Since I know there are non-academic PhDs who read this, there’s a survey out from Harvard researcher Melanie Sinche that is trying to gather data on the career trajectories of PhDs. The title of the article linked above, “Help solve the mystery of the disappearing Ph.D.s,” sounds really off to me — I know where the people I know from grad school ended up, and a quick glance through LinkedIn show that the “where” is not so much the issue as “how many.” For example, we talk a lot about how so many people from various flavors of theory end up in finance, but is it 50%? I suspect the number is much lower. Here’s a direct link to the survey. Fill it out and spread widely!

Filed under: Uncategorized ]]>

**Qualifications:** Ph.D., with expertise in the theoretical foundations of at least one of the research areas (algorithms, machine learning and statistics, data privacy). Willingness to work on a cross-disciplinary project.

**More about the project leaders:** Sofya Raskhodnikova, Adam Smith.

**Duration and compensation:** At least one year, renewable. Start date is

negotiable, though we slightly prefer candidates starting fall 2015. Salary is competitive.

Applicants should email a CV, short research statement and list of references directly to the project leaders (`{asmith,sofya}@cse.psu.edu`

) with “postdoc” in the subject line.

**Location:** The university is located in the beautiful college town of

State College in the center of Pennsylvania. The State College area has 130,000 inhabitants and offers a wide variety of cultural and outdoor recreational activities. The university offers outstanding events from collegiate sporting events to fine arts productions. Many major population centers on the east coast (New York, Philadelphia, Pittsburgh, Washington D.C., Baltimore) are only a few hours’ drive away and convenient air services to several major hubs are operated by three major airlines out of State College.

Penn State is an equal opportunity employer. We encourage applications from underrepresented minorities.

Filed under: Uncategorized ]]>

The 2015 IEEE Information Theory Workshop will take place in Jeju Island, Korea, from October 11 to October 15, 2015. Jeju Island is the largest island in Korea and is located in the Pacific Ocean just off the south-western tip of the Korean peninsula. Jeju Island is a volcanic island with a mountainous terrain, a dramatic rugged coastline and spectacular watershed courses. The Island has a unique culture as well as natural beauty. It is a living folk village, with approximately 540,000 people. As a result of its isolated location and romantic tropical image, Jeju Island has become a favorite retreat with honeymooners and tourists. The tour programs of the conference will also provide participants with the opportunity to feel and enjoy some of the island’s fascinating attractions.

Special topics of emphasis include:

- Big data
- Coding theory
- Communication theory
- Computational biology
- Interactive communication
- Machine learning
- Network information theory
- Privacy and security
- Signal processing

Filed under: Uncategorized ]]>

Petar Djuric gave two talks since his colleague Monica Bugallo couldn’t make it. The first talk was on “Sequential signal processing with Dirichlet mixture models” wherein he gave a tutorial on Dirichlet priors and nonparametric Bayesian methods and then used it to address a problem of annotating data from fetal heart rate signals. The nonparametric approach is important because we want to understand what the data suggests about the number of categories with which one could annotate a segment of signals. He also gave Monica’s talk on “Particle Filtering for Complex Systems,” which was a tutorial on particle filters for high-dimensional signals. This is challenging because of a curse of dimensionality — we need too many particles, making the filtering process too complex, computationally. The goal here was to reduce the complexity by generating parallel particle filters that can exchange information when the particles get close (e.g. for multiple target tracking). I am far from knowledgeable about particle filters, but the experimental results looked really compelling.

McGill student Milad Kharratzadeh, talked about a problem of modeling sparse signals which he called “Sparse Multivariate Factor Regression.” Basically they posit latent factors explicitly, so the model is that the outcome , where are vector inputs and outputs, but the dictionary decomposes into the product of two sparse, rank matrices. He showed an alternating minimization for the resulting LASSO-like biconvex minimization and then provided some experimental evidence on data from a bikesharing service in Montreal.

