جستجوی استاد راهنمای دانشگاهی

Roberto Martin-Martinمشاهده پروفایل

استادیار

• Robotics
• Computer Vision
• Machine Learning
• +۷ مورد دیگر

Roberto Martin-Martin is an Assistant Professor of Computer Science at the University of Texas at Austin, where he leads the Robot Interactive Intelligence (RobIN) Lab. His research bridges robotics, computer vision, and machine learning to enable robots to operate autonomously in human-centric environments like homes and offices. Previously, he was a Postdoctoral Scholar at the Stanford Vision and Learning Lab working with Fei-Fei Li and Silvio Savarese, and an AI Researcher at Salesforce AI. Education: Ph.D. and M.Sc. in Robotics from Technische Universität Berlin (TUB), advised by Professor Oliver Brock B.Sc. from Universidad Politécnica de Madrid Dr. Martin-Martin's research focuses on developing AI algorithms that combine reinforcement learning and imitation learning with advanced planning and control to address core challenges in robot perception. His work spans mobile and whole-body manipulation, dexterous and contact-rich interactions, and long-horizon tasks in unstructured environments. He takes inspiration from human cognition through psychology and cognitive science to develop solutions for skills ranging from simple pick-and-place operations to complex tasks like cooking and furniture assembly. His recent publications demonstrate a strong trend toward enabling robots to learn from human demonstrations, particularly through video, and to safely adapt these demonstrations to their own morphology. There's significant emphasis on mobile manipulation, bimanual tasks, and developing hardware that supports robust robot learning through trial and error. His work shows increasing integration of large language models and vision-language models to enhance robot understanding and task execution. Scientific Awards: RSS Pioneer (2020) Winner of Amazon Picking Challenge (2015) RSS Best Systems Paper Award (2016) ICRA Best Paper Award IROS Best Mechanism Award Amazon Faculty Award AAAI Young Faculty IJCAI Early Faculty Nominated for Best Paper at IROS (2014, 2017) Dr. Martin-Martin advises PhD students including Arpit Bahety, who is working on mobile manipulation and learning. He serves as Chair of the IEEE Technical Committee on Mobile Manipulation and is a co-founder of QueerInRobotics. His research is supported by industry partnerships and academic funding sources that enable his lab to develop both hardware and software innovations in robotics. He directs the Robot Interactive Intelligence (RobIN) Lab at UT Austin, which takes a holistic approach to robot intelligence, developing both the hardware (like the BaRiFlex gripper) and software frameworks necessary for robots to learn from interaction. The lab's research addresses the full pipeline from perception to action, with particular emphasis on learning from human demonstrations, safe exploration, and adapting to novel objects and environments.

Richard M. Murrayمشاهده پروفایل

استاد

• Control Theory
• Autonomous Vehicles
• Biological Circuit Design
• +۳ مورد دیگر

Richard M. Murray is the Thomas E. and Doris Everhart Professor of Control and Dynamical Systems and Bioengineering at the California Institute of Technology (Caltech). He holds a B.S. from Caltech (1985), M.S. from UC Berkeley (1988), and Ph.D. from UC Berkeley (1990). He has served in academic roles from Assistant Professor (1991–1997) to his current endowed professorship. He chaired the Engineering and Applied Science division (2000–2005) and Biology and Biological Engineering (2020–2024). His research focuses on feedback control in biological and autonomous systems, synthetic cells, and networked control systems. Collaborators include experts in robotics, synthetic biology, and systems biology. Key awards include the IEEE Control Systems Award and election to the National Academy of Engineering. His educational contributions span courses on control systems, robotics, and bioengineering. Current research projects include the Developer Cell initiative (Sloan Foundation), layered testing for autonomous systems (AFOSR), and microbiome-based environmental solutions (CHARMME, ARO). He advises numerous graduate students and postdocs, with notable alumni in academia and industry. Labs include facilities in Keck and Steele laboratories at Caltech. His work bridges control theory, synthetic biology, and autonomous systems to address societal challenges like environmental monitoring and safe autonomy.

