K&L Gates Presidential Fellows Bios
Maria Alejandra Arciniegas Gomez
Hailing from Bogotá, Colombia, María Alejandra Arciniegas Gómez is a PhD candidate studying ethics and technology. Gomez earned her bachelors and master’s degree in philosophy from la Universidad Nacional de Colombia. She then completed an additional Masters in Logic, Computation and Methodology from Carnegie Mellon University. Currently, Gomez is a PhD candidate in Philosophy at Carnegie Mellon University, writing her dissertation on protecting privacy a shift from 'data control' to 'use control'.
Christina Boyce-Jacino
Christina Boyce-Jacino received her B.S. in psychology and economics from the University of St. Andrews. She began her graduate education at Rutgers University where she received an M.S. in cognitive psychology with a certificate in cognitive science before transferring to Carnegie Mellon. She is now a Ph.D. student in social and decision sciences where she is advised by Simon DeDeo and Gretchen Chapman.
Her work sits at the intersection of cognitive psychology, data science and computational cognition and aims to provide insights into the extraordinary complexity of social interactions. In her dissertation work, she uses question asking as a context in which to understand components of this complexity; the ease of asking and answering questions belies a rich process by which each party has successfully inferred each other’s hidden knowledge and intentions. A combination of laboratory studies, data science investigations and machine learning methods allows her to explore the deeply intertwined process of asking and answering questions: how questions relate to answers, the features of difficult questions and the semantics of good answers.
Lingwei Cheng
Lingwei Cheng is a PhD student in Public Policy and Management at Carnegie Mellon University. Lingwei received a B.A. in Political Science and International Business from Dickinson College, and an M.S. in Computational Analysis and Public Policy from the University of Chicago. She worked as a research analyst at Inclusive Economy Lab, where she built predictive models and evaluated programs in homelessness, workforce development and post-secondary education. Lingweiʼ’s research interests include understanding the social and economic impact of algorithms, improving human-AI collaboration, and fairness, accountability, and transparency in machine learning (FATML).
Amanda Coston
Amanda Coston is a PhD student in Machine Learning and Public Policy at Carnegie Mellon University. Her research investigates how to make algorithmic decision-making more reliable and more equitable using causal inference and machine learning. Prior to her PhD, she worked at Microsoft, the consultancy Teneo, and the Nairobi-based startup HiviSasa. She earned a B.S.E from Princeton in computer science with a certificate in public policy. Amanda is a Meta Research PhD Fellow, K & L Gates Presidential Fellow in Ethics and Computational Technologies, and NSF GRFP Fellow, and has received several Rising Star honors.
Sanghamitra Dutta
Sanghamitra Dutta received a bachelor's degree in electronics and electrical communication engineering from the Indian Institute of Technology Kharagpur. She is currently a Ph.D. student in the Department of Electrical and Computer Engineering at Carnegie Mellon University.
The focus of her thesis is to responsibly address the emerging trust issues in machine learning concerning fairness, privacy and reliability. In one of her works, she has proposed a rigorous quantification of bias in machine learning with respect to protected attributes, such as gender, race, etc. that selectively quantifies the part of the bias that cannot be explained by critical features. This quantification is important because it enables us to check if the bias arose purely due to business necessities (e.g., merit and seniority in deciding salary or educational qualification in a job) or not, drawing inspiration from the business necessity defense of disparate impact law. Her quantification relies on deep ideas in information theory from the literature on “Partial Information Decomposition.” A related project examines fundamental limits on the so-called trade-off between fairness and accuracy with respect to a given dataset and examines ways of alleviating this trade-off.
Her work bridges the fields of information theory, causality, reliability and machine learning. In her prior work, she has also examined problems in reliable computing, proposing novel algorithmic solutions for large-scale machine-learning in the presence of faults and failures, using tools from information and coding theory (an emerging area called “coded computing”). Her results on coded computing address problems that have been open for several decades and have received substantial attention from across communities.
Anna Kawakami
Anna Kawakami is a second-year PhD student in the Human-Computer Interaction Institute at Carnegie Mellon University, where she is advised by Ken Holstein and Haiyi Zhu. She researches the design, evaluation, and governance of AI-based decision-making algorithms in complex, socio-organizational contexts like social services. She leverages empirical qualitative, design, and quantitative methods, to both critically examine and design improved forms of human-AI complementary systems. Ultimately, she hopes to generate knowledge and solutions that can easily be transferred to support relevant community members, policymakers, developers, and researchers.