Milad was followed by Jun Ye Yu, who talked about how to track a target using sensors that only sense bearings only. For targeting on the Earth, it turns out that the curvature of the surface matters, so his talk was entitled “Distributed Bearing-only Tracking on Spherical Surfaces.” He also used a particle filtering approach for the tracking but had to modify the updates to account for the curvature. It turns out that accounting for this effect has a pretty large impact on the empirical performance of the tracking algorithm.

I gave a talk on my work with Tara Javidi and Anusha Lalitha, based on our recent TAC paper and the journal version of our ISIT paper.

Mikael Johansson gave a talk on distributed optimization with delayed information. A lot of what we know is in sort of extreme versions of asynchrony — totally asynchronous or mostly not-asynchronous. He gave a really nice overview of known results before diving into the open middle ground and what he called “pseudo-contractions” to get some convergence rates for different types of delay. The talk concluded with some discussion of gradient-based algorithms like an asynchronous incremental gradient in a worker/coordinator model, and coordinate descent methods with delay.

Shohreh Shaghaghian described an opportunistic forwarding method for minimizing latency in when forwarding information in “social” networks. Basically each edge has a Poisson process associated to it which says when the two nodes “meet.” The goal is to get packets from source to destination: if I meet a friend, should I give them the packet or wait until I meet someone a bit closer? They show a distributed algorithm for each node to decide which neighbors should get the forwarded packet, and did some experiments from a data set generated at INFOCOM 2005 of real meetings between participants.

Ioannis Bakagiannis discussed some recent work on trying to apply accelerated gradient methods in the kind of optimization methods that Mikael discussed. The results here were pretty preliminary so I don’t want to give too much away… you’ll just have to wait for the paper.

Mark Coates gave a very interesting, but math-heavy, talk on a possible connection between certain approaches to particle filtering called the “particle flow filter,” which is a sort of continuous-time embedding of the Bayes update, and optimal transportation, which also seeks to link two distributions. This was all a bit new to me, but it did inspire me to check out Villani’s *Optimal Transport* book, which has made some interesting bedtime reading for the last few days.

Michael Rabbat gave a new model for a similarity search problem that looks a bit like associative memories. This reminded me a lot of problems in group testing and multiuser detection, but the model was a bit different. There’s a paper on ArXiV with more details, but basically the idea is to store sums of data points and then when presented with a new point, one can query the sum to find similarities. So the sums are “good enough” for a first round of detection. They did some experiments on image datasets which had promising results.

Finally, Yunpeng Li gave a really interesting talk about designing a “smart bra” that uses microwave pulses to detect breast tumors. Basically there is an array of transmitter/receiver pairs that send pulses and try to detect the presence of tumor tissue, which will have different propagation characteristics than healthy tissue. It’s safe, non-invasive, and potentially a game-changer for cancer screening. The hard part is the very low SNR and the large number of antennas. He basically used some ensemble tricks to get better classification performance. The goal is to get data from real patients now (they have mostly been working with physical “dummy” models) to get real tumor/healthy characteristics.

After all that research, really the only thing to do is to hit the beach and go for a nice swim. It’s a real luxury in February!

Filed under: Uncategorized ]]>

*Colorless Tsukuru Tazaki and His Years of Pilgrimage* [Haruki Murakami]: This also felt a bit slight with respect to other books of Murakami, but also “clean” in a way that I appreciated. I also now have to listen to more Liszt. Tsukuru Tazaki feels “colorless” and empty, shunned by his old childhood friends. He finally tries to seek out why, which turns out to be more surprising than he thought. As with much of Murakami’s work, the “mysteriousness” of women has this negative tint that makes me uncomfortable. This book, unlike *1Q84* or others, has very little magical realism going on, so it could be a good recommendation for someone who is less of a fan of that aspect of Murakami’s work.

