Pierre Baldi is a Distinguished Professor of Computer Science and Director of the Institute for Genomics and Bioinformatics at the University of California, Irvine (UCI). He is affiliated with the Donald Bren School of Information and Computer Sciences. His research spans artificial intelligence, machine learning, bioinformatics, and communication networks, with notable projects in protein structure prediction, gene expression modeling, and neutrino physics collaborations like DUNE. Baldi’s work bridges theoretical foundations (e.g., neural network theory) and applied domains, including medical imaging and fusion technology. Key research interests include AI-driven biomedical applications, neural network theory, and interdisciplinary projects such as the DUNE neutrino experiment. His contributions to neural network engineering were recognized with the 2023 INNS Dennis Gabor Award, highlighting his paradigm-changing impact on computational neuroscience and physics. Baldi’s academic leadership includes directing UCI’s Institute for Genomics and Bioinformatics, fostering collaborations in computational biology and AI. His recent work explores AI’s role in healthcare, climate modeling (e.g., ClimSim-Online), and fundamental physics challenges like neutrino oscillation studies.
Onur Varol is an Assistant Professor at Sabanci University's Computer Science Department and leads the VIRAL Lab, which focuses on computational social science, network science, and machine learning. He has affiliations with the Center of Excellence for Data Analytics. His research spans social bot detection, misinformation analysis, and online behavior modeling.
Lizi Liao is an Assistant Professor at the School of Computing and Information Systems , Singapore Management University (SMU) , specializing in Artificial Intelligence and Conversational AI . Her research bridges Machine Learning , Natural Language Processing , and Multimodal Systems , focusing on proactive dialogue systems, multimodal conversational search, and task-oriented interactions. Education : PhD in Computer Science (2019) from the National University of Singapore (NUS) , advised by Professor Tat-Seng Chua . Research Interests center on principles of human conversational understanding and machine implementation, particularly in proactive conversational agents , multimodal dialogue systems , and target-driven conversation planning . Key applications include emotional support systems , intelligent shopping assistants , and learning companions . Recent Publications (2024-2025) highlight her work on LLM-based proactive dialogue , multimodal emotion recognition , and dynamic graph modeling , often integrating NLP , Multimedia , and Knowledge Graphs . Collaborative projects with her CoAgent Lab team emphasize human-AI interaction and ethical response generation . Scientific Awards : Google South Asia & Southeast Asia Research Award 2023 Lee Kong Chian Fellow Teaching includes Visual Analytics for Business Intelligence (undergraduate) and Text Analytics and Application (graduate). She also serves as Associate Editor for TOIS and TOMM , and organizes tutorials at ACL , SIGIR , and WSDM .
Parv Venkitasubramaniam is a Professor in the Department of Electrical & Computer Engineering at Lehigh University, affiliated with the P.C. Rossin College of Engineering. Previously, he served as a postdoctoral researcher at UC Berkeley under Prof. Venkat Anantharam. His research focuses on theoretical foundations of privacy and security in networks, leveraging statistical signal processing, information theory, and game theory. Key application areas include smart grids, transportation systems, and peer production networks. Education includes a Ph.D. and M.S. in Electrical Engineering from Cornell University, and a B.Tech from the Indian Institute of Technology. His doctoral work concentrated on wireless sensor networks, particularly distributed communication and statistical inference. Research interests span privacy-utility tradeoffs, cybersecurity in control systems, and resilient network design. He explores topics like stealthy attacks on dynamical systems, privacy-aware stochastic games, and resilient energy storage systems. Recent work emphasizes transportation system resilience and cyber-physical system security. His publications address cutting-edge challenges in anonymizing networks, detecting cyber attacks, and optimizing privacy-preserving mechanisms. Notable projects include NSF-funded research on anonymous networking and information-theoretic security frameworks.
