Marc HON is an Assistant Professor at the National University of Singapore (NUS) under the NUS Presidential Young Professorship, specializing in time-domain astronomy and machine learning applications for NASA missions including Kepler, TESS, and the Roman Space Telescope. His work focuses on characterizing stellar populations and discovering novel astrophysical phenomena through data-driven methodologies. His research spans asteroseismology for probing stellar interiors and Galactic archaeology to map the Milky Way's evolution using variable stars, alongside exoplanetary science investigations into planetary system evolution, habitable worlds, and James Webb Space Telescope atmospheric characterization. A core methodology involves developing machine learning frameworks like deep learning classifiers and generative models for large-scale astronomical datasets. HON's publication trends (2018-2024) reveal consistent innovation at the astrophysics-ML intersection, with emphases on red giant asteroseismology, exoplanet dynamics, and scalable analysis pipelines for space telescope data. Key contributions include flow-based stellar evolution emulators, deep learning oscillation detectors, and large-scale TESS Galactic archaeology studies. Scientific recognition includes: NASA Hubble Fellowship (2020) He actively contributes to major international collaborations as a member of both the TESS and Kepler Asteroseismic Science Consortia, with direct involvement in MIT's TESS mission operations and data pipelines.
Joo Heung Yoon, MD is an Assistant Professor of Medicine in the Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine at the University of Pittsburgh School of Medicine. His research develops machine learning models for predicting hemodynamic instability in critical care settings, with applications extending to space medicine environments. His educational background includes: MD from Catholic University of Korea, Seoul, South Korea (2002) Internal Medicine Internship at Maimonides Medical Center - SUNY Downstate (2007) Internal Medicine Residency at New York Medical College (2009) Research Fellowship at Massachusetts General Hospital / Harvard Medical School (2011) Research Fellowship at Beth Israel Deaconess Medical Center / Harvard Medical School (2014) Fellowship in Pulmonary and Critical Care Medicine at University of Pittsburgh School of Medicine (2017) Dr. Yoon specializes in identifying hidden pathologic patterns through machine learning, developing prediction models for shock, hemorrhage, and tachycardia using large-scale clinical data. His work bridges critical care medicine with AI, focusing on real-world ICU implementation through alert systems and user interfaces. He actively explores microgravity applications, aiming to create feasible prediction algorithms for resource-constrained space missions where timely high-stake decisions are critical. His publication trend (2018-2020) reveals consistent advancement in hemodynamic prediction models, transitioning from theoretical frameworks to practical implementation strategies. These works integrate supervised ML and deep neural networks to address circulatory shock, hemorrhage identification, and instability surrogates, demonstrating strong interdisciplinary collaboration between clinical medicine and engineering. Notable awards include: SCCM Gold Snapshot Award (2019) ATS Abstract Award (2018) Excellence in Clinical Service Award (2010) Partners in Excellence Award (2009) Richard D. Levere Teaching Award (2008) As Principal Investigator for NIH K23 grant GM138984 (2020-2025), Dr. Yoon leads research on machine learning-driven shock prediction models. He mentors medical students and house staff daily in the ICU, specializing in cardiopulmonary physiology teaching. His grant portfolio focuses on therapeutic strategies for circulatory shock in critically-ill patients, with strong industry-academic partnerships. Based at UPMC Montefiore, Dr. Yoon collaborates with Carnegie Mellon University's Machine Learning School and Pitt Engineering to develop clinical decision support systems. His team is designing graphic user interfaces for spaceflight applications where resource limitations demand highly efficient predictive analytics for hemodynamic crises.
