Dr. Youngchan Kim is a Lecturer in Quantum Biology at the University of Surrey , serving as Director of the Quantum Biology Doctoral Training Centre (QB-DTC). He is affiliated with multiple departments including the School of Biosciences, Advanced Technology Institute, and Quantum Sciences Group. PhD in Physics (2011), Korea Advanced Institute of Science and Technology MSc in Physics (2008), KAIST BSc in Physics (2006), Chung-Ang University Graduate Certificate in Learning and Teaching (2022), Advance HE His research focuses on quantum phenomena in biological systems at physiological temperatures, particularly using femtosecond optical spectroscopy and genetically engineered fluorescent proteins to explore evolutionary adaptations and develop quantum-bio-inspired technologies like room-temperature single-photon sources. The 15 most recent publications span quantum biology, biophotonics, and optical spectroscopy, with particular emphasis on quantum coherence in biological systems , terahertz birefringence , fluorescent protein dynamics , and biomedical imaging innovations . These works demonstrate his interdisciplinary approach bridging physics, biology, and medical applications. As QB-DTC Director, he leads transdisciplinary initiatives fostering collaboration between quantum physics and biosciences. His technical expertise includes time-correlated single-photon counting , common-path interferometry , and ultrafast fluorescence depolarization techniques.
Prashanth Krishnamurthy is a Research Scientist in the Department of Electrical and Computer Engineering at New York University Tandon School of Engineering. His research focuses on robotics, control systems, and cybersecurity, particularly in cyber-physical systems such as power grids and embedded devices. He holds a Ph.D. in Electrical Engineering from NYU. Key research areas include hardware security (e.g., detecting Trojans in chips), anomaly detection in critical infrastructure, and resilient control strategies for robotic systems. He has led or contributed to projects funded by the U.S. Department of Energy (DOE), Office of Naval Research (ONR), and others, including the Tracking Real-time Anomalies in Power Systems (TRAPS) initiative and hardware Trojan detection using short-term aging phenomena. Education: Ph.D., Electrical Engineering, NYU His work bridges theoretical advancements and practical implementations, such as developing FPGA-based testbeds for hardware security validation and creating AI-driven cybersecurity tools like the CRAKEN LLM agent. Collaborators include institutions like SRI International, Karlsruhe Institute of Technology, and the NYU Center for Cybersecurity. Grants include a $1.94M DOE grant for TRAPS and a $359K DURIP grant for hardware Trojan detection. His technical contributions span control systems, anomaly detection algorithms, and cybersecurity frameworks for embedded systems. He is actively involved in advancing secure cyber-physical systems through innovations in real-time monitoring, robust control mechanisms, and AI-augmented security solutions.
Zeljko Pantic is an Associate Professor in the Department of Electrical and Computer Engineering at North Carolina State University. He holds a Ph.D. from NC State (2013) and B.S./M.S. degrees from the University of Belgrade (1998/2007). Before joining NC State in 2019, he served as an Assistant Professor and Associate Director of the Electric Vehicle and Roadway Research Facility at Utah State University. He is actively involved in editorial roles for IEEE Transactions on Transportation Electrification and serves on the IEEE IAS Transportation Systems Committee. Education: Ph.D., Electrical Engineering, North Carolina State University (2013) M.S., Electrical Engineering, University of Belgrade (2007) B.S., Electrical Engineering, University of Belgrade (1998) Research: Dr. Pantic specializes in electrified transportation systems, wireless power transfer (WPT), power converter design, and DC microgrid technologies. His work addresses challenges in EV charging infrastructure, magnetic circuit optimization, and energy conversion principles for transportation electrification. Recent projects include autonomous wireless charging systems for UAVs, marine DC microgrids, and road-embedded DWPT solutions. Awards & Recognition: 2019 IEEE JESTPE Second Prize Paper Award 2017 Outstanding Teacher of the Year (USU) 2012 NC State Mentored Teaching Assistantship Award Advisees & Grants: While specific student names are not listed, Dr. Pantic has advised graduate students on projects spanning WPT systems, EV infrastructure, and battery management. His work has been supported by grants focusing on dynamic charging, magnetic materials, and autonomous observatory nodes. Labs & Facilities: He leads research at NC State's Electric Vehicle and Roadway facility, focusing on roadway-integrated wireless charging and high-power WPT systems. Collaborations include ocean observatory development and autonomous system integration.
