Prof. Dr. Adam Andrzej Kurpisz is a Tenure Track Professor at the Bern University of Applied Sciences (BFH) and a senior researcher at the Institute for Operations Research (IFOR) at ETH Zürich. He leads the Ambizione Junior Research Group under Prof. Rico Zenklusen. His research bridges combinatorial optimization, robust optimization, and semi-algebraic proof systems, with applications in polynomial optimization and approximation algorithms. Education: PhD from Wrocław University of Science and Technology (supervised by Prof. Paweł Zieliński), postdoctoral work at IDSIA and Max-Planck-Institut. Grants: Secured over CHF 600,000 via Swiss National Science Foundation and other grants, including the AMBIZIONE project on linear/semidefinite relaxations. Research Interests: Focuses on semi-algebraic proof systems, approximation algorithms, robust optimization, and polynomial optimization. Applies techniques from algebraic geometry, Fourier analysis, and linear algebra to analyze optimization hierarchies. Teaching: Lectures on convex optimization, polynomial optimization, and discrete mathematics at ETH Zürich and BFH. Supervised courses in algorithms, programming, and mathematical analysis. Industry Collaboration: Co-founder of startups Silencions and Deeptale, advancing their ventures to global finals of MassChallenge Switzerland with over €5M in EU grants secured.
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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.
- Control Systems
- Optimization
- Machine Learning
- +۵ مورد دیگر
Joëlle NOAILLYمشاهده پروفایل
مدرس ارشد
Joëlle NOAILLY is a Senior Lecturer in the Department of International Economics at the Geneva Graduate Institute and a part-time Associate Professor in Environmental Economics at Vrije Universiteit Amsterdam. She is also a Research Fellow at the Tinbergen Institute and Co-Editor of Environmental and Resource Economics . Her research focuses on environmental policy design, energy economics, green technologies, and the clean energy transition. She holds a PhD from Vrije Universiteit Amsterdam and has received prestigious awards such as the Marie Skłodowska-Curie Fellowship and the EAERE Best Paper Award (2021 and 2022). Her academic career includes roles as Head of Research at the Geneva Graduate Institute’s Centre for International Environmental Studies and as a research economist at the Netherlands Bureau for Economic Policy Analysis (CPB). She advises international organizations like the OECD, WIPO, and the European Investment Bank on green growth and climate policy. Current research projects include critical minerals for the clean energy transition (SNF-funded, 2023–2027). Her work integrates econometric methods and machine learning for text analytics, addressing topics like green innovation incentives, environmental policy uncertainty, and climate transition risks. Education: PhD in Economics from Vrije Universiteit Amsterdam Grants: Swiss Science Foundation, Swiss Network of International Studies, Swiss Federal Office of Energy Affiliations: Tinbergen Institute, Geneva Graduate Institute, VU Amsterdam
Christian Rutzerمشاهده پروفایل
دانشگاهی
Dr. Christian Rutzer is Deputy Director and Research Associate at the Center for International Economics and Business (CIEB), Faculty of Economics, University of Basel. He specializes in international trade, European integration, and advanced econometric methods, including machine learning and theoretical modeling. Institution: University of Basel School: Faculty of Economics Department: Center for International Economics and Business (CIEB) Role: Deputy Head / Research Associate Email: christian.rutzer@unibas.ch His research focuses on quantitative and theoretical aspects of international economics, with strong methodological training in econometrics and data science. Dr. Rutzer applies modern analytical tools to issues in trade and economic integration, contributing to policy-relevant research at CIEB. There is no public information on his publications or advising activities in the provided texts. No scientific awards are mentioned in the available content. He leads research projects and supports the center's mission in advancing knowledge in international economics and business. No information is available about labs or research teams he may lead.