Chuanfei Dong is an Assistant Professor of Astronomy at Boston University's College of Arts & Sciences and of Electrical and Computer Engineering at the College of Engineering. His research focuses on understanding plasma physics and its applications to space science, planetary atmospheres, and fusion energy. Dong joined BU in January 2023 after working as a staff scientist at the Princeton Plasma Physics Laboratory. Education: B.S. in Space Science from University of Science and Technology of China M.S. in Earth and Atmospheric Sciences from Georgia Institute of Technology M.S.E. in Nuclear Engineering and Radiological Sciences from University of Michigan M.S. in Planetary and Space Sciences from University of Michigan Ph.D. in Scientific Computing from University of Michigan Research Interests: Dr. Dong's research spans multiple disciplines within space physics and plasma science. His primary interests include Star-Terrestrial Planet Interactions in our Solar System and beyond, magnetic reconnection and turbulence phenomena, wave-particle interactions in space plasmas, and applications of physics-informed machine learning to plasma problems. He also investigates high-intensity laser-plasma interactions with applications to fusion energy research. His work bridges the gap between theoretical plasma physics and observational space science, with particular focus on planetary atmospheres, solar wind interactions, and exoplanet habitability. Dong's interdisciplinary approach combines computational modeling, observational data analysis, and theoretical frameworks to address fundamental questions in space physics. Research Trends: Dong's recent publications demonstrate a strong focus on applying advanced computational techniques to space plasma physics problems. His work spans solar system bodies including Earth, Mars, Mercury, and the Moon, with increasing attention to exoplanet systems. A notable trend is the integration of machine learning approaches with traditional plasma physics modeling, particularly for complex phenomena like Landau damping and magnetic reconnection. His research has significant implications for understanding atmospheric evolution, space weather, and potential habitability of planetary bodies. Scientific Awards: DOE Early Career Research Award (2023) - $875,000 grant for plasma turbulence research Alfred P. Sloan Research Fellow (2024) Metcalf Travel Award Advising and Grants: Dr. Dong mentors undergraduate research assistants and plans to expand his research group with the support of his DOE Early Career Award, which will fund a graduate student and postdoctoral researcher. His research is supported by the Department of Energy and has connections to NASA missions including MAVEN (Mars) and BepiColombo (Mercury). Dong is also involved with the Mauve telescope project as BU institutional PI. His work has been featured in numerous media outlets including Phys.org, Science Daily, and German TV program zdf/3sat. Labs and Teams: Dr. Dong leads a research group focused on computational plasma physics at Boston University. He collaborates with researchers at Princeton Plasma Physics Laboratory and is involved with multiple NASA missions. His team develops advanced computational models to simulate space plasma phenomena, with particular expertise in magnetohydrodynamics (MHD), particle-in-cell methods, and physics-informed machine learning approaches. Dong is also affiliated with BU's Hariri Institute for Computing.
Angelo Cervone is a Professor at Delft University of Technology's Department of Astrodynamics & Space Missions within the Faculty of Aerospace Engineering. His research focuses on advanced propulsion systems, CubeSat technology, additive manufacturing for space applications, and space systems design. He leads projects like LUMIO, a CubeSat mission to monitor lunar meteoroid impacts, and has contributed to the development of green propellants and smart composite structures with embedded sensors. His work integrates cutting-edge manufacturing techniques like laser powder directed energy deposition with propulsion system optimization, emphasizing sustainable and robust space technologies. Cervone has authored over 130 publications and edited the book Adaptive On- and Off-Earth Environments , reflecting his expertise in off-world infrastructure and robotic production systems. He received the Rhizome Award (2021) for advancing autarkic systems in off-Earth habitat development. Key Projects: LUMIO CubeSat mission, Rhizome habitat system development, smart propellant tank design Research Themes: CubeSat propulsion, lunar exploration, additive manufacturing for space, in-situ resource utilization His articles highlight advancements in micro-thrusters, structural health monitoring via fiber optics, and autonomous navigation systems for deep-space CubeSats. Cervone collaborates globally on missions requiring innovative propulsion architectures and materials science breakthroughs.
