Dr. Jiliang Tang

Dr. Tang is a University Foundation Professor in the computer science and engineering department at Michigan State University. He got one early promotion to associate professor at 2021 and then a promotion to full professor (designated as MSU foundation professor) at 2022. Before that, he was a research scientist in Yahoo Research and got his PhD from Arizona State University in 2015 under Dr. Huan Liu. His research interests include graph machine learning,trustworthy AI and their applications in education and biology. He was the recipient of various awards including 2022 AI's 10 to Watch, 2022 IAPR J. K. AGGARWAL Award, 2022 SIAM/IBM Early Career Research Award, 2021 IEEE ICDM Tao Li Award, 2021 IEEE Big Data Security Junior Research Award, 2020 ACM SIGKDD Rising Star Award, 2020 Distinguished Withrow Research Award, 2019 NSF Career Award, and 8 best paper awards (or runner-ups). His dissertation won the 2015 KDD Best Dissertation runner up and Dean's Dissertation Award. He serves as conference organizers (e.g., KDD, SIGIR, WSDM and SDM) and journal editors (e.g., TKDD, TOIS and TKDE). 

Dr. Michael Murillo

Michael Murillo is a theoretical and computational physicist who applies particle-based simulation methods across diverse domains—from molecular dynamics and plasma kinetics to agent-based modeling of complex systems. As nearly a founding member of MSU's CMSE department, Prof. Murillo joined in 2016 after serving as a Director's Postdoctoral Fellow and staff scientist at Los Alamos National Laboratory for many years. He currently holds a joint appointment in CMSE and CHEMS.

Prof. Murillo leads the MIND (Modeling of Interacting Networks and Dynamics) Lab, where his team bridges traditional physics simulations with modern machine learning and network science approaches. Their work spans from understanding chronic wasting disease in wildlife and modeling conflict dynamics, to applying ML techniques in agriculture, food science, and cancer research. The lab also develops computational tools like Sarkas MD and ES-BGK kinetic codes. Prof. Murillo is a Fellow of the American Physical Society and has been instrumental in developing CMSE's interdisciplinary approach to computational science over the past decade. Prof. Murillo has developed the Foundations of Data Science course for the MSU master's in data science and the popular Applied Machine Learning course for graduate students from all disciplines.

Dr. Brian O'shea

Dr. O'shea is a professor at Michigan State University, with a joint appointment in The Department of Computational Mathematics, Science and Engineering, the Department of Physics and Astronomy, and the Facility for Rare Isotope Beams. He is a member of the Center for Hierarchical and Robust Modeling of Non-Equilibrium Transport, the Michigan Institute for Plasma Science and Engineering, and the Joint Institute for Nuclear Astrophysics. He is also the Director of MSU's Institute for Cyber-Enabled Research and the Interim Director of the MSU Bioinformatics Core.

Dr. O'shea is a computational and theoretical astrophysicist. His research involves numerical simulations and analytical modeling of cosmological structure formation, galaxy clusters, high-redshift galaxies, Milky Way-type galaxies, plasma turbulence, and terrestrial plasma physics (including the potential uses of machine learning in the modeling of plasma systems). In addition, his research interests have extended into computational science education research as part of the MSU Computational Education Research Lab.

Dr. John W. Luginsland

Dr. John W. Luginsland is a Principal General Engineer (DR-IV/GS-15) at the Air Force Research Laboratory Strategic Partnering (AFRL/SP). He serves as the Collaboration Director and AFRL Lead for AFRL’s MidAtlantic Regional Network, an innovation acceleration unit for dual-use technology anchored by Cornell University under a cooperative agreement. Previously, he worked at the Air Force Office of Scientific Research (2021 to 2024), where he served as the founding Program Officer for High Energy Radiation-Matter Systems, the acting Program Officer for Astrodynamics, as well as the Acting Branch Chief for the Physical and Biological Sciences Branch in 2022. In additional terms of government service, he worked as an Acting Division Chief, a Division Technical Advisor, as well as the program officer for plasma physics (2009-2014) and lasers and optics (2014-2017) at AFOSR. From 2013 to 2014, Dr. Luginsland was the program element monitor and action officer for Air Force basic research in the Office in the Assistant Secretary of the Air Force for Acquisitions (SAF/AQR). He was a staff member at the Air Force Research Laboratory’s Directed Energy Directorate in the late 1990s, having first joined AFRL as a National Research Council Postdoctoral Researcher. Dr. Luginsland has industrial experience at Confluent Sciences, LLC, NumerEx, LLC, and Science Applications International Corporation as well as academic experience as a professor at Michigan State University in the Departments of Computational Mathematics, Science and Engineering and Electrical and Computer Engineering. Additionally, he was an elected Chair of the IEEE’s Plasma Science and Applications Committee, a past member of the National Academies of Science, Engineering, and Medicine’s Intelligence Science and Technology Experts Group (ISTEG), and served as co-chair on a National Academy consensus study “Powering the Army of the Future.” Dr. Luginsland is a fellow of the IEEE and the Air Force Research Laboratory, and received the IEEE Nuclear and Plasma Science Society’s Early Achievement Award. He has degrees from the University of Michigan in Nuclear Engineering.

