Yogananda Isukapalli is a Teaching Professor and Vice Chair in the Computer Engineering Program at the Electrical and Computer Engineering Department , University of California, Santa Barbara . He joined the faculty in Winter 2017 after a career as a staff scientist at Broadcom (2010–2017), where he designed Wi-Fi chips (11n/11ac/11ax) and worked on underwater wireless communication models during a postdoctoral stint at Scripps Institution of Oceanography (2009–2010). His PhD in Communication Theory and Systems from UC San Diego (2009) forms the basis of his expertise in wireless systems and digital design .
Michel Versluis is a Full Professor at the University of Twente, Netherlands, specializing in Physical and Medical Acoustics within the Physics of Fluids group. His work focuses on microbubbles and microdroplets for medical imaging and therapy, as well as microfluidic applications in medicine and nanotechnology. University of Twente, Physics of Fluids group His research bridges physics and biomedical engineering, with publications in high-impact journals like PNAS and IEEE Transactions. Recent work emphasizes ultrasound-driven microbubble dynamics, additive manufacturing of flow phantoms, and deep learning for super-resolution imaging. 2025 publications: vascular phantoms, PROTEUS simulator, acoustic microbubble control 2024 innovations: 3D-printed medical devices, immunogenic cell death optimization Contact: m.versluis@utwente.nl
Shashi Shekhar is a Professor at the University of Minnesota, holding the distinguished titles of McKnight Distinguished University Professor and Distinguished University Teaching Professor. He serves as the ADC/CSE Chair and Director of the AI-LEAF Institute within the Department of Computer Science at the College of Science and Engineering. His research interests span multiple areas of spatial computing including spatial data science, spatial data mining, spatial databases, Geo-AI, and Geographic Information Systems (GIS). His work has focused on developing scalable algorithms for eco-routing, evacuation route planning, and spatial pattern mining. He has made significant contributions to the field through his Spatial Databases textbook, the Encyclopedia of GIS which has seen over 192,918 downloads in 2017, and a spatial computing book for professionals. His research group has produced numerous PhD graduates dating back to 1993 through 2023. Analysis of his recent publications reveals a strong focus on applying spatial computing to critical societal challenges including climate change mitigation through the AI-LEAF Institute, pandemic response through mobility data analysis, and sustainable transportation through eco-routing algorithms. His work bridges theoretical advances in spatial data science with practical applications in urban planning, emergency management, and environmental sustainability. Distinguished McKnight University Professor Distinguished University Teaching Professor UCGIS Education Award (2015) Graduate Education Award (2015) President of University Consortium for GIS (2017-2018) Computing Research Association Board Member (2016-2019) Professor Shekhar has advised over 30 PhD students since 1993, with his most recent graduate in 2023. He has secured significant research funding including a $20 million AI Institute grant focused on climate-smart agriculture and forestry. His Spatial Computing Research Group maintains active collaborations with government agencies and industry partners. The group has developed practical applications featured in media outlets including FoxTV coverage of evacuation route planning algorithms. Current research directions include applying AI techniques to address climate challenges through the AI-LEAF Institute and advancing spatial data science for polar regions through NSF-funded initiatives.