Ediz Cetin is an Associate Professor in Digital Electronics Engineering at Macquarie University's School of Engineering and a member of the Astrophysics and Space Technologies Research Centre. He serves as Course Director for the MEng Electronics Engineering program and Chair of the School's Postgraduate Coursework Committee. His research focuses on radio frequency interference mitigation, fault-tolerant reconfigurable circuits for space applications, machine learning in RF signal analysis, and low-power digital circuit design. Education: PhD in Signal Processing (Unsupervised Adaptive Signal Processing Techniques for Wireless Receivers) B.Eng. (Hons.) in Control and Computer Engineering Research Interests: RF interference detection and localization GNSS anti-jamming and spoofing detection FPGA-based reconfigurable systems Space instrumentation and CubeSat technologies Machine learning for signal processing Awards: Excellence in Learning Innovation (FSE Teaching Award, 2022) Highly Commended Finalist – Vice-Chancellor’s Award for Learning Innovation (2022) Innovative Approaches – Highly Commended (FSE Teaching Award, 2020) Key Projects: SmartSat CRC (2020–2026): Smart Satellite Technologies and Analytics Spacecraft Innovation Lab (2021–2022) CubeSat Biological Payload (2019–2022) Teaching Contributions: Led the 'Improving Student Engagement with Anywhere and Any-time Laboratory Access' initiative (2019–2020), enhancing remote lab accessibility for students.
Dr. Qian Zhang serves as Assistant Professor in the Robert M. Buchan Department of Mining at Queen's University's Smith Engineering, leading the Green Mining Value Chain (GreeMVC) Lab. His research develops strategic frameworks for sustainability and resilience throughout mining value chains, with emphasis on climate change mitigation and resource efficiency in global mineral systems. His academic foundation includes a Ph.D. in Urban Engineering from the University of Tokyo (awarded Japanese Government MEXT Scholarship), complemented by MSc and BSc degrees in Environmental Science plus a Minor in Economics from Peking University. Prior to his current role, he conducted postdoctoral research at the University of Victoria and University of Tokyo while consulting for the World Resources Institute on climate-energy initiatives. Dr. Zhang's expertise spans carbon footprint analysis , life-cycle assessment , and industrial ecology applied to mining systems. He employs advanced methodologies including input-output analysis and material flow accounting to model environmental pressures across urban infrastructure and mineral supply chains. His work specifically addresses greenhouse gas accounting, water-energy nexus challenges, and circular economy implementation in resource-intensive sectors. Recent publications reveal strong methodological convergence between artificial intelligence and environmental assessment, particularly in optimizing mining operations through reinforcement learning and geospatial analysis. Key thematic clusters include carbon accounting standardization, critical mineral sustainability, and policy-oriented modeling of environmental pressures throughout mineral value chains. His research program is supported by major competitive grants: NSERC Discovery Grant (2022-2027) SSHRC Institutional Grant (2023, 2025) NSERC Alliance Missions Grant (2023, 2024) Mitacs Accelerate Grant (2023, 2025) NFRF Exploration Grant (2025-2027) NRCan Energy Innovation Program (2025) Dr. Zhang actively mentors a dynamic research group comprising 10+ graduate students and postdocs, securing collaborative funding through institutional and federal channels. His GreeMVC Lab maintains active partnerships with industry leaders and government agencies to translate research into practical sustainability solutions for the mining sector, with current projects focusing on AI-driven fleet management and life-cycle assessment of mineral supply chains. The GreeMVC Lab operates as a multidisciplinary hub with structured mentorship programs, regular industry engagement events, and international collaborations including the COM symposium on sustainable circularity. The lab's physical space in Goodwin Hall supports advanced computational analysis of mining value chains while fostering innovation in green mining technologies through student-led research initiatives.
Byungwoon Park is a Professor in the Department of Aerospace Engineering at Sejong University, specializing in Global Navigation Satellite Systems (GNSS) and precision positioning technologies. His research focuses on advancing navigation systems through innovations in Real Time Kinematics (RTK), smartphone sensor integration, and aviation applications. Professor Park's primary research interests include Global Navigation Satellite System (GNSS), Real Time Kinematics (RTK), smartphone sensor integration, aviation navigation, and urban positioning systems. His work has significantly contributed to improving positioning accuracy in challenging environments such as urban canyons and deep urban areas. He has developed techniques for achieving sub-meter accuracy in smartphone positioning and has made substantial contributions to international GNSS standardization efforts. His recent research has focused on multi-constellation GNSS integration, lunar navigation systems, tropospheric error modeling using LEO satellites, and advanced smartphone positioning techniques. Professor Park has successfully implemented methods to achieve 1m horizontal accuracy in Android smartphone positioning using SFMC SBAS and has developed Compact Network RTK technology that reduces bandwidth requirements for GPS correction in 100x100 km areas to 700bps. 'Google Smartphone Decimeter Challenge 2022' Gold Medal Third Place Winner of the Smartphone Decimeter Challenge (2024) Professor Park leads the Navigation Systems Laboratory at Sejong University, which conducts research on various navigation systems including GNSS. His work spans theoretical research, practical implementation, and industry collaboration, with numerous publications in prestigious journals and conference proceedings. He has advised multiple graduate students and has been actively involved in both domestic and international research collaborations focused on advancing navigation technologies.
