جست‌وجوی استاد و پژوهشگر

Antoine Bosselutمشاهده پروفایل

استادیار

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Antoine Bosselut is a Tenure Track Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences, where he leads the EPFL NLP group. His research focuses on developing AI reasoning agents that can model, represent, and reason about human and world knowledge, with applications in health, education, and global fairness. His research interests span multiple critical areas in modern AI: LLM Representations of Knowledge: Understanding what language models know and how they represent that knowledge internally Reasoning Algorithms: Developing methods to improve LLMs' reasoning capabilities through symbolic systems, neuroscience, and cognitive science Large-scale AI Development: Creating open-source foundation models with multilingual capabilities AI Democratization: Ensuring equitable access to AI technologies across different cultural and regulatory contexts His recent publications reveal a strong focus on the intersection of language models and cognitive science, with multiple studies examining how LLMs align with human cognition. He's also deeply engaged in practical applications of NLP technology, particularly in education and multilingual settings, as evidenced by his work on evaluating AI's impact on higher education and developing culturally-aware language models. His scientific achievements have been recognized with several prestigious awards: Outstanding Paper Award at NAACL 2025 ELLIS Scholar designation in 2024 Outstanding Paper Award at ACL 2023 Inclusion in Forbes 30 Under 30 list for Science & Healthcare in 2021 Bosselut actively mentors numerous PhD students across diverse NLP research areas and has secured significant recognition for his work in both academic and media circles. His lab has been featured in major publications including Communications of the ACM, The Atlantic, and Quanta Magazine, highlighting the societal impact of his research on commonsense reasoning and AI capabilities.

Robert Westمشاهده پروفایل

دانشیار

• Natural Language Processing (NLP)
• Machine Learning
• Computational Social Science
• +۴ مورد دیگر

Robert West is an Associate Professor at EPFL (École polytechnique fédérale de Lausanne) in the School of Computer and Communication Sciences , leading the Data Science Lab (dlab) . His research focuses on Natural Language Processing , Machine Learning , and Computational Social Science , analyzing human-generated data from the web, social media, and online platforms. Education : PhD in Computer Science (2016) - Stanford University MSc in Computer Science (2010) - McGill University BSc in Computer Science (2007) - Technische Universität München Research Interests : West develops algorithms for analyzing large-scale web data, with emphasis on multilingual NLP , social network analysis , and AI ethics . His work bridges machine learning with social science to understand digital human behavior. Scientific Awards : ICWSM’22 Adamic–Glance Distinguished Young Researcher Award Google Faculty Research Award Facebook Research Award Multiple Outstanding Paper Awards at ICWSM and WWW Advising & Grants : He advises 12 PhD students and has secured funding from the Swiss National Science Foundation , Swiss Data Science Center , and industry partners. His lab maintains collaborations with Microsoft Research and CROSS . Labs & Collaborations : West leads the Data Science Lab at EPFL, which focuses on web-scale data analysis , privacy-preserving machine learning , and AI for social good . The lab develops tools like Wikispeedia and Quotebank for public data exploration.

Aarti Singhمشاهده پروفایل

استاد

• Reinforcement Learning
• Bandit Algorithms
• Active Learning
• +۷ مورد دیگر

Aarti Singh is a Professor in the Machine Learning Department at Carnegie Mellon University and Director of the NSF AI Institute for Societal Decision Making. She leads research at the intersection of machine learning, statistics, and decision making, with applications to scientific and societal domains. Her work focuses on designing principled interactive algorithms for learning and decision making under uncertainty. Education: Ph.D. in Electrical Engineering, University of Wisconsin-Madison (2008) M.S. in Electrical Engineering, University of Wisconsin-Madison (2003) B.E. in Electronics and Communication Engineering, University of Delhi (2001) Research Interests: Professor Singh's research centers on developing interactive machine learning algorithms that go beyond finding input-output associations to make higher-level decisions about the most informative data and actions. Her work spans autonomous decision making, including active sampling, stochastic optimization, bandits, and reinforcement learning that are statistically optimal, computationally tractable, and robust. She also investigates human factors in decision making, designing algorithms that model and leverage human feedback while accounting for bias, memory effects, and calibration. Her research has applications in material science, cosmology, and peer review systems. Research Trends: Professor Singh's recent publications demonstrate a strong focus on reinforcement learning, particularly in developing more efficient and robust algorithms for decision making under uncertainty. Her work bridges theoretical foundations with practical applications, spanning from fundamental algorithm development to real-world implementation in scientific domains. There's a clear trajectory toward integrating human factors into decision-making algorithms, with significant contributions to peer review systems and preference learning. Scientific Awards: NSF Career Award United States Air Force Young Investigator Award A. Nico Habermann Faculty Chair Award Harold A. Peterson Best Dissertation Award Multiple paper awards Advising and Grants: Professor Singh has advised numerous PhD and master's students, many of whom have gone on to faculty positions or research roles at leading institutions. Her research is supported by prestigious grants from ONR, Simons Foundation, AFRL, ARL, and NSF. She serves as General Chair (2025) and Program Chair (2020) for the International Conference on Machine Learning (ICML) and has held leadership roles in multiple professional organizations. Research Team: Professor Singh leads a vibrant research group within the Machine Learning Department at CMU, with current PhD students working on topics including reinforcement learning, human-AI collaboration, and decision making under uncertainty. She also directs the NSF AI Institute for Societal Decision Making, which brings together researchers from multiple disciplines to develop AI systems that support human decision making in societal contexts.

