Call for Applications: Rising Stars in Data Science CFA
Application Deadline (UPDATED): November 30th, 2020 11:59pm CT
Nomination Deadline: November 30th, 2020, 11:59pm CT
Workshop Dates: January 11-12th, 2021
The Rising Stars in Data Science workshop is a new initiative from the Center for Data and Computing (CDAC) at the University of Chicago, focusing on celebrating and fast tracking the careers of exceptional data scientists at a critical inflection point in their career: the transition from PhD to postdoctoral scholar, research scientist, or tenure track position. The workshop also aims to increase representation and diversity in data science by providing a platform and a supportive mentoring network to navigate academic careers in data science. Women and underrepresented minorities in computing are especially encouraged to apply.
The two-day, remote research workshop will feature career and research panels, networking and mentoring opportunities, and 30-minute student research talks. Students will gain insights from faculty panels on career development questions such as: how to start your academic career in data science; how to strategically sustain your career through research collaborations, publications, and skill development; and how to form meaningful interdisciplinary collaborations in data science with industry and government partners. Participants will also hear inspiring keynote talks from established, cutting-edge leaders in data science.
Eligibility & Guidelines
- Applicants must be full time graduate students within ~1-2 years of obtaining a PhD.
- Applicants should be pursuing doctoral degrees in computer science, statistics, data science, or a related computational field.
- Applicants both from and outside of the University of Chicago are encouraged to apply.
- Applicants may only submit one application.
- Applicants may have nomination letters from a maximum of 2 faculty members.
- Student research talks
- Panels (career development, data science research)
- Keynote address
- 1:1 meetings with faculty members
- Networking within the UChicago data science ecosystem
- Student Application Deadline (UPDATED): November 30th, 2020, 11:59pm CT
- Faculty Nomination Deadline: November 30th, 2020, 11:59pm CT
- Notification Deadline (Accepted Speakers): December 14th, 2020
- Workshop: January 11-12th, 2021
The application is available through InfoReady. If you have not previously used InfoReady, you will be required to create an account in order to submit your application.
- Biography (100 words)
- Research talk title
- Research talk abstract (250 words)
- Research statement outlining research goals, potential projects of interest, and long-term career goals (2 pg, standard font at a size 11 or larger)
- Letter of recommendation (1 pg maximum, standard font at a size 11 or larger, a recommendation request will be sent when you add your reference to the application system)
- Short answer (1,000 characters max per question)
- What long-term impact do you hope to have with your research on the field of data science?
- What is your timeline for going on the academic or industry job market?
- The Center for Data and Computing (CDAC) at UChicago focuses on early-stage, cutting-edge data science research to advance the establishment of this emerging field. In your opinion, what areas of data science research are currently missing or nascent, but the most promising?
- How do you hope your research will advance issues germane to data science ethics, such as biased datasets, privacy, and the ethical use of data?
- Please list 1-2 members from the UChicago organizing committee that you would be interested in having a 1:1 discussion with at the workshop.
Proposals will be reviewed by the Rising Stars in Data Science Committee using the following scoring rubric (0-3 points per criterion):
- Research Potential: Overall potential for research excellence, demonstrated by research statement, goals and long-term career goals.
- Academic Progress: Academic progress to date, as evidenced by publications and endorsements from their faculty advisor or nominator.
- Computational Background: Strong computational skills and expertise, ideally with coursework in computer science, statistics, data science, AI or a related field.
- Data Science Commitment: Experience with interdisciplinary research that advances research innovation in the fields of data science or artificial intelligence.
Due to the volume of applications we receive, we will be unable to provide reviewer feedback on applications that are not accepted.
We look forward to your application. Please use the link below to apply:
Rising Stars Committee
Bryon AragamAssistant Professor and Topel Faculty Scholar, Booth School of Business
Raul Castro FernandezAssistant Professor, Computer Science
Yuxin ChenAssistant Professor, Computer Science
Marshini ChettyAssistant Professor, Computer Science
Nick FeamsterFaculty Director, Center for Data and Computing; Neubauer Professor of Computer Science and The College
Andrew FergusonAssociate Professor of Molecular Engineering
Maryellen GigerA.N. Pritzker Professor of Radiology, Committee on Medical Physics, and the College at the University of Chicago
Eric JonasAssistant Professor, Computer Science
Sanjay KrishnanAssistant Professor, Computer Science
Karen LivescuAssociate Professor, Toyota Technological Institute at Chicago
David MillerAssociate Professor, Physics
Dan NicolaeChair and Professor, Statistics; Professor, Human Genetics, Medicine, Section of Genetic Medicine and the College
Chenhao TanAssistant Professor, Computer Science
Rebecca WillettProfessor, Statistics, Computer Science, and the College
Heather ZhengNeubauer Professor of Computer Science
Bryon Aragam is an Assistant Professor and Topel Faculty Scholar in the Booth School of Business at the University of Chicago. He studies high-dimensional statistics, machine learning, and optimization. His research focuses on mathematical aspects of data science and statistical machine learning in nontraditional settings. Some of his recent projects include problems in graphical modeling, nonparametric statistics, personalization, nonconvex optimization, and high-dimensional inference. He is also involved with developing open-source software and solving problems in interpretability, ethics, and fairness in artificial intelligence. His work has been published in top statistics and machine learning venues such as the Annals of Statistics, Neural Information Processing Systems, the International Conference on Machine Learning, and the Journal of Statistical Software.
