Marie Violay is an Associate Professor at the Laboratory of Experimental Rock Mechanics (LEMR) within the School of Architecture, Civil and Environmental Engineering (ENAC) at École Polytechnique Fédérale de Lausanne (EPFL). She also contributes to teaching and PhD program committees across multiple EPFL divisions. Specializes in rock mechanics, earthquake dynamics, and hydro-mechanical couplings Leads experimental studies on fluid-induced seismicity and reservoir deformation Research Focus : Investigates how pore fluid pressure variations affect rock strength, fault behavior, and permeability evolution. Her work spans from brittle fracture mechanics to ductile deformation processes under geothermal conditions, with applications to carbon storage, earthquake mitigation, and volcanic hazard assessment. Article Trends : Her recent publications examine stress biaxiality effects on fracture energy (2025), alteration impacts on geothermal reservoirs (2025), permeability changes in volcanic rocks (2024), and fundamental studies of water weakening mechanisms in sedimentary rocks (2024-2021). The work combines laboratory experiments, microstructural analysis, and numerical modeling. Labs & Collaborations : Coordinates the LEMR laboratory at EPFL. Collaborates with European Synchrotron Radiation Facility, Freie Universität Berlin, and industry partners like Emch&Berger AG. Leads PhD committee work in the EDME Doctoral School .
Britt Adamson is an Associate Professor in the Department of Molecular Biology and the Lewis-Sigler Institute for Integrative Genomics at Princeton University, where she serves as Director of the Undergraduate Program in Quantitative and Computational Biology. Her lab investigates molecular networks in human cells with focus on stress response mechanisms and genome editing technologies. She received her B.S. in Biology from the Massachusetts Institute of Technology (2005) and Ph.D. in Genetics and Genomics from Harvard University (2012), followed by postdoctoral training at UCSF under Jonathan Weissman supported by a Damon Runyon Cancer Research Foundation Fellowship. Adamson's research centers on how cells organize stress response networks during DNA damage and endoplasmic reticulum stress, developing CRISPR-based functional genomics and single-cell sequencing tools to map molecular behaviors. Her work bridges fundamental cell biology with therapeutic applications in genome editing. Analysis of her 15 most recent publications reveals dominant themes in precision genome editing (prime/base editing optimization) and systematic dissection of DNA repair pathways through combinatorial CRISPR screening. Her lab consistently integrates computational approaches with high-resolution experimental techniques to uncover context-dependent cellular behaviors. Her scientific recognitions include: Damon Runyon Cancer Research Foundation Postdoctoral Fellowship Princeton IP Accelerator Award (2025) STAT Who to Know: 10 Scientists leading a new generation of gene editors (2024) Adamson actively mentors eight graduate students (including alumni Ann Cirincione and Jun Hussmann) and two postdocs, with research funded through institutional awards and collaborative grants. Her lab's technological developments have enabled projects spanning virology, immunology, and developmental biology. The Adamson Lab operates within Princeton's Lewis-Sigler Institute for Integrative Genomics, fostering an interdisciplinary environment that merges cell biology, genomics, and computational science. Current projects focus on improving prime editing efficiency and understanding stress response adaptation in disease contexts.
