Shiyu Chang is an Associate Professor in the Department of Computer Science at the University of California, Santa Barbara , focusing on machine learning with applications in natural language processing and computer vision . He previously worked as a research scientist at the MIT-IBM Watson AI Lab alongside Prof. Regina Barzilay and Prof. Tommi Jaakkola, and earned both his B.S. and Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign , advised by Prof. Thomas S. Huang. Education : PhD, University of Illinois at Urbana-Champaign BS, University of Illinois at Urbana-Champaign His research centers on enhancing AI systems through human-AI interaction , aiming to improve interpretability , transferability , and adversarial robustness in LLMs. Recent work includes LLM watermarking defense , uncertainty decomposition , and self-denoised smoothing for model robustness. His publications span premier venues like ICML , NeurIPS , CVPR , and ACL , with recurring themes in diffusion models , LLM optimization , and ethical AI (e.g., hallucination detection, unlearning frameworks). He actively mentors students, several of whom are marked as advisees (☆) in his publications.
Ron Dror is the Cheriton Family Professor of Computer Science at the Stanford Artificial Intelligence Lab , with courtesy appointments in Structural Biology and Molecular & Cellular Physiology . He also holds affiliations with Bio-X, the Institute for Human-Centered Artificial Intelligence (HAI), the Institute for Computational and Mathematical Engineering (ICME), Sarafan ChEM-H, and the Wu Tsai Neurosciences Institute. Education: PhD in Electrical Engineering and Computer Science, MIT MPhil in Biological Sciences, University of Cambridge (Churchill Scholar) BS in Mathematics and Electrical & Computer Engineering, Rice University (summa cum laude) Ron leads a multidisciplinary research group that combines molecular simulation and machine learning to study biomolecular structure, dynamics, and function. His work focuses on developing computational methods to accelerate drug discovery by predicting molecular interactions and designing more effective therapeutics. Current projects include the PENSA software library for analyzing biomolecular ensembles and FRAME framework for structure-based ligand design. His research has produced groundbreaking work on G-protein-coupled receptors (GPCRs) , RNA structure prediction , and mitochondrial transport mechanisms . Key publications highlight applications of geometric deep learning and molecular dynamics simulations in structural biology. Scientific Awards: Cheriton Family Professorship (2023) Two Gordon Bell Prizes (2014, 2009) Best Paper Awards at NeurIPS (2021), IPDPS (2013), SC11 (2011), SC09 (2009), SC06 (2006) Science Magazine Top 10 Breakthrough (2010) Fulbright Scholarship , NSF Fellowship , DoD Fellowship , Whitaker Foundation Fellowship Ron has advised numerous doctoral and master’s students including EJ Fine , Masha Karelina , and Briana Sobecks . His lab collaborates with experimentalists across academia and industry, applying computational methods to diverse biomedical problems such as RNA structure prediction , GPCR signaling , and mitochondrial metabolism .
Associate Professor Wenhua Zhao is a globally recognized expert in offshore hydrodynamics and renewable energy technologies at The University of Queensland , School of Civil Engineering. With over 110 publications and 30 million AUD in secured research funding, his work bridges theoretical and practical advancements in marine engineering. Research focuses on Clean Energy , Artificial Intelligence , and Climate Change , specifically floating wind energy, floating solar, offshore aquaculture, and green hydrogen production. His 15 most recent articles (2024-2025) emphasize wave-structure interactions, gap resonance dynamics, and AI-driven wave prediction, published in top journals like Journal of Fluid Mechanics and Ocean Engineering . Scientific awards include the prestigious ARC Future Fellowship (2024-2028) and DECRA Fellowship (2019-2022) , recognizing his contributions to academia and industry. He teaches the 'Design of Offshore Energy Systems' course , training hundreds of students in coastal and ocean engineering, and serves as Deputy Editor for Ocean Engineering and Associate Editor for ASME's Journal of OMAE . Available for research supervision, Zhao actively collaborates with editorial boards of Applied Ocean Research and other Q1 journals.
