Professor Guy Williams is a leading academic at the University of Cambridge with a focus on imaging science and clinical neurosciences, affiliated with Downing College and the Wolfson Brain Imaging Centre . Holding a PhD in Physics from his initial Natural Sciences degree, he specializes in nuclear magnetic resonance (NMR) and MRI techniques for brain imaging. Education: BA, PhD in Physics His research centers on non-invasive imaging of brain structure and function, particularly in traumatic brain injury (TBI) and dementia. His work involves developing novel MRI pulse sequences and advanced data analysis algorithms, including AI-based diagnostic tools. He leads studies on white matter integrity post-trauma, longitudinal dementia assessment, and applications of MRI in disorders of consciousness and addiction. Recent publications highlight collaborations in traumatic brain injury outcomes, AI-guided dementia prediction, and neuroimaging of post-COVID cognitive deficits. His team's work on ultra-high field laminar fMRI and distortion correction methods has advanced clinical neuroscience applications. Key techniques include diffusion tensor imaging (DTI), 7 Tesla MRI, and positron emission tomography (PET/MR). His research spans from basic NMR physics to clinical translation, with a strong emphasis on multi-site studies and real-world diagnostic implementation.
George T.-C. Chiu is a Professor in the School of Mechanical Engineering at Purdue University, with courtesy appointments in Electrical and Computer Engineering and Psychological Sciences. He holds a 50% appointment as Assistant Dean for Global Engineering Programs and Partnerships. Previously, he served as a Program Director at the NSF, managing the Control Systems Program and National Robotics Initiative. His research focuses on mechatronics, dynamical systems, and control, with applications in printing, robotics, and human-machine interaction. Education: PhD (1994), University of California, Berkeley MS (1990), University of California, Berkeley BS (1985), National Taiwan University Research Interests: Functional printing technologies for biomedical and environmental sensors Robotics and human-robot interaction Control systems for manufacturing and dynamic systems Energy-efficient sensor design His work bridges mechanical engineering, materials science, and control theory, addressing challenges in precision manufacturing and sustainable technology. Awards: Fellow, ASME (2021) Fellow, Society for Imaging Science and Technology Grants & Projects: USDA-funded projects on food safety sensors and sustainable agriculture NSF initiatives in robotics and additive manufacturing Collaborative research with industry partners like HP and the Army Labs & Outreach: Founded the Purdue FIRST Programs, mentoring K-12 students in robotics. Co-developed experiential courses for student mentors, fostering leadership and project management skills.
Dr. Hongtu Zhu is the Kenan Distinguished Professor of Biostatistics, Statistics, Radiology, Computer Science, and Genetics at the University of North Carolina at Chapel Hill (UNC). He holds affiliations with the Gillings School of Global Public Health and leads the Biostatistics and Imaging Genomics Analysis Lab. His expertise spans statistical learning, medical imaging, AI, and big data integration, with a focus on precision medicine and biomedicine. Dr. Zhu earned his PhD in Statistics from The Chinese University of Hong Kong (2000) and has held prior roles including DiDi Fellow/Chief Scientist (2018-2020) and Bao-Shan Jing Endowed Professor at MD Anderson Cancer Center (2016-2018). He has published over 345 peer-reviewed articles in top-tier journals like Nature, Science, and JASA, and actively contributes to editorial roles including Coordinating Editor of JASA. His research interests include neuroimaging analysis, knowledge graphs, and AI applications in healthcare. Notable awards include the COPSS Snedecor Award (2025), IEEE Fellowship (2025), and IMS Medallion (2027). He has mentored over 80 PhD students/postdoctoral fellows and serves on NIH grant review panels and professional organizations like the ASA's Section on Statistics in Imaging. Key Contributions: Imaging genomics, brain connectivity studies, ridesharing market optimization, medical AI frameworks Lab Innovations: Brain Imaging Genetics Knowledge Portal, Biomedical Knowledge Graph Interface Teaching: Advanced biostatistics courses (Generalized Linear Models, Deep Learning in Biomedicine) Recent work explores causal inference in healthcare, X chromosome's role in neurobiology, and AI ethics in medical vision-language models. His interdisciplinary projects bridge statistics, computer science, and clinical practice to address complex biomedical challenges.