Tommi Jaakkolaمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Computational Biology
• +۷ مورد دیگر

Tommi Jaakkola is the Thomas Siebel Professor of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society at the Massachusetts Institute of Technology. He received his MSc in theoretical physics from Helsinki University of Technology in 1992 and his PhD from MIT in computational neuroscience in 1997. After completing a postdoctoral position in computational molecular biology as a DOE/Sloan fellow at UCSC, he joined the MIT EECS faculty in 1998. His research advances how machines can learn, predict or control, and do so at scale in an efficient, principled, and interpretable manner. His work in machine learning extends from foundational theory to modern applications, focusing especially on statistical inference and estimation tasks that lie at the heart of complex learning problems. He designs new methods, theory and algorithms to automate the use and generation of semi-structured data such as natural language text, images, molecules, or strategies. Jaakkola applies and develops algorithms to solve multi-faceted recommender, retrieval, or inferential tasks (particularly in biomedical contexts), design and optimize molecules or reactions for drug design, and model strategic, game theoretic interactions. His recent work heavily focuses on diffusion models, protein structure prediction, molecular design, and generative AI, with significant publications in top conferences including ICML, NeurIPS, and ICLR. His scientific contributions span multiple disciplines with significant impact in both theoretical machine learning and practical applications in computational biology and chemistry, including notable work on antibiotic discovery published in Cell. Current advisees: Julia Balla, Bowen Jing, Hannes Stärk, Peter Holderrieth, Chenyu Wang Recent graduates: Gabriele Corso (Boltz PBC), Ezra Erives (DE Shaw), Jason Yim (Xaira) Jaakkola maintains an active research program through MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Institute for Data, Systems, and Society (IDSS), with his office located in the Stata Center (32-G470). His work bridges theoretical machine learning with practical applications, making significant contributions to both the academic field and potential real-world impact in healthcare and drug discovery.

Adam Runionsمشاهده پروفایل

پژوهشگر

• Computational Modeling
• Developmental Biology
• Computer Science
• +۴ مورد دیگر

Adam Runions is a researcher in the Department of Computer Science at the University of Calgary, leading the MPG Partner Group in computational analysis of leaf development through collaborative work with Miltos Tsiantis. His group is embedded in the Graphics Cluster, focusing on interdisciplinary problems at the intersection of computer science and developmental biology. University of Calgary - Department of Computer Science MPG Partner Group (2022) Graphics Cluster affiliation His research explores computational modeling and analysis of plant form and development across multiple scales, integrating geometric modeling, physically-based simulation, and computer-aided design. Key themes include plant morphogenesis, self-organization of natural forms, and cross-disciplinary applications in computer graphics and animation. Recent publications emphasize plant development (leaf shape, bark patterning), mathematical modeling (auxin-driven patterning), and geometric techniques (subdivision surfaces, PUPs). Collaborations span institutions like the Max Planck Institute for Plant Breeding Research. Scientific Awards Marie Sklodowska-Curie Fellowship Best Paper Award (International Conference on Cyberworlds 2015) Best Student Paper Award (Computer Graphics International 2011) The group actively recruits BSc, MSc, and PhD students with backgrounds in computer science and mathematics for projects on plant form simulation and digital content creation. Research integrates evolutionary biology, biomechanical modeling, and computational techniques.

