MICDE Symposium: Emerging and Future Paradigms for High Performance Computing

By | Educational, Feature, General Interest, News, Research

By Eric Shaw
Office of the Vice President for Research

The Michigan Institute for Computational Discovery and Engineering (MICDE) welcomed distinguished scientists to discuss emerging and future paradigms for High Performance Computing during its 2023 symposium, held on Friday, March 24. 

“Computational advancements have reached a pivotal juncture, empowering researchers to make progress in scientific discoveries and engineering innovations. This is enabled by the confluence of algorithms, software and hardware, and it is critical for experts across disciplines to work together to continue the pace of progress and achieve desired outcomes,” said Karthik Duraisamy, Director of Michigan Institute for Computational Discovery and Engineering.

The symposium featured a wide range of topics related to high performance computing and other computational science-related issues, demonstrating the multidisciplinary nature of the field. These leading-edge developments highlight the vast potential for the field to address some of the most significant challenges facing society today. From improving weather and climate forecasting to advancing materials science and drug discovery to understanding nuclear fusion, the power of high-performance computing is truly remarkable.

Dr. Fariba Fahroo, program officer at the Air Force Office of Scientific Research (AFOSR), spoke on challenges, opportunities, and national needs in computational science. During the talk, she introduced AFOSR and the computational mathematics program she manages. She also discussed the challenges and new directions emerging in computational mathematics as a field bridging areas in applied mathematics and computational science. Dr. Fahroo shared her insights on large-scale projects in machine learning for modeling physical systems, rare events, data assimilation, and reduced order modeling. The talk highlighted the importance of basic research programs in various areas of computational math and control theory, such as multiscale modeling and computation, design under uncertainty, distributed, multi-agent control and estimation, and computational control theory.

Stanford professor, Alex Aiken, presented Legion, a programming model, and runtime system that is designed to handle the increasingly complex and hierarchical nature of modern machines. Aiken discussed the design of Legion, its rationale, and recent work in developing these libraries before highlighting the importance of considering data movement in parallel programming and the potential for Legion to improve the efficiency and productivity of programming for modern machines. Aiken also highlighted usability – particularly demonstrating how simple codes written for course projects scale to a massive number of GPU nodes.

Princeton University professor of astrophysical sciences, Amitava Bhattacharjee, presented his research on the High-Fidelity Whole Device Model of Magnetically Confined Fusion Plasma (WDMApp) as part of the Department of Energy’s Exascale Computing Project (ECP). Bhattacharjee explained that WDMApp is a ten-year project that involves plasma physicists, applied mathematicians, and computer scientists to simulate whole device burning plasmas applicable to an advanced tokamak regime. Bhattacharjee explained that the most crucial step of the project was coupling two existing, well-established, extreme-scale gyrokinetic codes, the GENE continuum code, and the XGC particle-in-cell (PIC) code, to develop novel algorithms for both GENE-XGC and GEM-XGC coupling. The WDMApp codes (GENE, GEM, and XGC) were optimized, leveraging the ECP Co-Design and Software Technologies projects for portability and performance.

Dr. Patty Lee, the chief scientist of hardware technology development at Quantinuum, presented the current capabilities of quantum computing hardware and discussed the scientific and industrial applications that have been run on the hardware. Dr. Lee also provided insights into the software development toolkits available to support the quantum programmer community and the outlook for achieving quantum advantage in the near term. She highlighted the exponential improvement in the computational capability of state-of-the-art quantum computing hardware compared to classical computers.

Dr. Christiane Jablonowski, a professor in the Department of Climate and Space Sciences and Engineering at the University of Michigan, gave a talk on “Computational Frontiers in Weather and Climate Modeling”. She reviewed the state-of-the-art weather and climate modeling approaches at NOAA, the National Center for Atmospheric Research (NCAR), and the Department of Energy, and discussed the emerging computational frontiers. The talk focused on high-resolution weather and climate modeling trends, the ‘digital twin’ concept, and emerging computational paradigms.

