Dr. Juan Manuel Berbel Pineda is a full Professor at the Department of Business Organization and Marketing at Universidad Pablo de Olavide, Spain. His academic work focuses on tourism economics, sustainable tourism practices, and international business strategies, particularly within the hotel industry and textile sector. Key research areas: Tourism Economics, Sustainable Tourism, International Business Strategy Affiliated with IMEGS (Innovation and Marketing for a Sustainable Global Environment) research group Doctoral Programs: Innovation, Entrepreneurship and Family Business His research explores tourism competitiveness and internationalization patterns through empirical studies on hotel chains in Latin America, fair trade impacts in emerging economies, and post-COVID-19 rural tourism development. Analysis of 15 recent publications reveals strong emphasis on structural equation modeling, cross-cultural comparisons, and market segmentation strategies. Current academic activity includes international collaborations with institutions like Massey University, Varna University of Management, and University of Mauritius. His 2020 work on Ecuadorian chia seed exports demonstrates methodology for EU market selection using 7-dimensional indicators, while his sustainable tourism studies provide frameworks for pandemic-era tourism revival through non-overcrowding strategies.
Laur Järv is an Associate Professor in Theoretical Physics at the University of Tartu, Faculty of Science and Technology, Institute of Physics. He has been serving as Associate Professor since 2021 and is currently the Head of the Laboratory of Theoretical Physics (since 2019). His academic career at the University of Tartu spans over 20 years, with progressive roles from Post-Doc to his current position. Dr. Järv received his education at the University of Tartu (B.Sc. in Fundamental Physics, 1996; M.Sc. in Theoretical Physics, 1998) and completed his Ph.D. in Mathematical Sciences at the University of Durham in 2002. His doctoral research focused on "The enhancon mechanism in string theory" under the supervision of Clifford V Johnson. Dr. Järv's primary research interests lie in gravitational physics and cosmology, with particular focus on modified theories of gravity including teleparallel gravity, scalar-tensor theories, and nonmetricity-based approaches. His work explores the cosmological implications of these theories, including inflationary models, black hole solutions, and gravitational wave propagation. His research bridges theoretical physics with observational cosmology, addressing fundamental questions about the nature of gravity and the evolution of the universe. His publication record demonstrates a strong focus on geometric foundations of gravity, with numerous high-impact papers in leading journals like Physical Review D and Classical and Quantum Gravity. Recent work shows increasing emphasis on alternative formulations of gravity (teleparallel, symmetric teleparallel) and their cosmological applications, often collaborating with international researchers in the field. Estonian National Research Award in exact sciences (2020) for the cycle of works "Extended geometric theories of gravity" with Manuel Hohmann and Margus Saal University of Tartu Badge of Distinction (2021) Best teaching staff in the UT Institute of Physics, recognized by students (2024) Letter of recognition for supervision of Joosep Lember's award-winning student work (2022) Dr. Järv has been actively involved in academic mentoring, serving as a supervisor for student research projects and as an opponent for PhD defenses internationally. He has organized multiple international conferences on gravitational physics in Tartu, establishing the university as a hub for research in modified gravity theories. As Head of the Laboratory of Theoretical Physics, he leads a research group focused on geometric foundations of gravity and cosmological applications. Dr. Järv's laboratory has become a recognized center for research on alternative gravity theories, particularly through the organization of the biennial "Geometric Foundations of Gravity" conference series since 2017, which has attracted leading researchers from around the world to Tartu.
Tae-Ho Lee is an Associate Professor in the Department of Psychology at Virginia Tech, with affiliated appointments in the School of Neuroscience and Translational Biology, Medicine, and Health. His research focuses on affective and cognitive neural development across the lifespan, neurodegeneration, and family-based neural dynamics. PhD in Brain and Cognitive Science, University of Southern California M.A. in Clinical Psychology, Korea University B.A. in Psychology, Korea University Dr. Lee’s work explores brain connectome dynamics, dyadic neural concordance in families, and age-related attentional control. He employs neuroimaging techniques to study how familial and environmental factors shape emotional and cognitive outcomes in adolescents and older adults. Recent publications highlight trends in longitudinal studies , parent-child neural similarity , and functional connectivity in emotion regulation . Key areas include autism spectrum disorder, substance misuse risk, and the role of socioeconomic factors in brain development. Rising Star , Association for Psychological Science (2020) Dr. Lee is not currently accepting students and leads the Affective Neurodynamics and Development (AND) Lab at Virginia Tech.
