People

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 Form.

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

Jesse Thaler
IAIFI Director
QCD and jet physics, point clouds, topic modeling, optimal transport
MIT
Mike Williams
IAIFI Deputy Director
dark sector physics, high-throughput real-time analysis, AI robustness
MIT
Marisa LaFleur
Marisa LaFleur
IAIFI Project Manager

MIT

Senior Investigators

Cora Dvorkin
IAIFI Institute Board (Harvard)
dark matter, inflation, CMB, gravitational lensing, galaxy clustering
Harvard
Jim Halverson
IAIFI Institute Board (Northeastern)
string theory, particle-cosmology, topology, RL, NN-QFT correspondence
Northeastern
Taritree Wongjirad
IAIFI Institute Board (Tufts)
neutrino physics, convolutional and generative neural networks, reinforcement learning
Tufts
William Freeman
IAIFI Institute Board (MIT)
mid-level vision, computational photography, black hole imaging
MIT
Tracy Slatyer
IAIFI Early Career and Equity Chair
Dark matter theory, particle astrophysics, early-universe cosmology
MIT
Matt Schwartz
IAIFI Coordination Board Chair
quantum field theory, collider physics
Harvard
Phiala Shanahan
IAIFI Research Coordinator for Physics Theory
Lattice QCD, flow models, unsupervised learning, interpretability
MIT
Philip Harris
IAIFI Research Coordinator for Physics Experiment
Deep Learning based Hardware Acceleration, FPGAs, GPUs, Dark Matter, QCD and jet physics
MIT
Demba Ba
IAIFI Research Coordinator for AI Foundations
Sparse representations, deep learning, computational neuroscience
Harvard
Marin Soljacic
IAIFI Postdoctoral Fellows Coordinator
nanophotonics, AI for nanophotonics and science in general, physics inspired AI algorithms
MIT
Isaac Chuang
IAIFI Education and Workforce Development Coordinator
quantum information science; machine learning; trapped ion quantum computation
MIT
Brent D. Nelson
IAIFI Outreach Coordinator
string phenomenology, dark sectors, AI transference, reinforcement learning
Northeastern
Todd Zickler
IAIFI Knowledge Transfer Coordinator
artificial visual perception, computational sensing, computer vision
Harvard
Max Tegmark
IAIFI Community Building Coordinator
AI for physics, physics for AI, intelligible intelligence
MIT
William Detmold
IAIFI Resource Cooordinator
strong intereactions, nuclear physics, AI for simulations
MIT
Shuchin Aeron

Information Theory, Generative Networks, Robust Representation Learning, Optimal Transport
Tufts
Pulkit Agrawal

sensorimotor control, deep learning, robotics, intuitive physics and behavior, reinforcement learning, computer vision
MIT
Lisa Barsotti

gravitational waves, laser interferometry, quantum optics, precision measurements
MIT
Edo Berger
IAIFI Early Career and Equity Committee Member
Time-domain astrophysics, gravitational wave astrophysics, machine learning classification
Harvard
Daniel Eisenstein

statistical cosmology, large-scale structure surveys
Harvard
Doug Finkbeiner

Interstellar dust, high-energy astrophysics, cosmology
Harvard
Alexander Rakhlin

machine learning, mathematical statistics, online learning
MIT
Yaron Singer

machine learning, approximation algorithms, optimization, information networks, mechanism design
Harvard
Tess Smidt

geometry, machine learning, computational physics, particle physics, materials physics
MIT
Justin Solomon

Geometry, large-scale optimization, numerical methods, machine learning
MIT

IAIFI Fellows, Post-Docs, and Research Scientists

Denis Boyda
Incoming IAIFI Fellow
lattice field theory, generative models, Markov Chain Monte Carlo, high performance computing
IAIFI
Carolina Cuesta
Carolina Cuesta
Incoming IAIFI Fellow
cosmology and AI, ML models, statistics
IAIFI
Anna Golubeva
Anna Golubeva
IAIFI Fellow, PhD Summer School Committee Member
Theory of Deep Learning, Condensed Matter Theory
IAIFI
Di Luo
IAIFI Fellow, Speaker Selection Committee Member
quantum algorithms and machine learning for condensed matter physics, high energy physics, and quantum information science
IAIFI
Jessie Micallef
Incoming IAIFI Fellow
machine learning, particle physics experiments, neutrinos
IAIFI
Siddharth Mishra-Sharma
IAIFI Fellow, IAIFI Early Career and Equity Committee Member
particle astrophysics, cosmology, simulation-based inference, probabilistic programming
IAIFI
Ge Yang
IAIFI Fellow
reinforcement learning, planning, optimal transport, robotics
IAIFI
Cristiano Fanelli

