The Carver Medal Committee of the Institute of Mathematical Statistics has selected **Jean Opsomer** to receive the 2019 Harry C. Carver Medal. The award is made in recognition of his outstanding contributions to IMS, especially through his steady service and guidance as two-term Treasurer of the IMS that put IMS finances on a healthy and stable path.

Jean Opsomer is a Vice President of Westat. Prior to this, he was a professor and chair of the Department of Statistics at Colorado State University, which he joined in 2007. Previously, Jean spent 12 years at Iowa State University as a faculty member in the Department of Statistics, affiliated with the Center for Survey Statistics and Methodology. He has a Master’s in Management Engineering from KU Leuven, Belgium, an MBA in Finance from the University of Chicago, and a PhD (1995) in Operations Research from Cornell University.

The author or coauthor of 65 peer-reviewed articles, Jean has introduced a number of influential novel statistical methodologies into survey estimation. His methodological and theoretical work is frequently motivated by questions that arise within federal statistical agencies with which he has long-term collaborations. His recent research has focused on the introduction of shape-constrained and nonparametric methods in survey estimation and on several interdisciplinary projects with survey components on a range of topics (higher education, public health, nutrition, employment, fisheries management, methane emissions, forest health, and agricultural erosion).

Jean is a Fellow of the IMS and the American Statistical Association, and an Elected Member of the International Statistical Institute. In recognition of his contributions to the field, he was named a Westat Senior Statistical Fellow and serves on their Statistical Fellows Committee, which provides consultation on important survey statistics issues and addresses recent advances in applied statistics.

The Carver Medal was created by the IMS in honor of Harry C. Carver, Founding Editor of the *Annals of Mathematical Statistics *and one of the founders of the IMS. The medal is for exceptional service specifically to the IMS. It will be presented to Jean Opsomer at the IMS Presidential Address and Awards session at JSM Denver (Monday, July 29). Also presented at that session are the **IMS Fellows **(see page 8), the **New Researcher Travel Awards **(page 5), the **Hannan Travel Awards **(page 16) and, of course, the Presidential Address (see page 10 for Xiao-Li Meng’s latest **President’s Column**). If you’re coming to JSM this year, please join us for the session, and the reception that follows it!

IMS Fellow **Kathryn Roeder **has been elected a member of the US National Academy of Sciences in recognition of her distinguished and continuing achievements in original research. NAS membership is a widely accepted mark of excellence in science and is considered one of the highest honors that a scientist can receive.

Kathryn Roeder is Professor of Statistics and Computational Biology, and Vice Provost for Faculty, at Carnegie Mellon University (CMU). She earned her PhD in statistics in 1988 at Pennsylvania State University (her dissertation, supervised by Bruce G. Lindsay, was *Method of Spacings for Semiparametric Inference*), then she worked at Yale University for the next six years before moving to CMU in 1994. In 1997 she received both the COPSS Presidents’ Award and the Snedecor Award, and, in 1999, gave an IMS Medallion Lecture. In 2013, she received the Janet L. Norwood Award for outstanding achievement by a woman in statistical sciences. She said she joined CMU’s Computational Biology Department as a voting faculty member in 2004, “to encourage a bridge between statistics, machine learning, genetics and genomics.” She is a fellow of the American Statistical Association as well as IMS.

She lists her research interests on her website: “A primary goal of my research group is to develop statistical tools for finding associations between patterns of genetic variation and complex disease. To solve biologically relevant problems, we utilize modern statistical methods such as high dimensional statistics, statistical machine learning, nonparametric methods and networks. Data arises from primarily from Next Generation Sequencing and gene expression arrays. Our methodological work is motivated by our studies of schizophrenia, autism and other genetic disorders.” See http://www.stat.cmu.edu/~roeder/index.html

]]>Congratulations to: **Gerda Claeskens, Keith Crank, Michael Fay, Michele Guindani, Sebastien Haneuse, Hongkai Ji, Jiashun Jin, Katerina Kechris, Charles Kooperberg, Eric Laber, Bo Li, Jia Li, Yehua Li, Samuel Mueller, Davy Paindaveine, Judea Pearl, Igor Pruenster, Cynthia Rudin, David Stephens, Pei Wang, William Welch, Xiangrong Yin **and** Hui Zou. **

The full list is at https://www.amstat.org/asa/files/pdfs/2019-ASAFellowAnnouncement.pdf

]]>Professor **Robert Tibshirani **is among the 51 eminent scientists who have become Fellows of the Royal Society, for their exceptional contributions to science. Rob Tibshirani is Professor of Biomedical Data Science and of Statistics, in the Departments of Biomedical Data Science and Statistics at Stanford University.