Michael Natusch is a Lecturer at The Open University and Head of Data Science Services for EMEA at Pivotal. With a PhD in theoretical physics from the University of Cambridge and an MBA, his expertise spans predictive analytics applications across telecommunications, financial services, retail, automotive, and aviation industries. Current roles: Pivotal (EMEA Data Science Services), Open University (Lecturer) Research interests: Machine learning, customer churn reduction, pricing optimization, logistics, and telecoms network deployment Academic background: PhD in Theoretical Physics (University of Cambridge), MBA His work focuses on transforming business data into actionable insights using the Pivotal platform. He has contributed to practical implementations in HR analytics, health insurance claims optimization, and machine-to-machine data processing. Scientific Awards Fellow of the Royal Statistical Society
- Machine Learning
- Predictive Analytics
- Data Science
- +۳ مورد دیگر
Michael Berthold is a Professor at the University of Konstanz and Co-Founder/President of KNIME.com AG. His career spans academia and industry, with notable roles at Carnegie Mellon University (CMU), UC Berkeley, Intel, and Tripos. His research focuses on descriptive data analysis, particularly in life sciences applications, and he has recently explored massively parallel approaches for machine learning algorithms. He co-authored the influential textbooks Intelligent Data Analysis and Guide to Intelligent Data Analysis with Springer Verlag. Dr. Berthold has delivered invited presentations at major academic and industrial conferences and received recognition as an IEEE Fellow and Honorary Professor at Obuda University, Budapest.
- Data Science
- Machine Learning
- Life Sciences
- +۲ مورد دیگر
Jingfeng Zhangمشاهده پروفایل
پژوهشگر
Jingfeng Zhang is a researcher at RIKEN Advanced Intelligence Project (RIKEN-AIP) working in the "Imperfect Information Learning Team" supervised by Prof. Masashi Sugiyama. Prior to RIKEN-AIP, he obtained his Ph.D. in 2020 from the School of Computing at the National University of Singapore under Prof. Mohan Kankanhalli, and his Bachelor's degree in 2016 from Taishan College at Shandong University, China. Dr. Zhang specializes in adversarial robustness in AI systems, focusing on making artificial intelligence safe for human beings. His research investigates how standard trained models fail when confronted with adversarial examples and develops methods to enhance model reliability through adversarial training. Key aspects of his work include: Developing techniques to make decision boundaries "thick" in machine learning models Simulating adversarial attacks during training to improve robustness Evaluating and enhancing reliability of AI-powered tools Addressing security vulnerabilities in deep learning systems Dr. Zhang has received significant recognition for his work through competitive funding and awards: Strategic Basic Research Programs ACT-X 2021-2023 funding JSPS Grants-in-Aid for Scientific Research (KAKENHI), Early-Career Scientists 2022-2023 RIKEN Ohbu Award 2022 He actively contributes to the machine learning community as a reviewer for top-tier conferences including ICLR, ICML, and NeurIPS. Dr. Zhang has presented his research at international venues such as the EPFL Center for Intelligent Systems, where he delivered talks on adversarial robustness in 2022 and 2023, demonstrating the practical applications of his theoretical work in real-world AI systems.