Dr. Alexei Bazavov

Alexei Bazavov is an associate professor at Michigan State University, jointly appointed to the Department of Computational Mathematics, Science & Engineering and the Department of Physics & Astronomy. He previously served as a research associate at the University of Arizona (2007-2010), Brookhaven National Laboratory (2010-2013) and a joint appointment between the University of California, Riverside and the University of Iowa (2013-2016).

He is a theoretical particle physicist specializing in study of strongly coupled theories, in particular, Quantum Chromodynamics. Items of particular interest to him include: Quantum field theory, Finite-temperature field theory, Lattice gauge theory with applications to particle and nuclear physics, Parallel algorithms, iterative solvers, molecular dynamics algorithm, Inverse problems and Bayesian inference Ultra-cold atomic systems and quantum simulation

Dr. Yuying Xie

Yuying Xie is an Associate Professor in the Department of Computational Mathematics, Science and Engineering and Department of Statistics and Probability at Michigan State University. He eceived his first Ph.D. in genetics  and his second Ph.D. in statistics from the University of North Carolina at Chapel Hill. His research mainly focuses on developing statistical machine learning and deep learning algorithms for single-cell, spatially resolved transcriptomics, genome-wide, and metagenomics data and their application on clinical data, including head and neck cancer, colitis, and SARS-CoV-2. 

Dr. Bin Chen

Bin Chen is an Associate Professor in the Department of Pediatrics and Human Development. His research focuses on developing computational methods and tools, in collaboration with bench scientists and clinicians, to discover new or better therapeutic candidates. Dr. Chen received his Ph.D. in Informatics from Indiana University and completed his postdoctoral training in bioinformatics at Stanford University. Prior to joining MSU, he was an Assistant Professor in the Institute for Computational Health Sciences at the University of California, San Francisco. He is also the founder of DahShu, a non-profit organization promoting research and education in data sciences.

The Chen Lab is interested in leveraging “big data” and artificial intelligence to connect the various components (patients, tissues, in vitro models and in vivo models) needed for translational research. Using a systems-based approach, Dr. Chen’s lab has successfully identified drug candidates for Ewing’s sarcoma, liver cancer, and basal cell carcinoma. In a pan-cancer analysis, the lab found that the potency to reverse cancer gene expression correlates to drug efficacy. Additionally, Dr. Chen’s team is developing methods to integrate single-cell data and deep learning approaches for personalized cancer therapy.

Dr. Adam Alessio

Adam Alessio is a professor in the departments of Computational Mathematics, Science, and Engineering (CMSE), Biomedical Engineering (BME), and Radiology. He is currently serving as the Interim Chair of the Department of Biomedical Engineering His research is focused on non-invasive quantification of disease through advanced imaging algorithms and integrated data analysis.

Prior joining MSU, Dr. Alessio was a professor of Radiology at the University of Washington.  He received his Ph.D. in Electrical Engineering at the University of Notre Dame and post-doctoral training in nuclear medicine physics at the University of Washington. He is the author of over 70 peer-reviewed publications, holds 6 patents, and has grant funding from the National Institutes of Health and the medical imaging industry to advance non-invasive cardiac and cancer imaging.

Dr. Alessio’s research group solves clinically motivated research problems at the intersection of imaging and medical decision-making.  Current efforts center on translational medical research projects for topics including machine learning for quantitative diagnostics, cardiac perfusion estimation, quantitative PET and CT imaging, radiation dose optimization, and system modeling. 