EIC, AI, generative, unsupervised, detector design, particle ID, near real-time, calibration, QIS
MIT
Daniel Johnson
Daniel Johnson

Dark-sector searches; hadron spectroscopy; high-throughput real-time analysis
MIT
Plamen Krastev

nuclear physics, dense matter, gravitational wave astrophysics, HPC, AI for physics and astrophysics
Harvard
Salvatore Cali

strong interactions, lattice QCD simulations, machine learning
MIT
Blaise Delaney
Blaise Delaney

Flavour and jet physics, graph learning and robustness.
MIT
Mehmet Demirtas
Mehmet Demirtas
IAIFI Community Building Committee Member & Early Career and Equity Committee Member
string theory, algebraic topology, deep learning
Northeastern
Harold Erbin
IAIFI Outreach Committee Member
string theory, lattice QFT, field theory exploration, physics for AI
MIT
Daniel Hackett
Daniel Hackett

lattice field theory, statistical mechanics, normalizing flows, Markov Chain Monte Carlo
MIT
Arthur Hennequin
Arthur Hennequin

Computer Architectures, Compilers, Code optimization, Machine Learning
MIT
Daichi Hiramatsu
Daichi Hiramatsu

supernovae, gravitational wave sources, transient surveys
Harvard
Samuel Homiller

particle physics beyond the standard model, collider physics, quantum field theory
Harvard
Yin Lin

lattice field theory and machine learning
MIT
Patrick McCormack
Patrick McCormack

Hardware acceleration, Anomaly detection, Dark matter searches
MIT
Rashmish Mishra
Rashmish Mishra
IAIFI Early Career and Equity Committee Member
collider physics, dark sectors, early universe cosmology
Harvard
Niklas Nolte
Niklas Nolte
IAIFI Community Building Committee Member
Programming Languages, AI, High Energy Physics
MIT
Bryan Ostdiek
Data and Applied Scientist
dark matter, strong lensing, gaia satellite, lhc, hst, anomaly detection, uncertainty reduction
Microsoft
Dylan Rankin
Dylan Rankin
PhD Summer School Committee Member
Fast inference, Heterogeneous computing, Semisupervised learning, Higgs measurements
MIT
Fernando Romero-Lopez
Fernando Romero-Lopez

lattice QCD, normalizing flows, Markov Chain Monte Carlo
MIT
Matthew Rosenberg
Matthew Rosenberg

neutrino physics, machine learning
Tufts
Ralitsa Sharankova
Ralitsa Sharankova

neutrino physics, accelerator physics, Arduino projects
Tufts
Sokratis Trifinopoulos
Sokratis Trifinopoulos

Phenomenology of Particle Physics, Beyond the Standard Model Physics, Jet Physics, Machine Learning
MIT
Georgios Valogiannis

theoretical/numerical cosmology, cosmological tests of fundamental physics, LSS
Harvard

Students

Polina Abratenko
Polina Abratenko

neutrino physics
Tufts
Aizhan Akhmetzhanova
Aizhan Akhmetzhanova

Cosmology, Machine Learning
Harvard
Omar Alterkait
Omar Alterkait

Neutrino Physics, Machine Learning
Tufts
Samuel Alipour-fard
Samuel Alipour-fard

Quantum field theory, collider physics, and machine learning in high energy physics.
MIT
Sean Benevedes
Sean Benevedes

Quantum field theory, theoretical particle physics, and interfaces of these with machine learning
MIT
Floor Broekgaarden

binary evolution, neutron stars and black holes, gravitational waves, star formation history, Monte Carlo Sampling, Uncertainty Quantification
Harvard
Zhuo Chen
Zhuo Chen

Quantum information, quantum computation and quantum simulation with machine learning.
MIT
Rumen Dangovski

self-supervised learning, meta learning, contrastive learning, natural language processing
MIT
Aurélien Dersy
Aurélien Dersy

