Henry Rutgers Term Chair in Data Science & Associate Professor of Mathematics and Computer Science, Rutgers University - Newark
I am Henry Rutgers Term Chair in Data Science and Associate Professor in the Department of Mathematics and Computer Science at Rutgers - Newark. I am also affiliated with the Institute for Data Science, Learning and Applications (I-DSLA) and have appointments in Psychology, Rutgers Business School, and the Center for Molecular and Behavioral Neuroscience (CMBN) at Rutgers.
See my Curriculum Vitae.
- DARPA XAI: (2017-2021)
- NSF Science of Learning collaborative: (2016-2018)
- NSF EAGER MAKER (CISE): (2016-2018)
- NSF Cyber-human systems (CISE): Perception and Augmented reality (2015-2018)
- NSF INSPIRE (CISE, SBE): Human algorithm interaction (2015-2018)
- NSF REESE, CAREER award: Investigating the implications of social reasoning for learning from teaching (2012-2017)
- DARPA XData: Developing tools to facilitate analysis of very large data sets (2012-2015)
Open source software projects
I am Post-Doctoral Associate in the Department of Mathematics and Computer Science at the University of Rutgers - Newark. I received my B.A. from Hanover College where I studied Art and Psychology and I received my PhD from the University of Louisville in Experimental Psychology working with Edward A. Essock.
My research focuses on understanding early visual processes in the human brain. My work looks at how the visual processing of the environment is biased by the environment one is experiencing. Recently, we have determined that adapting to different visual environments affects the viewer’s prior expectations about environmental structure. Using augmented reality, I study how the visual system learns about the statistics of recently viewed scenes.
I am a postdoctoral associate in the department of Mathematics and Computer Science at Rutgers University - Newark. I earned a B.A. psychology from Concordia University in Montreal and continued on to complete my Ph.D. investigating human sensitivity to natural scene statistics with Aaron Johnson and Bruce Hansen. I then spent 18 months in the UK as a postdoc in the Department of Psychology at the University of York working on a project investigating the neural mechanisms of binocular vision.
My research focuses on the study of early visual mechanisms. Specifically, I am interested in defining the association between the characteristics of early visual properties to the statistical properties of the natural environment. I rely a combination of psychophysical, neuroimaging, neurostimulation, and computational modelling methods in my research. In the CoDaS lab, I am part of the virtual reality project, which aims to modify human perception by altering the statistical properties of the environment.
I am a Post-Doctoral Associate in the Departments of Psychology, Mathematics and Computer Science at Rutgers University - Newark. I earned a B.S. in psychological sciences and world religious studies from Loyola University New Orleans before completing my Ph.D. at Vanderbilt University in psychology with a concentration in cognitive neuroscience and certification in quantitative methods.
I use behavioral, psychophysical, and compuational methods to study the factors that guide attention and memory in everyday events. I’m engaged in two major projects with the CODAS lab. First, I’m developing the Mobile Maker Center for the large-scale analysis of children’s exploratory play. Second, I’m adapting augmented reality technology to train users’ visual systems to better detect specific features in the environment.
I am a postdoctoral associate in the Department of Mathematics and Computer Science at the University of Rutgers - Newark. I received my PhD in biophysics from Simon Fraser University where I worked on developing probabilistic models of DNA replication. I then spent three years at University of Cambridge as a post-doc, investigating active sensing in human eye movement.
My current research focuses on formulating active learning and teaching with the view to understand the conditions that make one method more effective than the other. I hope to extend the formalism to model learning scenarios in the real world and be able to eventually understand what the best ways to learn and teach are.
Wai Keen Vong
Starting in December, I will be a Post-Doctoral Associate in the Department of Mathematics and Computer Science at the University of Rutgers - Newark. I am currently completing my PhD in Psychology at the University of Adelaide with Amy Perfors, Daniel Navarro and Drew Hendrickson studying the influence of category structure and labels in category learning, using a mixture of behavioural experiments and computational modeling.
My current research interests are in the learning and teaching of concepts and categories. From a cognitive science perspective I’m interested in the kinds of conceptual representations necessary that allows people to learn an infinite variety of concepts, structures and theories. I’m also interested in how teaching can help learning different kinds of knowledge, figuring out when and how teaching is effective and the implications of this for pedagogy and education. Finally, I’m interested the technical aspects of many of these problems, such as scaling inference algorithms to work on more interesting problems in teaching and the use of probabilistic programming languages for cognitive modeling.