Prior to joining the University of Chicago, he was a project scientist and postdoctoral researcher in the Machine Learning Department at Carnegie Mellon University. He completed his PhD in Statistics and a Masters in Applied Mathematics at UCLA, where he was an NSF graduate research fellow. Bryon has also served as a data science consultant for technology and marketing firms, where he has worked on problems in survey design and methodology, ranking, customer retention, and logistics.
Raul Castro Fernandez is an Assistant Professor of Computer Science at the University of Chicago. In his research he builds systems for discovering, preparing, and processing data. The goal of his research is to understand and exploit the value of data. He often uses techniques from data management, statistics, and machine learning. His main effort these days is on building platforms to support markets of data. This is part of a larger research effort on understanding the Economics of Data. He’s part of ChiData, the data systems research group at The University of Chicago.
Yuxin Chen is an assistant professor at the Department of Computer Science at the University of Chicago. Previously, he was a postdoctoral scholar in Computing and Mathematical Sciences at Caltech, hosted by Prof. Yisong Yue. He received my Ph.D. degree in Computer Science from ETH Zurich, under the supervision of Prof. Andreas Krause. He is a recipient of the PIMCO Postdoctoral Fellowship in Computing and Mathematical Sciences, a Swiss National Science Foundation Early Postdoc.Mobility fellowship, and a Google European Doctoral Fellowship in Interactive Machine Learning.
His research interest lies broadly in probabilistic reasoning and machine learning. He is currently working on developing interactive machine learning systems that involve active learning, sequential decision making, interpretable models and machine teaching. You can find more information in my Google scholar profile.
Marshini Chetty is an assistant professor in the Department of Computer Science at the University of Chicago, where she co-directs the Amyoli Internet Research Lab or AIR lab. She specializes in human-computer interaction, usable privacy and security, and ubiquitous computing. Marshini designs, implements, and evaluates technologies to help users manage different aspects of Internet use from privacy and security to performance, and costs. She often works in resource-constrained settings and uses her work to help inform Internet policy. She has a Ph.D. in Human-Centered Computing from Georgia Institute of Technology, USA and a Masters and Bachelors in Computer Science from the University of Cape Town, South Africa. In her former lives, Marshini was on the faculty in the Computer Science Department at Princeton University and the College of Information Studies at the University of Maryland, College Park. Her work has won best paper awards at SOUPS, CHI, and CSCW and has been funded by the National Science Foundation, the National Security Agency, Intel, Microsoft, Facebook, and multiple Google Faculty Research Awards.
Nick Feamster is Neubauer Professor in the Department of Computer Science and the College. He researches computer networking and networked systems, with a particular interest in Internet censorship, privacy, and the Internet of Things. His work on experimental networked systems and security aims to make networks easier to manage, more secure, and more available.
I lead an interdisciplinary computational and theoretical research group working on materials self-assembly, biomolecular simulation, viral dynamics, and vaccine design. My doctoral training provided me with expertise in molecular simulation, statistical mechanics, and machine learning, in which I developed new nonlinear machine learning approaches to study the conformations and dynamics of proteins, polymers, and confined water. During my post-doctoral fellowship, I acquired knowledge and skills in immunology and viral dynamics, and developed new computational tools for structure-free prediction of antibody binding sites, and the computational design of HIV vaccines using statistical mechanical principles.
Since establishing my independent research program in 2012, I have combined these expertise to establish a dynamic research program in computational materials science and computational virology for which I have attracted over $2.9M in federal research funding, established a strong publication record (60+ papers) in leading journals, and have been recognized with a number of national awards including a 2018 Royal Society of Chemistry Molecular Systems Design and Engineering Emerging Investigator Award, 2017 Dean’s Award for Excellence in Research, 2016 AIChE CoMSEF Young Investigator Award, 2015 ACS Outstanding Junior Faculty Award, 2014 ACS Petroleum Research Fund Doctoral New Investigator Award, 2013 NSF CAREER Award, and I was named the 2013 Institution of Chemical Engineers North America “Young Chemical Engineer of the Year”. I am engaged and active within my professional organization serving on the AIChE Area 1a Programming Committee and as CoMSEF Liaison Director, and in organizing multiple scientific sessions at our national meetings. In addition to independent theoretical work, my research interests lead naturally to close collaboration with experimentalists and clinicians, teaching me the power of mutually reinforcing theoretical and experimental work and the importance of effective communication, planning, budgeting, teamwork and leadership.