The much-anticipated exascale computing era is here, with the arrival of the Frontier system at Oak Ridge National Laboratory in the US. The US Department of Energy’s Exascale Computing Project (ECP) is poised to take full advantage of Frontier’s capabilities in tackling problems of national and international interest. According to Doug Kothe, the Director of the US Department of Energy Exascale Computing Project, and the day’s final speaker, “When we collaborate, we get the most powerful tools and discoveries.” The ECP’s mission is to deliver on targeted exascale systems such as Frontier, which are capable of addressing high-priority strategic problems of national interest that are intractable with at least 50 times the computational power of the HPC systems available in 2016, yet at a very high efficiency. The ECP’s software technology effort is developing an expanded and vertically integrated software stack that includes advanced mathematical libraries, extreme-scale programming environments, development tools, visualization libraries, and the software infrastructure to support large-scale data management and data science for science and security applications.

In addition to the inspiring talks, symposium attendees also had the opportunity to engage in a lively panel discussion with the day’s speakers. Moderated by Venkat Raman, Professor of Aerospace Engineering and Mechanical Engineering at the University of Michigan, the panelists – Fariba Fahroo, Doug Kothe, Amitava Bhattacharjee, Patty Lee, Christiane Jablonowski, and Alex Aiken – tackled some of the most pressing issues and challenges in the field of high performance computing and computational discovery. The audience was able to ask questions and participate in the discussion, making it an engaging experience. It was a fitting end to an informative and thought-provoking day at the MICDE Symposium.

The 2023 MICDE Symposium featured a poster competition where 58 participants showcased their research. The competition winners were announced on Friday, March 24. Tommy Waltmann secured the first place for his work on “Fast and Efficient Particle Trajectory Analysis with the freud Library.” Doruk Aksoy won the second place for “An Incremental Tensor Train Decomposition for High-Dimensional Data Streams,” and Archana Sridhar and Parameshwaran Pasupathy shared the third place for their respective works on “Simulation and modeling of particle-laden compressible flows” and “A Fractional Viscoelastic Model of the Axon in Brain White Matter.” The fourth place was shared among Keith Phuthi, Srinivasan Arunachalam, Kyle Bushick, and Vishal Subramanian, for their works on various topics related to simulation and modeling.

“It has truly been an honor hosting these distinguished speakers, and to attend the poster session which highlighted the incredible depth and breadth of research in computational science at the University of Michigan. It is a testament to our pursuit of knowledge and innovation and a reminder of the direct impact computational science has on science and society,” Duraisamy said.

MICDE 2023 Annual Symposium Thank you

Professor Karthik Duraisamy named new director of the Ph.D. in Scientific Computing program

By | Educational, Feature
Prof. Karthik Duraisamy infront of screen with turbulence simulation

Professor Karthik Duraisamy (Aerospace Engineering)

Karthik Duraisamy, associate professor of Aerospace Engineering, and an associate director of the Michigan Institute for Computational Discovery & Engineering, has been named director of the joint Ph.D. in Scientific Computing program effective on January 1, 2022. Professor Duraisamy’s research involves the development of theory and algorithms for computational modeling of complex physical systems. He was the principal investigador of ConFlux, an NSF Major Research Instrumentation project that led to the development of a first of its kind computing instrument specifically designed to enable High Performance Computing (HPC) clusters to communicate seamlessly and at interactive speeds with data-intensive operations. Currently he directs the Air Force Center of Excellence on Rocket Combustion modeling. He is invested in educating future researchers with a strong computational background capable of using the power of computing for problem solving. He worked with the group that launched the course Methods and Practice in Scientific Computing, and developed and teaches a course on data-driven analysis and modeling of complex systems. These two courses give students a solid foundation, enabling them to use HPC in their research. 

Portrait of Ken Powell

Professor Ken Powell (Aerospace Engineering)

Professor Duraisamy replaces Ken Powell, Arthur F. Thurnau Professor of Aerospace Engineering, who stepped down from the role after 18 years of service. As a young assistant professor, Professor Powell was an instrumental member of the original team that conceived and launched the program back in 1989. The field of computational fluid dynamics, where his research interests lie, has always included an active community of HPC users and developers, thus he was always actively involved in the program through research, teaching and student advising. In 2004 he succeeded Professor William Martin as director of the program. During his time as director, he met and advised every single one of the over 350 students that enrolled in the program. Through this time he became an expert on scientific computing courses across the university, and witnessed first hand the explosion in computational and data science usage, reflected in the research scope of the students enrolling in the program.