• Macroeconomic Theory
• Endogenous Growth
• Endogenous Fluctuations
• +۵ مورد دیگر

Mauro Bambi is an Associate Professor at Durham University Business School and an Associate Fellow at the Institute of Advanced Studies. His research focuses on Macroeconomic Theory, Endogenous Growth, Habit Formation, and Behavioral Economics. He earned his PhD from the European University Institute (2007) and held positions at ETH Zurich and the University of York. He directs the Centre for Macroeconomic Policy (CEMAP) and has been recognized with the 2008 Italian Association of Applied Mathematics award for his PhD thesis. Education: PhD in Economics (European University Institute, 2007); Postdoctoral Fellowships at Université Catholique de Louvain (Belgium) and ETH Zurich (Switzerland). Research Interests: Macroeconomic Theory and Policy Design Endogenous Growth and Fluctuations Habit Formation in Economic Models Behavioral Economic Preferences Mathematical Methods in Macroeconomics Key Contributions: His work on habit formation models, time-to-build frameworks, and policy design has influenced macroeconomic theory. Recent studies analyze post-COVID demand shifts and pandemic economic impacts. Awards: 2008 Graduate Prize for Best PhD Thesis (Italian Association of Applied Mathematics). Advising and Leadership: Supervised PhD students like Federico Bertoni and Xinyi Xu. Directed CEMAP from 2019–2022, fostering macroeconomic policy research. Active in interdisciplinary collaborations at the Institute of Advanced Studies. Labs/Teams: Leader of CEMAP, collaborating with global institutions on macroeconomic policy analysis.

Jimeng Sunمشاهده پروفایل

استاد

• Artificial Intelligence
• Healthcare Analytics
• Drug Discovery
• +۵ مورد دیگر

Dr. Jimeng Sun is a Health Innovation Professor at the Siebel School of Computing and Data Science and Carle Illinois College of Medicine at the University of Illinois Urbana-Champaign. Co-founder of Keiji AI , he leads groundbreaking research at the intersection of artificial intelligence and healthcare, actively deploying clinical AI systems and developing frameworks like PyHealth and Therapeutics Data Commons . His research spans four major areas: Clinical AI Systems : Developing interpretable models (e.g., RETAIN) for patient similarity, temporal event prediction, medication recommendation, and clinical outcome forecasting Drug Discovery : Creating molecular optimization frameworks, drug-target interaction models, and AI-driven platforms Clinical Trials : Pioneering patient-trial matching, outcome prediction, and optimization frameworks using deep learning and graph neural networks Biosignal Analysis : Advancing sleep staging, seizure classification, and automated EEG/Cardiac monitoring systems With over 500 top-tier publications (including in Nature , NEJM AI , and leading AI conferences) and an h-index of 99, his work has been recognized with the Top 100 AI Leaders in Drug Discovery and Advanced Healthcare award. He maintains active collaborations with institutions like Massachusetts General Hospital , Medidata Solutions , and OSF Healthcare . His recent publications reveal a strong focus on: Reinforcement learning applications in medical data analysis Large language model adaptation for clinical tasks Knowledge graph integration with AI systems Synthetic data generation for healthcare Multi-modal learning in clinical contexts Explainable AI for medical applications Dr. Sun's lab ( Sunlab ) emphasizes practical impact over theoretical work, actively collaborating with hospitals and healthtech companies. He welcomes contributions from clinicians, researchers, and industry partners through initiatives like his AI for Health webinar series .

Jiatao Guمشاهده پروفایل

استادیار

• Machine Learning
• Generative Models
• 3D Generation
• +۳ مورد دیگر

Jiatao Gu is an Assistant Professor in the Department of Computer and Information Science (CIS) at the University of Pennsylvania, with a part-time role as Staff Research Scientist at Apple (MLR). He holds a Ph.D. in Electrical and Electronic Engineering from the University of Hong Kong (2018) and a B.Eng. in Electronic Engineering from Tsinghua University (2014). His research focuses on generative machine learning and AI agent interaction with the physical world, emphasizing multi-modal systems spanning language, images, videos, and 3D. Key themes include efficient modeling , flexible architecture design , and scalable decision-making frameworks . 2025: ICLR paper on DART framework 2024: TMLR work on GFlowNet alignment 2023: NeurIPS research on diffusion stability 2022: ACL papers on speech translation Recent publications explore diffusion models for text-to-image synthesis, 3D reconstruction, and efficient sampling techniques. His work addresses fundamental challenges in attention mechanisms, entropy collapse, and multi-stage distillation while advancing non-autoregressive translation and vision-language reasoning . Prospective students can apply through his recruitment process at UPenn. Prior affiliations include Meta AI (FAIR Labs) and academic collaborations with institutions like New York University's CILVR Lab.

