جستجوی استاد راهنمای دانشگاهی

• Natural Language Generation
• Human Evaluation of NLP Systems
• Data-to-Text Generation
• +۶ مورد دیگر

Ehud Reiter is a Professor of Natural Language Generation at the University of Aberdeen's School of Natural and Computing Sciences, Department of Computing Science. With over three decades of research experience, he is recognized as one of the world's leading experts in Natural Language Generation (NLG), particularly in data-to-text systems, evaluation methodologies, and healthcare applications. Reiter's research primarily focuses on creating systems that generate accurate, useful, and understandable natural language from structured data. His work spans multiple domains including healthcare (medical note generation, patient-facing systems), sports reporting, and explainable AI. A significant portion of his recent research addresses the critical challenge of evaluating NLG systems, with particular emphasis on human evaluation methodologies, reproducibility of results, and factual accuracy in generated text. His work on Bayesian Networks and causal graph discovery represents his ongoing interest in knowledge representation and reasoning behind natural language explanations. His research has evolved from foundational work on reference generation and document planning to current projects addressing large language models, reproducibility crises in NLP evaluation, and human-AI collaboration frameworks. The SPHERE evaluation card framework he co-developed represents a systematic approach to evaluating human-AI interaction systems across five key dimensions. Reiter has been instrumental in organizing multiple shared tasks focused on reproducibility in NLG evaluations, demonstrating his commitment to improving research methodology in the field. His work on consultation checklists for medical note evaluation has introduced standardized protocols that increase objectivity in clinical text assessment. Through projects like BabyTalk (generating neonatal intensive care unit summaries) and DrivingBeacon (providing driving behavior feedback), Reiter has demonstrated the practical applications of NLG technology in critical domains. His research consistently bridges theoretical advances with real-world implementation challenges.

Franck Iutzelerمشاهده پروفایل

استاد

• Numerical optimization
• Statistical learning
• Stochastic programming
• +۵ مورد دیگر

Franck Iutzeler is a Professor of Applied Mathematics at Université de Toulouse, working within the Statistics & Optimization team of the Institut Mathématique de Toulouse and teaching in the Department of Mathematics. He previously served as an Assistant Professor at Université Grenoble Alpes from 2015 to 2023 and completed his Habilitation à Diriger des Recherches in 2021. His research focuses on the intersection of optimization, statistics, and optimal transport theory to develop robust data-driven models. Key areas include numerical optimization, statistical learning, stochastic programming, and optimal transport. He is particularly interested in distributionally robust optimization using Wasserstein metrics and has developed the skwdro Python library for implementing these methods. Iutzeler's recent publications demonstrate a strong focus on Wasserstein Distributionally Robust Optimization (WDRO), with multiple papers in top venues like NeurIPS and SIAM Journal on Optimization. His work bridges theoretical guarantees with practical implementation, particularly through the skwdro library which provides efficient code for WDRO in machine learning applications. ANR JCJC grant for project STROLL: Harnessing Structure in Optimization for Large-scale Learning Co-PI of ANITI chair on Trust and Responsibility in Artificial Intelligence led by JM. Loubes and J. Bolte Iutzeler actively supervises PhD students including Yu-Guan Hsieh (awarded Université Grenoble Alpes's PhD award), Gilles Bareilles, Waïss Azizian, and Victor Mercklé. He has secured research funding through the ANR (MAD project on Automatic Differentiation) and ANITI. His current research includes statistical fairness using optimal transport theory and automatic differentiation for stochastic optimization. He leads the development of the skwdro library for Wasserstein Distributionally Robust Optimization and is involved with ANITI (Toulouse's AI Cluster), where he also took responsibility for the 2nd year of the Master SID in Data Science & Engineering in September 2024.