Wing Lam is an Associate Research Scientist in the Department of Pharmacology at the Yale School of Medicine. He holds a BSc in Molecular Biology and a PhD in Biochemical Pharmacology from City University of Hong Kong, followed by postdoctoral training at Yale. His research focuses on developing traditional Chinese medicine (TCM) formulations as adjuvants for cancer therapy, notably YIV-906, which enhances chemotherapy efficacy and mitigates intestinal toxicity. Lam also pioneered the STAR database for herbal drug discovery and the Mechanism-Based Quality Control (MBQC) platform for botanical drug standardization. Education: BSc (Hons) Molecular Biology, City University of Hong Kong, 1995 PhD Biochemical Pharmacology, City University of Hong Kong, 1999 Postdoc, Pharmacology, Yale University, 1999-2002 His research interests span cancer pharmacology, TCM modernization, and mitochondrial toxicity mechanisms. Key projects include YIV-906’s role in enhancing anti-PD1 and CAR T-cell therapies, developing L-nucleoside analogs like troxacitabine, and investigating tylophorine analogs’ antitumor effects. Lam has co-chaired sessions at multiple Consortium for Globalization of Chinese Medicine (CGCM) meetings and contributed to patents on herbal drug formulations and quality control methods. Recent work explores YIV-906’s potential for inflammatory bowel disease (IBD) and phase II clinical trials for colon and liver cancers. Lam’s publications highlight synergistic drug interactions, mitochondrial DNA depletion mechanisms, and TCM’s evidence-based application in chronic diseases. His grants include studies on PHY906 as an adjuvant in rectal cancer therapy and collaborations with Yiviva, Inc. He maintains active roles in editorial boards, including a special issue on herbal drug quality control in Frontiers in Pharmacology . Lam’s lab is embedded within Dr. Yung-Chi Cheng’s group, focusing on translational pharmacology and botanical drug innovation.
Dr. Katerina Marcoulides is an Associate Professor in the Quantitative and Psychometric Methods Program at the University of Minnesota's Department of Psychology. She is affiliated with the Minnesota Population Center and serves as Co-Chair of the Structural Equation Modeling Special Interest Group (SEM SIG) for the American Educational Research Association. Her research focuses on advanced data mining and modeling techniques for complex longitudinal data, particularly applied to developmental processes in economically disadvantaged immigrant children. She holds a PhD in Quantitative Psychology from Arizona State University, an MA from UC Davis, and a BA from UC Santa Barbara. Education: PhD: Quantitative Psychology, Arizona State University MA: Quantitative Psychology, University of California, Davis BA: Psychology (minor in Education), University of California, Santa Barbara Research Interests: Dr. Marcoulides develops and applies statistical methods such as structural equation modeling (SEM), Bayesian synthesis, and data fusion to study developmental and educational processes. Her work emphasizes longitudinal data analysis, item response theory, and multilevel modeling. Recent projects include NIH-funded research on parenting, marginalization, and well-being during the pandemic. Awards: APS Rising Star Award (2021) NIH Grant Award Teaching & Collaboration: She teaches courses on SEM, multilevel modeling, and data analysis at the University of Minnesota. Previously at the University of Florida, she contributed to workshops on educational data mining and served as an APA Advanced Training Institute presenter. Her interdisciplinary collaborations span population studies, health inequities, and workforce research. Labs & Groups: She leads the Data Analytics and Visualization Lab and actively participates in the Minnesota Population Center, integrating computational and statistical innovations with real-world applications.
Assoc Prof Ng Teng Yong is an Associate Professor at the School of Mechanical & Aerospace Engineering (NTU), specializing in numerical modeling and simulation. With a background as Research Manager at A*STAR Institute of High Performance Computing, his work spans materials science, nanotechnology, and aerospace engineering. Current focus on graphene-based desalination membranes Expertise in molecular dynamics simulations Investigates nanoscale fluid mechanics and structural dynamics Recent publications highlight advancements in energy-efficient electrodialysis, smart robotics, and nonlinear vibration analysis. His interdisciplinary approach integrates computational methods with experimental validation in additive manufacturing and soft material mechanics.
