REsearch Exposure in Socially Relevant Computing


The EECS department of Syracuse University is proud to partner with Google Research to launch an undergraduate student engagement program, RESORC (Research Exposure in SOcially Relevant Computing).

We encourage students from historically-underrepresented groups within computer science to apply, which includes, but is not limited to, women, students of color, first-generation college students, and LGBTQ+ students. No prior research experience required! Our goal is to expose students to what socially relevant computing research can look like, to help them build self-efficacy and practical research skills, to help them become stronger candidates for doctoral programs and to influence the career ambitions and choices of URM students in computing research by providing a supportive community of peers.

About the Program

Who can apply:All current undergraduate students at EECS department can apply. The program will be virtual. However, majority of our sessions will be interactive and synchronous, hence it may be useful to attend the sessions to achieve the maximum benefit.

What experience needed: Prospective students should have completed at least ECS 102 course. It is better if the students have completed the introductory computer science course sequence till CIS 351 .No prior research experience necessary!

How much time commitment: You will need to dedicate 2 days (Saturdays) in April and May to attend the virtual workshops.

Any stipend: We recognize that participating in this program takes time. To help offset this opportunity cost, each participant will receive a stipend.

What will the program offer:

  • Workshop 1 (April 24, 2021): A virtual workshop on exposure to many research areas of CS and hearing from current graduate students.
  • Workshop 2 (May 1, 2021): A virtual workshop on introduction to research methodologies, how to prep for higher ed, preparing REU application, and post-graduate career opportunities.
  • Research opportunity: Tons of research exposure to socially relevant computing based research problems from domain experts and disciplinary pioneers.
  • Networking: Meet and connect and network with faculty and industry researchers and graduate students who are amazing role models to learn from.
  • Speakers

    Michael Mozer

    Affiliation: Google Research, Brain Team
    Department of Computer Science, U. Colorado Boulder

    Mike received a Ph.D. in Cognitive Science at the University of California at San Diego in 1987.  Following a postdoctoral fellowship with Geoffrey Hinton at the University of Toronto, he joined the faculty at the University of Colorado at Boulder and served for 30 years as a Professor in the Department of Computer Science and the Institute of Cognitive Science. He recently joined Google Brain (Mountain View) as a Research Scientist. He is Secretary of the Neural Information Processing Systems (NeurIPS) Foundation and is a former chair of the Cognitive Science Society.  He is interested in human-centric artificial intelligence, which involves designing machine learning methods that leverage insights from human cognition, and building machine learning based software tools to help people better learn, remember, and make decisions.

    Susan H. Rodger

    Affiliation: Professor of the Practice of Computer Science
    Department of Computer Science, Duke University

    Susan Rodger is a Professor of the Practice in the Department of Computer Science at Duke University. Over twenty years ago, she was a faculty member in the Computer Science Department at Rensselaer Polytechnic Institute. She received her Ph.D. in Computer Science from Purdue University and her B.S. in Computer Science and Mathematics from North Carolina State University. Her research is in visualization, algorithm animation, and computer science education. She has developed JFLAP, software for experimenting with formal languages and automata. JFLAP was recognized as one of two finalist candidates in the NEEDS Premier Award for Excellence in Engineering Education Courseware in 2007. Rodger leads the Adventures in Alice Programming project and has taught computing to over 300 K-12 teachers. Rodger has supervised over eighty undergraduate students in research projects. Rodger is a member of the SIGCSE Board as immediate past chair and is a member of the CRA-W Board and the ACM Education Policy Committee. She is an ACM Distinguished Educator and a recipient of the ACM 2013 Karl V. Karlstrom Outstanding Educator Award.

    Siobahn Grady

    Affiliation: Assistant Professor
    Department of Information Systems, North Carolina Central University

    Dr. Siobahn Day Grady is an Assistant Professor of Information Systems at North Carolina Central University. She is a black woman, second-generation college graduate, and AAAS IF/THEN® Ambassador. She seeks to broaden participation in computing, especially for women and girls of color in STEM. Her research includes human-computer interaction and machine learning.

