جست‌وجوی استاد و پژوهشگر

Ellen Vitercikمشاهده پروفایل

استادیار

• Machine Learning
• Algorithm Design
• Discrete Optimization
• +۳ مورد دیگر

Ellen Vitercik is an Assistant Professor at Stanford University with joint appointments in the Management Science and Engineering and Computer Science departments. She holds a PhD from Carnegie Mellon University, advised by Nina Balcan and Tuomas Sandholm, and was a Miller Fellow at UC Berkeley under Michael Jordan and Jennifer Chayes. Her research focuses on machine learning, algorithm design, discrete optimization, and the intersection of economics and computation. She explores how machine learning can enhance discrete optimization and algorithmic reasoning, with applications to fairness, efficiency, and decision-making systems. Her work has garnered prestigious awards including the Schmidt Sciences AI2050 Early Career Fellowship and the NSF CAREER Award. Her doctoral thesis received multiple accolades, including the SIGecom Doctoral Dissertation Award and the CMU School of Computer Science Distinguished Dissertation Award. Notable research contributions include developing neural algorithmic reasoning frameworks, evaluating LLMs' structural reasoning abilities, and advancing techniques for optimization formulation equivalence checking. Her academic contributions span algorithm design, learning-augmented optimization, and mechanism design. She has pioneered methods for offline tuning in online decision-making systems and explored data-driven approaches for algorithm selection and configuration. Her research often bridges theoretical foundations with practical applications, addressing challenges in marketing, resource allocation, and social network dynamics. Ellen's interdisciplinary work integrates computer science, economics, and operations research, with a focus on creating scalable, fair, and efficient systems. Her current projects include investigating cold-start algorithm configuration using LLMs, optimizing decision-making under uncertainty, and designing robust systems resilient to data limitations.

Martim Brandãoمشاهده پروفایل

استادیار

• Robotics
• Artificial Intelligence
• Responsible AI
• +۱۲ مورد دیگر

Martim Brandão is a Lecturer (Assistant Professor) in Robotics and Autonomous Systems at King’s College London, where he leads the Responsible Robotics and AI (RRAI) Lab and serves as Co-Director of the UKRI Centre for Doctoral Training in Safe and Trusted AI. His research focuses on ethical, explainable, and safe AI and robotics, with applications in human-robot interaction, motion planning, fairness, and societal impact. His research interests include: Explainable AI and Motion Planning Fairness and Bias in AI Systems Human-Robot Interaction and Social Robotics Adversarial Robustness in Robotics Value Alignment and Ethical AI Inclusive and Participatory Robotics Design His recent publications (2023–2025) reflect a strong trend toward socially responsible robotics, focusing on fairness in navigation, explainability of planning failures, worker-centered agricultural robotics, environmental justice in drone delivery, and the dangers of bias in drowsiness detection and LLM-driven robots. His work emphasizes user understanding, societal impact, and ethical safeguards in autonomous systems. He has advised and collaborated with numerous students and researchers across diverse topics in robotics and AI. He is actively involved in shaping responsible robotics through: Leadership in the RRAI Lab Co-directing a national CDT in Safe and Trusted AI Developing fairness-aware algorithms Advocating for inclusive and ethical design practices His lab and research group focus on: Responsible Robotics and AI Explainability in Multi-Agent Planning Fairness in Coverage and Navigation Human-Centered Evaluation of AI Systems

• Decision support models in the electricity sector
• Optimization techniques
• Electric power system modeling
• +۷ مورد دیگر

