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Zhi-Pei LiangView profile

Professor

• Magnetic Resonance Imaging
• Medical Imaging
• Biomedical Engineering
• +5 more

Zhi-Pei Liang is the Franklin W. Woeltge Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign, with joint appointments in the Department of Bioengineering, Beckman Institute for Advanced Science and Technology, and Coordinated Science Laboratory. His research spans biomedical engineering, medical imaging, and signal processing with a focus on advancing magnetic resonance imaging and spectroscopy technologies. His educational background includes a Ph.D. in Biomedical Engineering from Case Western Reserve University (1989) and a B.S. in Electrical Engineering from South-China University of Technology (1982), followed by postdoctoral training at UIUC (1989-1991). Professor Liang's research interests center on magnetic resonance imaging and spectroscopy , with particular emphasis on ultrafast imaging techniques , model-based reconstruction methods , and the integration of physics-based modeling with machine learning . His pioneering work on SPICE (SPectroscopic Imaging by exploiting spatiospectral CorrElation) has revolutionized high-resolution metabolic brain imaging by enabling label-free molecular imaging through the marriage of spin physics and machine learning. His research spans pattern recognition, parameter estimation, image formation theory, and algorithms for medical imaging applications. Analysis of his recent publications reveals a strong focus on high-resolution metabolic imaging , particularly using SPICE methodology to map brain metabolism with unprecedented detail. His work bridges fundamental physics of magnetic resonance with advanced computational methods to overcome traditional limitations in imaging speed and resolution. Current research directions include J-resolved spectroscopic imaging, deuterium-based metabolic mapping, and multimodal integration of PET and MRSI for studying neurological disorders. Elected to International Academy of Medical and Biological Engineering (2012) Gold Medal, International Society for Magnetic Resonance in Medicine (2022) Technical Achievement Award, IEEE Engineering in Medicine and Biology Society (2014) Fellow, National Academy of Inventors (2021) Author of influential book 'Principles of Magnetic Resonance Imaging' (1999) President of IEEE Engineering in Medicine and Biology Society (2011-2012) Professor Liang has advised numerous students and postdocs in biomedical imaging research and has received multiple teaching honors including the Ronald W. Pratt Outstanding Teaching Award (2005) and multiple listings among UIUC's Excellent Teachers. His research has been supported by various grants from NIH, NSF, and other funding agencies. He leads the SPICE (Spectroscopic Imaging by exploiting spatiospectral Correlation) research group which focuses on developing novel imaging techniques that combine physics-based modeling with machine learning for ultrafast metabolic imaging. His laboratory, part of the Beckman Institute's Integrative Imaging Theme, collaborates extensively with clinical researchers at Carle Illinois College of Medicine and other institutions to translate advanced imaging techniques into clinical applications for neurological disorders, cancer, and metabolic diseases. Current projects focus on high-resolution mapping of brain metabolism in Alzheimer's disease, stroke, and brain tumors using novel MR spectroscopic imaging techniques.

Jan Martin NordbottenView profile

Professor

• Applied Mathematics
• Porous Media
• Geological CO2 Storage
• +4 more

Jan Martin Nordbotten is a full-time Professor at the Department of Mathematics, University of Bergen (UiB), with adjunct positions at Princeton University and NORCE. His research focuses on applied mathematics, particularly in porous media, CO2 storage, fluid dynamics, and interdisciplinary applications in hydrology, biomedicine, and ecology. He completed his PhD at UiB in 2004 and became Norway's third youngest professor in 2007. His work emphasizes numerical methods, multiscale modeling, and experimental validation. Affiliations: UiB (full-time), Princeton (adjunct), NORCE (adjunct) Research Group: Center for Sustainable Subsurface Resources Research interests span mathematical modeling of subsurface processes, including flow in fractured media, geomechanics, and phase-field fracture. Notable contributions include analytical and numerical solutions for CO2 leakage, multiphase flow, and development of tools like DarSIA for image processing in porous media. Publications highlight advancements in mixed-dimensional models, finite element methods, and experimental validation of CO2 storage forecasts. His work bridges theoretical mathematics with practical applications in energy and environmental systems.

