The Capstone Kickoff was held in the Busch Student Center. Attending were 120 capstone students, 17 industry representatives, ECE faculty and staff.

Speakers included Dr. Hana Godrich, Dr. Narayan Mandayam (ECE Dept Chair), Dr. Yicheng Lu and Don Bachman from ASCO Power Technologies.

Food was served and two Arduino development kits were raffled off as door prizes. It was a great start to the Spring 2017 Capstone Program. More photos at the ECE Facebook page located at https://www.facebook.com/Department-of-Electrical-and-Computer-Engineeri...

The Novice2Expert Coding Club organized two MIT App Inventor workshops for middle school students this year on January 14th and 15th. The goal of the 3 hour workshops was to familiarize students with basic programming concepts while they learned how to make cool mobile apps. The workshops took place in ECE Computer Lab and was led by Parneet Kaur (Graduate Student) and Umama Ahmed(sophomore). A competition was also held on February 11, 2017, where the participants showcased their own apps the created with the program and 3 winners were selected by judges while all participants received prizes.

ECE graduate student Kazem Cheshmi working with Professor Maryam Mehri Dehnavi has won first place at the ACM student research competition of the 2017 International Symposium on Code Generation and Optimization. The ACM Student Research Competition (SRC), sponsored by Microsoft, offers a unique forum for undergraduate and graduate students to present their original research before a panel of judges and attendees at well-known ACM-sponsored and co-sponsored conferences.

Kazem won first place for his project entitled "Decoupling Symbolic Analysis from Numerical Factorization in Sparse Direct Solvers." This project seeks to build a domain-specific pattern-aware compiler for sparse matrix computations. A two-phase algorithm-aware compiler leverages domain knowledge to decouple the symbolic analysis stage in sparse codes from the numerical computation phase. The proposed approach not only reduces the overhead of symbolic analysis in sparse numerical codes but also enables the application of algorithm-specific and low-level compiler transformations. The algorithm-aware inspection exposes the dependence structure of the sparse matrix method and enables a space of optimization for applying aggressive compiler transformations not possible in specialized libraries. The compiler generates high-performance codes for low-level and high-level sparse linear algebra such as LU factorization, Cholesky factorization, QR decomposition, SVD, and triangular solvers used to find the solution to linear systems and singular values and eigenvalues of a matrix.

Congratulations to Kazem and Maryam on winning this competition!

Congratulations to Prof. Maryam Mehri Dehnavi on her new NSF award for the project titled "Performance-in-Depth Sparse Solvers for Heterogeneous Parallel Platforms." This is a two year project totaling $175,000 and is supported under the Computer and Information Science and Engineering (CISE) Research Initiation Initiative (CRII).

The project conducts an in-depth investigation of performance bottlenecks in sparse solvers and reformulates their standard variants to deliver end-to-end performance. Cross-layer solutions are developed to improve data locality, reduce communication, and increase inherent parallelism in sparse linear solvers. The solutions involve multi-level algorithm restructuring and performance tuning to significantly improve the scalability and performance of sparse computations while preserving their numerical accuracy, convergence, and stability. The proposed methods and algorithms are implemented as domain-specific high-performance software and a benchmark suite to promote iterative improvements of the developed algorithms and codes.

Professor Vishal Patel's recent work on de-raining is featured in the publication The Outline. Along with his PhD students He Zhang and Vishwanath Sindagi, Professor Patel has recently developed an algorithm for de-raining rainy images using a deep learning method based on conditional generative adversarial networks. Please read the article entitled "Computers are learning how to see in the rain" at  https://theoutline.com/post/979/scientists-remove-rain-and-snow-from-images-machine-learning.

Professor Peter Meer has been recognized as a 2016 AMiner Most Influential Scholar for his outstanding and vibrant contributions to the field of Computer Vision!

The AMiner Most Influential Scholar Annual List names the world's top-cited research scholars from the fields of science and engineering. The list is conferred in recognition of outstanding technical achievements with lasting contribution and impact to the research community. The 2016 winners are among the most-cited scholars from the top venues of their respective subject fields as of 2016. Recipients are automatically determined by a computer algorithm deployed in the AMiner system that tracks and ranks scholars based on citation counts collected by top-venue publications. The full list of the most influential scholars can be found here: https://aminer.org/mostinfluentialscholar/cv.