Javier García González is a Full Professor in the Department of Electrical Engineering at the School of Engineering, Comillas Pontifical University, Madrid. He has been a key researcher at the Technological Research Institute (IIT) since 1996 and served as Deputy Director of IIT from 2016 to 2022. He holds a Ph.D. in Industrial Engineering from Comillas, awarded the best thesis in its category by the Royal Academy of Doctors. He has held visiting positions at MIT and Lawrence Berkeley National Laboratory. Education: Ph.D. in Industrial Engineering, Comillas Pontifical University (2001) Industrial Engineering (Electricity), ETSEIB, UPC Barcelona (1996) His research focuses on decision support systems for the electricity sector , applying optimization techniques to power system modeling, energy market design, and renewable integration. He has led over 80 research projects for utilities and institutions, with expertise in hydro scheduling, energy storage, and market modeling. His recent work emphasizes microgrid optimization , deep reinforcement learning , and pumped storage hydropower . The trends in his publications reveal a strong focus on optimization under uncertainty , market participation of storage and renewables , and advanced modeling of hybrid AC/DC systems . His work bridges engineering and economics, often involving stochastic programming and real-world applications. Scientific Awards: Best doctoral thesis, Royal Academy of Doctors (2001) Outstanding reviewer, Electric Power Systems Research (2017) He has supervised six doctoral theses and currently mentors a Ph.D. student. He has been principal investigator in numerous national and EU-funded projects, including TWENTIES and OptiREC. He has also delivered training courses at MIT and for industry partners like Endesa. He is actively involved in professional service, having organized the IEEE PowerTech 2021 conference and served on thesis committees at UPC, TU Delft, and Universidad Politécnica de Madrid. He is a long-standing reviewer for top journals such as IEEE Transactions on Power Systems , Applied Energy , and Energy Policy .

Julian Antony Quickمشاهده پروفایل

پژوهشگر

• Wind Power
• Wind Turbine Engineering
• Compressed Air Motors
• +۳ مورد دیگر

Julian Antony Quick is a Researcher at the Department of Wind and Energy Systems within the Technical University of Denmark . His work focuses on wind farm optimization, energy management, and market-driven renewable energy systems. Active research in wind farm control and market integration Specializes in hybrid power plant design and uncertainty quantification Contributes to UN Sustainable Development Goals through wind energy research His research explores advanced optimization techniques for wind resource assessment, turbine design, and revenue maximization in electricity markets. Key areas include: Compressed air energy storage integration Wind farm layout optimization Surrogate-based system modeling Dynamic energy management strategies While not explicitly listed, his collaborative projects suggest active involvement in PhD supervision and industry partnerships across Europe. His recent publications demonstrate a strong focus on translating technical advances into economic benefits through: Market-aware wind farm design Data-driven flow control Hybrid system efficiency improvements SDG-aligned sustainable energy solutions

Lalit Jainمشاهده پروفایل

استادیار

• Machine Learning
• Data Science
• Artificial Intelligence
• +۹ مورد دیگر

Lalit Jain is an Assistant Professor at the Foster School of Business , University of Washington. His research bridges theoretical mathematics with practical machine learning systems, focusing on adaptive data collection under budget constraints, applied to marketing, cognitive psychology, and humor detection. Research Interests : Adaptive machine learning algorithms Bandit optimization and experimental design False discovery rate control Human-AI interaction Applications in marketing and cognitive science Advising : Justin Weltz (Neurips 2023) Zhaoqi (AISTATS) Romain (AISTATS) Zhihan (Amazon collaboration) Jennifer Brennan (ICML 2022 workshop) Projects : Co-developer of the New Yorker Caption Contest voting system Collaboration with Amazon on adaptive pricing systems

Hendrik Baierمشاهده پروفایل

استادیار

• Artificial Intelligence
• Planning
• Search Algorithms
• +۵ مورد دیگر

Hendrik Baier is an Assistant Professor in the Information Systems group at Eindhoven University of Technology (TU/e), where he joined in 2022. His research focuses on creating agents capable of succeeding in complex decision-making tasks to help human users solve real-world problems. His work spans planning for long-term goals, learning in unknown environments, and explainability of AI systems for effective human-AI interaction. Dr. Baier's research interests center on planning and search algorithms, reinforcement learning, and explainable AI systems. His work investigates how AI can think ahead and explain its reasoning process, particularly in sequential decision-making contexts. He applies these techniques to practical domains including logistics and transportation, smart manufacturing, and sustainable energy systems. His research bridges theoretical foundations with real-world applications through collaborative projects with industry partners. Analysis of his recent publications reveals a strong focus on explainability in sequential decision-making, with increasing integration of large language models to enhance traditional planning algorithms. His work spans theoretical foundations of Monte Carlo Tree Search, programmatic policy generation, multi-agent reinforcement learning, and practical applications of these techniques. A notable trend is the growing emphasis on human-AI collaboration, where AI systems must not only perform well but also effectively communicate their reasoning to human users. Dr. Baier actively collaborates with researchers across multiple institutions, including CWI Amsterdam where he maintains an affiliation, and has participated in significant interdisciplinary efforts such as the Dagstuhl Seminar on Explainable AI for Sequential Decision Making. His research group at TU/e works closely with industry partners to translate fundamental research into practical applications. He is affiliated with EAISI (Eindhoven Artificial Intelligence Systems Institute) and contributes to the Decision Making with Artificial Intelligence educational program at TU/e. His laboratory work focuses on developing benchmark environments and frameworks that enable rigorous evaluation of decision-making algorithms, with recent contributions including MOMAland for multi-objective multi-agent reinforcement learning.