Professor Duraisamy has big shoes to fill, but he is being assisted by the MICDE Management and Education Committee. The program’s mission, to train U-M students in scientific computing and to support the growing computational and data science community at the University of Michigan, will itself continue to expand.

The University of Michigan Ph.D. in Scientific Computing timeline. Read more.


Job Opening: Physics-AI Hybrid Modeling Research Engineer at Bosch

By | Feature, SC2 jobs

The Bosch Research and Technology Center in Sunnyvale, CA seeks to hire an outstanding research engineer to develop novel hybrid multiscale, cross-domain modeling and simulation tools for Bosch products. This engineer would join a team of PhDs with a variety of competences including high fidelity CFD-based multiphysics modeling, adjoint-based optimization, machine learning and high performance computing. The team focuses on design and optimization of novel clean and sustainable energy solutions such as fuel cells and electric vehicle components.

Primary responsibilities:

  • Build models which utilize machine learning and hybrid modeling approaches to capture complex physical phenomena and accelerate solution time of physical models
  • Develop multiscale models together with materials and systems modelers
  • Develop hybrid performance and aging models for Bosch products including polymer electrolyte fuel cells
  • Integrate hybrid performance and aging models into system simulation
  • Collaborate with experimentalists, top universities and our partners in Silicon Valley

Read more.


Postdoc Position: Computational Modeling in Immunology of Tuberculosis

By | Feature, SC2 jobs


About the Position:

An exciting opportunity is available for a strong mathematical/computational modeler to work in a multidisciplinary team on immune responses in the context of tuberculosis.  The position is available jointly in the laboratories of Jennifer Linderman in Chemical Engineering and Denise Kirschner in the Department of Microbiology and Immunology, both at the University of Michigan. The project uses a systems biology approach to integrate our multi-scale and multi-organ in silico models with data from humans and non-human primates derived by our collaborators. An estimated one-third of the human population is infected with the pathogen Mycobacterium tuberculosis, mostly in rural areas within developing countries, making it a critical global health issue.


  • Ph.D. degree (or equivalent) in engineering or mathematics or a closely related field
  • Strong computational skills and experience in mathematical modeling in biology
  • The ideal applicant will have extensive experience in object-oriented programming and/or use of MATLAB, R
  • Experience with python is a plus
  • Desire and ability to read scientific literature in immune response to tuberculosis
  • Good communication skills and the ability to work in an interdisciplinary team are essential

How to apply:

Send a CV, names of 3 references, and a letter describing research interests and summarizing Ph.D. work to both Jennifer Linderman linderma@umich.edu and Denise Kirschner kirschne@umich.edu. Copies of papers authored by the applicant are welcome.  Those under-represented in STEM are especially encouraged to apply.

New physics-based computation and AI framework to understand the agressive behavior of cancer cells

By | Feature, Research

Cancer is an illness caused by an uncontrolled division of transformed cells, which can originate in almost  any organ of the body.  Cancer is not a single disease, even when it arises in the same site of the body. Tremendous variability exists in progression of disease and response to therapy among different persons with the same general type of cancer, such as breast cancer. Even at the level of a single person, cancer cells show tremendous heterogeneity within a single tumor and among a primary tumor and metastases. This heterogeneity causes drug resistance and fatal disease. The prevailing dogma is that heterogeneity among cancer cells arises randomly, generating greedy individual cancer cells that compete for growth factors and optimal environments. The rare “winners” in this competition survive and metastasize. However, tumors consistently maintain heterogeneous subpopulations of cancer cells, some of which appear less able to grow and spread. This observation prompted Gary and Kathy Luker, cancer cell biologists at the University of Michigan, to hypothesize that cancer cells may actually collaborate under some circumstances to cause disease and not just compete. The idea that single, heterogeneous cancer cells work collectively within a constrained range of variability to drive population-level outputs in tumor progression is a ground-breaking concept that may revolutionize how we approach cancer biology and therapy.