Joy Arulraj is an Associate Professor in the School of Computer Science within the College of Computing at Georgia Institute of Technology. His research focuses on data systems, machine learning, and database systems, with a particular emphasis on video analytics and adaptive query processing. He leads the Data Systems and Analytics Group and is developing the EVA AI-Relational Data System. Dr. Arulraj's research interests span data systems, machine learning, database systems, video analytics, and adaptive query processing. His work centers on developing systems that efficiently process complex queries, particularly for video analytics and machine learning workloads. He has made significant contributions to GPU database systems, non-volatile memory database management, and adaptive query processing techniques. His research often bridges the gap between theoretical database principles and practical implementations for modern hardware architectures. His recent publications show a strong trend toward video analytics systems, adaptive query processing for machine learning workloads, and GPU-accelerated database systems. The EVA system represents a major focus of his recent work, providing end-to-end exploratory video analytics capabilities. His research also addresses fundamental database concepts like buffer management, query optimization, and storage management, adapting these principles for modern hardware and application requirements. Dr. Arulraj has advised numerous graduate students including Pramod Chunduri, Gaurav Tarkok Kakkar, Jiashen Cao, and Sayan Sinha. His graduated students have gone on to work at companies like ServiceNow, Meta Research, and the Korean Army. He actively teaches database system courses at Georgia Tech, including Database System Implementation (CS 4420/6422) and Advanced Database System Implementation (CS 4423/6423), where students build database systems from scratch using C++ and the BuzzDB framework. He maintains an active research program with consistent publication output across top database and systems conferences. His work spans from theoretical database principles to practical system implementations, with a recent emphasis on video analytics, machine learning integration with database systems, and leveraging modern hardware like GPUs and non-volatile memory for database applications.
Navid Azizan is the Alfred H. (1929) and Jean M. Hayes Career Development Assistant Professor at Massachusetts Institute of Technology (MIT), holding dual appointments in the Department of Mechanical Engineering (in Control, Instrumentation & Robotics) and the Schwarzman College of Computing's Institute for Data, Systems & Society (IDSS). He is also a Principal Investigator in the Laboratory for Information & Decision Systems (LIDS), and a faculty member of the MIT Statistics and Data Science Center, the Center for Computational Science and Engineering, and the Operations Research Center. Dr. Azizan received his PhD in Computing and Mathematical Sciences from the California Institute of Technology (Caltech) in 2020, his MSc in Electrical Engineering from the University of Southern California in 2015, and his BSc in Electrical Engineering with a minor in Physics from Sharif University of Technology in 2013. Prior to joining MIT, he completed a postdoc at Stanford University's Autonomous Systems Laboratory and was a research scientist intern at Google DeepMind. His research spans the intersection of machine learning, systems and control, mathematical optimization, and network science. Dr. Azizan's work focuses on developing principled learning and optimization algorithms for reliable intelligent systems, with applications to autonomy and sociotechnical systems. His research has significant implications for creating trustworthy AI systems that can operate effectively in complex, uncertain environments. Dr. Azizan's recent publications demonstrate a strong focus on uncertainty quantification, reliable AI systems, constrained optimization, and control-oriented learning. His work bridges theoretical foundations with practical applications, particularly in autonomous systems where safety and reliability are paramount. His research group has made notable contributions to areas including neural network verification, multi-agent reinforcement learning, and adaptive inference techniques for large language models, with several papers featured on MIT News and selected for oral presentations at top conferences. Alfred H. (1929) and Jean M. Hayes Career Development Professorship (2025-present) Frank E. Perkins Award for Excellence in Graduate Advising (2025) List of Outstanding Academic Leaders in Data from the CDO Magazine (2024, 2023) Amazon Science Hub Research Award (2023) Outstanding UROP Faculty Mentor (2023) Esther and Harold E. Edgerton (1927) Career Development Chair (2022-2025) Information Theory and Applications (ITA) Gold Graduation Award (2020) Dr. Azizan has been recognized for his excellence in graduate advising, receiving the Frank E. Perkins Award for Excellence in Graduate Advising in 2025. During the pandemic, he founded and co-organized the 'Control meets Learning' virtual seminar series, connecting researchers across disciplines. His work has attracted significant research funding from industry partners including Google, Amazon, and MathWorks, supporting both fundamental research and practical applications in reliable intelligent systems. The Azizan Lab at MIT brings together researchers from mechanical engineering, computer science, and applied mathematics to tackle challenges at the intersection of learning and control. The lab emphasizes both theoretical foundations and practical implementations, with a particular focus on developing algorithms that provide guarantees of performance and safety. Current research directions include uncertainty quantification in AI systems, constrained optimization for neural networks, and control-oriented learning for autonomous systems, with applications spanning robotics, transportation, and complex sociotechnical systems.
