Stephanie Milani will complete her Computer Science (B.S.) and Psychology (B.A.) degrees in 2019. She is a Grand Challenge Scholar, a URA Scholar, and a member of the Honors College.

Title of your research project: Creating a Scalable Framework for Model-Free Reinforcement Learning in Norm-Rich Environments

Describe your project:I developed a scalable framework for robots to incorporate human values and preferences, or norms, with traditional methods for learning how to perform tasks so that these robots can learn how to act in environments where there may be social ramifications to actions.

Who is your mentor for your project? Why did you choose them? My research mentor is Dr. Katia Sycara, Robotics Institute at Carnegie Mellon University.A couple of my friends from the Robotics Institute Summer Scholars program worked with her last summer and only had great things to say about working with her. When she and I met, I liked the research questions that she proposed. Based on that conversation and the research publications I had read from her lab, I decided that I wanted to work with her for the summer and the fall.

How did you become interested in this project?Dr. Sycara's lab is researching the general area of normative reasoning for social robots. While reading papers in this area, I realized that few people have looked into how to incorporate normative reasoning in a reinforcement learning setting. I've been interested in reinforcement learning for a few years now - both from its psychological underpinnings and in the area of machine learning - and I wanted to take the opportunity to explore it in a little-researched area with real-world impact. 

What has been the hardest part about your research/what was the most unexpected thing about being a researcher? The most difficult part about my research is finding a research problem that is both specific enough to make a tangible contribution and plan, but broad enough that it has greater impact outside of its original context.

What has been the most rewarding part?Probably presenting my research and being met with interest!

How will you disseminate your research?I will publish this research in the RISS Working Papers Journal. I plan to incorporate part of this work in my URCAD presentation, as well as other publications. 

What is your advice to other students about getting involved in research? If you find someone's research findings interesting, then contact them about it! They may have opportunities for you to work with them. 

What are your career goals?I plan to obtain a Ph.D. in Computer Science, specifically focusing on the field of reinforcement learning. After that, I plan to continue with research in machine learning in either an academic or industrial setting.

To learn more about becoming a URA Scholar:

https://ur.umbc.edu/ura/

or contact Dr. Householder at : aprilh@umbc.edu

Jordan Troutman is a Computer Science and Mathematics major and just spent his summer conducting research at Rutgers University-  and he’s only a SOPHOMORE!

List any Scholars/Honors programs you are a part of: Meyerhoff Scholars, Honors College

Title of your research project: Understanding Machine Learning Bias without a Ground Truth

Describe your project: My project aimed to understand how predictive computer algorithms can impose biases across protected groups (e.g. race, gender, etc.) when it is not possible to use common measures of comparing an algorithm's predicted outcome with its intended outcome. 

Who is your mentor(s) for your project? How did you find your mentor? Why did you choose them? My mentor was Dr. Anand Sarwate, Assistant Professor in the Department of Electrical and Computer Engineering at Rutgers University.  It is ironic, actually, because he found me. I applied to the RiSE Research Experience at Rutgers through the Big Ten Academic Alliance Summer Research Opportunities application, and he reached out to me because my interest in data science and machine learning would be a perfect fit for this project.  I chose him because I was primarily interested in this field, but also because I was honored someone would want to take me under their wing to conduct research.

How did you become interested in this project? This project sparked my interest because it lies at the intersection of ethics and computer science.  I have always loved coding. But also, I think it is important to understand how individual actions contribute to the structure of our society.  We inherently give power to computers when we start to depend on them to make very complex human decisions, like choosing from a pool of resumes who the most qualified applicants are for a job.  For the computer scientists who create these machine learning algorithms, it is important that we understand the impact to what our programs--discriminatory or not--can affect the world we live in. Since the field of machine learning fairness stems from an analysis that saw black men to be twice as likely of being miscategorized of being high risk of committing a crime again compared to white criminals, I wanted to make sure that people who look like me are not going to be marginally discriminated against based upon a computer's decision.

