Baike She is a Postdoctoral Fellow at the School of Electrical and Computer Engineering, Georgia Institute of Technology. Their research focuses on interdisciplinary topics at the intersection of control theory, network science, and epidemiological modeling. Key areas include epidemic spread analysis, distributed systems optimization, and privacy-preserving algorithms for networked models. Research interests emphasize mathematical frameworks for analyzing complex systems, including compositional control approaches (e.g., LQR analysis via category theory), robust epidemic control strategies, and leveraging differential privacy in sensitive data computations. Work spans both theoretical developments and applied methodologies for real-world systems such as SIR/SIS epidemic models and infrastructure networks. Recent publications (2022-2025) highlight contributions to distributed reproduction number computation, optimal epidemic mitigation under uncertainty, and the integration of opinion dynamics with vaccination strategies. Methodologies include Gaussian process regression, dissipativity theory, and model predictive control frameworks. No specific awards or grants are explicitly listed in the provided texts. Advising roles and laboratory affiliations remain unspecified based on available information.
Sakari Lahti is a Lecturer in the Department of Computing Sciences at Tampere University, within the Faculty of Information Technology and Communication Sciences. His primary responsibilities include teaching digital logic and hardware design. He holds an ORCID identifier: 0000-0002-9915-4784 . Lahti's research focuses on High-Level Synthesis (HLS) for FPGAs, with emphasis on optimizing embedded systems, real-time applications, and digital signal processing. His work spans FPGA implementation techniques, compiler optimizations for HLS tools, and practical applications in media processing and wireless communications. Notable projects include real-time HEVC video encoding and nonlinear self-interference cancellation systems. His publications reflect a sustained contribution to FPGA-based hardware design, with a decade of work from embedded systems (2002) to modern C++ integration in HLS (2023). Collaborations include colleagues like Teemu Hämäläinen and Jari Vanne, focusing on bridging software and hardware design methodologies. His research also extends to educational aspects, such as real-world product development in system design courses. Lahti’s work is peer-reviewed and published in prestigious venues like IEEE Transactions and conferences like DDECS and ISCAS. His research unit is the Unit of Computing Sciences at Tampere University.
Yonghyun Ha is an Associate Research Scientist in the Department of Radiology & Biomedical Imaging at Yale School of Medicine. His research focuses on advancing magnetic resonance imaging (MRI) technologies, particularly in low-field MRI systems, RF pulse design, and imaging hardware innovation. He collaborates with experts like Duy Phan, Haifan Lin, and Nikhil Malvankar, contributing to projects such as RF pulse distortion compensation and novel RF coil development. Research Interests: His work spans low-field MRI systems , RF engineering , imaging algorithm optimization , and hardware design . He explores applications like point-of-care imaging and cost-effective MRI solutions. Articles Trends: Recent publications address gradient-free imaging, field-cycling magnets, and deep learning for data compression. His work bridges engineering and clinical needs, emphasizing practical MRI advancements. Advising & Grants: No formal advisees are listed, but his collaborations suggest involvement in interdisciplinary research teams. No specific grants are mentioned, but his projects imply funding through institutional or NIH channels. Labs/Teams: Active in Yale’s Radiology & Biomedical Imaging department, contributing to MRI technology development and translational research initiatives.