Dr. Zhu Lailai serves as Assistant Professor in the Department of Mechanical Engineering at the National University of Singapore (NUS), appointed in January 2020. His research bridges fundamental fluid mechanics with cutting-edge engineering applications through computational and theoretical approaches. Dr. Zhu holds a PhD from KTH Royal Institute of Technology (Sweden) and completed postdoctoral training at Princeton University. His research program centers on: Low-Reynolds-number fluid-structure interactions and bio-inspired adaptive systems Active matter dynamics (Janus colloids, active droplets, flagella/cilia) Intelligent fluids integrating machine learning for fluid dynamics Microrobotics with reinforcement learning-based chemotactic navigation Non-Newtonian/multiphase flows and microfluidics applications Analysis of his 2017-2025 publications reveals a clear trajectory toward AI-enhanced fluid mechanics, evolving from foundational theoretical models to machine learning integration. Recent work emphasizes foundation models for fluid dynamics prediction and topology-adaptive microrobotic navigation, demonstrating interdisciplinary convergence of physics, AI, and bionics. Scientific Awards: No major scientific awards specified in source materials Advising and Grants: While specific advisees and grants aren't detailed, his active publication record across high-impact journals (Nature Communications, Journal of Fluid Mechanics) indicates ongoing supervised research and likely grant funding through NUS and collaborative projects. Research Group: Dr. Zhu leads a computational/theoretical research team at NUS investigating active and intelligent fluids, with current projects on PCM thermal systems, microrobotic navigation, and active matter phase transitions, collaborating with experimentalists globally.
Ferdinando Fioretto is an Assistant Professor of Computer Science at the University of Virginia, leading the Responsible AI for Science and Engineering (RAISE) group. His research focuses on foundational challenges in AI, privacy, fairness, and the intersection of machine learning and optimization. He holds a dual PhD in Computer Science from the University of Udine and New Mexico State University. Affiliations: University of Virginia (current), Syracuse University (former), Georgia Institute of Technology (postdoc), University of Michigan (research fellow) Education: PhD (Udine & NMSU), B.S. (University of Parma) Research Interests: Machine Learning, Responsible AI, Optimization, Differential Privacy, Algorithmic Fairness. His work emphasizes practical applications in energy systems, court scheduling, and privacy-preserving machine learning. Recent projects include neuro-symbolic diffusion models, fairness-aware optimization, and privacy guarantees in LLMs. Grants & Funding: NSF CAREER Award, Google Faculty Research Award, Amazon Research Award, NVIDIA Academic Grant, and grants from the LaCross Institute and 4-VA. His group collaborates with institutions like George Mason University and Virginia Tech. Key Awards: NSF CAREER (2022), IJCAI Early Career Spotlight (2022), Caspar Bowden PET Award (2022), ACP Early Career Researcher Award (2021) Labs/Teams: RAISE group at UVA, focused on trustworthy AI, fair optimization, and privacy-preserving systems. Active in organizing workshops like NeurIPS Algorithmic Fairness and AAAI Privacy-Preserving AI.