    Dilma Da Silva

    Affiliation: Ford Motor Company Design Professor II, Interim Director, Texas A&M Cybersecurity Center, Professor
    Department of Computer Science and Engineering, Texas A&M University

    Dilma Da Silva joined the Department of Computer Science and Engineering at Texas A&M University as its new department head on August 2014. Her primary research interests are cloud computing, operating systems, distributed computing, and high-end computing. Before joining Texas A&M, she worked at Qualcomm Research in California (2012-2014), IBM Thomas J. Watson Research Center in New York (2000-2012) and the University of Sao Paulo in Brazil (1996-2000). Da Silva is an ACM Distinguished Scientist, a member of the board of CRA-W (Computer Research Association’s Committee on the Status of Women in Computing Research), a member of CDC (Coalition for Diversifying Computing), co-founder of the Latinas in Computing group, and an event liaison with USENIX. She served as an officer at ACM SIGOPS from 2011 to 2015 and as chair of the ACM Senior Award Committee. In 2015 Da Silva is a very active member of her research community. She has chaired 27 scientific conferences and participated in 100+ program committees. She has published 72 articles in journals, books, refereed conferences and workshops, filed 15 patents, served on more than 30 thesis committees, and has had dozens of mentees, from middle school students to post-doctoral researchers. Da Silva received her doctoral degree in computer science from Georgia Tech in 1997 and her bachelor’s and master’s degrees from the University of São Paulo, Brazil. Besides pursuing her passion for computing, she spends time reading novels, knitting and keeping in touch with her friends across EIGHT time zones.

    Kelly Shaw

    Affiliation: Associate Professor
    Department of Computer Science, Williams College

    Kelly received her Ph.D. from Stanford University in 2007. Her graduate work focused on how to distribute computation and data across chip multiprocessors in order to take advantage of available hardware resources while reducing communication distance on-chip. She has since worked on projects related to the characterization of emerging application domains and on ways to effectively improve the use of graphics processors with respect to power and performance. More recently, she has begun exploring the correctness of Internet of Things platforms and applications with respect to data consistency.

    Sarah Masud Preum

    Affiliation: Assistant Professor
    Department of Computer Science, Dartmouth College

    Sarah will be joining the CS department at Dartmouth College as a tenure-track Assistant professor in July 2021. Currently, she is a postdoctoral fellow in the School of Computer Science at Carnegie Mellon University. She is interested in developing novel information extraction and fusion techniques using applied machine learning with applications in smart health, personalized assistant, and intelligent environments. she builds deep semantic inference models of multi-modal data generated from heterogeneous applications to provide interpretable and personalized decision support. Her vision is to advance the fundamental techniques and theories for data-driven and knowledge-integrated information fusion to improve human-in-the-loop intelligent systems.

    Chelsea Finn

    Affiliation: Assistant Professor
    Department of Computer Science and Electrical Engineering, Stanford University and Google

    Chelsea is an Assistant Professor in Computer Science and Electrical Engineering at Stanford University. Finn's research interests lie in the capability of robots and other agents to develop broadly intelligent behavior through learning and interaction. To this end, her work has included deep learning algorithms for concurrently learning visual perception and control in robotic manipulation skills, inverse reinforcement methods for learning reward functions underlying behavior, and meta-learning algorithms that can enable fast, few-shot adaptation in both visual perception and deep reinforcement learning. Finn received her Bachelor's degree in Electrical Engineering and Computer Science at MIT and her PhD in Computer Science at UC Berkeley. Her research has been recognized through the ACM doctoral dissertation award, the Microsoft Research Faculty Fellowship, the C.V. Ramamoorthy Distinguished Research Award, and the MIT Technology Review 35 under 35 Award, and her work has been covered by various media outlets, including the New York Times, Wired, and Bloomberg. Throughout her career, she has sought to increase the representation of underrepresented minorities within CS and AI by developing an AI outreach camp at Berkeley for underprivileged high school students, a mentoring program for underrepresented undergraduates across four universities, and leading efforts within the WiML and Berkeley WiCSE communities of women researchers.

