Research projects and faculty mentors
Northeastern seeks postdoctoral candidates from fields beyond computer science to grow its cadre of scholars at the Experiential AI Institute. During a two-year postdoctoral appointment, EAI Fellows will take classes in AI and work with Northeastern’s Experiential AI faculty mentors. Each mentor has identified one or more important research problem for a talented postdoctoral fellow to solve while establishing a career in AI.
Successful applicants will spend two plus years with a team of mentors at the Boston and/or Portland, Maine campuses, and, at the conclusion, may be considered for faculty and staff research appointments at the Roux Institute in Portland, Maine or at one of Northeastern’s other eight campuses. All questions can be emailed to Professor Jennifer Dy, Director of the Experiential AI Postdoc Program. The projects and affiliated teams working in Experiential AI and seeking postdoctoral fellows as part of this program can be found below. Deadline for submissions is March 15, 2021.
Life Sciences
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AI-Enabled Large-Scale Quantum Chemical Simulations
Team:
Sijia Dong, Deniz Erdogmus
More about AI-Enabled Large-Scale Quantum Chemical Simulations
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AI for Image Diagnosis and Treatment Planning for Skin Cancer
Team:
Jennifer Dy, Dana Brooks, Octavia Camps, Kivanc Kose, Milind Rajadhyaksha
More about AI for Image Diagnosis and Treatment Planning for Skin Cancer
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Augmented AI for Pose Estimation and Behavior Monitoring in Infants
Team:
Sarah Ostadabbas, Bharath Modayur
More about Augmented AI for Pose Estimation and Behavior Monitoring in Infants
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Computational approaches to multiple solutions and degeneracy in functional neuroanatomy
Team:
Ajay Satpute, Jennifer Dy, Sarah Ostaddabas
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Exploring drug resistance through leveraging mathematical modeling and machine learning
Team:
Eduardo Sontag, Jennifer Dy
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Interpretable Learning Models for Understanding Lung Disease
Team:
Jennifer Dy, Dr. Peter Castaldi, M.D., Dr. Michael Cho, M.D., M.P.H., Dr. Edwin Silverman, M.D., Ph.D.
More about Interpretable Learning Models for Understanding Lung Disease
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Merging AI and Network Science, with Applications from Epidemics to Network Medicine
Team:
Alessandro Vespignani, Albert-László, Barabási, Tina Eliassi-Rad
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Multimodal Brain Data Modeling and Analysis
Team:
Deniz Erdogmus, Susan Whitfield-Gabrielli
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Targeted Neural Text Summarization of Electronic Medical Records to Improve Imaging Diagnosis
Team:
Byron Wallace, Geoffrey Young
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Using machine learning to predict adaptation to complex environments, with application to conservation genomics
Team:
Predrag Radivojac, Kathleen Lotterhos
Engineering
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AI algorithms for smooth human-robot collaboration for object handover
Team:
Taskin Padir, Eugene Tunik, Deniz Erdogmus
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AI for Climate
Team:
Auroop Ganguly, Jennifer Dy, Moira Zellner, Tarik Gouhier, Ravi Sundaram
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Combining reinforcement learning and control theory for complex systems
Team:
Christopher Amato, Eduardo Sontag
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Efficient and Adaptive Real-Time Learning for Next Generation Wireless Systems
Team:
Stratis Ioannidis, Yanzhi Wang, Jennifer Dy, Kaushik Chowdhury
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Learning from Multimodal Sensors: Harnessing ML for Transforming The Internet of Things
Team:
Kaushik Chowdhury, Stratis Ioannidis, Jennifer Dy
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Learning to Control Safety Critical Systems
Team:
Mario Sznaier, Jennifer Dy
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Machine Learning for System Control Theory
Team:
Deniz Erdogmus, Alireza Ramezani
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Modeling of transcranial magnetic stimulation effects on brain and peripheral neural activity
Team:
Deniz Erdogmus, Eugene Tunik
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Multimodal Deep Representation Learning for Visual Understanding
Team:
Y. Raymond Fu
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Optimization of Deep Learning Applications through Hardware and Software Acceleration
Team:
David Kaeli
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Photonic Metamaterials Designed by Deep Learning
Team:
Yi Zheng, Paul Hand
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Shapeshifting Neural Networks for Effective, Efficient and Secure Hardware-based Deep Learning
Team:
Francesco Restuccia, Octavia Camps
Social Sciences
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AI Toolkit for Media Research: Adapting NLP Methods for Humanities and Social Science Scholars
Team:
David Smith, Rahul Bhargava, Catherine D'Ignazio
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Designing AI Infrastructure for Effective Human-Agent Teams
Team:
Christoph Riedl, Brooke Foucault Welles
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Cybersecurity
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Adversarial Machine Learning on Complex (Cyber) Networks
Team:
Tina Eliassi-Rad
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Protecting Confidentiality and Integrity of Deep Neural Networks against Side-Channel and Fault Attacks
Team:
Xue (Shelley) Lin, Yunsi Fei, Thomas Wahl