Prof. Jamie Paik is a Full Professor at the Swiss Federal Institute of Technology (EPFL), where she serves as Director of the Reconfigurable Robotics Lab (RRL) and is a core member of the Swiss NCCR robotics group. She holds multiple academic appointments across EPFL's School of Engineering, including positions in the Institute of Mechanical Engineering (IGM), STI-SMT SMT-ENS, and STI-SGM SGM-ENS. Additionally, she serves as a PhD program committee member for the Doctoral Program in Robotics, Control and Intelligent Systems. Her research focuses primarily on soft robotics , origami-inspired robotics , and wearable technologies . Prof. Paik's work leverages multi-material fabrication and smart material actuation to develop novel robotic designs that push the physical limits of materials and mechanisms. Her current research includes self-morphing Robogami (robotic origami) that transforms from planar shapes to 2D or 3D structures through predefined folding patterns, similar to traditional paper origami. Prof. Paik has published extensively on robotic origami, haptic feedback systems, and soft actuators, with her most recent work (2024-2025) focusing on vibration of soft twisted beams for locomotion, semi-autonomous surgical assistance, plug-and-play pneumatic systems, and data-driven kinematic modeling. Her research demonstrates a clear trajectory toward more adaptive, reconfigurable robotic systems with applications in surgery, human-robot interaction, and wearable technologies. Among her notable achievements, she developed a 7-DoF humanoid arm during her PhD at Seoul National University (sponsored by Samsung Electronics), which was the lightest in literature at that time (3.7kg including the 8-DoF hand). During postdoctoral work at Pierre Marie Curie University, she developed the internationally patented JAiMY laparoscopic tools now commercialized by Endocontrol-medical.com. PhD: Seoul National University (Humanoid Arm and Hand Design) Postdoc: Institut des Systems Intelligents et de Robotic, Université Pierre Marie Curie Postdoc: Harvard University's Microrobotics Laboratory Prof. Paik has supervised numerous PhD students, both current and past, including Bakir Alihan, Demirtas Serhat, Jiang Shaopeng, Kanno Ryo, Schüssler Alexander Michael, and Wang Ziqiao. Her teaching includes Topics in Autonomous Robotics, Mechanical Product Design and Development, and Advanced Design for Sustainable Future.
Philip Garner is a Lecturer at the Laboratory for Intelligent Data Analysis and Processing (LIDIAP), part of the School of Engineering (STI) at École Polytechnique Fédérale de Lausanne (EPFL). He is affiliated with the Institute of Electrical Engineering (IEM) and conducts research in intelligent data analysis, with a focus on biologically inspired neural models for speech and cognitive processing. Despite holding a 'Guest' status, he actively supervises PhD students and co-directs doctoral research, indicating a significant academic role. His research interests lie at the intersection of neuroscience and machine learning, particularly in spiking neural networks , speech recognition , neuromorphic computing , and deep learning architectures . He investigates how biologically plausible models can replicate brain-like computation for efficient information processing. His work often explores temporal dynamics, recurrence, memory mechanisms, and neural oscillations in artificial systems. The publications associated with his advisees and research group reflect a strong trend in developing energy-efficient, brain-inspired computing models for sequential data processing. These works frequently involve surrogate gradient learning, Bayesian recurrent units, and analysis of cross-frequency coupling in neural networks, contributing to both cognitive science and practical applications in speech technology. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: Philip Garner supervises multiple doctoral students, including Abdennadher Yesmine, Chen Haolin, Coppieters De Gibson Louise Clothilde, He Mutian, and has co-supervised several completed theses such as those of Bittar Alexandre, Honnet Pierre-Edouard, Schnell Bastian, Taghizadeh Mohammadjavad, and Tong Sibo. His advising role is central to the LIDIAP lab’s research output in neural computation. While specific grants are not listed, his involvement in cutting-edge AI and neuroscience research suggests active participation in funded projects. Labs and Teams: He is based at the IDIAP Research Institute, closely affiliated with EPFL’s LIDIAP laboratory, which specializes in intelligent data analysis and processing. The lab fosters interdisciplinary research in machine learning, signal processing, and cognitive modeling, often in collaboration with neuroscience and engineering groups.
