جست‌وجوی استاد و پژوهشگر

Marco Cuturiمشاهده پروفایل

استاد

• Machine Learning
• Optimal Transport
• Statistics
• +۳ مورد دیگر

Marco Cuturi is a Professor of Statistics at ENSAE (École Nationale de la Statistique et de l'Administration Économique), part of Institut Polytechnique de Paris, where he has been affiliated since 2016, working part-time since 2018. He is also a CREST Permanent Member and a Research Scientist at Apple ML Research in Paris, having previously worked at Google Brain from 2018 to 2022. His academic journey includes positions as an Associate Professor at Kyoto University's Graduate School of Informatics (2010-2016), a lecturer at Princeton University's ORFE department (2009-2010), and postdoctoral research at the Institute of Statistical Mathematics in Tokyo (2005-2007). Dr. Cuturi earned his Ph.D. from École des Mines de Paris in 2005 and holds a master's degree from ENS Cachan, having graduated from ENSAE in 2001. His research centers on the application of optimal transport theory to machine learning and statistics, with significant contributions to computational optimal transport, kernel methods, and time series analysis. His influential work includes the development of the Sinkhorn distance for fast computation of optimal transport, soft-DTW for time series alignment, and various applications of optimal transport in machine learning. His research program explores how geometric structures can be leveraged in statistical learning, with recent work focusing on entropic optimal transport solvers, Gromov-Wasserstein distances, and applications to single-cell biology and multimodal learning. Dr. Cuturi's publications reveal a consistent trajectory of developing theoretically grounded yet computationally efficient methods that bridge optimal transport theory with practical machine learning applications across diverse domains including computer vision, natural language processing, and computational biology. Among his professional recognitions is a Notable Paper Award at AISTATS 2020 for his work on regularization in optimal transport. He serves as an Associate Editor for JMLR and has held senior area chair positions at NeurIPS and ICML. Dr. Cuturi has organized major machine learning events including the Machine Learning Summer School in Kyoto (2012, 2015) and co-organized workshops on optimal transport at major conferences. Dr. Cuturi has supervised numerous doctoral students including Nina Vesseron, Théo Uscidda, Charlotte Bunne, and Othmane Sebbouh, with research spanning optimal transport applications in generative modeling, representation learning, and biological data analysis. His current research group at ENSAE and Apple continues to push the boundaries of optimal transport methodology and its applications to cutting-edge machine learning problems.

• Aerospace Propulsion
• Computational Fluid Dynamics
• Machine Learning
• +۶ مورد دیگر

Andrea Ferrero is an Associate Professor at the Polytechnic University of Turin , affiliated with the Department of Mechanical and Aerospace Engineering (DIMEAS) . He leads research in aerospace propulsion, computational fluid dynamics (CFD), machine learning, and reduced-order modeling, with a focus on turbulent flows, turbomachinery, and combustion. Scientific Director for projects like NextGenSProDesT (2023–2025) and PARSEC (2024–2027). Active in commercial research collaborations with companies like Avio Aero and GE. His work bridges theoretical and applied research, emphasizing AI/ML integration in aerospace systems. Research Interests Andrea Ferrero's research spans: Aerospace Propulsion : Advanced rocket nozzles, hybrid engines, and hydrogen turbofans. Computational Fluid Dynamics : Turbulent and compressible flow simulations, reduced-order models (ROM). Machine Learning : Data-driven turbulence modeling, stress correction with physical constraints. Combustion & Propellants : UV-curable solid propellants, photocurable materials for additive manufacturing. Recent Publications His 2025 articles address hydrogen turbofan fuel systems, rocket nozzle retro-flows, and UV-curable propellants, reflecting trends in sustainable propulsion and AI-enhanced CFD. Earlier works include turbomachinery simulations, resonant igniters, and microwave material uncertainty analysis. Teaching & Students He teaches Innovative Approaches to Turbulent Flow Simulation and Reduced Basis Methods in doctoral programs. His PhD advisees explore topics like: Turbulent flow modeling (Giacomo Gedda, Lorenzo Folcarelli) Hybrid rocket engine optimization (Leonardo Stumpo) Cryogenic fuel systems (Alessandra Zumbo) Topological heat exchanger design (Alessandro Chiodi)