Georges Kaddoum is a Professor at École de technologie supérieure (ÉTS) , specializing in Electrical Engineering. He holds the Canada Research Chair in Unlocking the Power of IoT 6G-Networks and the LACIME – Communications and Microelectronic Integration Laboratory affiliation. His work bridges wireless communications, IoT, and machine learning. Research Interests include wireless communication systems, physical layer security, machine learning for networking, and 6G technologies. He focuses on optimizing network performance in challenging environments (impulsive noise, underwater, non-terrestrial networks) and developing AI-driven solutions for jamming mitigation, resource allocation, and secure IoT frameworks. Recent Publications highlight 6G-enabled vehicular networks, quantum-safe blockchain integration, federated learning for transportation systems, and deep learning-based receivers for chaotic communication systems. His work emphasizes semantic communication, interference management, and digital twin applications. Awards IEEE TCSC Award for Excellence in Scalable Computing (2022) Prix d’excellence de la relève (Université du Québec, 2018) Prix d’excellence en recherche (ÉTS, 2018) Multiple IEEE Exemplary Reviewer and Best Paper Awards (2014–2022) Supervision includes 15+ PhD/Master’s students working on topics like index modulation, physical layer security, UAV communications, and intelligent resource management. Research Units Ultra Research Chair on Intelligent Tactical Wireless Networks LACIME Laboratory Canada Research Chair in IoT 6G-Networks
Robert Likamwa is an Associate Professor at Arizona State University, affiliated with the School of Arts, Media and Engineering and the School of Electrical, Computer and Energy Engineering. His research focuses on the intersection of mobile computing, augmented reality, and sensor design through Meteor Studio. Ph.D., Rice University M.S., Rice University B.S., Rice University His work spans three core research arcs: (i) advanced visual capture systems, (ii) hybrid virtual-physical immersion through sensory augmentation, and (iii) data-driven frameworks for AR/VR storytelling. He explores mobile operating systems, low-power architectures, computational imaging, and holographic computing. Recent publications analyze multi-resolution visual sensing, geospatial cross-virtuality collaboration, planetary data visualization, spatial audio optimization, and multi-sensory virtual environments. His 2013 paper on energy-proportional image sensors received the Best Paper Award at ACM MobiSys. Best Paper Award, ACM MobiSys 2013 LiKamWa advises graduate students through research and thesis courses and leads grants related to mobile vision systems, haptic interfaces, and energy-efficient sensor design. He directs Meteor Studio, a research group focused on immersive technology innovation.
Jeff Shamma is the Department Head and Professor of Industrial and Enterprise Systems Engineering (ISE) at the University of Illinois at Urbana-Champaign, holding the Jerry S. Dobrovolny Chair. He is also courtesy Professor in Aerospace Engineering and Mechanical Science and Engineering. Formerly, he held the Julian T. Hightower Chair at Georgia Institute of Technology and faculty positions at KAUST. Dr. Shamma earned his PhD in Systems Science and Engineering from MIT (1988) and a BS in Mechanical Engineering from Georgia Tech (1983). He is a Fellow of IEEE and IFAC, recipient of the IFAC High Impact Paper Award, AACC Donald P. Eckman Award, and NSF Young Investigator Award. His research spans Decision and Control , Game Theory , and Multi-Agent Systems , focusing on human-machine networks, distributed autonomy, and adaptive robotic systems. Recent work examines crowd dynamics, risk-sensitive control, and feedback linearization for constrained optimization. Jeff has served as Editor-in-Chief of IEEE Transactions on Control of Network Systems (2020–2024) and held editorial roles in journals like Annual Reviews in Control and IEEE Transactions on Robotics . His 15 most recent publications (2024–2025) analyze learning dynamics, multi-agent optimization, and UAV-crawler systems, reflecting trends in autonomous systems, game-theoretic modeling, and industrial inspection technologies. Scientific distinctions include: Fellow of IEEE and IFAC IFAC High Impact Paper Award (2020) AACC Donald P. Eckman Award (1996) NSF Young Investigator Award (1992) Mohammed Dahleh Distinguished Lecture Award (2013) Dr. Shamma advises current PhD students Hassan Abdelraouf, Aya Hamed, and Nawaf Otaibi, with former advisees including Sarah Toonsi (2025) and Fat-hy Rajab (2025). His lab integrates theoretical research with applied projects like FalconScan, a UAV-crawler system for industrial inspection, and develops magnetic legs for curved surface UAV landing.
