One of the great new NSF programs in recent years is the introduction of the “Algorithms in the Field” program, which is a joint initiative from the CCF, CNS, and IIS divisions in CISE. It’s goal is almost a direct match with what I try to do with my research: it “encourages closer collaboration between (i) theoretical computer science researchers [..] and (ii) other computing and information researchers [..] very broadly construed”. The projects it funds are meant to push the boundaries of theoretical tools and apply them in a application domain.
Of course this is perfectly suited to what we do in RSRG at Caltech! We missed the first year of the call due to bad timing, but we submitted this year and I’m happy to say it was funded (over the summer when I wasn’t blogging)!
The project is joint with Steven Low, Venkat Chandrasekaran, and Yisong Yue and has the (somewhat generic) title “Algorithmic Challenges in Smart Grids: Control, Optimization, and Learning.”
For those who are curious, here’s the quick and dirty summary of the goal…taken directly from the proposal.
At long last, we have gotten together and created a “Caltech-style” machine learning / big data / optimization group, and it’s called DOLCIT: Decision, Optimization, and Learning at the California Institute of Technology. The goal of the group is to take a broad and integrated view of research in data-driven intelligent systems. On the one hand, statistical machine learning is required to extract knowledge in the form of data-driven models. On the other hand, statistical decision theory is required to intelligently plan and make decisions given imperfect knowledge. Supporting both thrusts is optimization. DOLCIT envisions a world where intelligent systems seamlessly integrate learning and planning, as well as automatically balance computational and statistical tradeoffs in the underlying optimization problems.
In the Caltech style, research in DOLCIT spans traditional areas from applied math (e.g., statistics and optimization) to computer science (e.g., machine learning and distributed systems) to electrical engineering (e.g., signal processing and information theory). Further, we will look broadly at applications spanning information and communication systems to the physical sciences (neuroscience and biology) to social systems (economic markets and personalized medicine).
In some sense, the only thing that’s new is the name, since we’ve been doing all these things for years already. However, with the new name will come new activities like seminars, workshops, etc. It’ll be exciting to see how it morphs in the future!
(And, don’t worry, RSRG is still going strong — RSRG and DOLCIT should be complementary with their similar research style but differing focuses with respect to tools and applications.)
It’s been a while since I last wrote a post here… The spring was full of duties pulling me in lots of different directions, but now (finally) the summer has given me a chance to dig myself out and now I’m finally back.
Of course, lots has been going on at Caltech and in RSRG in the meantime…
For Caltech as a whole, the big recent news is of Gordon & Betty Moore’s $100 million dollar gift, which is particularly special because it’s a pure endowment gift with the goal of helping to provide graduate fellowships. This takes us a long way toward our ultimate goal of providing fellowships for every grad student on campus.
For the CMS department, the exciting news is that we’ll be welcoming two new interdisciplinary faculty into our mix:
- Omer Tamuz, who falls squarely in the middle of Economics, Mathematics, and Computer Science
- Fernando Brandao, who works on quantum information/complexity/algorithms
And finally, in RSRG, lots has been going on. This year we sent a number of students and postdocs off into the world…
- Lingwen Gan is now happily settled in at Facebook
- Subhonmesh Bose will be starting as an Assistant Professor at UIUC this coming year after finishing his postdoc at Cornell
- Enrique Mallada will be starting as an Assistant Professor at Johns Hopkins
- Siddharth Barman will be starting as an Assistant Professor at the Indian Institute of Science (IISc)
So, we’ve said a lot of goodbyes, but we also have new faces joining our ranks: Madeleine Udell & Hu Fu have joined as postdocs, and lots of new grad students will make their way onto campus in the coming month.
It’s that time again — our Center for the Mathematics of Informaion (CMI) call for postdoc applications is now up.
As I wrote last year at this same time, one of the things I really enjoy about Caltech is the fact that we always have lots of amazing postdocs floating around… This year, that is especially true. Siddharth Barman and Enrique Mallada are around RSRG for one more year, and we have a whole cast of new postdocs just getting settled in: Quentin Berthet, Dvijotham Krishnamurthy, Georgios Piliouras, and Piyush Srivastava.