Manoel Horta Ribeiro is an Assistant Professor in the Department of Computer Science at Princeton University, affiliated with the Princeton Center for Information Technology Policy (CITP). He holds a PhD from EPFL, an MSc and BSc from UFMG, and has collaborated with major tech firms including Meta, Reddit, and Microsoft. His research spans computational social science, artificial intelligence, and social computing, with a focus on real-world impact and policy implications. His research interests include computational social science, artificial intelligence, social computing, generative AI, online platforms, content moderation, algorithmic amplification, and human-AI interaction. He investigates how AI systems shape online discourse, influence user behavior, and affect platform governance, often using large-scale data analysis and experimental methods to derive actionable insights for design and policy. His recent publications reflect a strong trend in studying the societal impacts of AI, particularly generative models, in online environments. Themes include AI-assisted peer review, deplatforming effects, persuasive capabilities of LLMs, enterprise social media, and the ethics of automation in crowdsourcing. His work bridges technical AI research with social science theory, emphasizing empirical validation and real-world relevance. Forbes 30 Under 30 Class of 2023 in Science & Healthcare (Europe) ICWSM Best Paper Award Facebook Fellowship Best Reviewer Award at ICWSM 2022 Teaching Assistant Award at EPFL 3rd Place in SBC Master Thesis Awards Google Latin America Research Award Top 100 Most Influential Papers in 2019 (3rd in Computer Science) Manoel actively mentors students and is recruiting PhD candidates at Princeton to work on computational social science and the societal impact of generative AI. He has advised and collaborated on numerous studies involving content moderation, platform migration, and AI behavior. His lab engages in interdisciplinary research, often partnering with industry and policy institutions. He is also active in public discourse through media interviews, invited talks, and his Substack blog, where he critically examines AI trends and their broader implications.
Adway Girishمشاهده پروفایل
دانشگاهی
Adway Girish is a third-year Ph.D. candidate and Doctoral Assistant at the School of Computer and Communication Sciences (IC), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He is affiliated with the Information Processing Group (IPG) and the Laboratory for the Theory of Information (LTHI), working under the supervision of Prof. Emre Telatar. He has also collaborated with leading researchers including Michael Gastpar, Hyeji Kim, and Shlomo Shamai. His educational background includes a B.Tech. in Electrical Engineering with honors and a minor in Mathematics from the Indian Institute of Technology Bombay (IITB), completed in 2022. Adway's research centers on information theory and its applications in communication, security, and machine learning. He is particularly interested in foundational aspects of information measures, entropy optimization, and the theoretical underpinnings of modern deep learning architectures such as transformers and large language models. His recent work explores rate-distortion frameworks for prompt compression, learning dynamics in transformers, and entropy-constrained communication channels. His research bridges theoretical rigor with practical implications in AI and communication systems. The trend in his recent publications shows a shift from classical signal processing and micro-Doppler analysis during his undergraduate years to advanced topics in information theory and machine learning during his Ph.D. His contributions are published in top-tier venues such as ISIT, NeurIPS, ICLR, and ICML workshops, indicating a strong trajectory in theoretical computer science and applied mathematics. Scientific Awards and Recognitions: ICLR 2025 Spotlight Paper (awarded to top 5% of accepted papers) Oral presentation at ICML 2024 Workshop on Theoretical Foundations of Foundation Models (selected as one of top 4 out of 58 submissions) Adway advises no students currently, as he is himself a doctoral candidate. However, he plays an active role in collaborative research projects involving multiple co-authors across institutions. He has not received specific mention of external grants in the provided text, but his position as a Doctoral Assistant at EPFL suggests institutional funding. His collaborations with renowned researchers suggest involvement in larger research initiatives and potential access to grant-supported projects. He is a core member of the Information Processing Group (IPG) at EPFL, a research lab focused on theoretical and applied aspects of information science, including coding, communication, learning, and data analysis. The group fosters interdisciplinary research and hosts regular seminars, candidacy reviews, and internal presentations, all of which Adway actively participates in.
- Information Theory
- Coding Theory
- Machine Learning
- +۹ مورد دیگر
Malo Lucas Perez serves as a Doctoral Assistant at the Computer Vision Laboratory (CVLAB) within the Institute of Electrical Engineering at the School of Computer and Communication Sciences, Swiss Federal Institute of Technology in Lausanne (EPFL). He concurrently pursues doctoral studies in the Doctoral program in computer and communication sciences (EDIC). His research centers on Computer Vision and Machine Learning , with specialization in Artificial Intelligence and Image Processing . As part of CVLAB, he contributes to advanced projects in visual recognition systems, 3D scene reconstruction, and deep learning architectures for real-world vision applications. He maintains active affiliation with EPFL's Computer Vision Laboratory, a globally recognized research hub advancing state-of-the-art methodologies in computational imaging and visual understanding.
