Azarakhsh Jalalvand
azarakhsh.jalalvand@princeton.edu

Azarakhsh (Aza) is a Research Scholar in Princeton University with a Ph.D. in Computer Engineering from Ghent University (Belgium). His research focuses on Artificial Intelligence, signal processing and data-driven discovery research tracks including audio, visual and radar data analysis as well as multi-sensor signal processing for variety of applications such as object recognition, surveillance, predictive maintenance and anomaly detection. He investigates real-time, robust and adaptive data-driven models for condition monitoring and plasma control in the magnetic confinement devices to produce controlled thermonuclear fusion power.

SangKyeun Kim
skim2@pppl.gov

Google scholar
SangKyeun Kim is a staff research scientist at the Princeton Plasma Physics Laboratory (PPPL) with a focus on plasma physics and fusion research. His expertise encompasses a wide range of areas including linear PBM stability analysis using MISHKA and ELITE tools, nonlinear physics studies with the JOREK 3D MHD code, and the development of the kinetic-EFIT reconstruction system for the KSTAR tokamak. Currently, SangKyeun is engaged in ML-related plasma control at DIII-D, where he explores the utilization of machine learning techniques for 3D control, ELM suppression, and boundary control, contributing to advancements in these fields.

CheolSik Byun
cb3887@princeton.edu

CheolSik Byun is a postdoctoral researcher with expertise in simulation and experiment of particle transport. During his doctoral studies at Seoul National University, he focused on interpretative/predictive simulations and experiments for hybrid scenarios in  KSTAR. He also conducted research on particle transport driven by turbulence using perturbative analysis in KSTAR experiments. Currently, his primary research focus involves implementing various controllers, such as RMP-driven ELM suppression and radiation power control, utilizing RT-bolometer to manage heat loads on the plasma-facing components in KSTAR.

Max Curie
max.curie@princeton.edu

Max Curie is a postdoc in Princeton University. He received his Ph.D. in physics in the University of Texas at Austin in 2022. His focuses are
1. End-to-end prediction for Tokamak.
2. Signal processing for the spectral diagnostics.
3. Design, propose, and conduct experiments in DIII-D (future).

Alvin Garcia
ag6990@princeton.edu

Alvin Garcia is an ORISE FES Postdoctoral Research Fellow experienced in
computational modelling, analysis and techniques that can be used to study
mission critical topics in fusion energy. Prior to joining the Plasma Control
Group at Princeton University, he completed his PhD in Physics at the
University of California, Irvine on projects related to Spin Polarized Fuel and
Artificial Intelligence using data from the DIII-D National Fusion Facility. He
plans to design, propose, and execute experiments aimed at controlling
dangerous Alfvén eigenmodes on DIII-D as a contributing member of the
Plasma Control Group.

Yufan Xu
yxu@pppl.gov
yufanxu@princeton.edu

Yufan Xu is a postdoc at PPPL specialized in liquid metal magnetohydrodynamics, geophysical and astrophysical turbulence, and planetary and stellar dynamos. He received his PhD in geophysics and space physics at University of California, Los Angeles in 2023, on the experimental investigation of the heat and momentum transfer in liquid gallium rotating and non-rotating magnetoconvection, complemented with theoretical and numerical analyses. In Princeton, his focus is developing a flowing liquid lithium facility at PPPL for testing and validating liquid metal plasma facing component designs for fusion devices.

Rahul Gaur
rg6256@princeton.edu

I am a postdoctoral researcher working with the DESC optimization code. I am interested in the optimization of stellarators against instabilities and turbulent transport.
I completed my Ph.D. in Physics from the University of Maryland, College Park where I investigated the effect of ideal MHD and kinetic instabilities on high-beta tokamak and stellarator equilibria.

Dario Panici
dpanici@princeton.edu

Dario is a 4th-year graduate student in the MAE department interested in computational plasma physics, especially related to fusion energy. He completed his undergraduate in Nuclear, Plasma, and Radiological Engineering at UIUC before attending Princeton. Currently, Dario works on code development and error analysis of computational stellarator equilibria, as well as helical coil design and connections between 3D global MHD equilibria and the near-axis expansion.