Marilyn J Smith is the David S. Lewis Professor and Director of the Vertical Lift Research Center of Excellence (VLRCOE) at the Georgia Institute of Technology's Daniel Guggenheim School of Aerospace Engineering. She leads a seven-university consortium conducting vertical lift research for the U.S. Army, Navy, and NASA, and has secured over $200 million in collaborative research funding. Computational Nonlinear Computational Aeroelasticity Lab Director NASA FUN3D development team contributor Aerospace Systems Design Lab (ASDL) affiliate Her research spans unsteady aerodynamics, computational aeroelasticity, and sustainable energy applications across rotary-wing, fixed-wing, and launch vehicles. She serves on the Vertical Lift Consortium (VLC) Board of Directors and Vertical Flight Society (VFS) Board, while acting as VFS Deputy Technical Director for Aeromechanics and leading international NATO AVT panels on UAV aerodynamics. Recent publications focus on galaxy cluster cosmology, ship-helicopter dynamic interface modeling, and Type Ia supernova analysis. She has won prestigious awards including the AIAA Aerodynamics Award and multiple American Helicopter Society honors for research, mentoring, and service. 2022 AIAA Aerodynamics Award 2015 Best Paper Awards at AHS Forum 2014 & 2012 AHS Agusta-Westland International Fellowships Her laboratory work integrates high-performance computing with aerospace design and develops advanced turbulence models through partnerships with Georgia Tech Research Institute (GTRI). She contributes to public science communication with appearances on National Geographic, PBS, NPR, and local media.
Michael Lampson is Professor of Biology at the University of Pennsylvania's School of Arts and Sciences, with secondary appointments in the Department of Cell and Developmental Biology. He serves as faculty in the Cell and Molecular Biology (CAMB) and Biochemistry and Molecular Biophysics (BMB) Graduate Groups, and is affiliated with the American Society for Cell Biology (ASCB). Ph.D., Cornell University, Weill Medical College, 2002 AB, Harvard College, 1994 Dr. Lampson's research program focuses on fundamental mechanisms of chromosome biology, with particular emphasis on cell division, centromere inheritance, and meiotic drive. His lab investigates how selfish genetic elements can violate Mendel's First Law through meiotic drive, the stability of centromere chromatin through the germline, and the role of repetitive satellite DNA in chromosome segregation. Using innovative approaches including mouse model systems, optogenetic tools, and biochemical techniques, his work bridges cell biology, genetics, and evolutionary biology to address questions with implications for reproductive biology, cancer, and genetic inheritance. Analysis of Dr. Lampson's recent publications reveals a strong focus on the intersection of centromere biology, meiotic drive, and chromosome segregation mechanisms. His work increasingly incorporates computational approaches alongside experimental systems to study evolutionary aspects of centromere function. The research demonstrates consistent innovation in methodology, particularly in developing optogenetic tools for precise manipulation of cellular processes. Key themes include the role of satellite DNA variation, mechanisms of non-Mendelian inheritance, and the stability of chromatin structures through cell division and development. Searle Scholar Award American Association for the Advancement of Science (AAAS) fellow Dr. Lampson's research is supported by multiple NIH grants including from NIGMS, NHGRI, NICHD, and NCI, as well as University of Pennsylvania funding sources including the University Research Foundation, Abramson Cancer Center, and several specialized research centers. He collaborates extensively with researchers across disciplines, including Ben Black (Biochemistry), Dennis Discher (Chemical Engineering), Dave Chenoweth (Chemistry), and Roger Greenberg (Cancer Biology), reflecting the interdisciplinary nature of his work. His lab has trained numerous graduate students and postdocs who have gone on to successful careers in academia and industry. The Lampson Lab maintains state-of-the-art facilities for cell biological, genetic, and biochemical research, with specialized equipment for live-cell imaging, optogenetic manipulation, and mouse genetics. The lab fosters a collaborative environment that bridges molecular, cellular, and evolutionary perspectives on chromosome biology.