Andrea Del Prete is an Associate Professor in the Industrial Engineering Department at the University of Trento (Italy) since 2022. His research focuses on robot control, reinforcement learning, trajectory optimization, and numerical algorithms for dynamic systems. He leads the Interdepartmental Robotics Lab (IDRA) and has previously held roles as a tenure-track assistant professor at the University of Trento (2019-2021), a research scientist at the Max-Planck Institute for Intelligent Systems (2018), and an associated researcher at LAAS-CNRS (2014-2017) working with the HRP-2 humanoid robot. Earlier, he conducted PhD and post-doc research at the Italian Institute of Technology (2010-2013) on iCub robot control. PhD in Robotics (2013) - Italian Institute of Technology MEng in Computer Engineering (2009) - University of Bologna BSc in Computer Engineering (2006) - University of Bologna Dr. Del Prete specializes in merging learning and model-based techniques for safe robot control, particularly in legged systems. His work bridges trajectory optimization (TO) with reinforcement learning (RL) to overcome local minima challenges (CACTO/CACTO-SL algorithms) and develops robust controllers for humanoid and quadrupedal robots in unstructured environments. He explores viability kernels in MPC, safety certificates, and bi-level optimization for co-designing hardware/control policies. Key application areas include mountain rescue robotics (ALPINE platform), aerial maneuver recovery, and energy-efficient legged locomotion. His recent publications (2023-2025) emphasize numerical optimization algorithms, multi-contact locomotion, and hybrid control frameworks. Topics span from analytical integral optimization (2025) to climbing robots for mountain operations (2025), demonstrating a trajectory from theoretical algorithm development to real-world robotic applications. Research keywords include robotics, numerical optimization, and machine learning, with sub-fields like MPC for dynamic systems, humanoid control, and terrain adaptation. As an educator, he teaches advanced courses on: Optimization and Learning for Robot Control (48-hour master's course) Optimization-based Control of Legged Robots (12-hour PhD course) Task-Space Inverse Dynamics (3-hour PhD course) Current PhD advisees include Mohammad Hasan Yeganegi (generalization bounds for imitation learning), Pietro Noah Crestaz (numerically-efficient RL), Veronica Campana (ergodic control for defect detection), Elisa Alboni (data-efficient model-based RL), and Gianni Lunardi (MPC for legged locomotion).
Luca Sterpone is a Full Professor at the Department of Control and Computer Science (DAUIN), Politecnico di Torino. He serves as Head of the Control and Computer Engineering Department (2023-2027), coordinates the Aerospace and Safety Computing Lab, and is a member of the Academic Senate and Power Electronics Innovation Center (PEIC). His research spans reconfigurable computing, fault tolerance, and radiation effects analysis in electronic systems. Professor since 2021 Department Head (DAUIN) since 2023 Coordinates international collaborations with ESA, AMD Xilinx, NVIDIA, and Thales Alenia Space Develops radiation-hardened FPGA tools (SETA, VERI-Place, PyXEL) 2007 EDAA Outstanding Dissertation Award and 2005 IEEE Best Paper Award Research Focus : Designing radiation-tolerant systems for aerospace, including fault-tolerant AI accelerators, FPGA reliability, and software-based error mitigation. He investigates soft error propagation in nanoscale circuits and develops tools for radiation sensitivity analysis in VLSI. His work integrates hardware-software co-design for mission-critical applications. Awards : EDAA Outstanding Dissertation Award (2007) IEEE European Test Symposium Best Paper (2005) SMACD Best EDA Tool Award (2018) ARC Best Paper candidate (2018) Teaching : He leads courses in Reconfigurable Computing (PhD level), GPU Programming , and Operating Systems . He has formal responsibility for teaching roles across 9 bachelor's and 7 master's years, and mentors multiple PhD students. Collaborations : Coordinates with the European Space Agency (ESA), University of Bielefeld, Universidad de Sevilla, and industrial partners like AMD Xilinx, NVIDIA, and General Motors. He leads projects such as RESCHIP4EU, VEGAS, and TERRAC for radiation-hardened computing solutions.