The team is using innovative approaches to extract and merge data streams from models that generate heterogeneous cell behaviors

...cancer cell biologists have teamed up with computational scientists and experts in artificial intelligence to focus the power of these fields on understanding and overcoming heterogeneity in cancer.

To understand causes of single-cell heterogeneity in cancer and conditions that motivate cancer cells to collaborate, an interdisciplinary team of scientists at UM formulated an entirely new conceptual approach to this challenging problem. The cancer cell biologists have teamed up with computational scientists and experts in artificial intelligence to focus the power of these fields on understanding and overcoming heterogeneity in cancer. Building on large, single-cell data sets unique to the team, they will combine inverse reinforcement learning, an artificial intelligence method typically applied to discover motivations for human behaviors, with computational models inferred on the basis of the physics and chemistry of cell signaling and migration. They have proposed an entirely new conceptual approach combining single cell data, physics-based modeling and artificial intelligence to single-cell heterogeneity and intercellular interactions. By discovering  testable molecular processes underlying “decision-making” by single cells and their “motivations” for acting competitively or collaboratively, this research blazes a new path to understand and treat cancer. Their high-risk, high-reward approach to understand how each cell in a population processes information and translates that to action driving cancer progression, has attracted an award of $1 million dollars by the Keck Foundation. 

The team includes Gary Luker (Radiology, Microbiology and Immunology; Biomedical Engineering), and Kathryn Luker (Radiology), who are leading the experimental studies of cell signaling and migration; Jennifer Linderman (Chemical Engineering; Biomedical Engineering); and Krishna Garikipati (Mechanical Engineering; Mathematics), who are leading the machine learning and modeling side of the project. Nikola Banovic (Electrical Engineering and Computer Science) and Xun Huan (Mechanical Engineering) are using artificial intelligence approaches to discover decision-making policies and rewards for cancer cells, working with the rest of the investigators to incorporate experimental data and physics/chemistry-based models into their approaches.

The W. M. Keck Foundation was established in 1954 in Los Angeles by William Myron Keck, founder of The Superior Oil Company. One of the nation’s largest philanthropic organizations, the W. M. Keck Foundation supports outstanding science, engineering and medical research. The Foundation also supports undergraduate education and maintains a program within Southern California to support arts and culture, education, health and community service projects. This project incorporates elements from all the W. M. Keck Foundation’s focus research areas to tackle cancer with a novel, physics-based modeling and AI-centered approach.  The idea for this project originated in the 2020 MICDE faculty workshop in AI for Physically based Bio-medicine Workshop. The workshop brought together an interdisciplinary group of faculty members to discuss ways to advance artificial intelligence and machine learning methods for biomedical problems. After seeding the idea, a subset of these researchers were awarded an MICDE catalyst grant and a MIDAS PODS grant. These funds were used to establish the proof of concept and to generate preliminary results. 

Computational science is becoming increasingly indispensable in many areas of biomedical science. While the current proposal focuses on cancer, this innovative computational framework represents a transformative leap with widespread applications in multiple other biomedical, physical, and social sciences. MICDE supports innovative and interdisciplinary projects aiming to advance the current paradigms.

Portraits of Kathryn Luker, Gary Luker, Krishna Garikipati, Jennifer Linderman, Nikola Banovic and Xun Huan

Project’s principal investigators (left to right): Kathryn Luker (Radiology), Gary Luker (Radiology, Microbiology and Immonology, and Biomedical Engineering), Krishna Garikipati (Mechanical Engineering, and Mathematics), Jennifer Linderman (Chemical Engineering, and Mathematics), Nikola Banovic (Electrical Engineering and Computer Science) and Xun Huan (Mechanical Engineering).