*Soy Sauce For Beginners* [Kirstin Chen]: The story of Gretchen Lin, a 30-year old who has moved back to Singapore from SF to work at the family soy sauce factory after her marriage fell apart, this novel is part Gretchen’s painful journey towards self-discovery and resolution with her family, and partly an introduction to Singapore for the non-familiar reader. The latter part will appeal to some but at times I wanted less explanation and to be forced into trying to make sense of cultural elements myself. In this sense it’s a sort of novel of cultural translation. That being said, the best part of this book is how true and messy the story really felt. The family (and business) are dysfunctional, and Gretchen has a lot to come to terms with regarding herself, her marriage, and her relationship to this family.

*The Name of The Wind / The Wise Man’s Fear* [Patrick Rothfuss] : I should make myself promise to not read epic fantasy series that are not completed. Told in a kind of story-within-a-story, these books were a great way to unwind over the vacation. If you like those bards plus wizards coming of age stories, this one is for you. Also: plenty of unrequited love.

*The Lowland* [Jhumpa Lahiri] : I had read the opening of this book as a short story, but the novel is another beast entirely. Two brothers in Kolkata, one a Naxalite, the other looking to go to grad school in the US, and a torn apart and stitched together family in the US. While reading this I kept thinking of the movie *Boyhood*, which rather abruptly jumped years into the future to catch the family’s story at another time. This book does the same, but the shifts felt more jarring to me; I did not understand who there characters were quite as well. I think I had to suspend my disbelief a few times for some of the narrative choices. However, in retrospect it is because I think I didn’t quite get the characters, or I had misconceptions. Regardless, I think this is a story that helps complicate the story of middle-class Indian immigrant families, and is worth giving a read.

*House of Suns* [Alastair Reynolds] : Space opera, on a grand scale, but still grounded in our galaxy with humans, rather than the more distant and alien Culture novels of Banks. As Cosma would put it, mind candy, and a nice beach read.

Filed under: Uncategorized ]]>

Dear ISIT-2015-Submission Reviewers:

In an effort to ensure that each paper has an appropriate number of reviews, the deadline for the submission of all reviews has been extended to March 2nd. If you have not already done so, please submit your review by March 2nd as we are working to a very tight deadline.

In filling out your review, please keep in mind that

(a) all submissions are eligible to be considered for presentation in a semi-plenary session — Please ensure that your review provides an answer to Question 11

(b) in the case of a submission that is eligible for the 2015 IEEE Jack Keil Wolf ISIT Student Paper Award, the evaluation form contains a box at the top containing the text:

Notice: This paper is to be considered for the 2015 IEEE Jack Keil Wolf ISIT Student Paper Award, even if the manuscript itself does not contain a statement to that effect.

– Please ensure that your review provides an answer to Question 12 if this is the case.Thanks very much for helping out with the review process for ISIT, your inputs are of critical importance in ensuring that the high standards of an ISIT conference are maintained. We know that reviewing a paper takes much effort and we are grateful for all the time you have put in!

With regards,

Pierre, Suhas and Vijay

(TPC Co-Chairs, ISIT 2015)

Filed under: Uncategorized ]]>

Filed under: Uncategorized ]]>

I’ll just recap a few of the talks that I remember from my notes — I didn’t really take notes during the plenaries so I don’t have much to say about them. Mostly this was due to laziness, but finding the time to blog has been challenging in this last year, so I think I have to pick my battles. Here’s a smattering consisting of

*(Information theory)*

**Emina Soljanin** talked about designing codes that are good for fast access to the data in distributed storage. Initial work focused on how to repair codes under disk failures. She looked at how easy it is to retrieve the information afterwords to guarantee some QoS for the storage system. **Adam Kalai** talked about designing compression schemes that work for an “audience” of decoders. The decoders have different priors on the set of elements/messages so the idea is to design an encoder that works for this ensemble of decoders. I kind of missed the first part of the talk so I wasn’t quite sure how this relates to classical work in mismatched decoding as done in the information theory world. **Gireeja Ranade** gave a great talk about defining notions of capacity/rate need to control a system which as multiplicative uncertainty. That is, where has the uncertainty. She gave a couple of different notions of capacity, relating to the ratio — either the expected value of the square or the log, appropriately normalized. She used a “deterministic model” to give an explanation of how control in this setting is kind of like controlling the number of significant bits in the state: uncertainty increases this and you need a certain “amount” of control to cancel that growth.