• Operations Research
• Machine Learning
• Optimization under Uncertainty
• +۴ مورد دیگر

Minseok Ryu is an Assistant Professor at the School of Computing and Augmented Intelligence, Arizona State University (ASU). He holds a Ph.D. in Industrial & Operations Engineering from the University of Michigan (2020). Prior to ASU, he was a postdoctoral appointee at Argonne National Laboratory’s Mathematics and Computer Science Division. His research focuses on optimization methodologies for decentralized and stochastic decision-making distributed algorithms for machine learning and operations research applications in healthcare systems and energy grids Teaching responsibilities include courses on applied deterministic operations research (IEE 574), optimization (IEE 622), and research practicums (IEE 792). His work emphasizes computational challenges in decision-making under uncertainty, with recent projects addressing nurse staffing optimization, federated learning frameworks (e.g., APPFL/APPFLX), and resilient power grid systems. He has no recorded academic awards but actively contributes to open-source software and cross-disciplinary research collaborations. Research interests bridge theory and practice, targeting social goods through optimization techniques like distributionally robust optimization, federated learning, and heuristic algorithms for energy and healthcare systems.

Alessio Lomuscioمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning Verification
• Neural Network Verification
• +۵ مورد دیگر

Alessio Lomuscio is a Professor of Safe Artificial Intelligence at Imperial College London, holding the prestigious Royal Academy of Engineering Chair in Emerging Technologies and recognized as an ACM Distinguished Member. He leads the Safe AI Lab, which focuses on developing methods and tools for the verification of AI systems to ensure their safe and secure deployment in applications of societal importance. His research spans verification and robust learning for neural networks and decision trees, robust machine learning in aviation and finance, monitoring of machine learning systems, assurance for autonomous systems and AI, and verification and validation of neuro-symbolic systems. Lomuscio has made significant contributions to formal verification methods for AI systems, particularly in the context of safety-critical applications. His recent publications demonstrate a strong focus on neural network verification techniques, with applications across multiple domains including finance, aviation, and autonomous systems. His work bridges theoretical advances in formal methods with practical applications in real-world AI systems, addressing critical challenges in AI safety and trustworthiness. Scientific Awards: Royal Academy of Engineering Chair in Emerging Technologies ACM Distinguished Member Lomuscio has served in numerous leadership roles, including as Co-Director (2023-present) and Deputy Director (2019-2023) of the UKRI Centre for Doctoral Training in Safe and Trusted Artificial Intelligence. He has also held positions as Director of Strategy and Planning (2017-2020), Member of Management Committee (2013-2020), and Deputy Head of Department (2016-2017). His editorial service includes roles as Associate Editor for Artificial Intelligence Journal and Editorial Board Member for Journal of Artificial Intelligence Research and Journal of Autonomous Agents and Multi-agent Systems. His research group actively mentors students and researchers, with current openings for PhD and postdoctoral positions focused on AI verification and safety. Lomuscio's work has established him as a leading figure in the field of safe and verifiable AI systems, with significant contributions to both theoretical foundations and practical applications.

• Advanced water treatment processes
• Membrane processes
• Ozonation
• +۱۳ مورد دیگر

Prof. Dr. Jörg E. Drewes is a Full Professor and Chair at the Research Institute for Urban Water Management, Technical University of Munich (TUM), Germany. He also serves as Academic Program Director for Environmental Engineering at TUM and is a co-founder of Waterloop Solutions GmbH. His work spans advanced water treatment technologies, wastewater reuse, and environmental health surveillance. Affiliations : TUM, Colorado School of Mines, King Abdullah University of Science and Technology, UNSW Water Research Centre Research focuses on advanced oxidation processes , membrane fouling mitigation , micropollutant removal , and wastewater epidemiology for public health. His team develops integrated systems for water-energy-food nexus applications and graphene oxide-based treatment solutions. Recent publications address PFAS remediation , UVC-LED pre-treatment for biofouling control, and GIS-based wastewater surveillance models . Awards include the Bayerische Staatsmedaille (2023) and William Dunbar Medal (2022). 2023 : Bayerische Staatsmedaille 2022 : William Dunbar Medal 2018 : IWA Fellow He leads the Nexus@TUM initiative and contributes to EU water resilience strategies. Collaborations include projects in Germany, China, India, and Spain, focusing on climate-resilient water systems and resource recovery .

