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 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

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
Thomas Bradford
Thomas Bradford
IAIFI Project Coordinator

MIT
Maria Figueiredo
Maria Figueiredo
IAIFI Project Coordinator

MIT

Senior Investigators

Cora Dvorkin
IAIFI Institute Board (Harvard); Colloquium Organizer
dark matter, inflation, CMB, gravitational lensing, galaxy clustering
Harvard
Jim Halverson
IAIFI Institute Board (Northeastern); Summer School & Workshop Chair
string theory, particle-cosmology, topology, RL, NN-QFT correspondence
Northeastern
Taritree Wongjirad
IAIFI Institute Board (Tufts), Computing Committee Member
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
Edo Berger
IAIFI Early Career and Equity Committee Chair
Time-domain astrophysics, gravitational wave astrophysics, machine learning classification
Harvard
Matt Schwartz
IAIFI Community Building 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; Seminar Organizer
Sparse representations, deep learning, computational neuroscience
Harvard
Lisa Barsotti
IAIFI Fellowship Chair
gravitational waves, laser interferometry, quantum optics, precision measurements
MIT
Isaac Chuang
IAIFI Education and Workforce Development Coordinator
quantum information science; machine learning; trapped ion quantum computation
MIT
William Detmold
IAIFI Computing Chair
strong intereactions, nuclear physics, AI for simulations
MIT
Lina Necib
IAIFI Public Engagement Chair
Dark Matter, Galaxy Formation, Local Universe
MIT
Todd Zickler
IAIFI Industry Partnership Chair
artificial visual perception, computational sensing, computer vision
Harvard
Shuchin Aeron
Summer School & Workshop Committee Member
Information Theory, Generative Networks, Robust Representation Learning, Optimal Transport
Tufts
Pulkit Agrawal
IAIFI Communications Committee Chair
sensorimotor control, deep learning, robotics, intuitive physics and behavior, reinforcement learning, computer vision
MIT
Carlos Argüelles-Delgado
IAIFI Public Engagement Committee Member
neutrino experiment, neutrino phenomenology, astroparticle physics
Harvard University
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
Fabian Ruehle
IAIFI Summer School & Workshop Chair; Community Building Committee Member
string theory, string pheno, topology, geometry, ML
Northeastern
Tracy Slatyer
IAIFI Communications Committee Member
Dark matter theory, particle astrophysics, early-universe cosmology
MIT
Tess Smidt
IAIFI Early Career and Equity Committee Member
geometry, machine learning, computational physics, particle physics, materials physics
MIT
Marin Soljacic

nanophotonics, AI for nanophotonics and science in general, physics inspired AI algorithms
MIT
Max Tegmark

AI for physics, physics for AI, intelligible intelligence
MIT
Ashley Villar

Time-domain Astrophysics, Multimessenger Astrophysics, Representation Learning
Harvard

IAIFI Fellows

Michael Albergo
Incoming IAIFI Fellow
statistical physics, generative modeling, measure transport, dynamical systems

Denis Boyda
IAIFI Fellow, IAIFI Summer School & Workshop Committee Member and IAIFI Computing Committee Member
lattice field theory, generative models, Markov Chain Monte Carlo, high performance computing
IAIFI
Samuel Bright-Thonney
Incoming IAIFI Fellow
particle physics, self-supervised learning, dark matter, jet physics, AI robustness

Carolina Cuesta
Carolina Cuesta
IAIFI Fellow, IAIFI Early Career and Equity Committee and Industry Partnership Committee Member
cosmology and AI, ML models, statistics
IAIFI
Akshunna S. Dogra
Incoming IAIFI Fellow
Applied Mathematics, Dynamical Systems Theory, Machine Learning

Alexander Gagliano
IAIFI Fellow and IAIFI Journal Club Organizer
Time-domain astrophysics, machine-learning classification, gravitational-wave astrophysics, variational inference
IAIFI
Gaia Grosso
Gaia Grosso
IAIFI Fellow and IAIFI Public Engagement Committee Member
ML for particle physics, collider experiments, hypothesis testing, anomaly detection
IAIFI
Thomas Harvey
Incoming IAIFI Fellow
String Theory, Field Theory, Cosmology, and Geometry