Dr. Shuo Zhang is an Assistant Professor in the Department of Physics & Astronomy at Michigan State University's College of Natural Science. Her research focuses on observational high-energy astrophysics and particle astrophysics, with particular emphasis on supermassive black holes, Galactic cosmic-ray origins, and large dataset analysis. As a member of the Event Horizon Telescope collaboration, she leads X-ray observation campaigns of the Galactic center supermassive black hole and its vicinity. Dr. Zhang received her educational training at prestigious institutions: Ph.D. in Physics, Columbia University, 2016 B.S. in Engineering Physics, Tsinghua University, 2010 Her research interests span observational high-energy astrophysics and particle astrophysics, focusing on supermassive black holes including Sgr A* flaring activities, outburst history, and radiation in quiescence. She investigates Galactic cosmic-ray origins and exotic physics, particularly TeV electrons and PeV protons pointing to Galactic PeVatrons. Her work constrains MeV-GeV proton/electron populations in the central 1 kpc of the Galaxy and examines supernova remnant and molecular cloud interaction sites. Dr. Zhang's recent publications reveal a strong emphasis on multi-messenger astronomy, combining neutrino, X-ray, and radio observations to understand cosmic particle acceleration. Her work spans from Galactic center studies of Sgr A* to extragalactic investigations of active galactic nuclei like M87. The research demonstrates increasing sophistication in analyzing complex datasets from multiple observatories including IceCube, ALMA, NuSTAR, and Chandra. Her notable scientific achievements include: NASA Hubble/Einstein Fellowship at Boston University (2019-2020) Heising-Simons Fellowship at MIT (2016-2019) NASA Earth and Space Science Fellowship for research on Galactic center supermassive black hole Dr. Zhang's career path demonstrates a steady progression from her doctoral work at Columbia University through prestigious postdoctoral fellowships to her current faculty position. She has developed significant expertise in X-ray observations using the NuSTAR space telescope and has been instrumental in Galactic plane survey campaigns. Her research group combines high-energy photon and neutrino signals from PeVatron candidates to address fundamental questions about cosmic-ray origins and particle acceleration mechanisms. As a member of the Event Horizon Telescope collaboration, Dr. Zhang contributes to cutting-edge research on black hole physics, utilizing multi-wavelength observations to understand accretion, feedback, and particle acceleration mechanisms around supermassive black holes. Her work bridges observational astronomy with theoretical astrophysics to address some of the most fundamental questions in modern astrophysics.
Berrak Sisman is an Assistant Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, affiliated with the Data Science and AI Institute and the Center for Language and Speech Processing (CLSP). She leads the Speech & Machine Learning Lab (SmILe Lab), focusing on AI-driven speech technologies. She received her PhD from the National University of Singapore in 2020 and was previously a tenure-track faculty member at the University of Texas at Dallas (2022–2024). Research Interests: Her work spans artificial intelligence, speech synthesis, voice conversion, emotion analysis in speech, medical speech applications, and secure speech technology. She develops neural models for expressive and adaptive speech processing. Publications: Her recent articles (2024–2025) emphasize speech emotion recognition, zero-shot prosody control, accent conversion, and disentangled representations in TTS, reflecting a focus on cross-modal learning, robustness, and real-world applications. Awards & Grants: NSF CAREER Award (2024) Amazon Faculty Research Award (2022) Singapore Ministry of Education Award (2021) A*STAR Singapore International Graduate Award (2016–2020) Leadership: She directs the SmILe Lab, recruiting PhD/Master’s students for projects in neural speech modeling. Her grants include NSF and Amazon funding for voice conversion and emotion synthesis research.
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.
Dr. Kaibo Liu is the Grainger STAR Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison and serves as Associate Director of the UW-Madison IoT Systems Research Center. He earned his B.S. from the Hong Kong University of Science and Technology (2009), and M.S. and Ph.D. from Georgia Tech (2011/2013). His research focuses on system informatics, big data analytics, and data fusion for process modeling, monitoring, and decision-making. He has been funded by NSF, ONR, DOE, and industry partners. Notable awards include the 2024 Hromi Medal (ASQ), 2021 IISE Technical Innovation Award, and multiple early-career recognitions. Recent work emphasizes real-time cyber-physical security, reinforcement learning for data streams, and Bayesian methods for prognosis. He edits IEEE Transactions on Automation Science and Engineering and IISE Transactions on Data Science.
