Alan Hammond is a Professor in the Department of Statistics at the University of California, Berkeley. His research focuses on rigorous mathematical probability techniques applied to problems in statistical mechanics, including percolation theory, polymer models, and random growth processes. He has contributed to understanding critical phenomena, phase transitions, and universality classes in stochastic systems. Hammond's work spans topics such as KPZ universality, Brownian motion, and the geometry of random media. He has investigated models like last passage percolation, self-avoiding walks, and tug-of-war games, often uncovering deep connections between stochastic processes and nonlinear PDEs. His teaching includes courses on stochastic processes and statistical theory at both graduate and undergraduate levels. Notable research highlights include studies on fractal properties of Airy processes, stability in dynamical last passage percolation, and the behavior of geodesics in random environments. His contributions bridge probability theory with applications in physics and combinatorics.
Giancarlo Ferrari Trecate is an Adjunct Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) , affiliated with the School of Engineering and the SCI-STI-GFT department. He is also involved in teaching and research through the STI-SGM and EDRS-ENS programs. Research Interests : Automatic control, state estimation, system identification, machine learning, distributed control, hybrid systems, microgrids, biochemical networks, voltage and frequency stabilization in AC/DC microgrids. Publications Trends : His recent work focuses on integrating Neural ODEs and Hamiltonian structures for stable control systems, regret minimization in distributed control, and robust state estimation under uncertainty. Applications include autonomous mobility-on-demand , power grid optimization , and secure microgrid control against cyber-attacks. Scientific Awards : No specific awards mentioned in the provided data. Teaching & Advising : He supervises PhD students in mechanical engineering and teaches courses on Multivariable control and Networked control systems . His lab, DECODE , specializes in Dependable Control and Decision systems.
Prof. Dr. Tobias Gemmeke is a University Professor at RWTH Aachen University's Faculty of Electrical Engineering and Information Technology, leading the Chair of Integrated Digital Systems and Circuit Design. His work focuses on neuromorphic computing, hardware accelerators, and energy-efficient electronics. He has pioneered advancements in FPGA-based computational neuroscience simulators, neuromorphic processor architectures, and sensor integration for industrial and medical applications. Research interests include time-domain computing, ReRAM reliability, and co-optimization of neural networks with hardware. Notable contributions include the neuroAIx framework for accelerated neuroscience simulations and energy-efficient ASIC designs for post-quantum cryptography. He actively explores memristive devices and domain generalization techniques for edge computing. Recent publications highlight innovations in spiking neural networks, sensor systems for plain bearings, and time-domain compute-in-memory engines. His work bridges theoretical neuroscience with practical hardware implementations, emphasizing scalability and real-time performance.
Giorgia Ramponi is an Assistant Professor (tenure-track) in Artificial Intelligence for Cyber-Physical Systems at the University of Zurich (UZH), leading the Autonomous Learning and Predictive Intelligence Lab. She holds affiliations with ETH Zurich’s AI Center and Chalmers University of Technology. Previously, she was a postdoctoral researcher at ETH AI Center, sponsored by Google Brain. Her research focuses on machine learning and mathematical modeling, particularly Reinforcement Learning (RL), Multi-Agent Learning, and Imitation Learning. Notable contributions include work on non-cooperative Markov Decision Processes, mean-field games, and batch IRL for multiple intentions. Publications highlight advancements in constrained MDPs, policy optimization, and applications of RL in cyber-physical systems. Awards include the IBM Best Student Award (2016) and a Hassler Research Grant (2024). She advises students on topics ranging from RL theory to social network analysis. Her academic journey includes a PhD (Politecnico di Milano, 2021) and MSc/BSc (La Sapienza, Rome) with honors. She has taught courses on AI, data science, and machine learning, and contributed to open-source projects like GAN_Time_Series.
Zhibo Pang is an Adjunct Professor at KTH Royal Institute of Technology's Department of Intelligent Systems (EECS) and Senior Principal Scientist at ABB Corporate Research Sweden. His work focuses on digital transformation in industry and healthcare, spanning robotics, AI, control systems, and wireless communication. He leads projects in embodied intelligence, Industry 4.0, and Healthcare 4.0, with 23 granted patents and over 120 journal papers. Education: PhD in Electronic and Computer Systems (KTH, 2013), MBA in Innovation & Growth (University of Turku, 2012). Key Roles: IEEE Technical Committee Chair, Editor of 6 IEEE journals, ABB Inventor of the Year (2016, 2018, 2021). Research Interests: Robotics safety, wireless automation, federated learning, digital twins, and IoT security. Recent Projects: Cloud-fog automation frameworks, robot skin systems for healthcare, and latency-aware industrial control. His work bridges academia and industry through cross-functional collaborations.