• Human-Computer Interaction (HCI)
• Multimodal Interaction
• Visual Analytics
• +۵ مورد دیگر

Denis Lalanne is a full Professor in the Department of Informatics at the University of Fribourg, leading the 'human-building interaction' group at the Smart Living Lab and directing the Human-IST institute . He specializes in Human-Computer Interaction (HCI), focusing on multimodal interaction, visual analytics, and Human-Building Interaction (HBI). His work bridges computer science with architecture and urban design to improve building occupants' comfort and accessibility. His research spans topics such as assistive technologies for visually impaired individuals, energy-efficient lighting systems, and predictive models for indoor environmental quality. He has led projects like the Human-IST Lab and SWICE (Sustainable Well-being in Energy Transition), emphasizing interdisciplinary collaboration between academia, industry, and public institutions. Key contributions include the development of frameworks for human-centric lighting, wearable sensors for urban comfort analysis, and empathic systems to enhance HCI. His work is published in top venues like ACM Transactions on Accessible Computing , IEEE , and CHI . Education and career highlights include a PhD from EPFL (Swiss Federal Institute of Technology), postdoctoral research at IBM’s USER Group, and prior teaching roles at the University of Avignon. He is a Swiss representative at IFIP TC13 and has organized major conferences like IHM 2016.

Feiran Zhaoمشاهده پروفایل

پژوهشگر

• Data-Driven Control
• Adaptive Control
• Reinforcement Learning
• +۲ مورد دیگر

Feiran Zhao is a Researcher at the Institute of Automatic Control, part of the Department of Mechanical and Process Engineering at ETH Zürich. He holds a B.S. in Control Science and Engineering from Harbin Institute of Technology (2018) and a Ph.D. from Tsinghua University (2024). His research focuses on data-driven control, adaptive control, reinforcement learning, and their applications in engineering systems. Zhao is currently a postdoc under Prof. Florian Dorfler at ETH's Automatic Control Lab. Research interests span topics like policy optimization for LQR systems, quantized feedback control, and model predictive control acceleration. His work bridges machine learning and classical control theory, with applications in robotics, power systems, and aerospace engineering. His publications (2019–2025) explore theoretical foundations of policy gradient methods, convergence analysis, and practical implementations in autonomous systems. Though no awards are explicitly listed, his active research in high-impact areas suggests potential recognition. As part of the Automatic Control Lab, Zhao collaborates on projects involving data-enabled control strategies and real-world system applications. No student advisees are currently listed.

• Bankruptcy Law
• Corporate Law
• Corporate Reorganization
• +۳ مورد دیگر

Vincent S.J. Buccola is a Professor of Law at the University of Chicago Law School , focusing on bankruptcy, corporate law, and financial contracting. He has published extensively on creditor rights, corporate reorganization, and municipal debt. University of Chicago - Law School Research Interests include bankruptcy theory, corporate governance, leveraged finance, and municipal bankruptcy law. His work analyzes creditor control rights, non-pro rata workouts, and the intersection of corporate debt and legal frameworks. Recent Trends in his publications cover: Bankruptcy auctions and credit bidding Leveraged and syndicated loan markets Municipal bond cases and Puerto Rico Chapter 11 reorganization dynamics Key Collaborations involve scholars like Greg Nini (Drexel University) and David A. Hoffman (University of Pennsylvania). His work appears in journals such as the University of Chicago Law Review and Yale Journal on Regulation .

Seyed Armin Tajalliمشاهده پروفایل

استاد مهمان

• Ultra Low Power Circuits
• Analog/Mixed-Signal Circuits
• Data Converters
• +۶ مورد دیگر