Kumar Varoon Agrawal is an Associate Professor at École Polytechnique Fédérale de Lausanne (EPFL), holding the Gaznat Chair for Advanced Separations. He is affiliated with the School of Basic Sciences (SB), the Institute of Chemical Sciences and Engineering (ISIC), and the Laboratory of Advanced Separations (LAS) in Sion, Switzerland. Additionally, he contributes to the Swiss Doctoral School in Chemical and Bioengineering (SCGC) and serves as Vice President of the Confédération des Chimistes et des Génie Chimique (CCE). Research Focus: Material Chemistry & Engineering at the Ångström scale for high-performance inorganic and hybrid membranes, emphasizing energy-efficient molecular separations. Teaching: Courses include Fundamentals of separation processes , Diffusion and mass transfer , and Chemical engineering product design . Scientific Contributions: His 15 most recent publications (2025-2020) span topics like graphene pore engineering , 2D material synthesis , carbon capture , and gas separation membranes , with keywords such as Nanotechnology , Materials Science , and Molecular Transport . Subfields include Atomic-Scale Pores , Membrane Stability , and Industrial Scalability . Students and Collaborations: He advises 10 current PhD students and has mentored 9 past PhD candidates in areas like graphene membranes , ion separation , and MOF films . He is an Academic Referent for the EPFL Carbon Team and a committee member for the EDCH Doctoral Program in Chemistry and Chemical Engineering.
Dr. Vasant Honavar is a Professor of Computer Science and Informatics at Pennsylvania State University, holding the Edward Frymoyer Endowed Chair. He serves as Director of the Center for Artificial Intelligence Foundations and Scientific Applications and Associate Director of the Institute for Computational and Data Sciences. His expertise spans artificial intelligence, machine learning, causal inference, and bioinformatics. Honavar has led over $60M in research grants and mentored 36 PhD students, 30 MS students, and numerous undergraduates. He is a Fellow of the AAAS and recipient of NSF Director’s Awards. Education: Ph.D., Computer Science and Cognitive Science, University of Wisconsin–Madison (1990) M.S., Computer Science, University of Wisconsin–Madison (1989) M.S., Electrical and Computer Engineering, Drexel University (1984) B.E., Electronics Engineering, Bangalore University (1982) Research Interests: His work focuses on machine learning, causal inference, knowledge representation, health informatics, and algorithmic fairness. Notable contributions include scalable algorithms for big data analytics and predictive modeling, as well as computational infrastructure for interdisciplinary science. Awards: Fellow, AAAS (2018) ACM Distinguished Member NSF Director’s Award for Superior Accomplishment (2013) Edward Frymoyer Endowed Chair (2013) Leadership: Honavar co-founded the Penn State Center for Artificial Intelligence and led the NIH-funded Biomedical Data Sciences Ph.D. program. He is a Co-PI of the North East Big Data Innovation Hub and serves on editorial boards of journals like IEEE/ACM Transactions on Computational Biology and Bioinformatics. Lab & Teams: Directs the Artificial Intelligence Research Laboratory and the Center for Big Data Analytics and Discovery Informatics, fostering collaborations across computer science, life sciences, and health sciences.
