The IAIFI is comprised of both physics and AI researchers at MIT, Harvard, Northeastern, and Tufts.
We are currently accepting applications from senior researchers in both academia and industry to become IAIFI Affiliates. If this interests you, see our IAIFI Affiliates Application Form.
If you are a junior researcher interested in becoming involved in IAIFI, see our Junior Researcher Interest Page.
There are various levels of involvement in IAIFI:
Senior Investigators, Junior Investigators, IAIFI Affiliates: Members in these categories are actively working on IAIFI-related research and must report their IAIFI-related activities to the NSF. Everyone at these levels is listed on this page.
Friend of IAIFI: Friends of IAIFI are Boston-area researchers interested in IAIFI’s mission, but cannot receive NSF funding and have no reporting requirements. Friends of IAIFI are welcome to participate in internal IAIFI activities. If you are interested in becoming a Friend of IAIFI, complete the interest form.
Management
IAIFI Director
QCD and jet physics, point clouds, topic modeling, optimal transport
MIT
IAIFI Deputy Director
dark sector physics, high-throughput real-time analysis, AI robustness
MIT
Senior Investigators
IAIFI Institute Board (Harvard); Colloquium Organizer
dark matter, inflation, CMB, gravitational lensing, galaxy clustering
Harvard
IAIFI Institute Board (Northeastern); Summer School & Workshop Chair
string theory, particle-cosmology, topology, RL, NN-QFT correspondence
Northeastern
IAIFI Institute Board (Tufts), Computing Committee Member
neutrino physics, convolutional and generative neural networks, reinforcement learning
Tufts
IAIFI Institute Board (MIT)
mid-level vision, computational photography, black hole imaging
MIT
IAIFI Early Career and Equity Committee Chair
Time-domain astrophysics, gravitational wave astrophysics, machine learning classification
Harvard
IAIFI Community Building Chair
quantum field theory, collider physics
Harvard
IAIFI Research Coordinator for Physics Theory
Lattice QCD, flow models, unsupervised learning, interpretability
MIT
IAIFI Research Coordinator for Physics Experiment
Deep Learning based Hardware Acceleration, FPGAs, GPUs, Dark Matter, QCD and jet physics
MIT
IAIFI Research Coordinator for AI Foundations; Seminar Organizer
Sparse representations, deep learning, computational neuroscience
Harvard
IAIFI Fellowship Chair
gravitational waves, laser interferometry, quantum optics, precision measurements
MIT
IAIFI Education and Workforce Development Coordinator
quantum information science; machine learning; trapped ion quantum computation
MIT
IAIFI Computing Chair
strong intereactions, nuclear physics, AI for simulations
MIT
IAIFI Public Engagement Chair
Dark Matter, Galaxy Formation, Local Universe
MIT
IAIFI Industry Partnership Chair
artificial visual perception, computational sensing, computer vision
Harvard
Summer School & Workshop Committee Member
Information Theory, Generative Networks, Robust Representation Learning, Optimal Transport
Tufts
IAIFI Communications Committee Chair
sensorimotor control, deep learning, robotics, intuitive physics and behavior, reinforcement learning, computer vision
MIT
IAIFI Public Engagement Committee Member
neutrino experiment, neutrino phenomenology, astroparticle physics
Harvard University
statistical cosmology, large-scale structure surveys
Harvard
Interstellar dust, high-energy astrophysics, cosmology
Harvard
IAIFI Summer School & Workshop Chair; Community Building Committee Member
string theory, string pheno, topology, geometry, ML
Northeastern
IAIFI Communications Committee Member
Dark matter theory, particle astrophysics, early-universe cosmology
MIT
IAIFI Early Career and Equity Committee Member
geometry, machine learning, computational physics, particle physics, materials physics
MIT
nanophotonics, AI for nanophotonics and science in general, physics inspired AI algorithms
MIT
Time-domain Astrophysics, Multimessenger Astrophysics, Representation Learning
Harvard
IAIFI Fellows
Incoming IAIFI Fellow
statistical physics, generative modeling, measure transport, dynamical systems
Incoming IAIFI Fellow
particle physics, self-supervised learning, dark matter, jet physics, AI robustness
Carolina Cuesta
IAIFI Fellow, IAIFI Early Career and Equity Committee and Industry Partnership Committee Member
cosmology and AI, ML models, statistics
IAIFI
Incoming IAIFI Fellow
Applied Mathematics, Dynamical Systems Theory, Machine Learning
IAIFI Fellow and IAIFI Journal Club Organizer
Time-domain astrophysics, machine-learning classification, gravitational-wave astrophysics, variational inference
IAIFI
Gaia Grosso
IAIFI Fellow and IAIFI Public Engagement Committee Member
ML for particle physics, collider experiments, hypothesis testing, anomaly detection
