HPI∙MS | Hasso Plattner Institute for Digital Health at Mount Sinai

Open Faculty Recruitment

HPI･MS invites exceptional scholars with expertise in machine learning, artificial intelligence, and computer science to contribute to applications in human health at the Mount Sinai Health System, New York city’s #1 ranked hospital.  Seeking early and mid-career professorship candidates to fill at least four independent faculty positions at this time.

Learn More

Shaping the future of digital health

We are a global research institute rapidly developing digital health solutions that empower patients and healthcare providers.

A collaboration between

About Us

Turning the promise of digital health into a reality

Science has helped humanity to improve and extend life. Today, technological advances have allowed us to understand and analyze information in ways never before possible.

The Hasso Plattner Institute for Digital Health at Mount Sinai (HPI･MS) propels these possibilities through an extraordinary international academic collaboration between the Hasso Plattner Institute for Digital Engineering in Potsdam, Germany, and the Mount Sinai Health System in New York City, USA.

Learn more about us

FACULTY RECRUITMENT

Seeking early and mid-career professorship candidates with expertise in machine learning, AI, and computer science

More info

To build a world-class research program for AI- and data-driven innovation in human health, the Hasso Plattner Institute for Digital Health at Mount Sinai (HPI.MS) is seeking outstanding candidates for early- and mid-career positions with proven scholarship in computer science, artificial intelligence, and machine learning to join the Icahn School of Medicine at Mount Sinai faculty.

With the innovative platform programs Artificial Intelligence-Ready Mount Sinai (AIR.MS) and Digital Discovery Platform (DDP), HPI.MS offers candidates seamless and secure access to vast amounts of multimodal health data from New York City’s #1 Ranked Hospital.

Why HPI•MS

A new era of digital health

Combining an innovative health care system with world class engineering expertise to offer unprecedented opportunities for improved global well-being and medical discovery.

Expanding possibilities in medicine

Advancements in data science and engineering are rapidly translated into medical discovery in partnership with clinical experts.

Improving access to critical data

Data science powered by 7.6 million clinical records is connected to diverse genomic sequencing, comprehensive medical imaging, and biometric cardiology data, alongside innovative clinical research.

Optimizing human health and well-being

By linking comprehensive patient data from hospital visits, genetic testing, and remote monitoring devices, patients and their physicians will be able to monitor, diagnose and inform personalized treatment plans.

Advancing the state of the art

Leveraging the latest technologies, creating novel methodologies, and bringing together interdisciplinary expertise, HPI⋅MS is leading the digital health revolution.

Research

Leading the digital health revolution

AIR.MS - AI Ready Mount Sinai

Leveraging expertise in computer science and engineering, we've built a platform where researchers can access the vast datasets held at Mount Sinai for data science and machine learning endeavors.

View full project

Digital Discovery Program

The DDP comprehensive digital health research program of patient-centric health studies using wearable, mobile and sensor technologies serves as a scalable platform for digital trials in order to better understand complex diseases and optimize health and wellbeing.

View full project

Explore all research initiatives

Publications

World class research with a global impact

March, 2021

Genome-Wide Polygenic Risk Score for Retinopathy of Type 2 Diabetes

Journal

Human Molecular Genetics

HPI·MS Authors

.author__profile-picture.gbi-221404342-k51FxKsT6mGFqasuyxdiMG:before { opacity: 1; background-image: url('/static/06e843d62425f8b6e8fd6107468dc5a3/e9c03/profile-picture-placeholder.png'); }

Kumardeep Chaudhary

.author__profile-picture.gbi-221404342-nnVMmgGAdZdJ9aPE74okG4:before { opacity: 1; background-image: url('/static/06e843d62425f8b6e8fd6107468dc5a3/e9c03/profile-picture-placeholder.png'); }

Ishan Paranjpe

.author__profile-picture.gbi-221404342-iEhpAgWZtXhWxjTN12eyDG:before { opacity: 1; background-image: url('/static/06e843d62425f8b6e8fd6107468dc5a3/e9c03/profile-picture-placeholder.png'); }

Lili Chan

.author__profile-picture.gbi--748886666-rDY3SEjZ6qAexvxtTryPnK:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/2TVnDzADjRqgSryXlvWYqT/558c5c5d8e3c7b5139417bf21129df69/Girish-Nadkarni.png?w=24&q=100'); }