Francisco Saenz
fcastro@princeton.edu
fsaenz@pppl.gov

Francisco is a 4th-year PhD student in the MAE department interested in computational/experimental liquid metal magnetohydrodynamics, especially related to the design of plasma-facing component of fusion systems. His undergraduate degree is in Mechanical Engineering from Universidad de Costa Rica. Currently, Francisco works on simulations and experiments to evaluate the performance of the new liquid metal divertor concept called ‘Divertorlets’. Additionally, he works on simulations of free-surface liquid metal flows for MHD drag calculations.

Brian Wynne
brianwynne@princeton.edu

Brian is a 3rd year graduate student in MAE. Before joining the Kolemen group, he completed his Bachelor’s Degree in Chemical Engineering at The Ohio State University. Brian works with experiments and simulations on LMX-U (Liquid Metal eXperiment Upgrade) at PPPL. He is currently involved in testing  the effects of a gradient magnetic field on free surface flow, as well as designing a magnetically driven centrifuge for lithium hydride separation from liquid lithium. 

Andy Rothstein
arothstein@princeton.edu

Andy is a 3rd year graduate student in MAE interested in data-driven approaches to plasma control. His work involved building machine learning models to find correlations between tokamak diagnostics and magnetic instabilities to build models capable of predicting magnetic instabilities in real-time. Before Princeton, he got his Bachelor’s Degree in Physics from Caltech and while performing research in astrodynamics and quantum optics. 

Jalal Butt
jbutt@pppl.gov

Jalal studies the 3D field physics of edge-localized modes (ELMs) and their control in pursuit of long-pulse tokamak operation for KSTAR, ITER and beyond. Before Princeton, Jalal graduated from Columbia University, where he contributed to the disruption event characterization and forecasting code (DECAF), and CCSU. Jalal also worked as a research scientist at NASA Goddard Space Flight Center on novel satellite remote sensing techniques.

Minseok Kim
mseokim@princeton.edu

Minseok is a 1st-year PhD student in the MAE department. Before joining the Kolemen group, he completed his bachelor’s degree in Physics and Artificial intelligence at Korea University and master’s degree in Nuclear and Quantum engineering at Korea Advanced Institute of Science and Technology (KAIST). Minseok studied Bayesian statistics for KSTAR ion temperature profile reconstruction and machine learning application for fusion-grade hot plasma prediction during his master’s degree. His current research interest is to detect edge localized mode (ELM) induced by sawtooth instability at DIII-D tokamak using ECE diagnostics.

Yigit Gunsur Elmacioglu
ye2698@princeton.edu

Yigit is an incoming PhD student in the Mechanical and Aerospace Engineering department. He completed his bachelor’s degree in Mechanical Engineering along with a double major in Physics at Bogazici University. During his undergraduate studies, he worked on magnetic guidewire steering in ultrahigh magnetic fields and PIC-DSMC implementation of the ion thruster grid region. In the Kolemen group, he will work on plasma simulations for stellarators.

Nathaniel Chen
nathaniel@princeton.edu

Nathan is a PhD student in  the Mechanical and Aerospace Engineering department. He completed his bachelor’s degree in Physics and Cognitive Science at the University of California, Los Angeles. He works on machine learning and diagnostics in the plasma control group.

Hiro Farre
hf8585@princeton.edu

Hiro is a 1st year PhD student at the Princeton Plasma Physics Laboratory. He completed his master’s and bachelor’s degrees in Physics at the University of Cambridge, where he worked on simulating and detecting the Low to High Confinement Mode transition in tokamaks. By running HESEL simulations and analyzing experimental data from the DIII-D and MAST-U tokamaks, he studied the triggers leading to High Confinement Mode, a plasma state that is favorable for fusion reactors. In the Plasma Control group, he will work on plasma profile prediction for real time control on the DIII-D and ASDEX-U tokamaks.