“Get non-Real”: Department of Energy grant funds novel research in High-Performance Algorithms at U-M

By | Feature, Research

“Preparing for the future means that we must continue to invest in the development of next-generation algorithms for scientific computing,

Barbara Helland, Associate Director for Advanced Scientific Computing Research, DOE Office of Science
Source: www.energy.gov/science/articles/department-energy-invests-28-million-novel-research-high-performance-algorithms

New research from the University of Michigan will help revolutionize the data processing pipeline with state-of-the-art algorithms to optimize the collection and processing of any kind of data. Algorithms available now are built for real data, meaning real numbers, however, most of the data we see on the internet is non-real, like discrete data, or categorical. This project is part of a $2.8 million grant from the Department of Energy on algorithms research, which is the backbone of predictive modeling and simulation. The research will enable DOE to set new frontiers in physics, chemistry, biology, and other domains. 

“Preparing for the future means that we must continue to invest in the development of next-generation algorithms for scientific computing,” said Barbara Helland, Associate Director for Advanced Scientific Computing Research, DOE Office of Science. “Foundational research in algorithms is essential for ensuring their efficiency and reliability in meeting the emerging scientific needs of the DOE and the United States.”

The U-M project, led by associate professor Laura Balzano and assistant professor Hessam Mahdavifar, both of electrical engineering and computer science, is one of six chosen by DOE to cover several topics at the leading-edge of algorithms research. According to the DOE, researchers will explore algorithms for analyzing data from biology, energy storage, and other applications. They will develop fast and efficient algorithms as building blocks for tackling increasingly large data analysis problems from scientific measurements, simulations, and experiments. Projects will also address challenges in solving large-scale computational fluid dynamics and related problems.

Laura Balzano and Hessam Mahdavifar portraits

Laura Balzano, associate professor of electrical engineering and computer science (left); Hessam Mahdavifar assistant professor of electrical engineering and computer science (right)

Balzano and Mahdavifar, both Michigan Institute for Computational Discovery and Engineering (MICDE) affiliated faculty members, will use a $300,000 portion of the overall grant to study randomized sketching and compression for high-dimensional non-real-valued data with low-dimensional structures.

“Randomized sketching and subsampling algorithms are revolutionizing the data processing pipeline by allowing significant compression of redundant information,” said Balzano. “Sketches work well because scientific data are generally highly redundant in nature, often following a perturbed low-dimensional structure. Hence, low-rank models and sketching that preserves those model structures are ubiquitous in many machine learning and signal processing applications.” 

Even though a lot of the data used and processed in scientific and technological applications are best modeled mathematically as discrete, categorical or ordinal data, most state-of-the art randomized sketching algorithms focus on real-valued data. To add to this, in practical applications, treating high-dimensional data can be challenging in terms of computational and memory demands. Thus, the proposed project will significantly expand the applicability of randomized sketching.

“A key to data-driven modeling is to carefully reformulate the computational and data analysis challenges and take full advantage of the underlying mathematical structure that is often common across application areas,” said Krishna Garikipati, MICDE director and professor of mechanical engineering and mathematics.”This research and the work that Laura and Hessam are doing is critically important to the advancement of computational discovery.”

MICDE catalyst grant leads to new NSF funding to study cascade “ecohydromics” in the Amazonian headwater system

By | Feature, News, Research

The Amazon Basin cycles more water through streamflow and evaporation than any other contiguous forest in the world, and transpiration by trees is a critical part of this cycle. Understanding how plant roots, stems, and leaves interact with soil water to regulate forest transpiration across landscapes is a critical knowledge gap, especially as climate changes. Professor Valeriy Ivanov, from the Department of Civil and Environmental Engineering at U-M, is the lead investigator in a newly NSF funded project that links diverse disciplines – plant ecophysiology, ecology, and hydrology – and will build a unique modeling framework to characterize landscape variation in physiological and hydrological processes in the Amazon Basin. The framework will integrate a wide array of field observations with detailed watershed modeling for hypothesis testing. The team includes Tyeen Taylor, research fellow also from the Civil and Environmental Engineering Department at U-M, and many collaborators in the U.S. at the University of Arizona, University of West Virginia, University of Nebraska, as well as Brazilian researchers at the Federal University of Eastern Para, and Federal University of Amazonas, National Institute for Amazonian Research, and Eastern Amazon Agricultural Agency. Detailed, physical models of ecophysiology and above- and below-ground hydrology will be informed by observations of leaf physiology, tree morphological traits, soil moisture, groundwater, and streamflow. Data and models will be integrated employing novel tools in probabilistic learning and uncertainty quantification. The computational framework tools to be used in this project were developed in part with the support from MICDE Catalyst grant program for the 2018 project “Urban Flood Modeling at “Human Action” Scale: Harnessing the Power of Reduced-Order Approaches and Uncertainty Quantification” led by Prof. Ivanov. 