Di Luo
IAIFI Fellow and IAIFI Speaker Selection Committee Member
quantum algorithms and machine learning for condensed matter physics, high energy physics, and quantum information science
IAIFI
Jessie Micallef
IAIFI Fellow, Early Career and Equity Committee and Community Building Committee Member
machine learning, particle physics experiments, neutrinos
IAIFI
Siddharth Mishra-Sharma
IAIFI Fellow, IAIFI Computing Committee Member and IAIFI Speaker Selection Committee Member
particle astrophysics, cosmology, simulation-based inference, probabilistic programming
IAIFI
Ge Yang
IAIFI Fellow, IAIFI Industry Partnership Committee Member and IAIFI Public Engagement Committee Member
reinforcement learning, planning, optimal transport, robotics
IAIFI

Long-Term Visitors

Adam Zacharia Anil

Gravitational Wave astrophysics, Machine Learning, Artificial Intelligence
2024 Summer Intern
Helena Brittain
Helena Brittain

Dark Matter, Halo Substructures, Machine Learning, Early Universe Cosmology
2024 Summer Intern
Francisco Galvan
Francisco Galvan

Machine learning, quantum computing, deep learning, computational simulation, and particle physics
2024 Summer Intern
Roger Rusack

CMS event reconstruction, Advanced instrumentation, FAIR data, precision timing
University of Minnesota
Nathaniel Santiago
Nathaniel Santiago

Neutrino, Machine Learning, Particle, Detector, Accelerator
2024 MSRP Intern

IAIFI Affiliates

Aram Apyan

Electroweak interactions, collider physics, AI for event reconstruction, GPUs
Brandeis
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
Pierre-Hugues Beauchemin

Experimental High Energy physics
Tufts
Michael Douglas

ML for numerical geometry, string theory, working on a computational theory of mathematical thought
Harvard
Liang Fu

neural network; quantum materials; topological phases of matter
MIT
Cecilia Garraffo

Machine Learning, Astrophysics
Harvard-Smithsonian CFA
An Huang

Algebraic geometry, p-adic string theory, graph curvatures, ML for algebra
Brandeis
Tommi Jaakkola

machine learning, AI for Science, molecular modeling, protein design, material design
MIT
Erik Katsavounidis

gravitational-wave astrophysics, muti-messenger astronomy, astroparticle physics, machine learning applications in physics
MIT
Rahul Kulkarni

Reinforcement learning, nonequilibrium statistical mechanics, biological physics, machine learning
UMass Boston
Sudhir Malik

BSM Physics, Silicon Detectors, Software Training, Machine Learning, AI
University of Puerto Rico - Mayaguez
Vidya Manian

Predictive AI modeling, active learning, graph theory, neuroscience, hyperspectral imaging
University of Puerto Rico - Mayaguez
Tyler Maunu

Geometry, nonconvex optimization, machine learning
Brandeis
Brent D. Nelson
IAIFI Public Engagement Committee Member
string phenomenology, dark sectors, AI transference, reinforcement learning
Northeastern
Olga Goulko

Quantum many-body physics, statistical mechanics, quantum information theory, exact computational methods
UMass Boston
Cengiz Pehlevan

theoretical neuroscience, deep learning theory
Harvard
Dan Roberts
IAIFI Speaker Selection Committee Member
effective theory of deep learning, quantum field theory, black holes & quantum chaos, word play
MIT
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
Akira Sone

quantum information theory; statistical mechanics; quantum optics
UMass Boston
Christopher Stubbs

cosmology, gravitation, GAI applications to experiments, dark matter, dark energy
Harvard
Hidenori Tanaka

Physics of Natural and Artificial Intelligence for Trustworthy and Green AI
Harvard University / NTT Research, Inc.
Abiy Tasissa

Distance geometry, matrix completion, compressive sensing, manifold learning
Tufts
Washington Taylor

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

cosmological simulations, high-performance computing, galaxy/structure formation
MIT
Susanne Yelin
Susanne Yelin

Quantum machine learning, Quantum optics
Harvard

Post-Docs and Research Scientists

Steven Eulig
Steven Eulig

Machine learning for physics, neutrino physics
Harvard
Plamen Krastev

nuclear physics, dense matter, gravitational wave astrophysics, HPC, AI for physics and astrophysics
Harvard
Victor Samuel Pérez Díaz

AI for (astro)physics, foundation models, interpretability and robustness
Harvard
Cari Cesarotti

Beyond the Standard Model, Muon Colliders
MIT
Blaise Delaney
Blaise Delaney

Flavour and jet physics, graph learning and robustness.
MIT
Daniel Hackett
Daniel Hackett

lattice field theory, statistical mechanics, normalizing flows, Markov Chain Monte Carlo
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
Matheus Hostert
Matheus Hostert