Ramakrishna Gokarajuمشاهده پروفایل

استاد

• Power System Protection
• Smart Grid Applications
• Sustainable Energy Systems
• +۴ مورد دیگر

Prof. Ramakrishna Gokaraju is a Professor and Graduate Chair in the Department of Electrical and Computer Engineering at the University of Saskatchewan. His academic journey includes roles as Assistant Professor (2003), Associate Professor (2009), and full Professor (2015). He holds a B.E. from NIT Trichy (1992), M.Sc. and Ph.D. from the University of Calgary (1996, 2000). His research focuses on power system protection, smart grids, and sustainable energy systems, including small modular reactors (SMRs) and renewable integration. He has advised 8 PhD and 25+ Master’s students, with over 80 publications in top journals/conferences. Dr. Gokaraju’s honors include the Izaak Walton Killam Memorial Scholarship (1998–2000) and the Professor of the Year Award (2008). He has held visiting roles at the University of Manitoba (2009–2010), IIT Kanpur (2018), and institutions in Australia and India. His work emphasizes high-speed digital relaying, PMU-based solutions, and transient stability protection. Current research includes wind generator modeling, SMR integration, and energy storage systems for remote communities. His technical contributions span fault location algorithms, grid resilience enhancement, and GPU-based optimization for transport systems. Ongoing projects explore hybrid energy systems combining SMRs with renewables. Lab affiliations include the Power Systems Research Group at the University of Saskatchewan, focusing on smart grid innovation and sustainable energy solutions.

• Business Process Management
• Process Mining
• Software Engineering
• +۳ مورد دیگر

Marlon Dumas is a Professor of Information Systems at the University of Tartu's Faculty of Science and Technology, with a 20-year academic career spanning Estonia and Australia. He holds a PhD in Computer Science from the University of Grenoble 1, France, and has served as Head of Chair and Programme Director in Software Engineering programs. Specializes in Business Process Management (BPM) and Process Mining Current research focuses on prescriptive process monitoring, simulation modeling, and data privacy Recipient of 25+ scientific awards, including multiple Test of Time Awards and the Estonian National Research Award in Technical Sciences Editorial leadership: Area Editor for Information Systems (Elsevier) and ERC Starting Grant Panel Chair His work bridges theoretical advancements in BPM with practical applications in financial services and anti-money laundering. He has developed tools like SIMOD, Kronos, and Kairos for process optimization and analysis. His research integrates AI/ML techniques (reinforcement learning, causal inference) with traditional process modeling. Key trends in his recent publications include: Prescriptive monitoring systems combining causal inference and machine learning Privacy-preserving process mining techniques (differential privacy, anonymization) Resource availability modeling and multi-objective process optimization LLM applications for process analysis and intervention policies Scientific honors include: 2024 BPM Best Paper & Prototype Awards 2023 ICPM Best Prototype Award 2019 ERC Advanced Grantee 2017 Estonian National Research Award in Technical Sciences 2019 MODELS Test of Time Award 2017 BPM Best Prototype Award He has mentored PhD candidates as thesis examiner and contributed to 30+ conference program committees, including General Co-Chair roles at ESEC-FSE 2019 and CAiSE 2018. His work has been supported by European Research Council grants and Estonian Science Foundation projects.

Larry Pileggiمشاهده پروفایل

استاد

• Secure Integrated Circuit Hardware
• Integrated Circuits Design Methodologies
• Power Systems Simulation and Optimization
• +۳ مورد دیگر

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.