• Distributed Systems
• Computer Networks
• Distributed Computer Systems
• +۷ مورد دیگر

Oznur Ozkasap is a Professor of Computer Engineering and Head of Department at Koç University, Istanbul, Turkey. She leads the Distributed Systems and Reliable Networks (DISNET) Research Laboratory, focusing on cutting-edge research in distributed systems, networking, and related fields. Her educational background includes a PhD (2000), Master's Degree (1994), and Bachelor's degree (1992), all from Ege University. She has been instrumental in advancing research in distributed systems and networks through her academic and leadership roles. Professor Ozkasap's research interests span a wide range of topics in distributed systems, including distributed systems, computer networks, distributed computer systems, peer algorithms, energy efficiency, trusted network protocols, cloud and edge computing, mobile and vehicular networks, security in distributed systems, and artificial intelligence in distributed systems. Her work has significant applications in smart cities, stock exchanges, healthcare, air traffic control, intelligent transportation, and smart grids. Her research output shows a strong focus on the intersection of distributed systems with emerging technologies like blockchain, edge computing, and AI. Recent publications indicate growing interest in federated learning, energy-efficient systems, and blockchain applications for energy management, reflecting current trends in the field. The Informatics Association of Turkey Prof. Aydın Köksal Computer Engineering Science Award (2019) Best Paper Award at the 16th IEEE International Conference on Pervasive Intelligence and Computing (PiCom 2018) Professor Ozkasap actively contributes to the academic community through editorial roles, conference organization, and professional service. She serves as an Area Editor for Future Generation Computer Systems journal and has held leadership positions in conference organization. She is also deeply involved in promoting women in computing as Vice Chair of the Ankara ACM-W Professional Chapter and a member of the Center for Gender Studies at Koç University Management Board. Her DISNET Research Laboratory serves as a hub for collaborative research in distributed systems and reliable networks.

Carolyn McGregorمشاهده پروفایل

استاد

• Health Informatics
• Artificial Intelligence
• Big Data Analytics
• +۳ مورد دیگر

Professor Carolyn McGregor is a distinguished academic holding positions at both University of Technology Sydney (UTS) and University of Ontario Institute of Technology (UOIT). At UTS, she serves as Professor in the Faculty of Engineering and Information Technology and School of Computer Science. At UOIT, she holds multiple leadership roles including Research Chair in AI for Health and Wellness, Director of the Joint Research Centre in AI for Health and Wellness, and former Canada Research Chair in Health Informatics. Professor, University of Technology Sydney (2017-present) Co-Director, Joint Research Centre in AI for Health and Wellness (2019-present) Research Chair in AI for Health and Wellness (2020-present) Research Excellence Chair in Health Informatics (2018-present) Professor, University of Ontario Institute of Technology (2011-present) Dr. McGregor earned her PhD in Computer Science from University of Technology Sydney (1998-2003) where she graduated with first-class honors. Her academic journey began with a Bachelor of Applied Science in Computer Science from the same institution (1987-1990). Her research expertise spans health informatics with a particular focus on neonatal critical care, where she pioneered work applying big data analytics to bedside monitoring in intensive care units. Her research interests have expanded to include AI applications in extreme environments including space, firefighting, military operations, and police wellness. She has developed innovative frameworks for reliable big data platforms that process high-frequency physiological streams for real-time clinical decision support. McGregor's publication record demonstrates remarkable breadth, moving from neonatal care applications to space medicine, law enforcement wellness, and veterinary disease control. Her work consistently applies computational methods to healthcare challenges across diverse contexts, with recent publications showing increased focus on AI applications in extreme environments and stress monitoring systems. Her research bridges computer science with clinical practice, creating tools that directly impact patient care and professional wellness. Member of the Order of Australia (2014) UTS Alumni Award for Excellence (2015) FBIT Research Excellence Award (2018) Two-time Canada Research Chair in Health Informatics IBM Centenary Video feature as significant innovator Dr. McGregor has secured substantial research funding from major organizations including Canadian Institutes of Health Research (CIHR), Natural Sciences and Engineering Research Council (NSERC), and Australian Research Council. Her funded projects include Autonomic Modulation Training for police resilience, Resilience Assessment for Firefighters, and Improving Neonatal Critical Care through Real Time Analysis. She has supervised numerous students and collaborators across disciplines, fostering interdisciplinary research teams focused on health informatics applications. Her leadership extends to directing the Health Informatics Laboratory and serving as Associate Dean of Research at UOIT. As Director of the Joint Research Centre in AI for Health and Wellness, she leads a multidisciplinary team developing AI solutions for health monitoring across extreme environments. Her work with NASA-related projects on space medicine and astronaut health demonstrates the far-reaching impact of her research framework, which has been adapted from neonatal care to space exploration contexts.