Prof. Dr. Sebastian Steinhorst is an Associate Professor (W3-level with tenure) at the Technical University of Munich (TUM) within the Embedded Systems and Internet of Things group at the TUM School of Computation, Information and Technology . His research focuses on advancing the security, predictability, reliability, and interoperability of smart connected and autonomous systems, particularly for applications in Internet of Things (IoT) , Industry 4.0 , and automotive systems . PhD in Computer Science (2011) from Goethe University Frankfurt Postdoctoral roles at TUMCREATE Singapore (2011-2016) and Aarhus University (2016) Joined TUM in 2016 as Rudolf Moessbauer Tenure Track Professor His research areas include decentralized embedded systems, hardware/software co-design, modeling and verification of cyber-physical systems, security protocols for automotive networks, and time-sensitive networking (TSN) for industrial applications. Recent work explores blockchain-based data sovereignty, zero-knowledge proofs for vehicle authentication, and resilient architectures for autonomous systems. Key scientific contributions include the 2019 ACM TODAES Best Paper Award and pioneering work on CyberSecDome , LeapChain , and Simutack frameworks. He serves on editorial boards and conference committees, including co-organizing the Autonomous Systems Design initiative at DATE. His teaching portfolio spans lectures on System Design for IoT , Software Architecture for Distributed Systems , and IoT Security across multiple semesters. He also leads advanced seminars on embedded systems and IoT.
Geert Deconinck is a full professor at KU Leuven , leading the Electrical Energy Systems and Applications (ELECTA) research group within the Department of Electrical Engineering (ESAT). He also serves as scientific leader of the EnergyVille research center's algorithms domain, focusing on smart electrical networks and thermal systems. M.Sc. and Ph.D. from KU Leuven Head of ELECTA since 2012 (10 professors, 8 postdocs, 70+ PhDs) Over 8 million EUR research budget in last 5 years 44 completed PhDs and 10 current advisees IEEE Transactions editorial board member His research spans smart grid architectures , distributed control , and cyber-physical security , with recent focus on EV-grid integration , renewable energy democratization , and multi-carrier energy systems . Current projects include: Smart Charging - E-Mobility meets Renewable Energy Early Detection and Defense Systems for Smart Grids Open-source P2P energy sharing platforms Microgrid control strategies for PV-battery systems Awarded IET Fellow and IEEE Senior Member status, his work combines machine learning with power systems engineering through both theoretical modeling and experimental validation . He has contributed over 575 publications with 9800+ Google Scholar citations.
R. Edwin García is a Professor at the School of Materials Engineering at Purdue University, where he has been faculty since 2005. He holds appointments in the Materials Engineering department within Purdue's College of Engineering, specifically in the School of Materials Engineering located in the Neil Armstrong Hall of Engineering at Purdue's West Lafayette campus. His educational background includes: B.S. in Physics from the National University of Mexico (1996) M.S. in Materials Science and Engineering from Massachusetts Institute of Technology (2000) Ph.D. in Materials Science and Engineering with a minor in Applied Mathematics from Massachusetts Institute of Technology (2003) Professor García's research focuses on the design of materials and devices through the development of a fundamental understanding of the solid state physics of individual phases, their short and long range interactions, and associated microstructural properties and time evolution. His current research emphasizes establishing relationships between material properties and resultant performance and degradation in electrochemical systems. He integrates computational approaches ranging from kinetic Monte Carlo, phase field and level set methods, to finite elements, finite volumes, and symbolic computing. His work particularly addresses microstructure design, crystallographic texture, and grain boundary science and engineering to control the topology of underlying phases and establish practical relations between processing, microstructure, and material properties. His recent publications demonstrate a strong focus on lithium-ion battery technology, ferroelectric materials, and computational modeling of material behaviors. The research trends show increasing integration of machine learning with traditional computational methods, exploration of novel sintering techniques like flash sintering, and deeper investigation into the fundamental mechanisms of material degradation in energy storage systems. His work spans multiple length scales from atomistic to continuum modeling, reflecting a comprehensive approach to materials design and analysis. Professor García teaches several courses including MSE 230 (Structure and Properties of Materials), MSE 350 (Thermodynamics of Materials), MSE 597G (Modeling and Simulation of Materials), MSE 597I (Introduction to Computational Materials), and MSE 597N (Physical Properties of Crystals). He mentors graduate students in areas related to computational materials science, battery technology, and microstructural evolution. His research group, the Laboratory of Computational Microstructures, focuses on developing home-grown analytical theories and algorithms to resolve relevant time and length scales in materials systems. The group's work has significant implications for portable power sources, including rechargeable batteries and fuel cells, as well as for ferroelectric ceramic applications.