Given (a) a mechanistic model M (e.g., a stomatal conductance model), (b) one can treat its inputs ? (e.g., parameters) as random variables. These inputs are sampled and model simulations are carried out. Using (c) PCEs, we construct a surrogate model that best approximates the model output – left-hand-side of (c). The surrogate is then evaluated with Monte Carlo simulations and used for (d) parameter inference. (d.1) is the flow of outputs from the surrogate model into a likelihood function L (D | ?) to compare the surrogate model output and observed data D. This inference produces the posterior distribution for ?. This pdf can then be sent back to the surrogate in (d.2) to reduce the uncertainty in the inputs and to obtain pdf for a quantity of interest (e).

“The reduced-ordered modeling approach developed during the MICDE Catalyst grant project is a key element of the new project,” said Prof. Ivanov, “the MICDE seed funding has allowed us to build a general framework that is applicable to a wide range of computational applications in earth-system science, and thus made our project proposal more competitive”.

The MICDE Catalyst Grants program funds projects that have the potential to catalyze and reorient the directions of their research fields by developing and harnessing powerful paradigms of computational science. This new NSF project is an example of the reach of the program.

Read more.

We welcome 20 students to the 2021-22 class of MICDE graduate fellows

By | Feature, News

MICDE is proud to announce the recipients of the 2021 MICDE graduate fellowships. The fellows’ research projects involve the use and advancement of scientific computing techniques and practices. “This year, MICDE awarded fellowships in a wide array of disciplines ranging from chemistry to biostatistics and interdisciplinary mathematics to applied physics,” said Krishna Garikipati, MICDE director and professor of mechanical engineering and mathematics. “Engineering is also well represented with fellows focused on disciplines such as aerospace, biomedical, civil and environmental, climate and space, industrial and operations, materials science, mechanical, and naval architecture and marine engineering.”

For the past seven years, MICDE has awarded fellowships to over 135 graduate students from our large community of computational scientists. “I am so excited and honored to be a part of the MICDE Fellowship program. My research interest is in an interdisciplinary field between healthcare and data science. This fellowship symbolizes my core value for career development as a data scientist in healthcare,” said 2021 MICDE Fellowship recipient Hyeon Joo, Ph.D. pre-candidate in health infrastructure and learning systems and scientific computing. The MICDE graduate student top-off fellowship provides students with a stipend to use for supplies, technology, and other materials that will further their graduate education and research. Among other things, awards have helped many to travel to conferences and meetings around the world to share the rich and diverse research in computational science being carried out at U-M.

Yifu An, Climate and Space Sciences Engineering
Andre Antoine, Applied Physics
Shreyas Bhat, Industrial and Operations Engineering
Erin Burrell, Mechanical Engineering and Scientific Computing
Alanah Cardenas-O’Toole, Climate and Space Sciences Engineering
Brian Chen, Applied and Interdisciplinary Mathematics
Xinyu Fei, Industrial and Operations Engineering and Scientific Computing
Nicholas Galioto, Aerospace Engineering
Vishwas Goel, Materials Science and Engineering and Scientific Computing
Min-Chun Han, Civil and Environmental Engineering
Dalia Hassan, Chemistry and Scientific Computing
Alexander Hrabski, Naval Architecture and Marine Engineering and Scientific Computing
Javiera Jilberto Vallejos, Biomedical Engineering and Scientific Computing
Hyeon Joo, Learning Health Sciences and Scientific Computing
Timothy Jugovic, Chemistry and Scientific Computing
Ismael Mendoza, Physics and Scientific Computing
Aagnik (Nick) Pant, Applied Physics and Scientific Computing
Hardik Patil, Civil and Environmental Engineering
Amanda Wang, Materials Science and Engineering
Wenbo Wu, Biostatistics and Scientific Computing