Meik B. Frankeمشاهده پروفایل

استاد

• Process Optimization
• Reactive Distillation
• Hybrid Separation Processes
• +۶ مورد دیگر

Meik B. Franke is a Full Professor in Sustainable Process Technology at the Technical University of Dortmund. His research spans chemical engineering optimization, reactive distillation, hybrid separation processes, and sustainable energy systems. Key Affiliations: Technical University of Dortmund, Collaborations with E. Zondervan, K.V. Camarda Research Themes: Ferrofluidic extraction, Biodiesel production, Discrete-event simulation, Algae biorefinery optimization His recent work focuses on integrating MILP with discrete-event simulation and applying AI to chemical process optimization. He has presented at conferences on process optimization challenges in the chemical industry, emphasizing sustainability and energy efficiency. His publications reveal expertise in: Optimization algorithms (Benders decomposition, MILP-DES integration) Distillation and hybrid process design Biorefinery systems for biodiesel and algae-derived products Dynamic process modeling and control Heat integration for energy efficiency AI/ML applications in chemical engineering

Christof Teuscherمشاهده پروفایل

استاد

• Non-classical computation
• Biomolecular computation
• Neuromorphic computation
• +۷ مورد دیگر

Christof Teuscher is a Professor at Portland State University's Maseeh College of Engineering & Computer Science. His research focuses on developing disruptive computing paradigms through interdisciplinary approaches that merge computer science, physics, biology, and cognitive science. Education: PhD in Computer Science, Swiss Federal Institute of Technology Lausanne (EPFL), 2004 M.Sc. in Computer Science, EPFL, 2000 Research Interests: Non-classical computation Neuromorphic and thermodynamic computing Machine learning optimization Complex adaptive systems Reservoir computing in biological/chemical systems Recent Academic Contributions: 2025 course: Hardware for AI/ML (ECE 410/510) Labs developing reversible computing architectures NSF REU and altREU programs for undergraduates Artist-in-the-Lab (AiL) interdisciplinary program Advising & Lab Activities: Supervising PhD student Byron Gregg Published 300-page research book with students Presentations at IEEE ICRC and Green Computing conferences

Lucas C. Cordeiroمشاهده پروفایل

استاد

• Software Model Checking
• Automated Software Testing
• Program Synthesis
• +۴ مورد دیگر

Lucas C. Cordeiro is a Full Professor in the Department of Computer Science at the University of Manchester, where he leads the Systems and Software Security (S3) Research Group and serves as the Arm Centre of Excellence Director. He maintains affiliations with the Federal University of Amazonas (Brazil), Trusted Digital Systems Cluster, and Formal Methods Group. His industry roles include Chief Technology Officer at VeriBee and technical advisory board membership at Axiomise. His research focuses on: Formal verification techniques (bounded/unbounded model checking) Automated software testing and program synthesis Security of embedded and cyber-physical systems Applications of AI/ML in software verification Concurrent software verification Recent publications demonstrate strong emphasis on AI/formal method integration, including LLMs for verification tasks, neural network repair, and security vulnerability detection. His work frequently develops practical tools like ESBMC (bounded model checker) and FuSeBMC (fuzzer). Major scientific honors include: Most Influential Paper (ASE'23) Distinguished Paper Awards (ICSE'11, ASE'24) 50+ competition awards in SV-COMP/Test-Comp (2012-2025) He leads significant grants totaling over $13M from EPSRC, Intel, Ethereum Foundation, GCHQ, and others. Current projects include AICodeRepair (self-healing code via LLMs), SECCOM (hardware security), and EnnCore (neural network security). He directs the S3 Research Group and Arm Centre of Excellence, developing verification tools that consistently win international competitions.