George M. Church is a Professor of Genetics at Harvard Medical School and affiliated with MIT, where he directs PersonalGenomes.org, providing open-access genomic, environmental and trait data. His laboratory focuses on transformative technologies for reading and writing 3D/4D biological structures with attention to ethics, safety, and equitable access. Church has co-initiated major scientific initiatives including the BRAIN Initiative (2011) and multiple Genome Projects (GP-Read-1984, GP-Write-2016, PGP-2005). Church's research spans multiple cutting-edge domains including genome engineering, synthetic biology, aging reversal, and space genetics. His lab pioneered foundational methods for direct genome sequencing, molecular multiplexing and barcoding in 1984, leading to the first genome sequence in 1994. His innovations contributed to nearly all next-generation DNA sequencing methods and companies. Current research directions include machine learning for protein engineering, tissue reprogramming, organoids, gene therapy, and in situ 3D DNA/RNA/protein imaging. His work bridges fundamental biology with therapeutic applications across diverse fields from Alzheimer's disease to de-extinction biology. Church's recent publications reveal a remarkable breadth of scientific inquiry, spanning from fundamental genome editing techniques to applications in aging research, neuroscience, and space biology. His work increasingly integrates artificial intelligence with biological systems, as seen in papers on machine-guided cell-fate engineering and automation of systematic reviews with large language models. His research maintains a strong translational focus, with numerous papers addressing therapeutic applications in cancer immunotherapy, gene therapy, and diagnostics. The consistent theme across his diverse publications is the development and application of transformative technologies to address fundamental biological questions and medical challenges. National Academy of Sciences (NAS) membership National Academy of Engineering (NAE) membership Franklin Bower Laureate for Achievement in Science Co-initiator of the BRAIN Initiative (2011) Director of multiple NIH Centers for Excellence in Genomic Science (2004-2020) Church directs numerous research centers including the NIH-CEGS, Personal Genome Project (PGP), Lipper Center for Computational Genetics, and Wyss Institute Synthetic Biology center. His laboratory has trained PhD students across multiple Harvard and MIT programs including Biophysics, BBS, Biomedical Informatics, ChemBio, Chemistry, SSQB, MCO, Virology, HST, EE/CS, Physics and Applied Math. His commercial impact is extensive through companies spanning medical diagnostics (Knome/PierianDx, Alacris, Nebula, Veritas) and synthetic biology/therapeutics (AbVitro/Juno, Gen9/enEvolv/Zymergen/Warpdrive/Gingko, Editas, Egenesis). Church also pioneered new privacy, biosafety, ELSI, environmental and biosecurity policies. The Church Lab operates across multiple research domains including molecular multiplexing, next-generation sequencing, nanopore technology, and genome engineering. The lab maintains strong connections with the Personal Genome Project, Wyss Institute, and multiple commercial ventures. Current research directions include the Spatial Atlas of Human Anatomy (SAHA), human skin rejuvenation via mRNA, and space genetics research through the Consortium for Space Genetics and BioAstra. The lab's mission focuses on transformative technologies for reading and writing 3D/4D structures at any scale, inspired by but not limited by biology.