Seyed Armin Tajalli is a Visiting Professor at the École polytechnique fédérale de Lausanne (EPFL), affiliated with the School of Engineering (STI), Institute of Electrical Engineering (IEM), and Integrated Neurotechnologies Laboratory (INL). He has extensive experience in ultra-low power integrated circuit design and high-speed serial communication systems. Dr. Tajalli received his B.S.E.E. and M.S.E.E. degrees (with honors) from Sharif University of Technology, Tehran, and Tehran Polytechnic University in 1997 and 1999, respectively. He earned his Ph.D.E.E. on low-power integrated circuit design techniques in 2010 from EPFL. Prior to his academic career, he worked with Emad Semicon from 1998-2006 as a senior analog/RF design engineer and technical manager. His research focuses on ultra-low power circuit design, including subthreshold source-coupled logic, analog/mixed-signal circuits, data converters, and serial data transceivers. His work addresses critical challenges in low-power design, high-speed communication, and energy-efficient circuit implementations. Recent publications demonstrate his expertise in spectrum shaping techniques, crosstalk reduction, and high-speed serial links for memory interfaces and multi-drop communication systems. Dr. Tajalli's publications span high-impact journals and conferences including IEEE Journal of Solid-State Circuits, IEEE Transactions on Circuits and Systems, and major conferences like ISSCC, ISCAS, and VLSI Symposium. His research demonstrates consistent innovation in low-power circuit techniques with practical applications in high-speed communication systems. Kharazmi Award on Research and Development, 2000 Outstanding Design Engineer, Emad Semicon, 2001 Presidential Award of the best Iranian researchers, 2003 ACM/CICC Award, 2009 EPFL Prime Special Award, 2009 Dr. Tajalli has advised PhD students including Kiarash Gharibdoust, whose thesis focused on Hybrid NRZ/Multi-Tone Signaling for High-Speed Low-Power Wireline Transceivers. His collaborative work with the ALGO team has resulted in significant contributions to high-speed serial data transceiver architectures. His research has been supported by various academic and industrial partnerships focused on advancing integrated circuit design techniques. His work is primarily conducted within EPFL's Integrated Neurotechnologies Laboratory (INL) and previously with the Microelectronic Systems Laboratory (LSM). These labs provide state-of-the-art facilities for mixed-signal IC design, characterization, and testing, supporting his research in ultra-low power circuits and high-speed communication systems.

Antonio Carzanigaمشاهده پروفایل

استاد

• Distributed Systems
• Software Engineering
• Content-Based Networking
• +۷ مورد دیگر

Antonio Carzaniga is a Full Professor and founding member of the Faculty of Informatics at Università della Svizzera italiana (USI), where he has been active since 2004. Previously, he served as an Assistant Research Professor at the University of Colorado at Boulder from 2001 to 2007. He holds a Ph.D. in Computer Science and a Bachelor’s degree in Electronic Engineering from Politecnico di Milano. Full Professor, Faculty of Informatics, Università della Svizzera italiana (2004–Present) Assistant Research Professor, Department of Computer Science, University of Colorado at Boulder (2001–2007) Ph.D. in Computer Science, Politecnico di Milano Bachelor’s in Electronic Engineering, Politecnico di Milano His research spans distributed systems and software engineering, with a strong focus on content-based addressing networks, publish/subscribe systems, middleware, software fault tolerance, and verification. He has pioneered work in information-centric networking and developed the Siena project, a scalable publish/subscribe service. His recent work extends into programmable networks, GPU-accelerated matching, and performance annotations for cloud systems. The 15 most recent publications highlight a consistent trajectory in scalable, high-performance networking and adaptive software systems. Key themes include content-based communication, packet subscriptions, information-centric networking, and leveraging redundancy for fault tolerance and testing. His work bridges theoretical foundations with practical implementations, often involving system-level software and performance evaluation. Best Paper Award, ACM SIGCOMM Workshop on Information-Centric Networking (ICN'13) Carzaniga has advised multiple graduate students, including Michele Papalini, Koorosh Khazaei, and Daniele Rogora, and has collaborated on funded research projects in distributed systems and networking. He has contributed to software development through projects like the Siena Fast Forwarding engine and the Synthetic Workload Generator. His service includes organizing workshops and contributing to major conferences in software engineering and computer systems. He leads research initiatives such as Siena and Content-Based Networking, focusing on scalable, decentralized communication infrastructures. His lab has developed key tools for evaluating publish/subscribe performance and implementing high-speed forwarding algorithms.