Girish Nadkarni

4 HPI•MS authors

Full list of authors

Iain S Forrest , Kumardeep Chaudhary, Ishan Paranjpe , Ha My T Vy, Carla Marquez-Luna, Ghislain Rocheleau, Aparna Saha, Lili Chan, Tielman Van Vleck, Ruth J F Loos, Judy Cho, Louis R Pasquale, Girish N Nadkarni, Ron Do

Journal

Human Molecular Genetics

Open Publication

March, 2021

Relational Learning Improves Prediction of Mortality in COVID-19 in the Intensive Care Unit

Journal

IEEE Transactions on Big Data

HPI·MS Authors

.author__profile-picture.gbi--1231477022-2Sw7SnrHkD9tmLVNEp49mt:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/2Vq4xDseaUtItjYdQlzPWs/4660da881b69eae58073783da75b8af2/Tingyi_Wanyan.png?w=24&q=100'); }

Tingyi Wanyan

.author__profile-picture.gbi--714597874-pXcWaB6ahAEmfQxSvU7nRJ:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/2EpIOxIrCjA8ECAqfcn0gK/13ab77bc2f498ef753bdd80260312cce/Akhil_Vaid.png?w=24&q=100'); }

Akhil Vaid

.author__profile-picture.gbi-221404342-hq3D7CHxWr9MPAJT7E7Enc:before { opacity: 1; background-image: url('/static/06e843d62425f8b6e8fd6107468dc5a3/e9c03/profile-picture-placeholder.png'); }

Jessica De Freitas

.author__profile-picture.gbi-221404342-xqEWQr7w3LdHsexdeQf8vb:before { opacity: 1; background-image: url('/static/06e843d62425f8b6e8fd6107468dc5a3/e9c03/profile-picture-placeholder.png'); }

Sulaiman Somani

.author__profile-picture.gbi--748886666-bBe7zJ3c26Kz9HScSvd1qv:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/2TVnDzADjRqgSryXlvWYqT/558c5c5d8e3c7b5139417bf21129df69/Girish-Nadkarni.png?w=24&q=100'); }

Girish Nadkarni

.author__profile-picture.gbi--111367730-wRvBwv4eTdQ7SVBbAAz17G:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/5Hy8JqLpZ5AKug1PuY6CLO/b4838b545e7f0fe90f45b19bacf045d5/Ben-Glicksberg.png?w=24&q=100'); }

Benjamin Glicksberg

6 HPI•MS authors

Full list of authors

Tingyi Wanyan, Akhil Vaid, Jessica K De Freitas, Sulaiman Somani, Riccardo Miotto, Girish N Nadkarni, Ariful Azad, Ying Ding, Benjamin S Glicksberg

Journal

IEEE Transactions on Big Data

Open Publication

March, 2021

Using C-JAMP to investigate epistasis and pleiotropy

Journal

Epistasis - Methods and Protocols

HPI·MS Authors

.author__profile-picture.gbi-221404342-34G7aU9GzYvTLeMLJFyY2o:before { opacity: 1; background-image: url('/static/06e843d62425f8b6e8fd6107468dc5a3/e9c03/profile-picture-placeholder.png'); }

Stefan Konigorski

.author__profile-picture.gbi--111367730-jYLEGKqG2bxHiSYViMY47N:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/5Hy8JqLpZ5AKug1PuY6CLO/b4838b545e7f0fe90f45b19bacf045d5/Ben-Glicksberg.png?w=24&q=100'); }

Benjamin Glicksberg

2 HPI•MS authors

Full list of authors

Stefan Konigorski, Benjamin S. Glicksberg

Book Chapter

Epistasis - Methods and Protocols

Open Publication

March, 2021

Outcomes of Patients on Maintenance Dialysis Hospitalized with COVID-19

Journal

CJASN

HPI·MS Authors

.author__profile-picture.gbi-221404342-aVwZbMRubaCS81SzA6acNt:before { opacity: 1; background-image: url('/static/06e843d62425f8b6e8fd6107468dc5a3/e9c03/profile-picture-placeholder.png'); }