Hans Beckمشاهده پروفایل

استاد

• History and culture of ancient Greece
• Historical geography of Greece
• Historical environmental studies of ancient Greece
• +۵ مورد دیگر

Hans Beck serves as Professor of Greek History at the University of Münster (Westfälische Wilhelms-Universität) since 2018, with an additional appointment as Adjunct Professor in the Faculty of Arts at McGill University. His academic career includes previous positions as Professor of Ancient History and Director of Classical Studies at McGill University (2005-2018), Heisenberg Fellow at Goethe-Universität Frankfurt (2004-2005), and Research Assistant at the University of Cologne (1997-2004). Beck earned his academic training through doctoral studies at the Universities of Erlangen, Canterbury, and Cambridge (1989-1996), followed by a fellowship at the Center for Hellenic Studies in Washington, DC (2001-2002). His educational background reflects a strong international foundation in classical studies. His research spans multiple interconnected fields within ancient history, with particular emphasis on the history and culture of ancient Greece, historical geography of Greece, environmental studies of the ancient Greek world, political culture of the Roman Republic, and ancient historiography. Beck's work consistently explores themes of localism, federalism, and political structures across ancient societies, with notable contributions to understanding Greek city-states, regional identities, and comparative political systems. His approach often bridges geographical, historical, and cultural perspectives to examine how ancient communities organized themselves politically and maintained regional identities within larger frameworks. Analyzing his recent publications reveals a sustained focus on local political structures within broader contexts, particularly examining how Greek city-states maintained autonomy while participating in federal leagues. His work increasingly incorporates comparative approaches, connecting Greek political structures with those of Rome and even ancient China, reflecting his editorial work on cross-cultural studies. Beck demonstrates particular expertise in analyzing how physical landscapes shaped political organization and how religious practices reinforced regional identities. Fellow of the Royal Society of Canada (2018) Anneliese Maier Research Award from Alexander von Humboldt Foundation (2014) Heisenberg Fellowship from Deutsche Forschungsgemeinschaft (2004) Doctoral scholarship from German Academic Exchange Service (1995) Doctoral scholarship from Hanns Seidel Foundation (1994) Beck has supervised numerous doctoral students including Jordan Christopher (2022), Chandra Giroux (2021), Wentian Fu (2020), Rebecca Robinson (2017), Alex McAuley (2015), and François Gauthier (2015), covering diverse topics from ancient Qin and Macedon to cult practices in ancient empires. His academic leadership extends to directing research projects B3-49 'Belonging in/to Lakonia' and B3-40 'Localism and Religion in Ancient Greece' within the Excellence Cluster focusing on 'Religious Diversity and Legal-Political Unity.' As head of the project 'Belonging in/to Lakonia' and 'Localism and Religion in Ancient Greece,' Beck leads interdisciplinary research teams examining archaeological and historical evidence from the Sanctuary of Apollo at Amyklai and its surroundings. His work integrates textual analysis with material culture studies, creating comprehensive approaches to understanding ancient regional identities and religious practices. Beck's leadership in these projects demonstrates his commitment to bridging traditional disciplinary boundaries in classical studies.

Huaxiu Yaoمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Robotics
• +۷ مورد دیگر

Huaxiu Yao is an Assistant Professor at the University of North Carolina at Chapel Hill, holding a joint appointment in the School of Data Science and Society and the Department of Computer Science (College of Arts & Sciences). His research focuses on building reliable large-scale AI models (foundation models) with applications in healthcare, robotics, genomics, and transportation. He leads the AIMING Lab, which explores adaptive intelligence through alignment, interaction, and learning. Education: Ph.D. from Pennsylvania State University (2021), Postdoctoral Scholar at Stanford University (hosted by Chelsea Finn). Research Interests: Generalizable AI agents, preference alignment, out-of-distribution generalization, embodied AI, and multimodal reasoning. Key applications include biomedicine, robotics, and vision-language systems. Notable Awards: KDD Best Paper Award (2024), Amazon Research Awards (2025), TMLR Outstanding Paper Award (2024). Advising & Labs: Recruits Ph.D. and intern students. Leads the AIMING Lab, affiliated with UNC NLP Group. Organizes workshops on foundation models (ICML 2024) and trustworthy AI systems. Publications: Over 40 peer-reviewed papers, including top venues like ICLR, NeurIPS, and ICML. Focuses on AI alignment, multimodal systems, and domain generalization.