Susan E. Clark is an Assistant Professor of Physics at Stanford University, where she investigates cosmic magnetic fields, magnetohydrodynamic processes, and the interstellar medium (ISM) through observation, simulation, and analytic theory. Prior to Stanford, she was a NASA Hubble Fellow and postdoctoral member at the Institute for Advanced Study in Princeton, New Jersey, after earning her Ph.D. in Astrophysics from Columbia University (2017) and B.S. in Physics from the University of North Carolina at Chapel Hill (2012), supported by a Morehead-Cain scholarship. Education: Ph.D., Astrophysics, Columbia University (2017) — NSF Graduate Fellow B.S., Physics, University of North Carolina at Chapel Hill (2012) — Morehead-Cain scholarship Her research focuses on Galactic and extragalactic magnetism, interstellar turbulence, star formation, and polarized cosmological foregrounds. She leads an interdisciplinary group tackling these problems via observational data, numerical simulations, and machine learning techniques. Current projects include characterizing magnetic field alignment, modeling 3D ISM structure, and analyzing data from experiments like the Atacama Cosmology Telescope, Simons Observatory, CMB-S4, CCAT-Prime, LiteBIRD, and the Galactic Australian SKA Pathfinder (GASKAP). Recent publications highlight her work on dust filament misalignment, HI morphology for gas phase separation, equipartition magnetic field estimation, and tomographic MHD simulations of galactic magnetic fields. These studies integrate astrophysics, computational methods, and observational astronomy to decode the universe's magnetic structure and dynamics. Scientific Awards: Alfred P. Sloan Research Fellowship NSF Graduate Fellowship NASA Hubble Fellowship Clark co-founded the Pan-Experiment Galactic Science Group and co-directs Stanford's Center for Decoding the Universe, fostering interdisciplinary collaborations. Her lab includes postdocs, graduate students, and undergraduates, with alumni transitioning to roles in academia and industry. Funding comes from NSF, NASA, and the Sloan Foundation.
David Fouhey is an Assistant Professor at New York University, jointly appointed between the Courant Institute of Mathematical Sciences (Computer Science) and the Tandon School of Engineering (Electrical and Computer Engineering). He previously held positions at the University of Michigan and was a postdoctoral researcher at UC Berkeley. His research focuses on learning-based computer vision, particularly in 3D reconstruction, AI for science, and human-object interaction. Education: PhD in Robotics from Carnegie Mellon University (2013-2018) Bachelor of Arts in Computer Science from Middlebury College (2007-2011) Research Interests: His work spans 3D reconstruction from images , AI-driven scientific measurement (e.g., solar physics, evolutionary ecology), and human interaction modeling . Notable projects include Stereo4D for 3D motion analysis and SyntheticIA for solar magnetogram fusion. Recent Articles: Recent work emphasizes interdisciplinary applications of vision (e.g., bird morphology analysis) and robust 3D techniques like Perspective Fields for camera calibration. His 2025 Nature Scientific Data paper on bird skeletal traits highlights his AI-for-science focus. Grants & Collaborations: Secured a NASA grant for heliophysics tools and collaborates with institutions like NASA’s SDO mission and the Astrophysical Journal. Labs & Teams: Leads a NYU research group focused on vision and robotics, with active collaborations in astrophysics and ecology.
Giovanna Tinetti is a Professor of Astrophysics and Vice Dean (Research) at King's College London's Faculty of Natural, Mathematical & Engineering Sciences. She leads the European Space Agency's Ariel mission, a space telescope surveying exoplanet atmospheres, set to launch in 2029. As co-founder of the London Centre for Space Exochemistry Data and Blue Skies Space Ltd, she pioneers satellite technology for scientific data collection. She holds a PhD in Theoretical Physics from the University of Turin, with prior affiliations at Caltech/JPL, the Institute of Astrophysics in Paris, and University College London (UCL), where she was a Royal Society University Research Fellow. Her research focuses on exoplanetary atmospheres, molecular spectroscopy, and advanced data science techniques. With over 300 publications, her 2019 paper on water vapor in K2-18b's atmosphere achieved the highest altmetric score in Physical Sciences that year. She has delivered over 350 international talks and lectures. Education: PhD in Theoretical Physics (University of Turin) Affiliations: King's College London, UCL (past), ESA's Ariel Mission, Blue Skies Space Ltd Research Interests: Exoplanet atmospheres, molecular spectroscopy, space science, data-driven analysis methodologies, and atmospheric modeling. Her work bridges observational astronomy with computational chemistry to interpret exoplanet compositions and climates. Awards: Royal Society University Research Fellow Highest Altmetric Score (2019 Physical Sciences) Grants & Projects: Principal Investigator for ESA's Ariel mission Co-leader of the Ariel Data Challenge 2025 Labs/Teams: London Centre for Space Exochemistry Data, Blue Skies Space Ltd technical team, and the international Ariel collaboration network.