Bob Kirschمشاهده پروفایل

استاد

• Brain-Computer Interfaces
• Functional Electrical Stimulation
• Neuroprosthetics
• +۳ مورد دیگر

Bob Kirsch serves as Professor and Department Chair of Biomedical Engineering at Case Western Reserve University, where he also directs the Cleveland FES Center. His research focuses on restoring motor function for paralyzed individuals through neural engineering innovations in brain-computer interfaces and functional electrical stimulation systems. His educational background includes: Ph.D. in Biomedical Engineering from Northwestern University M.S. in Biomedical Engineering from Northwestern University (1986) B.S. in Electrical Engineering from the University of Cincinnati (1982) Dr. Kirsch's research spans intracortical brain-computer interfaces for movement restoration, neural decoding algorithms for grasp force and speech, and functional electrical stimulation integration with BCI systems. His work emphasizes closed-loop control architectures , artifact reduction in neural recordings , and reinforcement learning approaches for neuroprosthetic control, targeting clinical translation for tetraplegia rehabilitation. Analysis of his 2016-2020 publications reveals consistent focus on improving BCI calibration speed, enhancing movement decoding accuracy, and developing robust systems for real-world use. Key trends include leveraging human-generated rewards for controller training, addressing signal-independent noise limitations, and creating hybrid approaches combining cortical recordings with FES for functional movement restoration. His recognition includes: Faculty Distinguished Research Award from Case Western Reserve University (2023) As Department Chair and Cleveland FES Center Director, Dr. Kirsch leads multidisciplinary teams developing next-generation neuroprosthetics. His research program, supported by NIH and NSF grants, trains graduate students in neural engineering while advancing clinical applications through the BrainGate consortium collaboration. Current efforts focus on improving BCI communication rates and expanding movement restoration capabilities for paralyzed individuals. The Cleveland FES Center under his direction serves as a hub for neuroprosthetics innovation, integrating expertise in neural signal processing, biomechanics, and clinical rehabilitation to develop practical solutions for restoring lost motor function through implanted and non-invasive technologies.

• Data management and data science
• Artificial intelligence
• Distributed computing and systems software
• +۶ مورد دیگر

Thomas C Henderson is a tenured Professor at the School of Computing within the College of Engineering at the University of Utah. His research focuses on autonomous systems , computational models , and intelligent machine systems , with specific interests in biosystem simulation, distributed systems, and adaptive algorithms. He has contributed to Bayesian sensor networks , UAS traffic management , and probabilistic logic frameworks for intelligent agents. Current academic appointments since 1989 Adjunct roles in Biomedical Engineering Active in IEEE committees (2024-2025) Research trends in recent publications emphasize autonomous aircraft coordination , probabilistic reasoning , and multi-sensor integration . Key subfields include lane-based airspace modeling, reinforcement learning for UAS, and pseudogradient navigation techniques. His work has received recognition through a Best Paper Award (IEEE 2008) and the US Air Force Summer Faculty Fellowship . Strategic deconfliction protocols Dynamic data-driven applications Structural health monitoring systems He actively mentors undergraduate research through courses like Deep Learning Capstone and Senior Capstone Design . Current grants include "Deep Learning in AI and Robotics" (2024-2029) and "Robust Reasoning using Geometric SAT/PSAT" (2022-2023).