• Engineering Management
• Technology Management
• Systems Thinking
• +۶ مورد دیگر

Mehran Shahhosseini is a Professor in the Applied Engineering & Technology Management department at Indiana State University's Bailey College of Engineering & Technology, currently serving as PhD Coordinator since October 2023. With over 25 years of academic experience since earning his Ph.D. in 1999, he previously directed the College from 2014-2021 and has held Graduate Faculty status since 2009 with Chair Status since 2015. His interdisciplinary career bridges engineering practice, academic leadership, and cutting-edge research in technology management. His educational foundation includes: Ph.D. in Mechanical Engineering, Lamar University (1999) M.S. in Materials Engineering, Isfahan University of Technology (1991) B.S. in Metallurgical Engineering, Tehran University (1988) Shahhosseini's research integrates systems thinking with emerging technologies, focusing on AI applications in workforce development, engineering education innovation, and quality management systems. His work pioneers concept mapping techniques for diagnostic skills training and examines AI's transformative impact on middle-skilled labor markets. Recent publications reveal a strategic shift toward practical AI implementation frameworks and curriculum modernization for technology management education, addressing critical gaps between academic preparation and industry needs. His publication trajectory demonstrates growing emphasis on machine learning applications in healthcare administration, defense systems, and sustainable materials engineering, with increasing interdisciplinary collaboration across computer science, industrial engineering, and public policy domains. This evolution reflects his commitment to solving complex real-world problems through technological innovation and educational transformation. Key recognitions include: Outstanding Faculty Council Service of Bailey College of Engineering & Technology (2023) First Destination Survey recognition for student success contributions (2020) Outstanding Supervisor Award (2018) Shahhosseini has secured over $2.5 million in competitive funding, primarily from NSF, including the $999,019 'Upskilling and Reskilling Middle-skilled Workers for Jobs Created by AI' grant and the $999,999 STARS II scholarship program. His leadership extends to advising PhD candidates, directing curriculum revisions for doctoral programs, and developing assessment tools for systems thinking evaluation. Current projects focus on AI infrastructure integration and workforce adaptation strategies. He actively shapes academic policy through service on the Faculty Affairs Committee, Strategic Planning Steering Committee, and as former Secretary of the Graduate Council, demonstrating sustained institutional commitment beyond his research and teaching responsibilities.

Anita Rajaمشاهده پروفایل

استاد

• Artificial Intelligence
• Multi-agent Systems
• Machine Learning
• +۴ مورد دیگر

Anita Raja is a Professor of Computer Science at Hunter College and a member of the doctoral faculty in Computer Science at the Graduate Center, City University of New York (CUNY). She previously served as Acting Chair of the Department (2024-2025), Associate Dean of Research and Graduate Programs at The Cooper Union's Albert Nerken School of Engineering (2014-2019), and Associate Professor at The University of North Carolina at Charlotte (2003-2014). Her educational background includes a B.S. Honors in Computer Science with a minor in Mathematics summa cum laude from Temple University (1996), and M.S. and Ph.D. in Computer Science from the University of Massachusetts Amherst (1998, 2003). Anita Raja's research focuses on the intersection of bounded rationality, distributed problem-solving, and artificial intelligence. Her work particularly emphasizes designing and controlling rational agents in real-time, multi-agent environments under uncertainty and limited computational resources. She directs the Distributed Artificial Intelligence Research (DAIR) Lab, which has produced significant work in areas such as deep learning program refactoring, health data analysis using generative algorithms, and technical debt in machine learning systems. Her recent publications demonstrate a strong focus on improving deep learning systems through automated refactoring techniques and addressing challenges in migrating imperative deep learning programs to graph execution models. Her work bridges theoretical AI concepts with practical software engineering applications in machine learning systems. 2006 UNCC College of Computing Essam El-Kwae Student-Faculty research award Best Paper Award at the 2010 IEEE Intelligent Agent Technology Conference Distinguished Paper award at FASE 2025 2021 National Institutes of Health's Decoding Maternal Morbidity Challenge prize Named one of 75 "Notable Women in Tech" by Crain's New York Business in 2019 Elected to serve on the Executive Council of AAAI in 2022 Anita Raja has successfully advised multiple graduate students including Jeffrey Hsu (Masters thesis on "Intuiting Interaction: Meta-Reasoning and Meta-Learning as Foundations for Intelligent User Interfaces") and Dylan Dominguez. Her research is supported by grants from NSF, NIH, ONR, DARPA, DHS and PNNL. She serves as a co-investigator at the Civic-Led Urban Adaptation Research Center (CIVIC-UARC) at the Cornell Mui Ho Center for Cities. She directs the Distributed Artificial Intelligence Research (DAIR) Lab which has been actively working on projects related to deep learning systems, health informatics, and multi-agent systems. The lab has received funding from multiple prestigious sources including NSF and NIH, and has produced award-winning research in both academic conferences and real-world challenges.