Dr. Lisa Wang is a tenure-track Assistant Professor in the Department of Civil & Environmental Engineering at Old Dominion University (ODU). She holds a Ph.D. and Postdoctoral Fellowship in Structural Engineering from Colorado State University (CSU), with additional research at NIST’s Center for Risk-Based Community Resilience Planning. She is a licensed California Professional Engineer (PE) and has expertise in multidisciplinary community resilience assessment, mitigation strategies, and policy analysis. Her research focuses on integrating physical, socio-economic, and infrastructural systems to enhance disaster resilience in coastal and hazard-prone communities. Education: Ph.D. and Postdoc (CSU, 2022-2024), M.S. in Structural Engineering (University of Colorado Denver & Jilin University, 2015), B.S. in Civil Engineering (Jilin University, 2012). Professional licenses include CA PE #94251 and CO EIT #007558. Research Interests: Community resilience under multi-hazards (tornadoes, floods, climate change), resilience-based design of structural systems, data fusion across disciplines, and equitable decision-making for disaster resilience. Recent grants include $31k for AI-driven coastal resilience strategies and $10k for minimum building portfolio development. Grants & Awards: Principal Investigator (PI): Trustworthy AI in Coastal Resilience (ICAR, $31k), AI-Driven Building Portfolios (ODU PURS, $10k) Awards: O. H. Ammann Fellowship (ASCE), ICAR Travel Grant, AGU NSF Travel Grant, Jack E. Cermak Fellowship Labs & Teams: Wang Research Group and Structural Engineering Research Laboratory at ODU.
Gabriel A. Silva is a Professor in the Shu Chien-Gene Lay Department of Bioengineering at UC San Diego’s Jacobs School of Engineering, with a joint appointment as Assistant Professor in Ophthalmology. His research bridges neuroscience, theoretical physics, and applied mathematics to explore how the brain encodes and processes information, leveraging quantum logic and algorithms for advanced neural modeling. University: University of California, San Diego School: Jacobs School of Engineering Department: Shu Chien-Gene Lay Department of Bioengineering Academic Rank: Professor Joint Appointment: Assistant Professor in Ophthalmology Research Interests: Silva focuses on neural computation at cellular and network scales, aiming to abstract biological mechanisms into mathematical models that emulate brain-like processing. His work has implications for understanding neurological disorders, developing neural engineering nanotechnologies, and advancing AI systems through emergent complexity. Recent Article Trends: His publications span quantum-enhanced neural modeling, EEG-based disease detection, nonlinear dynamics in brain networks, and interdisciplinary applications of graph theory. Emerging themes include the integration of category theory for network analysis and AI optimization via emergence-promoting schemes. Labs & Teams: Affiliated with UC San Diego’s Institute of Engineering in Medicine, Silva leads research at the intersection of bioengineering, ophthalmology, and neural systems, fostering collaborations with neuroscience and quantum computing domains.
Kevin P. O'Brien is an Associate Professor in the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT), affiliated with the Research Laboratory of Electronics (RLE). He leads the Quantum Coherent Electronics (QCE) group, focusing on advancing superconducting quantum computing, microwave quantum optics, and quantum metamaterials. His research explores nonlinear and quantum-mechanical light-matter interactions using superconducting circuits, aiming to improve quantum technologies like qubits and amplifiers. Education: B.S. in Physics from Purdue University, Ph.D. in Physics from UC Berkeley, and postdoctoral research at UC Berkeley developing superconducting quantum processors. His group collaborates with MIT Lincoln Laboratory and institutions nationwide. Research Interests: Quantum computing hardware, superconducting circuits, parametric amplifiers, qubit measurement systems, and metamaterials for quantum applications. His work emphasizes scalable architecture design, noise reduction, and novel device concepts. Key projects include directional qubit readout resonators, Floquet-mode amplifiers, and quarton couplers for ultrafast readout. The group actively engages in training graduate students and postdocs, emphasizing open collaboration and problem-solving in quantum technologies. Advising & Grants: Supervises a dynamic team of graduate students and postdocs. Students like Bright Ye and Kaidong Peng have contributed to award-winning projects. The group receives support through fellowships (e.g., Jin Au Kong, NSF GRFP) and industry partnerships. Labs/Teams: Quantum Coherent Electronics Group at MIT, collaborating on quantum device fabrication, theoretical modeling, and experimental validation of quantum systems.