Robert Tibshirani has made important contributions to the statistical analysis of complex datasets. Some of his best-known contributions are the lasso, which uses *L*_{1} penalization in regression and related problems, generalized additive models and Significance Analysis of Microarrays (SAM). He also co-authored four widely used books: *Generalized Additive Models*, *An Introduction to the Bootstrap*, *The Elements of Statistical Learning*, and *Sparsity in Statistics: the Lasso and its generalizations*.

Professor Tibshirani co-authored the first study that linked cell phone usage with car accidents, a widely cited article that has played a role in the introduction of legislation that restricts the use of phones while driving. He is one of the most widely cited authors in the mathematical sciences field.

Robert Tibshirani trained at the University of Waterloo, University of Toronto, and Stanford University. He was elected to the US National Academy of Sciences in 2012.

]]>On the 16th of July 2018 the President of Vietnam awarded IMS Fellow **Klaus Krickeberg **the Friend of Vietnam Medal for “positive essential contributions to the development of the Vietnamese health sector.”

Klaus has worked in Vietnam since the 1980s in the research, teaching and practice of Public Health. He explained how his work uses probability theory and mathematical statistics: “Clinical trials to evaluate the action of medical treatments in populations are based on statistical models. The same is true for estimating the efficacy of preventive measures, for example vaccinations. Statistical models also allow the analysis of risk factors for non- infectious diseases like obesity, diabetes, various degenerative ailments and most forms of cancer. Smoking and alcohol are ‘classical’ risk factors; now environmental and nutritional factors and lack of physical exercise are the center of attention. The stochastic methods for dealing with infectious diseases are very different: they rest on stochastic modeling of the evolution of a disease, for example of a measles epidemic, in a given population. With its help we can estimate the smallest coverage by a vaccination of known efficacy that will lead to extinction of the disease. Nowadays planning a vaccination campaign is mainly an affair of stochastic modeling. Few people in the health sciences are aware of this particular aspect of this dichotomy, infectious disease versus non-infectious disease. It has important implications in organizing measures to control their evolution.”

]]>**IMS Presidential Address and Awards Ceremony** (Monday, July 29, 8:00 pm): Xiao-Li Meng: *011, 010111, and 011111100100*

**Wald Lectures **(Mon, July 29, 10:30 am; Tues, July 30, 2:00 pm; Weds, July 31, 10:30 am): Trevor J. Hastie: *Statistical Learning with Sparsity*

**Rietz Lecture **(Tuesday, July 30, 10:30 am): Yoav Benjamini: *Selective Inference: The Silent Killer of Replicability*

**Medallion Lecture I **(Sunday, July 28, 4:00 pm): Yee Whye Teh: *On Statistical Thinking in Deep Learning*

**Medallion Lecture II** (Mon, July 29, 8:30 am): David Dunson: *Learning and Exploiting Low-Dimensional Structure in High-Dimensional Data*

**Medallion Lecture III **(Mon, July 29, 2:00 pm): Helen Zhang: *Breaking Curse of Dimensionality in Nonparametrics*

**Medallion Lecture IV **(Weds, July 31, 8:30 am): Elizaveta (Liza) Levina: *Hierarchical Communities in Networks: Theory and Practice*

*More IMS members giving keynote lectures:*

**Deming Lecture **(Tuesday, July 30, 4:00 pm): Nicholas Fisher: *Walking with Giants: A Research Odyssey*

**ASA President’s Address and Awards **(Tuesday, July 30, 8:00 pm): Karen Kafadar

**Free Public Lecture** (Sunday, July 28, 6–7 pm): Mark Glickman: *Data Tripper: Distinguishing Authorship of Beatles Songs through Data Science*

The Committee of Presidents of Statistical Societies (COPSS) sponsors and presents the following awards at JSM Denver this year. The 2019 **George W. Snedecor Award** will be given to **Sudipto Banerjee**, University of California, Los Angeles, for groundbreaking and fundamental work on Bayesian hierarchical modeling and the analysis of large spatial datasets; for significant contributions to the mapping of disease incidence in space and time, and the analysis of environmental exposures. The Snedecor Award, established in 1976 and given biennially (odd years) since 1991, honors an individual who was instrumental in the development of statistical theory in biometry. The award is for a noteworthy publication in biometry in the past three years.