Sara Mahdizadeh Shahri
Sara Mahdizadeh Shahri is a Ph.D. student in the Electrical and Computer Engineering department at Carnegie Mellon University. She is advised by Prof. Akshitha Sriraman. Shahri’s research bridges computer architecture and software systems, demonstrating the importance of that bridge in enabling efficient and equitable large-scale web systems via solutions that span the compute stack. Shahri’s research has been recognized with the 2023 K&L Gates Presidential Fellowship, the 2023 CMU College of Engineering Presidential Fellowship, the 2023 Boeing Scholarship, and the 2021 Rackham Merit Ph.D. Fellowship. Before starting her Ph.D., Shahri completed her MS in Computer Science and Engineering at Penn State University.
Maggie Oates
Maggie Oates is a Ph.D. student in societal computing at Carnegie Mellon University's School of Computer Science. She received her B.S. in computer science from Indiana University, picking up minors in math, linguistics and international studies.
Her research focuses on understanding public opinion of emerging technology and privacy through participatory and arts-based methods. How can we study human attitudes about behavior with technology that has not reached a widespread public (Deep Fakes), or does not even exist yet (convincing conversational agents)? Immersive theater and other arts-based methods address these challenges by giving participants a fictional environment to inhabit and interact with where participants want to be part of a story, suspending their disbelief, to explore the yet impossible. Through qualitative and quantitative methods, storytelling and case examination her work covers the possibilities of tech research through the arts, and also examines the challenges and pitfalls of digital data in the art world.
Joseph Seering
Joseph Seering is a fourth-year Ph.D. student in the Human-Computer Interaction Institute at Carnegie Mellon University. His work builds from social identity theory and role theories, using a variety of methods to analyze social dynamics of online communities and design interventions. He focuses primarily on (1) improving moderation tools and strategies, (2) developing methods to increase democratic engagement and (3) exploring the space for integrating conversational agents into communities. He has received awards for his work at the ACM CHI, CSCW, and CHI PLAY conferences. Joseph has an M.S in human-computer interaction from Carnegie Mellon University and an A.B. in social studies from Harvard University. His work is funded primarily by the K&L Gates Presidential Fellowship for Ethics and Computational Technologies.
Qinlan Shen
Qinlan Shen is a Research Scientist in the Machine Learning Research Group within Oracle Labs. She received her B.S.E. in Computer Science from Princeton University and a Ph.D. in Language and Information Technologies from Carnegie Mellon University. Her research interests span a variety of areas within natural language processing, including multilingual modeling, computational social science, and bias/fairness issues in machine learning models. Her Ph.D. thesis, advised by Carolyn P. Rose, examined different strategies for measuring the social impact of interventions for mitigating abusive language in online discussion platforms. She is the recipient of a CMU Presidential Fellowship, a K&L Gates Presidential Fellowship for Ethics and Computational Technologies, and an NSF Graduate Research Fellowship.
Zeyu Tang
Zeyu Tang is a PhD student in Department of Philosophy at Carnegie Mellon University. He conducts research on algorithmic fairness (ML Fairness) to model and understand the social impact of AI, and also causal analysis related to machine learning (Causal ML/AI) to further enhance the capacity of intelligent systems. He pursues the safe and principled development of machine intelligence with the help of causal learning and reasoning, so that technologies can improve our lives in a responsible and effective way.
Helen Shuxuan Zeng
Helen Zeng is a PhD student in Information Systems & Management at Heinz College, Carnegie Mellon University (CMU). Her research lies in the intersection of Economics, Information Systems and public policy. Specifically, her work exams the harmful content on the Internet, and with the downsides brought by digital transformation how governance should adapt to that. She is advised by Prof. Michael D. Smith.
Helen is a member of Global Association of Human Trafficking Scholars (GAHTS), K & L Gates Presidential Fellow in Ethics and Computational Technologies. Her research won the 2022 George Duncan Award for Excellence in Doctoral Studies for the Second Research Paper and 2020 Suresh Konda Best First Student Research Paper Award from the Heinz College. Prior to PhD, she received a Bachelors of Science in Statistics & Math from the University of Hong Kong, and a Master of Arts in Quantitative Methods in Social Sciences from Columbia University.