Behnaam Aazhang is the J.S. Abercrombie Professor of Electrical and Computer Engineering at Rice University and Director of the Rice Neuroengineering Initiative (NEI). He holds a B.S., M.S., and Ph.D. from the University of Illinois at Urbana-Champaign. His roles include leading the multi-university Rice Neuroengineering Initiative and directing the Center for Neuroengineering. He has held an Academy of Finland Distinguished Visiting Professorship (FiDiPro) at the University of Oulu (2006-2014) and received an Honorary Doctorate from the University of Oulu in 2017. Education: Ph.D. in Electrical Engineering, University of Illinois at Urbana-Champaign (1986) M.S. in Electrical Engineering, University of Illinois at Urbana-Champaign (1983) B.S. in Electrical Engineering, University of Illinois at Urbana-Champaign (1981) Research Interests: Dr. Aazhang’s work focuses on signal/data processing, information theory, and neuroengineering applications. Key areas include: Neuronal circuit connectivity and learning impacts Real-time closed-loop neuromodulation for neurological disorders (epilepsy, Parkinson’s, depression) Patient-specific cardiac pacing systems Cybersecurity in cloud computing Awards & Honors: 2022 Rice Outstanding Doctoral Thesis Advisor Award 2019 SIGMOBILE Test of Time Award 2017 Honorary Doctorate (University of Oulu) 2013 IEEE Communication Society Advances in Communication Award AAAS and IEEE Fellowships (2012 and 1999) Grants & Advising: His research is supported by multi-university collaborations and grants. He has advised numerous graduate students in electrical engineering and neuroengineering, though specific names are not listed here. Labs & Teams: Leads the Aazhang Lab and the Rice Neuroengineering Initiative, focusing on translational technologies for neurological and cardiac disorders, including non-invasive neuromodulation and cloud security systems.
Bart Somers is an Associate Professor at Eindhoven University of Technology , affiliated with the Department of Mechanical Engineering . His primary affiliations include the Power & Flow Group and his own research group, Group Somers , alongside cross-cutting roles in EAISI (Eindhoven Artificial Intelligence Systems Institute) and EIRES (Eindhoven Research on Innovation and Sustainability in Energy Systems). He focuses on advancing combustion science , sustainable fuels , and engine efficiency , leveraging computational fluid dynamics (CFD) and experimental methods. His research interests span alternative fuels (hydrogen, bio-oils, biofuels), high-pressure spray combustion , and low-emission engine design . He investigates combustion optimization through CFD tools like large-eddy simulation (LES) and flamelet-generated manifolds (FGM), emphasizing fuel stratification , ignition dynamics , and emission control . His work bridges experimental diagnostics (e.g., spray visualization, OH* chemiluminescence) and numerical modeling. Academically, he teaches courses such as Thermodynamics , Clean Engines and Future Fuels , and Sustainable Vehicles , integrating practical projects into curricula. His educational activities emphasize interdisciplinary sustainability and innovation, including honors programs focused on professional development. Recent publications highlight his contributions to hydrogen injection strategies, biofuel applications in genset engines, and optimization of diesel-biofuel blends. His work aligns with global sustainability goals, addressing energy transition challenges through advanced combustion technologies.
Michael O. Wood is an Associate Professor in the School of Environment, Enterprise and Development (SEED) at the University of Waterloo and currently serves as the Associate Dean of Work-Integrated Learning in the Faculty of Environment. He holds a Ph.D. in Strategy and Sustainability from the Ivey Business School at Western University, an M.A. in Environmental Studies in Sustainability Management from Western University, and a B.A. in Science from the University of Guelph. Ph.D. in Strategy and Sustainability, Ivey Business School, Western University M.A. in Environmental Studies in Sustainability Management, Western University B.A. in Science, University of Guelph Dr. Wood's research focuses on organizational responses to sustainability challenges through the lenses of space, time, scale, and social license to operate. His work spans industries like insurance, mining, carbon management, waste management, and global security, with a particular emphasis on the Blue Economy and climate change. He has contributed to frameworks for water risk assessment, sustainable packaging practices, and the role of governance in sustainability-financial performance linkages. His recent publications address transdisciplinary tools for water risk management, workforce practices' impact on sustainability, ETS policy effects, and disaster response patterns in multinational corporations. While no scientific awards are explicitly mentioned, his work has been published in journals such as the International Journal of Disaster Risk Reduction and Economic Systems Research . Dr. Wood teaches courses like Introduction to Environment and Business and Environment and Business Project , and he collaborates with researchers like Sandhu, Weber, and Bansal on sustainability-related projects. His studies often incorporate mixed-methods analysis and policy reviews for sustainable resource management in Ontario and Canada.
