جستجوی استاد راهنمای دانشگاهی

• Energy systems optimization
• Conceptual process design
• Life cycle assessment
• +۷ مورد دیگر

André Bardow is a Full Professor at the Department of Mechanical and Process Engineering, ETH Zürich. His research focuses on energy systems optimization, life cycle assessment, computer-aided molecular design, and CO2 capture/utilization. Professor (ETH Zürich, 2020–present) Head of Institute of Technical Thermodynamics (RWTH Aachen University, 2010–2020) Visiting Professor (University of California, Santa Barbara, 2015/16) Part-time Director (Forschungszentrum Jülich, 2017–2022) Associate Professor (TU Delft, 2007–2010) Research Interests: His work spans energy and process systems engineering, with emphasis on sustainable technologies. Key areas include: Computer-aided molecular and process design Machine learning for chemical engineering Carbon capture and utilization (CCU) Life cycle assessment (LCA) of industrial processes Thermo-economic modeling of energy systems Multiphase equilibrium analysis Publication Trends: Recent articles focus on integrating machine learning with process design, optimizing CO2 capture in steel production, and advancing electrochemical cooling technologies. Subfields include sustainable plastics, ORC working fluids, and solvent mixture design. Scientific Awards: Fellow of the Royal Chemical Society Recent Innovative Contribution Award (EFCE, 2019) PSE Model-Based Innovation Prize (2018) Covestro Science Award (first recipient) Arnold-Eucken-Award (VDI-GVC) Highly Cited Researcher (Clarivate, 2024) Advising and Grants: Professor Bardow mentors students in process optimization and leads projects like Systemic expansion of territorial CIRCULAR Ecosystems for end-of-life FOAM (Grant 101036854, EC).

Philipp Baumannمشاهده پروفایل

استاد

• Operations Research
• Optimization
• Machine Learning
• +۵ مورد دیگر

Prof. Dr. Philipp Baumann is a Professor at the University of Bern, affiliated with the Group for Business Analytics, Operations Research and Quantitative Methods. His research focuses on optimization algorithms, clustering techniques, and operations research applications. He has contributed to advancements in constraint-based clustering, fault-tolerant facility location, and fair-capacitated clustering, leveraging mathematical programming and heuristic methods. His work bridges theoretical optimization and practical applications in industries like printing and healthcare. Research interests include combinatorial optimization, algorithm design for machine learning, and large-scale data mining. Notable contributions include the MPFCC algorithm for fair clustering and FT-KMEANS for fault-tolerant systems. His recent publications emphasize scalable solutions for real-world scheduling and resource allocation challenges. No scientific awards are explicitly mentioned. He advises students on thesis guidelines (Bachelor and Master levels) but no specific advisees are listed. His research group collaborates on projects involving mathematical programming in operations and finance, with applications to production scheduling and portfolio optimization.

Norbert Trautmannمشاهده پروفایل

استاد

• Combinatorial Optimization
• Project Management
• Production Planning
• +۳ مورد دیگر

Prof. Dr. Norbert Trautmann is a Full Professor of Quantitative Methods in Business Administration at the University of Bern, leading the Group for Business Analytics, Operations Research, and Quantitative Methods. He holds a PhD from the University of Karlsruhe (2000) and a Habilitation degree in Operations Research from the University of Karlsruhe (2004). His research focuses on Combinatorial Optimization , Project Management , Production Planning , and Portfolio Optimization . Prof. Trautmann has extensive professional experience, including roles as Head of the Department of Business Administration (2010–2012), and leadership in academic societies like the Swiss Operations Research Society (President since 2017). He has authored over 100 peer-reviewed publications and supervised numerous PhD students, including Philipp Baumann, Adrian Zimmermann, and Tom Rihm. His research has led to awards such as the 2010 IEEE Honourable Mention Award and the 2001 German Society of Operations Research Doctoral Thesis Award. Key projects include scheduling optimization for assessment centers, production planning in process industries, and portfolio tracking models for small investors.