Camillo De Lellis is a Professor at the Institute for Advanced Study since July 2018, with a distinguished career spanning multiple institutions including the University of Zürich, where he served as Full Professor from 2005 and Assistant Professor in 2004. Prior to that, he held postdoctoral positions at the Max Planck Institute for Mathematics in the Sciences (Leipzig) and ETH Zürich. Research Interests encompass calculus of variations , geometric measure theory , partial differential equations , and incompressible fluid dynamics . His work bridges deep analytical techniques with geometric insights, particularly in understanding regularity theory for area-minimizing surfaces and anomalous dissipation in fluid flows. Publications highlight groundbreaking contributions to geometric analysis and fluid dynamics, including regularity theory for currents, Onsager's conjecture, and convex integration methods for Euler equations. These works span subfields like center manifold theory , blow-up analysis , Hölder continuous flows , and Q-valued functions . Scientific Awards Maryam Mirzakhani Prize (2022) Feltrinelli Prize (2021) Bôcher Memorial Prize (2020) Caccioppoli Prize (2014) Stampacchia Medal (2009)
Dr. Jenna Yentes is an Associate Professor in the Department of Kinesiology and Sport Management at Texas A&M University, affiliated with the College of Education and Human Development. Her research focuses on functional resiliency in aging populations, biomechanics of chronic obstructive pulmonary disease (COPD), and nonlinear analysis of human movement. She leads studies on gait stability, respiratory-gait coupling, and exoskeleton-assisted walking. Notable contributions include quantifying locomotor reserve and exploring firefighter performance in protective gear. Education: Ph.D. in Biomechanics (University of Nebraska, 2013), M.S. in Kinesiology (California State University Fullerton, 2006), B.A. in Kinesiology (University of Northern Colorado, 2000). Research Interests: Reserve capacity in older adults' mobility and cognition Biomechanical adaptations in COPD patients Methodological rigor in nonlinear data analysis (e.g., entropy metrics) Firefighter physical performance under protective gear Recent Articles Trends: Focus on entropy-based gait analysis, COPD biomechanics, dual-task interference in aging, and exoskeleton effects on interlimb coordination. Methodological rigor in parameter selection for nonlinear algorithms is a recurring theme. Awards: 2023 Faculty Climate Award (Texas A&M), 2019 Promising Scientist Award (International Society of Posture and Gait Research), and 2019 Chancellor's Commission on the Status of Women Award (University of Nebraska). Advising & Grants: Mentors graduate students (noted in publications) and collaborates with TEEX Fire Academy and multiple Texas A&M research centers including the Huffines Institute for Sports Medicine and the Center for Population Health and Aging. Research supported by institutional and federal grants. Labs/Teams: Active in Texas A&M's Human Movement and Aging Lab, collaborating with interdisciplinary teams in sports medicine, rehabilitation engineering, and pulmonary research.
Noa Marom is an Associate Professor in the Department of Materials Science and Engineering at Carnegie Mellon University (CMU), holding courtesy appointments in Chemistry and Physics. She is a member of the Pittsburgh Quantum Institute (PQI) and an affiliate of the Wilton E. Scott Institute for Energy Innovation. Her research focuses on computational materials science, energy security, and quantum materials. Marom earned a B.A. in Physics and B.S. in Materials Engineering (cum laude) from the Technion-Israel Institute of Technology (2003) and a Ph.D. in Chemistry from the Weizmann Institute of Science (2010). She held postdoctoral positions at the University of Texas at Austin’s Institute for Computational Engineering and Sciences (ICES) before joining Tulane University as an Assistant Professor (2013–2016) and CMU in 2016. Her research interests include computational design of semiconductor materials, topological quantum computing, and crystal structure prediction. Key projects involve machine learning for materials discovery and quantum computing applications, such as optimizing semiconductor interfaces for stable qubits. Marom has received numerous awards, including the NSF CAREER Award (2016), DOE INCITE Awards (2017–2019), and the IUPAP Young Scientist Prize (2018). She serves as Associate Editor of npj Computational Materials. Her work spans collaborations with institutions like the Paul Scherrer Institute (Switzerland) and the Pittsburgh Supercomputing Center. Research highlights include computational studies of InAs/InSb semiconductors for quantum bits and machine learning-driven discovery of organic semiconductors.
Dr. Amit Goyal is the SUNY Empire Innovation Professor and SUNY Distinguished Professor in the Department of Chemical and Biological Engineering at the University at Buffalo. He serves as Director of the UB Initiative on Plastics Recycling and Innovation (a New York State Center of Excellence) and previously founded and led the RENEW Institute (2015–2021), focusing on interdisciplinary research in energy, environment, and water. His academic roles include leadership in both research and industry, with expertise spanning clean energy technologies, superconducting materials, and flexible electronics. Education: B. Tech in Metallurgical Engineering, Indian Institute of Technology (1996) MS in Mechanical & Aerospace Engineering, University of Rochester (1988) PhD in Materials Science & Engineering, University of Rochester (1991) Executive MBA, Purdue University International Executive MBA, Tilburg University Research Interests: Dr. Goyal’s work focuses on heteroepitaxial growth , strain-driven self-assembly , and advanced energy materials . He develops clean energy technologies, including superconducting devices and photovoltaic systems, while pioneering innovations in plastics recycling. His recent projects involve high-throughput plastic sorting techniques using molecular vibrational signatures and nanoscale defect optimization in superconducting wires. Advising & Industry: He co-founded TapeSolar Inc. and TexMat LLC, demonstrating expertise in venture capital engagement and technical project management. His RENEW Institute has attracted multidisciplinary collaborations across six schools at UB, advancing solutions for global sustainability challenges. Labs & Initiatives: Leads the NYS Center for Plastics Recycling & Innovation and oversees the UB Initiative on Plastics Recycling, integrating engineering, economics, and communication strategies to address plastic waste challenges.