*(Learning and statistics)*

I learned about active regression approaches from **Sivan Sabato** that provably work better than passive learning. The idea there is do to use a partition of the X space and then do piecewise constant approximations to a weight function that they use in a rejection sampler. The rejection sampler (which I thought of as sort of doing importance sampling to make sure they cover the space) helps limit the number of labels requested by the algorithm. Somehow I had never met **Raj Rao Nadakuditi** until now, and I wish I had gotten a chance to talk to him further. He gave a nice talk on robust PCA, and in particular how outliers “break” regular PCA. He proposed a combination of shrinkage and truncation to help make PCA a bit more stable/robust. **Laura Balzano** talked about “estimating subspace projections from incomplete data.” She proposed an iterative algorithm for doing estimation on the Grassmann manifold that can do subspace tracking. **Constantine Caramanis** talked about a convex formulation for mixed regression that gives a guaranteed solution, along with minimax sample complexity bounds showing that it is basically optimal. **Yingbin Liang** talked about testing approaches for understanding if there is an “anomalous structure” in a sequence of data. Basically for a sequence , the null hypothesis is that they are all i.i.d. and the (composite) alternative is that there an interval of indices which are instead. She proposed a RKHS-based discrepancy measure and a threshold test on this measure. **Pradeep Ravikumar** talked about a “simple” estimator that was a “fix” for ordinary least squares with some soft thresholding. He showed consistency for linear regression in several senses, competitive with LASSO in some settings. Pretty neat, all said, although he also claimed that least squares was “something you all know from high school” — I went to a pretty good high school, and I don’t think we did least squares! **Sanmi Koyejo** talked about a Bayesian devision theory approach to variable selection that involved minimizing some KL-divergence. Unfortunately, the resulting optimization ended up being NP-hard (for reasons I can’t remember) and so they use a greedy algorithm that seems to work pretty well.

*(Privacy)*

**Cynthia Dwork** gave a tutorial on differential privacy with an emphasis on the recent work involving false discovery rate. In addition to her plenary there were several talks on differential privacy and other privacy measures. **Kunal Talwar** talked about their improved analysis of the SuLQ method for differentially private PCA. Unfortunately there were two privacy sessions in parallel so I hopped over to see **John Duchi** talk about definitions of privacy and how definitions based on testing are equivalent to differential privacy. The testing framework makes it easier to prove minimax bounds, though, so it may be a more useful view at times. **Nadia Fawaz** talked about privacy for time-series data such as smart meter data. She defined different types of attacks in this setting and showed that they correspond to mutual information or directed mutual information, as well as empirical results on a real data set. **Raef Bassily** studied a estimation problem in the streaming setting where you want to get a histogram of the most frequent items in the stream. They reduce the problem to one of finding a “unique heavy hitter” and develop a protocol that looks sort of like a code for the MAC: they encode bits into a real vector, had noise, and then add those up over the reals. It’s accepted to STOC 2015 and he said the preprint will be up soon.

Filed under: Uncategorized ]]>

In case you hadn’t heard, the IEEE Signal Processing Society is currently running a campaign that allows IEEE Student and Graduate Student members to join the SPS for free for the 2015 membership year. The promotion is running now through 15 August 2015. Only IEEE Student and Graduate Students are eligible, as this offer does not apply to SPS Student or Graduate Student members renewing their membership for 2015.

This link directs to the IEEE website with both IEEE Student membership and the free SPS Student membership in the cart.

If a student is already an IEEE Student of Graduate Student member, he/she can use the code SP15STUAD at checkout to obtain his/her free membership.

If you have any questions regarding the SPS Free Student Membership campaign or other membership items, please don’t hesitate to contact Jessica Perry at jessica.perry@ieee.org.

Please spread the news to others who may be interested in joining the SP Society.

Filed under: Uncategorized ]]>

Filed under: Uncategorized ]]>