Emily FalkView profile

Professor

• Communication Neuroscience
• Behavior Change
• Persuasion
• +7 more

Emily Falk is a Professor of Communication, Psychology, Marketing, and Operations, Information, and Decisions at the University of Pennsylvania, where she serves as Vice Dean of the Annenberg School for Communication, Director of the Communication Neuroscience Lab, and Director of the Climate Communication Division of the Annenberg Public Policy Center. Her interdisciplinary work bridges communication science, psychology, and neuroscience to understand behavior change and message effectiveness. Dr. Falk received her B.A. in Neuroscience from Brown University and her Ph.D. in Psychology from the University of California, Los Angeles. Her educational background reflects the interdisciplinary approach that characterizes her research program. Dr. Falk's research focuses on the science of behavior change, examining what makes messages persuasive, why and how ideas spread, and what makes people effective communicators. Her work employs tools from psychology, neuroscience, and communication to investigate neural predictors of message effectiveness, social influence, and the spread of ideas through networks. Key research areas include health communication (particularly tobacco use), climate communication, political communication, and the neuroscience of choice and decision-making. Her groundbreaking work has demonstrated how fMRI brain imaging in small groups can predict large-scale public health campaign success. Dr. Falk's research has been recognized with numerous prestigious awards, including early career awards from the International Communication Association and the Society for Personality and Social Psychology Attitudes Division, a Fulbright grant, Social and Affective Neuroscience Society award, DARPA Young Faculty Award, and the NIH Director's New Innovator Award. She was also named a Rising Star by the Association for Psychological Science. As an advisor, Dr. Falk has mentored numerous graduate students who have gone on to successful careers in academia, government, non-profit, and business sectors. Her lab, the Communication Neuroscience Lab, is funded by major organizations including DARPA, NIH, Google, and the Mind & Life Institute. The lab operates with a mission to increase health and happiness for people and the planet through communication science. The Communication Neuroscience Lab is an interdisciplinary research group that uses tools from biological, social, and network sciences to motivate choices that benefit individuals, communities, and the planet. Current major research projects include BB-PRIME (Brain-based Prediction of Message Effectiveness), BB-PRIME Phase II focusing on climate change interventions, and the GeoScan Smoking Study examining tobacco marketing effects.

• Cancer Genomics
• Liquid Biopsies
• Machine Learning in Oncology
• +7 more

Ash A. Alizadeh is the Moghadam Family Professor of Medicine, Oncology, and Hematology (by courtesy) at Stanford University, where he serves as leader of the Cancer Genomics Program at Stanford Cancer Institute. He holds multiple academic appointments including Professor in Medicine - Oncology, and membership in Bio-X, the Institute for Stem Cell Biology and Regenerative Medicine, and the Maternal & Child Health Research Institute (MCHRI). Dr. Alizadeh received his BS in Biochemistry from UCLA (1994), MD from Stanford Medical School, and PhD in Biophysics from Stanford. He completed additional training at the National Cancer Institute (NCI), the National Institutes of Health (NIH), and the Howard Hughes Medical Institute (HHMI). His primary research focuses on developing and applying genome technologies and computing (machine learning & data science) to problems in human disease, with special emphasis on cancer detection, classification, monitoring, and tumor immunology. His laboratory pioneers noninvasive cancer genomic techniques including CAPP-Seq, PhasED-Seq, and EPIC-Seq for "liquid biopsies" that analyze circulating nucleic acids for early cancer detection and monitoring therapeutic response. Using machine learning approaches, his group studies how cellular compositional variation impacts cancer behavior and therapeutic response, including anti-tumor immunity. His work spans molecular, cellular, organism and population levels of tumor behavior analysis. Dr. Alizadeh has received numerous prestigious awards including the Scholar Award from the American Society of Hematology (ASH), the Leukemia & Lymphoma Society (LLS), the V-Foundation, as well as awards from the American Red Cross, Damon Runyon Cancer Research Foundation, and Doris Duke Charitable Research Foundation. He is an elected member of the American Society for Clinical Investigation (ASCI) and serves on the Scientific Advisory Board of the Lymphoma Research Foundation (LRF). As an educator and mentor, Dr. Alizadeh advises numerous doctoral students, postdoctoral fellows, and medical scholars. He teaches in the Department of Medicine and Immunology and serves on various admissions panels at Stanford. His laboratory, the Alizadeh Lab, is a hub for interdisciplinary cancer genomics research that combines computational biology, molecular genetics, and clinical oncology to develop novel cancer diagnostics and therapeutics.