Dr. Matt Bonney is a Lecturer in Space Engineering at Swansea University, affiliated with the School of Aerospace, Civil, Electrical and Mechanical Engineering. He holds a position in the Department of Aerospace Engineering and is actively involved in postgraduate supervision. His research focuses on digital twin technology, nonlinear structural dynamics, mechanical joint modeling, seismic reliability, and uncertainty quantification, with recent emphasis on digital twin security and thermo-mechanical coupling in assembled structures. Dr. Bonney's expertise spans multi-physics joint modeling and multi-disciplinary development of digital twins, with international collaborations. He teaches modules such as 'Advanced Space Systems' (EG-M334) and 'Aerospace Systems' (EGA220), emphasizing space system design, orbital mechanics, and cyber-physical security. His research highlights include the development of a Python Flask-based digital twin operational platform, contextualization of information in digital twin processes, and experimental studies on frictional interfaces. His work on uncertainty quantification and seismic reliability has applications in nuclear reactor systems and civil engineering structures. Dr. Bonney currently supervises a PhD student focusing on nonlinearities in thermal-mechanical joints. His research outputs include over 30 peer-reviewed publications, with contributions to journals like Mechanical Systems and Signal Processing and Data-Centric Engineering .
Dr. Jonathan Hu is a Professor in the Department of Electrical and Computer Engineering at Baylor University's School of Engineering and Computer Science. He holds a PhD from the University of Maryland Baltimore County (2008) and completed a postdoctoral fellowship at Princeton University (2009–2011). He is an active researcher in optics and photonics, leading the Photonics Research Laboratory and advising both graduate and undergraduate research assistants. Research Interests: Nanophotonics and metamaterials for photovoltaic and biomedical applications Mid-IR supercontinuum generation using chalcogenide photonic crystal fibers 2D materials such as graphene and their alignment via magnetic fields Coherent optical communication and quantum optical Fredkin gates Numerical simulation of electromagnetic problems and leaky mode analysis His recent publications (2019–2024) demonstrate a strong focus on quantum plasmonics, specialty optical fibers, optofluidics, and nonlinear optical phenomena, with high-impact work in journals like Science Advances , ACS Photonics , and Advanced Materials . The research shows a clear trend toward integrating photonics with 2D materials and quantum systems, with applications in sensing, communication, and materials characterization. Scientific Awards and Recognition: 35 Baylor faculty named among top 2% most cited researchers (2023) Editor’s Pick, Journal of Applied Physics (2018) Top three downloads in OSA journals for three consecutive months (2009) NSF Graduate Research Fellowship (awarded to advisee) Chinese Government Award for Outstanding Self-Financed Students Abroad (awarded to advisee) Second Place in FiO + LS Student Competition (awarded to advisee) Advising and Grants: Dr. Hu actively mentors students at all levels, with current graduate research assistants including Wei Zhang, Zhihao Hu, and Sterling Walzel. His lab is supported by external funding, though specific grants are not detailed in the text. He has advised PhD students such as Joshua Young, Chao Niu, and Chengli Wei, many of whom have gone on to successful academic and industry careers. His teaching includes core courses like EGR 1302, ELC 2320, and ELC 4320, as well as advanced topics in computational photonics and integrated photonics. Labs and Teams: He leads the Photonics Research Laboratory at Baylor University, located at the BRIC facility. He is also involved with the Baylor University Optica Student Chapter, promoting optics outreach and networking among students and researchers.