Karthik P. N.مشاهده پروفایل

استادیار

• Probability Theory
• Detection and Estimation Theory
• Markov Decision Processes
• +۶ مورد دیگر

Karthik P. N. is an Assistant Professor in the Department of Artificial Intelligence at the Indian Institute of Technology Hyderabad (IIT Hyderabad). He previously served as a Research Fellow at the Institute of Data Science, National University of Singapore (NUS), and completed his Ph.D. and Master of Science (Engineering) at the Department of Electrical Communication Engineering, Indian Institute of Science (IISc), Bengaluru, under the guidance of Prof. Rajesh Sundaresan. Earlier, he worked as a Project Assistant in Prof. Chandra R. Murthy's lab at IISc and earned a Bachelor of Engineering in Electronics and Communications from R V College of Engineering, Bengaluru. Ph.D., Electrical Communication Engineering, IISc Bengaluru M.Sc. (Engineering), IISc Bengaluru B.E., Electronics and Communications, R V College of Engineering His research focuses on Probability Theory, Detection and Estimation Theory, Markov Decision Processes , and Multi-armed Bandits , with applications in Federated Learning, Differential Privacy, Reinforcement Learning, Information Theory , and Statistics . Recent work explores optimal strategies for best arm identification in restless bandits and privacy-preserving machine learning. He has received high instructor ratings for courses like Stochastic Processes and Programming for AI , and was honored with the Faculty Teaching Excellence Award 2025 at IIT Hyderabad. His Google Scholar articles highlight advances in 3D point cloud security, railway accident prevention, and vision-language model robustness . Scientific awards include the Faculty Teaching Excellence Award 2025 and First place in the INAE Kanpur Chapter 100 Seconds Competition (2021) . He collaborates with researchers like Prof. Vincent Y. F. Tan and Prof. Yeow Meng Chee, and has served on technical program committees for conferences such as APWDSIT 2025 and ISIT 2025 . Teaching roles at IIT Hyderabad involve graduate-level courses on Probability and Stochastic Processes , cross-listed with Electrical Engineering.

Ioannis Havoutisمشاهده پروفایل

دانشیار

• Robotics
• Machine Learning
• Dynamic Motion Planning
• +۵ مورد دیگر

Ioannis Havoutis is an Associate Professor in Engineering Science at the Oxford Robotics Institute , University of Oxford. His research integrates dynamic whole-body motion planning , control systems , and machine learning for robotics with arms and legs . He holds a PhD (2011) and MSc with distinction (2007) from the University of Edinburgh, focusing on machine learning for motion planning of articulated robots. University of Edinburgh: PhD in Robotics (2011), MSc in Machine Learning (2007) His work spans dynamic legged locomotion , learning from demonstration , and terrain-adaptive control , with applications in quadruped robots and teleoperation . Key contributions include symplectic meta-learning , skill switching criteria , and flow-based trajectory optimization . Recent publications focus on 2025 advancements in adaptive manipulation , generalist parkour policies , and diffusion models for locomotion. He has received awards such as the Frontiers in Robotics & AI Best Paper Award (2019) and CLAWAR 2015 Best Technical Paper Award . Teaching roles include B16 Operating Systems courses at Oxford and Advanced Robotics lectures at IIT. Contact: havoutis@robots.ox.ac.uk | ioannis@robots.ox.ac.uk | Address: Oxford Robotics Institute, 17 Parks Road, Oxford, OX1 3PJ, UK

• Biostatistics
• Causal Inference
• Network Data Analysis
• +۶ مورد دیگر

Laura Forastiere is an Associate Professor of Biostatistics at Yale School of Public Health and affiliated faculty at the Yale Institute for Global Health. Her research focuses on causal inference methods for complex clustered and network data, addressing challenges in global health, environmental health, and social epidemiology. Ph.D. in Statistics, University of Florence (2015) Postdoctoral training at Harvard University Former Postdoctoral Associate at Yale Institute for Network Science Research domains include: Methodological development for causal inference under network interference Behavioral interventions leveraging social networks Applications in low/middle-income countries (malaria, HIV, maternal/child health) Environmental exposure modeling (PM2.5, pollution transport) Her 15 most recent publications (2023-2025) span journals in biostatistics, epidemiology, and network science. Key themes involve network interference modeling (6/15), global health applications (5/15), and environmental health impacts (3/15). Scientific awards: Yale Global Health Spark Award (2023), Young Protagonist Researchers (2016) Collaborations: Yale Institute for Network Science, Center for Methods in Implementation and Prevention Science Active in public health practice across Africa and Latin America