Yiping Lu is an Assistant Professor in the Department of Industrial Engineering and Management Sciences at Northwestern University's McCormick School of Engineering. His research focuses on developing interdisciplinary approaches combining domain knowledge (differential equations, stochastic processes), machine learning, and experiments. Key interests include scientific machine learning (AI4Science), stochastic simulation, and robust machine learning. Education: Ph.D. in Applied and Computational Mathematics, Stanford University (2023) B.S. in Computational Mathematics, Peking University (2019) Research Highlights: Hybrid research integrating ML with scientific domains like PDEs and inverse problems Development of Physics-Informed Learning frameworks Contributions to deep learning theory (ResNets, neural collapse) Advances in kernel operator learning and adversarial robustness Awards: CPAL Rising Star Award (2024) University of Chicago Data Science Rising Star (2022) Stanford Interdisciplinary Graduate Fellowship (2021) Labs/Teams: SCALE Lab (Scientific Computation and Learning at Northwestern) Collaborations with NYU's Courant Institute and Stanford
Professor Paul D. Sclavounos is a faculty member in the Department of Mechanical Engineering at the Massachusetts Institute of Technology (MIT). He earned his B.Sc. from the National Technical University of Athens in 1977 and Ph.D. from MIT in 1981. Research Interests : Marine hydrodynamics, stochastic control, offshore wind/wave/tidal/solar energy, machine learning applications, magnetohydrodynamic propulsion systems. Notable Contributions : Development of computational tools like SWAN and SML software suites; analysis of nonlinear wave dynamics; integration of AI/ML in marine hydrodynamics. Scientific Recognition : First Prize in National Mathematics Competition (1972), Georg Weinblum Memorial Lecturer (2010-2011), Best Paper Award at OMAE 2019, AEOLOS Scientific Award (2024). Leadership : Director of the Laboratory for Ship and Platform Flows since 1985; advisory roles for US Navy, US Department of Energy, and Det Norske Veritas (DNV). Teaching : Courses in Hydrodynamics (2.016), Advanced Fluid Mechanics (2.25), and Naval Architecture (2.701).
Prof. Liam Murphy is a Full Professor of Computer Science & Informatics at University College Dublin (UCD) and Director of the Performance Engineering Laboratory. He holds a B.E. from UCD, M.Sc. and Ph.D. from UC Berkeley. His research focuses on performance engineering of networks, software systems, and multimedia transmissions. He has published over 150 peer-reviewed papers and is an IEEE member and Fellow of the Irish Computer Society. Education: B.E. in Electrical Engineering, UCD (1985) M.Sc. & Ph.D. in Electrical Engineering & Computer Sciences, UC Berkeley (1988, 1992) Research Interests: Dynamic resource allocation in networks Cloud computing efficiency Software performance engineering Wireless multimedia systems Quality of Service (QoS) optimization Recent work emphasizes energy-efficient cloud workflows, multi-objective data center optimization, and decentralized traffic simulation. Grants & Awards: Fellow of the Irish Computer Society (2007) Conference Paper Awards (2004, 2002, 2001) Principal Investigator in multiple funded projects (e.g., EU-funded traffic simulation, cloud resource allocation) Advising & Labs: Directed 24 Ph.D. and 8 M.Sc. students. Leads the Performance Engineering Laboratory (PEL), focusing on distributed systems, cloud efficiency, and network performance. Collaborates on industry-relevant projects like crovan (UCD/DCU campus company). Teaching: Coordinates courses on computer science fundamentals, distributed systems performance, and software engineering at UCD.
