Anna Fang is a Ph.D. student in the Human-Computer Interaction Institute (HCII), advised by Haiyi Zhu.  Her research is in social computing and focuses on the networks and mechanisms of online communities. Anna is interested in how the network algorithms and structures of online communities can enable both personal well-being (e.g., mental wellness) and community well-being (e.g., productive discourse). Her work is mostly in network science, social science and applied artificial intelligence.

Fellowship Research: "Measuring the Impact of Online Mental Health Communities on Mental Health Outcomes"

Venkat Sivaraman is a Ph.D. student in the HCII, advised by Assistant Professor Adam Perer. His research focuses on human-centered AI for high-stakes decision making, particularly in the domains of social work and health care. He seeks to understand how AI decision support systems work in practice, and to design new algorithms and interfaces to improve human-AI decisions. Venkat received his undergraduate degree in computational biology from MIT.

Fellowship Research: "Designing Dynamic Human-Centered AI Decision Support for ICU Sepsis Treatment"

Nathan Roblin is a Ph.D. candidate in the Materials Science and Engineering Department, advised by Professor Rosalyn Abbott. His research focuses on tailoring the degradation kinetics of silk biomaterial scaffolds to patient-specific regenerative environments for tissue engineering applications, and developing a computational model to predict degradation kinetics from digital health data and patient inputs. He received a bachelor's degree in materials science and engineering with an additional major in biomedical engineering from CMU's College of Engineering.

Fellowship Research: "Improving Regenerative Outcomes by the Development of a Computational Model that Tailors the Degradation Profile of Silk Fibroin Scaffolds to Individual Patients"

Yunzhi Li is an HCII Ph.D. student advised by Professor Patrick Carrington. His research focuses on developing novel assistive technologies and inclusive health tracking systems for people with disabilities by combining sensing and machine learning techniques. Prior to joining the HCII, Yunzhi earned his bachelor’s degree in computer science from the University of Chinese Academy of Sciences and a master’s degree in computer science from Georgia Tech.

Fellowship Research: "Wearable-Based Upper Extremity Motion and Health Tracking for Wheelchair Users"

Soyong Shin is a Ph.D. student in the Mechanical Engineering Department, advised by Assistant Professor Eni Halilaj. Currently, Soyong’s research focuses on adapting machine learning to human biomechanics to capture 3D body kinematics from videos, IMUs or both, enabling fast and easy-to-use motion tracking at clinics and patients’ homes. Previously, Soyong completed a bachelor’s degree in mechanical engineering at Seoul National University and a master’s in mechanical engineering at CMU. 

Fellowship Research: "Easy and Fast 3D Human Motion Tracking in Clinics"

Shane Elder is a Ph.D. student in the Computational Biology Department, advised by Professor Carl Kingsford. He earned his bachelor's degree in computer science and mathematics from Rhodes College. His research focuses on developing and applying machine learning methods with applications to drug target identification using the relatively new area of actionable recourse. 

Fellowship Research: "Target Identification Using Actionable Recourse"

Jingyi Wu is a Ph.D. candidate in the Biomedical Engineering Department, advised by Professor Jana Kainerstorfer. His research focuses on improving maternal and fetal health during childbirth. Currently, he is developing algorithms for transabdominal fetal pulse oximetry — a device that uses light to measure fetal arterial oxygen saturation noninvasively through the maternal abdomen. He received his master's in biomedical engineering and a bachelor's degree in physics from CMU.

Fellowship Research: "Calibration Free Transabdominal Fetal Pulse Oximetry"

Morgan Evans is a Ph.D. student in the Software and Societal Systems Department co-advised by professors Jessica Hammer and Geoff Kaufman. Her research focuses on the design and evaluation of theoretically driven, transformational game design. Prior to joining CMU, Morgan earned a bachelor’s degree in computer science from Bard College.

Fellowship Research: "Designing and Evaluating a Digital Game Intervention To Promote Health Self-Efficacy and Empowerment in Historically Marginalized Communities"

Mohsen Ferdosi is a Ph.D. candidate in the Computational Biology Department advised by Hosein Mohimani, and a secondary master’s student in the Machine Learning Department. His research focuses on designing efficient algorithms and machine learning models for inference in large datasets. He is developing computational techniques for annotating and discovering bioactive small molecules that can be used for drug development. Before joining CMU, he earned a bachelor’s degree in computer engineering from Sharif University of Technology. 

Fellowship Research: "Estimating Statistical Significance of Metabolomics Annotations Using Markov Chain Monte Carlo"

Shahriar Noroozizadeh is a Ph.D. student in the Information Systems and Management program, advised by George Chen and Jeremy Weiss. His research aims to build trustworthy machine learning models that go beyond prediction and can guide decision making. His current research focuses on interpretable representation learning for temporal data in healthcare. He received master’s degrees in both machine learning and biomedical engineering from CMU. Prior to joining CMU, Shahriar earned a bachelor’s in engineering physics with an EECS specialization from the University of British Columbia.

Fellowship Research: "Interpretable Hospital Discharge Delay Prediction and Data-Driven Decision Support System Development for Patient Transport Scheduling"