The **2019 Fisher Lecture **will be delivered at JSM by **Paul R. Rosenbaum**, University of Pennsylvania. Paul was selected for his pioneering contributions to statistical methodology for observational studies, important applications of such methodology to health outcomes studies, lucid books on statistical principles and methodology for observational studies and excellent mentoring. The lecture is titled, *“An Observational Study Used to Illustrate Methodology for Such Studies.”* Paul R. Rosenbaum is the Robert G. Putzel Professor in the Department of Statistics at the Wharton School of the University of Pennsylvania. He is the author of three books, *Observational Studies *(Springer 1995, 2002), *Design of Observational Studies *(Springer 2010), and *Observation and Experiment: An Introduction to Causal Inference *(Harvard University Press 2017). He received his BA in Statistics from Hampshire College and his AM and PhD in statistics from Harvard University. Before joining the Wharton School in 1986, he worked at the US Environmental Protection Agency, the University of Wisconsin at Madison, and the Educational Testing Service. The R.A. Fisher Lectureship honors both the contributions of Sir Ronald Aylmer Fisher and the work of a present-day statistician for their advancement of statistical theory and applications.

The **2019 Florence N. David Award **will be presented to **Susan S. Ellenberg**, University of Pennsylvania, for her impactful leadership roles at the NIH, FDA and the University of Pennsylvania, developing and evaluating new methodologies and specialized approaches to improve the conduct of clinical trials; for influencing ethical practice and leading development of important regulatory policies; for leadership in setting standards for clinical trial data monitoring committees; for senior statistical leadership for many multicenter clinical research network clinical trials; for distinguished leadership in numerous professional societies and national and international committees addressing major public health challenges; and for serving as an exceptional academic role model for faculty and students. Susan Ellenberg will deliver the **F.N. David Lecture** at JSM in Denver on the Tuesday afternoon: *“The Evolution of The Randomized Clinical Trial.”*

The **COPSS Presidents’ Award** is given annually to a young member of the statistical community in recognition of outstanding contributions to the profession of statistics. The award is announced and presented at the Joint Statistical Meetings.

Étienne Pardoux received his PhD in 1975 from the Université Paris-Sud Orsay, under the joint supervision of Alain Bensoussan and Roger Temam. He held a position at CNRS, before joining the Université de Provence at Marseille (now Aix Marseille Université) in 1979, where he has worked ever since and, since 2017, is professor emeritus. Étienne’s research interests include stochastic partial differential equations, nonlinear filtering, anticipating stochastic calculus, backward stochastic differential equations, homogenization of PDEs with periodic and random coefficients, and, more recently, probabilistic models in evolutionary biology and epidemics. He received the Monthyon Prize from the French Academy of Sciences in 1993. Étienne’s Medallion Lecture will be delivered at the Stochastic Processes and their Applications (SPA) meeting, 8-12 July 2019, in Evanston, IL, USA: https://sites.math.northwestern.edu/SPA2019/.

We consider epidemic models where there is a constant flux of susceptible individuals, either because the infected individuals, when they recover, don’t gain any immunity, or they lose their immunity after some time, or because of demography (birth or immigration of susceptible individuals). Under certain conditions on the parameters, the associated deterministic epidemic model, which is an ODE, has a stable endemic equilibrium. This ODE is a large population law of large numbers limit of a system of stochastic Poisson driven SDEs. The stochastic model has a disease free absorbing state, which by irreducibility, is reached soon or later by the process. It might however be that the time it takes for this to happen, i.e. for the random fluctuations to drive the system out of the basin of attraction of the endemic equilibrium of the deterministic limiting ODE is enormous, and does not give any encouraging information concerning the epidemic.

It is therefore of interest to try to predict the time it takes for the random fluctuations inherent in the model to drive the system to the disease-free absorbing state. This can be done using the central limit theorem, moderate and large deviations. The relevance of each approach will depend upon the size of the population.