Necmiye Ozay is an Associate Professor in Robotics and Electrical and Computer Engineering at the University of Michigan. Her research focuses on control systems, formal methods, and cyber-physical systems, with applications in autonomy, system identification, and verification. She leads a diverse research group encompassing PhD, MS, and undergraduate students, as well as postdoctoral researchers. Her work bridges theory and practice, addressing challenges in safety-critical systems design and data-driven control. Her educational background includes a PhD in Electrical and Computer Engineering from Northeastern University and a Master’s from Penn State. She has received significant funding from NSF, ONR, and industry partners, supporting projects like the CLEVR-AI initiative and Scenic ecosystem development. Her research has been recognized through awards and collaborations at institutions like MIT, Berkeley, and Johns Hopkins. Key research areas include model-based control synthesis, robust system identification, and anomaly detection in cyber-physical systems. Notable contributions include methods for correct-by-construction control, hybrid system analysis, and learning-based approaches for autonomous systems. She actively contributes to conferences such as HSCC and CDC, and her lab’s work impacts automotive safety, energy systems, and robotics. Ozay’s advising spans over 50 students and postdocs, many of whom hold academic or industry roles. Her lab maintains active collaborations across disciplines, emphasizing interdisciplinary solutions to real-world control challenges.
Jerelle A. Joseph is an Assistant Professor at Princeton University , affiliated with the Department of Chemical and Biological Engineering and the Omenn-Darling Bioengineering Institute . They also hold associated faculty roles in the Department of Chemistry , Andlinger Center for Energy and the Environment , Princeton Institute for Computational Science and Engineering , and the Biophysics Graduate Program . Research Interests : The Joseph Group investigates the physicochemical principles governing biomolecular condensate formation, dissolution, and misregulation . Their work focuses on phase separation mechanisms , computational modeling of protein-RNA interactions , and engineering condensates for biomedical and sustainability applications , including therapeutic targeting of neurodegenerative diseases and design of synthetic microreactors . Scientific Awards : NIGMS MIRA (R35) Award (2024) Biophysical Society Award Lecture (2024) Chan Zuckerberg Initiative Investigator (2023) Postdoctoral Award, Biophysical Society IDP Subgroup (2022) Rising Star in Soft and Biological Matter (University of Chicago, 2020) Advising : Dr. Joseph advises graduate students including Ananya Chakravarti , Dominic Curtis , and Pablo Garcia . The group develops chemically-specific coarse-grained models using molecular dynamics , Monte Carlo sampling , and machine learning to study condensate microstructure , aging dynamics , and surface electrostatics .
Mohammed Aledhari is an Assistant Professor at the University of North Texas, specializing in cybersecurity, machine learning, and data science. His research focuses on applications in computational medicine, bioinformatics, and autonomous systems. He holds a Ph.D. from Western Michigan University and degrees from the University of Basrah and the University of Anbar. His research interests include social cybersecurity techniques, federated learning in IoT, and AI-driven solutions for healthcare and transportation. Recent work explores blockchain-enabled digital twins, DDoS attack detection, and equitable ASD diagnostics using machine learning. His publications span cybersecurity frameworks, autonomous vehicle communication protocols, and biomedical IoT innovations. Notable contributions include optimizing intrusion detection in IoMT networks and developing interpretable machine learning models for healthcare. While no formal awards or grants are listed, his work emphasizes interdisciplinary applications of AI in healthcare, transportation, and energy markets. His email is Mohammed.Aledhari@unt.edu .
