Rutgers ECE GAANN Fellowships for Graduate Students

In case there are any potential grad school applicants to Rutgers who read this blog, we recently were awarded a GAAAN award to help fund some graduate fellowships for US citizens or permanent residents interested in bioelectrical engineering (somewhat broadly construed). Application review will start soon, so if you’re interested in this opportunity, read on.

The Rutgers ECE Department is proud to announce the Graduate Assistance in Areas of National Need (GAANN) Fellowship. The GAANN Fellowship program provides need-based financial support to Ph.D. students pursuing a degree in areas related to bioelectrical engineering at the Department of Electrical and Computer Engineering, Rutgers University. Each GAANN Fellow receives a stipend to cover the Fellow’s financial need. A typical stipend is $34,000 per year for up to 5 years, subject to satisfactory performance. ECE is pleased to announce 5 GAANN Fellowships. Minority students, women and other underrepresented groups are particularly encouraged to apply.

Applicants must:

  • Be U.S. citizens or permanent residents
  • Have a GPA of 3.5/4.0 or higher
  • Plan to pursue a Ph.D. degree in Electrical and Computer Engineering at Rutgers University
  • Have Financial Need
  • Demonstrate excellent academic performance
  • Submit an application and supporting documents

Deadline: To apply, please email the application and supporting documents to Arletta Hoscilowicz AS SOON AS POSSIBLE.

Effective early anti-plagiarism interventions for (mostly international) Masters students

My department at Rutgers, like many engineering departments across the country, has a somewhat sizable Master’s program, mostly because it “makes money” for the department [1]. The vast majority of the students in the program are international students, many of whom have English as a second or third language, and whose undergraduate instruction was not necessarily in English. As a consequence, they face considerable challenges in writing in general, and academic writing in particular. Faced with the prospect of writing an introduction to a project report and wanting to sound impressive or sophisticated, many seem tempted into copying sentences or even paragraphs from references without citation. This is, of course, plagiarism, and what distresses me and many colleagues is that the students often don’t understand what they did wrong or how to write appropriately in an academic setting. Is this because most non-American universities don’t teach about referencing, citation, and plagiarism? I hesitate to lay the blame elsewhere — it’s hard (initially) to write formally in a foreign language. However, the students I have met say things like “oh, I thought you didn’t need to reference tutorials,” so there is definitely an element of ill-preparedness. Adding to this of course is that students are stressed, find it expedient, and hope that nobody will notice.

Most undergrad programs in the US have some sort of composition requirement, and at least at my high school, we learned basic MLA citation rules as part of English senior year. However, without assuming this background/pre-req, what can we do? My colleague Waheed Bajwa was asking if there are additional resources out there to help students learn about plagiarism before they turn in their assignments. Of course we put links to resources in syllabi, but as we all know, students tend to not read the syllabus, especially what seem like administrative and legalistic things. Academic misconduct is serious and can result in expulsion, but unless you’re a vindictive type, the goal shouldn’t be to have a “one strike and you’re out” policy. I’ve heard someone else suggest that students sign a contract at the beginning of the semester so they are forced to read it. Then, if they are given an automatic F for the class you can point to the policy. That also seems like dodging the underlying issue, pedagogically speaking.

Another strategy I have tried is to have students turn in a draft of a final project, which I then run through TurnItIn [2] or I manually search for copied sentences. I then issue a stern/threatening warning with links to information about plagiarism. Waheed does the same thing, but this is pretty time-intensive and also means that some students get the attention and some don’t. Students who are here for a Masters lack some incentives to do the right thing the first time — if this is the last semester of their program and suddenly this whole plagiarism thing rears its head in their last class, they may be tempted to just fix the issues raised in the draft and move on without really internalizing the ethics. I’m not saying students are unethical. However, part of engineering/academics, especially at the graduate level, is teaching the ethics around citation and attribution. I pointed out to one student that copying from sources without attribution is stealing and that kind of behavior could get them fired at a company, especially if they violate a law. They seemed surprised by this metaphor. That’s just an anecdote, but I find it telling.