Most results are given for a homogeneous model (i.e. where each infectious individual is likely to infect with equal likelihood each susceptible individual in the population). However, there are extensions of those results for a population distributed over space. Another model of interest is the so-called “household model,” where there are both local infections in each household, and global infections between households. In that model, the law of large numbers limit is given by a type of “propagation of chaos” result.

This is joint work with R. Forien, P. Kratz, B. Samegni-Kepgnou and T. Yeo.

]]>*Statistics conference in honour of Aad van der Vaart’s 60th birthday* (June 17–21, Leiden, The Netherlands): **Bo Ning**, Yale.

*IMS China Meeting* (July 6–10, Dalian): **Boxiang Wang**, Univ. Iowa, **Guannan Wang**, College of William and Mary, **Yuanyuan Zhang**, Univ. Manchester, UK.

*International Conference on Computer Age Statistics in the Era of Big and High Dimensional Data* (which was in January): **Marcelo Bourguignon Pereira**, UF Rio Grande do Norte.

*Joint Statistical Meetings* (July 27–August 1, Denver): **Abhishek Chakrabortty**, Univ. of Pennsylvania, **Xinyi Li**, UNC Chapel Hill, **Quan Zhou**, Rice Univ.

*O’Bayes* (June 29–July 2, Warwick, UK): **Justin Strait**, Univ. Georgia.

*Stochastic Processes and their Applications* (July 8–12, Evanston): **Mikołaj Kasprzak**, Univ. Luxembourg.

*WNAR/IMS/JR Meeting* (June 23–26, Portland): **Lu Mao**, University of Wisconsin–Madison.

In 1912, James W. Glover, a Professor of Mathematics who specialized in actuarial science, taught the first course devoted entirely to statistical theory at the University of Michigan. In 1930, Harry C. Carver founded the *Annals of Mathematical Statistics* in Ann Arbor. That journal was edited at the University of Michigan until 1938 and has since grown into *Annals of Statistics* and *Annals of Probabilit*y, two flagship journals of IMS. So far, four University of Michigan faculty have served as Editor of *Annals of Statistics*.

In September 1969 the Department of Statistics was officially founded within the College of Literature, Science, and the Arts at the University of Michigan. The original department, established in Mason Hall, consisted of Bill Ericson (the first chair), Chuck Bell, Paul Dwyer, Bruce Hill, Norm Starr, and Michael Woodroofe. Ed Rothman joined shortly thereafter.

For the first few years, the Department awarded only Master’s and PhD degrees. It wasn’t until the fall of 1977 that the department introduced an undergraduate concentration program in Statistics, replacing the previous option that had been offered through the Department of Mathematics.

Since its foundation, the Department has grown at an exponential rate and has risen to become a leader in statistical education and research in the United States. “Faculty excellence in scholarship is always a hallmark of the department, and education is a core mission for us,” said Xuming He, Department Chair and H.C. Carver Professor of Statistics at the University of Michigan. “Our faculty are renowned researchers who are dedicated to their work and strive to provide the best resources and opportunities for our students.”

Notable former colleagues include the current IMS President-Elect Susan Murphy, former H.C Carver Professor Chien-Fu Jeff Wu, former ISI President Vijay Nair, and former L.J. Savage Professor Michael Woodroofe. The current faculty include former editors of major statistics journals, including *Annals of Statistics*, *Biometrics*, and *Journal of the American Statistical Association*.

Several junior faculty have joined the department just in the past three years, including Yang Chen (PhD 2018, Harvard), Snidgha Panigrahi (PhD 2019, Stanford), Yukai Sun (PhD 2015, Stanford), Jonathan Terhorst (PhD 2017, Berkeley), Gongjun Xu (PhD 2013, Columbia), and Ziwei Zhu (PhD 2018, Princeton). They have brought with them great talent and expertise in several emerging areas of statistics and data science, such as biosciences, big data computation, and post selection inference. Another internationally renowned statistician, Ya’acov Ritov, joined the faculty in 2016.

“Since I joined the University of Michigan, I have always been inspired by our outstanding faculty, students, and staff,” said Professor He, who joined the faculty in 2011. “They love what they are doing and excel in what they do. I am very proud that we are able to attract exceptional talent in statistics and data science to our department, and they will continue to take us to new heights in the data science era.”