Kyle DeMars is an Associate Professor and Associate Department Head for Theoretical and Computational Research in the Department of Aerospace Engineering at Texas A&M University. He holds a Ph.D. from The University of Texas at Austin (2010) and has expertise in space situational awareness, navigation systems, Bayesian filtering, and information theory. His work focuses on advanced estimation techniques for spacecraft autonomy and space surveillance. Dr. DeMars' research emphasizes robust nonlinear filtering, multitarget tracking, and information-theoretic approaches to orbital dynamics. He has developed innovative methods for spacecraft navigation, including terrain-relative systems and anonymous feature processing. His contributions address challenges in uncertainty quantification, sensor fusion, and cislunar space domain awareness. Education: Ph.D./M.S.E./B.S. in Aerospace Engineering (UT Austin, 2004–2010) Awards: AIAA Young Professional Award (2017), NASA Innovation Award (2014), and multiple teaching/research recognitions Labs/Teams: Active in space situational awareness, guidance & control, and probabilistic navigation systems Key trends in his publications include: Advances in particle flow and Gaussian mixture methods for nonlinear estimation Cislunar trajectory analysis and resonance-based surveillance strategies Development of fault-resistant and anonymous navigation frameworks Integration of information theory into sensor tasking and uncertainty management His work bridges theoretical developments with practical applications in planetary landing navigation, space traffic management, and autonomous spacecraft systems.
Professor Kathryn Fairfull-Smith is a Professor in the School of Chemistry & Physics at Queensland University of Technology (QUT). Her research focuses on organic synthesis for applications in materials science and medicinal chemistry, with a particular emphasis on developing strategies to combat bacterial biofilms through nitroxide-functionalized compounds and surface coatings. She leads projects funded by grants such as the QLD-Bavaria collaborative seed grant (2024) and an ARC Linkage Grant (2024). Her work spans organic radical batteries, profluorescent sensors, and antimicrobial hybrids, addressing challenges in biomedical and environmental materials. Education: PhD from Griffith University. Professional memberships include MRACI CChem and MRSC. Awards include the QUT Vice-Chancellor's Award for Excellence in Research (2017) and an ARC Future Fellowship (2014). She actively supervises postgraduate students in topics like nitroxide therapeutics and polymer materials. Her research group is part of the Centre for Materials Science at QUT. Research Grants: QLD-Bavaria collaborative seed grant (2024), ARC Linkage Grant (2024), DP210101317 (2021), FT140100746 (2015) Key Projects: Biofilm eradication via nitroxide-antimicrobial hybrids, light-triggered surface coatings, and polymer degradation monitoring Publications include over 30 peer-reviewed articles in journals such as Advanced Materials Technologies and Antimicrobial Agents and Chemotherapy . Her work bridges fundamental chemistry with real-world applications in healthcare and sustainability.
Indrani Bhattacharya, PhD, is an Assistant Professor in the Department of Biomedical Data Science and the Center for Precision Health and Artificial Intelligence (CPHAI) at Dartmouth College's Geisel School of Medicine. Her research focuses on developing human-centered AI systems for healthcare, particularly in multimodal medical imaging and behavioral health analytics. She holds a BS in Electrical Engineering from Jadavpur University (India), and MS/PhD from Rensselaer Polytechnic Institute (USA). Postdoctoral training at Stanford University's Department of Radiology further specialized her in biomedical imaging informatics. Research interests include: Integrating imaging and non-imaging data for precision medicine AI-driven prostate cancer detection/classification Multimodal behavior estimation for doctor-patient interactions Privacy-preserving sensor systems for group interaction analysis Her work bridges computer vision, medicine, and social science, with recent breakthroughs in MRI-ultrasound fusion AI outperforming radiologist interpretations in multi-center studies. Active in AI ethics and translational research, she leads teams developing clinical decision support tools for oncology and behavioral health. Key career milestones include: Postdoctoral scholar at Stanford University School of Medicine (2016-2021) Academic research staff at Stanford Radiology (2021-2022) Founding member of Dartmouth CPHAI precision health initiatives Labs/Teams: Leads the Biomedical AI for Healthcare group at Dartmouth, collaborating with Stanford and industry partners on AI-driven diagnostic systems.
