Anzalee Khan, Ph.D.

Anzalee Khan, Ph.D.

Associate Director (Data Sciences)
Manhattan Schizophrenia Research Program (MSRP)

Dr. Anzalee Khan has a broad background in statistical modeling, with specific expertise in data science, statistics, psychometrics, Bayesian and item response theory applications, and digital technologies in mental health.

Dr. Khan’s research focuses on the use of novel statistical models to study treatment effects in schizophrenia and other psychotic disorders, and applications of multimodal approaches to understanding the relationships between patient functional recovery trajectories and neurobiological and clinical assessment scores. Dr. Khan’s research receives funding from various sources including the National Institute of Mental Health.

Dr. Khan has received several research awards, including the American Society for Clinical Psychopharmacology’s New Investigator Award, the Schizophrenia International Research Society’s Young Investigator Award, the NY Chapter of Portuguese and Spanish Association’s Excellence Award, and the Early Career Scientist Award in psychometrics.

Current Research Focus

Dr. Khan is currently conducting several projects in data science, digital therapeutics, and psychopharmacology, including applications of item response analysis models to clinical assessments for characterizing test items and estimating human ability from assessment performance.

Characterizing test items can be identified by fitting a joint model of human ability and item characteristics to response patterns for test items. Test items that do not fit the model are removed and the remaining items can be utilized as a scale to evaluate performance. The goal of this work is to improve assessment of treatment effects by identifying an appropriate set of items that measure ability and symptom severity.

Another project being conducted by Dr. Khan uses a virtual reality intervention to identify integrative digital and psychopharmacological treatments that improve aggressive behaviors in schizophrenia. This project also aims to reveal the underlying mechanisms mediating brain changes induced by virtual reality treatment interventions for aggression. The impact of virtual reality has been felt in a wide range of fields over the past years and has been shown to be an effective treatment for many indications. The emerging application of virtual reality in conjunction with functional magnetic resonance imaging (fMRI) is helping to improve upon current virtual reality systems and may aid in creating more effective treatments for patients. The goal of this study is to show that by using virtual reality for aggression, in combination with assessing brain activation patterns using fMRI can lead to a new emergence of data about previously elusive functions of the brain and the efficacy of virtual reality treatment.

In collaboration with Modality AI, Dr. Khan is currently working on using natural language processing (NLP) and machine learning techniques to understand negative symptoms underlying schizophrenia. Objective digital phenotyping assessments that address some of the intrinsic limitations of clinical rating scales can allow for more efficient clinical assessment of negative symptoms in clinical trials. The goal of the study is to evaluate the psychometric properties of a digital phenotyping method using vocal and facial metrics captured through a digital system.

Another of Dr. Khan’s projects uses multimodal approaches to determine the relationship between unique psychopathological symptom profiles and later functional recovery outcomes. This study utilizes data from the Positive and Negative Syndrome Scale (PANSS) and other clinical and global assessments, with the goal of identifying and better characterizing individual longitudinal patterns of functional recovery, and exploring the potential risk associated with functional outcome.

The Manhattan Schizophrenia Research Program (MSRP) led by Dr. Khan uses a computerized cognitive training program (CRT) to improve certain cognitive functions (such as memory and attention) and psychosocial outcomes in patients with schizophrenia. In recent years, CRT programs based on learning-induced neuroplasticity have shown improvement in cognitive functioning, indicating that CRT induces neurobiological remodeling applicable to a selection of cognitive functions.

The Manhattan Schizophrenia Research Program participates in international scientific collaborations, including various psychopharmacology research trials for new antipsychotic compounds with Dr. Jean Pierre Lindenmayer. 


  • Data Science

  • Statistical Analysis

  • Central Nervous System (CNS) disorders

Education & Training

  • PhD, Statistics (focus on Psychometrics), May 2010

Fordham University, New York, NY

  • MS, Psychometric and Quantitative Psychology, May 2007

Fordham University, New York, NY

  • MA, Psychology and Statistics, May 2002

University at Albany, New York, NY

  • BA, Experimental Psychology, December 1999

Stony Brook University, New York, NY

Select Publications

  • Khan A, Lindenmayer JP, Insel B, Seddo M, Demirli E, DeFazio K, Sullivan M, Hoptman MJ, Ahmed AO. Computerized cognitive and social cognition training in schizophrenia for impulsive aggression. Schizophr Res. 2022 Nov 21:S0920-9964(22)00418-2. PMID: 36424289.

  • Harvey PD, Khan A, Atkins A, Walker TM, Keefe RSE. Comprehensive review of the research employing the schizophrenia cognition rating scale (SCoRS). Schizophr Res. 2019 Aug;210:30-38. PMID: 31196736.

  • Harvey PD, Khan A, Atkins A, Keefe RS. Virtual reality assessment of functional capacity in people with Schizophrenia: Associations with reduced emotional experience and prediction of functional outcomes. Psychiatry Res. 2019 Jul;277:58-63. PMID: 30679049.

  • Harvey PD, Khan A, Keefe RSE. Using the Positive and Negative Syndrome Scale (PANSS) to Define Different Domains of Negative Symptoms: Prediction of Everyday Functioning by Impairments in Emotional Expression and Emotional Experience. Innov Clin Neurosci. 2017 Dec 1;14(11-12):18-22. PMID: 29410933.

  • Lindenmayer JP, Khan A, Lachman H, McGurk SR, Goldring A, Thanju A, Kaushik S. COMT genotype and response to cognitive remediation in schizophrenia. Schizophr Res. 2015 Oct;168(1-2):279-84. PMID: 26255563.

  • Khan A, Lindenmayer JP, Opler M, Kelly M, White L, Compton M, Gao Z, Harvey P. The evolution of illness phases in schizophrenia: a non-parametric item response analysis of the Positive and Negative Syndrome Scale. Schizophrenia Research: Cognition, 2014, 1 (2): 53-89.

  • Khan A, Yavorsky C, DiClemente G, Opler M, Liechti S, Rothman B, Jovic S. Reliability of the Alzheimer’s disease assessment scale (ADAS-Cog) in longitudinal studies. Curr Alzheimer Res. 2013 Nov;10(9):952-63. PMID: 24117118.

  • Khan A, Lindenmayer JP, Opler M, Yavorsky C, Rothman B, Lucic L. A new Integrated Negative Symptom structure of the Positive and Negative Syndrome Scale (PANSS) in schizophrenia using item response analysis. Schizophr Res. 2013 Oct;150(1):185-96. PMID: 23911252.

  • Khan A, Yavorsky WC, Liechti S, DiClemente G, Rothman B, Opler M, DeFries A, Jovic S. Assessing the sources of unreliability (rater, subject, time-point) in a failed clinical trial using items of the Positive and Negative Syndrome Scale (PANSS). J Clin Psychopharmacol. 2013 Feb;33(1):109-17. PMID: 23277234.

  • Khan A, Lewis C, Lindenmayer JP. Use of non-parametric item response theory to develop a shortened version of the Positive and Negative Syndrome Scale (PANSS). BMC Psychiatry. 2011 Nov 16;11:178. PMID: 22087503.