Prof. Dr.-Ing. Hans-Georg Herzog is a Professor of Energy Conversion Technology at the Technical University of Munich (TUM), School of Engineering and Design. He has headed the Energy Conversion Technology group at TUM since 2002 and is a Senior Member of IEEE and member of VDE and VDI professional organizations. His research focuses on energy-efficient electromechanical drives and related technologies critical for modern electric and hybrid vehicles. Prof. Herzog's research interests encompass energy-efficient electromechanical drives, with key expertise in design and optimization of hybrid-electric and battery-electric powertrains, automated design methods for electromechanical actuators, energy and power management systems, and analysis of loss mechanisms in soft magnetic materials. His work bridges fundamental electromagnetic theory with practical automotive applications, particularly in fault-tolerant systems and reliability engineering for electric propulsion. His recent publication trends show a strong focus on vehicular power systems, with particular emphasis on electronic fuses, fault diagnosis in multiphase machines, wireless power transfer, and reliability analysis of electric aircraft propulsion systems. The research spans from fundamental electromagnetic modeling to practical automotive applications, with increasing attention to autonomous driving power requirements and next-generation vehicle electrical architectures. Prize for Good Teaching of the Free State of Bavaria (2010) Prof. Herzog leads a substantial research team including doctoral candidates and postdoctoral researchers who contribute to his extensive publication record. His research group collaborates with automotive industry partners on various grants focused on electric vehicle technology, power system reliability, and advanced electromagnetic systems. The team regularly develops novel methodologies for machine design, fault tolerance analysis, and power system optimization. The research is conducted within TUM's Energy Technology Workshop with specialized facilities for electrical machine testing, power electronics development, and automotive power system simulation. The team maintains strong connections with industry partners in the automotive and aerospace sectors, facilitating technology transfer from academic research to practical applications.
Luigi Acerbi is an Associate Professor in the Department of Computer Science at the University of Helsinki, where he leads the Machine and Human Intelligence research group. He is also an active member of the Finnish Center for Artificial Intelligence (FCAI) and ELLIS (European Laboratory for Learning and Intelligent Systems). His research focuses on probabilistic machine learning and computational neuroscience, particularly on developing efficient methods for statistical inference, Bayesian models of perception, and resource-constrained rationality. His work bridges machine learning and cognitive science, with applications in Bayesian optimization, simulation-based inference, and image completion. The recent publications highlight a strong trend toward unifying probabilistic conditioning across diverse tasks using transformer-based meta-learning frameworks like the Amortized Conditioning Engine (ACE). These works emphasize amortized inference, flexible latent variable modeling, and the integration of prior knowledge at runtime, enabling efficient and scalable Bayesian methods for complex problems. Scientific Affiliations: University of Helsinki, Department of Computer Science Finnish Center for Artificial Intelligence (FCAI) ELLIS (European Laboratory for Learning and Intelligent Systems) Education: PhD in Computational Neuroscience, Doctoral Training Centre, Edinburgh, UK Advisor: Sethu Vijayakumar and Daniel Wolpert Visiting work at Computational and Biological Learning Lab, Cambridge Postdoctoral Experience: Alex Pouget’s lab, University of Geneva, Switzerland Wei Ji Ma, New York University, USA Collaboration with the International Brain Laboratory Luigi Acerbi mentors PhD students including Daolang Huang and Nasrulloh Loka, and collaborates widely with researchers such as Samuel Kaski. He has contributed to open-source tools like PyVBMC and is involved in community initiatives such as the EurIPS conference. His work is supported by grants from the Research Council of Finland, Business Finland, and the UKRI Turing AI World-Leading Researcher Fellowship. He leads a research lab focused on amortized probabilistic inference, with ongoing projects including PriorGuide and Stacked VBMC, aiming to make Bayesian methods more practical and accessible for real-world scientific and engineering applications.