• Control Systems
• Optimization
• Machine Learning
• +۵ مورد دیگر

Prof. Maryam Kamgarpour is a faculty member at ETH Zurich's Automatic Control Laboratory within the Department of Information Technology and Electrical Engineering. Her research focuses on control systems, optimization, and machine learning, with applications in robotics, power systems, and multi-agent systems. She has contributed significantly to safe reinforcement learning, stochastic control, and distributed optimization frameworks. Her work bridges theoretical foundations and practical implementations in areas like safe trajectory planning, market mechanisms for energy systems, and algorithmic guarantees for greedy methods. Key technical areas include model-based multi-agent reinforcement learning, robust control policies under uncertainty, and parameterization techniques in control theory. Collaborations with institutions like ETH Zurich's Automatic Control Lab and researchers such as Andreas Krause highlight her interdisciplinary approach. Her research has been supported by grants from the Swiss National Science Foundation (NCCR Automation) and the European Union (Reliable Data-Driven Decision Making in Cyber-Physical Systems). Publications span top journals and conferences in control systems, machine learning, and robotics, emphasizing rigorous analysis and practical scalability. Recent work addresses challenges in safe black-box optimization, stochastic hazard management in robotics, and market-based resource allocation in power grids.

Anastasios Tsiamisمشاهده پروفایل

مدرس

• Statistical Learning and Control
• Data-Driven Control
• Online Learning
• +۳ مورد دیگر

Dr. Anastasios Tsiamis is a Lecturer at the Department of Information Technology and Electrical Engineering at ETH Zürich, working in the Automatic Control Laboratory (Professur Control and Computation). His research focuses on the intersection of control theory and machine learning, specifically investigating how system theoretic properties affect the statistical difficulty of learning in system identification, online estimation, and control. Dr. Tsiamis received his Diploma (MEng, five-year degree) in Electrical and Computer Engineering from the National Technical University of Athens (NTUA). He completed his Ph.D. in Electrical and Systems Engineering at the University of Pennsylvania under Professor George Pappas, following graduate research with Professor Petros Maragos and undergraduate work with Professor Kostas J. Kyriakopoulos at NTUA. His primary research areas include Statistical Learning and Control, Data-Driven Control, Online Learning, Risk-Aware Control, and Security and Privacy in Networked Control Systems. Dr. Tsiamis has made significant contributions to understanding the fundamental statistical limits of learning in control systems, particularly focusing on sample complexity. His work on risk-aware optimization develops algorithms that safeguard against catastrophic events while maintaining good average performance, and his security research addresses eavesdropping attacks in remote estimation and motion planning. Analysis of Dr. Tsiamis's recent publications reveals a strong focus on data-driven approaches to control theory with emphasis on distributionally robust methods, risk-aware optimization, and finite sample guarantees. His work bridges theoretical foundations with practical applications across system identification, online learning, and adaptive control, providing rigorous non-asymptotic guarantees for learning-based control algorithms. Dr. Tsiamis has received several notable research recognitions: Best student paper award at IEEE 61th Conference on Decision and Control (2022) Spotlight Presentation at 41st International Conference on Machine Learning (2024) Finalist for best student paper award at American Control Conference (2019) Finalist for young author prize at IFAC World Congress (2017) Oral presentation at 2nd L4DC Conference (2020) Dr. Tsiamis teaches Linear System Theory (227-0225-00L) at ETH Zürich and collaborates extensively with Professor John Lygeros, Professor Manfred Morari, and researchers from the University of Pennsylvania. His publication record demonstrates strong collaborative research across multiple institutions while advancing theoretical foundations of learning-based control. As an active member of the Automatic Control Laboratory at ETH Zürich, Dr. Tsiamis contributes to advancing control systems science through rigorous mathematical analysis and innovative algorithmic development, with applications spanning robotics, energy systems, and networked control.