Andrea Ianiro is a Full Professor in the Aerospace Engineering Department at Universidad Carlos III de Madrid (UC3M), where he leads research in fluid dynamics, turbulence, and heat transfer. His work bridges experimental techniques and machine learning applications for flow analysis and control. He serves as Associate Editor of the International Journal of Heat and Mass Transfer (2025-2028) and directs the EFM Lab (Experimental Fluid Mechanics Laboratory) at UC3M. Professor Ianiro's research focuses on turbulence characterization, boundary layer flows, and the application of machine learning to fluid mechanics problems. His work spans experimental techniques including Particle Image Velocimetry (PIV), infrared thermography, and advanced data processing methods. Recent research emphasizes data-driven approaches for flow field reconstruction, turbulence control, and heat transfer optimization in wall-bounded flows. His projects often combine theoretical, experimental, and computational approaches to address complex fluid mechanics challenges. The analysis of his recent publications reveals a strong trend toward integrating machine learning with traditional fluid mechanics. His work increasingly focuses on using deep learning techniques (particularly CNNs and GANs) for flow field prediction from limited measurements, developing meshless computational methods for flow analysis, and applying optimization techniques (including genetic algorithms) to heat transfer enhancement. His research maintains a strong experimental foundation while embracing data-driven approaches to tackle turbulence modeling challenges. Associate Editor of the International Journal of Heat and Mass Transfer (2025-2028) Professor Ianiro leads multiple significant research projects including SPANDRELS (SParse AND paRsimonious Event-based fLow Sensing, 2025-2030), HumanIC (Human-Centric Indoor Climate for Healthcare Facilities, 2024-2027), and EXCALIBUR (Extraction of machine learning strategies for turbulent flow control, 2023-2026). His work has attracted funding from the European Commission, Spanish National Research Agency, and industry partners including Airbus. He has supervised numerous theses on topics including AI-based sensing of turbulent flows, convective heat transfer control, and turbulent boundary layers. At UC3M, Professor Ianiro directs the Experimental Fluid Mechanics Laboratory (EFM Lab), which focuses on advanced measurement techniques for fluid flow and heat transfer characterization. The lab specializes in PIV/PTV techniques, infrared thermography, and the development of novel experimental approaches for turbulence research. Current research directions include machine learning applications for flow field reconstruction, plasma-based flow control, and heat transfer optimization in complex flow configurations.
Karl-Erik Årzén is Professor and Head of the Department of Control Engineering at Lund University's Faculty of Engineering. He is also Co-director of the Wallenberg AI, Autonomous Systems and Software Program (WASP) and a key member of ELLIIT, the excellence center in information technology. His roles include leadership in AI and digitalization profile areas at both LTH and Lund University. His research lies at the intersection of control engineering and computer science, with a focus on cyber-physical systems, real-time systems, embedded control, and resource management in cloud and edge computing environments. He has pioneered methods for predictable performance in cloud applications and dynamic resource allocation using control-theoretic approaches. The recent publications highlight a strong trend in control over the cloud and edge, real-time scheduling co-design, distributed camera systems, and reinforcement learning for auto-scaling. Key topics include model predictive control, LQG-based scheduling, bandwidth allocation, and robustness in cyber-physical systems. The work spans theoretical control design and practical implementation in distributed systems. His scientific awards include multiple Best Paper Awards from IEEE and ACM conferences in 2018, 2016, and 2004, recognizing excellence in autonomic computing, edge computing, and real-time systems. Best Paper Award, IEEE International Conference on Autonomic Computing, 2018 Best Paper Award, IEEE International Conference on Edge Computing (EDGE), July 2018 Best Paper Award - RTNS 2016 Best Paper Award - RTCSA 2004 Årzén has supervised over 20 PhD students, including Mikael Johansson, Anton Cervin, Yang Xu, and Per Skarin, and currently supervises Ahmed Al Bayati and Max Nyberg Carlsson. His grant portfolio includes major projects such as WASP, AORTA (VINNOVA), and ELLIIT's 'Robust and Secure Control over the Cloud'. He has also contributed to innovation through tools like TrueTime and Jitterbug. He leads the RobotLab LTH initiative and is involved in the Nordic University Hub on Industrial Internet of Things (HI2OT). His work bridges academia and industry, with collaborations on adaptive control, cloud-native systems, and autonomous robotics.