Kian Orr
kianorr@princeton.edu

Liangjun Shao

ls7087@princeton.edu

Liangjun Shao is a 5th-year PhD student at Tsinghua University under the supervision of Assoc. Prof. Timing Qu. He received the B.S. degree in Mechanical Engineering from Tsinghua University in 2019. His research interests include electromagnetic modeling of high-field High-Temperature Superconducting magnets and analysis of screening-current effect in HTS conductors.

João Biu

joaopedrofbiu@tecnico.ulisboa.pt

João is an Engineering Physics MSc student from Instituto Superior Técnico, Portugal. His primary interests are plasma physics and computational physics, having worked in Stellarator coil optimization. Currently, his focus is optimizing nuclear fusion devices using automatic differentiation tools.

Minsoo Cha

cmsandy@snu.ac.kr

MinSoo is a graduate student at Seoul National University, South Korea, in Professor Yong-Su Na’s group. He is working on the feedback control of neoclassical tearing mode, and its stability. He worked on KSTAR plasma control system and have done experiment in KSTAR. He is now collaborating with Egemen’s group for the stability code for neoclassical tearing mode.

Jinsu Kim

wlstn5376@gmail.com

Jinsu is a graduate school student at Seoul National University, South Korea, majoring fusion plasma. He has an interest in machine learning application in KSTAR, including disruption prediction and tokamak plasma autonomous control based on RL. During his master’s degree in SNU, he has developed disruption prediction models based on multimodal learning and NN-based simulators for describing tokamak plasma. He is now focusing on physics-informed neural network and Neural ODE to solve complex dynamics in fusion plasma.

Sara Dubbioso

sara.dubbioso@unina.it

In 2020 I enrolled in the Fusion Science and Engineering program, a jointed PhD program between the Università degli Studi di Napoli Federico II and the Università degli Studi di Padova. I obtained my Master’s and Bachelor’s degree in Automation/Control Engineering at the University of Naples Federico II. In 2023  spent six months in Princeton collaborating with Plasma Control. My research focuses on control system applications, mainly for the Vertical Stabilization problem in tokamak plasma, pursuing model-free and data-drive approaches.

Jabir Al-Salami

j.s.asalami@gmail.com

Jabir Al-Salami is a PhD student at Kyushu University, Japan, majoring in computational fluid dynamics (CFD). He obtained his undergraduate degree in Mechatronics from Sultan Qaboos University, Oman. His research interests include applying particle and high-order methods to challenging free-surface phenomena, such as magneto-hydrodynamical flows. He has been developing CFD codes to aid in the design of liquid metal plasma-facing components for use in nuclear fusion reactors.

Jackson Crocker

Jackson.Crocker@princeton.edu

Jackson is an undergraduate student at Princeton studying Mechanical and Aerospace engineering. Jackson worked the summer of 2023 with the group using the DESC code to help implement the Gamma-C metric, a proxy metric for fast particle confinement that is important for optimizing stellarator fusion reactors.

Kayla Xu

kaylaxu@princeton.edu

Kayla is an undergraduate student at Princeton studying computer science, with possible tracks in statistics and machine learning, and cognitive science. She is a summer intern with the machine learning group, working on developing a machine learning model to detect appearances of the NT-edge mode in plasma through the analysis of ECE data, and raw and enhanced spectrograms.

Daniel Vergara

dv9484@princeton.edu

Daniel is a second year undergraduate in the Electrical and Computer Engineering department. Throughout the summer of 2023, he has researched the possible application of Contactless Inductive Flow Tomography to the LMX-U experimental set up, as well as performing material characterization studies on tungsten meshes. Research interests include droplet ejection reduction in liquid metal plasma facing components (LM-PFCs) and the development of liquid metal stability diagnostics.

Tal Shpigel

Princeton University

Tal is an undergraduate student at Princeton studying physics and applied math. He was part of the stellarator team and studied stellarator optimization under physics and engineering trade-offs.