Dr. Saiprasad Ravishankar

Dr. Ravishankar received the B.Tech. degree in Electrical Engineering from the Indian Institute of Technology Madras, in 2008. He received the M.S. and Ph.D. degrees in Electrical and Computer Engineering, in 2010 and 2014 respectively, from the University of Illinois at Urbana-Champaign. After my Ph.D., I was an Adjunct Lecturer in the Department of Electrical and Computer Engineering, and a Postdoctoral Research Associate in the Coordinated Science Laboratory at the University of Illinois. From August 2015, He was a Postdoc in the Department of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor. I was a Postdoc Research Associate in the Theoretical Division at Los Alamos National Laboratory from August 2018 to February 2019. He is currently an Associate Professor in the Departments of Computational Mathematics, Science and Engineering, and Biomedical Engineering at Michigan State University (MSU), and lead the Signals, Learning, and Imaging (SLIM) Group at MSU. His research interests include signal and image processing, computational and biomedical imaging, machine learning, deep learning, inverse problems, compressed sensing, dictionary learning, data science, image analysis, neuroscience, and large-scale data processing and optimization. 

Dr. Danny Caballero

Dr. Caballero works in the Department of Physics and Astronomy and the Department of Computational Mathematics, Science and Engineering at Michigan State University. He hold the Lappan-Phillips Chair of Physics Education, co-direct the Physics Education Research Lab, served as a principal investigator for the Learning Machines Lab, conduct research as part of the newly-founded Computational Education Research Lab, and hold an appointment as research faculty at the University of Oslo’s Center for Computing in Science Education. He studies how tools and science practices affect student learning in physics and computational science, and the conditions and environments that support or inhibit that learning.

His work spans from the high school to the upper-division. I am particularly interested in how students learn through their use of tools such as mathematics and computing. His work employs cognitive and sociocultural theories of learning and aims to blend these perspectives to enhance physics and computational science instruction at all levels. His projects range from the fine-grained (e.g., how students engage with particular computing ideas) to the course-scale (e.g., what kind of things that students are able to do after instruction in computing) to the very broad (e.g., how do departments value computing and work to integrate it). His work includes the use of data science to address questions in STEM education as well as concerns about diversity, equity, inclusion, and belonging.

Dr. Ron Buckmire

Profesor Buckmire is the Dean of the School of COmputer Science and Mathematics and Professor of Mathematics at Marist University. He was on the faculty of Occidental College for more than 30 years after receiving a Ph.D. in Mathematics from Rensselaer Polytechnic Institute in 1994. There he served as department chair, associate dean for curricular affairs and director of the Core (general education) program and taught classes such as Calculus, History of Mathematics, Differential Equations, and Mathematical Modeling. 

He has served as a Program Officer in the Division of Undergraduate Education at the National Science Foundation twice (2011-2013 and 2016-2018). His published articles are in an eclectic collection of peer-reviewed journals such as Data, Notices of the American Mathematical Society, Numerical Methods for Partial Differential Equations, Journal of Humanistic Mathematics, Journal of Machine Learning for Modeling and Computing, andAlbany Law Review. 

In 2023, he was named a Fellow of the Society for Industrial and Applied Mathematics (SIAM), the first person from a small liberal arts college, the fourth Black person, and the first openly LGBTQ+ person to receive this prestigious honor. He was SIAM’s inaugural Vice-President for Equity, Diversity, and Inclusion from 2021 to 2024.

Dr. Michael Lachney

Dr. Michael Lachney is an Assistant Professor in Michigan State University’s Educational Psychology and Educational Technology program. He has a background in science and technology studies and contributes to the field of computer science education. His research focuses on the cultural and racial politics of computing in school and community settings. His research has appeared in the journals Learning, Media and Technology, Science as Culture, Computer Science Education, among others. His research focuses on K-12 computer science education, culturally responsive computing, science and technology studies, educational technology

Dr. Devin Silvia

Dr Silvia is currently the Director of Undergraduate Studies and a teaching specialist in the Department of Computational Mathematics, Science, and Engineering at Michigan State University. He is a computational astrophysicist and education researcher working to understand how the gas around galaxies influences intergalactic star formation and how students learn to do computational science.