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

Maryam Kamgarpourمشاهده پروفایل

استادیار

• Control Theory
• Game Theory
• Mechanism Design
• +۶ مورد دیگر

Maryam Kamgarpour is a Tenure Track Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL), School of Engineering. She previously held faculty positions at the University of British Columbia and ETH Zürich. Her work bridges stochastic control , multiagent learning , and game theory , focusing on safety-critical systems. Education: PhD in Engineering from UC Berkeley, BSc in Applied Science from University of Waterloo. Research Interests: Control under uncertainty, game theory, mechanism design, mixed-integer optimization, and applications to transportation, robotics, power grids, and healthcare. Her recent publications emphasize safe reinforcement learning , multirobot coordination , and stochastic trajectory planning , with applications to aircraft navigation and energy systems. She has received the European Union ERC Starting Grant, NASA High Potential Individual Award, and IEEE Transactions on Control of Network Systems Outstanding Paper Award. Scientific Awards: ERC Starting Grant (2016-2021) NASA High Potential Individual Award (2010) NASA Excellence in Publication Award IEEE Outstanding Paper Award (2022) PhD Students: Jordan Philip Christopher Maddux Anna Maria Ni Tingting Ren Kai Salizzoni Giulio Schlaginhaufen Andreas Vaishampayan Saurabh Dilip Vallat Gabriel Rémi Former EPFL student: Guo Baiwei

Fazl Barezمشاهده پروفایل

پژوهشگر ارشد

• AI Safety
• Mechanistic Interpretability
• Technical AI Governance
• +۳ مورد دیگر

Fazl Barez is a Senior Research Fellow at the University of Oxford leading research on Technical AI Safety and Governance. He is also affiliated with Cambridge's CSER, NTU's Digital Trust Centre, Edinburgh's Informatics, and is a member of ELLIS. Previously, he was a researcher at Amazon and Huawei, and Co-director and Head of Research at Apart Research. He currently serves as an advisor to Martian and has worked with Anthropic's Alignment team (2024-2025). University of Oxford: Senior Research Fellow Cambridge CSER: Affiliate NTU Digital Trust Centre: Affiliate Edinburgh Informatics: Affiliate ELLIS: Member Anthropic: Alignment Team Collaborator (2024-2025) Martian: Advisor Dr. Barez's research focuses on ensuring AI systems remain safe, interpretable, and beneficial as they grow in capability. His work spans four interconnected areas: Interpretability (developing methods to reveal how AI models process information internally), Safety and Alignment (creating tools to detect and address deceptive behaviors), Technical Governance (translating technical insights into governance frameworks), and Societal Impact (examining broader implications of AI on society). His research is funded by OpenAI, Anthropic, Schmidt Sciences, Future of Life Institute, and NVIDIA. The trends in Dr. Barez's publications show a consistent focus on making AI systems more transparent and safer. His recent work explores mechanistic interpretability techniques like sparse autoencoders, investigates how language models relearn removed concepts, examines machine unlearning for safety applications, and develops frameworks for value alignment measurement. His publications appear in top venues including NeurIPS, ICML, ICLR, ACL, and EMNLP, reflecting his significant contributions to both theoretical and practical aspects of AI safety. Future of Humanity Institute PhD Affiliate (2022-2024) EPSRC PhD Student Scholarship (2019-2023) MSc Scholarship (2017-2018) BA (Hons) Sports Performance Scholarship (2013-2017) Dr. Barez has mentored numerous students who have gone on to prominent positions at organizations like Microsoft Research, DeepMind, and Martian. His research is generously funded by major AI organizations including OpenAI, Anthropic, Schmidt Sciences, Future of Life Institute, and NVIDIA. He has served as an Area Chair for ACL 2025 and on program committees for major conferences including ECAI 2024. His work has practical impact, with algorithms like N2G adopted by OpenAI to evaluate sparse autoencoders for interpretability. Dr. Barez leads research at the intersection of technical AI safety and governance. His work connects with multiple research groups including the UK AI Security Institute, Alan Turing Institute, and various university centers. He has co-organized workshops such as the first Mechanistic Interpretability workshop at ICML 2024 and actively collaborates with researchers across the AI safety ecosystem. His research bridges the gap between theoretical safety research and practical implementation in real-world AI systems.

Ethan Monaghan Ackelsbergمشاهده پروفایل

پژوهشگر

• Ergodic Theory
• Ramsey Theory
• Combinatorial Number Theory
• +۱ مورد دیگر

Ethan Monaghan Ackelsberg is a postdoctoral researcher at the Institute of Mathematics, École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Basic Sciences (SB) and the Ergodic Theory group (ERG). He also holds a lecturing position in the SMA-ENS program within the SB-SMA department at EPFL, focusing on teaching mathematics. His research bridges ergodic theory, Ramsey theory, and combinatorial number theory, with a focus on recurrence properties and polynomial configurations. Education: PhD in Mathematics from Ohio State University (2022), MS in Mathematics (2019), BA in Mathematics and Physics from Bard College at Simon's Rock (2016). Ethan's research explores measure-preserving actions of abelian groups, polynomial patterns in large subsets, and connections to number theory. His recent work includes advancements in equidistribution in nilpotent groups and counterexamples to Erdős-type problems. He has received academic honors including summa cum laude at Bard College and highest honors from Budapest Semesters in Mathematics. Ethan actively contributes to teaching, including courses on measure theory.