Ayomikun Gbadamosi

ayomikun@princeton.edu

Ayo was an undergraduate student at Princeton studying Mechanical and Aerospace engineering. She worked on her senior thesis under the guidance of Egemen Kolemen and the DESC code group, working on using machine learning techniques to create a surrogate model to solve the ideal MHD equilibrium equations.

Kaya Unalmis

Princeton University

Kaya is an undergraduate student at Princeton studying Electrical Engineering and Engineering Physics. As part of the stellarator team in the summer of 2022, Kaya contributed to the development of DESC, a stellarator equilibrium and optimization code. Outside of research, Kaya enjoys swimming and lifting.

Samantha O’Sullivan

Harvard University

Sam is a visiting undergraduate student from Harvard who is studying the effect of Neutral Beam Injection on Edge Localized Modes for her SULI project.

Shayaan Subzwari

Yale University

Shayaan is an undergraduate student from Yale who is working on implementing models for radiation losses in tokamaks. These models are to be implemented in the FAROES fusion systems code.

Nigel Mesta

University of California, Berkeley

Nigel is undergraduate student at University of California Berkeley studying degrees in Nuclear Engineering and Physics. He is participating in the Summer 2021 SULI Program where he constructed a linear programming model of a fusion power plant with an attached thermal storage system using estimated hourly electricity price series for a 2030s-era wind-dominated grid. We study whether adding thermal storage to a fusion reactor can mitigate losses in availability associated with unexpected shutdowns and increase profits.

Laura Fang

Princeton University

Laura is a summer student working on error analysis and uncertainty quantification for machine learning models in fusion.

Yuno Iwasaki

Princeton University

Yuno is a summer student working on machine learning driven real-time Bayesian estimation of the state of plasmas in fusion devices.

Azmaine Iqtidar

Princeton University

Azmaine was a senior thesis student (2020-2021) continuing work on autoencoder models for learning controllable linear dynamics. He also developed a model predictive controller using these machine learning models.

Aaron Wu

Princeton University

Aaron was a summer intern in 2020 working on using autoencoders to find control oriented linear embeddings of nonlinear plasma dynamics. He has since continued on as an undergraduate researcher to work on GPU algorithms for real time plasma stability analysis

Milan A. Wolff

The College of William & Mary

Milan was a summer student through the SULI program in 2020. He used machine learning techniques for automatic classification of tokamak experiments and identification of different regimes of plasma operation.

Bora Kiyan

Princeton University

Bora was a senior thesis student (2019-2020) examining linear and auto-regressive reduced order models for predicting plasma profile evolution.

Alex Liu

Princeton University

Alex was a summer student in 2019, helping to develop machine learning models for plasma profile prediction. He performed extensive sensitivity analysis to determine which signals were the most important for making predictions, and which actuators had the greatest effect on the state of the plasma.

Jalal Butt

Central Connecticut State University

Jalal was a summer student through the SULI program in 2018. He developed machine learning models using a Long Short-Term Memory recurrent neural network (LSTM-RNN) to predict the evolution of temperature and density profiles in tokamaks.

Nathaniel Barbour

Yale University

Nathaniel was a summer student through the SULI program in 2017. He used regression trees to attempt to predict the probability of high current disruptions in tokamaks using only low current disruptions as training data, a key need for ITER where accurate predictions will be needed with limited training data.

Neil Slighton
nts@princeton.edu

Neil is an undergraduate engineering student at Princeton university with an interest in energy science and engineering. Neil has worked on calibration techniques and low friction modifications to Lorentz force velocimeters. Outside of research and studying Neil enjoys distance running, cycling and baseball.

Gerrit Bruhaug
bruhgerr@isu.edu

Gerrit was a member of the Kolemen group during Summer 2017 as part of the SULI program at PPPL. Gerrit developed a non-contact liquid metal depth sensor. He built several prototypes of the sensor and compared measured data with predictions from a finite element analysis software called FEMM. Gerrit is interested in nuclear energy (fission and fusion) as well as particle accelerators. He is an NRC-licensed reactor operator on the ISU AGN-201 nuclear fission reactor. Gerrit is also an avid snowboarder and loves to work on his 1971 Chevrolet Nova SS.