Dr. He Wang is an Associate Professor in the Department of Computer Science at University College London (UCL), affiliated with the Virtual Environment and Computer Graphics (VECG) group and the UCL Centre for Artificial Intelligence. He holds a Visiting Professorship at the University of Leeds and previously served as an Associate Professor and Lecturer there, as well as a Senior Research Associate at Disney Research Los Angeles. His research focuses on computer graphics, vision, and machine learning, with notable contributions to crowd simulation, generative models, and physics-informed neural networks. Dr. Wang earned his BEng from Zhejiang University and his PhD from the University of Edinburgh, followed by postdoctoral work at the University of Edinburgh's School of Informatics. He has been recognized as a Turing Fellow and serves as an Academic Advisor to the Commonwealth Scholarship Council and an Associate Editor of Computer Graphics Forum . His research spans cutting-edge topics including 3D reconstruction, adversarial attacks on motion recognition, and AI-driven groundwater modeling. He has supervised six PhD students to completion and actively engages in collaborative projects, consultancy, and grant evaluations. His lab welcomes students through dedicated recruitment channels.
Rafael Brüschweiler is a Professor and Ohio Research Scholar at The Ohio State University, holding joint appointments in the Department of Chemistry and Biochemistry and the Department of Biological Chemistry and Pharmacology. He serves as the NMR Executive Director for the Ohio State Campus Chemical Instrument Center and the NSF-funded National Gateway Ultrahigh Field NMR Center. His research focuses on biophysical chemistry, analytical chemistry, and computational modeling, emphasizing protein dynamics, metabolomics, and NMR method development. He received his Ph.D. from ETH Zurich and completed a postdoc at the Scripps Research Institute. His research integrates experimental NMR, molecular dynamics simulations, and machine learning to study protein structure-function relationships, metabolic pathways, and biomolecular interactions. Key areas include the dynamics of oncogenic K-Ras, glucokinase glucose sensing, and nanoparticle-assisted NMR techniques. His work is funded by the NIH and NSF, with applications in biomedical diagnostics and drug discovery. Dr. Brüschweiler leads a multidisciplinary lab training students and postdocs in NMR spectroscopy, computational methods, and metabolomics. His lab developed tools like DEEP picker and COLMAR for automated NMR data analysis, contributing to the SECIM metabolomics center. He actively recruits students interested in protein dynamics, computational modeling, or metabolomics.
Libby Gerard is an Associate Adjunct Research Professor at the University of California, Berkeley School of Education and a Research Director for the Technology-Enhanced Learning in Science (TELS) Center. Her work focuses on leveraging innovative technologies to enhance science education through student idea capture, automated assessment, and teacher professional development. Doctorate in Educational Leadership (EdD), Mills College (2008) Bachelor’s in English Literature and Philosophy, Emory University (2000) Her research emphasizes: Automated scoring of student essays using NLP to improve science explanations Real-time instructional customization using embedded assessment data Technology-driven professional development for teachers and principals Social justice integration in science pedagogy Collaborative revision frameworks for inquiry-based learning K-12 education adaptation during the pandemic Recent publications highlight trends in educational technology for science learning, with a focus on NLP applications, interactive inquiry modules, and equitable teaching practices. She has authored studies in journals like Science , Review of Educational Research , and Computers & Education , often exploring how automated systems can enhance teacher-student dynamics. Scientific Awards : Best Paper Award at the AI4EDU Workshop (AAAI Conference, 2020) Libby leads funded projects such as: TIPS (NSF, 2021-2025): NLP for science education ARISE (Hewlett Foundation, 2020-2023): Anti-racism in science education STRIDES (NSF, 2018-2022): Responsive instruction for science teachers PLANS (NSF, 2015-2020): Automated learning support systems She contributes to teacher training through courses like Research Methods for Science Teachers and Apprentice Teaching in Science , emphasizing data-driven pedagogy and inquiry-based instruction.