Maryellen L. Giger, Ph.D. is the A.N. Pritzker Professor of Radiology, Committee on Medical Physics, and the College at the University of Chicago. She is also the Vice-Chair of Radiology (Basic Science Research) and the immediate past Director of the CAMPEP-accredited Graduate Programs in Medical Physics/ Chair of the Committee on Medical Physics at the University. For over 30 years, she has conducted research on computer-aided diagnosis, including computer vision, machine learning, and deep learning, in the areas of breast cancer, lung cancer, prostate cancer, lupus, and bone diseases.
Over her career, she has served on various NIH, DOD, and other funding agencies’ study sections, and is now a member of the NIBIB Advisory Council of NIH. She is a former president of the American Association of Physicists in Medicine and a former president of the SPIE (the International Society of Optics and Photonics) and was the inaugural Editor-in-Chief of the SPIE Journal of Medical Imaging. She is a member of the National Academy of Engineering (NAE) and was awarded the William D. Coolidge Gold Medal from the American Association of Physicists in Medicine, the highest award given by the AAPM. She is a Fellow of AAPM, AIMBE, SPIE, SBMR, and IEEE, a recipient of the EMBS Academic Career Achievement Award, and is a current Hagler Institute Fellow at Texas A&M University. In 2013, Giger was named by the International Congress on Medical Physics (ICMP) as one of the 50 medical physicists with the most impact on the field in the last 50 years. In 2018, she received the iBIO iCON Innovator award.
She has more than 200 peer-reviewed publications (over 300 publications), has more than 30 patents and has mentored over 100 graduate students, residents, medical students, and undergraduate students. Her research in computational image-based analyses of breast cancer for risk assessment, diagnosis, prognosis, and response to therapy has yielded various translated components, and she is now using these image-based phenotypes, i.e., these “virtual biopsies” in imaging genomics association studies for discovery.
She is a cofounder, equity holder, and scientific advisor of Quantitative Insights, Inc., which started through the 2009-2010 New Venture Challenge at the University of Chicago. QI produces QuantX, the first FDA-cleared, machine-learning-driven system to aid in cancer diagnosis (CADx). In 2019, QuantX was named one of TIME magazine’s inventions of the year.
Eric Jonas is a new professor in the Department of Computer Science at the University of Chicago. His research interests include biological signal acqusition, inverse problems, machine learning, heliophysics, neuroscience, and other exciting ways of exploiting scalable computation to understand the world. Previously he was at the Berkeley Center for Computational Imaging and RISELab at UC Berkeley EECS working with Ben Recht.
Sanjay Krishnan is an Assistant Professor of Computer Science. His research group studies the theory and practice of building decision systems that are robust to corrupted, missing, or otherwise uncertain data. His research brings together ideas from statistics/machine learning and database systems. His research group is currently studying systems that can analyze large amounts of video, certifiable accuracy guarantees in partially complete databases, and theoretical lower-bounds for lossy compression in relational databases.
My main research interests are in speech and language processing, as well as related aspects of machine learning.
I am an Associate Professor at TTI-Chicago, a philanthropically endowed academic computer science institute located on the University of Chicago campus. We are recruiting students to our PhD program and visiting student program, as well as additional faculty, including in speech and language-related areas (more on Speech and Language at TTIC).
I completed my PhD in 2005 at MIT in the Spoken Language Systems group of the Computer Science and Artificial Intelligence Laboratory. In 2005-2007 I was a post-doctoral lecturer in the MIT EECS department. In Feb.-Aug. 2008 I was a Research Assistant Professor at TTI-Chicago.
David Miller’s research focuses on answering open questions about the fundamental structure of matter. By studying the quarks and gluons -—the particles that comprise everyday protons and neutrons —produced in the energetic collisions of protons at the Large Hadron Collider (LHC) at CERN in Geneva, Switzerland, Miller conducts measurements using the ATLAS Detector that will seek out the existence of never-before-seen particles, and characterize the particles and forces that we know of with greater precision. Miller’s work into the properties and measurements of the experimental signatures of these quarks and gluons –orjets” –is an integral piece of the puzzle used in the recent discovery of the Higgs bosons, searches for new massive particles that decay into boosted top quarks, as well as the hints that the elusive quark-gluon-plasma may have finally been observed in collisions of lead ions.
Besides studying these phenomena, Miller has worked extensively on the construction and operation of the ATLAS detector, including the calorimeter and tracking systems that allow for these detailed measurements. Upgrades to these systems involving colleagues at Argonne National Laboratory, CERN, and elsewhere present an enormous challenge and a significant amount of research over the next several years. Miller is also working with state-of-the art high-speed electronics for quickly deciphering the data collected by the ATLAS detector.
