National Medals of Science and Technology (including Cynthia Dwork for differential privacy)

 In one of the final acts of his administration, President Biden celebrates 25 distinguished scientists and engineers. (I'm particularly glad to see Cynthia Dwork recognized for her work on differential privacy.)

 Forbes has the story:

Biden Names 25 Recipients Of National Medals Of Science, Technology, by Michael T. Nietzel

In a statement from the White House, Biden said, “those who earn these awards embody the promise of America by pushing the boundaries of what is possible. These trailblazers have harnessed the power of science and technology to tackle challenging problems and deliver innovative solutions for Americans and for communities around the world.”

...

"The 14 recipients of the National Medal of Science are:

    Richard B. Alley, the Evan Pugh University Professor of Geosciences at Pennsylvania State University. Alley researches the great ice sheets to help predict future changes in climate and sea levels.
    Larry Martin Bartels, University Distinguished Professor of Political Science and Law and the May Werthan Shayne Chair of Public Policy and Social Science at Vanderbilt University. His scholarship focuses on public opinion, public policy, election science, and political economy.
    Bonnie L. Bassler, Squibb Professor in Molecular Biology and chair of the Department of Molecular Biology at Princeton University, for her research on the molecular mechanisms that bacteria use for intercellular communication.
    Angela Marie Belcher, the James Mason Crafts Professor of Biological Engineering and Materials Science and Engineering at MIT and a member of the Koch Institute for Integrative Cancer Research. She was honored for designing materials for applications in solar cells, batteries, and medical imaging.
    Helen M. Blau, Donald E. and Delia B. Baxter Foundation Professor and the Director of the Baxter Laboratory for Stem Cell Biology at Stanford University for her research on muscle diseases, regeneration and aging, including the use of stem cells for tissue repair.
    Emery Neal Brown, the Edward Hood Taplin Professor of Medical Engineering and Computational Neuroscience at MIT, was recognized for his work revealing how anesthesia affects the brain.
    John O. Dabiri, Centennial Chair Professor at the California Institute of Technology, in the Graduate Aerospace Laboratories and Mechanical Engineering. His research focuses on fluid mechanics and flow physics, with an emphasis on topics relevant to biology, energy, and the environment.
    Ingrid Daubechies, the James B. Duke Distinguished Professor Emerita of Mathematics at Duke University, was honored for her pioneering work on signal processing.
    Cynthia Dwork, Gordon McKay Professor of Computer Science at Harvard University, was recognized for research that has transformed the way data privacy is handled in the age of big data and AI.
    R. Lawrence Edwards, Regents and Distinguished McKnight University Professor, Department of Earth and Environmental Sciences at the University of Minnesota. Edwards is known for his refinement of radiocarbon dating techniques to study climate history and ocean chemistry.
    Wendy L. Freedman, the John and Marion Sullivan University Professor in Astronomy and Astrophysics at the University of Chicago, for her observational cosmology research, including pioneering uses of the Hubble Space Telescope.
    Keivan G. Stassun, Stevenson Professor of Physics & Astronomy at Vanderbilt University for his work in astrophysics, including the study of star formation and exoplanets.
    G. David Tilman is Regents Professor and the McKnight Presidential Chair in Ecology, Evolution, and Behavior at the University of Minnesota. He studies biological diversity, the structure and benefits of ecosystems and ways to assure sustainability despite global increases in human consumption and population.
    Teresa Kaye Woodruff is the MSU Research Foundation Professor of Obstetrics, Gynecology and Reproductive Biology and Biomedical Engineering at Michigan State University. She is an internationally recognized expert in ovarian biology and reproductive science.

The nine individual recipients of the National Medal of Technology and Innovation are:

    Martin Cooper for his work in advancing in personal wireless communications for over 50 years. Cited in the Guinness Book of World Records for making the first cellular telephone call, Cooper, known as the “father of the cell phone,” spent much of his career at Motorola.
    Jennifer A. Doudna, a Nobel Laureate in Chemistry and the Li Ka Shing Chancellor’s Chair in Biomedical and Health Sciences at the University of California, Berkeley. She is a pioneer of CRISPR gene editing.
    Eric R. Fossum is the John H. Krehbiel Sr. Professor for Emerging Technologies at Dartmouth College. He invented the CMOS active pixel image sensor used in cell-phone cameras, webcams, and medical imaging.
    Paula T. Hammond, an MIT Institute Professor, vice provost for faculty, and member of the Koch Institute, was honored for developing methods for assembling thin films that can be used for drug delivery, wound healing, and other applications.
    Kristina M. Johnson, former president of The Ohio State University was recognized for research in photonics, nanotechnology, and optoelectronics. Her discoveries have contributed to sustainable energy solutions and advanced manufacturing technologies.
    Victor B. Lawrence spent much of his career at Bell Laboratories, working on new developments in multiple forms of communications. He is a Research Professor and Director of the Center for Intelligent Networked Systems at Stevens Institute of Technology.
    David R. Walt is a faculty member of the Wyss Institute at Harvard University and is the Hansjörg Wyss Professor of Bioinspired Engineering at Harvard Medical School and Professor of Pathology at Harvard Medical School and Brigham and Women’s Hospital. He was honored for co-inventing the DNA microarray, enabling large-scale genetic analysis and better personalized medicine.
    Paul G. Yock is an emeritus faculty member at Stanford University. A physician, Yock is known for inventing, developing and testing new cardiovascular intervention devices, including the stent.
    Feng Zhang, the James and Patricia Poitras Professor of Neuroscience at MIT and a professor of brain and cognitive sciences and biological engineering, was recognized for his work developing molecular tools, including the CRISPR genome-editing system."

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Here's my post from ten years ago:

Saturday, February 7, 2015 Differential Privacy: an appreciation of Cynthia Dwork

 

Fairness in algorithms: Hans Sigrist Prize to Aaron Roth

 The University of Bern's Hans Sigrist Prize has been awarded to Penn computer scientist Aaron Roth, and will be celebrated today.

Here are today's symposium details and schedule:

The Hans Sigrist Symposium

Data Science: The Power of the Human Mind for the Sake of Humankind

Here's an interview:

Aaron Roth: Pioneer of fair algorithms  In December 2023, the most highly endowed prize of the University of Bern will go to the US computer scientist Aaron Roth. His research aims to incorporate social norms into algorithms and to better protect privacy.  by Ivo Schmucki 

"There are researchers who sit down and take on long-standing problems and just solve them, but I am not smart enough to do that," says Aaron Roth. "So, I have to be the other kind of researcher. I try to define a new problem that no one has worked on yet but that might be interesting."

"Aaron Roth's own modesty may stand in the way of understanding the depth of his contributions. In fact, when he authored his doctoral thesis on differential privacy about 15 years ago and then wrote on the fairness of algorithms a few years later, terms like “Artificial Intelligence” and “Machine Learning” were far from being as firmly anchored in our everyday lives as they are today. Aaron Roth was thus a pioneer, laying the foundation for a new branch of research.

"I am interested in real problems. Issues like data protection are becoming increasingly important as more and more data is generated and collected about all of us," says Aaron Roth about his research during the Hans Sigrist Foundation’s traditional interview with the prize winner. He focuses on algorithmic fairness, differential privacy, and their applications in machine learning and data analysis.

...

"It is important that more attention is paid to these topics," says Mathematics Professor Christiane Tretter, chair of this year's Hans Sigrist Prize Committee. Tretter says that many people perceive fairness and algorithms as two completely different poles, situated in different disciplines and incompatible with each other. "It is fascinating that Aaron Roth’s work shows that this is not a contradiction."

...

"The first step to improving the analysis of large data sets is to be aware of the problem: "We need to realize that data analysis can be problematic. Once we agree on this, we can consider how we can solve the problems," says Aaron Roth."

Computer science award

A computer scientist whose work I follow has won an award that reflects on both his teachers and students.

Aaron Roth receives 2023 CyLab Distinguished Alumni Award 

"Aaron Roth, the Henry Salvatori Professor of Computer Science and Cognitive Science at the University of Pennsylvania, has been named CyLab's 2023 Distinguished Alumni Award winner.

...

"Roth earned his Ph.D. in Computer Science from Carnegie Mellon University in 2010, where he was advised by former CMU Professor Avrim Blum. His dissertation, 'New Algorithms for Preserving Differential Privacy,' gave new methods for performing computations on private data.

"Nominated by his former advisee, now Assistant Professor in CMU's School of Computer Science, Steven Wu, the award recognizes Roth's excellence in algorithms and machine learning, leadership in the field, and commitment to his students.