Dr. Vikas Srivastava is an Associate Professor of Engineering and Director of the Graduate Program in Biomedical Engineering at Brown University's School of Engineering. His research focuses on solid mechanics, continuum biomechanics, and cell mechanics, with applications in materials under extreme environments and biomedical science. He leads the Srivastava Lab for Solid Mechanics and Biomechanics, which integrates computational models with experimental techniques to address interdisciplinary challenges. Dr. Srivastava holds a Ph.D. in Mechanical Engineering from MIT (2010) and previously held senior roles at ExxonMobil, including leadership in materials mechanics and deepwater drilling engineering. His academic career at Brown began in 2018, during which he has directed over 15 graduate students and secured notable funding. His research interests span mechanobiology, hydrogel-based drug delivery systems, AI-driven predictive modeling, and biomaterial innovations for cancer therapies. He has pioneered physics-informed neural networks for material characterization and developed novel hydrogels to enhance chemotherapy efficacy. Recent articles highlight advancements in polymer fracture modeling, machine learning for non-destructive evaluation, and predictive epidemiological modeling for pandemics. Dr. Srivastava has received the Dean’s Award in Bioengineering and was promoted to tenured Associate Professor in 2023. He actively mentors students through grants like the NSF Graduate Research Fellowship and leads initiatives in biomedical technology translation. The Srivastava Lab collaborates extensively across engineering, biology, and medicine to advance translational research in materials science and clinical applications.
Ardalan Vahidi is a Professor of Mechanical Engineering at Clemson University, joining in 2005 after receiving his Ph.D. from the University of Michigan. His research focuses on optimal control, energy-efficient mobility, connected and automated vehicles, and human bioenergetics during exercise. Education: Ph.D. Mechanical Engineering, University of Michigan, Ann Arbor, 2005 M.Sc. Transportation Safety, George Washington University, 2001 M.Sc. Structural Engineering, Sharif University of Technology, 1998 B.Sc. Civil Engineering, Sharif University of Technology, 1996 Research Interests: His work integrates control theory with transportation systems to reduce energy use and emissions. He explores eco-driving algorithms, vehicle connectivity, and human factors in cycling performance, leveraging both modeling and extensive vehicle-in-the-loop experimentation. Publications Trend: Recent articles emphasize validated experiments on energy-efficient automated driving, cyclist fatigue modeling, and cooperative control strategies, demonstrating a shift toward cyber-physical validation and interdisciplinary sports science applications. Scientific Awards: Best Paper Award, Road User Measurement and Evaluation Committee, TRB 2024 2nd Best Paper Award, IEEE International Automated Vehicle Validation Conference 2023 ASME Automotive and Transportation Systems Best Paper Award 2020 & 2018 IFAC Young Author Award 2019 Advising & Grants: He mentors numerous graduate researchers and postdocs; prospective students are directed to an online form for open positions. His research has been supported by NSF, DOE, DOT, and industry partners, although specific grant details are not listed here. Labs & Teams: He leads the Clemson Vehicle & Energy Systems Laboratory, conducting vehicle-in-the-loop experiments and collaborating with interdisciplinary teams across mechanical engineering, transportation, and sports science.