Mohamed Abdalmoatyمشاهده پروفایل

پژوهشگر

• Data-Driven Modelling
• Control Systems
• Signal Processing
• +۵ مورد دیگر

Dr. Mohamed Abdalmoaty is a Researcher at ETH Zürich's Institut für Automatik, specializing in the Department of Automatic Control. His work focuses on Data-Driven Modelling and Control , with expertise in system identification, stochastic systems, and optimal control. He holds an affiliation within the Professorship for Complex Systems Control. His research interests emphasize data-driven approaches for predictive control, frequency-domain identification, and nonlinear dynamical systems. He has contributed to advancements in Kalman filter formulations, stochastic Wiener models, and cybersecurity in control systems, particularly in medical applications like the artificial pancreas. Abdalmoaty’s recent publications (2022–2024) highlight innovations in time-varying normalizing flows, privacy-preserving network control, and robust parameter estimation under uncertainty. His work bridges theoretical control systems with practical applications in machine learning and biomedical engineering. He has developed software tools for system identification and simulation, including implementations for frequency-domain analysis and nonparametric closed-loop identification. His research aligns with emerging trends in hybrid machine learning-control frameworks and resilient cyber-physical systems.

Kimia Chavoshi Boroujeniمشاهده پروفایل

استادیار

• Nonlinear systems
• Cooperative control center networks
• Model Predictive Control (MPC)
• +۴ مورد دیگر

Dr. Kimia Chavoshi Boroujeni is an Assistant at the Chair of Control and Computation under the Institute for Automatic Control at ETH Zurich. Her research focuses on advanced traffic management systems for autonomous and connected vehicles. PhD in Civil Engineering (Road Traffic Engineering), ETH Zurich (2019–2024) MSc in Electrical Engineering (Control Systems), Sharif University of Technology (2016–2018) BSc in Electrical Engineering (Control Systems), Sharif University of Technology (2011–2015) Her work addresses complex control challenges in nonlinear systems , model predictive control (MPC) , and autonomous vehicle coordination , particularly in lane-free and direction-free highway systems. She has developed innovative strategies like feedback linearization and Karma games to enhance fairness and efficiency in traffic networks. Notable projects include ASTRA BGT for automated driving in road tunnels and RECCE for real-time traffic estimation. Her publications span journals like IEEE Transactions on Intelligent Transportation Systems and Electronics, alongside conferences like TRB and STRC.

• Natural Language Processing
• Ethics in NLP
• Bias Detection
• +۸ مورد دیگر

Margot Mieskes is a Researcher in Natural Language Processing with a focus on Ethics in NLP , Bias Detection , Autism Detection , and Reproducibility in Research . She has contributed to Multilingual NLP , Multimodal Analysis , and Peer Review Practices through numerous publications and workshops. Research Trends: Her work addresses ethical guidelines for NLP, bias in multilingual models, emotion detection, and the challenges of replicating human evaluations. Key Contributions: Margot co-authored the 2024 survey on autism detection in speech, 2023 studies on cross-lingual bias evaluation, and 2019 analyses of summarization metrics and OCR impacts. Workshops: She actively participates in teaching NLP workshops (2016–2023) and tutorials on ethical research (2025) and reviewing practices (2020, 2021). Collaborations: Margot has collaborated with researchers like Nadine Probol, Karën Fort, and Michael Strube on topics ranging from disfluency detection to OCR preprocessing.

Nicolás Vila Blancoمشاهده پروفایل

استادیار

• Medical Image Processing
• Deep Learning
• Biomedical Engineering
• +۴ مورد دیگر

Nicolás Vila Blanco is an Assistant Professor at the University of Santiago de Compostela, primarily affiliated with the Information Technologies Research Center (CiTiUS). His work bridges Medical Image Processing and Deep Learning through software development and algorithm optimization. Education includes: PhD in Information Technology Research (Universities of Santiago de Compostela and Coruña) M.Sc. in Data Science and Computer Engineering (University of Granada) B.Sc. in Computer Science (University of Vigo) Research focuses on applying Artificial Intelligence to biomedical challenges, particularly in dental imaging and cardiac diagnostics . Key contributions include: Developing IDALib (Image Data Augmentation Library) Creating DenTiUS Deep Plaque software for dental plaque analysis Designing PrimerEvalPy for microbiome primer evaluation Building gVARVI for heart rate response analysis Recent publications demonstrate expertise in: Clinical software development Deep learning architectures (U-Net, Few-Shot Learning) Medical imaging applications Biomedical data processing AI in dental diagnostics Microbiome sequencing techniques

