Vittorio Solina is a Researcher at the Department of Mechanical, Energy and Management Engineering (DIMEG) of the University of Calabria, specializing in industrial systems simulation and digital twin technologies. His work spans Industry 4.0/5.0 applications, supply chain optimization, and human-robot collaboration. Affiliation: University of Calabria Academic Role: Researcher (Legge 240/10) Research Focus: Digital twins, simulation modeling, sustainable manufacturing His research emphasizes advanced simulation tools for decision support, including applications in Industry 4.0 , Smart Manufacturing , and Agri-Food Supply Chains . Recent work explores adaptive simulation optimization for Industry 5.0 and 5G-enabled robotics. Key publication trends include: (1) Digital twin integration for supply chain resilience (2024-2025), (2) Simulation automation methodologies (2025), and (3) Human-robot collaboration frameworks (2024).
Matthew Louis Mauriello is an Assistant Professor in the Department of Computer and Information Sciences at the University of Delaware , where he directs the Sensify Lab . He holds a PhD in Computer Science from the University of Maryland (2018) and completed postdoctoral work at Stanford University (School of Medicine, 2020; School of Public Policy & Environmental Engineering Department, 2019). Research interests span Human-Computer Interaction (HCI), Ubiquitous Computing, and User-Centered Design with applications in: Sustainability Human-Building Interaction Wearable Technology Personal Informatics Educational Game Design Mental Health Interventions Recent publications highlight trends in stress monitoring (skin-like biosensors, workplace wellbeing), energy auditing (thermography systems), and educational technology (block-based programming tools for teachers). His work appears in ACM CHI, ACM Human-Computer Interaction, and Building and Environment. Scientific awards include: Best Paper Honorable Mention, CHI 2016 Best Paper Honorable Mention, CHI 2015 Best of WebSci'18 Teaching at University of Delaware encompasses courses like Educational Game Design Operating Systems Advanced Web Technologies Computing for Social Good with a focus on project-based learning and systems thinking.
Stefano Ermon is an Associate Professor in the Department of Computer Science at Stanford University, affiliated with the Artificial Intelligence Laboratory and a Senior Fellow at the Woods Institute for the Environment. His research focuses on advancing machine learning and generative AI techniques to address societal and environmental challenges, including computational sustainability, geospatial analysis, and climate science. He holds a Ph.D. from Cornell University (2015). Education: Ph.D. in Computer Science, Cornell University (2015). Research Interests: Ermon’s work bridges foundational machine learning (e.g., diffusion models, generative AI, and optimization) with applications in sustainability, geospatial analysis (via satellite imagery), and earth observation systems. Notable contributions include predicting poverty using satellite data and developing scalable methods for molecule generation. Articles Trends: His recent work emphasizes diffusion models for generative tasks (e.g., text-to-image, molecule design), geospatial AI (e.g., environmental monitoring), and ethical AI (e.g., bias mitigation in LLMs). He also explores applications in robotics and scientific computing. Awards: He has received prestigious awards, including the ICML 2024 Best Paper Award, Sloan Research Fellowship, Microsoft Research Faculty Fellowship, and the IJCAI Computers and Thought Award. Advising and Grants: Ermon teaches courses like Probabilistic Graphical Models (CS228) and has secured grants from NSF, ONR, AFOSR, and private foundations. His lab develops tools for climate science and sustainable development. Labs/Teams: Leads the Stanford AI Lab group focused on computational sustainability and generative AI, collaborating with institutions like the Woods Institute for environmental applications.