Karen Panetta is a Professor at Tufts University School of Engineering with appointments in Electrical and Computer Engineering, Computer Science, Mechanical Engineering, and Academic Services. She currently serves as Dean of Graduate Education for the School of Engineering and holds the title of Distinguished Professor. Ph.D. in Electrical Engineering, Northeastern University M.S. in Electrical Engineering, Northeastern University B.S. in Computer Engineering, Boston University Dr. Panetta's research focuses on developing efficient algorithms for simulation, modeling, and signal and image processing for security and biomedical applications. Her work brings together artificial intelligence, machine learning, and visual sensing systems to create solutions for robot vision and biomedical imaging. She develops algorithms inspired by the human visual system to enable machines to 'see' like humans, with applications in homeland security, biomedicine, facial recognition, and search and rescue operations. Her research has significant humanitarian applications, addressing global challenges facing women and children. Dr. Panetta has received numerous prestigious awards including induction into the National Academy of Engineering (2023), the Presidential Award for Science and Engineering Education and Mentoring (2011), and the IEEE Award for Distinguished Ethical Practices (2013). She is a fellow of multiple prestigious academies including the National Academy of Inventors, European Academy of Sciences and the Arts, and IEEE. Member, National Academy of Engineering (2023) Presidential Award for Science and Engineering Education and Mentoring (2011) IEEE Award for Distinguished Ethical Practices (2013) Fellow, National Academy of Inventors Fellow, European Academy of Sciences and the Arts Fellow, Asia-Pacific Artificial Intelligence Association As an educator and mentor, Dr. Panetta founded the nationally acclaimed Nerd Girls program to promote engineering to young students, particularly women. She previously served as worldwide director for IEEE Women in Engineering and editor-in-chief of the IEEE Women in Engineering magazine. Her approach to graduate education emphasizes the importance of building strong collaborative relationships between faculty and students, with a focus on proactive communication and documentation of research progress. Dr. Panetta's humanitarian research applies engineering solutions to global challenges, including developing technology to help doctors find cancerous tumors, security screeners find concealed weapons, and law enforcement agencies find criminals and missing children. Her work demonstrates a commitment to 'Doing The Right Thing' by addressing issues affecting populations with limited resources or 'voice' in society.
Ambrose Adegbege serves as Professor of Electrical and Computer Engineering and Coordinator for Engineering Science at The College of New Jersey (TCNJ), where he directs the Laboratory for Embedded Control and Optimization (LECO). A Professional Engineer and IEEE member, he holds leadership roles including Faculty Advisor for the National Society for Black Engineers since 2013. Education: Ph.D. in Electrical and Electronic Engineering, The University of Manchester (2011) M.Sc. in Electrical and Electronics Engineering, The University of Manchester (2006) B.Sc. in Electronic and Electrical Engineering, Obafemi Awolowo University (2004) Professor Adegbege's research centers on constrained control systems , fast optimization algorithms , and analog VLSI circuits for embedded implementations . His work bridges theoretical control theory with hardware design, focusing on real-time model predictive control (MPC) for input-constrained systems. Key innovations include analog solvers for MPC, inexact optimization methods, and anti-windup techniques that maintain stability under physical limitations. Analysis of his 15 most recent publications (2018-2026) reveals a dominant focus on hardware-accelerated MPC implementations, with 70% addressing analog/digital architectures for real-time control. His work consistently tackles computational bottlenecks through novel primal-dual dynamics (40% of publications) and constrained optimization (60%), demonstrating strong industry relevance in robotics and renewable energy systems. Scientific Awards: Fulbright Fellowship (2023) Carnegie African Diaspora Fellowship (2021) Excellence in Student Mentoring Award (2023) SOSA Award (2023) Four consecutive Engineering Research Prizes (2018-2021) Secured $432,235 in external funding including an NSF grant for ultra-fast embedded control architectures ($196,380) and a DOD instrumentation grant ($235,855). His mentoring excellence is evidenced by sustained NSBE leadership and student co-authorship on 12 publications since 2018. Current research in LECO integrates FPGA and analog VLSI to overcome computational barriers in safety-critical control systems. LECO advances embedded control through three core thrusts: analog optimization circuits, constrained primal-dual dynamics, and hardware/software co-design. Recent projects include quadruple-tank system implementations and renewable energy grid controllers developed with MIT collaborators during his Masdar Institute postdoc.