Joydeep Biswas is an Associate Professor in the Computer Science Department at the University of Texas at Austin, where he serves as the Director of the Autonomous Mobile Robotics Laboratory (AMRL). He is also affiliated with Texas Robotics, the UT Machine Learning Laboratory, and UT Good Systems. Previously, he was an Assistant Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst. Dr. Biswas earned his PhD in Robotics from Carnegie Mellon University in 2014 and his B.Tech in Engineering Physics from the Indian Institute of Technology Bombay in 2008. His educational background has provided him with a strong foundation in both theoretical and applied aspects of robotics and artificial intelligence. Dr. Biswas's research focuses on enabling long-term autonomy for mobile robots operating in human environments. His work spans robot perception, motion planning, control systems, and AI, with the ultimate goal of creating self-sufficient autonomous mobile robots that can perform tasks accurately and robustly in real-world settings. He is particularly interested in perception, planning, and failure recovery for autonomous mobile robots, which supports his vision of having autonomous service mobile robots deployed at campus-to-city scale, both indoors and outdoors, performing assistive tasks over deployments spanning years. His IJCAI 2019 Early Career Spotlight talk summarizes much of his research to date and ongoing interests. His recent research has shown a strong trend toward social navigation, human-robot interaction, and the application of machine learning techniques to robotics problems. There's a clear progression from fundamental robotics research toward more complex, real-world applications that require robots to understand and navigate human social spaces effectively. His work increasingly integrates large language models and other advanced AI techniques with traditional robotics approaches, as evidenced by his recent publications on topics like preference-conditioned navigation, social navigation benchmarks, and instruction-following navigation systems. Dr. Biswas has received numerous prestigious awards including the NSF CAREER Award (2021), J.P. Morgan Faculty Research Award (2019), Amazon Research Award (2019), and a grant from Northrop Grumman Mission Systems (2018). These awards recognize his innovative contributions to the field of robotics and autonomous systems. As a dedicated educator and mentor, Dr. Biswas actively supervises PhD and master's students, with his PhD student Sadegh Rabiee winning the student poster award at the Northrop Grumman University Symposium 2019. He has secured significant grant funding from the National Science Foundation for projects including 'Introspective Perception and Planning for Long-Term Autonomy' and 'Interactive Synthesis and Repair For Robot Programs,' demonstrating his ability to secure competitive research funding and his commitment to advancing the field. Dr. Biswas leads the Autonomous Mobile Robotics Laboratory (AMRL), which serves as a hub for interdisciplinary research in mobile robotics. The lab has developed notable resources such as the UT Campus Object Dataset (CODA) for 3D perception research and SOCIALGYM, a framework for benchmarking social robot navigation. His team regularly deploys robots on the UT Austin campus and in urban environments to test and refine their approaches in realistic settings, bridging the gap between simulation and real-world application.
Richard E. Turner is a Professor of Machine Learning at the University of Cambridge's Department of Engineering and Research Lead for AI for Weather Prediction at the Alan Turing Institute. He serves as Cambridge Lead for the EPSRC Probabilistic AI Hub and previously held roles including Visiting Researcher at Microsoft Research, Co-Director of the AI4ER CDT, and Course Director for the Machine Learning and Machine Intelligence MPhil program. Current research focuses on probabilistic machine learning fundamentals, environmental prediction (weather/climate), and spatio-temporal modeling combining deep learning with Bayesian methods Supervised 26 PhD students (13 graduated) and 7 research assistants/associates Secured over £30M in research funding from EPSRC, Microsoft, Toyota, Google, DeepMind, Amazon, and Improbable Featured in BBC Radio 5 Live's The Naked Scientist, BBC World Service's Click, and Wired Magazine His recent publications demonstrate expertise in diffusion models for PDE simulations, Gaussian Processes for environmental applications, and Bayesian methods for spatio-temporal forecasting. Key trends include climate modeling using ML, neural PDE solvers, and scalable probabilistic inference. Awards : Cambridge Students' Union Teaching Award for Lecturing; supervised Qualcomm Innovation Fellowship winner Collaborations : Microsoft Research (AI4Science), Alan Turing Institute, EPSRC Probabilistic AI Hub Turner leads the Turner Group within Cambridge's Machine Learning Group, focusing on uncertainty-aware ML for scientific applications. Current research assistants work on topics like meta-learning, Bayesian inference, and climate science applications.
Subhashis Ghoshal is a Goodnight Distinguished Professor in the Department of Statistics at North Carolina State University (NCSU). He holds a Ph.D. in Statistics from the Indian Statistical Institute (1995). His research focuses on Bayesian nonparametrics, high-dimensional models, asymptotic theory, and functional data analysis. He has authored influential books like *Fundamentals of Nonparametric Bayesian Inference* (2017) and contributed to methodologies in image processing and statistical inference. Key awards include the Goodnight Distinguished Professorship (2021), Dr. Cavell Brownie Mentoring Award (2014-15), and the De Groot Prize (2019). He has held editorial roles in journals like *Statistical Science* and *Annals of Statistics*. His work bridges theory and applications, addressing challenges in modern statistical problems such as uncertainty quantification and causal inference. He advises on graduate programs and actively contributes to academic leadership at NCSU.
