Sponsor
RESORC program is funded by Google Research. 
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.
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:
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.
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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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) SCAN360.com. 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.
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 |
Do you have more question about RESORC? Please send an email to syracuseresorc@gmail.com
RESORC program is funded by Google Research.