Today, the Department of Statistics at the University of Michigan offers three undergraduate majors (Statistics, Informatics, and Data Science), two undergraduate minors (Statistics and Applied Statistics), as well as Master’s programs in Applied Statistics and Data Science, and a PhD program in Statistics. Currently, the department is host to more than 800 students across those programs. The Michigan alumni have their presence in major companies of the modern age, such as Google and Amazon, and have joined the faculty in top research universities in the country.

On September 20 and 21 of this year, the Department of Statistics will be celebrating its 50th anniversary on campus in Ann Arbor, Michigan. The celebration will feature talks led by former professors and alumni, as well as panel discussions. All in all, it will be an opportunity to reflect and celebrate the continual hard work and progress that has brought the Department of Statistics to the level of excellence it operates on today. “Our department has undergone many changes in the past 50 years” said Professor He. “We are now extremely well-positioned for another 50 years as a premier statistics department in the country.”

Visit https://lsa.umich.edu/stats for more information about the department.

]]>

** Edoardo M. Airoldi, **Co-Director, Data Science Institute, and Millard E Gladfelter Professor of Statistics & Data Science, Temple University:

** Cristina Butucea, **Professor, ENSAE, Institut Polytechnique de Paris:

** Victor Chernozhukov, **International Ford Professor, Department of Economics and Center for Statistics & Data Science, Massachusetts Institute of Technology:

** Jeng-Min Chiou, **Distinguished Research Fellow, Academia Sinica:

** Bertrand Salem Clarke, **Professor and Chair of the Department of Statistics, University of Nebraska-Lincoln:

** Michael Cranston, **Professor, University of California, Irvine:

** Robert C. Dalang, **Professor of Mathematics, École Polytechnique Fédérale de Lausanne:

** Christina Goldschmidt, **Professor, University of Oxford:

** Yongdai Kim, **Professor, Seoul National University, Korea:

** Alois Kneip, **Professor of Statistics, University of Bonn:

** Shiqing Ling, **Professor, Hong Kong University of Science and Technology:

** Jinchi Lv, **Kenneth King Stonier Chair in Business Administration and Professor of Data Sciences and Operations, and Mathematics, University of Southern California:

** Elizabeth S. Meckes, **Professor of Mathematics, Case Western Reserve University:

** Victor M. Panaretos, **Professor of Mathematical Statistics, École Polytechnique Fédérale de Lausanne:

** Victor Pătrângenaru, **Professor of Statistics, Florida State University:

** Debashis Paul, **Professor, Department of Statistics, University of California, Davis:

** Firas Rassoul-Agha, **Professor, University of Utah:

** Bruno N. Rémillard, **Professor, HEC Montréal:

** Adrian Röllin, **Associate Professor, National University of Singapore:

** Cynthia Rudin, **Professor of Computer Science, Electrical and Computer Engineering, and Statistical Science, Duke University:

** Xiaofeng Shao, **Professor, University of Illinois, Urbana-Champaign:

** Yuedong Wang, **Professor, University of California, Santa Barbara:

** Christopher K. Wikle, **Curators’ Distinguished Professor and Chair, Department of Statistics, University of Missouri:

** Hongquan Xu, **Professor and Graduate Vice Chair of Statistics, University of California, Los Angeles:

** Xiangrong Yin, **Professor of Statistics, University of Kentucky:

We statisticians have successfully—perhaps too successfully—taught everyone that the larger the size, the higher the power to lend credence to an alternative. This is evident from the 2017 *Nature Human Behaviour*’s “Redefine Statistical Significance,” which has over 70 authors, and from the 2019 *Nature*’s “Retire Statistical Significance,” with its more than 800 signatories. The statistical community’s organized responses regarding the troubled *p*-value have been led most visibly by American Statistical Association (ASA), via the 2016 ASA’s Statement on *p*-Values, the 2017 ASA Symposium on A World Beyond *p*<0.05, and the post-symposium special issue in *The American Statistician* (TAS 2019), with its 43 articles on what do to in a world in which *p*-value has been de-valued.

Given the increased attention to the issue of replicability, what can IMS contribute to the larger conversation? Inspired by a predecessor, I have a somewhat unusual idea, which requires your thoughtfulness in order to be consummated. So please, read on.