Beatriz Noheda is a Full Professor of Functional Nanomaterials at the University of Groningen's Faculty of Science and Engineering, where she chairs the Solid State Materials for Electronics group at the Zernike Institute for Advanced Materials. She also serves as the founding Director of the Groningen Cognitive Systems and Materials center (CogniGron). Her academic journey began with a PhD in Physics from the Autonomous University of Madrid in 1996, followed by research positions at Brookhaven National Laboratory and various European institutions before joining Groningen through the prestigious Rosalind Franklin Fellowship program in 2004. Her research interests span the physics of functional materials with particular emphasis on ferroelectric, piezoelectric, and multiferroic thin films . She investigates the relationship between structure and functionality, focusing on nano-domain control through strain engineering and the unique properties of domain walls. Her work bridges fundamental physics with two promising application areas: piezoelectric energy harvesting for low-power electronics and the development of novel materials for neuromorphic computing . This dual focus reflects her vision of enabling the next technological revolution through materials science. Noheda's publication record shows a clear evolution from fundamental structural studies of ferroelectric materials toward cutting-edge research in hafnia-based ferroelectrics and neuromorphic computing materials. Her most recent work focuses on oxygen migration in hafnium-zirconium oxide systems, metal-insulator transitions in nickelates, and the development of novel ferroelectric phases suitable for next-generation electronic devices. These publications demonstrate her leadership in advancing the field from basic understanding toward practical applications in memory devices and cognitive computing systems. Fellow of the American Physical Society (2011) - awarded for fundamental structural studies of new phases in perovskite-type ferroelectric materials and domain nanostructures IEEE Robert E. Newnham Ferroelectrics Award (2020) - for outstanding contributions to understanding giant piezoelectricity in lead zirconate titanate Member of the Netherlands Academy of Technology and Innovation (AcTI) (2022) Elected Senior member IEEE (2021) Rosalind Franklin Fellowship (2004) - enabling her successful academic career in Groningen Noheda has secured substantial research funding throughout her career, including a Rosalind Franklin Fellowship (2004-2009), VIDI-NWO Fellowship (2004-2008), TOP-NWO project on Functional Nanowalls (2007-2012), multiple Zernike Institute Dieptestrategie grants, and a significant TOP-PUNT grant (2016-2021). She has supervised numerous students and early-career researchers, contributing to the development of the next generation of materials scientists. Her leadership extends to editorial roles on prestigious journals including Science, Physical Review Applied, and npj Quantum Materials. As Director of CogniGron, Noheda leads an interdisciplinary center focused on developing materials and systems for cognitive computing. Her team combines expertise in functional oxides, nanoelectronics, and neuromorphic engineering to create novel computing paradigms inspired by the human brain. The center represents a strategic initiative at the University of Groningen to position itself at the forefront of cognitive systems research.
Gary Rochelle is the Carol and Henry Groppe Professor in Chemical Engineering and a faculty member at the University of Texas at Austin . His research focuses on developing fundamental insights into kinetic and mass transfer phenomena in aqueous technologies for air pollution control and acid gas treating, particularly for carbon dioxide and mercury removal. Education: Ph.D. in Chemical Engineering from UC Berkeley (1977), M.S./B.S. from MIT (1971) His work addresses critical challenges in CO2 capture using amine scrubbing, including process design optimization, solvent degradation mitigation, and pilot plant validation. Recent studies emphasize energy efficiency, oxidation inhibition, and environmental impacts such as amine aerosol emissions. The Texas Carbon Management Program , which he contributes to, aims to improve amine scrubbing technologies for retrofitting power plants and enabling geological sequestration or enhanced oil recovery. His group has validated concentrated aqueous piperazine (PZ) with an advanced flash stripper as the most efficient open-literature system.