Dhruval Patel

Dhruval worked on liquid metals, developing a diagnostic tool to resolve the surface wave topology of a flowing liquid metal. The setup uses a single camera to acquire an image being reflected off the liquid metal surface. Methods and algorithms were developed with the goal of decreasing the computation time and error. A 1D simulation of the problem is created to study the efficacy of each algorithm. This paper discusses the differences between he methods and compares the relative error and computation time required for each method. It was concluded that images with lower resolution can be used to get reliable results using any of these methods. Crude approximation using the linear method can yield results quickly with a reasonable margin of error.

Dhruvit Patel
dhruvit.patel@rutgers.edu

Dhruvit was with the Kolemen group during Summer 2017 as part of the SULI program at PPPL. During that time, he designed and build several flow-straighteners and nozzles for liquid metal flows and tested them on the Liquid Metal Experiment (LMX). Dhruvit has pursued research in various fields of engineering and physics, such as aerodynamics, condensed matter physics, fluid dynamics (MHD) as well as computational analysis and simulations in order to better understand his future goals. He will be applying to graduate school Fall 2017 to various physics/engineering programs in Atomic/Laser/Plasma Physics.

Rory Conlin
wconlin@pppl.gov

Rory is a 6th-year graduate student in MAE, with an undergraduate background in mechanical engineering, physics, and film studies. He is developing machine learning algorithms for data driven control of fusion plasmas to predict and avoid instabilities and achieve new confinement regimes. He has developed new methods to streamline and automate the conversion and deployment of such algorithms for real time applications. He is also working on new numerical techniques for physics based predictions of restive instabilities in plasmas, and helping to develop a new stellarator equilibrium code.

Ricardo Shousha
rshousha@princeton.edu
rshousha@pppl.gov

Ricardo completed his Ph.D. program at Princeton in 2023, after joining in 2018. He is currently a post-doctoral researcher at PPPL, specializing in plasma control. Ricardo holds B.Sc. and M.Sc. degrees in Mechanical Engineering, as well as an M.Sc. in Science and Technology of Nuclear Fusion from the University of Technology Eindhoven, The Netherlands. His expertise lies in developing control systems for fusion plasmas, particularly the Feedback ELM Controller for ELM suppression and enhancing the plasma state observer (rtEFIT) with kinetic constraints for real-time control.

Joe Abbate
jabbate@pppl.gov

Joe is a 6th-year graduate student working on model-predictive control to help operators achieve desirable plasma states in experimental fusion reactors. He and Rory are running a campaign of realtime tests on the algorithm at the DIII-D tokamak in San Diego.

Daniel Dudt
ddudt@princeton.edu

Daniel is a 6th-year PhD student in the MAE department. Before joining the Kolemen group, he completed his bachelor’s degree in Mechanical Engineering at Bucknell University. Daniel’s research is on computational tools for calculating the equilibrium magnetic fields in stellarators, a type of fusion reactor with three-dimensional geometries. He also assists with work on flowing liquid metals for fusion applications.

Susan Redmond

sredmond@princeton.edu

Susan is a 5th-year PhD student in the MAE department. Before joining the Kolemen group, she completed her bachelor’s degree in Mechanical Engineering at Memorial University of Newfoundland and her master’s degree in Aerospace Engineering at the University of Toronto. Susan’s research is focused on optical control systems for balloon-borne telescopes and exoplanet-imaging space telescopes. Susan works in partnership with the Jones group on adaptive optics systems for the SuperBIT and GigaBIT balloon-borne telescopes as well as with the Kasdin group (emeritus) on high contrast imaging control algorithms for space telescopes.

Josiah Wai
jwai@princeton.edu

Josiah is a graduated PhD student focused on magnetic control of plasmas. His research is focused on modelling and controlling the plasma shape boundary, with the aim of being able to control the plasma during the highly dynamic ramp-up phase at NSTXU. Accurately controling the ramp-up phase will allow for more consistent H-mode transitions in spherical tokamaks.