György HetényiView profile

Associate Professor

• Geophysics
• Tectonics
• Seismology
• +5 more

György Hetényi is an Associate Professor at the Institute of Earth Sciences, University of Lausanne, specializing in large-scale geophysics. His research focuses on mountain-building processes, earthquake dynamics, and tectonic deformation of the Himalayas and Alps. He leads the AlpArray project, deploying the largest academic seismic network in Europe. Since 2015, he has held an SNSF Assistant Professorship, advancing to his current role in 2020. Education: Bachelor's in Geophysics at Eötvös University (Budapest) Master's and PhD at École Normale Supérieure (Paris), studying Himalayan deformation Research Interests: Combining seismic and gravity data to model crustal structures, numerical modeling of orogenic processes, and educational seismology initiatives in Nepal. Active in field campaigns across Bhutan, Nepal, and the Ivrea-Verbano Zone. Publications: Over 48 peer-reviewed articles since 2007, emphasizing crustal imaging, seismic tomography, and Himalayan tectonics. Recent work includes participatory gravity modeling challenges and pan-Alpine gravity database development. Awards: Prize of the Chancellery of the Universities of Paris (2007) for doctoral research on Himalayan deformation. Teaching & Outreach: Developed the 'Geophysics Across Scales for Geologists' module in the UNIL-UNIGE program. Co-leads the Nepal School Seismology Network, integrating low-cost seismic education tools. Grants & Projects: AlpArray, DIVE (scientific drilling in Ivrea Zone), and seismic hazard assessments in Bhutan. Collaborates with international networks like ICDP and European seismic consortia. Labs/Teams: Part of the Institute of Earth Sciences (UNIL) and leads the OROG3NY project on mountain-building dynamics.

Vedran DunjkoView profile

Professor

• Quantum Computing
• Quantum Machine Learning
• Quantum Artificial Intelligence
• +7 more

Professor Vedran Dunjko is a faculty member at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University, with affiliations to the Leiden Institute of Physics (LION). He leads the Applied Quantum Algorithms group and co-founded the Quantum@LIACS initiative, focusing on the intersection of quantum computing, machine learning, and artificial intelligence. His research interests include quantum machine learning, quantum-enhanced reinforcement learning, quantum heuristics, and the application of AI to quantum computing challenges. Dunjko's work bridges theoretical foundations with experimental implementations on near-term quantum devices, exploring both quantum advantages in learning and the use of classical AI for quantum system design. The recent publications show a strong trend toward proving quantum advantages in learning tasks, optimization, and topological data analysis, with publications in Nature , Nature Communications , and NeurIPS . Key themes include quantum policy gradients, quantum TDA, and reinforcement learning for quantum circuit optimization. ERC Consolidator Grant (2024) PNAS Cozzarelli Prize (2018) Editor’s Suggestion in Physical Review Letters (2014, 2018) Featured in Physics (American Physical Society) (2014, 2018) Dunjko advises several PhD candidates and postdocs, including Rahul Bandyopadhyay, Sofiene Jerbi, and Lea Trenkwalder. He has received competitive grants, most notably the ERC Consolidator Grant in 2024. His group fosters international collaborations with institutions across Europe and industry partners. The Applied Quantum Algorithms group and the Quantum@LIACS team combine theoretical investigations with practical implementations on quantum hardware, focusing on scalable quantum algorithms and AI-driven quantum discovery.

• RF and Microwave Engineering
• Power Amplifiers
• Digital Transmitters
• +4 more

Leonardus Cornelis Nicolaas de Vreede is a Professor at Delft University of Technology in the Faculty of Electrical Engineering, Mathematics and Computer Science. With over 237 research publications and extensive conference activities, he is a leading researcher in RF and microwave engineering with specialization in power amplifiers, digital transmitters, and mm-wave circuits for wireless communications applications. Dr. de Vreede's research focuses on the intersection of circuit design and signal processing for next-generation wireless systems: Advanced Power Amplifier Architectures including Doherty and Out-phasing techniques Energy-Efficient Digital Transmitters with high linearity and power efficiency mm-Wave Circuit Design for 5G/6G applications Machine Learning Applications for Digital Predistortion CMOS RF Integrated Circuit Implementation Wideband Signal Processing Techniques His recent publications demonstrate a clear research trajectory toward integrating machine learning with traditional RF circuit design to solve the efficiency-linearity tradeoff in wireless transmitters. This work is particularly relevant for current and future wireless infrastructure requiring high spectral efficiency across wide bandwidths while maintaining energy efficiency. Dr. de Vreede has received significant recognition for his contributions to the field: EuMC Microwave Prize (2024) for groundbreaking work on wideband Doherty amplifiers Recognition for innovative characterization techniques for high-power RF transistors (2015) As an active researcher and educator, Dr. de Vreede has supervised 16 students and regularly participates in major international conferences including serving on program committees for the IEEE MTT-S International Microwave Symposium. His work bridges theoretical advances with practical implementations for wireless infrastructure applications, with numerous patents and industry collaborations evident from his research portfolio.