Miles M. Ishigaki serves as Professor of Music in the Department of Music within California State University, Fresno's College of Arts and Humanities. He has taught applied clarinet, chamber music, music theory, and music appreciation at Fresno State since 1987 while directing the university clarinet choir. From 1989-2017, he held the position of state chair for the National Association of College Wind and Percussion Instructors, demonstrating sustained institutional commitment. His academic credentials include: Doctor of Musical Arts in Performance, University of Oklahoma (1988) Doctoral study in Music Performance, University of Arizona (1981-1982) Master of Music in Performance, Colorado State University (1981) Dual Bachelor's degrees: Music Education and Music Performance, University of Northern Colorado (1978) Dr. Ishigaki's research centers on the dialectic between intuition and analytical understanding in musical performance, particularly regarding clarinet repertoire. His seminal work documents interpretive approaches of legendary clarinetists including Larry Combs, Stanley Hasty, and Leon Russianoff, focusing on Stravinsky's Three Pieces for Solo Clarinet. His collaborative research with Dr. Michael Rogers bridges music theory and performance practice, revealing how structural analysis informs expressive execution. This scholarly trajectory consistently examines the cognitive and technical dimensions of artistic mastery. His publications from 1988-2017 reveal evolving emphases from symposium documentation and performance analysis toward technology integration in music education, culminating in the 2017 Music Fundamentals ebook. Collectively, his work establishes clarinet pedagogy as both scientific discipline and artistic practice, with growing attention to digital learning environments while maintaining core focus on interpretive authenticity. His recognitions include: 2013 Provost's Teaching Award in Technology for innovative digital pedagogy 2006 Honorary Alumnus distinction from University of Northern Colorado Dr. Ishigaki has mentored exceptional students who have earned Fulbright Scholarships, McNair Doctoral grants, and participation in national honor ensembles. His studio consistently produces competition winners while he maintains active performance engagements with regional orchestras and international venues. Beyond academia, he serves as COO of I&M, Inc., applying sustainable technology principles to water and energy systems, demonstrating interdisciplinary impact beyond music. As founder of both the West Coast Clarinet Congress and Fresno State Faculty Clarinet Quartet, he cultivates professional communities while maintaining Yamaha Artist status. His community outreach through organizations like the Japanese American Citizen's League and Valley Children's Hospital reflects deep civic engagement alongside scholarly pursuits.
Dr. Nicola Mingotti is a Research Associate in the Department of Earth Sciences at the University of Cambridge, affiliated with the Institute for Energy & Environmental Flows (IEEF). His experimental research focuses on fluid mechanics in the context of energy transition, particularly carbon sequestration and underground hydrogen storage, while also addressing oceanic microplastics dispersion and atmospheric volcanic ash transport. Research Associate, University of Cambridge IEEF Flow Laboratory His work on multiphase flows spans volcanic eruptions, oceanic microplastics separation, and contaminant dispersion in hospital environments. Recent studies include Journal of Fluid Mechanics publications on bubble plumes (2023) and sediment-laden oceanic plumes (2022), alongside Communications Earth & Environment articles on CO2 storage (2024) and volcanic dynamics (2021). Scientific awards and honors: None explicitly mentioned in the text. As an engineering researcher, he led investigations on droplet transport in ventilation systems (Royal Papworth Hospital, 2021) and historical surgical environment design (2021). His publications demonstrate interdisciplinary expertise across environmental fluid mechanics, geological engineering, and public health.
Saurabh Amin is a Professor in the Department of Civil and Environmental Engineering at the Massachusetts Institute of Technology (MIT), where he also serves as the Edmund K. Turner Professor and Undergraduate Officer. He is a Principal Investigator at the Laboratory of Information and Decision Systems and holds affiliations with the Operations Research Center and the Center for Computational Science and Engineering. His educational background includes: B.Tech. 2002, Indian Institute of Technology (IIT) Roorkee M.S. 2004, University of Texas (UT) Austin Ph.D. 2011, University of California (UC) Berkeley Saurabh Amin's research focuses on the design and control of infrastructure systems using game theory and optimization in networks. His work spans three main areas: resilient network control, information systems and incentive design, and optimal resource allocation in large-scale infrastructure systems. By concentrating on critical infrastructure domains including highway transportation, electric power distribution, and urban water networks, his research develops innovative theory and tools to enhance system performance against both stochastic and adversarial disruptions. His approach involves modeling cyber-physical interactions in infrastructures to assess