"As my advisor, Aaron is nothing less than a beacon of inspiration, marked by his relentless curiosity, exceptional instinct for identifying the most exciting questions, creative problem-solving acumen, and impeccable eloquence in communication," said Wu.

"Advising is one of the best parts of my job," said Roth. "Being recognized by one of my former students at the University where I earned my Ph.D. is really special."

Ethics of machine learning--an interview with Michael Kearns and Aaron Roth

 Amazon Scholars Michael Kearns and Aaron Roth discuss the ethics of machine learning--Two of the world’s leading experts on algorithmic bias look back at the events of the past year and reflect on what we’ve learned, what we’re still grappling with, and how far we have to go.  By Stephen Zorio

"In November of 2019, University of Pennsylvania computer science professors Michael Kearns and Aaron Roth released The Ethical Algorithm: The Science of Socially Aware Algorithm Design. Kearns is the founding director of the Warren Center for Network and Data Sciences, and the faculty founder and former director of Penn Engineering’s Networked and Social Systems Engineering program. Roth is the co-director of Penn’s program in Networked and Social Systems Engineering and co-authored The Algorithmic Foundations of Differential Privacy with Cynthia Dwork. Kearns and Roth are leading researchers in machine learning, focusing on both the design and real-world application of algorithms.

Their book’s central thesis, which involves “the science of designing algorithms that embed social norms such as fairness and privacy into their code,” was already pertinent when the book was released. Fast forward one year, and the book’s themes have taken on even greater significance.

Amazon Science sat down with Kearns and Roth, both of whom recently became Amazon Scholars, to find out whether the events of the past year have influenced their outlook. We talked about what it means to define and pursue fairness, how differential privacy is being applied in the real world and what it can achieve, the challenges faced by regulators, what advice the two University of Pennsylvania professors would give to students studying artificial intelligence and machine learning, and much more."

Avinatan Hassidim (and market design) and Katrina Ligett (and privacy) celebrated in Israel

"The Marker," the biggest economic newspaper in Israel, includes two researchers who will be familiar to many readers of this blog in their list of "40 under 40" .

Here's their writeup on Avinatan Hassidim, a computer scientist and market designer at Bar Ilan University:
אבינתן חסידים, 37
"Among other things, Hassidim led the development of an algorithm for embedding doctors in Israel in a residency internship in hospitals (instead of the lottery method) and in recruiting students for a master's degree in psychology for the study programs. Today he is working on developing a system for placing graduates of law studies in Israel in places of specialization."
***********

Here's his web page: Avinatan Hassidim
"My main research interests are auction theory, mechanism design, cake cutting, algorithmic game theory and approximation algorithms. My works have been used to devise the Israelli Medical Interns Lottery, the Israelli Psychology Match and to assign children to schools in various cities in Israel. "
**************

Earlier related posts:

Thursday, March 26, 2015

Redesigning the Israeli Medical Internship Match

Monday, July 14, 2014

Two new labor market matching mechanisms in Israel: for psychologists, and doctors

***************
And here's The Marker on computer scientist Katrina Ligett,
קתרינה ליגת,
who is singled out for her work on differential privacy (including how privacy can have counterintuitive consequences in equilibrium), among other things.


HT: Ran Shorrer
***********

Update: Itai Ashlagi points out to me that Ya'akov Babichenko of the Technion, who studies learning in games, is also on the list:

יעקב בביצ'נקו

Match Up 2017: April 20-21 at Microsoft Research New England

MATCH-UP 2017, the fourth workshop in the series of interdisciplinary and international workshops on matching under preferences, will take place April 20-21, 2017.

