engineering

Marzyeh Ghassemi

Marzyeh Ghassemi is a engineering topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Marzyeh Ghassemi rather than just read about it. In short: Marzyeh Ghassemi is currently a professor at MIT, leading the Healthy ML lab which develops robust machine-learning algorithms, and works to understand how such models can best inform and improve health-care decisions. She was formerly an assistant professor at the University of Toronto's Department of Computer Science and Faculty of Medicine, holding a Canada CIFAR Artificial Intelligence (AI) chair and Canada Rese…

Key takeaways

Marzyeh Ghassemi belongs to engineering; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Marzyeh Ghassemi to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Marzyeh Ghassemi from memory before moving on to harder problems.

Reference excerpt

Marzyeh Ghassemi is currently a professor at MIT, leading the Healthy ML lab which develops robust machine-learning algorithms, and works to understand how such models can best inform and improve health-care decisions. She was formerly an assistant professor at the University of Toronto's Department of Computer Science and Faculty of Medicine, holding a Canada CIFAR Artificial Intelligence (AI) chair and Canada Research Chair (Tier Two) in machine learning for health.

Research career Ghassemi pursued a bachelors of science degree in computer science and electrical engineering at New Mexico State University, a master's degree in biomedical engineering from Oxford University, and a PhD at the Massachusetts Institute of Technology (MIT). During her PhD, Ghassemi collaborated with doctors based within Beth Israel Deaconess Medical Center's intensive care unit and noted the extensive amount of clinical data available. She then developed machine-learning algorithms to take in diverse clinical inputs and predict risks and mortality, such as the length of the patient's stay within the hospital, and whether additional interventions (such as blood transfusions) are necessary. In 2012, Ghassemi was a member of the Sana AudioPulse team, who won the GSMA Mobile Health Challenge as a result of developing a mobile phone app to screen for hearing impairment remotely. Ghassemi was also the lead PhD student in a study where accelerometer data collected from smart wearable devices to successfully detect differences between patients with muscle tension dysphonia (MTD) and those without MTD. Upon completing her PhD, Ghassemi was affiliated with both Alphabet’s Verily (as a visiting researcher) and at MIT (as a part-time post-doctoral researcher in Peter Szolovits' Computer Science and Artificial Intelligence Lab). Ghassemi joined the University of Toronto in fall 2018, where she was co-appointed to the Department of Computer Science and the University of Toronto's Faculty of Medicine, making her the first joint hire in computational medicine for the university. Ghassemi's lab was titled the Machine Learning for Health (ML4H) lab. Ghassemi was also a faculty member at the Vector Institute. She held the Canada CIFAR Artificial Intelligence (AI) Chair position. In June 2019, Ghassemi was appointed a Canada Research Chair (Tier Two) in machine learning for health. Ghassemi joined MIT in 2021, as a professor in Electrical Engineering and Computer Science, and the Institute for Medical Engineering and Science. Ghassemi was selected for a National Science Foundation CAREER award, and has been cited over 13,000 times with an h-index and i-10 index of 54 and 122 respectively. During her PhD she was named as one of the 35 Innovators Under 35, in the visionaries category, in MIT Technology Review's annual list., and prior to her PhD she was awarded the Barry M. Goldwater Scholarship and the Marshall Scholarship.

Selected bibliography Ethical machine learning in healthcare. Irene Chen, Emma Pierson, Sherri Rose, Shalmali Joshi, Kadija Ferryman, Marzyeh Ghassemi. Annual Review of Biomedical Data Science 4, 123-144. The false hope of current approaches to explainable artificial intelligence in health care. Marzyeh Ghassemi, Luke Oakden-Rayner, Andrew L. Beam. The Lancet Digital Health 3 (11), e745-e750. Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Laleh Seyyed-Kalantari, Haoran Zhang, Matthew McDermott, Irene Chen, Marzyeh Ghassemi. Nature Medicine 27 (12), 2176-2182.

References

Worked examples

Example 1 — a first encounter with Marzyeh Ghassemi

Start with the simplest possible case. Write down what Marzyeh Ghassemi claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In engineering, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Marzyeh Ghassemi before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Marzyeh Ghassemi ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Marzyeh Ghassemi

In research: Marzyeh Ghassemi appears in engineering research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Marzyeh Ghassemi in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Marzyeh Ghassemi is common in secondary-school and first-year university syllabi. It links to neighbouring topics Academic staff of the University of Toronto, Alumni of the University of Oxford, Canadian biomedical engineers, so understanding it makes those chapters shorter.
In everyday life: Look for Marzyeh Ghassemi outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Marzyeh Ghassemi in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Marzyeh Ghassemi means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Marzyeh Ghassemi out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Marzyeh Ghassemi in simple terms?

Why does Marzyeh Ghassemi matter?

Because it connects several engineering ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Marzyeh Ghassemi?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Marzyeh Ghassemi.