If the number 43 is too large for you (because you have taught many that *n*=30 is a good approximation for n=∞ under normal circumstances), the editorial of *TAS* 2019 by Wasserstein, Schirm, and Lazar is a gentle and humble tour guide. It summarizes the key recommendations by an ATOM: “**A**ccept uncertainty. Be **t**houghtful, **o**pen, and **m**odest.” Indeed, the thoughtfulness and modesty of our profession are well-reflected by the very fact that many statisticians endorse the call to abandon the term “statistical significance.” I have yet to identify another discipline with quite so many members who endorse the idea of abandoning its publicly most-recognized concept.

To a layperson, saying something is “statistically significant” is analogous to saying it is “mathematically proven” or “scientifically valid.” Such colloquial associations are in fact what motivated the call to abandon the term “statistical significance,” because the methods behind it are far less rigorous than mathematical proofs, and far too simplistic for establishing scientific validity. Yet we should not overlook the epistemological effectiveness of such confidence- inducing terms in promoting and sustaining the public awareness and appreciation of the societal relevance of a discipline (e.g., mathematics) or a collection of them (e.g., science). As Aristotle reminds us, our expectations of absolute exactitude should be qualified when it comes to matters of human opinion and action.

The question, then, is what alternative statistical concept could conceivably maintain the virtues of “statistical significance” without much of its vice? How about we simply drop the word “significance” ?Just as we question if a finding is *scientific*, a study is *ethical*, a project is *economical*, an action is *legal*, or a policy is *moral*, we can—and should—ask of any study, “Is it *statistical*?” While the concepts of being *scientific*, *ethical, economical, legal, *and *moral *are endlessly contested, they have considerable use as yardsticks in both common and specialized parlance. Experts and laypersons alike may ask “Is it X?” with the term “X” signifying what something is or is not. The point is not to lay down incontrovertible definitions but rather to open up questions about what “X” is. Indeed, the lack of such routine questioning would itself be a troubling sign for a society or a historical period.

I dare to suggest that in the light of the dramatically increased societal attention to data science, we should promulgate the use of “statistical” as a yardstick. “Unstatistical” studies can do much harm to our societies in both the short and long term, just as unethical studies or uneconomical projects can. The concept of *being statistical* will not be any more perplexing than any of the concepts mentioned above, and its pithiness will enhance its effectiveness in public discourse and research communications, as well as in private conversations. IMS, as the world’s leading learned society in foundational thinking and the building-up of statistics and probability, can play a vital role in framing its core rhetorical components. Indeed, to the best of my knowledge, “Is it statistical?” was first posed by Bernard Silverman, 2000–2001 IMS President (in a private conversation years ago), as a parallel to the question “Is it legal?” or “Is it ethical?”

In the spirit of “casting stones to attract jades” (테漏多圖 in Chinese), I list below my proposal on the virtues of being statistical, the practice of which should help to reduce the prevalence of irreplicable research findings. I purposefully set the bar high in order to provoke, and hence, I would be happy to praise a study as being “significantly statistical” if it demonstrates—with due diligence—all of the following virtues, as called for by the purposes and design of the study:

Discuss the collection, pre-processing, quality and limitations of the data, and the implications of these;

Elucidate, assess, and discuss data analysis and modeling assumptions, as well as their consequences;

Investigate and evince a good understanding of selection biases, confounding factors, and when/whether causal conclusions can be drawn;

Exhibit coherent probabilistic thinking and treatments of multivariate relationships and distributions;

Apply statistical methods with reasonable justifications and acknowledge their shortcomings;

Conduct appropriate uncertainty propagation, quantification, and representations;

Show good understanding of statistical principles, such as conditioning and the bias–variance trade-off.

A list of virtues can never be exhaustive. There are also other virtues that are critical for data science, but they are not purely or primarily statistical considerations. For example, it is a virtue to understand trade-offs between statistical and computational efficiency, to ensure computational stability and scalability, to consider carefully policy implications, and to describe the essential scientific background, etc.

**An invitation to you**

My list here is only an invitation for IMS members to contemplate what should be the core considerations of “statistical” or “significantly statistical”. I would greatly appreciate hearing from you. Please either comment (below) or send your thoughts to meng@stat.harvard.edu as I prepare for my IMS Presidential Address at JSM 2019.

Of course, I’d appreciate it most if we all can practice what we preach, by constantly asking ourselves, “Is my study *statistical?*”

Kimiko Osada Bowman, age 91, passed away on January 13, 2019.