Jay Strader is a Professor in the Department of Physics and Astronomy at Michigan State University, where he serves as Graduate Director for the astronomy PhD program and Associate Chair for astronomy. His research focuses on compact objects, particularly black holes and neutron stars in globular clusters, neutron star binaries in Fermi gamma-ray sources, and intermediate-mass black holes. He has received a Packard Fellowship for Science and Engineering and grants from NSF and NASA. His research group includes postdoc Ryan Urquhart, graduate students Thomas Do and Rebecca Kyer, and several undergraduates, with past students like Teresa Panurach (now director of NoVEL Consortium) and Samuel Swihart (NRC fellow at Naval Research Lab). Education: PhD in Astronomy, UC-Santa Cruz/Lick Observatory Awards: Packard Fellowship Collaborations: Member of Rubin Observatory's Stars, Milky Way, and Local Volume science collaboration since 2008 Previous Positions: Hubble Fellow and Menzel Fellow at Harvard-Smithsonian Center for Astrophysics (2007-2012) Program Initiatives: Co-founder of PAREDS program for early research opportunities at MSU His work has been supported by NSF and NASA grants, and he has contributed to studies on black holes in M22, hypervelocity globular clusters around M87, and transitional millisecond pulsars. His group collaborates with Laura Chomiuk and contributes to data catalogs like the M31 globular cluster velocity dispersion database.
Cathryn Mitchell is a Professor of Radio Science and Royal Society Industry Fellow at the University of Bath, specializing in ionospheric physics, position, navigation, and timing (PNT). She leads research in the Space & Telecoms Research Group (STAR), focusing on radio propagation, data assimilation, and space weather impacts on communication systems. Her work bridges theoretical, computational, and experimental approaches, with applications in satellite navigation, climate monitoring, and defense sectors. Her research interests include ionospheric tomography, HF communications, and the development of robust PNT systems. Mitchell collaborates extensively with industry partners like Spirent Communications on future navigation technologies and space weather resilience. She has held roles such as Academic Director of the Doctoral College and contributes to interdisciplinary projects like the DRIIVE initiative exploring ionospheric variability with EISCAT-3D radar. Recent work emphasizes ionospheric effects during geomagnetic storms (e.g., the 2024 Gannon Storm) and cooperative autonomous systems under communication constraints. Her projects are funded by the Royal Society, Natural Environment Research Council (NERC), and ESA, addressing challenges in space weather forecasting and PNT system reliability. Awards: Royal Society Industry Fellow (2022–present) Key Projects: Royal Society Industry Fellowship on Future PNT Technologies DRIVERS (DRIIVE): Ionospheric Variability Studies EISCAT-3D FINESSE: Ionospheric Structuring Analysis Mitchell’s lab, STAR, integrates academic and industrial partnerships to advance space weather applications and sustainable navigation systems, contributing to UN Sustainable Development Goals related to climate action and innovation.
Bérénice Benayoun, PhD is an Associate Professor at the USC Leonard Davis School of Gerontology , with secondary appointments in the Department of Molecular and Computational Biology (USC Dornsife College of Letters, Arts and Sciences) and the USC Norris Comprehensive Cancer Center . Her research bridges aging biology , epigenetics , and sex differences using vertebrate models like the African turquoise killifish and machine learning . Education : École Normale Supérieure (BSc, MSc), Paris Diderot-Paris 7 University (PhD in Genetics and Cell Biology) Her lab investigates epigenome and transcriptome remodeling during aging , focusing on how biological sex influences these processes. Key themes include inflamm-aging , genomic instability , and immune senescence , with applications in neurodegeneration and reproductive longevity . Recent publications highlight sex-dimorphic gene regulation in neutrophils , macrophages , and brain aging , alongside novel insights into transposable elements and MOTS-c mitochondrial signaling . She pioneers the use of single-cell transcriptomics and multi-omics in aging research. Scientific awards include: 2024 Vincent Cristofalo Rising Star in Aging Research Award 2023 AGHE Rising Star Early Career Faculty Award 2023 USC Mentoring Award 2023 Rising Star in Reproductive Biology 2021 Nathan Shock New Investigator Award 2019 Rosalind Franklin Young Investigator Award Her editorial roles include Geroscience , Translational Medicine of Aging , and eLife . She mentors students across PhD programs in Biology of Aging , Neuroscience , and Molecular Medicine , as well as Master's and undergraduate trainees.
