DEVDEEPTA BOSE, Postdoc
Devdeepta Bose is a Postdoctoral Scholar in Economics and Psychology in the Camerer Team. He holds a PhD in Economics from the University of Arizona, an MS in Applied Economics from the University of Michigan, and a B.Eng in Electrical Engineering from Nanyang Technological University, Singapore. Devdeepta uses a combination of eye-tracking, machine learning, laboratory and field experiments to study how people make sub-optimal choices in different economic decision-making environments, ranging from consumer purchases in the supermarket to asset purchases in financial markets. He is interested in how social preferences inform equilibrium selection in infinitely repeated games, and how adaptive design can be used to infer social preferences in representative populations.
TANAZ MOLAPOUR, Postdoc
Tanaz Molapour is a Postdoctoral Scholar in Computational Affective Science in Colin Camerer and Dean Mobbs’s labs. She holds a PhD in Clinical Neuroscience from Karolinska Institutet in Stockholm. Tanaz has a background in neurocognitive mechanisms of emotional learning in social situations. She uses a combination of brain imaging, psychophysiological and behavioral measures to investigate how people perceive threats in social interactions and how it influences behavior. Currently she is interested in understanding how social networks are formed, and how behaviors (e.g., punishments) are spread within the network.
SARAH M TASHJIAN, Postdoc
Sarah M. Tashjian is a Postdoctoral Scholar in Affective Neuroscience, working both in the Camerer Lab and in Dean Mobbs Lab. She holds a PhD in Psychology from UCLA. Sarah’s research aims to understand the neural and behavioral dynamics of human social and emotional experiences, particularly in the domain of threat.
She focuses on developmental periods from preadolescence through early adulthood. Her work is multimodal including novel experimental paradigms, neuroimaging, skin conductance, and inflammatory markers.
XIAOMIN LI, Postdoc
Xiaomin Li holds a M.A. in Economics from NYU, and a B.S. in Finance from Shanghai University of Finance and Economics. Currently, she is trying to understand strategic decision making through various methods: behavioral data, eye-tracking, or brain recordings. She is also interested in single choice decision making broadly related to economics and is open to any relevant future projects.
ANASTASIA BUYALSKAYA, PhD Student
Anastasia Buyalskaya is a fourth-year PhD student in the Social and Decision Neuroscience program at Caltech and a Chen Graduate Fellow. She holds an MSc in Economics and Stategy for Business from Imperail College London, and a BA in Economics and Interdisciplinary Studies from Hunter College in New York City. Anastasia studies the neural and physiological mechanisms which underpin economics decision making. Her current research includes an empirical analysis of habit formation and an experimental analysis of optimal mechanisms to reduce cheating behavior.
MARCOS GALLO, PhD Student
Marcos Gallo is a third-year Ph.D. student in the Social and Decision Neuroscience program and a Chen Graduate Fellow. He has a background in Economics, including an MSc from Shanghai Jiao Tong University. Marcos’s motivation to research the neuroscience of poverty and discrimination directly stems from his experiences growing up in a disadvantaged neighborhood in Brazil. When he started his studying at Brigham Young University, he became determined to dedicate his career to helping inform and create effective policies to lift those in poverty. Marcos is fluent in Portuguese, Spanish, English, and Mandarin Chinese. When he is not working in the lab, he sings in a gospel choir, experiments with cooking, and weightlifts.
Tony Kukavica is a Rose Hills Foundation SURF Fellow. He is a rising third-year undergraduate double-majoring in mathematics and economics at Caltech. He is also the concertmaster of the Caltech Symphony, a first violinist in the American Youth Symphony, President of the Caltech Chess Club, and a National Chess Master. His SURF project concerns the use of reinforcement learning algorithms to investigate whether habit-based “model-free” actions can be statistically predicted by a carefully selected range of state variables.
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