Akihiko Nishimura is an Assistant Professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. He holds a PhD from Duke University (2017) and MS and BS degrees from Stanford University (2011 and 2010). His research focuses on Bayesian methods, statistical computing, and public health data science, with applications in precision medicine and observational health data analytics. PhD, Duke University, 2017 MS, Stanford University, 2011 BS, Stanford University, 2010 Nishimura's research centers on developing advanced statistical and computational methodologies for real-world health data. His work emphasizes Bayesian inference, large-scale computing, and software development for reproducible research. He is particularly interested in using observational health data to improve clinical decision-making and advance precision medicine. He co-leads the Bayesian Learning and Spatio-Temporal modeling group (BLAST Group) and the inHealth/OHDSI Lab , collaborating with clinicians and data scientists across institutions. His recent publications reflect a strong trend in methodological innovation in Monte Carlo methods (e.g., Hamiltonian and Zigzag samplers), scalable Bayesian inference, and applications in pharmacovigilance, diabetes management, and infectious disease modeling. The articles span disciplines including biostatistics, computational statistics, public health, and bioinformatics, demonstrating a consistent focus on high-impact, computationally intensive problems in health data science. Nishimura actively contributes to the scientific community through methodological development and open science. He develops statistical software and shares teaching materials on GitHub, emphasizing reproducibility and performant computing. His involvement in the OHDSI community enables large-scale, multi-institutional studies that would not be feasible with single-source data. His work has been recognized through publications in top-tier journals such as the Journal of the American Statistical Association , Biometrika , and JAMA Ophthalmology , and has been picked up by numerous news outlets and social media platforms, indicating broad scientific and public impact. Nishimura teaches courses on performant statistical computing and advanced Monte Carlo methods, training the next generation of data scientists in efficient algorithm and software design. He mentors students and collaborators in statistical methodology and software development, fostering a culture of rigorous, reproducible, and impactful research.
Patrick Phelan is a Professor and Associate Dean of Graduate Programs at the Ira A. Fulton Schools of Engineering, Arizona State University (ASU). He holds additional roles as a Senior Global Futures Scientist and Editor-in-Chief of Frontiers in Energy Efficiency . His research focuses on sustainable energy systems, thermal management, and energy efficiency, with notable contributions to solar energy, thermal transport processes, and industrial cooling technologies. Phelan has extensive administrative experience, including managing the U.S. Department of Energy’s Emerging Technologies Program and the National Science Foundation’s Thermal Transport Processes Program. Education: Postdoctoral Fellow, Tokyo Institute of Technology (1990–1992) Ph.D., Mechanical Engineering, University of California, Berkeley (1990) M.S., Mechanical Engineering, Massachusetts Institute of Technology (1987) B.S., Mechanical Engineering, Tulane University (1985) Research Interests: Thermal engineering and heat transfer Sustainable energy systems and cooling Energy efficiency in buildings and industry Thermogalvanic systems and advanced materials Decarbonization and community benefit strategies Professional Associations: Fellow, American Society of Mechanical Engineers (ASME) Member, American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Current Activities: Leading the ASU Energy Efficiency Center Contributing to the Energy for Rural Arizona initiative Advancing agricultural cold chain efficiency Teaching courses on heat transfer and energy systems (e.g., MAE 589, MAE 576)
Stephane Cotin is a Research Director at Inria and leader of the MIMESIS team, specializing in real-time physics-based medical simulations. His work focuses on surgical training, planning, and image-guided therapy, with over 200 scientific articles and the development of the open-source SOFA framework. He co-founded InSimo, Twinical, and EVE, and previously held roles at Harvard Medical School and Mitsubishi Electric Research Lab. Cotin’s research bridges imaging, robotics, and medicine to improve healthcare outcomes, emphasizing patient-specific biophysical modeling and real-time computation. His awards include the Academy of Sciences Award (2018) and Dirk Bartz Medical Prize (2015). He has advised numerous PhD students and led projects like MediTwin and PREMYOM, advancing digital twin technologies for precision medicine.