    Endadul Hoque

    Affiliation: Assistant Professor
    Department of Electrical Engineering & Computer Science, Syracuse University

    His research focuses on the security of computer networks and systems. The software of computer networks and systems continues to have exploitable vulnerabilities, which are lucrative targets for adversaries. Within this broad domain, his particular emphasis is on automated detection of vulnerabilities as well as creating resilient protocols and systems. His research primarily builds on and expands program analysis, software engineering, and formal verification. His interests span several domains of computing, including network communication protocols, operating systems, distributed systems, internet-of-things (IoT) systems and embedded devices.

    Sucheta Soundarajan

    Affiliation: Assistant Professor, CISE Doctoral Program Coordinator
    Department of Electrical Engineering & Computer Science, Syracuse University

    Dr. Soundarajan’s research focuses on the structure of social and other real-world networks. She is interested in a variety of problems related to social network analysis, including community detection, link prediction, and network similarity. She is currently studying how communities change over time and, in particular, the structural factors that influence a community’s evolution. She is also interested in developing methods to obtain accurate samples of large network.

    Kristopher Micinski

    Affiliation: Assistant Professor
    Department of Electrical Engineering & Computer Science, Syracuse University

    My research lies at the intersection of the theory and application of program analyses. Program analyses are tools that examine programs and determine (prove) facts about them. For example, a program analysis might prove that a program can never crash due to a type error. In general, however, program analyses can be arbitrarily complex and infer subtle program invariants relating to myriad applications (such as computer security). Because program analyses must always approximate program behavior (otherwise they could solve the halting problem), there is an inherent tradeoff between analysis precision and analysis performance. Currently, program analyses are often applied only in limited contexts, as gaining acceptable performance requires too many compromises in terms of analysis precision. My current work focuses on three concurrent threads: tackling fundamental issues relating to scaling static analysis (specifically, scaling analyses to run on supercomputers rather than a single machine as all current analyses do); engineering those analyses (to allow analysis reuse); and applying those analyses to computer security (e.g., to check properties such as information flow and to support complex reverse engineering tasks).

    Ferdinando Fioretto

    Affiliation: Assistant Professor
    Department of Electrical Engineering & Computer Science, Syracuse University

    I work on artificial intelligence, privacy, and machine learning. My recent work focuses on (1) how to make AI algorithms better aligned with societal values, especially privacy and fairness, and (2) how to use machine learning for solving complex optimization problems. I study these questions using methods and models from optimization, statistics, differential privacy, and multiagent systems.

    Asif Salekin

    Affiliation: Assistant Professor
    Department of Electrical Engineering & Computer Science, Syracuse University

    My research takes a multi-disciplinary approach to develop novel and practical human behavioral and physical event sensing technologies that overlap with machine learning, human-centered Computing (e.g., health computing, human-machine interaction, and wellness monitoring applications), internet of things, cyber-physical systems and natural language processing. I enjoy building data-driven, application-specific novel technologies, as well as new systems and applications that involve sensors, mobile devices and cloud services. Research challenges that I deal with are the uncertainties in physical world sensing, human factors, such as, the user-context and mobility, limitation of current technologies, and resource constraints of the sensing data and platform. Technologies and systems that I develop are human-centric, several of them are attributed to health and wellness, and in general, they are in the scope of ubiquitous computing.