IAIFI
Incoming IAIFI Fellow
String Theory, Field Theory, Cosmology, and Geometry
IAIFI Fellow, Early Career and Equity Committee and Community Building Committee Member
machine learning, particle physics experiments, neutrinos
IAIFI
Long-Term Visitors
CMS event reconstruction, Advanced instrumentation, FAIR data, precision timing
University of Minnesota
Victor Verschuren
Machine-learning, particle-physics, CMS
MIT
IAIFI Affiliates
Electroweak interactions, collider physics, AI for event reconstruction, GPUs
Brandeis
Quantum information theory, in particular as it relates to high energy physics AdS/CFT; Complexity theory, both classical and quantum; Quantum Field Theory
Northeastern
Jonathan Blazek
cosmology with galaxy surveys, statistical inference, simulation-based modeling, emulation with ML
Northeastern University
ML for numerical geometry, string theory, working on a computational theory of mathematical thought
Harvard
Algebraic geometry, p-adic string theory, graph curvatures, ML for algebra
Brandeis
machine learning, AI for Science, molecular modeling, protein design, material design
MIT
gravitational-wave astrophysics, muti-messenger astronomy, astroparticle physics, machine learning applications in physics
MIT
Reinforcement learning, nonequilibrium statistical mechanics, biological physics, machine learning
UMass Boston
BSM Physics, Silicon Detectors, Software Training, Machine Learning, AI
University of Puerto Rico - Mayaguez
Predictive AI modeling, active learning, graph theory, neuroscience, hyperspectral imaging
University of Puerto Rico - Mayaguez
IAIFI Public Engagement Committee Member
string phenomenology, dark sectors, AI transference, reinforcement learning
Northeastern
Quantum many-body physics, statistical mechanics, quantum information theory, exact computational methods
UMass Boston
IAIFI Speaker Selection Committee Member
effective theory of deep learning, quantum field theory, black holes & quantum chaos, word play
MIT
Algebraic Geometry (Gromov-Witten theory, Donaldson-Thomas theory), Derived Algebraic Geometry, Mathematics of String theory, Mathematical AI
Center for QGM / Harvard / U. Miami
quantum information theory; statistical mechanics; quantum optics
UMass Boston
cosmology, gravitation, GAI applications to experiments, dark matter, dark energy
Harvard
Physics of Natural and Artificial Intelligence for Trustworthy and Green AI
Harvard University / NTT Research, Inc.
Distance geometry, matrix completion, compressive sensing, manifold learning
Tufts
Quantum gravity, string theory, geometry, particle physics, energy, ecology, computational physics
MIT
cosmological simulations, high-performance computing, galaxy/structure formation
MIT
Geometric Deep Learning, Equivariant Neural Networks, Mathematical Foundations of AI, AI for Science, Robot Learning
Northeastern University
Post-Docs and Research Scientists
nuclear physics, dense matter, gravitational wave astrophysics, HPC, AI for physics and astrophysics
Harvard
Time-Domain Astronomy, Transients, Supernovae, Gamma-Ray Bursts, Time-Domain Surveys
Harvard University
String theory, Mathematical Physics, Particle Physics, Jet Physics, Machine Learning
University of Puerto Rico Mayaguez
Christian Ferko
neural network field theories; machine learning patterns of quantum entanglement
Northeastern University
Daichi Hiramatsu
supernovae, gravitational wave sources, transient surveys
Harvard
particle physics beyond the standard model, collider physics, quantum field theory
Harvard
Matheus Hostert
Neutrino and dark matter theory, astroparticle physics
Harvard
Nicholas Kamp
high-energy reconstruction neutrinos transformers
Harvard University
Rashmish Mishra
IAIFI Early Career and Equity Committee Member
collider physics, dark sectors, early universe cosmology
Harvard
Gravitational-wave astrophysics, Bayesian inference, machine-learning, likelihood-free methods
MIT
gravitational waves, laser interferometry, AI-based sensing and control, embedded machine learning
MIT
Christina Reissel
High-energy particle physics, Gravitational wave astronomy, AI for Anomaly detection
MIT
IAIFI Public Engagement Committee Member
Phenomenology of Particle Physics, Beyond the Standard Model Physics, Jet Physics, Machine Learning
MIT
computational QFT, probabilistic modeling, generative machine learning, numerical optimization, inverse problems
MIT
Students and Post-Bacs
Polina Abratenko
IAIFI Public Engagement Committee Member
neutrino physics
Tufts
reinforcement learning, non-equilibrium statistical mechanics
UMass Boston
Omar Alterkait
IAIFI Community Building Committee Member
Neutrino Physics, Machine Learning
Tufts
Samuel Alipour-fard
Quantum field theory, collider physics, and machine learning in high energy physics.