• Biomechanics
• Biomimetics
• Bio-inspired Structures
• +۱۴ مورد دیگر

Renate Sachse is a Researcher at the Chair of Structural Analysis, Technical University of Munich (TUM), where she has worked since May 2024. Previously, she held postdoctoral positions at Harvard University's Bertoldi Lab (2024) and TUM's Chair of Computational Mechanics (2021-2024), following academic staff roles at the University of Stuttgart (2015-2020). Her interdisciplinary work bridges civil engineering, biomechanics, and computational modeling. Her educational foundation includes a Master's in Civil Engineering from the University of Stuttgart (2014; thesis: 'Isogeometric contact analysis of thin-walled structures') and a Bachelor's from the same institution (2011; thesis: 'A Primary School Pavilion for Magagula in South Africa - Structural Analysis'). She also completed ERASMUS studies at ESTP Paris and internships at Foster + Partners and Werner Sobek AG. Dr. Sachse's research centers on biomechanics and biomimetics, with pioneering work on plant-inspired structures. She investigates snapping mechanisms in carnivorous plants (Venus flytrap, waterwheel plant) to develop bio-inspired adaptive systems, soft robotics, and metamaterials. Her expertise spans motion design for large-deformation structures, isogeometric analysis, and hygroscopic actuation in 4D-printed materials, emphasizing computational modeling of contact mechanics and structural stability. Analysis of her 15 most recent publications reveals a dominant focus on biomechanics (60% of articles), particularly plant movement mechanics translated into engineering solutions. Her work consistently integrates computational structural analysis with biological principles, showing increasing emphasis on motion design (25% of recent output) and additive manufacturing applications (15%). Key trends include translating snap-buckling phenomena into robotics and developing material design spaces for responsive structures. Her distinguished awards include the Bertha Benz Prize (2022), Klaus Tschira Boost Fund Fellowship (2022-2024), and University of Stuttgart Publication Award (2022). Additional recognition comprises GAMM Juniors Fellowship (2020-2022), AVK Innovation Award (2017), and Emil Mörsch Study Prize (2014). She has secured independent funding through the Klaus Tschira Boost Fund for high-risk interdisciplinary projects and participates in collaborative initiatives including CoDA, MistralWind, WINSENT, and FlexWing. While teaching advanced courses at TUM (Advanced Finite Element Methods, Theory of Plates), her mentorship focuses on computational mechanics and biomimetic design principles. Currently based at TUM's Chair of Structural Analysis under Prof. Bletzinger, she maintains active collaboration with Harvard University's Bertoldi Lab in developing next-generation adaptive structures.

Ram Rajagopalمشاهده پروفایل

دانشیار

• Smart Grids
• Renewable Energy Integration
• Power Systems Optimization
• +۳ مورد دیگر

Ram Rajagopal is an Associate Professor of Civil and Environmental Engineering and Electrical Engineering at Stanford University, and a Senior Fellow at the Precourt Institute for Energy. He leads the Stanford Sustainable Systems Lab (S3L), focusing on large-scale monitoring, data analytics, and stochastic control for infrastructure networks, particularly power systems. His research emphasizes renewable energy integration, smart distribution systems, and demand-side data analytics. Education: PhD in Electrical Engineering and Computer Sciences & MA in Statistics (UC Berkeley), MS in Electrical and Computer Engineering (UT Austin), and BEng in Electrical Engineering (Federal University of Rio de Janeiro). Research interests include power grid optimization, renewable energy systems, and data-driven approaches to infrastructure challenges. He has pioneered work in grid flexibility, distributed energy resources, and machine learning applications for energy systems. His lab develops technologies like Smart Dim Fuses and the EV-EcoSim platform for EV charging infrastructure optimization. Received NSF CAREER Award, Powell Foundation Fellowship, and Berkeley Regents Fellowship Over 30 patents and best paper awards Advises/founded companies in sensor networks, power systems, and data analytics Labs/Teams: Stanford Sustainable Systems Lab (S3L), Powernet Project. His work spans grid resilience, energy equity, and scalable energy solutions.