Miller received his PhD from Stanford University in 2011 and his BA in Physics from the University of Chicago in 2005. He was a McCormick Fellow in the Enrico Fermi Institute from 2011-2013.
Dan Nicolae obtained his Ph.D. in statistics from The University of Chicago and has been a faculty at the same institution since 1999, with appointments in Statistics (since 1999) and Medicine (since 2006). His research focus is on developing statistical and computational methods for understanding the human genetic variation and its influence on the risk for complex traits, with an emphasis on asthma related phenotypes. The current focus in his statistical genetics research is centered on data integration and system-level approaches using large datasets that include clinical and environmental data as well as various genetics/genomics data types: DNA variation, gene expression (RNA-seq), methylation and microbiome.
Chenhao Tan is an assistant professor at the Department of Computer Science and the Department of Information Science (by courtesy) at University of Colorado Boulder. His main research interests include language and social dynamics, human-centered machine learning, and multi-community engagement. He is also broadly interested in computational social science, natural language processing, and artificial intelligence.
Rebecca Willett is a Professor of Statistics and Computer Science at the University of Chicago. She completed her PhD in Electrical and Computer Engineering at Rice University in 2005 and was an Assistant then tenured Associate Professor of Electrical and Computer Engineering at Duke University from 2005 to 2013. She was an Associate Professor of Electrical and Computer Engineering, Harvey D. Spangler Faculty Scholar, and Fellow of the Wisconsin Institutes for Discovery at the University of Wisconsin-Madison from 2013 to 2018. Prof. Willett received the National Science Foundation CAREER Award in 2007, was a member of the DARPA Computer Science Study Group 2007-2011, and received an Air Force Office of Scientific Research Young Investigator Program award in 2010. Prof. Willett has also held visiting researcher positions at the Institute for Pure and Applied Mathematics at UCLA in 2004, the University of Wisconsin-Madison 2003-2005, the French National Institute for Research in Computer Science and Control (INRIA) in 2003, and the Applied Science Research and Development Laboratory at GE Medical Systems (now GE Healthcare) in 2002. Her research interests include network and imaging science with applications in medical imaging, wireless sensor networks, astronomy, and social networks. She is also an instructor for FEMMES (Females Excelling More in Math Engineering and Science; news article here) and a local exhibit leader for Sally Ride Festivals. She was a recipient of the National Science Foundation Graduate Research Fellowship, the Rice University Presidential Scholarship, the Society of Women Engineers Caterpillar Scholarship, and the Angier B. Duke Memorial Scholarship.
Heather Zheng is the Neubauer Professor of Computer Science at University of Chicago. She received my PhD in Electrical and Computer Engineering from University of Maryland, College Park in 1999. Prior to joining University of Chicago in 2017, she spent 6 years in industry labs (Bell-Labs, NJ and Microsoft Research Asia), and 12 years at University of California at Santa Barbara. At UChicago, she co-directs the SAND Lab (Systems, Algorithms, Networking and Data) together with Prof. Ben Y. Zhao.
- Rising Stars FAQ
- When is the application due?
The student application is due Monday November 30th by 11:59pm CT. On the student application, you will be able to add the emails of your recommender(s). Once you submit your application, they will receive an email notification requesting their letter. LORs are also due Monday November 30th by 11:59pm CT.
- Where can I apply?
- How many letters of recommendation can I request?
You can request up to two letters of recommendation, and are required to request at least one.
- Who can I request a letter of recommendation from?
You are encouraged to request a letter from a faculty member or advisor who can speak first-hand to your research and academic strengths and qualifications for the program.
- Is there a specific format for the letter of recommendation(s)?
There is not a preset format. The letter must be 1 pg maximum, standard font at a size 11 or larger.
- Do I have to be in a data science program to apply?
We do not require that applicants are currently in a Data Science program to apply. However, applicants should be pursuing doctoral degrees in computer science, statistics, data science, or a related computational field.
- Is the application limited to graduating PhD students?
Applicants should be within approximately 1-2 years of obtaining their PhD in order to apply.
- What should the research talk cover?
The research talk should highlight your research interests in data science and computing, and ideally showcase your unique approaches to the nascent field as it takes shape.
- Do I need to cover established research in my talk, or can it cover early-stage projects?
Your talk can use research work that is either early-stage or published, so long as you are confident that it appropriately and best showcases your research methodology and approaches.
- Who is the audience for the student research talks?
The primary audience for the student research talks are the Rising Stars Committee and researchers, faculty, and staff in the data science ecosystem at UChicago. The talks will also be streamed live as part of the virtual event. Certain panels and workshop events will be open to the public. Registration for the event will open in December.
- When will the workshop agenda be available?
The workshop agenda will be posted in December.