What if we could design neurotherapeutics like we engineer a chip?

Lilianne R. Mujica-Parodi is Director of the Laboratory for Computational Neurodiagnostics (www.lcneuro.org), Baszucki Endowed Chair of Metabolic Neuroscience, and Professor in the State University of New York at Stony Brook Department of Biomedical Engineering.   Additionally, she is Neuroscientist at the Athinoula A. Martinos Center for Biomedical Imaging (Massachusetts General Hospital and Harvard Medical School).  Dr. Mujica-Parodi received her B.A. from Georgetown University and Ph.D. from Columbia University in mathematical logic and foundations of physics. After her Ph.D. (Niles G. Whiting Fellow), she continued as faculty at Columbia’s College of Physicians and Surgeons (Psychiatry, Clinical Neuroscience) until being recruited to Stony Brook.  She is a recipient of the National Science Foundation’s Career Award, the White House’s Presidential Early Career Award in Science and Engineering, and the Fulbright Distinguished Scholar Award.  Dr. Mujica-Parodi has made fundamental contributions to clinical neuroscience through the application of control systems engineering and dynamical systems to human neuroimaging data (7T fMRI, MRS, MEEG, fNIRS, PET), and is a co-inventor of commercialized MR hardware for clinical neuroimaging.  Neurodiagnostic applications of this work have ranged from neurological (dementia, epilepsy) and psychiatric (anxiety, depression, addiction, bipolar, schizophrenia) disorders, to screening protocols for U.S. Navy SEALs. She has been the Lead Principal Investigator of Neuroblox since its inception.

Alan Edelman is a Professor of Applied Mathematics at MIT and a Principal Investigator at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). He is widely recognized for his groundbreaking contributions to numerical linear algebra, random matrix theory, and high-performance scientific computing. Edelman is the co-creator of the Julia programming language, a high-level, high-performance dynamic programming language designed for technical computing that has revolutionized scientific computation across numerous fields. His research spans computational mathematics, with particular expertise in eigenvalue problems, matrix computations, and the mathematical foundations of machine learning. In addition to Neuroblox, Dr. Edelman has co-founded two other companies:  the first of which was acquired by Microsoft, and the second of which has raised $43.6 million.  He has received many honors for his innovations, including the prestigious Gordon Bell Prize.  Edelman's interdisciplinary approach combines deep mathematical theory with practical computational solutions, making him a leading figure in the intersection of mathematics, computer science, and scientific computing.

Christopher Rackauckas has deep technical expertise in the computational side of the pharmaceutical industry as a founding member of PumasAI and the original architect of the Pumas nonlinear mixed-effects software. Chris's work with Pumas is well known in the field of clinical pharmacology, having won four research awards and the highest early-career award, the Emerging Scientist Award from the International Society of Pharmacometrics. He has collaborated with top industry scientists from prominent companies, including Pfizer, Moderna, and others. Since 2021, Chris has served as the VP of Modeling and Simulation and chief architect of Dyad at JuliaHub, where he has led the commercialization of a new systems-level modeling framework in the automotive and aerospace industries. Before deciding to work full-time in industry, Chris was an Applied Mathematics Instructor at the Massachusetts Institute of Technology and remains a Research Affiliate at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). His work on numerical differential equations (in particular the open source Julia numerical ecosystem, including DifferentialEquations.jl and ModelingToolkit.jl) and scientific machine learning (SciML) has earned 13 separate research and paper awards, including the US Air Force Artificial Intelligence Accelerator Scientific Excellence Award for fundamental work on integrating machine learning into scientific computing.

Earl K. Miller is the Picower Professor of Neuroscience at the Massachusetts Institute of Technology, with appointments in the Picower Institute for Learning and Memory and the Department of Brain and Cognitive Sciences. He earned a B.A. from Kent State University and subsequently completed an M.A. and a Ph.D. at Princeton University.  Dr. Miller’s work combines experimental and computational approaches to study the neural basis of cognition, including working memory, attention, and consciousness. He is known for theories on executive brain functions and the role of the prefrontal cortex. Dr. Miller’s honors include election to the American Academy of Arts and Sciences, the George Miller Prize in Cognitive Neuroscience, and the Goldman-Rakic Prize for Outstanding Achievement in Cognitive Neuroscience. His paper with Jonathan Cohen (Miller and Cohen, 2001), proposing a new theory of executive control, is among the most cited in the history of neuroscience.

Richard Granger received his Bachelor’s from MIT and Ph.D. in Computer Science from Yale. He is a Professor at Dartmouth, holding joint appointments in the Department of Psychological and Brain Sciences, the Thayer School of Engineering, and the Cognitive Science Program. He directs Dartmouth’s interdisciplinary Brain Engineering Laboratory (brainengineering.org), with publications and patents spanning computation, cognition, and neuroscience. He is an elected Fellow of the American Association for the Advancement of Science (AAAS). He advises multiple technology corporations and government research agencies, is a co-inventor of FDA-approved devices and drugs in clinical trials, and has been the principal architect of a series of advanced computational systems for military, commercial, and medical applications.

Dr. Helmut H. Strey is an Associate Professor of Biomedical Engineering at Stony Brook University and Director of the Laboratory for Micro- and Nanotechnologies for Quantitative Biology, with current affiliations as an MRI Researcher at the Martinos Center at Massachusetts General Hospital/Harvard Medical School and Affiliate Faculty at MIT's Picower Institute and Computer Science and Artificial Intelligence Laboratory (CSAIL). He leads interdisciplinary research, developing cutting-edge technologies and analytic methods for neuroscience, cancer research, genomics, and biophysics. With a Ph.D. in Physics from the Technical University of Munich and postdoctoral training at NIH, Dr. Strey has made significant innovations in scientific machine learning, the inverse problem, single-molecule biophysics, and microfluidics. A recipient of the National Science Foundation Career Award and the American Physical Society's Dillon Medal, Dr. Strey is a Member of the National Academy of Inventors with multiple patents in microfluidics, neuroimaging technologies, and data analysis methods.  He has been the Lead Software Engineer of Neuroblox since its inception.