This year, we’re looking pretty broadly for postdocs again. The CMI program considers postdocs in all areas of applied math, CS, and EE — basically, it just looks for strong people doing interesting, theoretically-oriented work. And, it’s important to say that it’s not just Katrina, Mani, Steven, and myself who will be looking at applicants to these programs. Faculty all across CS, EE, Economics, CDS, and Applied Math will be looking for strong applications. That said, we (Katrina, Mani, Steven, and I) do have a few specific areas in which we’re looking for folks. We are hoping to find strong candidates in CS/Econ, Energy, Privacy, and Data Markets. We’d love to find 2-3 people just across these areas…
A great thing about the CMI program is that the postdocs that are selected are not tied to any one faculty member or project. They can come in and define their own agendas, and work with whomever they’d like. This flexibility has led to really exciting results, and a high rate of placing postdocs in top faculty positions. Especially in the CS/Econ area, this flexibility is really unique. There are lots of Economists here at Caltech that work very closely with postdocs in the CS/Econ area, including Federico Echenique, Matt Elliott, John Ledyard, and Leeat Yariv (among others). It’s really rare to have a group of such strong economists that put up with us CS folk on a daily basis…
Back in January I wrote an excited post announcing our new Computing and Mathematical Sciences (CMS) PhD program. Unfortunately, the approval did not happen in time for students to apply to it last year, but everything is up and running now, so we’re looking forward to seeing the first round of applicants this winter! (Though, we actually had 6 students who applied to other options last year switch into CMS for this fall — so the first class of CMS students is on-campus already!)
To prime the pump, I’m hoping that all the readers of this blog can spread the word about our exciting (& unique) new degree. I know it’s what I wish I could’ve done when I was a grad student… Please feel free to point prospective students to this post or to simply have them send me email.
Why did we start a new degree program?
We are in the midst of an unprecedented convergence of ideas & tools across disciplines. This is happening among both traditionally “close” areas in the information sciences such as electrical engineering, computer science, and applied math as well as between the information sciences and seemingly disparate fields such as economics, biology, statistics, physics, and operations research.
The mathematics of planet earth is a joint initiative from a consortium of mathematical sciences organizations around the world (organized nominally by DIMACS) that has the goal of showcasing how mathematics can be useful in tackling our world’s problems. It started last year as a year-long focus, but has now expanded and will continue for the coming years as well. I’ve been to a few events organized under this program, but the reason for this post is to highlight the recent workshop on “Data-aware energy use” organized at UCSD a week or so ago.
…Mani Chandy (one of our own) and Leslie Lamport (whom we’ve blogged about before). Congrats, Mani!
The Dijkstra prize was awarded for one of Mani’s most well-known and influential papers: Distributed snapshots: determining global states of distributed systems. Though, as an aside, when I first came to Caltech, I knew Mani as the “C” in the BCMP theorem, which laid the basis for the study of queueing networks. Given that my thesis was primarily on queueing and scheduling, he was one of the godfathers… But, as influential as the BCMP theorem has been, I’d say it still falls second to the distributed snapshots paper, which has laid the foundation for the implementation of distributed algorithms and distributed systems.
As the citation for the award says: “The paper provides the first clear understanding of the definition of consistent global states in distributed systems. […] It has led to concepts such as vector time, isomorphism of executions, global predicate detection, and concurrent common knowledge. Applications of the results of observing the system in consistent states include the development of vector clocks, checkpointing and message logging protocols, correct protocols for detecting stable properties such as distributed deadlocks and termination, mutual exclusion algorithms, garbage collection protocols, cache coherency and file coherency protocols in distributed replicated file systems, distributed debugging protocols, protocols for total message order and causal message order in group communication systems, global virtual time algorithms used particularly in parallel and distributed simulations of discrete event systems, and collaborative sessions and editing protocols in wide area systems and on the grid.”