MIT
Oscar Barrera
IAIFI Computing Committee Member
High Performance Computing, Black Holes, Cosmology, Quantum Field Theory
Harvard University
Juvenal Bassa
Deep Learning, Image Classification, Particle Physics, Cross-Validation, Ensemble Learning
University of Puerto Rico at Mayagüez
Sean Benevedes
IAIFI Early Career and Equity Committee Member
Quantum field theory, theoretical particle physics, and interfaces of these with machine learning
MIT
Meghana Borse
Experimental Particle Physics, Neural Network, Plasma Physics, FPGA
University of Puerto Rico, Mayagüez
Shu-Fan Chen
Cosmology, Large-Scale Structure, Early Universe, Machine Learning
Harvard University
Zhuo Chen
Physics for AI, AI for physics, quantum many-body physics, quantum information theory
MIT
IAIFI Speaker Selection Committee Member
Generative models for molecules and learning dynamics
MIT
self-supervised learning, meta learning, contrastive learning, natural language processing
MIT
Aurélien Dersy
Quantum Field Theory, Particle Physics, Machine Learning
Harvard
Atakan Hilmi Firat
String theory, string field theory, and machine learning for geometry
MIT
Interested in Particle Physics, QFT, Phenomenology, Machine Learning, and Information Theory!
MIT
Computer Vision, Distributed COmputing, Information Theory, Deep Learning
MIT
Statistical learning; Optimization; Computer Vision; Imaging
Harvard
Elyssa Hofgard
Symmetry equivariant neural networks, computational physics, physics inspired AI
MIT
Reinforcement learning, sequential decision making, biologically inspired computing
MIT
Zev Imani
Early Career and Equity Committee Member
Neutrino Physics & Machine Learning
Tufts
generative models, lattice field theory, physics beyond the standard model
Universität Bern, ITP
Geometry, Systems for Machine Learning, GPUs, High Performance Computing, Domain Specific Languages, Hardware Accelaration
MIT
Jeffrey Krupa
high energy physics, dark matter, deep learning, heterogeneous computing, accelerated architectures, high-throughput computing
MIT
Zhaoyi Li
Quantum Computing, Machine Learning, Quantum Error Correction, Many-Body Physics, Quantum Neural Networks
MIT
Joshua Lin
Lattice Quantum Chromodynamics, Neural Network Quantum States, Hamiltonian Simulations, Gauge Theories
MIT
Charlotte Loh
self-supervised learning, uncertainty quantification, AI for scientific applications
MIT
Lesbia Lopez
Neuroscience, machine learning, deep learning
University of Puerto Rico at Mayagüez
Silviu-Marian Udrescu
Physics for AI, nuclear physics, high energy physics, atomic molecular and optical physics
MIT
Trevor McCourt
Computational & systems neuroscience, physics-inspired computing, probabilistic computing
MIT
Eric Moreno
Anomaly detection, Higgs measurements, Graph Neural Networks, Fast inference, Dark matter searches
MIT
Nayantara Mudur
IAIFI Summer School & Workshop Committee and IAIFI Speaker Selection Committee Member
Interstellar dust, Scalable Bayesian Inference, Cosmology, Astrostatistics
Harvard
Joydeep Naskar
Quantum gravity, quantum information theory, quantum field theory, machine learning theory
Northeastern
High Energy Physics, Dark Matter Searches, Machine Learning, High Performance Computing
MIT
IAIFI Summer School & Workshop Committee Member and IAIFI Public Engagement Committee Member
NN scaling laws, machine learning, cosmology
Northeastern
IAIFI Industry Partnership Committee Member and IAIFI Public Engagement Committee Member
Collider Data Analysis, FPGAs, Probabilistic Modeling and Inference
MIT
Diego Fernando Vasques Plaza
Artificial Intelligence, Machine Learning, Data Analysis
University of Puerto Rico at Mayagüez
machine learning, online learning, reinforcement learning, offline estimation
MIT
Anmol Raina
Cosmology, Cosmic Microwave Background, Wavelet Scattering Transforms, non-Gaussianity in data
Harvard University
Simon Rothman
Collider physics, dark matter searches, Higgs physics, novel machine learning approaches
MIT
IAIFI Industry Partnership Committee Member
Deep learning theory, structured representations, nonlinear optimization, algebraic geometry
Harvard
Felix Yu
deep learning, high energy physics
Ethan Silver
Gravitational wave astrophysics, machine learning, time-domain astrophysics
Harvard University
AI for (astro)physics, foundation models, interpretability and robustness
Harvard
Machine Learning, Algorithms, Probability/Statistics, Origami
MIT
Nino Ephremidze
Cosmology, dark matter, gravitational lensing
Harvard
Varun Hariprasad
Large language models, AI for physics, Automated planning, Control systems
MIT
Machine Learning, Software Engineering, Physics-inspired algorithms
MIT
Elizabeth Panner
Neutrinos, dark matter, neural networks, quantum sensing
Tufts
Graph Neural Networks, Theoretical Particle Physics, Machine Learning for Physics
MIT