Venue:Microsoft Research New England Cambridge, MA 02142

	DAY 1
8:00 A.M.	Breakfast
8.45 A.M.	Invited Talk 1 —Estelle Cantillon, Université libre de Bruxelles The efficiency – stability tradeoff in school choice: Lessons for market design Abstract: A well-known result for the school choice problem is that ex-post efficiency and stability may not be compatible. In the field, that trade-off is sometimes small, sometimes big.  This talk will summarize existing and new results on the drivers of this trade-off and derive the implications for the design of priorities and tie-breaking rules.
9.30 A.M.	Session 1
10.30 A.M.	Break
10.50 A.M.	Session 2
12.30 P.M.	Lunch
1:00 P.M.	Outlook Talk 1 – Al Roth, Stanford Frontiers of Kidney Exchange Abstract: Kidney exchange is different from many market design efforts I’ve been involved in, because it affects the everyday conduct of transplant centers, so we’re constantly adapting to their big strategy sets…(in contrast to e.g. annual labor markets or school choice which don’t affect the daily conduct of residency programs and schools …)The early design challenges in kidney exchange mostly involved dealing with congestion (and the solutions involved long chains, standard acquisition charges, and attempts to better elicit surgeons’ preferences over kidneys).The current challenges to kidney exchange involve creating more thickness in the markets, and I’ll touch on several new initiatives: 1. Frequent flier programs to encourage transplant centers to enroll more of their easy to match pairs; 2. Global kidney exchange; 3. Information deserts: populations of Americans who don’t get transplants; 4. Deceased donor initiated chains ; a. Increasing deceased donation: military share, priority in Israel
2:00 P.M.	Session 3
3.40 P.M.	Break
4:00 P.M.	Session 4
5:00 P.M.	Invited Talk 2 – Aaron Roth, UPENN Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy) Abstract: In this talk, we will walk through a case study of how techniques developed to design “stable” algorithms can be brought to bear to design asymptotically dominant strategy truthful mechanisms in large markets, without the need to make any assumptions about the structure of individual preferences. Specifically, we will consider the many-to-one matching problem, and see a mechanism for computing school optimal stable matchings, that makes truthful reporting an approximately dominant strategy for the student side of the market. The approximation parameter becomes perfect at a polynomial rate as the number of students grows large, and the analysis holds even for worst-case preferences for both students and schools. Joint work with: Sampath Kannan, Jamie Morgenstern, and Zhiwei Steven Wu.
5.45 P.M.	Break
6:00 P.M.	Poster Lightning Talks
6.30 P.M.	Reception and Poster Session
8:00 P.M.	END
	DAY 2
8:00 A.M.	Breakfast
8.45 A.M.	Invited Talk 3 — Michael Ostrovsky, Stanford Matching under preferences: beyond the two-sided case Abstract: I will present an overview of several recent papers showing that most of the key results of matching theory generalize naturally to a much richer setting: trading networks. These networks do not need to be two-sided, and agents do not have to be grouped into classes (“firms”, “workers”, and so on). What is essential for the generalization is that the bilateral contracts representing relationships in the network have a direction (e.g., one agent is the seller and the other is the buyer), and that agents’ preferences satisfy a suitably adapted substitutability notion. For this setting, for the cases of discrete and continuous sets of possible contracts, I will discuss the existence of stable outcomes, the lattice structure of the sets of stable outcomes, the relationship between various solution concepts (stability, core, competitive equilibrium, etc.), and other results familiar from the literature on two-sided markets.
9.30 A.M.	Session 5
10.30 A.M.	Break
10.50 A.M.	Session 6
12.30 P.M.	Lunch
1:00 P.M.	Lunch w/Outlook Talk 2 — David Manlove, University of Glasgow Selected Algorithmic Open Problems in Matching Under Preferences Abstract: The research community working on matching problems involving preferences has grown in recent years, but even so, plenty of interesting open problems still exist, many with large-scale practical applications.  In this talk I will outline some of these open problems that are of an algorithmic flavour, thus giving an outlook on some of the research challenges in matching under preferences that the computer science community might seek to tackle over the next decade.
2:00 P.M.	Session 7
	Making it Safe to Use Centralized Markets: Epsilon - Dominant Individual Rationality and Applications to Market Design Speakers: Ben Roth and Ran Shorrer Abstract: A critical, yet under-appreciated feature of market design is that centralized markets operate within a broader context; often market designers cannot force participants to join a centralized market. Well-designed centralized markets must induce participants to join voluntarily, in spite of pre-existing decentralized institutions they may already be using. We take the view that centralizing a market is akin to designing a mechanism to which people may voluntarily sign away their decision rights. We study the ways in which market designers can provide robust incentives that guarantee agents will participate in a centralized market. Our first result is negative and derives from adverse selection concerns. Near any game with at least one pure strategy equilibrium, we prove there is another game in which no mechanism can eliminate the equilibrium of the original game. In light of this result we offer a new desideratum for mechanism and market design, which we term epsilon-dominant individual rationality. After noting its robustness, we establish two positive results about centralizing large markets. The first offers a novel justification for stable matching mechanisms and an insight to guide their design to achieve epsilon-dominant individual rationality. Our second result demonstrates that in large games, any mechanism with the property that every player wants to use it conditional on sufficiently many others using it as well can be modified to satisfy epsilon-dominant individual rationality while preserving its behavior conditional on sufficient participation. The modification relies on a class of mechanisms we refer to as random threshold mechanisms and resembles insights from the differential privacy literature.
3.40 P.M.	Break
4:00 P.M.	Session 8
5.20 P.M.	Break
5.30 P.M.	Invited Talk 4 — Marek Pycia, UCLA Invariance and Matching Market Outcomes Abstract: The empirical studies of school choice provide evidence that standard measures of admission outcomes are the same for many Pareto efficient mechanisms that determine the market allocation based on ordinal rankings of individual outcomes. The paper shows that two factors drive this intriguing puzzle: market size and the invariance properties of the measures for which the regularity has been documented. In addition, the talk will explore the consequences of these findings: the usefulness of non-invariant outcome measures and of mechanisms that elicit preference intensities.
6.15 P.M.	Closing Remarks
6.30 P.M.	END