Lili Chan

.author__profile-picture.gbi-221404342-47JsErjmsPDsN19STaB4ze:before { opacity: 1; background-image: url('/static/06e843d62425f8b6e8fd6107468dc5a3/e9c03/profile-picture-placeholder.png'); }

Sulaiman Somani

.author__profile-picture.gbi-221404342-x4FQ61YQVrn2bFJukZ7Yyj:before { opacity: 1; background-image: url('/static/06e843d62425f8b6e8fd6107468dc5a3/e9c03/profile-picture-placeholder.png'); }

Ishan Paranjpe

.author__profile-picture.gbi-221404342-2RL3YAvpWVbqnFCUZY7LCw:before { opacity: 1; background-image: url('/static/06e843d62425f8b6e8fd6107468dc5a3/e9c03/profile-picture-placeholder.png'); }

Shan Zhao

.author__profile-picture.gbi--1423126066-eaHD1aKMhaWQWp2yV8xzma:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/2QywYUc22Tw3W4WzkbwPJa/3e6bd4cb020a8f5a68b6530fc215c57c/Zahi-Fayad.png?w=24&q=100'); }

Zahi Fayad

.author__profile-picture.gbi--172148350-v63VPPQMPhNjQf3YSs3dR9:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/19qKAMLjqjuCigL5nC3yMO/d7c2dad6c2d4ad947ce9b2ea49fa7746/Erwin-Bottinger_web.jpg?w=24&q=100'); }

Erwin Bottinger

.author__profile-picture.gbi-1854195918-moiMtsuxSqECskz12CPjjn:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/3hEWbF5T0Y79sYLLsLslAB/ce19591f01acd3536bb4dd0e133dfa67/Alex_Charney.png?w=24&q=100'); }

Alex Charney

.author__profile-picture.gbi--111367730-6HRcM6CGZiXNubeYG46Vom:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/5Hy8JqLpZ5AKug1PuY6CLO/b4838b545e7f0fe90f45b19bacf045d5/Ben-Glicksberg.png?w=24&q=100'); }

Benjamin Glicksberg

.author__profile-picture.gbi--748886666-aivDzUyW7XceoEvYaV78DF:before { opacity: 1; background-image: url('//images.ctfassets.net/6cma8zf36gz0/2TVnDzADjRqgSryXlvWYqT/558c5c5d8e3c7b5139417bf21129df69/Girish-Nadkarni.png?w=24&q=100'); }

Girish Nadkarni

9 HPI•MS authors

Full list of authors

Lili Chan, Suraj K Jaladanki, Sulaiman Somani, Ishan Paranjpe, Arvind Kumar, Shan Zhao, Lewis Kaufman, Staci Leisman, Shuchita Sharma, John Cijiang He, Barbara Murphy, Zahi A Fayad, Matthew A Levin, Erwin P Bottinger, Alexander W Charney, Benjamin S Glicksberg, Steven G Coca, Girish N Nadkarni

Journal

CJASN

Open Publication

See all publications

Press

HPI·MS in the news

EurekAlert!

Researchers create machine learning model to predict treatment with dialysis or death for hospitalized COVID-19 patients

MedCityNews

In scramble to respond to Covid-19, hospitals turned to models with high risk of bias

Mount Sinai Press Release

Shaping the future of digital health

Turning the promise of digital health into a reality

Seeking early and mid-career professorship candidates with expertise in machine learning, AI, and computer science

A new era of digital health

Leading the digital health revolution

AIR.MS - AI Ready Mount Sinai

Digital Discovery Program

World class research with a global impact

Genome-Wide Polygenic Risk Score for Retinopathy of Type 2 Diabetes

Relational Learning Improves Prediction of Mortality in COVID-19 in the Intensive Care Unit

Using C-JAMP to investigate epistasis and pleiotropy

Outcomes of Patients on Maintenance Dialysis Hospitalized with COVID-19

HPI·MS in the news

Researchers create machine learning model to predict treatment with dialysis or death for hospitalized COVID-19 patients

In scramble to respond to Covid-19, hospitals turned to models with high risk of bias

Icahn School of Medicine at Mount Sinai Unveils New PhD Concentration in Artificial Intelligence and Emerging Technologies in Medicine