Stephen Licht is an Associate Professor of Ocean Engineering and Graduate Director at the University of Rhode Island's College of Engineering, where he directs the Robotics Laboratory for Complex Underwater Environments (R-CUE). His research focuses on developing maritime robots capable of operating in dynamic and unpredictable environments through biologically inspired propulsion, distributed pressure sensing, model-based optimal control, and compliant underwater manipulation technologies. Ph.D. in Oceanographic and Mechanical Engineering from MIT/WHOI Joint Program (2008) B.S. in Mechanical Engineering from Yale University (1998) Former Senior Research Scientist at iRobot and Senior Robotics Engineer at Vecna Robotics Current Research Affiliate with MIT Department of Mechanical Engineering Former Visiting Faculty at Libera Università di Bolzano (2019-2020) Dr. Licht's research spans marine robotics with emphasis on biologically inspired propulsion systems that provide high authority and bandwidth thrust, nonlinear attitude control for maneuvering in dynamic conditions, compliant underwater manipulation technologies, and unmanned aerial monitoring of coastal structures. His work bridges mechanical engineering principles with oceanographic applications to create more capable underwater robotic systems that can operate in complex marine environments. His recent publications demonstrate a strong trend toward soft robotics applications for deep-sea exploration, with particular focus on jamming grippers and neutrally buoyant manipulation systems. The research also shows increasing integration of additive manufacturing techniques for field-deployable solutions and computational methods for autonomous systems operating in challenging marine environments. His work spans fundamental control theory, mechanical design, and practical field applications. Dr. Licht has secured significant research funding as both Principal Investigator and Co-Principal Investigator from major organizations including the Office of Naval Research, NOAA, NSF, and various university collaborations. His grants focus on advancing unmanned underwater vehicle technology, soft robotics for deep-sea applications, and coastal monitoring systems. Active mentor to numerous graduate and undergraduate students in Ocean Engineering Successful track record of student placements at organizations including Jaia Robotics, Scripps Institution of Oceanography, FORSSEA Robotics, and government research labs Collaborates with researchers at MIT, WHOI, University of Connecticut, University of Maine, and international institutions Licht leads the R-CUE lab which develops innovative solutions for underwater robotics challenges, with particular expertise in biomimetic propulsion, soft robotics for deep-sea applications, and autonomous systems for environmental monitoring. The lab maintains strong industry connections with OceanGate Inc. and FabNewport, and engages with local educational institutions through outreach programs with Roger Williams Middle School.
Massachusetts Institute of TechnologyUnited States
Laurent Demanet is a Professor of Applied Mathematics at the Massachusetts Institute of Technology (MIT), with a primary appointment in the Department of Mathematics and a joint appointment in Earth, Atmospheric, and Planetary Sciences (EAPS). He served as Director of MIT’s Earth Resources Laboratory from 2018 to 2024, focusing on seismic imaging and wave propagation to visualize Earth's subsurface structures. Undergraduate: Mathematical Engineering and Theoretical Physics, Université de Louvain, Belgium PhD: Applied and Computational Mathematics, California Institute of Technology (Caltech), 2006 (advisor: Emmanuel Candès, William P. Carey Prize for best dissertation in mathematical sciences) Postdoctoral: Szegö Assistant Professorship, Stanford University (2006-2009) His research centers on applied mathematics and scientific computing for geophysics, particularly inverse problems, wave propagation, and signal processing. He develops numerical algorithms and machine learning tools to analyze indirect, incomplete, and noisy data, enabling accurate subsurface modeling for environmental monitoring, carbon sequestration, hydrogen storage, and geothermal energy. His work also addresses seismic hazards induced by resource exploration and storage activities. Scientific awards and honors include: National Science Foundation CAREER Award (2012) Alfred P. Sloan Research Fellowship (2011) Air Force Young Investigator Award (2011) MIT Class of 1954 Career Development Professorship (2013-2016) As Director of MIT’s Earth Resources Laboratory and through his research group, Demanet has advanced computational methods for geophysical imaging, mentored students, and secured grants for interdisciplinary projects. His work bridges applied mathematics, computational science, and geophysics to solve pressing environmental and energy challenges. He leads research on subsurface visualization, integrating machine learning and numerical algorithms to improve seismic imaging and quantify predictive uncertainty. His lab's applications span natural hazard assessment, resource management, and sustainable energy solutions.