My research interests include: non-equilibrium ionization chemistry,  the intergalactic & circumgalactic media,  galactic chemical evolution, cosmological hydrodynamics and computational science education

Amanda Bowerman

Amanda Bowerman is a doctoral research fellow at the University of Oslo in the department of Mathematics and Natural Science. Her research is centered around the use of Natural Language Processing (NLP) techniques combine with Large-Language Models (LLMs) to do qualitative data analysis at scale. Amanda received her Master’s degree in Computational Mathematics Science and Engineering from Michigan State University, where she also received a Bachelor’s degree in Data Science. During her time at Michigan State, Amanda was a part of the Computational Education Research Group (CERL) where she did research and curriculum development for some of the entry-level data science courses. She was also an undergraduate learning assistant (ULA) and graduate teaching assistant (TA) for a combined total of 4 years. 

Dr. Dirk Colbry

Dr. Dirk Colbry is faculty in the Department of Mathematics, Science and Engineering at Michigan State University. An alumnus of MSU, Colbry has a Ph.D. in Computer Science and his principle areas of research include machine vision and pattern recognition (specializing in scientific imaging). Dr. Colbry also does research in computational education and high performance computing. From 2009 until 2015, Dr. Colbry worked for the Institute for Cyber Enabled Research as a computational consultant and Director of the HPCC. Dr. Colbry collaborates with scientists from multiple disciplines including Engineering, Toxicology, Plant and Soil Sciences, Zoology, Mathematics, Statistics and Biology. Recent projects include research in Image Phenomics; developing a commercially-viable large scale, cloud based image pathology tool; and helping develop methods for measuring the Carbon stored inside of soil. Dr. Colbry has taught a range of courses, including; communication "soft" skills, introduction to computational modeling, microprocessors, artificial intelligence, scientific image analysis, compilers, exascale programing, and courses in programming and algorithm analysis.

Dr. Nathan Brugnone 

Dr. Brugnone serves as associate research scientist in the Department of Environmental Health and Engineering at Johns Hopkins University, faculty scholar in the Department of Community Sustainability at Michigan State University, and senior research scientist at Two Six Technologies. His work focuses developing, applying, and evaluating machine learning and AI methods for the study of complex social-ecological systems with government partners who include DARPA, NOAA, and NIST. He earned his Ph.D. in Computational Mathematics, Science, and Engineering and Community Sustainability from Michigan State University in 2023.

Dr. He Lyu

I am currently a Research Scientist at Meta Platforms, Inc. My work focuses on optimizing advertisement ranking systems to maximize the relevance and effectiveness of ads shown to users. I received my PhD degree from CMSE in 2023, and before that I completed my undergraduate studies at Fudan University.  At CMSE, I had the opportunity to collaborate with many distinguished faculty members and talented CMSE peer students. CMSE also provided me with extensive hands-on experience in computational mathematics, statistics, and programming, which were essential in building the technical skills for my career. 

MS. Katrina Gensterblum 

Katrina Gensterblum is a Data Science Manager at Kellanova (formerly Kellogg’s), where she leads the Demand Data Science team in improving forecasting accuracy and extracting actionable insights from demand trends. Her other projects have included commodity forecasting and multidimensional scenario planning, contributing over $12 million in annual value. Katrina holds an MS in Computational Mathematics, Science, and Engineering (CMSE) from Michigan State University

Dr. Runze Su

Dr. Su is a Senior Machine Learning Engineer on the Ads Performance team at Pinterest, where my work focuses on applying novel research to solve challenges in large-scale advertising systems. This has allowed me to co-author numerous publications in the field of machine learning during my time here. I hold two PhDs from Michigan State University in Statistics and in Computational Mathematics, Science, and Engineering (CMSE), and previously completed research internships at Expedia, TikTok, and Kwai.

Dr Hongzhi Wen 

Dr. Wen is the Co-founder & Chief AI Scientist at CartaBio, a start-up company providing innovative AI Solutions For Decoding Biology System. Dr. Wen received his PhD from Michigan State University in the Department of Computer Science and Engineering and have published numerous AI algorithms for single cell and spatial transcriptomices data anlayses including the first Cell centric Foundation model, CellPLM.