Zhen Sun
zsun@pppl.gov

Sun is a postdoc of Princeton Plasma Physics Laboratory, working to develop liquid metal plasma-facing component in the divertor and/or wall of a fusion system. Using the LMX (Liquid Metal eXperiment) planform and simulation, Sun is studying on several key issues for fast liquid metal flow, such as velocity measurement, MHD, stability of open channel flow, tritium removal, and so on. Sun is also developing a new concept of liquid metal divertor, referred as ’Divertorlets’. Before attending Princeton, Sun led the experimental physics research on the effect of active Lithium/Boron injection on EAST plasma performance and involved in building liquid lithium limiter/circuit and their experiments in EAST and HT-7. Google scholar

J. A. Schwartz
jacobas@princeton.edu

Jacob Schwartz is a postdoc working to understand what sort of fusion reactors could be economically attractive as part of the electric grid, and more broadly, for industry and energy systems, in 2050 and beyond. He is also working on a “systems code” to generate and optimize candidate fusion reactors for various missions.

Jaemin Seo

js7146@princeton.edu

Jaemin is a postdoc working on machine learning based plasma prediction and control in KSTAR and DIII-D. He got a Ph.D. degree from the department of nuclear engineering at Seoul National University. He developed a feedforward plasma control algorithm using a reinforcement learning technique in KSTAR during his Ph.D., and is now mainly working on the tearing mode prediction and control algorithm in DIII-D. He is also interested in a fast surrogate neural network model for the kinetic equilibrium reconstruction for a real-time implementation.

Pat Vail
pjv@princeton.edu

Pat is an 8th-year PhD student in the MAE department. His research focuses on the development of control systems for fusion plasmas in order to address the challenges of plasma shape and heat flux control in tokamaks. Pat is currently leading the development of the advanced divertor control systems for the NSTX-U and DIII-D tokamaks. This work includes physics-based control algorithm design and simulation, implementation of the real-time algorithms for use during plasma operations, and experiments to assess controller performance and divertor physics. Before attending Princeton, Pat worked as a full-time propulsion engineer at SpaceX.

Tim Chen
tc8@princeton.edu

Tim is a 5th-year graduate student co-advised by Prof. Kolemen and Prof. Yiguang Ju. His research entails utilizing laser diagnostics to study plasma-assisted combustion and fuel reforming.

Oak Nelson
anelson@pppl.gov

Oak is a 5th-year graduate student in the Plasma Physics Program. His thesis (“Comprehensive dynamic analysis of the H-mode pedestal in DIII-D”) is a combination of three separate experimental thrusts: (1) an investigation into the relationship between the neutral fueling profile and the pedestal structure, (2) experimental identification of turbulent processes that dictate transport through the pedestal and (3) 2D modeling of pedestal/SOL interactions with UEDGE. Oak was worked on several additional projects, including the development of several OMFIT modules for DIII-D analysis, studies of advanced ECE implementation, investigations into locked mode dynamics, implementation of automated ELM control, and surface chemistry experiments regarding the sputtering of lithium surfaces.

Adam Fisher
aefisher@princeton.edu

Adam completed his PhD in 2020, working on thin fast-flowing liquid metals for fusion reactor applications. Specifically, his research aims to study flow relevant to a liquid metal divertor on a tokamak. He is currently working on LMX-U at PPPL, a galinstan (GaInSn) channel flow within a magnetic field that can be injected with external electric currents.

Yichen Fu
yfu@pppl.gov

Yichen is a 4th-year graduate student at Princeton. He is interested in plasma physics, especially in theory and computation. Yichen is currently working on using machine learning algorithms to predict the onset of several important tokamak phenomena, including disruptions and neoclassical tearing nodes (NTMs).

Mike Hvasta
mhvasta@pppl.gov

Mike has extensive experience working with liquid metals. From 2008-2013, he worked with high-temperature sodium systems at the University of Wisconsin-Madison. From 2013-2016, he was the Lead Research Engineer for the Mechanisms Engineering Test Facility at Argonne National Laboratory. Since May 2016, Mike has been working on the Liquid Metal eXperiment (LMX) and the Flowing Liquid Metal Torus (FLIT) experiment at Princeton University and Princeton Plasma Physics Laboratory.