Prof. Roland Pail is a full Professor of Astronomical and Physical Geodesy at the Technical University of Munich (TUM). He leads the Chair of Astronomical and Physical Geodesy, part of the TUM School of Engineering and Design. His research focuses on physical and numerical geodesy, global/regional gravity field modeling, and satellite gravity missions like GOCE, GRACE, and future initiatives like MAGIC. He has held leadership roles, including President of IAG Commission 2 (2015–2019) and Vice Dean of TUM's Department of Aerospace and Geodesy. Pail earned his doctorate (sub auspiciis praesidentis) from TU Graz (1999) and habilitation in 2002. He is a Fellow of the International Association of Geodesy and has received numerous awards for his contributions to geodesy. His work integrates satellite data with geophysical modeling to monitor mass transport processes (e.g., ocean circulation, ice melt) and Earth's interior dynamics. He collaborates internationally on missions such as the DFG Research Training Group UPLIFT and the MAGIC constellation. Key publications include gravity field models (e.g., XGM2016, GOCO06s) and studies on future mission design, stochastic modeling, and climate monitoring. Pail’s scientific awards include the IAG Fellowship (2011), Young Authors Award (2006), and the Allmer-Löschner Prize (2000). His research also addresses quantum sensor applications in satellite gravimetry and the development of next-generation gravity field retrieval techniques.
Luís B. Elvas is an Assistant Professor at ISCTE-University Institute of Lisbon's Department of Social and Business Sciences (SINTRA) and a Research Assistant at ISTAR-Iscte Research Center. He holds qualifications including a Technical Specialization in TensorFlow for AI (Coursera, 2021) and certifications in IoT/Blockchain from ISCTE and cybersecurity from Palo Alto Networks. His research spans artificial intelligence, healthcare informatics, smart cities, and blockchain, with applied work in medical imaging, data sharing, and urban analytics. Research interests include: Healthcare AI : Developing deep learning models for cardiac diagnostics, medical imaging analysis, and blockchain-based health data systems Smart Cities : Implementing IoT solutions for urban mobility optimization, disaster management, and sustainable transportation Data Science : Creating predictive analytics frameworks for clinical decision support and urban planning His publications demonstrate a strong focus on AI-driven healthcare solutions (67% of recent works) and smart city technologies (33%), with emerging interests in blockchain and NLP. Research consistently targets real-world applications in clinical settings and urban environments. Awards: Award for best internship, Order of Engineers (2022) Distinction for best internship, Order of Engineers (2021) He leads/contributes to multiple EU research consortia including AMR-EDUCare (antimicrobial resistance education), NEEM (e-health in Nepal), and Blockchain.PT. Coordinates the IEEE Computational Intelligence Society Student Branch Chapter at ISCTE and developed the ManagiDiTH master's program in digital health transformation.
Dr. Yu Zhong is an Assistant Professor in the Department of Materials Science and Engineering at Cornell University's College of Engineering, where he leads the Yu Zhong Group. His research laboratory focuses on the design and synthesis of novel soft materials and nanomaterials for applications in electronics, energy, healthcare, and sustainability. As a principal investigator, he oversees a dynamic research team comprising postdoctoral associates, graduate students, and undergraduate researchers working on cutting-edge materials science projects. Dr. Zhong received his educational training at prestigious institutions, earning his B.S. in Chemistry from the University of Science and Technology of China (USTC) in 2011, followed by a Ph.D. in Chemistry from Columbia University in 2017 under the supervision of Prof. Colin Nuckolls. His doctoral research centered on designing contorted molecules for electronic and energy applications including organic solar cells, photodetectors, and gas sensors. He then conducted postdoctoral research at the University of Chicago in Prof. Jiwoong Park's group, where he worked on the design and