Kim immigrated to the USA from her native Japan in 1951. In the course of only five years, she completed an undergraduate degree in mathematics and chemistry at Radford College, and MS and PhD degrees in statistics at Virginia Tech. Many years later, she was also awarded a doctorate in mathematical engineering from Tokyo University. Her close and active collaboration with L.R. Shenton, her PhD advisor at Virginia Tech, focused on the distributional properties of estimators based on non-normal data, and continued for 45 years.

Kim was a member of the scientific staff of Oak Ridge National Laboratory for 50 years, and remained active as a researcher and collaborator with ORNL staff for many years after her retirement in 1994. Kim is fondly remembered by colleagues at ORNL as a tireless, enthusiastic and dedicated researcher. She authored or co-authored three books and more than 200 articles during her career. She was the recipient of many awards, was a fellow of the American Statistical Association and the American Association for the Advancement of Science, and was an elected fellow of the International Statistical Institute and the Institute of Mathematical Statistics. Her remarkable career was featured in “Statisticians in History,” a special issue of *Amstat News *(September, 2008).

A victim of polio herself, Kim took an active leadership role in advocacy for individuals with disabilities. She served on the National Science Foundation Equal Opportunities for Science and Engineering Advisory Committee, and chaired the NSF Committee on People with Disabilities. She also chaired the Statistical Tracking of Employment of People with Disabilities Task Force for the President’s Committee on Employment of People with Disabilities.

Kim is survived by a son, Robert Noah Bowman and spouse Cheryl, two grandsons, and one great grandson.

Donations in her name can be made to the Kingwood Church, MAPS Honduras Alliance, 100 Harvest Way, Alabaster, AL 35007.

*—*

*Written by Max Morris, Iowa State University*

On March 29, 2019, Harry Kesten lost a decade-long battle with Parkinson’s disease. He died in Ithaca, aged 87.

Harry was born in Duisburg, Germany, on November 19, 1931. His parents escaped from the Nazis in 1933 and moved to Amsterdam. After undergraduate studies in Amsterdam, he worked as a research assistant at the Mathematical Center there until 1956, when he came to Cornell. He received his PhD in 1958 at Cornell University under supervision of Mark Kac.

In his 1958 thesis on *Symmetric Random Walks*, he showed that the spectral radius equals the exponential decay rate of the return to 0, and the latter is strictly less than 1 if and only if the group is non-amenable This work has been cited 206 times and is his second most-cited publication (according to MathSciNet). Harry was an instructor at Princeton University for one year and at the Hebrew University for two years before returning to Cornell, where he spent the rest of his career. While in Israel, he and Furstenberg wrote their classic paper on *Products of Random Matrices*.

In the 1960s, he wrote a number of papers that proved sharp or very general results on random walks, branching processes, etc. One of the most famous of these is the 1966 Kesten–Stigum theorem, which shows that a normalized branching process *Z _{n}*/

Harry’s almost 200 papers have been cited 3781 times by 2329 authors. However, these statistics underestimate his impact. In baseball terms, Harry was a closer. When he wrote a paper about a topic, his results often eliminated the need for future work on it. Harry was almost too smart. When most of us are confronted with a problem, we need to try different approaches to find a route to a solution. Harry simply bulldozed over all obstacles. He needed 129 pages in the *Memoirs of the AMS *to answer the question: “Which processes with stationary independent increments hit points?”—a topic he spoke about at the International Congress in Nice in 1970.

In 1980 Harry wrote a paper titled, “The critical probability of bond percolation on the square lattice equals ½,” which was published in *Communications in Mathematical Physics*. This was followed by an explosion of results by him that literally filled a book: *Percolation Theory for Mathematicians*. I visited Cornell in 1980–81 and had the pleasure of watching him lecture on these results. I feel sorry for the graduate students in the course who were trying to take notes. My guess is that Harry planned his lectures while swimming laps in the pool at noon. Often he would start giving a proof and then go back and insert a lemma writing diagonally on the board. The lectures were often chaotic, but it was wonderful for me to see how he thought.