Andrea Iannelli is a Tenure-Track Assistant Professor at the Institute for Systems Theory and Automatic Control (IST) , University of Stuttgart, Germany. He also serves as a faculty member of the International Max Planck Research School for Intelligent Systems (IMPRS-IS) and participates in the Cluster of Excellence Data-Integrated Simulation Science (SimTech) . His research focuses on reconciling model-based and data-driven approaches for robust and adaptive control of uncertain dynamical systems. Ph.D. : Control and Dynamical Systems, University of Bristol (UK), 2019 Postdoctoral Researcher : ETH Zürich (Switzerland), 2019–2022 Harnessing the intersection of control theory, optimization, and machine learning , Iannelli’s work addresses data-driven modeling, uncertainty quantification, and robust control with applications in energy systems, intelligent transportation, and industry 4.0 . His recent publications highlight trends in LPV frameworks, online convex optimization, and hybrid control systems , emphasizing safety and efficiency. He contributes to the academic community as an Associate Editor for the International Journal of Robust and Nonlinear Control and as a member of international conference IPCs. His group, Trustworthy Autonomy for Smart Adaptive Systems (TASAS) , mentors PhD students in projects spanning adaptive control, uncertainty quantification, and reinforcement learning .
Will Smith is a Professor in Computer Vision at the University of York, leading the Vision, Graphics and Learning (VGL) research group. He previously held a Royal Academy of Engineering/The Leverhulme Trust Senior Research Fellow (2019-2020) and serves as Associate Editor of Pattern Recognition . PhD in Computer Vision (2007) and BSc in Computer Science (2002), both from University of York His research bridges computer vision, graphics, and machine learning, focusing on physics-based 3D vision , shape/appearance modeling , and statistical/machine learning applications in areas like face/body analysis, surveying, object capture, and inverse rendering. Methodologically, he works with convex/nonlinear optimization, manifold learning, and computational geometry. Recent publications emphasize neural rendering (ECCV 2024), document symbol detection (ICDAR 2023), and rotation-equivariant spherical neural fields (NeurIPS 2022). These works reflect trends in 3D-aware machine learning, outdoor scene modeling, and geometrically constrained optimization. Royal Academy of Engineering/The Leverhulme Trust Senior Research Fellow (2019-2020) Associate Editor, Pattern Recognition (2019–Present) Smith supervises nine PhD students including Evgenii Kashin, James Gardner, and Tejas Pandey. He has participated in numerous service roles: Area Chair for ICCV 2023, Programme Chair for BMVC 2020, and long-term reviewer for CVPR/ICCV/ECCV conferences since 2008. His lab engages in projects like Branching Out (historic tree mapping) and Google Daydream collaborations on VR/AR head modeling.
Assoc. Prof. Dr. Ayhan Gün is an Associate Professor in the Department of Electrical and Electronics Engineering at Kütahya Dumlupınar University's Faculty of Engineering. With a career spanning over two decades, he has held various academic positions including Research Assistant, Assistant Professor, and currently Associate Professor since 2024. His extensive administrative experience includes serving as Head of the Control and Command Systems Department (2007-2021) and various leadership roles in university-industry collaboration initiatives. Dr. Gün completed his Bachelor's degree at Near East University (1991-1996), Master's at Dumlupınar University (1998-2001), and PhD at Eskişehir Osmangazi University (2001-2007). His research focuses on control systems, mathematical modeling, artificial neural networks, robotics, SCADA, PLC programming, electromechanical systems, nonlinear control, fuzzy logic, optimization techniques, automation, biomechanics, and mechatronics. His recent publications demonstrate a consistent research trajectory in control engineering, with particular emphasis on optimization algorithms applied to quadrotor control, inverted pendulum systems, and electrical motor design. His work bridges theoretical control concepts with practical implementations in robotics and power systems. A significant portion of his research involves applying swarm intelligence and evolutionary algorithms to solve complex control problems. Bilim, Sanayi ve Teknoloji Bakanlığı Kurumsal Kapasitenin Arttırılması (2016) BİLİM SANAYİ VE TEKNOLOJİ BAKANLIĞI Çift Beslemeli İndüksiyon Generatörü Tasarımı ve İmalatı (2016) Dr. Gün has supervised multiple graduate students and managed numerous research projects, including the current 'Robotic Arm Design and Implementation for Patients with Hemiparetic Arms' project. His external roles include serving as an expert witness for judicial institutions, project referee for TÜBİTAK, and publication reviewer for IEEE Transactions. He has also contributed to regional development through his work with Kütahya Governorship's Planning and Development Board.