Dr. Hassan Qudrat-Ullah is a Professor at the School of Administrative Studies, York University, and Coordinator of the Certificate in Logistics Management. He holds a PhD in Decision Sciences from NUS Business School and completed a post-doctoral fellowship at Carnegie Mellon University. His research focuses on dynamic decision making, system dynamics modeling, energy planning, and interactive learning environments. He teaches courses on quantitative methods, logistics, and decision analysis, informed by global industry experience across 20+ countries. Research interests include sustainability, climate change, systems thinking, and educational applications of decision sciences. He serves as Editor-in-Chief of the International Journal of Complexity in Applied Science and Technology and is a member of IEEE, DSI, and the International System Dynamics Society. His work has been published in Energy , Decision Support Systems , and others. Key projects include studies on 'structured-debriefing in dynamic decision making' and renewable energy policies in Africa. His recent articles (2023–2025) address AI integration in energy governance, system dynamics for supply chain resilience, and education for sustainability. Hassan advocates for systems thinking in K-12 education and enjoys traveling (visited 129 countries) and bird-watching.
Sicun Gao is an Associate Professor in the Computer Science and Engineering department at the University of California, San Diego. His research focuses on practical algorithms for NP-hard search and optimization problems in computational systems, emphasizing combinatorial perspectives in numerical and statistical contexts to achieve reliable autonomy. Research Interests: Automated reasoning, Hamilton-Jacobi reachability, safe reinforcement learning, control barrier functions, and optimization in cyber-physical systems. Teaching: Courses on AI search, optimization, and graduate research seminars. The 15 most recent publications highlight advancements in safe AI control, motion planning, and policy optimization, often integrating neural networks with formal verification. Awards include the IEEE Power & Energy Society Technical Committee Prize Paper Award and the IROS RoboCup Best Paper Award. He advises PhD students working on AI-driven control and robotics, with alumni placed at institutions like Seoul National University, Amazon, and Apple. Grants include NSF Career, Air Force Young Investigator, and DARPA Assured Autonomy funding. His lab develops tools like dReal for automated reasoning in nonlinear theories over the reals.
Farzad Mashayek is a Professor and Department Head of Aerospace and Mechanical Engineering at the University of Arizona, College of Engineering. He is a member of the Graduate Faculty and leads the Computational Multiphase Transport Laboratory. His research integrates high-fidelity simulations, machine learning, and experimental validation across diverse domains in fluid dynamics and energy systems. Educational Background: PhD in Mechanical Engineering, State University of New York at Buffalo, Buffalo, NY MS in Mechanical Engineering, Sharif University of Technology, Tehran, Iran BS in Mechanical Engineering, Sharif University of Technology, Tehran, Iran His research interests include turbulent reacting flows, plasma dynamics, electrostatic atomization, solid-ion and lithium batteries, computational fluid dynamics, and machine learning applications in engineering. He employs high-order spectral element methods, phase-field modeling, and deep neural networks to study complex multiphysics phenomena such as drop impact, battery degradation, and turbulence modeling. The recent publications reflect a strong trend toward integrating machine learning with multiphysics simulations, particularly in battery safety (thermal runaway prediction), materials characterization (STEM image analysis), and fluid dynamics (modal analysis of turbulence). His work often involves collaboration with experimental groups to validate models, especially in dental aerosol suppression and electrohydrodynamics. Scientific Awards: Sustained Service Award, American Institute of Aeronautics and Astronautics (AIAA), Spring 2022 Best Presentation Award, The 20th International Conference on Computational Mathematics, Parallel and Distributed Computing, Summer I 2018 Dr. Mashayek has secured funding from NSF (GOALI program) for controlled coating via charged droplet deposition. He advises graduate students and postdoctoral researchers in computational mechanics and energy systems, fostering interdisciplinary research. He has contributed to engineering education, particularly during the pandemic, with active learning strategies in online instruction. He leads a dynamic research team focused on advancing simulation tools and applying them to real-world challenges in energy, manufacturing, and public health.