Harry was invited to give a talk at the 1982 International Congress in Warsaw on his work in percolation. His title was “Percolation theory and resistance of random electrical networks.” However, due to demonstrations in Poland in 1982 by members of Solidarity, which were suppressed by the communist regime using deadly force and the imposition of martial law, the meeting was delayed until the summer of 1983.[For an interesting account see Anthony Ralston’s article in the *Mathematical Intelligencer, ***6**(1)]. Sixteen of the 125 people giving 45-minute talks did not attend. I believe that Harry did not go in order to protest the human rights violations but that is what you would expect from a man who had a slide in his 2002 plenary talk at the ICM in Beijing listing the names of scientists who had “received long jail sentences for peaceful activities.”

In 1984 Harry gave lectures on first passage percolation at Saint-Flour. This subject dates back to Hammersley’s 1966 paper and was greatly advanced by Smythe and Weirman’s 1978 book. However, Harry’s paper attracted a number of people to work on the subject and it has continued to be a very active area. [See *50 years of First Passage Percolation* by Auffinger, Damron, and Hanson: https://arxiv.org/abs/1511.03262].

I find it interesting that Harry listed only six papers on his Cornell web page. Five have already been mentioned; the sixth is “On the speed of convergence in first-passage percolation,” *Ann. Appl. Probab. ***3**(2)(1993), 296–338.

Harry worked in a large number of areas. There is not enough space for a systematic treatment so I will just tease you with a list of titles. Sums of stationary sequences cannot grow slower than linearly. Random difference equations and renewal theory for products of random matrices. Subdiffusive behavior of a random walk on a random cluster. Greedy lattice animals. How long are the arms of DLA? If you want to try to solve a problem Harry couldn’t, look at his papers on Diffusion Limited Aggregation.

In the late 1990s, Maury Bramson and I organized a conference in honor for Harry’s 66 2/3’s birthday. (We missed 65 and didn’t want to wait for 70.) A distinguished collection of researchers gave talks and many contributed to a volume of papers in his honor called *Perplexing Problems in Probability*. The 21 papers in the volume provide an interesting snapshot of research at the time. If you want to know more about Harry’s first 150 papers, you can read my 32-page summary of his work that appears in that volume.

According to math genealogy, Harry supervised 17 Cornell PhD students who received their degrees between 1962–2003. Maury Bramson and Steve Kalikow were part of the Cornell class of 1977 that included Larry Gray and David Griffeath who worked with Frank Spitzer. (Fortunately, I graduated in 1976!). Yu Zhang followed in Harry’s footsteps and made a number of contributions to percolation and first passage percolation. I’ll let you use Google to find out about the work of Kenji Ichihara, Antal Jarai, Sungchul Lee, Henry Matzinger and David Tandy.

Another “broader impact” of Harry’s work came from his collaborations with a long list of distinguished co-authors: Vladas Sidoravicius (12 papers), Ross Maller (10), Frank Spitzer (8), Geoffrey Grimmett (7), Yu Zhang (7), Itai Benjamini (6), J.T. Runnenberg (5), Roberto Schonmann (4), Rob van den Berg (4), … I wrote four papers with him, all of which were catalyzed by an interaction with another person. In response to question asked by Larry Shepp, we wrote a paper about an inhomogeneous percolation which was a precursor to work by Bollobas, Janson, and Riordan. “Making money from fair games,” joint work with Harry and Greg Lawler, arose from a letter A. Spataru wrote to Frank Spitzer. I left it to Harry and Greg to sort out the necessary conditions.

Harry wrote three papers with Jennifer Chayes. With a leather-jacketed Cornell postdoc, her husband Lincoln Chayes, Geoff Grimmett and Roberto Schonmann, he studied “The correlation length for the high density phase.” With the manager of the Microsoft Research Group, her husband Christian Borgs, and Joel Spencer, he wrote two papers, one on the birth of the infinite component in percolation and another on conditions implying hyperscaling.

As you might guess from my narrative, Harry Kesten received a number of honors. He won the Brouwer medal in 1981. Named after L.E.J. Brouwer, it is The Netherlands’ most prestigious award in mathematics. In 1983, he was elected to the National Academy of Science. He gave the 1986 IMS Wald Lectures. In 1994, he won the SIAM’s Pólya Prize. In 2001 he won the AMS Steele Prize for lifetime achievement.

Being a devout Orthodox Jew, Harry never worked on the Sabbath. On Saturdays in Ithaca, I would often drive past him taking a long walk on the aptly named Freese Road, lost in thought.

Sadly, Harry is now gone, but his influence on the subject of probability will not be forgotten.

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*Written by Rick Durrett, Duke University*