AMiner (https://aminer.org) is a free online service for academic social network analysis and mining. As of 2016, the system has collected information on over 136 million researchers, 230 million publication papers, and 300,000 venues. The system has been in operation on the Internet since 2006 and has been visited by nearly 8.32 million independent IP accesses. It provides various search/mining services for publishers, NSFC, and research venues such as ACM/IEEE Transactions, ACM SIGKDD, ACM WSDM, and IEEE ICDM. Further details can be found online at the AMiner Wikipedia page: https://en.wikipedia.org/wiki/Arnetminer. For your information, you can sign up for an AMiner account and keep your profile and publications updated (https://aminer.org/profile/53f49fd9dabfaec18777b60d).

Congratulations on this fantastic recognition.

Shunqiao Sun, a recent Ph.D. graduate of the Rutgers Electrical and Computer Engineering (ECE) department, has won the 2016 Robert T. Hill Memorial Best Dissertation Award, given by the Institute of Electrical and Electronics Engineers (IEEE) Aerospace and Electronics Systems Society (AESS).

Shunqiao’s Ph.D. thesis, entitled ``MIMO Radars with Sparse Sensing,’’ proposed a new radar concept that enables high target scene surveillance while requiring substantially reduced volume of data as compared to state-of-art radar systems. Shunqiao was a member of the Communications and Signal Processing Laboratory (CSPL) and worked under the supervision of Prof. Athina Petropulu.

Shunqiao joined CSPL in 2011, after completing his bachelor’s and master’s degrees in Electrical Engineering from Southern Yangtze University and Fudan University, respectively. Following the completion of his Ph.D. in January 2016, he joined the radar core team of Delphi Electronics & Safety, where he now works on millimeter-wave radar signal processing and machine learning algorithms for self-driving cars.

The Best Dissertation Award, in honor of Robert T. Hill, is an annual AESS award to recognize candidates that have recently received a Ph.D. degree and have written an outstanding Ph.D. dissertation that has made particularly noteworthy contributions in a field of interest of the Aerospace and Electronic Systems Society. Its purpose is to grant international recognition for the most outstanding Ph.D. dissertation by an AESS member in the year she/he is nominated. The award consists of an honorarium of $1,000 and a plaque, and will be presented at the 2017 IEEE Radar Conference that will be held in Seattle, Washington May 8-12, 2017.

Shunqiao is also the winner of 2015-2016 Graduate Program Academic Achievement Award given by the ECE department of Rutgers University.

ECE Graduate Student Sijie Xiong was selected to participate in the 2017 CRA-W Grad Cohort Workshop organized by the Computing Research Association. This NSF-funded program’s aim is "to increase the ranks of senior women in computing-related studies and research by building and mentoring nationwide communities of women through their graduate studies.” The workshop will take place at the Marriott Marquis in Washington D.C. on April 7-8, 2017.

Sijie is a Graduate Assistant in the ECE Department working with her advisor, Prof. Anand Sarwate.

More information can be found at http://cra.org/cra-w/grad-cohort-workshop

ECE PhD student Zahra Shakeri has been selected to attend the 2017 CRA-W (Computing Research Association-Women) Grad Cohort Workshop in Washington, D.C. (fully expense paid). The CRA-Women Graduate Cohort Workshop is a mentoring program, for graduate women in computing or related fields, designed to help build a networking cohort during graduate school and throughout your computing career. The workshop will take place at the Marriott Marquis in Washington D.C. on April 7-8, 2017.

Zahra works in the INSPIRE (Information, Networks, and Signal Processing Research) Laboratory with her advisor, Prof.Waheed Bajwa.

More information about the Grad Cohort Workshop Event Page can be found at http://cra.org/cra-w/events/grad-cohort-workshop-2017/

Rutgers ECE PhD student Xianyi Gao was featured in Rutgers Today. A direct link to the Rutgers Today feature article is here: http://news.rutgers.edu/feature/misfortune-motivated-doctoral-student-ac...

Xianyi works with Professor Janne Lindqvist on problems in human-computer interaction.

Congratulations to Xianyi on his wonderful accomplishments!