Quantum Field Theory, Particle Physics, Machine Learning
Harvard
Arkopal Dutt
Speaker Selection Committee Member
Quantum Computation, Machine Learning, Graphical Models
MIT
Gokhan Egri

Computer Vision, Machine Learning
Harvard
Katherine Fraser

BSM phenomenology, cosmology, axions, jet physics
Harvard
Rikab Gambhir

Interested in Particle Physics, QFT, Phenomenology, Machine Learning, and Information Theory!
MIT
Sebastian Gomez

Time-domain astronomy, supernovae, transients, telescope surveys
Harvard
Qi Guo

Computational sensing, computer vision, machine learning, optics.
Harvard
Mark Hamilton

Computer Vision, Distributed COmputing, Information Theory, Deep Learning
MIT
Duc Hoang

Deep learning based hardware acceleration, FPGAs, dark matter, higgs
MIT
Zhang-Wei Hong

Reinforcement learning, sequential decision making, biologically inspired computing
MIT
Maryam Hussaini
Maryam Hussaini

Electromagnetic counterpart of gravitational wave events
Harvard
Zev Imani
Zev Imani

Neutrino Physics & Machine Learning
Tufts
Gurtej Kanwar
Postdoc
generative models, lattice field theory, physics beyond the standard model
Universität Bern, ITP
Ouail Kitouni
IAIFI Community Building Committee Member
jet physics, graph learning, ML interpretability and robustness.
MIT
Samuel Kim
Samuel Kim

Photonics, symbolic regression, optimization, ML for physics
MIT
Patrick Komiske

Jets, QCD, Machine Learning
MIT
Jeffrey Krupa
Jeffrey Krupa

high energy physics, dark matter, deep learning, heterogeneous computing, accelerated architectures, high-throughput computing
MIT
Jeffrey Lazar
PhD Summer School Committee Member
Quantum computing in HEP. Machine-learning aided simulation
Harvard
Alexander Lin

deep learning, scalable Bayesian inference, medical imaging, computational neuroscience
Harvard
Ziming Liu

AI for Physics & Physics for AI
MIT
Peter Y. Lu
PhD Summer School Committee Member
physics-informed machine learning, condensed matter physics, nonlinear dynamical systems, photonics
MIT
Andrew Ma
Andrew Ma

photonics, materials science, machine learning for physics
MIT
Anindita Maiti
IAIFI Early Career and Equity Committee Member
AI and ML for string theory (towards quantum gravity); connecting AI with fundamental physics
Northeastern
Silviu-Marian Udrescu
Silviu-Marian Udrescu

Physics for AI, nuclear physics, high energy physics, atomic molecular and optical physics
MIT
John Martyn
John Martyn

Scientific machine learning, quantum information, tensor networks
MIT
Katie Mason

machine learning for image reconstruction in neutrino physics
Tufts
Eric Michaud

science/theory of deep learning, information theory
MIT
Joshua Mills

ML for image analysis in particle physics, video games, creative writing.
Tufts
Eric Moreno
Eric Moreno

Anomaly detection, Higgs measurements, Graph Neural Networks, Fast inference, Dark matter searches
MIT
Nayantara Mudur
Nayantara Mudur
Speaker Selection Committee Member
Interstellar dust, Scalable Bayesian Inference, Cosmology, Astrostatistics
Harvard
Tri Nguyen
IAIFI Outreach Committee Member
Dark matter, Galactic Dynamics, Galaxy Mergers, Dwarf Galaxies, Graph Neural Networks
MIT
Noah Paladino

High Energy Physics, Dark Matter Searches, Machine Learning, High Performance Computing
MIT
Sneh Pandya

NN scaling laws, machine learning, cosmology
Northeastern
Sangeon Park

Collider Data Analysis, FPGAs, Probabilistic Modeling and Inference
MIT
Kate Richardson
Kate Richardson

Dark matter, machine learning, and high energy physics.
MIT
Simon Rothman
Simon Rothman