Neutrino and dark matter theory, astroparticle physics
Harvard
Harsh Kumar
Harsh Kumar

Kilonovae, GRBs, FRBs, SLSN
Harvard
Yin Lin

lattice field theory and machine learning
MIT
Rashmish Mishra
Rashmish Mishra
IAIFI Early Career and Equity Committee Member
collider physics, dark sectors, early universe cosmology
Harvard
Matthew Mould

Gravitational-wave astrophysics, Bayesian inference, machine-learning, likelihood-free methods
MIT
Nikhil Mukund

gravitational waves, laser interferometry, AI-based sensing and control, embedded machine learning
MIT
Andrzej Novak
Andrzej Novak

Higgs physics, Algorithmic Robustness, FPGAs
MIT
Fernando Romero-Lopez
Fernando Romero-Lopez

lattice QCD, normalizing flows, Markov Chain Monte Carlo
MIT
Christina Reissel
Christina Reissel

High-energy particle physics, Gravitational wave astronomy, AI for Anomaly detection
MIT
Matthew Rosenberg
Matthew Rosenberg

neutrino physics, machine learning
Tufts
Carlos Sarasty
Carlos Sarasty

Neutrino Physics, Graph Neural Networks, Optimal Transport, Reinforcement Learning, Computer Vision
University of Puerto Rico - Mayaguez
Sokratis Trifinopoulos
Sokratis Trifinopoulos
IAIFI Public Engagement Committee Member
Phenomenology of Particle Physics, Beyond the Standard Model Physics, Jet Physics, Machine Learning
MIT
Julian Urban

computational QFT, probabilistic modeling, generative machine learning, numerical optimization, inverse problems
MIT
Georgios Valogiannis

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

Students

Ryan Abbott
Ryan Abbott

Lattice QCD, Normalizing Flows
MIT
Polina Abratenko
Polina Abratenko
IAIFI Public Engagement Committee Member
neutrino physics
Tufts
Jacob Adamczyk

reinforcement learning, non-equilibrium statistical mechanics
UMass Boston
Aizhan Akhmetzhanova
Aizhan Akhmetzhanova

Cosmology, Machine Learning
Harvard
Omar Alterkait
Omar Alterkait
IAIFI Community Building Committee Member
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
IAIFI Early Career and Equity Committee Member
Quantum field theory, theoretical particle physics, and interfaces of these with machine learning
MIT
Kiara Carloni
Kiara Carloni

Neutrino Astrophysics, Statistical Methods
Harvard
Chandrika Chandrashekar
Chandrika Chandrashekar

dark matter, cosmology
Harvard
Zhuo Chen
Zhuo Chen

Quantum information, quantum computation and quantum simulation with machine learning.
MIT
Ameya Shrikant Daigavane
IAIFI Speaker Selection Committee Member
Generative models for molecules and learning dynamics
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
Gokhan Egri

Computer Vision, Machine Learning
Harvard
Atakan Hilmi Firat
Atakan Hilmi Firat

String theory, string field theory, and machine learning for geometry
MIT
Katherine Fraser

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

Interested in Particle Physics, QFT, Phenomenology, Machine Learning, and Information Theory!
MIT
Mark Hamilton

Computer Vision, Distributed COmputing, Information Theory, Deep Learning
MIT
Dean Hazineh

Statistical learning; Optimization; Computer Vision; Imaging
Harvard
Duc Hoang

Deep learning based hardware acceleration, FPGAs, dark matter, higgs
MIT
Elyssa Hofgard
Elyssa Hofgard

Symmetry equivariant neural networks, computational physics, physics inspired AI
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
Early Career and Equity Committee Member
Neutrino Physics & Machine Learning
Tufts
Gurtej Kanwar

generative models, lattice field theory, physics beyond the standard model
Universität Bern, ITP
Ouail Kitouni
Summer School & Workshop Committee Member and 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
Mit Kotak

Geometry, Systems for Machine Learning, GPUs, High Performance Computing, Domain Specific Languages, Hardware Accelaration
MIT
Jeffrey Krupa
Jeffrey Krupa

high energy physics, dark matter, deep learning, heterogeneous computing, accelerated architectures, high-throughput computing
MIT
Zhaoyi Li
Zhaoyi Li

Quantum Computing, Machine Learning, Quantum Error Correction, Many-Body Physics, Quantum Neural Networks
MIT
Ziming Liu