Andrew Bishara, MD, is an Assistant Professor in Residence in the Department of Anesthesiology within the School of Medicine at the University of California, San Francisco (UCSF). He is affiliated with multiple UCSF clinical sites, including Mission Bay, Mount Zion, and Parnassus, and is actively involved in the AI Clinical Innovation Lab, Transplant Anesthesia Research Group, and POCCO (PeriOperative Cardiac Complications Observatory). His clinical practice as an anesthesiologist is deeply integrated with his research in machine learning and artificial intelligence for perioperative care. Dr. Bishara's educational background includes a BSE in Mechanical Engineering from MIT (2009), an MD from Harvard Medical School (2014), and a D.ABA. in Anesthesiology from UCSF (2019). He also completed specialized training in Medical Informatics and Artificial Intelligence through the Bakar Computational Health Sciences Institute (2020) and a Diversity, Equity, and Inclusion Champion program at UCSF (2022). His research focuses on developing and validating machine learning models to predict and prevent surgical complications such as acute kidney injury, postoperative delirium, pain, and blood loss in real time. He emphasizes creating clinically usable models and improving AI-human interfaces for seamless integration into clinical workflows. His work also explores gender-based disparities in coronary artery disease diagnosis using EHR data analytics. His recent publications demonstrate expertise in AI quality improvement, model implementation in acute care, and predictive modeling across diverse surgical and critical care domains. He co-founded Bezel Health, a company focused on healthcare quality measurement, reflecting his commitment to translating research into real-world impact. Clinical Artificial Intelligence Quality Improvement Real-time Risk Assessment in Surgery AI Integration in Anesthesia Gender Disparities in Cardiac Care Transplant Anesthesia Research Regulatory Aspects of AI in Medicine Dr. Bishara is actively engaged in advancing perioperative medicine through innovation in data science and AI, with a strong emphasis on improving patient outcomes, equity, and clinical workflow efficiency.
Dr. Mohammad Yazdani-Asrami is a Lecturer in Electrically Powered Aircraft and Operations at the Autonomous Systems & Connectivity (ASC) division of the James Watt School of Engineering, University of Glasgow. He leads research in electrification and cryo-electrification of transportation, particularly in aviation, leveraging applied superconductivity and AI techniques. His research interests span the Electrification and cryo-electrification of power and transportation systems Design of superconducting components (machines, cables, fault current limiters) for aviation Application of AI, machine learning, and big data in engineering and superconductivity Hydrogen electrolysis, production, and integration in aerospace and power networks His recent publications demonstrate a strong trend toward intelligent modeling and AI-driven solutions in superconducting technologies, with a focus on electric aircraft, fault protection, and thermal management using cryogenic fluids. Dr. Yazdani-Asrami has received notable scientific recognition, including: UK Royal Academy of Engineering Global Talent (2021) Young Professional of the Year, Cryogenic Society of America (2023) He actively supervises PhD students and hosts visiting researchers. His advising portfolio includes Alireza Sadeghi, Kerr Smith, Dedao Yan, Giacomo Russo, and Fábio Gregório. He has secured funding from the EPSRC, University of Glasgow, and CSC for PhD students. He also supports postdoctoral fellowships from the Royal Academy of Engineering, Leverhulme Trust, and Marie Skłodowska-Curie actions. He is involved in several research groups and collaborations, particularly within the Aerodynamics, Propulsion and Electrification group. His editorial roles include serving on the boards of Superconductor Science and Technology , World Journal of Engineering , Aerospace Systems , and others. He regularly contributes to major conferences such as the Applied Superconductivity Conference and the International Conference on Magnet Technology.
