Meet the February Member of the Month, Dr. K. Jane Grande-Allen, Isabel C. Cameron Professor of Bioengineering. Dr. Grande-Allen's research applies engineering analysis to understand and fight heart valve disease. This involves mechanical testing, biochemical measurements, and microstructural analysis of critical components found in the extracellular matrix (ECM) that makes up cardiac tissue. Her studies into the basic and applied physiology of heart valve tissue have shown that the ECM – collagen, elastin, glycosaminoglycans and proteoglycans – forms an intricate network of connective tissue that is influenced by valvular function, growth, and abnormalities.
How do you explain your research in one sentence?
We develop tools to investigate how cells and tissues are influenced by their mechanical environment.
What challenges do you see in your work that you didn't expect?
When I was getting started in my academic career, I was very naïve about the need for mentor training. There is need for mentoring at so many levels in our research at Rice. Undergraduates are having their first research experiences. Our graduate students need mentoring as they learn to balance investigating a topic deeply with taking on adult responsibilities. Faculty need mentoring as well – but few of us get training in how to do this properly. I’m grateful for the mentoring engagements that I’ve experienced at Rice, and yet I believe that this remains a challenge for us.
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Why Venture Capitalists are Investing in 'Software Beyond the Screen'
Sunil Nagaraj | March 9, 2022 | 4:00 - 5:00pm CST | Virtual
Interested in venture capital, technology, and startups? Learn about how entrepreneurs and venture capitalists have capitalized on software.
Software has had an amazing decade, as it has transitioned from desktop computers into the cloud and onto smartphones. In the process, entrepreneurs and venture capitalists have smartly capitalized on this trend. The next decade will focus on software making an even more important jump: moving beyond the screen and into the real world around us.
In this talk, Sunil Nagaraj of Ubiquity Ventures will explore how software is beginning to animate, understand and navigate the real world with profound implications. Using examples ranging from hospital robots to rocket engines to dairy cows, he will explain how powerful this shift will be to our daily lives and our global economies. He will touch on how the software engineering toolchain (APIs, scrum, object-oriented programming, over-the-air updates, etc.) has been perfected for computer software and how this powerful software development paradigm is being applied conceptually and tactically to enable smart hardware in the real world. He will also cover several applications of the machine learning branch of software and its ability to connect the dots and navigate our complex physical world. |
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Office Hours with Sunil Nagaraj
March 23, 2022 | 5:00 - 7:00pm CST | Virtual
Want to meet with Sunil Nagaraj? Virtual office hours will be on Wednesday, March 23 from 5-7 PM CST. Apply here. |
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Covid-19 Symposium
Distinguished Speakers | March 11, 2022 | 10:00am - 2:00pm CST | Virtual
Reflecting on 2 years of scientific accomplishments since the start of the COVID-19 pandemic.
On March 11, 2020, the World Health Organization (WHO) declared the novel coronavirus (COVID-19) a global pandemic. Two years later, hear from six distinguished speakers about several topics surrounding the challenges and successes throughout the COVID-19 pandemic.
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Discovering your Homerun, Lessons from a Rice Owl (& Engineer) Turned Silicon Valley VC
Suman Talukdar | April 5, 2022 | 4:00 - 5:00pm CST | Virtual
Conversation with Suman Talukdar on his Silicon Valley career, today’s trends, what he looks for in companies, and how you might find the next big opportunity.
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Office Hours with Suman Talukdar
April 5, 2022 | 12:00 - 3:30pm CST | Virtual
Want to meet with Suman Talukdar? Virtual office hours will be on Tuesday, April 5 from 12-3:30 PM CST. Apply here. |
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Statistics Colloquium Lecture Series
Chris Tunnell | February 28, 2022 | 4:00 - 5:00 PM CST | How Domain Knowledge and Symmetries are Used in Data-Intensive Astroparticle Physics | Duncan Hall 1070
This talk will discuss the data analysis, statistical, and machine learning challenges that our XENON collaboration encoun-tered while performing the most-sensitive dark-matter search to date. These challenges are representative of a trend that has been underway in many data-intensive sciences, where teams analyze petabytes of data and likelihoods are in-tractable. After describing our astroparticle physics objectives at a high level (i.e. detecting dark matter particles from Big Bang), I will mainly explain how modern computational physics within the experimental community has matured with an emphasis on common techniques for parameter estimation and modeling. I will explain methodological advances for un-derstanding vast amounts of hierarchical data, and how large teams of scientists historically encode domain knowledge into this data analysis. The core goal of this talk is to translate domain problems from astroparticle physics into the lan-guage of data science and machine learning researchers, with an emphasis on where methodological gaps are and why. Along these lines, I will then explain some research directions in my group ranging from domain-informed neural net-works (DiNNs) for domain-inspired architectural constraints, to probabilistic graphical models to encode domain knowledge for interaction localization, to self-organizing maps for understanding signals present in the detector (done in conjunction with this department). Lastly, I will explain how many approaches from the domain side are similar to open questions in the machine learning community related to symmetries and invariances.
Also available on Zoom.
P & A Colloquium - Giulia Semeghini
Giulia Semeghini | February 28, 2022 | 4:00 - 5:00 PM CST | New Frontiers in Quantum Simulation and Computation with Neutral Atom Arrays | Brockman Hall for Physics 101 and online
Learning how to create, study, and manipulate highly entangled states of matter is key to understanding exotic phenomena in condensed matter and high energy physics, as well as to the development of useful quantum computers. In this talk, I will discuss recent experiments where we demonstrated the realization of a quantum spin liquid phase using Rydberg atoms on frustrated lattices and a new architecture based on the coherent transport of entangled atoms through a 2D array. Combining these results with novel technical tools on atom array platforms could open a broad range of possibilities for the exploration of entangled matter, with powerful applications in quantum simulation and information. BIOGRAPHY: Giulia Semeghini is an experimental physicist with expertise in atomic physics, quantum optics, and quantum information. Her main scientific interests are the investigation of complex quantum many-body phases and the development of efficient protocols for quantum information processing using neutral atom platforms. She is currently an MPHQ postdoctoral fellow at Harvard University in the group of Mikhail Lukin, where she helped develop the second generation of atom array platform with hundreds of Rydberg atoms in two-dimensional arrays, and worked on several projects on quantum simulation and computation. She holds a PhD in Physics from the University of Florence, where she worked on Bose-Einstein condensates with tunable interactions to study complex quantum phenomena such as Anderson localization and quantum droplets.
Learn more here.
Research Experience for Undergraduates: Data Science
Department of Computer Science | Apply by March 1, 2022
Undergraduates are welcome to apply for a paid summer research internship providing one-one-one access to faculty and graduate mentors for an immersive, cutting-edge data science experience. The 10-week program runs from Monday, May 23, 2022 to Friday, July 29, 2022 and takes place on campus in Houston, TX.
Participants will receive a $6000 stipend, $400 for travel expenses (if traveling from outside of Houston), and complimentary on-campus accommodations.
Sample research opportunities could include designing and implementing new machine learning algorithms, applying machine learning algorithms to solve specific problems, or developing methods to manage huge data sets. Majors in computer science, statistics, applied mathematics, electrical engineering, or applied/pure mathematics are encouraged to apply. Some knowledge of computer programming in a language such as Python or Java is required. International students must be enrolled at a US university.
For more information visit cs.rice.edu/reu.
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