Aditi Das is a Full Professor in the School of Chemistry and Biochemistry at the Georgia Institute of Technology, College of Sciences. She leads the Das Laboratory, which focuses on the biochemistry and chemical biology of lipids, particularly studying cytochrome P450 enzymes and their role in lipid metabolism, endocannabinoid systems, and inflammatory pathways. Her educational background includes: B.Sc. in Chemistry from St. Stephen's College M.Sc. in Chemistry from Indian Institute of Technology, Kanpur (I.I.T) Ph.D. in Chemistry from Princeton University Postdoctoral research at Northwestern University (NSF-NSEC fellow) and Beckman Institute for Advanced Science and Technology, University of Illinois UC Professor Das's research interests center around understanding the physiological role of lipids in sustaining homeostasis and their implications in disease states such as neurodegenerative disorders, cancer, and cardiovascular diseases. Her laboratory specializes in: Enzymology of cytochrome P450s, particularly CYP2J2 epoxygenase Metabolism of ω-3 and ω-6 fatty acids and their derivatives Minor cannabinoid metabolism by cytochrome P450 enzymes Discovery of novel anti-inflammatory lipid metabolites and endocannabinoids Mechanistic studies of membrane proteins using nanodisc technology Her work bridges biochemistry, chemical biology, and pharmacology to uncover novel therapeutic targets related to lipid signaling pathways. Analysis of Professor Das's recent publications (2023-2025) reveals a strong focus on cannabinoid metabolism by cytochrome P450 enzymes, with particular emphasis on how these metabolic processes generate bioactive compounds that interact with the endocannabinoid system. Her research increasingly explores the therapeutic potential of omega-3 derived endocannabinoid epoxides in inflammatory and neurodegenerative conditions. The use of nanodisc technology for studying membrane proteins in near-native environments remains a consistent methodological thread throughout her work, enabling detailed mechanistic insights into enzyme function. Professor Das has received numerous prestigious awards recognizing her research excellence and teaching: 2024 NIH Outstanding Researcher Award (MIRA R35) for established investigators 2024 Vasser Woolley Faculty Fellowship 2023 Plenary Lecture at the International Society of the Study of Xenobiotics (ISSX) 2021 E.L.R. Stokstad Award 2019-2021 List of Teachers Ranked as Excellent 2019 Eicosanoid Research Foundation Young Investigator Award 2019 Zoetis Research Excellence Award 2019 Mary Swartz Rose Young Investigator Award 2015 National Scientist Development Award from the American Heart Association 2022 El Sohly Award from the American Chemical Society Professor Das actively mentors a diverse group of students and postdoctoral researchers, with several former lab members now holding faculty positions or working at prestigious institutions. Her laboratory has secured significant funding from NIH, NSF, and other sources to support research on lipid metabolism, cannabinoid pharmacology, and membrane protein biochemistry. Notable grants include an NIH R35 Outstanding Investigator Award (MIRA), an NIH R21 grant from NIDA, and multiple collaborative grants with other research groups. The Das Laboratory operates within the Petit Institute of Bioengineering and Biosciences (IBB) at Georgia Tech, utilizing state-of-the-art facilities for biochemical and biophysical studies. The lab specializes in nanodisc technology to study membrane proteins in near-native environments, with particular expertise in cytochrome P450 enzymes and their interactions with lipid substrates. Recent work has expanded into collaborative projects involving lipidomics, structural biology, and translational applications of lipid signaling research.
Alexandros G. Dimakis is a Professor at the University of California, Berkeley's Department of Electrical Engineering and Computer Sciences (EECS), College of Engineering. He is also Co-Director of the National AI Institute for Foundations of Machine Learning and Co-Founder of BespokeLabs.ai. PhD (2008) and Diploma (2003) in Electrical Engineering His research focuses on Generative AI , Information Theory , and Machine Learning . Recent work includes advancements in diffusion models, compressed sensing, and causal inference. His publications (150+) emphasize inverse problems, neural network verification, and generative model optimization. Recent publications highlight trends in Diffusion Models for inverse problems, Language Model Scaling , and 3D-Aware Generative Systems . Collaborative projects span biomedical applications, large-scale dataset curation (Datacomp-LM), and parameter-efficient model fine-tuning. Scientific Awards : IEEE Fellow (2022) James Massey Award (2018) NSF CAREER Award (2011) Google Research Faculty Award Best Paper awards at UAI workshops Eli Jury Dissertation Award (UC Berkeley) He advises PhD students in generative modeling, compressed sensing, and information theory. His research group collaborates with institutions like MIT, NYU, and IBM Research. Former students hold positions at Google, Amazon, and academic institutions like Purdue University.