Kevin John Grimm is a Professor in the Department of Psychology at Arizona State University (ASU), serving as Director of Operations and Research within the College of Health Solutions. He holds a B.A. in Mathematics and Psychology from Gettysburg College (2000), and M.A. (2003) and Ph.D. (2006) in Psychology from the University of Virginia. Previously, he served as faculty at UC Davis before joining ASU in 2014. His research focuses on multivariate methods for analyzing developmental change, including nonlinear growth modeling, latent class analysis, and integrating machine learning with psychological data. Notable contributions include co-authoring the textbook *Growth Modeling: Structural Equation and Multilevel Modeling Approaches* (Guilford Press, 2017). Teaches courses like Structural Equation Modeling, Longitudinal Growth Modeling, and Machine Learning in Psychology at ASU. Active in professional service: Associate Editor of *Structural Equation Modeling: A Multidisciplinary Journal* since 2012. Recipient of NIH/NIDA grants for drug abuse/HIV prevention research and NICHD-funded studies on sleep health in children. His methodological work bridges quantitative innovation with substantive developmental research, emphasizing rigorous model specification and cross-disciplinary applications.
Murat Arcak is a Professor of Electrical Engineering and Computer Sciences and Mechanical Engineering at the University of California, Berkeley, holding the Robert M. Saunders Endowed Chair in the College of Engineering. His research spans control theory, autonomous systems, and multi-agent systems with applications in transportation, energy, and biology. Dr. Arcak received his Ph.D. in Electrical Engineering from the University of California, Santa Barbara in 2000, following an M.S. from the same institution in 1997 and a B.S. from Bogazici University in Istanbul, Turkey in 1996. His research interests focus on developing scalable control design and verification methods for complex systems with many interconnected components, nonlinear dynamics, and learning capabilities. He has made significant contributions to control theory, particularly in areas like reachability analysis, dissipative systems, and compositional verification methods. His work bridges theoretical advances with practical applications in transportation systems, energy networks, and biological systems. A leading researcher in control systems, Dr. Arcak's recent publications demonstrate a strong focus on data-driven approaches for system verification, synthetic biology applications, and formal methods for traffic control. His research combines mathematical rigor with practical implementation, often developing novel theoretical frameworks that address real-world engineering challenges. CAREER Award from the National Science Foundation (2003) Donald P. Eckman Award from the American Automatic Control Council (2006) Control and Systems Theory Prize from SIAM (2007) Antonio Ruberti Young Researcher Prize from IEEE Control Systems Society (2014) Brockett-Willems Outstanding Paper Award (2021) IFAC Fellow (2020) IFAC Automatica Paper Prize (2020) CSS Transactions on Control of Network Systems Outstanding Paper Award (2017) Electrical Engineering Award for Outstanding Teaching (2014) CSS Antonio Ruberti Young Researcher Prize (2014) IEEE Fellow (2012) SIAM Activity Group Control and Systems Theory Prize (2007) Dr. Arcak has advised numerous graduate students and postdoctoral researchers, though specific names are not listed in the available information. His research has been supported by various grants from the National Science Foundation and other funding agencies, enabling his work on control theory and applications across multiple domains. He is affiliated with several research centers at UC Berkeley including the Berkeley Artificial Intelligence Research Lab (BAIR), Berkeley Deep Drive (BDD), the Center for the Theoretical Foundations of Learning, Inference, Information, Intelligence, Mathematics and Microeconomics at Berkeley (CLIMB), the Institute of Transportation Studies (ITS), and Partners for Advanced Transit and Highways (PATH).
Claudia Ceci is a Full Professor at the Department of Methods and Models for Economy, Territory, and Finance (MEMOTEF) at Sapienza University of Rome. Her academic work focuses on stochastic models in economics, finance, and insurance, with a particular emphasis on optimal stochastic control, filtering, asset pricing, credit risk, and self-protection strategies. Coordinates internationalization initiatives within MEMOTEF Leads the Rome Sapienza unit in the 2022 PRIN project "Stochastic control and games and the role of information" Manages the 2023 Grande Sapienza project "Stochastic Optimization Problems in Insurance, Finance and Economics" Member of the UMI-PRISMA group (Probability in Statistics, Mathematics, and Applications) Featured in the "100 Esperte STEM" initiative (Mathematics category) Her research explores risk management, counterparty credit risk, and reinsurance optimization under partial observation. She has contributed extensively to journals in quantitative finance, insurance mathematics, and stochastic control. Recent projects analyze climate-related financial risks and reinsurance strategies under contagion models. Claudia teaches foundational mathematics and risk management courses across multiple campuses. Exam procedures for her mathematics course involve computerized written tests with mandatory oral components under specific conditions, reflecting her analytical approach to assessment.