Collider physics, dark matter searches, Higgs physics, novel machine learning approaches
MIT
Andrew Saydjari

equivariant ML, wavelet/kernel methods, non-gaussianity, astrophysics
Harvard
Atınç Çağan ŞENGÜL

dark matter, gravitational lensing, galaxy clustering, inflation
Harvard
Christopher Shallue
Christopher Shallue

deep learning, cosmology
Harvard
Yitian Sun
Yitian Sun

Particle astrophysics and applications of machine learning in data processing and simulations.
MIT
Andrew K. Tan

Statistical physics, quantum information theory, theory of deep learning
MIT
Emmanouil Theodosis

Deep learning theory, structured representations, nonlinear optimization, algebraic geometry
Harvard
Arthur Tsang

Dark matter and gravitational lensing
Harvard
Dor Verbin

computer vision, machine learning
Harvard
Chris Whittle
Chris Whittle

gravitational-wave detection, quantum optics
MIT
Mikaeel Yunus
Mikaeel Yunus

quark/gluon jets, anomaly detection, unsupervised machine learning, normalizing flows, variational autoencoders
MIT
Xiyu Zhai
Xiyu Zhai

ML
MIT
Gemma Zhang
Gemma Zhang

cosmology, machine learning
Harvard
Alexander Zlokapa

Quantum information, theory of deep learning
MIT
Akshunna S. Dogra

Applied Mathematics, Dynamical Systems Theory, Machine Learning
Harvard
Alec Gunny

Deep learning, gravitational waves, ML infrastructure
MIT
Oreoluwa Alao
Oreoluwa Alao

ML interpretability, reservoir computing, dynamical systems
MIT
Julia Balla

interpretable machine learning, geometric deep learning
MIT
Elias Benghiat
Elias Benghiat

Machine learning for physics
Tufts
Aidan Chambers

Experimental and computational approaches to High Energy and Particle Physics
MIT
Anugrah Chemparathy

Machine Learning, Algorithms, Probability/Statistics, Origami
MIT
Owen Dugan

Neurosymbolic Regression, Interpretable Machine Learning, Neural Simulation of Quantum Systems
MIT
Benjamin Harris
Benjamin Harris

neutrino physics, machine learning
Tufts
Adriano Hernandez

Machine Learning, Software Engineering, Physics-inspired algorithms
MIT
Jared Hwang

Machine Learning, data storage/processing, neutrino physics
Tufts
Manami Kanemura
Manami Kanemura

Machine Learning, astrophysics, particle physics
Northeastern
Serhii Kryhin
Serhii Kryhin

high energy physics, field theory, machine learning
MIT
Keiran Lewellen
Keiran Lewellen

ML for particle physics, Theoretical condensed matter physics, Extremal Combinatorics
MIT
Marco Pretell
Marco Pretell

Machine Learning
Tufts
Nikita Saxena
IAIFI Early Career and Equity Committee Member
Deep Learning for Physics
Tufts
Ray Wynne
Ray Wynne

Collider physics, machine learning, anomaly detection
MIT
Felix Yu
Felix Yu

deep learning, high energy physics
Tufts

IAIFI Affiliates

Carlos Argüelles-Delgado

neutrino experiment, neutrino phenomenology, astroparticle physics
Harvard University
Ning Bao

Quantum information theory, in particular as it relates to high energy physics AdS/CFT; Complexity theory, both classical and quantum; Quantum Field Theory
Northeastern
George Barbastathis

computational imaging, inverse problems
MIT
Michael Douglas

ML for numerical geometry, string theory, working on a computational theory of mathematical thought
Harvard
Lina Necib

Dark Matter, Galaxy Formation, Local Universe
MIT
Cengiz Pehlevan

theoretical neuroscience, deep learning theory
Harvard
Kerstin Perez

dark matter, high-energy astrophysics, space-based detector technology
MIT
Dan Roberts

effective theory of deep learning, quantum field theory, black holes & quantum chaos, word play
MIT
Fabian Ruehle

string theory, string pheno, topology, geometry, ML
Northeastern
Artan Sheshmani

Algebraic Geometry (Gromov-Witten theory, Donaldson-Thomas theory), Derived Algebraic Geometry, Mathematics of String theory, Mathematical AI
Center for QGM / Harvard / U. Miami
Haim Sompolinsky

theoretical neuroscience, deep learning, AI
Harvard/Hebrew University
Washington Taylor

Quantum gravity, string theory, geometry, particle physics, energy, ecology, computational physics
MIT