About the Student Research Competition

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.

Recognizing the value of student participation at conferences, ACM started the program in 2003, but it is much more than just a travel funding program. The ACM SRC provides participants a chance to meet other students and to get direct feedback on their work from experts.

This year's competitions took place at 22 participating ACM SIG conferences, sponsored by SIGACCESS, SIGAI, SIGAPP, SIGARCH, SIGBED, SIGCHI, SIGCOMM, SIGCSE, SIGDA,SIGDOC, SIGGRAPH, SIGHPC,  SIGMICRO, SIGMOBILE, SIGMOD, SIGOPS, SIGPLAN,  SIGSOFT and SIGSPATIAL as well as Grace Hoppper and TAPIA and included more than 356 student participants.

The program is administered by Nanette Hernandez at ACM, Dr. Laurie Ann Williams at North Carolina State University, Douglas Baldwin at SUNY Geneseo and Dr. Evelyne Viegas at Microsoft, Redmond, WA.

2020 SRC Grand Finals Winners

Peter Li, Massachusetts Institute of Technology,  James Davis, Virginia Tech,  Hasindu Gamaarachchi, University of New South Wales,  Zhaowei Xi, Tsinghua University, Alexander Zlokapa, California Institute of Technology,  and Ocean Hurd, University of California, Santa Cruz were the 2020 Grand Finals winners of ACM’s Student Research Competition.

The SRC Grand Finals are the culmination of a year-long competition that involved more than 356 computer science students presenting research projects at 22 major ACM conferences.

[Read the news release]

Students can gain many tangible and intangible rewards from participating in one of ACM’s Student Research Competitions. With a generous sponsorship of $120,000 per competition year from Microsoft, the ACM Student Research Competition is an internationally recognized venue enabling undergraduate and graduate students to earn:

  • Awards: cash prizes, medals, and ACM student memberships
  • Prestige: Grand Finalists and their advisors are invited to the Annual ACM Awards Banquet, where they are recognized for their accomplishments
  • Visibility: opportunities to meet with researchers in their field of interest and make important connections
  • Experience: opportunities to sharpen communication, visual, organizational, and presentation skills in preparation for the SRC experience

Graduate Category: First Place

Peter Li, Massachusetts Institue of Technology

"A Mutual Information Accelerator for Autonomous Robot Exploration" (MICRO 2019)

Exploration problems are fundamental to robotics, arising in various domains, ranging from search and rescue to space exploration. In these domains and beyond, exploration algorithms that allow the robot to rapidly create the map of the unknown environment can reduce the time and energy for the robot to complete its mission. Many effective exploration algorithms rely on the computation of Shannon mutual information (MI) which allow the robot to select the best location to explore in order to gain the most information about the unknown environment. [Read more]

Graduate Category: Second Place

James Davis, Virginia Tech

"On the Impact and Defeat of Regex DoS" (ESEC/FSE 19)

From their obscure origins in neuron modeling [26], regular expressions (regexes) have emerged as a widely used string manipulation tool. Regexes are commonly used to bring order to unstructured text, e.g., by web services to sanitize untrusted input. Unfortunately, regexes are risky in most mainstream programming languages: most regex engines have worst-case exponential matching behavior. This worst-case property has been known for decades [2, 39], and has been proposed as a vector for an algorithmic complexity attack [13]. Indeed, several major services have had outages caused by this behavior, including Stack Overflow [19] and Cloudflare [24]. [Read more]

Graduate Category: Third Place

Hasindu Gamaarachchi, University of New South Wales

"Real-time, Portable and Lightweight Nanopore DNA Sequence Analysis using System-on-Chip" (ESWEEK 2019)

The future of healthcare is decidedly dependent upon precision medicine. Precision medicine takes into account the genetic makeup of an individual to develop customised medicines and doses that are effective and safe (Fig. 1). The key to precision medicine is DNA sequence analysis. DNA sequence analysis is also beneficial in fields such as epidemiology, virology, forensics and evolutionary biology. Over the last two decades, DNA sequencing machines have evolved from >500kg machines to pocket-sized devices such as the 87g Oxford Nanopore MinION (Fig. 2). [Read more]

Undergraduate Category: First Place

Zhaowei Xi, Tsinghua University

"High-performance Flexible Packet Generator Using Programmable Switching ASIC" (SIGCOMM 19)

Packet generator is widely used to generate traffic with customized properties (e.g., rate, packet type) and plays a vital role in network researches and network operations. Network researchers use packet generators to examine the performance of purposed prototypes [1]. For network operators, packet generators are required in latency measurement [2][3] and failure troubleshooting [4]. The development of current network gives rise to new demands on packet generators in two ways. Firstly, packet generators need to be highperformance to meet expanding network bandwidth (from 10Gbps to 100Gbps). Secondly, packet generators should be capable of customizing packets flexibly to satisfy constantly emerging network functions and protocols. [Read more]

Undergraduate Category: Second Place

Alexander Zlokapa, California Institue of Technology

"A deep learning approach to noise prediction and circuit optimization for near-term quantum devices" (SC 2019)

Noisy intermediate-scale quantum devices face challenges in achieving high-fidelity computations due to hardware-specific noise. We present a framework for a deep-learning compiler of quantum circuits, designed to reduce the output noise of circuits run on a specific device. Our approach is to first train a convolutional neural network on experimental data from a quantum chip, so as to learn a noise model for that device. We then view the trained network as a noise predictor for quantum circuits and design a compiler that rewrites circuits so as to minimize expected noise, as predicted by the network. We tested this approach using the IBM 5-qubit devices and benchmarked the compiled circuits against the IBM Qiskit compilation algorithm. [Read more]

Undergraduate Category: Third Place

Ocean Hurd, UC Santa Cruz

"Insights for More Usable Virtual Reality Games for People with Ambylopia" (ASSETS 2019)

Amblyopia, or "lazy eye", is the world’s most common neurological eye disorder. Yet, very little has been done looking into how to make virtual reality (VR) more usable for people with Amblyopia. Furthermore, a trend of using VR for Amblyopia therapy has arisen, making such a study more essential than ever. Our study asks our user base of people with Amblyopia questions through two surveys, verbal feedback, and interviews about their experience with our VR video game therapy. We found patterns encoded in this information, which we use to create preliminary hypotheses for making VR experiences as usable as possible for people with Amblyopia. [Read more]