    Layla Bouzoubaa

    Affiliation: Lead Research Analyst
    University of Miami (UM) Miller School of Medicine's Department of Public Health Sciences (DPHS)

    Layla Bouzoubaa is a Lead Research Analyst for the University of Miami (UM) Miller School of Medicine's Department of Public Health Sciences (DPHS). Additionally, she is the lead Data Scientist on UM's Sylvester Comprehensive Cancer Center's (SCCC) Currently, she is working in substance abuse research to help target interventions to patients suffering from opioid use disorder which includes developing Shiny Apps for resource mapping, data harmonization, and population statistics/visualizations to build models that will inform proper treatment. Layla also has the pleasure of co-instructing "Data Science & Machine Learning for Health Research", a summer course for masters and PhD level students within DPHS. Outside of business hours, Layla co-organizes the Miami chapter of R-Ladies, an international organization that promotes gender equity among the data science community, and Google Developers Group (GDG) Miami, a community group that encourages and enables the discussion of new technologies in South Florida. She also co-organizes the annual Women in Data Science (WiDS) Miami conference, is a Google Women Techmakers (WTM) ambassador, and is a facilitator of Google's #IamRemarkable initiative. Aside from her professional loves, Layla is an over-zealous plant mom and an aspirant wine connoisseur and food critic.

    Program Goals

  • Train the next generation of scholars to conduct cross-cutting computing research
  • Provide research exposure to undergraduate students from populations that are under-represented in computing by introducing them to research
  • Increase students’ understanding of computing research methodologies
  • Introduce students to graduate education and research career opportunities through preparatory workshops and one-on-one mentoring
  • Provide students with multiple points of support from a diverse group of peers and faculty mentors
  • Tentative Program Schedule 

    The table below shows the major activities of the RESORC program. Updates are still in progress!

    All times are in EDT

    Mar 20   Applications open
    Mar 30   Application deadline
    April 12   Admission decisions announced
    Apr 24

    09:30 - 9:50 AM Introduction to socially relevant computing - Farzana Rahman, Syracuse University
    10:00 - 10:50 AM "Applying to Graduate School and Preparing a Stellar Application" - Susan Rodger, Duke University
    11:00 - 12:00 PM "Post-PhD Career Opportunities" - Chelsea Finn, Sarah Masud, Lina Battestilli, Priyam Biswas
    12:00 - 01:00 PM Lunch Break
    01:00 - 02:00 PM NO SESSION
    02:00 - 02:50 PM "Finding an Advisor & Developing an Effective Working Relationship with them" - Siobahn Day Grady, NC A & T State University
    03:00 - 03:50 PM "Tips for Developing Everyday Research Skills" - Kelly Shaw, Williams College
    04:00 - 04:25 PM "Fairness and Equity in Social Networks" -  Sucheta Soundarajan, Syracuse University
    May 1 09:30 - 9:50 AM Getting Started

    10:00 - 10:25 AM "Massively-Parallel Program Analysis" - Kristopher K Micinski, Syracuse University

     10:30 - 11:55 AM "Ubiquitous and human-centric computing in healthcare" - Asif Salekin, Syracuse University

     11:00 - 11:25 AM "Deep Constrained Learning with Privacy Guarantees" - Ferdinando Fioretto, Syracuse University

     11:30 - 11:55 AM "Application preparation for REU and similar opportunities" - Katie Siek, Indiana University

    12:00 - 01:00 PM Lunch Break

    01:00 - 01:50 PM "Financially supporting your graduate education" - Dilma Da Silva, Texas A&M University
    02:00 - 02:50 PM "AI to support human learning and decision making" - Michael Mozer, Google Research, Brain Team and UC Boulder
    03:00 - 03:50 PM "Journey beyond 10 min" - Minute talks and panel -  Graduate students, Syracuse University
    04:00 - 05:30 PM "Hands-on Workshop: Introduction to Data Science and Visualization in Healthcare" - Layla Bouzoubaa, University of Miami

    Contact Us

    Do you have more question about RESORC? Please send an email to


    RESORC program is funded by Google Research. Google Research Image