Florian Pommereningمشاهده پروفایل

استاد

• Classical Planning
• Heuristic Search
• Linear Programming
• +۳ مورد دیگر

Dr. Florian Pommerening is a Professor at the Department of Mathematics and Computer Science within the Faculty of Science at the University of Basel, Switzerland. He holds an email address of florian.pommerening@unibas.ch and can be reached via +41 61 207 05 37. His academic career is deeply rooted in classical domain-independent planning, with a focus on optimal planning through heuristic search and the application of linear and mixed integer programming in planning domains. University of Basel, Switzerland Faculty of Science Department of Mathematics and Computer Science Professor Rank Florian completed his PhD at the University of Basel in 2017 under the supervision of Malte Helmert. He also holds a Master's degree in Computer Science from the University of Freiburg (2012) and a Master's in Information Technology from Monash University (2010). His research spans topics such as classical planning, heuristic search, linear programming, mixed integer programming, and automated planning systems. His publication record from 2012 to 2025 demonstrates sustained contributions to planning algorithms, including cost partitioning heuristics, Lagrangian decomposition, and transition landmark generation. His work has been recognized with multiple awards, including the ICAPS 2024 Influential Paper Award, ECAI 2023 Quality Champion PC award, and the Dux of the Master of Information Technology at Monash University. ICAPS 2024 Influential Paper Award Quality Champion PC award (ECAI 2023) Runner Up, Best Student Paper Award (ICAPS 2023) CoRe Challenge 2022 Awards Best Student Paper Runner-Up (ICAPS 2022) Best System Demonstration (ICAPS 2022) Outstanding Senior Program Committee Award (AAAI 2021) ICAPS 2019 Best Paper Award Faculty Award 2018 (University of Basel) EurAI Artificial Intelligence Dissertation Award 2017 ICAPS 2018 Best Dissertation Award Outstanding PC Member Award (SoCS 2016) Winner, Unsolvability IPC 2016 AAAI 2015 Outstanding Paper Award ICAPS 2014 Outstanding Paper Award Dux of the Master of Information Technology 2010 (Monash University) Florian actively contributes to the planning community through tutorials at AAAI 2019 and ICAPS 2015, and through software development such as the Fast Downward planner and Planutils framework. His notable side project includes creating a quine in the Shakespeare Programming Language (SPL) with Thomas Mayer, showcasing technical creativity beyond core research.

Jonas Schnidrigمشاهده پروفایل

مدرس

• Sustainable Energy System Modelling
• Life Cycle Assessment
• Renewable Energy Planning
• +۷ مورد دیگر

Jonas Schnidrig is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Engineering (STI) and the School of Architecture, Civil and Environmental Engineering (ENAC). He works as an External Employee in the SCI STI FM Group at EPFL Valais Wallis, based at the IPESE institute in Sion. His research focuses on sustainable energy policy and planning, specializing in energy system modeling, optimization, and decision support for the energy transition. He actively bridges research, industry, government, and society by developing strategies that balance ecological, social, and economic dimensions of energy transitions. Dr. Schnidrig's educational background includes: PhD in Mechanical Engineering (Energy and Technologies) from EPFL-HES-So Valais Wallis (2020-2024) Master's in Mechanical Engineering (Thermodynamics & Energetics) from EPFL (2018-2020) Bachelor's in Mechanical Engineering from EPFL (2013-2018) His research interests span sustainable energy system modeling, life cycle assessment, renewable energy planning, rational use of energy, and thermoeconomic optimization. He specializes in energy system integration, incorporating sustainability metrics into energy planning, policy support for sustainable energy transitions, and renewable energy finance and infrastructure planning. His work emphasizes a life-cycle perspective to evaluate the ecological, social, and economic dimensions of energy systems. Analysis of his recent publications reveals a strong focus on decentralized energy systems, multi-actor energy planning, and the integration of environmental metrics into energy modeling. His research demonstrates how strategic decentralization can reduce system costs by up to 10% while increasing self-consumption by up to 68%. He has pioneered methodologies that integrate life-cycle impact assessment into energy system modeling, showing potential for 15-47% cost reductions alongside 31-81% reductions in environmental impacts. His notable scientific achievement includes: Zanelli Prize for outstanding contribution to sustainable development (2020) Dr. Schnidrig coordinates the EnergyScope Community, which brings together over 50 researchers and practitioners, and contributes to major international projects including EnergyScope Community, Net Zero Valais, and collaborations with Polytechnique Montréal, Hydro Québec, and Swiss energy institutions. He supervises semester and master projects at EPFL and teaches the course "Sustainability, climate and energy" (ENV-421). He leads research on digital twins for cities within the Blue City project and focuses on understanding the role of infrastructure in high share renewable energy systems, investigating synergies across different scales, and developing sustainable energy systems that align economic efficiency with environmental sustainability.