Jonathan Conway is an Assistant Professor in the Department of Chemical and Biological Engineering at Princeton University and an associated faculty member of the High Meadows Environmental Institute (HMEI). He leads the Conway Lab, which focuses on engineering plant-microbe interactions for applications in bioagriculture, bioenergy, and biochemical industries. Education: B.S. Chemical Engineering, University of Notre Dame (2011) M.S. Chemical Engineering, North Carolina State University (2013) Ph.D. Chemical Engineering, North Carolina State University (2017) Postdoctoral Fellow, University of North Carolina Chapel Hill & Howard Hughes Medical Institute (2017-2021) Research Interests: The Conway Lab develops genetic engineering approaches for non-model bacteria at plant-microbe interfaces. Key research areas include: chemical signaling between plants and microbes, microbiome impacts on plant immunity, environmental stress responses in agricultural systems, and enzymatic degradation of lignocellulosic biomass using thermophilic bacteria. The lab employs bacterial genetics, systems biology, and biomolecular engineering to create technologies for sustainable bioindustries. Publication Trends: Recent work demonstrates strong emphasis on molecular mechanisms of plant-microbe communication (2020-2024), enzyme characterization in biomass degradation (2024-2025), and development of synthetic microbial communities for climate resilience (2024). Earlier research focused on extremophile enzymology and metabolic engineering (2012-2019). Student Advising: Currently mentors 4 graduate students and 8 undergraduates. Alumni include 9 former advisees who graduated between 2022-2024. The lab actively recruits students through Princeton's Chemical Engineering graduate program and undergraduate research initiatives. Laboratory: The Conway Lab develops microfluidic systems for root microbiome studies and genetic tools for engineering plant-associated bacteria. Current projects include designing thermophilic microbial consortia for consolidated bioprocessing and characterizing bacterial immune evasion strategies.
Mike Kirby is a Professor at the Kahlert School of Computing, University of Utah. He also holds adjunct professorships in the Department of Bioengineering and the Department of Mathematics. His current roles include leadership in scientific computing and informatics initiatives, including former directorships of the Utah Informatics Initiative (2019-2023) and the Multi-Scale Multidisciplinary Modeling of Electronic Materials (MSME) Collaborative Research Alliance (2016-2022). He has extensive experience in strategic research initiatives, including serving as Assistant Vice President for Research (2024-2025). Education: Dr. Kirby earned a PhD in Applied Mathematics (2002) and MS in Computer Science (2001) from Brown University, and a BS in Applied Mathematics and Computer Science from Florida State University (1997). Research Interests: Focus on large-scale scientific computing, physics-informed machine learning, computational science and engineering, high-order numerical methods, and visualization. His work bridges applied mathematics and computer science to address real-world engineering challenges. Publications: Over 150 peer-reviewed articles, including high-impact contributions in journals like Journal of Computational Physics and SIAM Journal on Scientific Computing . Recent work emphasizes machine learning for differential equations, topology optimization under uncertainty, and multi-fidelity modeling. Awards: Recognized for leadership in computational science and informatics, including contributions to University of Utah’s Clery Compliance Program. Advising & Grants: Supervised over 50 graduate students and postdocs. Secured funding from NSF, DOE, and industry partnerships, totaling millions in research grants. Active in interdisciplinary collaborations across engineering, materials science, and medicine. Labs/Teams: Scientific Computing and Imaging (SCI) Institute, Utah Informatics Initiative, and the Center for Multiscale Modeling of Electronic Materials (MSME).
Tamás Budavári is an Associate Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University (JHU), with joint appointments in Physics and Astronomy and a secondary appointment in Computer Science. He is affiliated with the Whiting School of Engineering and the Institute for Data-Intensive Engineering and Science (IDIES). His research focuses on computational and statistical methods for big data in astronomy and interdisciplinary applications such as urban blight analysis. Education: PhD in Astrophysics (2001), Eötvös Loránd University, Budapest Master’s in Theoretical Physics (1997), Eötvös Loránd University Research Interests: Budavári develops algorithms for handling large astronomical datasets, including Bayesian inference, streaming algorithms, and GPU-accelerated processing. His work includes SkyQuery (an online astronomy data tool), photometric redshift estimation, and cross-matching catalogs. He also applies computational methods to urban planning, such as optimizing strategies to address vacant housing in Baltimore City. Publications & Tools: Budavári’s recent work spans topics like deep learning for astronomical image restoration, combinatorial optimization for urban policy, and probabilistic catalog matching. His tools, such as CUDAHM and NWAY, enable scalable analysis of multi-epoch survey data and N-way catalog cross-identification. Awards & Grants: Recipient of the Gordon and Betty Moore Fellowship and SAMSI Research Fellowship Funded by NSF, STScI, NIH, and others Leadership & Outreach: He serves on the Steering Committee of the 21st Centuries Cities Initiative and is a founding editor of the Journal of Astronomy and Computing. His interdisciplinary work bridges astrophysics, data science, and urban systems.