Zhe Zeng is an incoming Assistant Professor in the Department of Computer Science at the University of Virginia starting July 2025. Currently, she serves as a Faculty Fellow in the Computer Science Department at New York University. She earned her Ph.D. in Computer Science from UCLA in 2024 under Professor Guy Van den Broeck, and her B.S. in Mathematics from Zhejiang University in 2018. Research Focus: Dr. Zeng specializes in neurosymbolic AI and probabilistic machine learning, developing methods that integrate symbolic knowledge (logical constraints, graph structures) with probabilistic uncertainty. Her work spans three core areas: Reasoning: Probabilistic inference, tractable probabilistic models Learning: Constrained deep learning, graph ML, weakly supervised learning Trustworthiness: Explainability, uncertainty quantification, domain-knowledge integration Awards & Honors: Rising Star in EECS (2023) Amazon Doctoral Fellowship (2022) NEC Research Fellowship (2021) ICML Travel Award (2018) Outstanding Graduate, Zhejiang University (2018) Advising & Mentoring: Has supervised six students including PhD candidates and undergraduates at UCLA, Tsinghua, and CAS, with placements at Princeton and UT Austin. Academic Service: Regularly reviews for NeurIPS, ICML, ICLR, UAI; served as UAI 2023 discussant; active in WiML mentorship programs.
Paul Withers is a Professor and Chair of the Department of Astronomy at Boston University. He leads research on planetary atmospheres and ionospheres, with a focus on Mars and Venus, and serves as Principal Investigator on multiple NASA-funded research projects. Education: B.A. in Physics, 1998, Queens' College, Cambridge University M.S. in Physics, 1998, Queens' College, Cambridge University M.A., 2001, Queens' College, Cambridge University Ph.D. in Planetary Science, 2003, University of Arizona Professor Withers' research focuses on the upper atmospheres and ionospheres of terrestrial planets, particularly Mars and Venus. His work involves analyzing spacecraft data and developing theoretical models to understand how solar flux, neutral atmospheres, magnetic fields, and ionospheres interact under unique planetary conditions. He has made significant contributions to understanding the response of the Martian ionosphere to solar flares, the structure of the Venus ionosphere, and meteoric plasma layers in planetary ionospheres. His research often involves multi-instrument campaigns and coordinated observations across different spacecraft missions including Mars Express, MAVEN, and Venus Express. Analysis of Professor Withers' recent publications reveals a strong emphasis on Martian ionospheric dynamics, particularly its response to solar activity and its variability under different conditions. His work frequently combines data from multiple missions to create comprehensive models of planetary upper atmospheres. He has developed important methods for analyzing radio occultation data and reconstructing atmospheric properties from entry, descent, and landing measurements. Major Funded Projects: "Characterizing the topside bulge in the ionosphere of Mars" (NASA Mars Data Analysis Program, 2014, $144K) "Integration of MAVEN neutral and plasma observations" (NASA MAVEN Participating Scientist Program, 2013, $284K) "Radio occultation studies at Mars" (NASA Early Career Fellowship Program, 2013, $99K) "EDL reconstruction for MSL" (NASA, JPL contract, 2012, $199K) "Meteoric plasma layers on Venus and Mars" (NASA Planetary Atmospheres Program, 2012, $232K) Professor Withers has been actively involved in mentoring students and collaborating with international researchers. He serves as a key member of the Mars Upper Atmosphere Network (MUAN) and has contributed to community white papers for planetary science decadal surveys. His work supports future Mars landers through atmospheric modeling and surface pressure prediction, with direct applications to mission planning and execution. He has presented his research at numerous international conferences including the American Geophysical Union meetings, Division for Planetary Sciences meetings, and European Planetary Science Congress. His work has important implications for understanding planetary climate evolution, space weather effects on technological systems, and the search for habitable environments beyond Earth.