Martin BerggrenView profile

Professor

• Computational Design Optimization
• Numerical Optimization Algorithms
• Gradient-Based Algorithms
• +8 more

Martin Berggren is a Professor at the Department of Computing Science , Umeå University , Sweden. His work focuses on Computational Design Optimization , combining computer simulations and numerical optimization to enhance engineering designs for devices like antennas, microwave components, and loudspeakers. Berggren is also active in mathematical modeling of physical phenomena, particularly wave propagation and fluid mechanics, with a strong emphasis on finite-element methods . His research addresses large-scale conceptual design problems using thousands to millions of design variables, relying on gradient-based algorithms and adjoint-based computations of design sensitivities—similar to back-propagation in deep learning. Key application areas include acoustic and electromagnetic devices, where he investigates damping mechanisms, boundary conditions, and material distribution. Other interests, though less active, involve flow control and unsteady fluid–structure interaction . Berggren collaborates extensively on projects such as Structured Regularization , Topology Optimization of Acoustic Black Holes , and Design of Microstrip-to-Waveguide Transitions . His publications span journals like Journal of Computational Physics , Pattern Analysis and Applications , and IEEE Transactions on Antennas and Propagation , often co-authored with researchers like Linus Hägg , Eddie Wadbro , and Disi Lin .

Ellen ZweibelView profile

Professor

• Plasma Astrophysics
• Astrophysical Magnetic Fields
• Interstellar Medium
• +6 more

Ellen Zweibel is the W. L. Kraushaar Professor of Astronomy and Physics at the University of Wisconsin–Madison , where she has been a faculty member since 2003. She holds a joint appointment in the Department of Astronomy and Physics . Zweibel earned her undergraduate degree in Mathematics from the University of Chicago and her Ph.D. in Astrophysical Sciences from Princeton University. Her research focuses on plasma astrophysics , particularly the evolution of astrophysical magnetic fields , cosmic ray feedback in galactic and intergalactic environments, and stellar differential rotation dynamics. Recent work examines cosmic ray interactions with the interstellar medium, magnetic instabilities in galaxy clusters, and turbulence-driven dynamo processes. Zweibel's publications highlight collaborations on missions like HelioSwarm and SOFIA/HAWC+ , including the discovery of a magnetized dust ring in the Galactic Center . She leads NSF-funded research on microscale plasma processes in high-beta environments and contributes to understanding magnetic reconnection across astrophysical contexts.

• Virology
• Molecular Pathology
• Host-Pathogen Interactions
• +5 more

Dr. Richard Y. Zhao is a tenured Professor in the Department of Pathology and Microbiology-Immunology at the University of Maryland School of Medicine. His research combines molecular biology, fission yeast genetics, mammalian biology, and virology to study virus-host interactions, particularly for HIV and Zika virus. He previously held academic positions at Northwestern University and Columbia University and has contributed to over 120 peer-reviewed articles. B.S., China Oceanography University (1981) M.S., Oregon State University (1995) Ph.D., Oregon State University (1991) Postdoctoral Training, Columbia University (1991-1992) Dr. Zhao's research focuses on: Virus-host interactions and pathogenicity High-throughput drug screening for antivirals Role of viral proteins in neuroinflammation and cancer Translational genomics in precision medicine His recent publications highlight SARS-CoV-2 ORF3a, Zika envelope proteins, and HIV protease inhibitors, emphasizing host-pathogen mechanisms across species. He has served on NIH panels and editorial boards for journals like Cell Research and Retrovirology . Scientific awards include: Fellow, American Academy of Microbiology (2019) Bernard L Mirkin Endowed Chair (2001-2004) Honorary Director, Shandong Gallo Institute (2009) Distinguished Service from SCBA (2015) Outstanding Service from CBA-USA (2016) Dr. Zhao also contributes to clinical diagnostics and personalized medicine through molecular testing and pharmacogenetics programs.