Dr Sue Caton is a Senior Lecturer in the Department of Social Care and Social Work at Manchester Metropolitan University, within the Faculty of Health and Education. Her research focuses on social and health inequalities affecting people with intellectual disabilities, particularly digital inclusion, mental health medication decision-making, and pandemic impacts. She leads projects such as Digital Lifeline (NIHR-funded), Medications and My Mental Health (NIHR RfPB), and co-leads Our Digital Health (NIHR RfSC), emphasizing co-produced methodologies. Research Projects: Digital Lifeline: Evaluating tablet impact on social connections post-pandemic. Medications and My Mental Health: Empowering informed medication decisions for people with learning disabilities. Our Digital Health: Assessing digital health participation barriers. Key Expertise: Qualitative research, health inequalities, inclusive research design, and participatory methodologies. Her work addresses pandemic-related challenges faced by marginalized groups, including access to healthcare and social support. She has supervised four completed PhD students and currently mentors four PhD candidates exploring topics such as digital inclusion, animal-assisted interventions, and familial perspectives in parenting support. Dr Caton has led evaluations for initiatives like the Shared Lives 16+ project and the Us Too project on domestic abuse, demonstrating expertise in policy-informed research. Her contributions include over 50 peer-reviewed articles, focusing on digital participation, mental health, and pandemic resilience among people with intellectual disabilities. Awards: No specific scientific awards mentioned, though her work is funded by prestigious bodies like NIHR and UKRI. Labs/Teams: Collaborates with interdisciplinary teams including Dudley Voices for Choice, Liverpool John Moores University, and the Universities of Dundee, Warwick, and Birmingham City.
Farhad Pourkamali Anaraki is an Assistant Professor in the Department of Mathematical and Statistical Sciences at the University of Colorado Denver, part of the College of Liberal Arts and Sciences. His research focuses on Machine Learning, Data Science, and Computational Mathematics, with interdisciplinary applications in engineering, materials science, and uncertainty quantification. He specializes in developing data-driven methodologies for complex systems, including composite materials, seismic response prediction, and additive manufacturing. Key research themes include probabilistic neural networks, adaptive machine learning for sparse data, and computational techniques for engineering challenges. His work integrates advanced algorithms with domain-specific problems, such as optimizing material properties and enhancing predictive models in civil and mechanical engineering contexts. Despite his prolific publication record, no specific scientific awards or grants are explicitly listed in the provided information. He maintains an active profile in teaching and mentoring, though formal advisee details are not documented here.
Dr. Carolyn Conner Seepersad is a Woodruff Professor in the George W. Woodruff School of Mechanical Engineering at Georgia Institute of Technology. She leads the Digital Design and Manufacturing research group and previously founded the Center for Additive Manufacturing and Design Innovation at The University of Texas at Austin. Her research focuses on additive manufacturing, materials design, and process innovation. She holds editorial roles, including Editor-in-Chief of the ASME Journal of Mechanical Design, and has received numerous awards for research and teaching. Education: PhD, Mechanical Engineering, Georgia Tech, 2004 MS, Mechanical Engineering, Georgia Tech, 2001 BA, Philosophy, Politics, and Economics, Oxford University, 1998 BS, Mechanical Engineering, West Virginia University, 1996 Her research interests span design for additive manufacturing, simulation-based materials and structures, and metamaterials. She emphasizes manufacturing-aware design and sustainability. Key contributions include lattice structure optimization, negative stiffness composites, and process-aware manufacturing techniques. Her publications reflect advancements in additive manufacturing processes, materials characterization, and design methodologies. Awards include the ASME Design Automation Award and recognition as a University of Texas System Academy of Distinguished Teachers. Seepersad has advised on grants such as the LEAP-HI GOALI project and contributed to initiatives like the Solid Freeform Fabrication Symposium. Her work bridges academia and industry, emphasizing practical applications and innovation. Labs/Teams: Leads the Digital Design and Manufacturing group at Georgia Tech, previously directed the UT Austin Additive Manufacturing Center.