Olivier Izacard
oizacard@pppl.gov

Olivier works on theoretical and computational plasma physics. His recent results include (1) tokamak edge simulations with the multi-fluid transport code UEDGE and the turbulence code BOUT++, (2) development of the grid generator Gingred in collaboration with M. Umansky (LLNL) for single-null, double-null, snowflake divertor geometries, or field-reversed-configurations, (3) participation in the creation of the integrated framework OMFIT that allows accessing experimental data, running codes and creating post-analyses, (4) development of a new analytic non-Maxwellian distribution function based on non-orthogonal basis sets, (5) development of the generalized fluid theory that avoids using ad-hoc nonlocal transport coefficients, and (6) development of a new 5D kinetic theory called the gyrointegrated kinetic theory valid for arbitrary gradient scale lengths, Larmor radii, magnetic geometry and distribution functions. Olivier also has extensive experience peer-reviewing articles and was awarded as the “Reviewer of the Year” for the journal Plasma Physics and Controlled Fusion in 2017.

Florian Laggner
flaggner@pppl.gov

Florian is a post-doc working primarily on plasma edge and pedestal physics. During his PhD studies at TU Wien, he investigated the inter-ELM pedestal evolution on ASDEX Upgrade and operated the lithium beam diagnostic. After receiving his PhD, he joined the Kolemen group in June 2017. Florian’s current research includes the implementation of a real-time Thomson Scattering diagnostic on NSTX-U as well as the running of experiments on DIII-D, which aim to develop a deeper understanding of pedestal fluctuations. Furthermore, he is involved in the development of pedestal controllers and studies the impact of low-Z impurities on the pedestal.

David Eldon
eldond@fusion.gat.com

David is interested in control systems, primarily focused on tokamak divertors and heat flux, as well as general plasma control, boundary physics, and pedestal physics.

Alexandre Fil
alexandre.fil@outlook.com

Alex worked on simulation of fueling of fusion reactors and control of the plasma edge. He analyzed the penetration of Deuterium/Tritium and Lithium pellets into the fusion core using M3D-C1 and developed control algorithms for EAST tokamak. He is now working at University of York. 

Mikhail Modestov

Mikhail worked on simulation of liquid metals under magnetohydrodynamical conditions with an International Postdoc Grant from the Swedish Research Council (VR) for collaborative research in Princeton University. He is now working at Instituto de Astrofísica de Canarias.

Matthijs Roelofs
m.a.roelofs@gmail.com

Matthijs is a graduate student at the University of Eindhoven studying for a dual degree in Mechanical Engineering and Science and Technology of Nuclear Fusion. Before enrolling at Eindhoven, he earned his bachelor’s degree in Mechanical Engineering from the University of Twente. In the Kolemen group, Matthijs developed new methods for automatically reconstructing tokamak plasma equilibria with kinetic constraints and employed a technique called the unscented transform to compute uncertainty estimates of the ideal MHD stability criterion (δW).

Kornee Kleijwegt
kleijwegtkornee@gmail.com

Kornee is a graduate student at the University of Eindhoven studying for a dual degree in Systems and Control and Science and Technology of Nuclear Fusion. He earned a bachelor’s degree in Mechanical Engineering from the University of Twente. In the Kolemen group, Kornee used various machine learning algorithms such as regression trees to create plasma disruption predictors for the DIII-D and NSTX-U tokamaks.

Koji Kusumi
kusumi.koji.87e@st.kyoto-u.ac.jp

Koji works on heat transfer in various heat exchanger systems. He also studies hydrodynamics flow in oil plants.

Shoki Nakamura
nakasyo316@gmail.com

Shoki studies the measurement of MHD pressure drops under high magnetic field (3T). He describes himself as full of curiosity for everything and having a passion for talking. Shoki enjoys sports – especially volleyball and baseball – and also fine foreign cuisine, including the American hot dog.