Dr. Xinwei Ye serves as a Researcher in the Inorganic Chemistry and Catalysis division at Utrecht University's Faculty of Science. His primary affiliation is with the Department of Chemistry, where he conducts cutting-edge research on heterogeneous catalysis for environmental applications, particularly focusing on selective catalytic reduction (SCR) systems for automotive emissions control. With a strong background in inorganic materials and advanced characterization techniques, Dr. Ye contributes significantly to understanding catalyst structure-performance relationships. Educational Background: Master of Science (MSc) - Institution not specified in source Doctor of Philosophy (PhD) in Chemistry, Utrecht University (2022) Dr. Ye's research program centers on the development and mechanistic investigation of copper-exchanged zeolite catalysts for NH 3 -SCR processes. His work integrates multiple advanced characterization methodologies including operando spectroscopy, scanning transmission X-ray microscopy (STXM), and atom probe tomography to probe catalyst behavior under working conditions at nanometer resolution. This multi-technique approach enables unprecedented insights into active site speciation, reaction mechanisms, and deactivation pathways in emission control catalysts. Analysis of Dr. Ye's publication record from 2018-2022 reveals a cohesive research trajectory focused on copper-zeolite SCR catalysts. His work consistently addresses critical challenges in catalyst durability and performance optimization through fundamental understanding of structure-activity relationships. The publications demonstrate increasing sophistication in experimental approaches, moving from membrane synthesis (2018) to nanoscale deactivation studies (2020) and ultimately to comprehensive structure-performance correlations in his doctoral thesis (2022). As a core member of Utrecht University's catalysis research community, Dr. Ye collaborates extensively with the renowned Weckhuysen group. His research is conducted within well-equipped laboratories featuring state-of-the-art instrumentation for catalyst synthesis, testing, and characterization, including access to synchrotron radiation facilities for advanced X-ray techniques.
Lindsey Draper, MD serves as an Adjunct Instructor in the Roybal Laboratory within the Department of Medicine, Division of Hematology and Oncology at the University of California San Francisco School of Medicine. Her clinical practice focuses on the treatment of patients with recurrent ovarian cancer using systemic therapies including chemotherapy and immunotherapy, with a commitment to evaluating and offering clinical trial opportunities as part of individualized patient care. Dr. Draper completed her educational training with a B.S. in Biology from Juniata College (2010), an M.D. from the University of Maryland School of Medicine (2017), an Internal Medicine Residency at The Mount Sinai Hospital (2022), and is currently completing a Medical Oncology Fellowship at UCSF (2025-2027). Her research interests center on cancer immunotherapy, particularly T cell-based approaches for HPV-associated cancers and ovarian cancer. She investigates engineered T cell therapies targeting viral antigens in cervical and other HPV-related cancers, with a focus on overcoming treatment resistance and improving clinical outcomes through novel immunotherapeutic strategies. Her work bridges fundamental immunological mechanisms with clinical applications, emphasizing the translation of laboratory discoveries into patient treatments. Dr. Draper's publication record demonstrates consistent contributions to the field of cancer immunotherapy, with recent work focusing on TCR-engineered T cells for leukemia and HPV-associated malignancies. Her research trajectory shows increasing sophistication in T cell engineering approaches and a growing emphasis on addressing inflammatory responses and treatment resistance mechanisms. Parker Institute for Cancer Immunotherapy Early Career Research Award: Parker Scholar (2024) Gladstone-UCSF Institute of Genomic Immunology Symbiont Seed Grant for 'Antigen Discovery for Ovarian Cancer' (2024) Conquer Cancer Young Investigator Award, American Society of Clinical Oncology (2024) Women in Cancer Immunotherapy Network Leadership Institute Selected Participant, Society for Immunotherapy of Cancer (2024) Physician-Scientist Fellow, Chan Zuckerberg Biohub - San Francisco (2023-2025) As a physician-scientist, Dr. Draper maintains an active research program while providing clinical care, with particular emphasis on developing novel immunotherapies for gynecologic cancers. Her current fellowship at UCSF and multiple early-career awards indicate strong potential for continued contributions to the field of cancer immunotherapy. Dr. Draper works within the Roybal Laboratory at UCSF, which focuses on innovative approaches to cancer immunotherapy and T cell engineering. Her research intersects with multiple collaborative initiatives at UCSF, including the Parker Institute for Cancer Immunotherapy and the Gladstone-UCSF Institute of Genomic Immunology.