Andrea Liu is the Hepburn Professor of Physics at the University of Pennsylvania, leading the Department of Physics and Astronomy. As Director of the Penn Center for Soft and Living Matter, she bridges physics, biology, and materials science. She joined Penn in 2004 after faculty roles at UCLA (1994-2004) and postdoctoral research at Exxon and UCSB. Her research focuses on theoretical studies of soft and living matter, particularly jamming transitions, glass physics, and emergent phenomena in biological systems. She pioneers the application of machine learning to physical systems, designing self-learning materials and circuits. Education Ph.D., Cornell University (1989) B.A., University of California, Berkeley (1984) Research Interests Soft matter: Glass transition, jamming, and plasticity in disordered solids Living matter: Collective behavior in tissues, fluidization mechanisms, and biopolymer networks Machine learning: Physical implementations, energy-efficient circuits, and adaptive systems Her work combines analytical theory and computation to explain how complex systems achieve functionality through structural and dynamical principles. Publications Trends Recent work emphasizes physical learning networks, clogging dynamics in granular systems, and biophysical tissue mechanics. Key themes include emergent learning in analog systems, topology-driven material design, and interdisciplinary approaches to biological and engineering challenges. Awards 2025 American Physical Society Leo P. Kadanoff Prize 2021-2025 Simons Investigator in Theoretical Physics Member, National Academy of Sciences (2017) Labs & Teams Her research group collaborates on the Center for Soft and Living Matter, advancing theoretical frameworks for adaptive materials and biological systems. Ongoing initiatives focus on machine learning-informed materials design and experimental validation of theoretical models.
Anna Choromanska is an Associate Professor in the Department of Electrical and Computer Engineering at NYU Tandon School of Engineering, with affiliations to NYU Center for Data Science (CDS), NYU Center for Urban Science and Progress (CUSP), NYU Center for Advanced Technology in Communications (CATT), and the C2SMART Center. Her research focuses on deep learning optimization, generalization, and applications in autonomous driving and large-scale data analysis. She holds an Alfred P. Sloan Fellowship and NSF CAREER Award, and her work impacts industries like NVIDIA and Facebook. She directs the Learning Systems Laboratory (LSL), emphasizing interdisciplinary experimental/theoretical work. Research Interests: Machine Learning fundamentals, DL optimization, continual learning, autonomous vehicle systems, large data analysis. Her lab explores DNN learning dynamics, training architecture design, and scalable algorithms. Professional Impact: Over 50 invited talks, workshop organization for top ML conferences, and contributions to open-source projects like Vowpal Wabbit. Her algorithms are deployed in production systems at Facebook and Baidu. Awards: NSF CAREER Award Alfred P. Sloan Fellowship IBM Global University Program Academic Award (2x) Columbia University Presidential Fellowship Advising & Labs: Leads LSL, supervising interdisciplinary projects in optimization and autonomy. Actively involved in NYU's Modern AI seminar series and industry partnerships through CATT. Personal Interests: Accomplished pianist, salsa dancer, and fashion design enthusiast with notable performances and certifications in dance and music.
Prof. Martin Haenggi is the Frank M. Freimann Professor of Electrical Engineering and Concurrent Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame. He holds a Dr.sc.techn. (Ph.D.) from ETH Zurich and has been at Notre Dame since 2000. His research focuses on stochastic geometry and wireless networks, including cellular, heterogeneous, vehicular, and millimeter-wave systems. He has held sabbaticals at UCSD (2007–2008), EPFL (2014–2015), and ETH Zurich (2021–2022). Education: Dipl.-Ing. (M.Sc.), ETH Zurich, 1995 Dr.sc.techn. (Ph.D.), ETH Zurich, 1999 Research interests emphasize stochastic geometry for analyzing network performance, including coverage, interference, and reliability in wireless systems. Key areas include meta distributions, spatial-temporal analysis, and network optimization. His work has been recognized with IEEE Fellow status, Clarivate Highly Cited Researcher distinction, and NSF CAREER Award (2005). Grants and Awards: NSF Award (Deep Stochastic Geometry: 2020–2023) NSF Award (Toward a Stochastic Geometry for Cellular Systems: 2015–2019) Rice Prize (2017), Best Survey Paper Award (2017), and Best Tutorial Paper Award (2010) from IEEE Communications Society Teaching includes advanced courses on stochastic geometry, wireless networks, and signal processing. His lab focuses on theoretical and applied aspects of network modeling, with collaborations in industry and academia.