Miaki IshiiView profile

Professor

• Seismology
• Geophysics
• Earthquake Studies
• +5 more

Miaki Ishii is a Professor of Earth and Planetary Sciences at Harvard University, affiliated with the Department of Earth and Planetary Sciences. She leads the Harvard Seismology Group and has held academic roles at Harvard since 2006, progressing from Assistant to Associate Professor and then Full Professor. Education: Ph.D. in Geophysics (2003), Harvard University Hon.B.Sc. in Physics (1998), University of Toronto Research Interests: Ishii specializes in seismic imaging of Earth's internal structure, including the mantle and core. Her work focuses on earthquake mechanisms, signal processing, and theoretical seismology. She uses seismic data to study rupture dynamics, subduction zone processes, and free oscillations of the Earth. Key Contributions: Notable projects include analyzing the 2011 Tohoku-Oki earthquake rupture, developing the DigitSeis software for analog seismogram digitization, and studying inner core anisotropy using normal mode splitting. Her research integrates high-performance computing and waveform inversion techniques. Awards: James B. Macelwane Medal (2009) Kavli Fellow (2012) Charles F. Richter Award (2008) Alice Wilson Award (2004) Labs/Teams: Directs the Harvard Seismology Group, collaborating internationally on seismic networks like Hi-net and USArray. Her work bridges computational seismology with observational geophysics.

Will PerkinsView profile

Associate Professor

• Theory of Algorithms
• Probability
• Combinatorics
• +2 more

Will Perkins is an Associate Professor in the School of Computer Science at Georgia Institute of Technology. Previously, he held faculty positions at the University of Illinois at Chicago, the University of Birmingham (UK), and was an NSF Postdoc at Georgia Tech. He earned his PhD in 2011 from New York University's Courant Institute under Joel Spencer. His research focuses on algorithms, statistical physics, and discrete mathematics, particularly exploring algorithmic tractability of random computational problems, statistical physics spin models, and combinatorial methods derived from algorithmic intuition. Research Interests : Algorithms, statistical physics, combinatorics, phase transitions, random graphs, and Gibbs measures. His work bridges theoretical computer science and statistical mechanics, addressing questions about sampling, phase coexistence, and algorithmic barriers. Recent Activities : Director of the Algorithms and Randomness Center at Georgia Tech, Managing Editor of Combinatorial Theory , and Associate Editor of Random Structures and Algorithms and SIAM Journal on Discrete Mathematics . Upcoming engagements include the Rocky Mountain Summer Workshop (2024), Park City Mathematics Institute (2024), and conferences on Random Structures and Algorithms (2025). Teaching : Courses include Design and Analysis of Algorithms (CS 3510), Advanced Algorithms (CS 4540), and specialized topics like Statistical Physics in Algorithms and Combinatorics (CS 8803). He has taught across institutions, including at the University of Birmingham and University of Illinois at Chicago. Key Contributions : His work on phase transitions in combinatorial structures, algorithmic sampling in statistical physics models, and rigorous analysis of Gibbs measures has been published in top venues like FOCS, STOC, and Communications in Mathematical Physics. Notable results include hardness of sampling for anti-ferromagnetic Ising models and novel contour methods for Pirogov-Sinai theory.

Enrico ArrigoniView profile

Professor

• Quantum Physics
• Condensed Matter Physics
• Theoretical Physics
• +3 more

Enrico Arrigoni is a Professor at the Institute of Theoretical Physics - Computational Physics at Graz University of Technology (TU Graz). His research focuses on correlated quantum systems, many-body physics, and nonequilibrium dynamics, with applications to Mott insulators, quantum transport, and photovoltaic systems. He teaches courses such as 'Green's functions in Many-Particle Physics' and 'Atom Physics - Quantum Mechanics'. Recent work explores phonon effects in Mott systems, neural network approaches to quantum states, and impact ionization processes in photodriven materials. His methods include auxiliary master equation techniques and variational cluster approaches. Publications span topics like nonequilibrium steady states, quantum impurity models, and disordered systems. While no specific awards are listed, his contributions to theoretical physics and computational methods are evident through his prolific research output. Advising and grants details are not explicitly mentioned, though his involvement in graduate theses and research projects is implied via available master's and bachelor's thesis topics.

