Field of Study: 
Computer Science
University of Illinois, Chicago

I am a computer science Ph.D. student at the University of Illinois at Chicago, in the computational population biology lab. In my research, I develop data mining and machine learning methods for prediction in networks. I focus on interdisciplinary computational sciences, including ecology, biology, and atmospheric science. Science has always been a passion of mine; interdisciplinary research has allowed me to gain familiarity with several areas of science from a computational perspective, and I have produced work that would not be possible without close collaboration.

As a person living with cerebral palsy, I am active in advocacy and inclusion within computer science for women and other underrepresented groups. I am a 2014 Google Lime Scholar, and I volunteer with the Broadening Participation in Data Mining workshop. In my youth, I lacked the peer support I’ve now gained through these activities. At that time, I had lowered expectations for my career path and discomfort in my own body. I want to ensure that young students have the support and encouragement to succeed in research.

In a crowded professional field, I see my disability as a slight but valuable distinction and an opportunity to be a positive representation to colleagues who may not know others with cerebral palsy. By making connections and proactively sharing research ideas, I have gained research-oriented internships at Technicolor Research, Lawrence Livermore National Laboratory, and Microsoft. Furthermore, I have gained a strong intellectual connection to data mining and machine learning. I aim to contribute to these areas throughout my career.

I feel that interdisciplinary computational science dramatically extends the capability of scientific investigation. Combining deep scientific knowledge with statistics and computational methods allows scientists to develop novel hypotheses on data too large and complex to directly visualize and understand. In my research, I develop computational methods that extract repeatable and significant patterns in complex data, while scientists investigate why these patterns emerge.

I wish to see more top minds in data mining and machine learning dedicated to creating novel scientific and algorithmic understanding. Many of the grand open questions in science are now data-driven questions: how does consciousness emerge from biological structure? Does life exist elsewhere in the universe? Aside from these grand visions, new and more sophisticated data are opening up several scientific disciplines to new opportunities for interdisciplinary research. I am tremendously excited to enter such a vibrant and expanding landscape of data-driven science. I have big ideas about how data-driven science can change the world.

I recently attend the Web Conference in Lyon, France. Attending this conference allowed me to build new connections and collaborations during a crucial time when I transition to a career in industry research. The Web Conference is a unique and valuable experience for students of many different backgrounds. In recent years, it has become a top venue for research in large-scale machine learning. However, it has strong communities in accessibility, security and privacy, and human-computer interaction. They also co-host the Web For All Conference, which focuses on developing accessible features and websites with respect to disability, socioeconomics, and other barriers. Due to this co-located event, the Web Conference itself also has better disability accommodation than similar top-tier conferences, both onsite and providing relevant local information. Outside of the Tapia Conference, I feel the Web Conference is the most diverse and valuable to students with disabilities who are in the relevant areas of computing.

The conference also hosts many sponsors and industry partners recruiting students at all levels. I was able to make direct contact with recruiters and researchers from Amazon, Facebook, Yahoo! Research, and Walmart Labs. Due to this, the conference is a great opportunity for masters and Ph.D. students, as well as undergraduates considering graduate school. There are several applied research tracks that are suitable for more junior students, including the "Web and Society" track, which focuses on empirical studies of websites and social networks, and recent emerging problems such as bias in machine learning, online abuse, and journalism and misinformation.

I presented my research in the BigNet International Workshop on Learning Representations for Big Networks. The workshop was very well-attended, in part due to notable keynote talks by Jon Kleinberg and Jure Leskovec. The research in this workshop focused on how to extract higher‑order information from networks (e.g. using deep learning) for recommender systems, knowledge-bases, or biological networks. These alternative representations mitigate some complexity of networks to build better predictive models, but are still not well-understood.

During my time in Lyon, I was largely focused on my work for the conference, and meeting a few closer colleagues. When I travel, I tend to meet many different groups of colleagues, and take time with them to see the city. These have been very important experiences and connections later on. However, the focus was very fruitful. This yielded good presentation slides, and a small breakthrough on research.

I am looking forward to attending future Web Conference iterations and facilitating junior students to attend as I work with the community in industry.