Tushar Krishna is an Associate Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology, with a courtesy appointment in the School of Computer Science. He earned his PhD in Electrical Engineering and Computer Science from MIT in 2014, an MSE in Electrical Engineering from Princeton University in 2009, and a B.Tech in Electrical Engineering from IIT Delhi in 2007. His research spans computer architecture, interconnection networks, networks-on-chip (NoC), and AI/ML accelerator systems, with a focus on optimizing data movement in modern computing platforms. His work is funded by NSF, DARPA, IARPA, SRC, Department of Energy, Intel, Google, Meta, Qualcomm, and TSMC. His papers have been cited over 17,000 times, with three receiving IEEE Micro's Top Picks recognition, one earning an honorable mention, and four winning best paper awards. Dr. Krishna leads the Synergy Lab at Georgia Tech and has developed several influential tools including ASTRA-sim for distributed AI/ML training, MAESTRO and SCALE-sim for accelerator design space exploration, and Garnet2.0 for NoC simulation. His recent work focuses on large language model acceleration, distributed training systems, and neuro-symbolic AI architectures. He has received numerous teaching and research awards including induction into the HPCA Hall of Fame (2022), the Class of 1940 Teaching Effectiveness Award (2018), and the Roger P. Webb Outstanding Mid-career Faculty Award (2024). HPCA Hall of Fame Inductee (2022) Roger P. Webb Outstanding Mid-career Faculty Award (2024) Richard M. Bass/Eta Kappa Nu Outstanding Junior Teacher Award (2023) Roger P. Webb Outstanding Junior Faculty Award (2021) Class of 1940 Course Survey Teaching Effectiveness Award (2018) Dr. Krishna currently serves as Associate Director for the Center for Research into Novel Computing Hierarchies (CRNCH) and co-chair of the Chakra Execution Traces and Benchmarks Working Group. He has held the ON Semiconductor (Endowed) Junior Professorship at Georgia Tech (2019-2021) and has been a visiting professor at MIT EECS, Harvard University CS, and a researcher at Intel's VSSAD group.
Simon Birrer is an Assistant Professor in Physics and Astronomy at Stony Brook University, specializing in cosmology and gravitational lensing. He holds a PhD from ETH Zurich (2016) and previously served as Kavli Fellow at Stanford University. Birrer leads research probing dark matter and dark energy using gravitational lensing phenomena. His group develops computational tools for analyzing strong gravitational lensing data to study cosmic expansion and dark matter distribution. Research areas include time-delay cosmography, Hubble constant measurements, and machine learning applications in astrophysics. Recent publications focus on multi-messenger gravitational lensing (2025), LSST survey applications (2025), and AI-powered lens modeling pipelines (2025). His work consistently addresses fundamental cosmological tensions like the Hubble constant discrepancy. Awards: Kavli Postdoctoral Fellowship (2019-2022) Kugelpyramide Lifetime Achievement Award Experimental Innovation Award (ETH Zurich) Research Group: Leads the SBU Strong Lensing group with 9+ graduate students and postdocs. The group participates in major collaborations including LSST Strong Lensing Science Collaboration (co-chair), LSST Dark Energy Science Collaboration, and TDCOSMO.
Karen I. Winey serves as the Harold Pender Professor in the Department of Chemical and Biomolecular Engineering and Materials Science and Engineering at the University of Pennsylvania's School of Engineering and Applied Science. Her research group employs experimental and computational tools to investigate advanced polymers for energy applications, particularly focusing on proton and ion conductivity for fuel cells and batteries within the Laboratory for Research on the Structure of Matter (LRSM). Dr. Winey's research encompasses: Designing functional polymers to improve proton, hydroxide, and ion conductivity Studying polymer nanocomposites and nanoparticle dynamics in electrochemical devices Developing polymer-to-polymer upcycling methods to convert waste polyolefins to higher value polymers Revolutionizing understanding of ionomer morphologies beyond traditional spherical aggregate models Her recent publications reveal significant advances in sustainable energy materials, particularly fluorine-free alternatives to conventional polymer electrolytes. The research spans fundamental studies of ion transport mechanisms in precisely engineered polymers to practical applications in energy devices. A critical focus is understanding how nanoscale morphology affects macroscopic properties, with particular attention to structure-property relationships in ion-conducting polymers for fuel cells and batteries. Scientific recognition includes: 2025 Turner J. Alfrey Visiting Professor Harold Pender Professorship (endowed chair) NSF DMR Polymers grant awarded July 2025 Dr. Winey actively mentors a diverse research group including PhD students, postdoctoral researchers, and undergraduates. Her lab recently welcomed six summer undergraduates and has celebrated numerous student achievements including qualifying exam successes and award-winning presentations. Current major projects include the Port5 collaboration and the PolyUp project focused on polymer upcycling through dehydrogenation and functionalization strategies. The Winey Group operates within Penn's state-of-the-art Vagelos Laboratory for Energy Science and Technology (VLEST), maintaining active collaborations with institutions including the University of Konstanz in Germany, Florida State University, and Michigan State University. The group regularly hosts international visitors and participates in major conferences including the APS Global Physics Summit.
