Post Doctoral Associates

Andre Eliatamby

Andre Eliatamby

Information
ae644@ruccs.rutgers.edu

André is a first language acquisition researcher interested in the acquisition of determiner phrase semantics and logical connectives, the early acquisition of functional categories, and theoretical issues concerning the semantics/pragmatics interface. He has a secondary interest in statistical methods, inductive inference, and the role of input in language acquisition. He received his Ph.D. in Linguistics from the CUNY Graduate Center in 2024.

Callan Howland

Callan Howland

Information
cal.howland@rutgers.edu

Callan (Cal for short) received their PhD in Philosophy from Rutgers University. They work at the interface of semantics, pragmatics and psycholinguistics, with a particular eye toward the role of perspectives in lexical meaning. Drawing on both experimental and theoretical methodology, their research revolves around questions like: How capable are hearers in accommodating stubborn speakers? How conversationally flexible are lexically encoded perspectives? Do different categories of lexical items (e.g. predicates of personal taste, relative motion verbs, socio-cultural relative terms) exercise different constraints on the perspectives they invoke? Topics they have worked on include relative locative terms, socio-cultural relatives, and epithets and thick terms.

Sten Knutsen

Sten Knutsen

Information
sten.knutsen@rutgers.edu

 Sten’s research focuses on spoken language, especially prosody and its role in comprehension and production. He is interested in both the acoustic realization of prosody and its functional role in communication, examining how patterns vary across gender, neurodivergent populations, and language backgrounds. His work draws on psychology, linguistics, and computer science to better understand the cognitive underpinnings of speech. Sten received his Ph.D. in psychology from Rutgers University in 2025.

Andre Oliver

Andre Oliver

Information
andre.oliver@rutgers.edu

Andre earned his PhD in Psychology from the Graduate Center—City University of New York (CUNY) in 2025. His research broadly focuses on social identity, person perception, and social inequality. In this work, he investigates how social identity shapes people's mental representations of faces from specific social groups and how those mental representations influence decisions and judgements about social inequality. He also studies how social identity-related cognitions impact intergroup interactions and expectations to develop interventions to improve those interactions. 

Elif Poyraz

Elif Poyraz

Information
elifnur@ruccs.rutgers.edu

 Elif received her Ph.D. in Cognitive Psychology from Rutgers University in 2025, along with a certificate in Cognitive Science. She earned her B.S. in Psychology from Middle East Technical University in Türkiye. Her research investigates how people understand and reason about others’ actions without direct access to their thoughts, with a particular focus on the developmental origins of this ability. She examines the factors that shape children’s performance on tasks requiring reasoning about mental states, as well as developmental changes in variability over time. At the Cognition and Learning Center, she studies how children’s inferences about others’ mental states extend to different domains, such as numerical quantities of resources.

Jocelyn Yuxing Wang

Information
yw678@ruccs.rutgers.edu

Jocelyn received her PhD in philosophy from MIT in 2024, prior to which she received her BA in philosophy from Yale. Her research interests are at the intersection of cognitive science, philosophy of mind, and epistemology. Her research broadly considers creative thought, and other closely related forms of unguided thoughts, such as mind wandering. Some of her research theorizes about the role of attention, memory and other underlying processes of creative thoughts, as well as how understanding them better brings out implications in epistemology. She also has broader research interests in providing philosophical interpretations of computational models in cognitive science.

Post Doctoral Affiliates

Theodoros Bermperidis

Information
tb642@psych.rutgers.edu

Theo is an Electrical Engineer with a strong mathematical training interested in advancing methods of machine learning and AI for smart health and applications to sports and the performing arts. He has completed his MSci in Psychology and developed several analytical models to automatically classify complex behaviors and distinguish different phenotypes. More recently, Theo has developed new methods of analyses to interrogate the transcriptome in humans and mice models.

Mona Elsayed

Information
mona.elsayed@rutgers.edu

Mona is a doctorate student under Dr. Torres supervision. Mona received her B.S. in Biology with a minor in Psychology at the College of New Jersey. She has completed her MSci in Psychology, and is currently finishing up her PhD in Psychology. In addition to her rigorous science, Mona is an excellent teacher. She has significantly contributed to the development of the Rutgers Autism Certification to be launched in the Fall of 2023 and offered through the Rutgers Continuing Education Program.

Amritpal Singh

Information
as5302@psych.rutgers.edu

Amrit received his PhD in Developmental Psychology at Cornell University in 2024. He completed his BA in a Great Books Program at St. John’s College. His research focuses on the contextual and developmental forces that shape how abstractly we think and how we reason about abstract entities. His dissertation work investigated differences in abstract thought across and within cultures, operationalizing abstract thought in different ways (i.e., event cognition and analogical reasoning). In the Quad Lab, he studies how context and time may shape the way we think about quantitative information, such as proportions and probabilities.

Samuel Sohn

Information
sss286@scarletmail.rutgers.edu

Sam received his PhD in Computer Science at Rutgers University in 2024. His doctoral studies centered around simulating and predicting human navigation in built environments at both the individual level and the crowd level with thousands of agents. This body of work carefully grounded its models in spatial cognition and leveraged machine learning techniques to eliminate a long-standing computational barrier to simulating at scale. His focus now is on investigating behavioral nuances that manifest in navigation and speech among individuals with diverse physical and neurocognitive abilities.