Guangyu Cao is a Professor at the Department of Energy and Process Engineering, Norwegian University of Science and Technology (NTNU). He holds leadership roles in multiple international organizations, including the European standards working group CEN TC156 WG18, REHVA's technical committee, and the editorial board of the Journal of Building Engineering (Impact Factor 5.318). His research focuses on indoor airflow dynamics, ventilation systems in healthcare settings, and airborne disease transmission mitigation, with a strong emphasis on surgical environments and school buildings. Education: PhD in Energy Engineering (2009), Helsinki University of Technology Senior Researcher at VTT Technical Research Center (2009-2014) Research Interests: Hospital ventilation optimization, thermal comfort in clinical settings, airborne infection control, and sustainable building environmental quality. He combines experimental studies with mathematical modeling to evaluate ventilation strategies, particularly in operating rooms and isolation wards. Projects: EU Marie Curie DTN HumanIC (2024–2027) EU H2020 iclimabuilt (2021–2025) NFR POSIred (2020-2024) Labs/Teams: Collaborates with St. Olav's Hospital on indoor environment projects and leads teams in Cold Climate HVAC and Healthy Building Europe initiatives.
Michael Pyrcz is a Professor in the Hildebrand Department of Petroleum and Geosystems Engineering and holds the rank of Associate Professor in the Jackson School of Geosciences at the University of Texas at Austin. He is the recipient of the B. J. Lancaster Professorship in Petroleum Engineering and the George H. Fancher Centennial Teaching Fellowship in Petroleum Engineering. His research focuses on subsurface data analytics, geostatistics, and machine learning applications in energy systems and CO2 sequestration. Pyrcz teaches widely, including through online lectures and GitHub workflows, and has authored over 50 peer-reviewed publications and a textbook on spatial data analytics. His work integrates machine learning with geoscience challenges, such as uncertainty quantification in reservoir modeling and CO2 storage site evaluation. He leads initiatives in energy data analytics through the Freshman Research Initiative and collaborates with industry on workflow development. Key research areas include generative AI for subsurface models, stochastic methods for fracture networks, and anomaly detection in geologic monitoring. Education: Background in petroleum engineering and geosciences (details not explicitly provided). Grants/Advising: Extensive industry collaboration and mentorship roles at Chevron prior to UT Austin. Labs/Teams: Maintains active GitHub repositories (GeostatsGuy), YouTube lecture series (GeostatsGuyLectures), and social media outreach (X/GeostatsGuy).
Dr. Likun Zhu is a Professor of Mechanical Engineering at Purdue University's School of Mechanical Engineering in Indianapolis. His research focuses on advanced battery technologies, including lithium-ion and solid-state batteries, with an emphasis on in situ and operando characterization, modeling, and micro/nano fabrication. Dr. Zhu's work addresses critical challenges in battery energy density, safety, and longevity through innovative materials and manufacturing processes. Education: Ph.D. Mechanical Engineering, University of Maryland (2006); M.S./B.S., Tsinghua University (2001/1998). His lab is affiliated with the Birck Nanotechnology Center and equipped with advanced facilities such as gloveboxes, electrochemical analyzers, and microscopy systems. Recent milestones include securing an NSF grant for solid-state battery research (2023) and advising over 30 graduate students. Research Interests: Solid-state batteries, micro/nano fabrication, operando characterization, and sustainable energy materials. His group develops novel electrode materials and designs for high-performance batteries, leveraging cutting-edge in situ techniques to study dynamic processes during cycling. Grants & Awards: NSF grant (2023) for solid-state battery research. Advising: Notable students include Hua Wang (Ph.D. 2024), Xintong Li, and Tianyi Li. Collaborations include work with Professors Hazim El-Mounayri and Andres Tovar on Bayesian optimization of battery materials. Labs & Facilities: The lab, located at ET 118, houses equipment like Arbin battery cyclers, FIB-SEM systems, and Comsol Multiphysics software. Dr. Zhu teaches courses including ME 330 (Dynamic Systems), ME 509 (Fluid Mechanics), and ME 597 (Renewable Energy).