Benjamin Carrison-Schafer is an Associate Professor and Assistant Dean for Graduate Student Success at the Department of Electrical and Computer Engineering, Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. He leads the Design Automation and Reconfigurable Computing Laboratory (DARClab), focusing on systems, high-level design, and programming methodologies for VLSI computing systems and FPGAs. Education: MBA from McGill University, Canada (2012) PhD in Electrical and Electronic Engineering from University of Birmingham, UK (2003) Research Interests: His work spans modeling, analysis, synthesis, optimization, and implementation of VLSI systems. Current projects include: Approximate Computing, High-Level Synthesis Design Space Exploration, Hardware Security, Behavioral MPSoC Optimization, and Automatic Fault-Tolerant System Generation. His research blends theory and practice, using analytical and experimental techniques to solve real-world problems. Publication Trends: Recent publications demonstrate strong focus on hardware security, FPGA optimization, and automated design methodologies. Key themes include High-Level Synthesis innovations, hardware acceleration techniques, and cross-disciplinary applications of reconfigurable computing. His work frequently addresses challenges in hardware trustworthiness, energy efficiency, and design automation scalability. Professional Activities: Associate Editor: IEEE Transactions on Sustainable Computing (2022-present) Conference Chair: ICCD 2024, DCAS 2024 Program Committee: ASP-DAC, DATE, FCCM, GLSVLSI Advising & Labs: Supervises multiple PhD and Master's students at DARClab. Current research includes domain-specific architecture design, hardware security, sustainable computing, and ML for VLSI design. The lab collaborates with industry partners like Renesas Electronics and develops commercial tools through spin-off company highX Technologies.
Larry Pileggi is the Coraluppi Head and Tanoto Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU). He is also the Department Head of CMU’s ECE Department and has held prior roles at Westinghouse Research and Development and the University of Texas at Austin. His work bridges academic research and industry innovation, with co-founding ventures like Fabbrix Inc., Extreme DA, and Pearl Street Technologies. Dr. Pileggi earned his Ph.D. in Electrical and Computer Engineering from CMU (1989), following an M.S. (1984) and B.S. (1983) in Electrical Engineering from the University of Pittsburgh. His research focuses on three core areas: secure integrated circuit hardware (mitigating supply-chain threats), integrated circuits design methodologies (supporting sub-20nm CMOS and heterogeneous technologies), and power systems simulation (developing robust grid analysis tools like SUGAR). He has been recognized with numerous awards, including the prestigious 2023 Phil Kaufman Award for contributions to electronic system design, and is an IEEE Fellow. His academic leadership includes fostering maker initiatives and interdisciplinary research programs to address future challenges in energy and computing systems. Pileggi’s advising record includes over 47 Ph.D. students, many of whom collaborate with him in industry ventures. Current and past grants support his work on resilient power grids and novel memory technologies, reflecting a commitment to both theoretical and applied research. His lab, the Pileggi Lab, develops cutting-edge solutions for energy and integrated systems, emphasizing scalable simulation, secure hardware design, and next-generation memory architectures. Collaborations span institutions like ETH Zurich and industry partners in EDA and semiconductor sectors.
