talked about optimal mechanisms for differentially private composition — I had to miss his talk, unfortunately. Delaram talked about cryptosystems based on group theory and I had to try and check back in all the things I learned in 18.701/702 and the graduate algebra class I (mistakenly) took my first year of graduate school. I am not sure I could even do justice to it, but I took a lot of notes. Joerg talked about using polar codes to enable private function computation — initially privacy was measured by equivocation but towards the end he made a connection to differential privacy. Since most folks (myself included) are not experts on polar codes, he gave a rather nice tutorial (I thought) on polar coding. It being the last day of the workshop, the audience had unfortunately thinned out a bit.

Jon spoke about estimating marginal distributions for high-dimensional problems. There were some nice connections to composite hypothesis testing problems that came out of the discussion during the talk — the model seems a bit complex to get into based on my notes, but I think readers who are experts on hypothesis testing might want to check out his work. Sasho rounded off the workshop with a talk about the sensitivity polytope of linear queries on a statistical database and connections to Gaussian widths. The main result was on the sample complexity of answering the queries in time polynomial in the number of individuals, size of the universe, and size of the query set.

CFP: IEEE T-SIPN Special Issue on Distributed Information Processing in Social Networks

IEEE Signal Processing Society
IEEE Transactions on Signal and Information Processing over Networks
Special Issue on Distributed Information Processing in Social Networks

Over the past few decades, online social networks such as Facebook and Twitter have significantly changed the way people communicate and share information with each other. The opinion and behavior of each individual are heavily influenced through interacting with others. These local interactions lead to many interesting collective phenomena such as herding, consensus, and rumor spreading. At the same time, there is always the danger of mob mentality of following crowds, celebrities, or gurus who might provide misleading or even malicious information. Many efforts have been devoted to investigating the collective behavior in the context of various network topologies and the robustness of social networks in the presence of malicious threats. On the other hand, activities in social networks (clicks, searches, transactions, posts, and tweets) generate a massive amount of decentralized data, which is not only big in size but also complex in terms of its structure. Processing these data requires significant advances in accurate mathematical modeling and computationally efficient algorithm design. Many modern technological systems such as wireless sensor and robot networks are virtually the same as social networks in the sense that the nodes in both networks carry disparate information and communicate with constraints. Thus, investigating social networks will bring insightful principles on the system and algorithmic designs of many engineering networks. An example of such is the implementation of consensus algorithms for coordination and control in robot networks. Additionally, more and more research projects nowadays are data-driven. Social networks are natural sources of massive and diverse big data, which present unique opportunities and challenges to further develop theoretical data processing toolsets and investigate novel applications. This special issue aims to focus on addressing distributed information (signal, data, etc.) processing problems in social networks and also invites submissions from all other related disciplines to present comprehensive and diverse perspectives. Topics of interest include, but are not limited to:

  • Dynamic social networks: time varying network topology, edge weights, etc.
  • Social learning, distributed decision-making, estimation, and filtering
  • Consensus and coordination in multi-agent networks
  • Modeling and inference for information diffusion and rumor spreading
  • Multi-layered social networks where social interactions take place at different scales or modalities
  • Resource allocation, optimization, and control in multi-agent networks
  • Modeling and strategic considerations for malicious behavior in networks
  • Social media computing and networking
  • Data mining, machine learning, and statistical inference frameworks and algorithms for handling big data from social networks
  • Data-driven applications: attribution models for marketing and advertising, trend prediction, recommendation systems, crowdsourcing, etc.
  • Other topics associated with social networks: graphical modeling, trust, privacy, engineering applications, etc.

Important Dates:

  • Manuscript submission due: September 15, 2016
  • First review completed: November 1, 2016
  • Revised manuscript due: December 15, 2016
  • Second review completed: February 1, 2017
  • Final manuscript due: March 15, 2017
  • Publication: June 1, 2017

Guest Editors:

tracks: response the sky’s convulsion

Old and new for a rainy day.

  1. If You Find Yourself Caught In LoveBelle and Sebastian
  2. Something About YouLucius
  3. The Natural WorldCymbals
  4. GutsThao & The Get Down Stay Down
  5. New Old FriendsJack & Jeffrey Lewis
  6. PatriarchaethGwenno
  7. Bright Shiny MorningMe’Shell NdegeOcello
  8. PowerEskimeaux
  9. ClayManatee Commune (feat. Marina Price)
  10. Little Drop Of PoisonTom Waits
  11. What Would I Do Without YouRay Charles
  12. Should Have Known BetterSufjan Stevens
  13. Survive ItGhostpoet
  14. I Think I Need A New HeartThe Magnetic Fields

IHP “Nexus” Workshop on Privacy and Security: Day 3

I’m doggedly completing these notes because in a fit of ambition I actually started posts for each of the workshop days and now I feel like I need to finish it up. Day 3 was a day of differential privacy: Adam Smith, Cynthia Dwork, and Kamalika Chaudhuri.

Adam gave a tutorial on differential privacy that had a bit of a different flavor from tutorials I have seen before (and given). He started out by highlighting a taxonomy of potential attacks on released data to make a distinction between re-identification, reconstruction, membership, and correlation inferences before going into the definitions, composition theory, Bayesian interpretation, and so on. With the attacks, he focused a bit more on the reconstruction story. The algorithms view of things (as I get it) is to think of, say, an LP relaxation of a combinatorial problem: you solve the LP and round the solution to integers and prove that it’s either correct or close to correct. This has more connections to things we think about in information theory (e.g. compressed sensing) but the way of stating the problem was a bit different. He also described the Homer et al. attack on GWAS. The last part of his talk was on multiplicative weights and algorithms for learning distributions over the data domain, which I think got a bit hairy for the IT folks who hadn’t seen MW before. This made me wonder if these connections between mirror descent on the simplex, information projections, and other topics can be taught in a “first principles” way that doesn’t require you to have a lot of familiarity with one interpretation of the method before bridging to another.

Cynthia gave a talk on false discovery control and how to use differential privacy ideas in a version of the Benjamini-Hochberg BHq procedure for controlling the false discovery rate. A key primitive is the the report noisy argmax procedure, which gives the index of the argmax but not its value (which would entail a further privacy loss). Since most people are not familiar with FDR control, she spent a lot of her talk on that and so the full details of the private version were deferred to the paper. I covered FDR in my detection and estimation class partly from some of the extra attention it has received in the privacy workshops over the last few years.

Kamalika’s talk was on a model for privacy when data may be correlated between individuals. This involves using the Pufferfish model for privacy in which there is an explicit class of probability distribution on parameters and a set of explicit secrets which the algorithm wants to obfuscate: the differential privacy guarantee should hold for the output distribution of the mechanism conditioned on any valid data distribution and any pair of secrets. Since the class of data distributions is arbitrary, we can also consider joint distributions on individuals’ data — if the distribution class has some structure, then there might be a hope to efficiently produce an output of a function. She talked about using the \ell_{\infty} Wasserstein distance to measure the sensitivity of a function, and that adding noise that scales with this sensitivity would guarantee privacy in the Pufferfish model. She then gave an example for Bayesian networks and Markov chains. As we discussed, it seems like for each dependence structure you need to come up with a sort of covering of the dependencies to add noise appropriately. This seems pretty challenging in general now, but maybe after a bit more work there will be a clearer “general” strategy to handle dependence along these lines.