Michele DolceView profile

Lecturer

• Partial Differential Equations
• Fluid Dynamics
• Kinetic Theory
• +4 more

Michele Dolce is a Lecturer and Scientist at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Basic Sciences (SB) and the Chair of Mathematical Analysis, Calculus of Variations and PDEs (AMCV). He previously held positions as a Postdoc at EPFL and a Research Associate at Imperial College London, where he worked under Prof. Michele Coti Zelati. His academic journey includes a PhD from the Gran Sasso Science Institute. Current affiliation: EPFL, School of Basic Sciences (SB), Department of Mathematics (MATH), AMCV Past affiliation: Imperial College London His research focuses on the mathematical analysis of Partial Differential Equations (PDEs) in fluid dynamics and kinetic theory. Key areas include hydrodynamic stability, long-time behavior of viscous vortex systems, and time-decay properties of kinetic models like the Boltzmann and Wave Kinetic Equations. Recent work explores vortex merging phenomena and Taylor dispersion in rotationally symmetric flows. Scientific activities include organizing workshops such as "Long time dynamics in random and deterministic systems" (2025) and co-organizing events like the "Deterministic and random features of fluids" summer school (2023) and the "Enjoying Probability and Fluids in Lausanne" workshop (2023). His publications span journals including Communications in Mathematical Physics, Archive for Rational Mechanics and Analysis, and Journal of Mathematical Fluid Mechanics. Supported by Swiss National Science Foundation (SNF Ambizione grant PZ00P2_223294) Partially funded by GNAMPA (INdAM group)

Micheline B. SoleyView profile

Assistant Professor

• Quantum Computing
• Quantum Dynamics
• Ultracold Chemistry
• +5 more

Micheline B. Soley is an Assistant Professor in the Department of Chemistry at the University of Wisconsin-Madison with an affiliate appointment in Physics. She leads the Soley Research Group, which focuses on developing quantum computing algorithms, tensor-network methods, and quantum control strategies to address fundamental challenges in quantum dynamics and ultracold chemistry. Her research bridges theoretical chemistry, quantum information science, and computational physics. Education: Ph.D. in Chemical Physics, Harvard University (2020) A.M. in Chemistry, Harvard University (2016) Yale Quantum Institute Postdoctoral Fellow (2020-2022) Fulbright Fellow, Max Born Institute (2013-2014) B.S. in Chemistry and Music, Yale University, Magna Cum Laude (2013) Research Interests: Her work centers on three interconnected pillars: (1) Quantum computing algorithms and tensor-network methods for exact quantum dynamics, overcoming dimensionality limitations in chemical simulations; (2) Ultracold chemistry and quantum control, developing schemes to manipulate chemical reactions and analyze ultracold collisions; and (3) Theoretical spectroscopy, creating tools to simulate UV/X-ray pump-probe experiments for mechanistic studies of processes like isomerization and proton transfer. Publication Trends: Recent articles (2023-2025) demonstrate a strong focus on quantum algorithm development (error mitigation, amplitude estimation), tensor-network applications in quantum dynamics and biomolecular simulations, quantum hardware compilation, and fundamental studies of PT symmetry and ultracold collisions. Her work consistently integrates theoretical chemistry with quantum information science. Awards and Fellowships: American Chemical Society Kavli Emerging Leader in Chemistry Award (2023) Institute for Pure and Applied Mathematics Fellow (2021) Yale Quantum Institute Postdoctoral Fellowship (2020) National Science Foundation Graduate Research Fellowship (2014) Fulbright Fellowship (2013-2014) DAAD Graduate Scholarship (2013-2014) Beckman Scholars Fellowship (2012-2013) Phi Beta Kappa (2012) Advising and Group: She mentors graduate students from Chemistry and Physics programs, including Jingcheng Dai (Chemistry), Atharva Vidwans (Chemistry/Physics), and Henry Lin (Physics-Quantum Computing). Former advisees include Preetham Tikkireddi (Quantum Circuits Inc.) and Jaden Coles (Yale PhD). Her group explores quantum computing, tensor networks, ultracold collisions, and PT symmetry.