I am a postdoctoral associate in the Psychology Department at the University of Rutgers - Newark. I received my PhD in developmental psychology from Cornell University under Tamar Kushnir. I was originally from Shanghai, and did my undergraduate studies in Beijing University.
My research focuses on understanding children’s learning in a social environment. Specifically, I am interested in 1) how contextual factors and individual differences influence children’s imitation behavior, and how that affect what children have learned; 2) how social and pragmatic cues shape children’s causal inferences; and 3) whether children’s experiences and expectations of social interactions differ between formal and informal settings (such as in a lab vs. in a science museum), and its implications for developmental research.
Arash is a PhD student in pure mathematics. He got his B.Sc. and M.Sc. in computer science from Isfahan University and Isfahan University of Technology respectively. His research interests include mathematical analysis and machine learning.
Sophia Ray Searcy
Sophie is a graduate student working toward a PhD in the Department of Psychological and Brain Sciences at the University of Louisville. After pursuing an interest in robotics and machine learning, Sophie received a BS and MEng in Electrical and Computer Engineering from U of L. Her recent work includes efficient models of teaching and communication, expressivity in models of concept learning, and pseudo-rational factors that affect causal inference..
Anderson is an undergraduate student at Rutgers University-Newark. He is earning his B.S. in Computer Science with a minor in Mathematics. His research interests are machine learning and computer vision.
Jake Alden Whritner
Jake is a Laboratory/Tech Assistant in the Department of Mathematics and Computer Science at the University of Rutgers - Newark. He earned his B.A. in Cinema Studies from New York University and his M.A. in Film from the University of Kent. His MA Dissertation—supervised by Prof. Murray Smith—investigates ways of theorizing film engagement other than character-centric approaches, and explores autistic film viewers’ experiences as a case study. For the past two years, Jake has also collaborated with Dr. Pascal Wallisch at NYU—exploring the cognitive neuroscience of film appraisal and categorization. At the CoDaS Lab, Jake primarily works on a visual perception project that uses augmented reality to study and train the visual system.
Moving forward, Jake will be joining the Center for Perceptual Systems at the University of Texas at Austin as a Ph.D student in the Perception, Brain and Behavior program. There, he will continue to investigate visual perception and natural scene statistics.
Ravi is a Research Assistant in the Department of Mathematics and Computer Science at Rutgers University-Newark, where he earned his B.A. in Biology and Philosophy. Advised by Dr. Mark Gluck, his undergraduate honors thesis examined age differences in learning from probabilistic feedback. After graduating, he was brought on to study the effects of age, exercise, and genetics on learning and memory. During that time, he was taken by computational approaches and models as well as community outreach work, both of which motivate his research interests in machine learning.
At the CoDaS Lab, Ravi is formalizing limitations of human-algorithm interactions in modern recommender systems and building models to address these challenges as they occur in real time.
I am a developmental psychologist interested in the mechanisms underlying social learning. Asheley has joined the faculty at Texas Tech.
I am now a Data Scientist at Monsanto. I was postdoctoral associate in the Department of Mathematics and Computer Science at the University of Rutgers - Newark. Before that I was a research engineer with the MIT Probabilistic computing project. And Before that I was an experimental psychology graduate student at the University of Louisville.
Broadly speaking, my research focuses on learning. On the psychological side, I seek to reverse-engineer human social learning by defining it in the language of mathematics—the ultimate goal being to endow computers with the ability to communicate concepts to humans in a natural, pedagogical, human way. On the data science side, I develop tools that improve subject-area experts’ ability to answer the questions important to them without them having to mangle their data to fit into off-the-shelf software and without them having to spend time becoming machine learning and programming experts.
I am currently a Research Associate at the Peabody Research Institute at Vanderbuilt. I was a postdoc in the CoDaS Lab. I received my PhD in Developmental Psychology from Vanderbilt University under Bethany Rittle-Johnson. I then spent an additional year working as a postdoc in mathematics education with Jon Star.
My research focuses on how ideas from cognitive and developmental psychology, such as using incorrect examples and comparing the reliability of informants, can be applied in educational settings to improve learning. I use quantitative and qualitative analyses to investigate how children’s knowledge changes over time, and I conduct this research in tutoring and classroom settings.