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 (anand.sarwate@rutgers.edu):

  • 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