Kazushi Ueda is an Associate Professor at the Graduate School of Mathematical Sciences, The University of Tokyo, where he has been since April 2015. His research spans algebraic geometry, symplectic geometry, and mathematical physics, with a focus on homological mirror symmetry and its applications to moduli spaces, Calabi-Yau manifolds, and singularities. He previously held academic positions at Osaka University from 2006 to 2015, including roles as Assistant Professor and Associate Professor. Ueda has also had visiting appointments at institutions such as the University of Oxford, Max Planck Institute for Mathematics, and Korea Institute for Advanced Study. Bachelor of Science, Kyoto University (1997-2001) Master of Science, Kyoto University (2001-2003) Doctor of Science, Kyoto University (2003-2006) Ueda's research explores the deep interplay between complex and symplectic geometry through mirror symmetry, particularly in the context of Calabi-Yau varieties, toric degenerations, and dimer models. His work addresses derived categories, stability conditions, and moduli problems, with recent contributions to noncommutative algebraic geometry and applications in mathematical physics. He has collaborated extensively with researchers like Akira Ishii, Masahiro Futaki, and Shinnosuke Okawa. His publications highlight homological mirror symmetry for K3 surfaces, Grassmannians, and singularities, as well as studies on modular forms, cluster transformations, and the Grothendieck ring. Ueda is a member of the Mathematical Society of Japan and has contributed to educational programs, including graduate lectures on mirror symmetry and symplectic geometry.
Amalia Foka is an Assistant Professor in Computer Science Applications for the Arts at the Department of Fine Arts & Art Sciences , School of Fine Arts , University of Ioannina , Greece. She has held academic positions at the University of Patras (2005-2013), University of Ioannina (2005-2008), and Computer Technology Institute & Press "Diophantus" (2014-2015). Her research bridges Artificial Intelligence with Digital Art , focusing on Generative AI , Social Media Mining , and Human-Computer Interaction within artistic contexts. Education: BEng in Computer Systems Engineering (1998) from the University of Manchester Institute of Science & Technology (UMIST) , UK MSc in Advanced Control (1999) from UMIST PhD in Robotics (2005) from the Department of Computer Science , University of Crete Her artistic research includes projects like Bushwalking (StyleGAN2 landscape generation), Breaking the Silence (NLP analysis of taboo topics), and The Invisible Structures of the Artworld (social media-driven network visualization). She leads the Multimedia Lab at the University of Ioannina, focusing on AI in Creative Processes and Digital Interaction methodologies.
Chao Wang is an Associate Professor at the Department of Chemical and Biomolecular Engineering within the Whiting School of Engineering at Johns Hopkins University. He also serves as the Director of the Nano Energy Laboratory and the department’s Master’s Admissions Director. His research focuses on sustainable energy systems and nanomaterials for CO2 capture and conversion, electrocatalysis, thermocatalysis, and green chemical engineering. Education: Bachelor’s degree, University of Science and Technology of China (2004) Doctorate, Brown University (2009) Wang’s research targets efficient energy conversion and storage via nanomaterials with tailored atomic structures, emphasizing catalytic activity, selectivity, and stability. His group explores electrochemical and thermochemical processes for reduced carbon footprints, including CO2 and methane conversion, ammonia recovery, and phosphorus/nitrogen nutrient recycling using zeolite-based systems. Recent publications (2021–2024) highlight his work in high-entropy alloys, solid-state battery materials, CO2 electroreduction, and biomedical nanotechnologies. Collaborative efforts span catalysis, nanoparticle dynamics, and environmental applications. Grants include a $1M DOE award for multi-university research, a $625K DOE grant for electrified transportation systems, and $3M in startup funding for carbon-removal technology commercialization. Alumni under his mentorship include Ph.D. graduates like Michael J. Manto (2018) and Master’s students like Mitchell Keller (2018), with notable achievements in catalyst development for ammonia/phosphorus recovery and industry placements at Grace & Co. and GEA Engineering.
