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); Summer School & Workshop 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
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
neutrino experiment, neutrino phenomenology, astroparticle physics
Harvard University
statistical cosmology, large-scale structure surveys
Harvard
Interstellar dust, high-energy astrophysics, cosmology
Harvard
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
IAIFI Fellows
IAIFI Fellow; Summer School & Workshop Committee
lattice field theory, generative models, Markov Chain Monte Carlo, high performance computing
IAIFI
Carolina Cuesta
IAIFI Fellow, Early Career and Equity Committee and Industry Partnership Committee Member
cosmology and AI, ML models, statistics
IAIFI
IAIFI Fellow
Time-domain astrophysics, machine-learning classification, gravitational-wave astrophysics, variational inference
IAIFI
Anna Golubeva
IAIFI Fellow; Summer School & Workshop Committee and Communications Committee Member
Theory of Deep Learning, Condensed Matter Theory
IAIFI
Gaia Grosso
IAIFI Fellow
ML for particle physics, collider experiments, hypothesis testing, anomaly detection
IAIFI
IAIFI Fellow, Speaker Selection Committee Member
quantum algorithms and machine learning for condensed matter physics, high energy physics, and quantum information science
IAIFI
IAIFI Fellow, Early Career and Equity Committee and Community Building Committee Member
machine learning, particle physics experiments, neutrinos
IAIFI
IAIFI Fellow, IAIFI Computing Committee Member
particle astrophysics, cosmology, simulation-based inference, probabilistic programming
IAIFI
IAIFI Fellow
reinforcement learning, planning, optimal transport, robotics
IAIFI
Long-Term Visitors
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
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
gravitational-wave astrophysics, muti-messenger astronomy, astroparticle physics, machine learning applications in physics
MIT
string phenomenology, dark sectors, AI transference, reinforcement learning
Northeastern
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
Geometry, large-scale optimization, numerical methods, machine learning
MIT
theoretical neuroscience, deep learning, AI
Harvard/Hebrew University
Distance geometry, matrix completion, compressive sensing, manifold learning
Tufts
Quantum gravity, string theory, geometry, particle physics, energy, ecology, computational physics
MIT
Time-domain Astrophysics, Multimessenger Astrophysics, Representation Learning
Harvard
cosmological simulations, high-performance computing, galaxy/structure formation
MIT
Post-Docs and Research Scientists
Daniel Johnson
Dark-sector searches; hadron spectroscopy; high-throughput real-time analysis
MIT
nuclear physics, dense matter, gravitational wave astrophysics, HPC, AI for physics and astrophysics
Harvard
Mehmet Demirtas
IAIFI Community Building Committee Member & Early Career and Equity Committee Member
string theory, algebraic topology, deep learning
Northeastern
Lena Funcke
lattice field theory, physics beyond the Standard Model, machine learning, quantum computing
MIT
Daniel Hackett
lattice field theory, statistical mechanics, normalizing flows, Markov Chain Monte Carlo
MIT
Arthur Hennequin
Computer Architectures, Compilers, Code optimization, Machine Learning
MIT
Daichi Hiramatsu
supernovae, gravitational wave sources, transient surveys
Harvard
particle physics beyond the standard model, collider physics, quantum field theory
Harvard
Patrick McCormack
Hardware acceleration, Anomaly detection, Dark matter searches
MIT
Rashmish Mishra
IAIFI Early Career and Equity Committee Member
collider physics, dark sectors, early universe cosmology
Harvard
gravitational waves, laser interferometry, AI-based sensing and control, embedded machine learning
MIT
dark matter, strong lensing, gaia satellite, lhc, hst, anomaly detection, uncertainty reduction
Microsoft
Sokratis Trifinopoulos
Phenomenology of Particle Physics, Beyond the Standard Model Physics, Jet Physics, Machine Learning
MIT
theoretical/numerical cosmology, cosmological tests of fundamental physics, LSS
Harvard
Students
Samuel Alipour-fard
Quantum field theory, collider physics, and machine learning in high energy physics.
MIT
Sean Benevedes
IAIFI Early Career and Equity Committee Member
Quantum field theory, theoretical particle physics, and interfaces of these with machine learning
MIT
binary evolution, neutron stars and black holes, gravitational waves, star formation history, Monte Carlo Sampling, Uncertainty Quantification
Harvard
Zhuo Chen
Quantum information, quantum computation and quantum simulation with machine learning.
MIT
self-supervised learning, meta learning, contrastive learning, natural language processing
MIT
Aurélien Dersy
Quantum Field Theory, Particle Physics, Machine Learning
Harvard
Speaker Selection Committee Member
Quantum Computation, Machine Learning, Graphical Models
MIT
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
Time-domain astronomy, supernovae, transients, telescope surveys
Harvard
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
Maryam Hussaini
Electromagnetic counterpart of gravitational wave events
Harvard
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
Summer School & Workshop Committee; Community Building Committee
jet physics, graph learning, ML interpretability and robustness.
MIT
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
IAIFI Summer School & Workshop Committee Member
Quantum computing in HEP. Machine-learning aided simulation
Harvard
deep learning, scalable Bayesian inference, medical imaging, computational neuroscience
Harvard
physics-informed machine learning, condensed matter physics, nonlinear dynamical systems, photonics
MIT
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
Summer School & Workshop Committee; Speaker Selection Committee
Interstellar dust, Scalable Bayesian Inference, Cosmology, Astrostatistics
Harvard
IAIFI Outreach Committee Member
Dark matter, Galactic Dynamics, Galaxy Mergers, Dwarf Galaxies, Graph Neural Networks
MIT
High Energy Physics, Dark Matter Searches, Machine Learning, High Performance Computing
MIT
Summer School & Workshop Committee
NN scaling laws, machine learning, cosmology
Northeastern
Collider Data Analysis, FPGAs, Probabilistic Modeling and Inference
MIT
Simon Rothman
Collider physics, dark matter searches, Higgs physics, novel machine learning approaches
MIT
equivariant ML, wavelet/kernel methods, non-gaussianity, astrophysics
Harvard
dark matter, gravitational lensing, galaxy clustering, inflation
Harvard
Yitian Sun
Particle astrophysics and applications of machine learning in data processing and simulations.
MIT
Statistical physics, quantum information theory, theory of deep learning
MIT
Deep learning theory, structured representations, nonlinear optimization, algebraic geometry
Harvard
Experimental and computational approaches to High Energy and Particle Physics
MIT
Machine Learning, Algorithms, Probability/Statistics, Origami
MIT
Neurosymbolic Regression, Interpretable Machine Learning, Neural Simulation of Quantum Systems
MIT
Varun Hariprasad
Large language models, AI for physics, Automated planning, Control systems
MIT
Machine Learning, Software Engineering, Physics-inspired algorithms
MIT
Manami Kanemura
Machine Learning, astrophysics, particle physics
Northeastern
Graph Neural Networks, Theoretical Particle Physics, Machine Learning for Physics
MIT