The major issues I see are that:

  • Undergrad-focused models for plagiarism education do not seem to address the issue of ESL-writers or the particulars of scientific/engineering writing.
  • Educating short-term graduate students (M.S.) about plagiarism in classes alone results in uneven learning and outcomes.

What we (and I think most programs) really need is an earlier and better educational intervention that helps address the particulars of these programs. I was Googling around for possible solutions and came across a paper by Gunnarsson, Kulesza, and Pettersson on “Teaching International Students How to Avoid Plagiarism: Librarians and Faculty in Collaboration”:

This paper presents how a plagiarism component has been integrated in a Research Methodology course for Engineering Master students at Blekinge Institute of Technology, Sweden. The plagiarism issue was approached from an educational perspective, rather than a punitive. The course director and librarians developed this part of the course in close collaboration. One part of the course is dedicated to how to cite, paraphrase and reference, while another part stresses the legal and ethical aspects of research. Currently, the majority of the students are international, which means there are intercultural and language aspects to consider. In order to evaluate our approach to teaching about plagiarism, we conducted a survey. The results of the survey indicate a need for education on how to cite and reference properly in order to avoid plagiarism, a result which is also supported by students’ assignment results. Some suggestions are given for future development of the course.

This seems to be exactly the kind of thing we need. The premises of the paper are exactly as we experience in the US: reasons for plagiarism are complex, and most students plagiarize “unintentionally” in the sense that the balance between ethics and expediency is fraught. One issue the authors raise is that “views of the concept of plagiarism… may vary greatly among students from one country” so we must be “cautious about making assumptions based on students’ cultural background.” When I’ve talked to professional colleagues (in my field and in other technical fields) I often hear statements like “students from country X don’t understand plagiarism” — we have to be careful about generalizations!

The key aspect of the above intervention is partnering with librarians, who are the experts in teaching these concepts, as part of a research methods course. Many humanities programs offer field-specific research methods courses. These provide important training for academic work. We can do the same in engineering, but it would require more effort and resources. For those readers interested in the ESL issues, there are a lot of studies in the references that describe the multifaceted aspects of plagiarism, especially among international students. A major component of the authors’ proposed intervention is the Refero tutorial, which is a web course for students to take as part of the course. We can’t delegating plagiarism education to a web tutorial, but we have to start somewhere. Another resource I found was this large collection of tutorials collected by Macie Hall from Johns Hopkins, but these are focused more at US undergraduates.

Does your institution have a good anti-plagiarism orientation unit? Does it work? When and how do you provide this orientation?

[1] There is much ink to be spilled debating this claim.
[2] I have many mixed feeling about the ethics of TurnItIn, especially after discussions with others.

Salim El Rouayheb’s Shannon Channel: Pulkit Grover at 1300 EST

Salim El Rouayheb has started an exciting new initiative inspired by the TCS+ series. TCS+ is a seminar series on theoretical computer science (plus more) given over Google Hangout so that people across the world can attend the talk (and even ask questions). Nobody has to travel anywhere. Salim’s version is for information theory and he’s calling it Shannon’s Channel. If you’re interested in getting announcements you can sign up for the mailing list.

Salim told me about this at Allerton and I meant to plug it here on the blog earlier but then the semester plus excessive travel ate me. He just sent a reminder yesterday that the inimitable Pulkit Grover will be giving a seminar today (Monday) at 1 PM:

Error-correction and suppression in communication and computing: a tradeoff between information and energy dissipation

Abstract: Information naturally tends to dissipate. This dissipation can be slowed down, but this requires increased energy dissipation. Shannon’s capacity theorem can be interpreted as the first word in this information-energy dissipation tradeoff, but it barely scratches the surface. I will begin with a survey of recent results on minimal energy dissipation for reliable information communication. I will discuss how incorporating energy dissipated in transmitter/receiver circuitry as well as in transmission leads to radically different fundamental limits on information-energy interactions than those obtained by Shannon. I’ll also talk about practical applications in short distance wired and wireless communications.