vulnerabilities, developing detection and response tools for failures at various scales, and designing economic incentive schemes that improve aggregate public good while accounting for dependencies and private information among strategic entities. Amin's work bridges mathematical systems theory with practical civil engineering applications, creating a rigorous theoretical foundation for infrastructure resilience that addresses diverse failure mechanisms from natural disasters to deliberate malicious actions. His recent publications demonstrate a strong focus on decarbonization of energy systems, resilient infrastructure planning under climate uncertainty, optimization methods for complex networked systems, and game-theoretic approaches to sustainable infrastructure management. His work increasingly integrates artificial intelligence and machine learning techniques with traditional control theory to address contemporary challenges in infrastructure resilience and sustainability. The research shows a clear trajectory toward addressing climate change impacts on infrastructure systems while maintaining economic efficiency and operational reliability. Professor Amin has received numerous prestigious awards and honors: Common Ground Excellence in Teaching Award, 2025 HSCC Test-of-Time Award, 2024 MIT CEE, Distinguished Service and Leadership Award, 2023 Samuel M. Seegal Prize (SoE) – inspiring students in pursuing and achieving excellence, 2022 Earll M. Murman for Excellence in Undergraduate Advising, 2022 C3.ai Digital Transformation Institute Research Award, 2020 MIT, Ole Madsen Mentoring Award, 2020 MIT, Energy Initiative Research Award, 2020 National Academy of Engineering, China-America Frontiers of Engineering Symposium speaker, 2019 MIT, Robert N. Noyce Career Development Professor, 2015-2018 Google Faculty Research Award, 2015 National Science Foundation CAREER Award, 2015 Siebel Energy Institute Research Award, 2015 MIT, Solomon Buchsbaum AT&T Research Fund Award, 2012 Professor Amin has been actively involved in significant research projects including the C3.ai DTI project on Causal Reasoning for Real-Time Attack Identification in Cyber-Physical Systems and another on Learning in Routing Games for Sustainable Electromobility. He serves as the chief scientist on multi-institutional NSF grants, including the $9 million Foundations of Resilient Cyber-Physical Systems (CPS) project. His teaching portfolio includes courses such as 1.008 Engineering for a Sustainable World, 1.104 Sensing and Intelligent Systems, 1.020 Engineering Sustainability: Analysis and Design, and 1.208 Resilient Networks. As Undergraduate Officer, he plays a key role in shaping the educational experience for civil and environmental engineering students at MIT. Professor Amin leads the Resilient Infrastructure Networks Lab at MIT, where his team develops theoretical foundations and practical tools for infrastructure resilience. The lab focuses on the intersection of control theory, game theory, and optimization applied to cyber-physical infrastructure systems. Current research directions include pandemic-resilient urban mobility and hurricane-resilient smart grid operations, reflecting the lab's commitment to addressing pressing societal challenges through rigorous systems engineering approaches.
Justin Yim is an Assistant Professor at the Department of Mechanical Science and Engineering at the University of Illinois Urbana-Champaign (UIUC), where he runs the Novel Mobile Robots Lab (NMbL). His research focuses on enabling high-performance locomotion in robots through concurrent design of mechanisms and controllers, inspired by biological systems. He previously earned his PhD in Electrical Engineering from UC Berkeley (2020) and dual BS degrees in Mechanical Engineering and Applied Mechanics/Electrical Engineering from the University of Pennsylvania (2015), followed by a postdoctoral researcher role at Carnegie Mellon University (2020-2022). PhD, Electrical Engineering, University of California, Berkeley (2020) MSE, Robotics, University of Pennsylvania (2015) BSE, Mechanical Engineering and Applied Mechanics/Electrical Engineering, University of Pennsylvania (2015) His research explores legged robot design, bioinspired robotics, and locomotion dynamics, with a focus on overcoming terrain challenges through minimalist mechanical systems and control strategies. Recent work emphasizes squirrel-inspired jumping and landing mechanics, programmable substrates for locomotion studies, and energy-efficient robot mobility. Selected article trends highlight innovations in monopedal hopping with series-elastic actuators, bioinspired balance control, underactuated bipedal walkers, and cooperative cable-driven modular robots. His work bridges theoretical insights with practical applications in extreme-terrain mobility. NSF CAREER Award (2025): 'Extreme Robot Walking: Speed, Agility, and Efficiency via Reduced Degrees of Freedom' NASA Innovative Advanced Concepts Fellow (2025) Justin Yim actively mentors graduate students and leads research projects in the NMbL lab, which develops robots capable of walking, hopping, and rolling in complex environments. Recent lab achievements include a Best Demo award at the 2nd Unconventional Robots Workshop (2025) and awards for outstanding locomotion papers. He teaches courses such as ME 370 Mechanical Design I and SE 422 (ME 446, ECE 489) Robot Dynamics and Control.