Antonio Pietrabissaمشاهده پروفایل

دانشیار

• Networked Systems
• Time-delay Systems
• Non-cooperative Control
• +۴ مورد دیگر

Antonio Pietrabissa is an Associate Professor at the Department of Computer, Control, and Management Engineering “Antonio Ruberti” (DIAG) of the University of Rome Sapienza, where he earned his degree in Electronics Engineering (2000) and PhD in Systems Engineering (2004). He has been teaching Automatic Control and Process Automation since 2010 and holds the National Scientific Qualification as Full Professor in Systems and Control Engineering (09/G1). Research interests include networked systems, robust control, Markov decision processes, and deep reinforcement learning. His work spans telecommunications, biomedical applications, and space systems, with a focus on federated learning and decentralized control. He has authored ~70 journal papers (Scopus h-index 22) and co-invented a patent for model predictive control in motor disability assistance. Awards include the 2021 Cybersecurity Award and ETRI Journal Best Paper. Current projects are NANCY (6G networks) and CADUCEO (AI-driven medical diagnostics). He is also CEO of Sapienza startup Automation Intelligence and Control (AICO), commercializing AI solutions for space, telecom, and biomedical sectors.

• Computational Linguistics
• Natural Language Processing
• Artificial Intelligence
• +۸ مورد دیگر

Bing Qin is a Researcher specializing in computational linguistics, artificial intelligence, and multimodal learning. Their work focuses on enhancing large language models' capabilities in temporal knowledge graph forecasting, cross-lingual alignment, and safety mechanisms. Core Research Areas: Knowledge graphs, multimodal systems, reasoning frameworks Technical Innovations: Analogical replay, gain signal estimation, cross-modal attention intervention Recent Trends: 2025 publications emphasize training-free methods and preference alignment in LLMs

Thomas Weberمشاهده پروفایل

استاد

• Credit collections
• Decision making under risk
• Behavioral economics
• +۸ مورد دیگر

Thomas Weber is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) , holding the Chair of Operations, Economics, and Strategy (OES) within the College of Management (CDM) . He serves as Director of the Doctoral Program in Management of Technology and contributes to academic governance through roles in committees such as the CDM Academic Evaluation Committee. PhD Students: Zhang Ru, Han Jun, Mark Michael, Razeghian Jahromi Maryam Email: thomas.weber@epfl.ch His research spans behavioral economics, risk analysis, and optimization in dynamic systems, with a focus on sharing economy applications, inventory management, and cryptocurrency market dynamics. Recent publications address robust decision-making frameworks, self-exciting point processes, and economic implications of information endogeneity. Key teaching activities include: Information: Strategy & Economics Innovation & Entrepreneurship in Engineering Microeconomics

• Machine Learning
• Computer Vision
• Natural Language Processing
• +۳ مورد دیگر

Professor Andreas Geiger leads the Autonomous Vision Group (AVG) at the University of Tübingen , heading the Department of Computer Science and serving as core faculty at the Tübingen AI Center . He is Principal Investigator in the ML in Science cluster of excellence and CRC Robust Vision , while coordinating the ELLIS PhD program . Develops machine learning models for computer vision, NLP, and robotics Focus on 2D/3D representations, geometry/material reconstruction, and robust AI Applications in autonomous vehicles, VR/AR, and document analysis His research has produced hundreds of publications with significant impact, including multiple best paper awards at top venues. The Scholar Inbox platform he co-created revolutionizes academic paper discovery, winning business model awards at Tübingen AI Center spinoff events. Key research areas include: Neural rendering and 3D scene understanding Self-driving perception and planning systems Simulation frameworks for autonomous validation Efficient reinforcement learning architectures Recent awards include: CVPR 2024 Best Paper Sage 10-Year Impact Award 2024 IEEE PAMI Young Researcher Award 2018 Active in CyberValley and ELLIS Institute Tübingen , he maintains strong industry collaborations through initiatives like the ML ⇌ Science Colaboratory . His group's work appears in journals like TPAMI and conferences including SIGGRAPH 2025.