• Combinatorial Optimization
• Operations Research
• Algorithms
• +۵ مورد دیگر

Adam Kurpisz is a Tenure Track Professor at Bern Business School (BFH) and a senior researcher at the Institute for Operations Research (IFOR) at ETH Zürich. He holds a PhD from Wrocław University of Science and Technology and has held postdoctoral and visiting researcher positions at IDSIA and the Max-Planck-Institut für Informatik. His research bridges theoretical computer science and applied optimization, focusing on combinatorial optimization, semi-algebraic proof systems, and robust optimization. He has secured over CHF 600,000 in grants, including leadership in the Ambizione Junior Research Group. His teaching spans convex optimization, polynomial optimization, and discrete mathematics at ETH Zürich and BFH. He co-founded startups Silencions and Deeptale, advancing to international competitions and securing EU grants. Research interests include leveraging algebraic geometry and Fourier analysis to develop algorithms for optimization problems. He has published extensively on sum-of-squares hierarchies, approximation algorithms, and scheduling under uncertainty. His work has been presented at venues like ISSAC, ICALP, and SIAM Conferences.

Fabrizio Grandoniمشاهده پروفایل

استاد

• Algorithms Design and Analysis
• Approximation Algorithms
• Parameterized Algorithms
• +۳ مورد دیگر

Fabrizio Grandoni is a Professor in approximation algorithms and Area Responsible at the Department of Innovative Technologies (DTI) of SUPSI. Previously, he held positions at institutions including the University of Rome Tor Vergata (2007–2011), Max Planck Institut für Informatik (Germany), and École Polytechnique Fédérale de Lausanne. He earned a Master’s (cum laude) and PhD in Computer Science from the University of Rome Tor Vergata, with a Marie-Curie Fellowship at the Max Planck Institute during his PhD. His research focuses on algorithms design and analysis, particularly approximation algorithms, parameterized algorithms, distributed systems, dynamic algorithms, and fault-tolerant data structures. He has authored over 100 papers, co-authored with 80+ researchers, achieving 3,000+ citations and an h-index of 31. Notable awards include the ERC Starting Grant (2011), Best Paper Award at STOC (2010), and the EATCS-IPEC Nerode Prize (2017). He leads projects on network design, clustering algorithms, and optimization, and is affiliated with the IDSIA (Dalle Molle Institute for Artificial Intelligence).

• Operations Research
• Optimization
• Integer Programming
• +۴ مورد دیگر

Dr. Peter Fusek is a researcher at the Zurich University of Applied Sciences (ZHAW) School of Engineering, affiliated with the Institute of Data Analysis and Process Design. He specializes in operations research and optimization, focusing on integer programming applications for staff scheduling and business efficiency improvements. Affiliation: Zurich University of Applied Sciences School: School of Engineering Department: Institute of Data Analysis and Process Design Email: peter.fusek@zhaw.ch Research Focus: Integer programming, staff scheduling, energy optimization, business process automation His recent publications explore advanced scheduling techniques for airports and energy systems, demonstrating practical implementations of optimization algorithms. Projects include airport ground staff scheduling (2023), operational staff planning (2021), and automated staff scheduling (2020).