Ljubisa Miskovicمشاهده پروفایل

پژوهشگر ارشد

• Systems Biology
• Machine Learning
• Dynamical Systems
• +۴ مورد دیگر

Ljubisa Miskovic is a Senior Scientist at École Polytechnique Fédérale de Lausanne (EPFL), holding multiple positions including at the Laboratory of Computational Systems Biotechnology (LCSB) within the School of Basic Sciences (SB) and Institute of Chemical Sciences and Engineering (ISIC), as well as at the Swiss Center for Scientific Computing (SCGC) and the School Council SB. He earned his Ph.D. degree in Automatic Control from EPFL in 2006 under the co-supervision of Dominique Bonvin and Alireza Karimi. Following his doctoral studies, he pursued postdoctoral research at the Centre for Systems Engineering and Applied Mechanics at Universite Catholique de Louvain with Michel Gevers before returning to EPFL to work with Vassily Hatzimanikatis. Dr. Miskovic's research spans systems biology, metabolic engineering, synthetic biology, data-driven control design, system identification, stochastic processes, and estimation theory. His work focuses on developing computational approaches for metabolic network analysis, kinetic modeling of genome-scale metabolic networks, and applying machine learning techniques to metabolic engineering problems. His recent publications demonstrate increasing integration of generative AI methods with traditional metabolic modeling approaches. His publication trends over the last five years reveal a clear progression from traditional metabolic modeling to sophisticated machine learning-enhanced approaches, with particular emphasis on genome-scale kinetic modeling, strain optimization for biochemical production, and uncertainty quantification in biological systems. Much of his recent work centers on applications in S. cerevisiae and P. putida for producing valuable biochemicals including p-coumaric acid and muconic acid. Dr. Miskovic has advised several doctoral students including Toumpe Ilias, Andreozzi Stefano, Choudhury Subham, and Tokic Milenko. He teaches courses in numerical methods and chemical process control at EPFL, demonstrating his commitment to both research and education in systems biotechnology. As a member of the Laboratory of Computational Systems Biotechnology led by Professor Vassily Hatzimanikatis, Dr. Miskovic contributes to a multidisciplinary team that combines expertise in computational biology, systems engineering, and metabolic modeling to advance the field of systems biotechnology.

Alisa Rupenyan-Vasilevaمشاهده پروفایل

استاد

• Industrial AI
• Automation of manufacturing systems
• Robotics
• +۴ مورد دیگر

Alisa Rupenyan-Vasileva is a Professor of Industrial AI at the ZHAW School of Engineering, Zurich University for Applied Sciences (ZHAW), where she leads the Industrial AI group at the ZHAW Center for AI. Her research focuses on integrating machine learning and control theory to advance manufacturing systems, robotics, and process optimization. She holds a PhD in Physics from Vrije Universiteit Amsterdam (2009), an MSc in Laser Physics and Optics from Sofia University (2005), and a BSc in Engineering Physics (2003), also from Sofia University. Her professional experience includes roles as Senior Scientist at ETH Zurich’s Automatic Control Laboratory, Group Leader at inspire AG (ETH Zurich’s technology transfer organization), and Head of Application Development at Qualysense AG. She has led projects such as 'Continuous optimization and control for advanced manufacturing' and 'Intelligent planning for robot-based manufacturing.' Research interests span industrial control systems, neurosymbolic AI, and data-driven optimization with applications in additive manufacturing and robotics. Notable contributions include Bayesian Optimization for controller tuning and melt pool control in laser powder bed fusion. She is an IEEE member and contributes to networks like the International Federation for Automatic Control and NCCR Automation. Her work emphasizes safe and efficient learning algorithms, with applications in centrifugal compressors, gas turbines, and precision motion systems. Recent talks include keynotes at the European Control Conference (2024) and the Benelux meeting on Systems and Control (2024).