synthesis of 2D polymers for ultrathin electronic circuits and energy conversion. Dr. Zhong's research program spans three primary directions: (1) the bottom-up synthesis of ultrathin nanoporous membranes using techniques like laminar assembly polymerization (LAP) for applications in water desalination, nanofiltration, and gas separation; (2) the study of transport behaviors in hybrid organic-inorganic 2D heterostructures created through layer-by-layer assembly for use in optical, electronic, and thermal management devices; and (3) the development of mixed ionic-electronic materials for bio-inspired and bioelectronic devices. His group employs advanced synthesis methods including organic/polymer synthesis, supramolecular and reticular chemistry, and 2D materials characterization to explore novel scientific phenomena and technological applications. An analysis of Dr. Zhong's recent publications reveals a strong focus on the synthesis and characterization of 2D polymers and organic-inorganic hybrid materials. His work bridges fundamental materials science with practical applications in energy conversion, electronics, and separation technologies. A notable trend is his development of innovative synthesis techniques like laminar assembly polymerization that enable precise control over material structure at the molecular level, leading to breakthroughs in areas such as lithium-ion transport, osmotic power generation, and ultra-narrowband photodetection. Dr. Zhong's scientific achievements have been recognized with several prestigious awards: Pegram Award for Meritorious Graduate Research, Columbia University (2016) Camille and Henry Dreyfus Postdoctoral Fellowship, Dreyfus Foundation (2016) Arun Guthikonda Memorial Fellowship, Columbia University (2015) Jack Miller Award for Excellence in Teaching, Columbia University (2014) As an advisor, Dr. Zhong mentors a diverse group of researchers including postdoctoral associate Qiyi Fang, multiple Ph.D. students (Yuhe Zhang, Kaushik Chivukula, William Xie), M.S. students, and undergraduate researchers. His group has secured funding for research on soft and nanomaterials, with projects spanning organic electronics, 2D materials synthesis, and biomimetic membranes. Dr. Zhong actively seeks motivated graduate students and postdoctoral fellows to join his research team, emphasizing the importance of interdisciplinary collaboration in advancing materials science. The Yu Zhong Group operates state-of-the-art laboratories in Bard Hall at Cornell University, equipped for organic synthesis, materials characterization, and device fabrication. The research team works collaboratively across disciplines, partnering with experts in physics, chemistry, and engineering to tackle complex challenges in materials science. Current projects focus on developing novel synthesis methodologies and exploring structure-property relationships in soft materials to enable next-generation electronic, energy, and healthcare technologies.
Jung Soo Lim is an Assistant Professor in the Department of Computer Science at California State University, Los Angeles, within the College of Engineering, Computer Science, and Technology. He earned his B.S. from Cal State LA and M.S. and Ph.D. from UCLA, returning to his alma mater as a part-time lecturer in 2014 before transitioning to a full-time assistant professor role in 2019. Education: B.S. (Cal State LA), M.S. (UCLA), Ph.D. (UCLA) His research focuses on Internet of Things (IoT) , Cyber-Physical Systems , Wireless Networking , Software Engineering , and Medical Computing . He has active projects in IoT applications for healthcare, urban safety, and wastewater monitoring, including the Center for Inclusive Computing (CIC) Transfer Pathways Project. Recent publications highlight interdisciplinary work bridging IoT, healthcare diagnostics, and smart city infrastructure. Notable topics include stroke detection algorithms , IoT communication protocols , and sensor-based environmental monitoring . Scientific Awards: Outstanding Senior at Cal State LA (1997) He teaches core computer science courses such as Computer Programming Fundamentals and Analysis of Algorithms, and actively mentors graduate students. His lab focuses on developing embedded systems and IoT solutions for real-world challenges.