Maria Gorlatova is an Associate Professor of Electrical and Computer Engineering at Duke University's Pratt School of Engineering, where she leads the Intelligent Interactive Internet of Things (I3T) Lab. She also holds a secondary affiliation as Faculty Network Member of the Duke Institute for Brain Sciences and has previously served as Assistant Professor of Computer Science. Dr. Gorlatova earned her Ph.D. in Electrical Engineering from Columbia University (2013), following M.Sc. and B.Sc. (Summa Cum Laude) degrees in Electrical Engineering from University of Ottawa, Canada. Prior to joining Duke, she was an Associate Research Scholar in the Electrical Engineering Department and Associate Director of the Princeton EDGE Lab at Princeton University (2016-2018). She also has industry experience with Telcordia Technologies, IBM, and D. E. Shaw Research. Her research focuses on advancing intelligent behavior in Internet of Things systems and applications, particularly in mobile pervasive systems and the Internet of Things. Her work crosses traditional discipline boundaries, requiring thinking across multiple layers of system and protocol stacks. Current research themes include breaking barriers for technologies that enable fundamentally new deployments and experiences, such as energy harvesting, artificial intelligence adapted to IoT constraints, and augmented reality. Her lab specifically develops edge- and IoT-enabled intelligent augmented reality platforms, with applications in healthcare and human-robot collaboration. Analyzing her recent publications reveals a strong focus on augmented reality systems, particularly for medical applications. Her work spans computer vision for AR, spatial tracking, SLAM systems, vision-language models for AR security, and VR/AR applications in neurosurgery and rehabilitation. A significant portion of her recent work addresses challenges in mixed reality for medical procedures, demonstrating the translational impact of her research. Google Anita Borg USA Fellowship Canadian Graduate Scholar CGS NSERC Fellowships Columbia University Presidential Fellowship Columbia University Jury Award for Outstanding Achievement in Communications ACM SenSys Best Student Demonstration Award IEEE Communications Society Young Author Best Paper Award IEEE Communications Society Award for Advances in Communications Best Research Artifact Award, IEEE IPSN (2020) N2 Women Rising Star, Networking Networking Women (N2Women) (2019) Dr. Gorlatova's research has been supported by various funding sources that enable her work on edge computing for augmented reality, IoT systems, and medical applications. She actively mentors graduate students who frequently appear as first authors on her publications, indicating strong student involvement in her research. Her I3T Lab at Duke focuses on creating human-facing pervasive mobile computing platforms that enable transformative applications, with recent emphasis on creating advanced augmented reality platforms that integrate edge computing and IoT technologies. The I3T Lab is developing next-generation AR systems with capabilities in edge AI, collaborative spatial awareness, AR user cognitive context sensing, and AR QoS/QoE evaluation. Current projects include applications in healthcare (particularly neurosurgery guidance and rehabilitation) and human-robot collaboration scenarios, demonstrating the lab's focus on real-world impact of pervasive computing technologies.
Gretchen A. Macht is an Associate Professor in the Department of Mechanical, Industrial and Systems Engineering at the University of Rhode Island, College of Engineering. She specializes in Human Systems Engineering, with research emphasizing team performance, sustainability, electric vehicles, solar energy systems, and voting systems. Her work bridges engineering, ergonomics, and public policy. Her research interests focus on understanding and optimizing human-centered systems. She investigates team communication and performance through fuzzy cognitive maps and entropy modeling, develops sustainable design practices for products and energy systems, and analyzes user behavior in electric vehicles and solar installations. A significant portion of her research is dedicated to election systems, where she uses simulation to improve accessibility, safety, and efficiency of in-person voting, particularly during public health crises. Her recent publications span topics such as EV charging behavior, solar installation safety, voting simulation, and team dynamics. These works reflect interdisciplinary trends combining systems engineering, data analytics, ergonomics, and sustainability science. She frequently employs simulation, naturalistic data analysis, and human factors methodologies. Dr. Macht has secured substantial grant funding from organizations including the U.S. Election Assistance Commission, National Science Foundation, MIT, and Democracy Fund. She serves as Principal Investigator (PI) or Co-PI on projects related to election systems (e.g., SMILE, URI VOTES), ergonomic redesign of protective gear, and community solar development. She leads the SIS Lab and the URI VOTES initiative, fostering collaborative research on simulation and system design. Her lab supports graduate students in conducting impactful research at the intersection of engineering and societal challenges. Notable grants include: PI, MIT: “Voting in Person - White Paper” (2023) PI, US EAC: “SMILE 2.0” (2022) PI, Democracy Fund: “URI VOTES Pocket Guide Tool” (2022) Co-PI, NSF: “Election Science: Convergence Accelerator Workshop Proposal” (2021) PI, US EAC: “SMILE: Simulation Models for an Immersive Learning Environment” (2021) She has advised several graduate students, including Mehrsa Khaleghikarahrodi, Nicholas D. Bernardo Jr., and Tim Jonas, whose research aligns closely with her focus areas in EVs, elections, and team performance.