Dejan Milojevicمشاهده پروفایل

مدرس

• Robotics
• Autonomous Vehicles
• Control Systems
• +۵ مورد دیگر

Dejan Milojevic is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich, where he works in Prof. Emilio Frazzoli's group at the Institute for Dynamic Systems and Control. He received his Ph.D. in robotics from ETH Zürich with affiliations at both the Institute for Dynamic Systems and Control and the Chemical Energy Carriers and Vehicle Systems Laboratory at Empa. He also earned his BSc. and MSc. in Mechanical Engineering from ETH Zürich. His educational background includes: Ph.D. in Robotics, ETH Zürich (affiliated with Institute for Dynamic Systems and Control and Empa) MSc. in Mechanical Engineering, ETH Zürich BSc. in Mechanical Engineering, ETH Zürich Dr. Milojevic's research focuses on the co-design of embodied intelligence systems, with particular interest in sensor selection and placement for mobile robots. His work explores the interplay between perception, decision-making, and control in autonomous systems. He is also keen on advancing uncertainty estimation and explainable AI within machine learning frameworks. His research has practical applications in autonomous vehicles and robotics. His recent publications demonstrate a consistent focus on optimizing the integration of perception systems and decision-making for mobile robots. The research shows how resource prioritization influences sensor choice, with cameras preferred for cost-effective designs while lidar sensors are chosen for better energy and computational efficiency. His work provides actionable information for designers of autonomous systems. His notable scientific contributions include: CODEI framework for Co-design of Embodied Intelligence Research on occupancy queries to quantify perception requirements for motion planners Integer Linear Programming approach for sensor and algorithm selection Dr. Milojevic has conducted research at Stanford University under Prof. Marco Pavone and has professional experience as a software engineer at Vay in Berlin, Germany. He has led significant research projects including the Automated Driving Sensor Test Vehicle project with multiple industry partners.

Emir Demirovićمشاهده پروفایل

استادیار

• Constraint Programming
• Combinatorial Optimisation
• Explainable AI
• +۴ مورد دیگر

Dr. Emir Demirović is an Assistant Professor in the Department of Computer Science at Delft University of Technology (TU Delft), The Netherlands. He leads the Constraint Solving ("ConSol") research group and co-directs the Explainable AI in Transportation Lab ("XAIT") as part of Delft AI Labs. Prior roles: Postdoc at University of Melbourne (2017-2020), PhD at Vienna University of Technology (2017) Collaborations: Civil Engineering, QuTech, and industry partners Funding sources: Dutch national funding agency, TU Delft, VoestAlpine Research Focus: Constraint programming and combinatorial optimisation Explainable AI methods for decision-making systems Integration of optimisation with machine learning Robust/resilient optimisation for industrial applications Optimal decision trees with dynamic programming Quantum computing scheduling techniques Scientific Contributions: Pioneered "Pseudo-Boolean Reasoning" for algorithm certification Developed "Blossom" algorithm for optimal decision trees Advances in "Predict+Optimise" frameworks Created Pumpkin constraint programming solver Bridge between SAT/CP and Machine Learning Awards: First Place, MaxSAT Evaluation 2018+ First Place, ROADEF/EURO 2012 Adoption of methods in Google OR-Tools Collaborations: Research visits to EPFL, ANITI/CNRS, CUHK, Monash University, TU Wien Participated in Dagstuhl seminars, Lorentz workshops, and Simons-Berkeley programme

Bartolomeo Stellatoمشاهده پروفایل

استادیار

• Mathematical Optimization
• Machine Learning
• Optimal Control
• +۳ مورد دیگر

Bartolomeo Stellato is an Assistant Professor at Princeton University's Department of Operations Research and Financial Engineering. He is associated faculty in Electrical and Computer Engineering and Computer Science, and affiliated with Princeton's AI, Robotics, and Center for Statistics and Machine Learning initiatives. His research bridges mathematical optimization, machine learning, and optimal control for real-time decision-making in uncertain environments. PhD advisors: Georgina Hall, Brandon Amos Key collaborators: Bart Van Parys (MIT), Jaime Fernandez Fisac (UPenn), Adrien Taylor (INRIA) His work on Mean Robust Optimization combines robust and distributionally robust optimization through data clustering, achieving 3+ orders of magnitude speedups in portfolio optimization. He also develops learned optimizers (e.g., L2WS, GeNIOS) and OSQP , which won the Beale–Orchard-Hays Prize. Recent grants include NSF CAREER (2023) and ONR Young Investigator (2025) awards. Article Trends : His research spans optimization verification (3/7 of 2025 articles), data-driven methods (5/7 of 2024-2025 articles), control applications (4/15 articles), and equitable resource allocation (2/15 articles). Key subfields include algorithm verification, stochastic optimization, multi-agent systems, and hardware acceleration. Scientific Awards : ONR Young Investigator (2025), Howard B. Wentz Award (2024), NSF CAREER (2023), ISSNAF Strazzabosco (2022) Students : Mentored 12+ graduate students/postdocs including Irina Wang (Wallace Fellowship), Rajiv Sambharya (Best Paper Award at JMLR), and Vinit Ranjan (co-author Mathematical Programming paper) He leads the Princeton Optimization, Learning, and Control Workshop and collaborates with institutions like MIT, INRIA, and Los Alamos National Laboratory. His group develops open-source tools (OSQP, L2WS) with over 300,000 downloads.