Ashutosh Trivediمشاهده پروفایل

دانشیار

• Formal Methods
• Reinforcement Learning
• Software Engineering
• +۴ مورد دیگر

Ashutosh Trivedi is an Associate Professor of Computer Science at the University of Colorado Boulder, specializing in formal methods for safety-critical learning-enabled systems. His research bridges computer science, control theory, and machine learning with a focus on trustworthy AI systems that operate safely, fairly, and responsibly. His research interests encompass Formal Methods , Reinforcement Learning , Software Fairness , and Software Accountability . Trivedi develops mathematical approaches to bring precision to AI system design, using formal languages, automata, and logic to transform vague natural-language instructions into clear specifications. His work emphasizes principled interaction with AI, creating explainable and reliable systems through techniques like reinforcement learning algorithms for cardiac pacemaker design, SAT solvers for grounding LLM outputs, and formal logic for capturing legal obligations. His recent publications reveal a strong trend toward integrating formal verification with machine learning systems, particularly focusing on fairness testing, safety constraints in reinforcement learning, and neurosymbolic approaches. The research spans applications from legal-critical software to medical devices, with consistent emphasis on accountability and ethical considerations in AI systems. 2022 NSF CAREER award Liverpool Fellowship Distinguished Paper Award at CAV for Regular Reinforcement Learning Senior Member of the ACM Royal Society Wolfson Visiting Fellowship NeuS 2025 Disruptive Idea Award Trivedi actively mentors PhD students, having supervised multiple successful thesis defenses including Shadi Tasdighi Kalat on multi-agent games, Mateo Perez on formal languages for reinforcement learning, John Komp on pacemaker therapy, Vishnu Murali on functional proofs, and Taylor Dohmen on sequential optimization. His group, the Programming Languages and Verification (CUPLV) at CU Boulder, focuses on making AI systems more trustworthy through mathematical precision and formal verification techniques. Currently on sabbatical at the University of Liverpool, Trivedi continues to bridge the gap between machine learning and responsible system design, helping AI systems earn the trust we increasingly place in them for critical applications.

