Established in 2002, the BU Computer Science Research Excellence Award (REA) is presented annually to the PhD student or students who have produced outstanding research results over the course of their studies in the department. To be considered for this award, BU\/CS PhD students must first be nominated by their advisor. The winners are then recommended by a faculty REA selection committee and approved by the entire BU\/CS faculty.<\/p>\n
The following are commendations by the REA selection committee for distinguished winners from past academic years.<\/p>\n
Departmental Research Excellence:\u00a0 Satchit Sivakumar, Hao Yu & Aneesh Raman<\/span><\/p>\n <\/div>\n<\/div>\n\n Departmental Research Excellence:\u00a0 Luowen Qian, Zichen Zhu & Shahin Roozkhosh<\/span><\/p>\n <\/div>\n<\/div>\n\n Departmental Research Excellence:\u00a0 \u00a0Gavin Brown<\/span><\/p>\n <\/div>\n<\/div>\n\n Departmental Research Excellence:\u00a0 Tarikul Islam Papon & Yi Zheng<\/span><\/p>\n <\/div>\n<\/div>\n\n Departmental Research Excellence:\u00a0 \u00a0Sarah Scheffler & Kostas Sotiropoulos<\/span><\/p>\n <\/div>\n<\/div>\n\n Ramesh Pallavoor Suresh<\/a><\/p>\n Ramesh\u2019s research focuses on sublinear-time algorithms. The goal is to understand what can be computed extremely quickly (after looking only at a tiny portion of the input), but with reasonable accuracy. Ramesh has recent results on sublinear-time algorithms that compute on graph data with erasures. This work defines a new model, appropriate for situations when the input graph is corrupted or access to the input is denied, for example, because of privacy concerns. Ramesh investigated testing connectedness and approximating the average degree in this model. Ramesh is a coauthor on a SODA 2020 paper on approximating the distance of a given function to the nearest monotone function. This work has resolved an important open question in sublinear algorithms from Property Testing Review 2014 for the case of nonadaptive algorithms. These are algorithms that make all their queries in advance, before receiving any answers. The approximation algorithm in the SODA paper is based on a novel use of a directed isoperimetric inequality for the Boolean hypercube. Ramesh is also a coauthor of the best approximation algorithms and hardness results for testing whether a function is unate in different parameter regimes. A function is called unate if it is nonincreasing or nondecreasing in every variable. This work was published in ICALP 2017, and the full version has been accepted to the Theory of Computing journal. Ramesh has also done interesting work on parametrized property testing (ITCS 2017, ACM Transactions on Computation Theory 2018). Usually, the running time of sublinear-time algorithms is measured with respect to the size of the input. Ramesh showed that by measuring it with respect to other parameters, we can sometimes overcome the known hardness results. Notably, he gave an algorithm for testing whether the data is sorted which is extremely efficient for data in a bounded range and beats the long-time lower bound for general range. Ramesh also contributed significantly to the success of the Algorithms and Theory group at BU, helping run our Algorithms\/Theory seminar and assisting in TCS classes.\u00a0<\/span><\/p>\n Xingchao Peng<\/a><\/p>\n Xingchao Peng is a final year PhD student with an excellent track record in both research and service. Xingchao\u2019s PhD work has focused on designing Machine Learning algorithms that can learn from less data. He developed several algorithms that transfer learned knowledge from one application domain to another, thus reducing the need for extra data annotation. These were published in nine peer-reviewed papers in top AI conferences, including an oral paper at ICCV19 that provided both theoretical insights and practical algorithms for knowledge transfer from multiple labeled source domains to an unlabeled target domain. Xingchao has also served the AI research community extensively, acting as a conference reviewer for over 35 conferences and journals in the last five years, and collecting two large-scale datasets to facilitate transfer learning research, <\/span>VisDA<\/span><\/a> (28K images) and <\/span>DomainNet<\/span><\/a> (0.6 million images).<\/span><\/p>\n <\/div>\n<\/div>\n\n Jacob Harer<\/a><\/p>\n In the short four years that Jacob Harer has been in our department, he has not only transitioned successfully from his old field EE (in which he has his BA and MS), but has also blossomed into an indecent, original and fancifully imaginative researcher in his own right. He has made a critical contribution to a four year research program at DARPA called MUSE (Mining and Understanding of Software Enclaves), which culminated in a paper in NIPS 2018, titled “<\/span>Learning to Repair Software Vulnerabilities with Generative Adversarial Networks<\/b>.\u201d He has developed novel learning algorithms to understand patterns in various signals (neural spikes and local field potentials in brains, RF and magnetic signals from integrated circuits, etc.), and in his final year of the doctoral program, he tackled a difficult challenge of Tree-to-Tree learning with potential applications for various NLP tasks. He was the first member of my group and a valuable senior PhD within the group, as he is always willing to help his follow students with I-can-do-this-for-you attitude. He is always full of great ideas and more importantly full of heart.<\/span><\/p>\n Oxana Poburinnaya<\/a><\/p>\n <\/div>\n<\/div>\n\n <\/div>\n<\/div>\n\n <\/div>\n<\/div>\n\n Omer Paneth, PhD ’16<\/p>\n Omer has written papers that have significantly influenced the state of the art in cryptographic research, including seminal works on the construction of program obfuscation schemes and their use within cryptography and beyond, on succinct delegation of computation, on zero knowledge proofs with low round complexity, on resettable and concurrent protocols, on functional encryption, and much more. Omer also contributed significantly to the success of the BUSEC group, mentoring younger students. and assisting in crypto and security classes.<\/span><\/p>\n <\/div>\n<\/div>\n\n Ye Li<\/a>, PhD ’152023\/24 Research Excellence Award Winners<\/h4>
2022\/23 Research Excellence Award Winners<\/h4>
2021\/22 Research Excellence Award Winners<\/h4>
2020\/21 Research Excellence Award Winners<\/h4>
2019\/20 Research Excellence Award Winners<\/h4>
2018\/19 Research Excellence Award Winners<\/h4>
2017\/18 Research Excellence Award Winners<\/h4>
2016\/17 Research Excellence Award Winners<\/h4>
2015\/16 Research Excellence Award Winners<\/h4>
2014\/15 Research Excellence Award Winners<\/h4>
Ye’s research focuses on the development of operating system kernels for real-time and embedded computing. His PhD is on the design of the Quest-V separation kernel for mixed-criticality systems, which he has been co-developing with his advisor, Prof. Rich West, and other systems students. Ye’s contribution is on the use of hardware virtualization techniques to sandbox and, hence, separate components of a system into different criticality domains. The resulting Quest-V system takes a radically different system structure to normal OSes: rather than being an SMP-based system with one image running on every core, it looks like a chip-level distributed system. CPU, memory and I\/O resources are partitioned amongst sandbox domains that manage resources directly without hypervisor (or virtual machine monitor) intervention. This makes the system far more efficient than with traditional hypervisor-based systems. Ye has also contributed to the design and development of secure and predictable inter-sandbox communication techniques. Application use-cases for such a system include automotive, avionics, healthcare, factory automation and robotics, where safety-critical system components must be separated so that failures and timing violations do not have global, and potentially catastrophic, consequences. Ye has led or been co-author on papers in top conferences for his area, including RTSS 2014, VEE 2014, PACT 2014, and RTAS (2013 and 2011), amongst others. His work has also been submitted to ACM TOCS and USENIX ATC, where it is currently under review.<\/p>\n