These techniques can also be applied to obtain fundamental limits to information-energy dissipation for reliable computation using unreliable/noisy components (first considered in [von Neumann ’56]). Recent work on strong data-processing inequality points out the fundamental difficulty in noisy computing: information-dissipation across multiple computation steps. We ask the question: what is the minimum energy-dissipation needed to keep information intact (reliability constant) as the computation proceeds? I’ll describe our novel ENCODED strategy (ENcoded COmputation with DEcoders EmbeddeD) for linear computations on noisy substrates, that outperforms uncoded/repetition-based strategies and keeps error-probability bounded below a constant. The key insight is that for computing in noisy environments, repeated error-suppression (that dissipates energy) is essential to keep information from dissipating. Application to emerging devices and circuit design techniques will also be discussed.

Finally, I’ll talk about a high-density noninvasive biopotential sensing problem, which is closely related to the problem of compressing a Markov source distributedly. Here, energy constraints limit the number of sensors. I’ll discuss how a novel “hierarchical” architecture that contains error-accumulation turns out to have a substantially improved energy-information dissipation tradeoff than simply “compressing innovations” (a strategy known to be suboptimal from a work of Kim and Berger).

The Hangout link is here and the talk will be on YouTube afterwards.

Unfortunately, I have to teach during that time, otherwise I would totally be there, virtually.

DIMACS Workshop on Network Coding: the Next 15 Years

DIMACS Workshop on Network Coding: the Next 15 Years
December 15 – 17, 2015
DIMACS Center, CoRE Building, Rutgers University


  • Michael Langberg, SUNY Buffalo
  • Emina Soljanin, Bell Labs, emina at
  • Alex Sprintson, Texas A&M

Presented under the auspices of the Special Focus on Cybersecurity, the Special Focus on Information Sharing and Dynamic Data Analysis and the Special Focus on Energy and Algorithms.

Since its introduction at the turn of the millennium, network coding has evolved from a simple idea to a mature interdisciplinary filed, with a solid body of knowledge generated by a diverse group of researchers. Over the years, network coding techniques have had significant impacts and benefits in throughput, reliability, security, and energy efficiency. We believe that in the next fifteen years the field will expand even further into new multidisciplinary area and provide a fertile ground for the next generation of researchers, leading to new breakthroughs, discoveries, and solutions to long standing open problems.

The goal of this workshop is to discuss the long-term horizons of the field and identify key areas and research problems that will be in the focus of the research community. The workshop will include speakers from a broad spectrum of backgrounds, from theoreticians to practitioners, and from the founders of the field to younger faculty and students.

This is list of Confirmed Participants:

  • Alexander Barg, UMD
  • Eimear Byrne, University College Dublin
  • Viveck Cadambe, Penn State
  • Chandra Chekuri, UIUC
  • Hoang Dau, UIUC
  • Alex Dimakis, UT Austin
  • Michelle Effros, Caltech
  • Salim El Rouayheb, Illinois Institute of Technology
  • Sid Jaggi, CUHK
  • Shirin Jallali, Bell Labs
  • Sudeep Kamath, Princeton
  • Young-Han Kim, UCSD
  • Joerg Kliewer, New Jersey Institute of Technology
  • Oliver Kosut, Arizona State University
  • Gerhard Kramer, TU Munich
  • Michael Langberg, SUNY Buffalo
  • Mohammad Ali Maddah-Ali, Bell Labs
  • Muriel Medard, MIT
  • Bobak Nazer, Boston University
  • Aditya Ramamoorthy, Iowa State University
  • Parastoo Sadeghi, Australian National University
  • Emina Solanin, Bell Labs
  • Alex Sprintson, Texas A&M
  • Raymond Yeung, CUHK

Call for Participation:

Attendance at the workshop is open to all interested participants (subject to space limitations). Please register if you would like to attend this workshop.