What has been the hardest part about your research/what was the most unexpected thing about being a researcher? The hardest part about researching this summer was overcoming the jargon barrier used in this specific field of machine learning fairness.  Being fairly new to research, the  novel vocabulary made research feel very advanced and out of my scope of understanding.  Once I got over the language barrier, I remembered that research is simply asking questions and then seeing if those questions can be answered.  Reading papers and getting acclimated to the conventions of contemporary researchers took a lot of time and effort, but it was overall worthwhile because research is really fun!

What has been the most rewarding part? The most rewarding part about researching this summer is that I can talk about the importance of making fair predictive algorithms to anyone.  Making people aware that machine learning algorithms, like Google search results, learn from observations (data), and if the scope of their observations is biased, then the algorithms will inherit those biases as well.  Since this field is very interdisciplinary, I believe it is crucial for researchers to be able to express their ideas in ways that individuals of varying fields and expertise can easily understand why their work is important.  This allows for more collaboration from other academic areas as well as making the essence of research a more inclusive experience.  Bringing more viewpoints on an important topic like machine learning fairness will point us in the direction of the best solution.

How will you disseminate your research? I have presented my research at the RiSE at Rutgers Symposium and the UMBC Summer Undergraduate Research Fest (SURF).  I plan on presenting at URCAD in April, and at an undergraduate research conference this year.

What is your advice to other students about getting involved in research? For UMBC students wanting to get into research, there is no requirement to be in a scholars program or have honors achievement.  Research is a simple concept that anyone can do.  Anyone who has ever identified a problem and envisioned the problem being solved is a researcher at heart.  It may be really intimidating to be working with people who have high degrees and extensive amounts of experience, but those garnishes will never replace the foundation of a researcher.  When anyone enters the world of research, it is important for them to bring their own experiences, perspective, and culture.  Diversity of thought makes problem-solving progress faster.  Additionally, treat attending class as an opportunity to understand rather than to get good grades; this skill and knowledge-base will easily transfer into research.  Get in contact with a professor who has interesting research, talk with them about their research, then get started!  If research may be too much for the school year, applying to summer research opportunities is a great option!

What are your career goals? After going to graduate school and getting my Ph.D., I would love to work at a university as a professor, then maybe become the dean of a college.  I want everyone to feel that learning is an exciting, thriving, living aspect of our everyday lives.  I would also like to work with computer scientists, educators, and policymakers to ensure developing technologies are being developed for the overall social good of our world.

Want to know how you can find a summer research experience?

Want to be the next Researcher of the Week? 

Email: aprilh@umbc.edu or visit ur.umbc.edu

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Getting involved in research helps you network with faculty both on and off campus, strengthen your resume for graduate school, professional school, and employment, support your artistic vision, and helps you apply what they’ve learned in the classroom in a research setting. UMBC has several programs that support undergraduate research:

Undergraduate Research and Creative Achievement Day (URCAD) features research, scholarship, and creative work carried out by UMBC undergraduates. Student work is shared through oral presentations, posters, artistic exhibits, performances, and film.

Undergraduate Research Awards (URA) provide up to $1,500 to undergraduate students to support their research or creative work with a UMBC faculty mentor on an original project. UMBC students of all years and disciplines are invited to apply.

UMBC Review (Journal of Undergraduate Research) Published annually, this peer-reviewed journal features academic papers written by UMBC undergraduates.

Travel Awards Students who are invited to present their work at a conference or festival may apply for funds to support their travel.

Research Opportunities Assistance with finding and applying to summer REUs and other research opportunities.

Prestigious Scholarships Support for students applying for prestigious scholarships such as Rhodes, Gates-Cambridge, Marshall, Truman, Goldwater, and others.

For more information, contact Dr. April Householder at aprilh@umbc.edu or visit:

ur.umbc.edu