An interview with computer scientist Cynthia Dwork

Quanta Magazine, a publication of the Simons foundation, has an interview with Cynthia Dwork on differential privacy and fairness among other things.

How to Force Our Machines to Play Fair
The computer scientist Cynthia Dwork takes abstract concepts like privacy and fairness and adapts them into machine code for the algorithmic age.

Here are some earlier news stories about Apple's introduction of differential privacy to the iPhone,  which I've been following for a number of reasons.

From TechCrunch: What Apple’s differential privacy means for your data and the future of machine learning

From Wired: Apple’s ‘Differential Privacy’ Is About Collecting Your Data—But Not ​Your Data

Apple's differential privacy analyzes the group, protects the individual
Posted on June 21, 2016 

Designing privacy (differential privacy) at the Institute for Advanced Study

Differential Privacy Symposium

Differential privacy disentangles learning about a dataset as a whole from learning about an individual data contributor. Just now entering practice on a global scale, the demand for advanced differential privacy techniques and knowledge of basic skills is pressing. This symposium will provide an in-depth look at the current context for privacy-preserving statistical data analysis and an agenda for future research. This event is organized by Cynthia Dwork, of Microsoft Research, with support from the Alfred P. Sloan Foundation.

Speakers include:

Helen Nissenbaum, Cornell Tech and NYU
Aaron Roth, University of Pennsylvania
Guy Rothblum, Weizmann Institute
Kunal Talwar, Google Brain
Jonathan Ullman, Northeastern University

Differential privacy at Apple

The MIT Technology Review has an article about Apple's use of differential privacy, that caught my eye for several reasons: Apple’s New Privacy Technology May Pressure Competitors to Better Protect Our Data: The technology is almost a decade-old idea that’s finally coming to fruition.

"On a quarterly investor call last week, Apple CEO Tim Cook boasted that the technology would let his company “deliver the kinds of services we dream of without compromising on individual privacy.” Apple will initially use the technique to track trends in what people type and tap on their phones to improve its predictive keyboard and Spotlight search tool, without learning what exactly any individual typed or clicked.
...
“It’s exciting that things we knew how to do in principle are being embraced and widely deployed,” says Aaron Roth, an associate professor at University of Pennsylvania who has written a textbook on differential privacy. “Apple seems to be betting that by including privacy protections, and advertising that fact, they will make their product more attractive.”

In the version of differential privacy Apple is using, known as the local model, software on a person’s device adds noise to data before it is transmitted to Apple. The company never gets hold of the raw data. Its data scientists can still examine trends in how people use their phones by accounting for the noise, but are unable to tell anything about the specific activity of any one individual.

Apple is not the first technology giant to implement differential privacy. In 2014 Google released code for a system called RAPPOR that it uses to collect data from the Chrome Web browser using the local model of differential privacy. But Google has not promoted its use of the technology as aggressively as Apple, which has this year made a new effort to highlight its attention to privacy (see “Apple Rolls Out Privacy-Sensitive Artificial Intelligence”)."