Family leave for graduate students: how does it work at your school?

I am trying to understand how family leave works for graduate students at different schools. More specifically, I am interested in how the finances for family leave work. Graduate students at Rutgers (as at many schools) are covered by a union contract. The contract specifies that in case of a pregnancy, the mother can take 6 weeks of paid leave recovery time plus an additional 8 weeks of paid leave family time. Non-carrying parents can take 8 weeks of paid leave for family time. While not generous by European standards, it’s better than what I would expect (ah, low expectations) here in the US.

This raises the question of how the university pays for the leave time. Students are either teaching or research assistants. 14 weeks off from teaching might include most of a semester, so the department needs a substitute. Trying to give the student an “easy TA” and still expecting them to come and teach when they are entitled to the leave is shady (although I have heard this idea floated). If they are paid through a grant, how should the leave time be charged?

I recently contacted authorities at Rutgers about this, and their response was not encouraging. Rutgers foists all charges off onto the department or grant/PI. If you are a TA and have a baby, the department is on the hook, financially, for finding a replacement. If you are a research assistant, they just charge the paid leave to the grant, as per the fringe rules in OMB Circular A-21.

I wrote a letter back about how disappointing this all is. The current system creates strong incentives for departments and PIs to deny appointments to students who have or may develop family obligations. This lack of support from the University could result in systematic discrimination against student parents. Whether examples of such discrimination exist is not clear, but I wouldn’t be surprised. Allocating the financial burden of leave to departments creates great inequities based on department size and budget, and not all departments can “close ranks” so easily.

For PIs covering students on grants with “deliverables,” the system encourages not supporting students on such grants. The rules in OMB Circular A-21 say that costs should be “distributed to all institutional activities in proportion to the relative amount of time or effort actually devoted by the employees.” It also implies that leave time should be charged via fringe benefits and not salary. It’s not entirely clear to be how a particular grant should be charged if a student participant goes on family leave, but the Rutgers policy seems to be to stick it to the PI.

The current situation leaves students in a predicament: when should they tell their advisor or department that they are pregnant? Many students are afraid of retribution or discrimination: I have heard from students that their friends say advisors “don’t like it when their students have kids.” The university’s policy on this issues only serves to legitimize these fears by creating uncertainty for them about whether they will be reappointed.

My question to the readers of this blog is this: how does your university manage paying for family leave for grad students?

LabTV, research stories, and video outreach

My lab was visited by Charlie Chalkin a few weeks ago. He was here to interview me and various students on our experiences in research for LabTV. LabTV was founded by Jay Walker and the NIH director Dr. Francis Collins with the aim of profiling NIH-funded researchers (as I now am). It was a great opportunity and a really short informal process, and I guess I can get some more hits from YouTube on the LabTV channel.

This experience got me thinking about how hard it is to connect with students at times. In particular, I think that many students don’t really see the process of how we got to where we are as their professors. Unless they have an academic in the family and also paid attention to their life story, they seem a bit mystified by it all. Obviously pop culture has a lot to do with this — movie and TV depictions of the professoriat are pretty far from reality. I have heard, however, from Ram Rajagopal that San Andreas has pretty much the most amazing interactions between professors and grad students. Heroism — that’s what we want.

But this experience got me thinking that departments might benefit from having short 2 minute profiles of their faculty members, but not from the technical achievements view. Instead, let them talk about what got them interested in the problems they are interested in, how they ended up in this position, and why they like the job. The answers may be surprising, but I think students might see a different side than they get in the lecture hall.

Call for Papers: 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:

Zhenliang Zhang, Qualcomm Corporate R&D (
Wee Peng Tay, Nanyang Technological University (
Moez Draief, Imperial College London (
Xiaodong Wang, Columbia University (
Edwin K. P. Chong, Colorado State University (
Alfred O. Hero III, University of Michigan (