Albert Lau is an Associate Professor of Civil and Environmental Engineering at the Norwegian University of Science and Technology (NTNU), located in Trondheim, Norway. He specializes in railway engineering, structural dynamics, and transportation systems. Lau holds leadership roles as the Study Program Leader for the MSc in Road, Railway, and Transportation Engineering, overseeing curriculum development and program coordination. His research focuses on railway track design, dynamic modeling of train-track interactions, and infrastructure maintenance, with projects such as the MeTinT initiative (Measurement with Train in Regular Traffic). He has extensive experience supervising master’s and PhD students, and his work emphasizes innovation in rail infrastructure and sustainable transportation solutions. Education and Professional Background: Lau earned his PhD from NTNU in 2018, focusing on numerical simulations of railway turnouts. Prior roles include Postdoc (2018–2020) and Assistant Professor (2017–2018) at NTNU, and teaching at Oslo Metropolitan University (2020). His industry experience includes roles as a Design Engineer (2010–2012) and Project Engineer (2013–2014) in Malaysia, where he managed construction projects and structural design. Research Interests: Lau’s work spans railway track dynamics, infrastructure health monitoring, and machine learning applications in transportation. Key projects include developing digital twins for railway test sites and analyzing ground displacement impacts on track anomalies. His contributions to the Road, Railway and Transport Group at NTNU aim to advance rail safety and efficiency through interdisciplinary approaches. Teaching and Outreach: Lau coordinates courses such as TBA4225 (Railway Engineering) and BA6012 (Fundamental Railway Technology). His outreach includes expert commentary on railway incidents, such as an interview on NRK (2024) discussing potential causes of a train accident. Current initiatives focus on revitalizing regional rail services and optimizing train positioning systems.
Thomas Faulkner is an Associate Professor in the Department of Physics at the University of Illinois at Urbana-Champaign, where he has been a faculty member since 2014. His research bridges condensed matter physics, high energy physics, and quantum information science through the framework of holographic duality (AdS/CFT correspondence), exploring connections between quantum field theories and gravitational theories. Dr. Faulkner received his BSc in Physics from the University of Melbourne in 2003 and his PhD from MIT in 2009 under Hong Liu and Krishna Rajagopal. He held postdoctoral positions at the Kavli Institute for Theoretical Physics (2009-2012) and the Institute for Advanced Study in Princeton (2012-2013) before joining the Illinois faculty. His primary research focuses on three interconnected areas: entanglement entropy as a tool to study quantum phases and gravity; string-inspired models of strongly correlated phenomena including non-Fermi liquids and quantum criticality; and holographic approaches to QCD under extreme conditions. His work leverages theoretical tools from both condensed matter and string theory communities to address fundamental questions in quantum gravity and many-body physics. Dr. Faulkner's publication record shows an evolving research trajectory from early work on strange metal transport and QCD applications toward increasingly sophisticated investigations of entanglement structure, quantum information aspects of holography, and fundamental constraints on quantum field theories. His recent work demonstrates deep connections between quantum information theory, gravitational physics, and condensed matter phenomena. DOE Early Career Award (2018) DARPA Young Faculty Award (2015) Dr. Faulkner has taught a comprehensive range of physics courses from undergraduate College Physics to advanced graduate-level field theory courses. His research program receives significant external funding, supporting his investigations into the quantum structure of spacetime and its connections to condensed matter phenomena. He participates in a vibrant research ecosystem exploring the quantum information foundations of spacetime geometry, contributing to collaborative efforts that are reshaping our understanding of the relationship between quantum mechanics and gravity.