Learn more about the fellows and the MICDE Fellowship program

2021-2022 Catalyst Grant awardees continue to forge new fronts in computational science

By | Feature, News, Research

The Michigan Institute for Computational Discovery and Engineering (MICDE) announced the awardees of the 2021-2022 round of Catalyst Grants. Since 2017 MICDE Catalyst Grants program has funded a wide spectrum of cutting-edge research, this year focusing on AI for physically-based biomedicine, quantum computing, convergence of natural hazards with economic dislocation, and computational integration across scales and disciplines in biology. The five projects awarded in this round represent these new frontiers of computational research spearheaded by the Institute through its initiatives.

Prof. Shravan Veerapaneni (Mathematics) is working on advancing quantum algorithm research. His team will develop a Variational Quantum Monte Carlo algorithm that can potentially be applied to a wide range of linear algebraic tasks, like QR and Singular Value Decomposition (SVD). 

Profs. Salar Fattahi (Industrial and Operations Engineering) and Arvind Rao (Computational Medicine and Bioinformatics, Biostatistics) are revisiting existing theoretically powerful maximum-likelihood estimation mathematical methods to identify areas of weakness and strengthen them for use in biomedical, largely genomic, applications.

Profs. Gary Luker (Microbiology and Immunology), Nikola Banovic (Electrical Engineering and Computer Science), Xun Huan (Mechanical Engineering), Jennifer Linderman (Biomedical Engineering and Chemical Engineering), and Kathryn Luker (Radiology), will develop a physics/chemistry-aware inverse reinforcement learning (IRL) computational framework that will support the understanding single-cell and cooperative decision-making that drive tumor growth, metastasis, and recurrence.

Profs. Seth Guikema (Civil and Environmental Engineering and Industrial and Operations Engineering) and Jeremy Bricker (Civil and Environmental Engineering) will develop an integrated computational modeling approach to studying equity and resilience during natural hazard events, specifically estimating what essential services are the main constraints on individuals returning to a more normal life post-hazard, and assess inequities in resilience to coastal flooding events. 

Prof. Jesse Capecelatro (Mechanical Engineering and Aerospace Engineering) and Alberto Figueroa (Biomedical Engineering and Vascular Surgery), will develop a versatile, physics-driven, computationally efficient, and massively parallel numerical framework to simulate the interaction between fluids and biological particles in patient-specific vasculature geometries. This framework will enable next-generation computer-aided diagnostics.

“This year’s cohort of MICDE Catalyst Grants range from quantum computing for engineering science, AI for the physics of cancer, and computational advances in hazards engineering, through mathematical advances in data science, and bioengineering,” said MICDE Director Krishna Garikpati, a professor of mathematics and mechanical engineering. “These projects represent new frontiers of computational research spearheaded by MICDE through its initiatives.”

Learn more about MICDE’s Catalyst Grant program and funded projects here.

“This year’s cohort of MICDE Catalyst Grants … represent new frontiers of computational research spearheaded by MICDE through its initiatives.”

Krishna Garikipati
Director, MICDE

miRcore is looking for group leaders to guide high school students in performing computational biomedical research

By | Feature, SC2 jobs

miRcore, a 501(c)(3) non-profit org., is looking for group leaders to guide groups of high school students in performing computational biomedical research. Group leaders will be paired with a younger co-lead. During our remote camps, students will finish a research project within about one week, while learning and applying new tools every day. Group leaders will be expected to assist and support these students.

2017 miRcore GIDAS Biotechnology Summer Camp participants

Ideal group leaders are:
1) Graduate or college students (professionals with PhDs welcome)
2) Experienced (or interested) in computational biomedical research
3) Have a passion to inspire young minds
4) Able to commit to a specific camp

Camps run June-August, and leaders will be compensated with $450 per camp session.
Group leaders usually describe the experience as fun, meaningful, an opportunity to learn new research skills, and reigniting their passion for science.

Learn more and apply here: https://forms.gle/6oD9cDqULebxarCE7