AI for Physics & Physics for AI
MIT
Charlotte Loh
Charlotte Loh

self-supervised learning, uncertainty quantification, AI for scientific applications
MIT
Andrew Ma
Andrew Ma

photonics, materials science, machine learning for physics
MIT
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
Ethan Marx
Ethan Marx

Data Analysis of Gravitational Waves with Machine Learning
MIT
Trevor McCourt
Trevor McCourt

Computational & systems neuroscience, physics-inspired computing, probabilistic computing
MIT
Eric Michaud

science/theory of deep learning, information theory
MIT
Eric Moreno
Eric Moreno

Anomaly detection, Higgs measurements, Graph Neural Networks, Fast inference, Dark matter searches
MIT
Nayantara Mudur
Nayantara Mudur
IAIFI Summer School & Workshop Committee and IAIFI Speaker Selection Committee Member
Interstellar dust, Scalable Bayesian Inference, Cosmology, Astrostatistics
Harvard
Joydeep Naskar
Joydeep Naskar

Quantum gravity, quantum information theory, quantum field theory, machine learning theory
Northeastern
Aviv Netanyahu

Distribution shift, inverse reinforcement learning
MIT
Tri Nguyen
IAIFI Public Engagement 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
IAIFI Summer School & Workshop Committee Member and IAIFI Public Engagement Committee Member
NN scaling laws, machine learning, cosmology
Northeastern
Sangeon Park
IAIFI Industry Partnership Committee Member and IAIFI Public Engagement Committee Member
Collider Data Analysis, FPGAs, Probabilistic Modeling and Inference
MIT
Yidi Qi
Yidi Qi

ML for numerical geometry and string theory
Northeastern
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
IAIFI Computing Committee Member
equivariant ML, wavelet/kernel methods, non-gaussianity, astrophysics
Harvard
Atınç Çağan ŞENGÜL
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
Emmanouil Theodosis
IAIFI Industry Partnership Committee Member
Deep learning theory, structured representations, nonlinear optimization, algebraic geometry
Harvard
Arthur Tsang

Dark matter and gravitational lensing
Harvard
Alex Wen
Alex Wen

neutrinos, data, analysis
Harvard
Noah Wolfe
Noah Wolfe

gravitational-wave astrophysics, normalizing flows
MIT
Felix Yu
Felix Yu

Sparse neural networks, deep learning, neutrino astrophysics
Harvard
Xiyu Zhai
Xiyu Zhai

ML
MIT
Gemma Zhang
Gemma Zhang

cosmology, machine learning
Harvard
Pavel Zhelnin
Pavel Zhelnin

Quantum Computing in HEP, neutrinos
Harvard
Alexander Zlokapa

Quantum information, theory of deep learning
MIT
Oreoluwa Alao
Oreoluwa Alao

ML interpretability, reservoir computing, dynamical systems
MIT
Julia Balla

interpretable machine learning, geometric deep learning
MIT
Donato Jimenez Beneto
Donato Jimenez Beneto


MIT
Elias Benghiat
Elias Benghiat

Machine learning for physics
Tufts
Anugrah Chemparathy

Machine Learning, Algorithms, Probability/Statistics, Origami
MIT
Matthew Chen
Matthew Chen

Large Language Models, Machine Learning, Deep Learning
MIT
Fiona Daly
Fiona Daly

ML for Anomaly Detection
MIT
Owen Dugan

Neurosymbolic Regression, Interpretable Machine Learning, Neural Simulation of Quantum Systems
MIT
Nino Ephremidze
Nino Ephremidze

Cosmology, dark matter, gravitational lensing
Harvard
Orion Foo

deep learning, FPGAs, machine learning for particle physics
MIT
Varun Hariprasad
Varun Hariprasad

Large language models, AI for physics, Automated planning, Control systems
MIT
Adriano Hernandez

Machine Learning, Software Engineering, Physics-inspired algorithms
MIT
Elizabeth Panner
Elizabeth Panner

Neutrinos, dark matter, neural networks, quantum sensing
Tufts
Marco Pretell
Marco Pretell

Machine Learning
Tufts
Marshall Taylor
Marshall Taylor

Physics for AI, ML Interpretability
MIT
Vinh Tran
Vinh Tran

Machine Learning, Particle Physics
MIT
Nate Woodward

Graph Neural Networks, Theoretical Particle Physics, Machine Learning for Physics
MIT
Lana Xu
Lana Xu

Machine learning and artificial intelligence
MIT