Julia Rubinمشاهده پروفایل

دانشیار

• Software Quality
• Software Security
• AI Reliability
• +۷ مورد دیگر

Julia Rubin is an Associate Professor in the Department of Electrical and Computer Engineering at the University of British Columbia, Canada, holding the prestigious Canada Research Chair in Trustworthy Software. She leads the UBC Research Excellence Cluster on Trustworthy ML and serves as an Associate Faculty Member in the Department of Computer Science. Her academic journey includes a PhD from the University of Toronto and postdoctoral research at MIT, complemented by nearly a decade of industry experience at IBM Research where she worked as a research staff member and group manager. PhD in Computer Science, University of Toronto Postdoctoral Research, MIT Department of Electrical Engineering and Computer Science Research Staff Member and Group Manager, IBM Research (10 years) Professor Rubin's research centers on quality, security, and reliability of software and AI systems. Her work develops automated solutions for analyzing, auditing, and improving these systems, with particular focus on reliable approaches for malware detection, code management techniques combining generative AI with program analysis, and technology for regulatory compliance of AI systems. Her research spans mobile security, smart contract vulnerabilities, program analysis techniques, and trustworthy machine learning. Analysis of her 15 most recent publications reveals strong emphasis on software security (particularly in mobile and blockchain applications), program analysis techniques (especially slicing methods), and microservice architecture. Her work consistently bridges theoretical foundations with practical applications, often involving empirical studies and tool development. The publications demonstrate evolution from traditional software analysis toward AI-integrated approaches while maintaining rigorous methodology. 2023 Alexander von Humboldt Research Fellowship for Experienced Researchers 2023 Killam Faculty Research Fellowship 2022 CS-Can | Info-Can Outstanding Early Career Computer Science Researcher Award Multiple Distinguished/Best Paper Awards at major conferences Canada Research Chair, Tier II IBM CAS Project of the Year Award Professor Rubin actively mentors students and contributes to the academic community through program committee roles, including serving as Program Co-Chair for ASE 2022. She has secured significant research funding through fellowships and industry partnerships, including IBM collaborations. Her research group (ReSeSS Research Lab) focuses on developing practical solutions for software trustworthiness challenges. She leads the ReSeSS Research Lab at UBC, which focuses on developing automated solutions for analyzing, auditing, and improving software and AI systems. The lab's work spans multiple projects in trustworthy software, with particular emphasis on reliable and explainable approaches for security analysis, code management, and regulatory compliance of AI systems.

NIANYU LIمشاهده پروفایل

پژوهشگر

• Self-adaptive Systems
• Cyber-Physical Systems
• Formal Verification
• +۴ مورد دیگر

NIANYU LI is an active researcher at ZGC Lab, China specializing in human-involved self-adaptive systems. Their work integrates rigorous modeling techniques to ensure software safety, security, and reliability in dynamic environments, with significant contributions to cyber-physical systems and formal verification methodologies. They earned a Ph.D. in Computer Software and Theory from Peking University in 2021 under Prof. Zhi Jin and Prof. Wenpin Jiao, with additional research experience at the National Institute of Informatics (NII) and Carnegie Mellon University (CMU) under Prof. Zhenjiang Hu and Prof. David Garlan respectively. Research focuses on human-in-the-loop adaptation mechanisms, with expertise spanning requirements modeling, system verification, and safety-critical cyber-physical implementations. Their methodology combines formal control paradigms with practical human factors considerations to address environmental uncertainty in adaptive systems. Publication trends reveal consistent contributions to ASE, ICSE, and specialized conferences like SASMS, with recent work exploring LLM applications in adaptation, federated learning robustness, and SBOM generation. Key themes include verification-driven design, human-system coordination, and empirical validation of adaptive mechanisms in real-world scenarios. As an active community contributor, they serve on program committees for ASE, ACSOS, and ICSE events while maintaining leadership roles in artifact evaluation. Their work bridges theoretical formal methods with practical implementation challenges in adaptive systems. NIANYU LI maintains research continuity through collaborations with Peking University and international institutions, directing focus toward explainable adaptation mechanisms and safety assurance in increasingly complex human-software ecosystems.

Xin Sunمشاهده پروفایل

دانشیار

• Precision Agriculture
• Field Robotics
• Artificial Intelligence
• +۶ مورد دیگر

Dr. Xin Sun serves as Associate Professor and inaugural Endowed Chair/Director of the Peltier Institute for Advancement in Agricultural Technology at North Dakota State University's College of Engineering, Department of Agricultural and Biosystems Engineering. His research integrates artificial intelligence, field robotics, and remote sensing to transform data-driven decision-making in agriculture. Education: Nanjing Agricultural University (2013) Yantai University (2007) His primary research spans precision agriculture, field robotics, AI/ML, and hyperspectral imaging, with specialized focus on edge-AI deployment for low-connectivity farms, multi-sensor data fusion, human-robot interaction in agricultural systems, and resilient solutions for tribal/underserved farming communities. His work bridges cutting-edge science with practical field applications through technology transfer initiatives. Analysis of 2024-2025 publications reveals dominant trends in AI-driven weed management (using UAVs/UGVs), crop stress detection, soil moisture prediction, and hyperspectral applications, emphasizing real-world deployment on resource-constrained edge devices. Awards: 2024 NDSU Agriculture and Extension Faculty/Staff Award: Larson/Yaggie Excellence in Research Award 2023 NDSU College of Engineering Early Researcher Award Secured >$23M in competitive funding (>$5M as PI) for projects including autonomous weed control systems, soybean disease identification, and resilient crop production. Mentors students through PAG courses (115, 115L, 215, 315, 496) and directs the Peltier Institute's interdisciplinary team developing field-deployable agtech solutions with strong grower engagement.