Yuzhe Yang is an Assistant Professor of Computational Medicine and Computer Science at UCLA, with a visiting research scientist role at Google Health. He holds a PhD in Computer Science from MIT (2024), advised by Dina Katabi, and a B.S. with honors from Peking University. Research Focus: Machine learning for healthcare, medical AI fairness, and AI-driven biomedical discovery Key Contributions: Ten Notable Advances (Nature Medicine) and Ten Crucial Advances (The Lancet Neurology) His lab develops Trustworthy Learning Algorithms and Generalist Health Models that integrate Multimodal Data for personalized health coaching. Notable projects include AI-based Parkinson's Disease Biomarkers via nocturnal breathing and Foundation Models for Equitable Medicine . Recent publications at ICLR 2025 (wearable foundation models), Nature Medicine 2024 (medical AI fairness), and Science Advances 2025 (vision-language medical bias) highlight his interdisciplinary work. He serves on ML4H workshops and reviews for top conferences like NeurIPS and ICML. Awards include Forbes 30 Under 30 , Takeda Fellowship , and Baidu PhD Fellowship . Advising opportunities: Recruiting PhD students (CS/CompMed) and postdocs in AI for health. Lab: Health Intelligence Lab (HAIL)
Songbin Gong is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana Champaign, where he has been a faculty member since August 2013. He was promoted from Assistant Professor to Associate Professor in August 2019 and holds the Intel Alumni Fellowship. His research is centered at the Micro and Nanotechnology Lab, where he leads the Integrated RF Microsystems research group. Professor Gong's research focuses on RF and microwave photonics, microwave acoustics, and Micro-Electro-Mechanical Systems, with particular expertise in lithium niobate-based devices. His work spans the development of acoustic resonators, filters, and transducers operating from VHF to Sub-THz frequencies. Recent publications demonstrate significant advances in high-frequency acoustic devices, including GHz resonators with high electromechanical coupling and low loss characteristics. His research has direct applications in 5G communications, wireless sensing, and imaging systems. Gong has established himself as a leader in the field of RF MEMS and acoustic devices, with numerous high-impact publications in top journals including IEEE Transactions on Microwave Theory and Techniques, Journal of Microelectromechanical Systems, and Optics Express. His work shows a clear progression toward higher frequency operation, improved device performance, and novel integration approaches for next-generation communication systems. Among his notable achievements is the development of thin-film lithium niobate devices that overcome traditional frequency limitations of MEMS resonators, enabling operation beyond 10 GHz. This work addresses critical challenges in 5G and future wireless technologies where conventional approaches face scaling limitations. IEEE Ultrasonics Early Career Investigator Award DARPA Young Faculty Award 2014 NASA Early Career Faculty Award 2017 Intel Alumni Fellow 2017-present Multiple Best Paper Awards at major conferences including International Ultrasonic Symposium and International Microwave Symposium Professor Gong actively mentors graduate and undergraduate students, with several of his PhD students achieving notable success, including Ruochen Lu who joined UT Austin as a tenure-track assistant professor. His research group has secured significant funding from agencies including DARPA and NASA, supporting cutting-edge work in RF microsystems. The group maintains strong industry connections, particularly with Intel, reflecting the practical relevance of their research to commercial communication technologies. The Gong Research Group leverages micro/nano electro mechanical systems (N/MEMS), integrated photonic, and compound semiconductor technologies to develop chip-scale hybrid microsystems for RF communication, sensing, and imaging applications. Their current work focuses on pushing the boundaries of acoustic device performance while maintaining compatibility with standard semiconductor manufacturing processes.
Miler T. Lee is an Associate Professor at the University of Pittsburgh , focusing on gene regulation during early embryonic development through high-throughput experimental and computational genomics. He earned his Ph.D. in Genomics and Computational Biology in 2009 from the University of Pennsylvania under Dr. Junhyong Kim, followed by postdoctoral work with Dr. Antonio Giraldez at Yale University. Joining the university in 2016, his research spans maternal-to-zygotic transition (MZT), RNA stability, pluripotency networks, and evolutionary developmental biology, utilizing model organisms like zebrafish, Xenopus, and Hydractinia symbiolongicarpus. Key Research Themes: Maternally inherited RNA dynamics during embryogenesis Mechanisms of RNA degradation and transcriptome remodeling Evolution of pluripotency networks in hybrid species Role of zinc signaling in fertilization barriers Computational tools for RNA regulation and sensing Scientific Awards: Pan-American Society for Evolutionary Developmental Biology Junior Faculty Award (2024) Outstanding New Investigator – International Xenopus Board (2023) Basil O'Connor Scholar – March of Dimes (2017-2019) Recent publications highlight his work on enhancer classification, RNA degradation mechanisms, and cross-species MZT comparisons. His lab develops innovative methods like RESA for regulatory sequence analysis and studies evolutionary divergence in RNA localization patterns. While the articles span computational and experimental approaches, they consistently address RNA's role in cellular identity, developmental timing, and evolutionary adaptation. Applications include understanding pluripotency, designing RNA biosensors, and elucidating fertilization barriers. Prospective Ph.D. students are encouraged to contact him for opportunities in gene regulation, development, evo-devo, and computational genomics.
