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I am Asma Ghandeharioun, currently a PhD candidate at Affective Computing group, MIT Media Lab. I am fortunate to have Roz as my advisor and a great source of inspiration. I have had research experiences at Google Research, Microsoft Research, and EPFL, many of which turned into long-term exciting collaborations.

RESEARCH SUMMARY

Towards Human-Centered Optimality Criteria

Building a system that is optimal from a human-centered point of view is challenging. There are many variables that influence the problem definition itself let alone its solution. In my research, I have approached this problem from three perspectives by aiming to build systems that: 1) better interpret humans; 2) are better interpreted by humans; 3) augment humans' capabilities.

PROJECTS

Generative Disentangled Interpretations via Concept Traversals

Generative Disentangled Interpretations via Concept Traversals

Active Project

Machine Learning; Computer Vision; Deep Learning;

Using little external supervision, we propose a method for algorithmic discovery of multiple concepts that are important in the decision making of a black-box classifier. We show that our proposed method, GenDict, generates Concet Traversals that (1) represent concepts influential to a black-box classifier’s decision outputs, (2) are composed of realistic samples when compared to actual samples, (3) and are distinct from each other. By jointly training a generative model from a classifier’s signal, GenDict offers a way towards understanding a classifier’s inherent notion of distinct concepts rather than relying on user-predefined concepts. We validate our approach using synthetic and real datasets and show that GenDict successfully discovers undesirable biases of a classifier in a simulated experiment.

Towards Empathy Learning, Socially-Aware Agents

Towards Empathy Learning, Socially-Aware Agents

Active Project

Machine Learning; Natural Language Processing; Reinforcement Learning; Deep Learning;

We introduce a novel, model-agnostic, and dataset-agnostic method to approximate interactive human evaluation in open-domain dialog. We develop an off-policy reinforcement learning (RL) scenario and show that solely relying on explicit human preferences is not as effective as training with implicit human rewards. We build a novel hierarchical RL model and demonstrate its effectiveness in reducing repetitiveness or toxicity.

Publications: NeurIPS'19, EMNLP'20 (to appear), AAAI'20, NeurIPS'19 Conv. AI workshop

Talks: NeurIPS'19 WiML workshop, NeurIPS'19 Conv. AI workshop

Awards: MIT Quest for Intelligence, MIT Stephen A. Schwarzman College of Computing, Machine Learning Across Disciplines Challenge

More: Deployment Website, Modeling code, Server code

Assessing depressive symptoms through physiological and behavioral data

Assessing Depressive Symptoms through Physiological and Behavioral Data

Active Project

Machine Learning; Affective Computing; Computational Psychiatry; Deep Learning;

In collaboration with Massachusetts General Hospital, we are conducting a longitudinal clinical trial exploring data-driven methods for assessing depression and its severity.

Publications: Froentiers in Psychiatry'20, ACII'17

Abstracts/Poster Presentations: ABCT'20, ABCT'18, ABCT'17, APS'17, ADAA'17, ADAA'17, CHC'17

Patents: US 2019/0117143 A1

Awards: NIH 1R01MH118274, J-Clinic, MGH-MIT Grand Challenge

More: Project Website, Video

Interpretability benefits of uncertainty quantification

Interpretability Benefits of Uncertainty Quantification

Machine Learning; Computer Vision; Deep Learning; Affective Computing;

We use a simple modification of a classical network inference using Monte Carlo dropout to give measures of uncertainty. We characterize sources of uncertainty to proxy calibration and disambiguate annotator and data bias.

Publications: ICCVW'19

Awards: MIT Stephen A. Schwarzman College of Computing, Machine Learning Across Disciplines Challenge

More: Code

Empathic Breath

Empathic Breath

Affective Computing; Human-Computer Interaction;

Motivated by the effectiveness of controlled breathing, this work studies the potential use of breathing interventions while driving to help manage stress.

Publications: UMAP'20

More: Project Website

EMMA

EMMA

Affective Computing; Positive Computing; Human-Computer Interaction;

EMMA (EMotion-Aware mHealth Agent) is a chatbot that conducts experience sampling in an empathetic manner and provides emotionally appropriate micro-activities.

Publications: ACII'19, ACII'19

Press: Wall Street Journal Dec. 2018

Breath-Based Music Therapy

Breath-Based Music Therapy

Affective Computing; Human-Computer Interaction;

We engineered an interactive music system that influences a user’s breathing rate to induce a relaxation response.

Publications: ACII'19

Analysis of Online Suicide Risk

Analysis of Online Suicide Risk

Machine Learning; Natural Language Processing; Affective Computing;

We developed and compared multiple methods to analyze suicide risk from online reddit posts.

Publications: ACIIW'19

Personalization of Photo Edits with Deep Generative Models

Personalization of Photo Edits with Deep Generative Models

Machine Learning; Deep Learning;

We develop a statistical hierarchical model using deep generative models to propose multiple diverse, high-quality photo edits while also learning from and adapting to a user's aesthetic preferences.

Publications: AISTATS'18

BrightBeat

BrightBeat

Affective Computing; Positive Computing; Human-Computer Interaction;

BrightBeat is a set of seamless visual, auditory, and tactile interventions that mimic a calming breathing oscillation, with the aim of influencing physiological syncing and consequently bringing a sense of focus and calmness.

Publications: CHI-EA'17, Master's thesis

Press: Wired Jan. 2019, New Scientist Jul. 2019

Other: Project Website

Kind and Grateful

Kind and Grateful

Positive Computing; Human-Computer Interaction;

We leverage pervasive technologies to infer optimal moments for stimulating contextually relevant thankfulness and appreciation in order to promote kindness and gratitude.

Publications: Psychology of Wellbeing'16

Abstracts/Poster Presentations: Annals of Behavioral Medicine'16

Hierarchical Infinite HMM

Hierarchical Infinite HMM

Machine Learning;

We develop a simple hierarchical infinite HMM (iHMM) model, an extension to (iHMM) with efficient inference scheme. The model can capture dynamics of a sequence in two timescales and does not suffer from the problems of other related models in terms of implementation and time complexity.

Publications: NIPS Workshop'15

SNAPSHOT

SNAPSHOT

Affective Computing; Machine Learning;

SNAPSHOT is a large-scale longitudinal study to measure: Sleep, Networks, Affect, Performance, Stress, and Health using Objective Techniques.

Publications: ACII'15

Abstracts/Poster Presentations: SLEEP'16

Awards: NIH 1R01GM105018

More: Project Website, Video

CONTACT

MIT Media Lab, 75 Amherst Street, E14-464K, Cambridge, MA 02139
Email: asma_gh [AT] mit [DOT] edu