Chelsea Finnمشاهده پروفایل

استادیار

• Robotics
• Machine Learning
• Reinforcement Learning
• +۵ مورد دیگر

Chelsea Finn is an Assistant Professor in Computer Science and Electrical Engineering at Stanford University, and co-founder of Pi. Her research focuses on enabling robots to learn intelligent behavior through interaction and learning, bridging machine learning and robotic control. She is affiliated with the Stanford Artificial Intelligence Laboratory (SAIL) and the ML Group. Finn holds a B.S. in Electrical Engineering and Computer Science from MIT and a Ph.D. in Computer Science from UC Berkeley. Education: Bachelor's Degree in Electrical Engineering and Computer Science, MIT Ph.D. in Computer Science, UC Berkeley Research Interests: Her work emphasizes end-to-end learning for visual perception and robotic manipulation, reinforcement learning, meta-learning, and scalable robotic learning. She explores how robots can autonomously collect data to develop general skills and adapt to new tasks rapidly. Key Contributions: Pioneered methods like Aloha for low-cost robotic teleoperation, OpenVLA for open-source vision-language-action models, and frameworks for surgical robotics (SRT-H). Her work has been recognized through awards like the ACM Doctoral Dissertation Award and the MIT TR35. Awards: ACM Doctoral Dissertation Award Intel Rising Star Faculty Award Microsoft Faculty Fellowship Presidential Early Career Award MIT Technology Review 35 Under 35 Teaching and Outreach: Taught courses such as CS224R (Deep Reinforcement Learning) and CS330 (Deep Multi-Task and Meta Learning at Stanford. Active in promoting diversity in AI through outreach programs for underrepresented students and mentoring initiatives. Labs and Teams: Leads the IRIS Lab, which studies intelligence through robotic interaction at scale. Collaborates with industry (e.g., Google Brain) and academic institutions to advance AI and robotics research.

• Sustainable Orchestration in Edge-Cloud Environments
• Multi-Cloud Workload Distribution
• Large Language Models (LLMs) Deployment
• +۳ مورد دیگر

Dr. Hadi Tabatabaee Malazi is an Assistant Professor at the University of College Dublin (UCD), leading the Sustainable Orchestration in Computing Continuum (SOC² Lab). He holds an Orcid identifier (0000-0002-2960-6896) and has extensive academic experience across institutions like Maynooth University, Shahid Beheshti University, and Trinity College Dublin. His research focuses on sustainable computing, edge-cloud continuum orchestration, AI/ML deployment (e.g., LLMs), and energy efficiency. He is a Senior Member of IEEE, serves as an Associate Editor for IEEE Access, and chairs the COST Action CA22151 (CYPHER) on decarbonizing energy-intensive industries. Education: PhD (2012): University of Isfahan (Computer Engineering) MSc (prior to 2012): University of Isfahan Research Visit (2010-2011): Delft University of Technology Research Interests: Sustainable edge-cloud systems, AI-driven workload optimization, carbon-aware resource management, distributed LLMs, and IoT interoperability. Key contributions include over 20 journal articles in IEEE, Elsevier, and Springer venues, with topics ranging from vehicular network security to smart city event processing. Professional Activities: Committee member of COST Action CA22151, IEEE Access Associate Editor, and Technical Program Committee member for IEEE conferences. Authored certifications on research leadership and research integrity from Epigeum. Labs/Teams: SOC² Lab (UCD), leading projects on edge-cloud sustainability and AI application deployment.