Maria Boulougouri is a Doctoral Assistant at the Signal Processing Laboratory 2 (LTS2) at École Polytechnique Fédérale de Lausanne (EPFL), within the School of Engineering and the Institute of Electrical Engineering. She is concurrently pursuing her doctoral studies in the Doctoral Program in Computational and Quantitative Biology (EDCB) under the EDOC Doctoral School at EPFL. Research Interests: Her work bridges signal processing and computational biology, focusing on the development and application of quantitative methods for biological data analysis. Her research interests include signal processing, computational biology, bioinformatics, machine learning in biological systems, and data-driven approaches in life sciences. This interdisciplinary profile suggests strong expertise in algorithmic modeling and analysis of complex biological signals and datasets. No publications are listed in the provided text, so no trends can be derived at this time. Scientific Awards: Advising and Grants: There is no information available regarding student advising or research grants in the provided text. Labs and Teams: Maria is affiliated with the Signal Processing Laboratory 2 (LTS2) at EPFL, a research group focused on advancing theoretical and applied aspects of signal processing with applications in biology, medicine, and engineering.
Pradeep Ravikumar is a Professor in the Machine Learning Department at Carnegie Mellon University's School of Computer Science. His research focuses on foundational aspects of statistical machine learning, particularly on developing explainable, robust, and resilient machine learning systems that perform well under resource constraints and distribution shifts. He previously served as Associate Director at the Center for Big Data Analytics at the University of Texas at Austin. Research Interests: Explainable AI Adversarial robustness Structured learning Distribution shift resilience Resource-constrained ML Scientific Awards: Sloan Fellow Siebel Scholar NSF CAREER Award ACM SIGKDD Dissertation Award honorable mention CMU School of Computer Science Distinguished Dissertation award honorable mention He has served as Program Chair for AISTATS 2013 and holds editorial roles as Associate Editor-in-Chief for IEEE TPAMI, and action editor for Machine Learning Journal and JMLR. Recent work demonstrates novel approaches to ensemble learning through margin-boosting games and improved cross-entropy loss functions.
Mathias Humbert is an Associate Professor at the University of Lausanne, with affiliations at the Department of Information Systems in the Faculty of Business and Economics. Previously, he held roles as a Scientific Project Manager at the Cyber-Defence Campus, Senior Data Scientist at the Swiss Data Science Center (SDSC) at ETHZ and EPFL, and Postdoctoral Researcher at CISPA in Saarbrücken. He earned his Ph.D. in 2015 from EPFL (Switzerland) after completing B.Sc./M.Sc. studies at EPFL and UC Berkeley. His research focuses on privacy, cybersecurity, and machine learning, particularly examining privacy risks in MLaaS, online social networks, wearable devices, and spectrum monitoring, while developing novel privacy-preserving frameworks for biomedical data and social graphs. Key research areas: Privacy in data sharing, interdependent privacy, genomic privacy, location privacy, and graph-based machine learning Notable contributions: GraphEraser for graph unlearning, KGP Meter for genomic privacy awareness, and SVT² for differential privacy in methylation data His recent publications explore machine unlearning vulnerabilities, privacy risks in DNA methylation data, and usability challenges in web security mechanisms, with a focus on empirical studies and cryptographic solutions. He received a Distinguished Paper Award at NDSS 2019 for his work on MBeacon and has contributed extensively to privacy research across ACM CCS, IEEE EuroS&P, and USENIX Security venues.
- Privacy
- Cybersecurity
- Machine Learning
- +۸ مورد دیگر
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