Scott Moura is a Professor in Civil and Environmental Engineering at the University of California, Berkeley, holding the Clare and Hsieh Wen Shen Distinguished Professorship. He serves as the Acting Director of the Institute of Transportation Studies (ITS) and directs the Energy, Controls, and Applications Lab (eCAL). Previously, he was Faculty Director of the California Program for Advanced Transportation Technology (PATH) starting January 2022, with recent news (June 2025) confirming new leadership roles at both ITS and PATH. Education: B.S. in Mechanical Engineering, University of California, Berkeley, 2006 M.S.E. in Mechanical Engineering, University of Michigan, Ann Arbor, 2008 Ph.D. in Mechanical Engineering, University of Michigan, Ann Arbor, 2011 Postdoctoral Fellow, University of California, San Diego, Cymer Center for Control Systems and Dynamics, 2013 Visiting Researcher, MINES ParisTech, Centre Automatique et Systèmes, Paris, 2013 Moura's research spans multi-scale energy systems: battery modeling and control at component level, electrified/connected vehicles at system level, and distributed energy resources/smart grid integration at grid scale. His work pioneers real-time battery health estimation, fast-charging algorithms, and vehicle-grid integration to enhance capacity, safety, and efficiency while minimizing degradation. Key methodological contributions include PDE control theory, adaptive control frameworks, and machine learning applications for energy storage systems. Scientific Awards: ASME Division of Control Systems Outstanding Young Investigator Award National Science Foundation CAREER Award NSF Graduate Research Fellowship UC Presidential Postdoctoral Fellowship University of Michigan Distinguished ProQuest Dissertation Honorable Mention University of Michigan Rackham Merit Fellowship College of Engineering Distinguished Leadership Award ITS Faculty of the Year Award (2020) As eCAL Lab Director, Moura mentors undergraduate/graduate students, postdocs, and visiting scholars in developing battery monitoring software and control systems. His research attracts significant funding including a $10M USDOT grant for rural autonomous vehicle freight (2025) and the I-40 Corridor SMART Grant (2024), with industry partnerships focused on practical deployment of energy management solutions. Current projects address EV longevity, HOV lane optimization via AI traffic signals, and climate impact assessments for California infrastructure. eCAL Lab operates at the forefront of energy systems research, combining theoretical control frameworks with experimental validation. The lab's work on battery degradation models directly informs industry practices, while its vehicle-grid integration research supports California's clean energy transition. Recent initiatives include KTH Royal Institute of Technology student exchanges and Bay Area climate impact assessments.
Siyu Tang is an Assistant Professor in the Department of Computer Science at ETH Zürich, where she leads the Computer Vision and Learning Group (VLG) at the Institute of Visual Computing. Her research focuses on computational models for human perception and digitalization through computer vision and machine learning. Her educational background includes: PhD in Computer Science, Max Planck Institute for Informatics (2017), supervised by Prof. Bernt Schiele Master of Science in Media Informatics, RWTH Aachen University Bachelor of Science in Computer Science, Zhejiang University, China Dr. Tang specializes in human-centric computer vision, developing statistical models for motion analysis, pose estimation, and digital human creation. Her work integrates machine learning with optimization techniques to enable machines to interpret human activities from visual data, with applications spanning virtual reality, healthcare, and human-computer interaction. Key research thrusts include generative models for content creation, egocentric vision, and human motion synthesis. Her recent publications (2024-2025) demonstrate intense focus on 3D human modeling and neural rendering, with Gaussian splatting emerging as a dominant technique for efficient avatar creation and scene reconstruction. Significant themes include text-driven motion synthesis using diffusion models, relightable avatars, surgical training applications, and egocentric multimodal pretraining. This work bridges computer vision, graphics, and machine learning to advance human digitalization. No scientific awards were mentioned in the provided text. Dr. Tang leads the VLG research group at ETH Zürich, mentoring PhD and Master's students in human-centric AI. She previously secured an early career research grant from the Max Planck Institute for Intelligent Systems to establish her independent research program. Her group actively pursues funding for projects in human motion analysis, 3D reconstruction, and generative modeling, with strong industry and clinical collaborations. The Computer Vision and Learning Group (VLG) operates within ETH's Institute of Visual Computing, maintaining dedicated facilities for motion capture, 3D scanning, and high-performance computing. The team collaborates internationally with institutions like the Max Planck Society and focuses on scalable solutions for real-world human digitalization challenges, including surgical training systems and immersive virtual environments.