Vivek Shenoyمشاهده پروفایل

استاد

• Mechanobiology
• Multiscale Modeling
• Biomaterials
• +۶ مورد دیگر

Vivek Shenoy is the Eduardo D. Glandt President's Distinguished Professor at the University of Pennsylvania, with primary appointments in the Department of Materials Science and Engineering and secondary appointments in Bioengineering and Mechanical Engineering and Applied Mechanics. He leads the Multiscale Mechanobiology and Biomaterials Laboratory, which focuses on developing theoretical frameworks and numerical methods to understand complex biological and engineering systems across multiple length scales. Shenoy's research spans mechanobiology, chromatin organization, cell mechanics, and biomaterials. His work addresses the fundamental challenge of modeling how small-scale cellular phenomena couple with long-range tissue-level interactions across micrometers to centimeters. By integrating insights from soft matter physics, solid mechanics, chemistry, and applied mathematics, his group develops multiphysics continuum and mesoscale theories to elucidate mechanisms controlling both biological and engineering systems. His recent publications demonstrate an increasing focus on nuclear mechanics, chromatin organization, and the interplay between mechanical forces and gene regulation. Analysis of Shenoy's publication record reveals a strong interdisciplinary approach, with high-impact papers spanning biophysics, materials science, and cell biology. His work shows consistent evolution from fundamental mechanics of materials to complex biological systems, with recent emphasis on the mechanical regulation of chromatin architecture, cell migration dynamics in 3D environments, and mechanotransduction in development and disease. His publications appear regularly in top journals including Nature, Science, and their affiliated publications, demonstrating significant influence across multiple fields. Eduardo D. Glandt President's Distinguished Professor Multiple publications in Nature, Science, and PNAS Active research program with publications through 2025 Shenoy actively mentors students and postdocs through his laboratory, with numerous co-authored publications indicating strong mentorship. His research program appears to be well-funded through multiple grants supporting his work in mechanobiology and biomaterials. The Multiscale Mechanobiology and Biomaterials Laboratory maintains active collaborations across disciplines and institutions, reflecting the interdisciplinary nature of his research. The Multiscale Mechanobiology and Biomaterials Laboratory, housed within the Department of Materials Science and Engineering at the University of Pennsylvania, serves as the primary research hub for Shenoy's work. The lab maintains an active presence on social media (Twitter: @ShenoyLab) for updates on activities and publications. Their research approach combines theoretical modeling with experimental validation to address fundamental questions at the interface of mechanics, materials science, and biology.

Dai Zhongxiangمشاهده پروفایل

استادیار

• Large Language Models Inference
• Prompt Optimization
• In-context Learning
• +۵ مورد دیگر

Dai Zhongxiang is an Assistant Professor and Presidential Young Fellow at the School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHKSZ), where he joined in August 2024. Previously, he was a Postdoctoral Associate at MIT's Laboratory for Information and Decision Systems (January-June 2024) and a Postdoctoral Fellow at the National University of Singapore's Department of Computer Science (April 2021-December 2023). He completed his Ph.D. in Artificial Intelligence at NUS under the supervision of Bryan Kian Hsiang Low and Patrick Jaillet. Dr. Dai's research focuses on the intersection of theoretical and practical AI, with particular emphasis on large language models (LLMs) and optimization techniques. His work spans both theoretical foundations of multi-armed bandits and Bayesian optimization, as well as practical applications in LLM inference, including prompt optimization, in-context learning, personalization of LLMs, LLM-based agents, and scaling up test-time computation of LLMs. His research approach often bridges theoretical principles with real-world applications, particularly in AI4Science problems. His recent publications demonstrate a clear trend toward advancing LLM capabilities through optimization techniques, with increasing focus on practical deployment challenges. The research spans both theoretical contributions to optimization theory and applied work on enhancing LLM performance in real-world scenarios. His work on dueling bandits, neural bandits, and zeroth-order optimization has been consistently published in top-tier venues including NeurIPS, ICML, ICLR, and ACL. Presidential Young Fellow, CUHKSZ (2024) Dean's Graduate Research Excellence Award, NUS (2021) Research Achievement Award × 2, NUS (2019 & 2020) Singapore-MIT Alliance Graduate Fellowship (2017) Dr. Dai actively mentors multiple Ph.D. students and research assistants, with several of his students' papers accepted to top conferences. His research has received significant attention, with invitations to serve as Area Chair for NeurIPS 2025 and ICLR 2025, reflecting his growing influence in the machine learning community. His work bridges theoretical machine learning with practical applications in large-scale AI systems.

Alexandre Jacquillatمشاهده پروفایل

دانشیار

• Data-driven decision-making
• Stochastic optimization
• Integer optimization
• +۶ مورد دیگر

Alexandre Jacquillat is the Maurice F. Strong Career Development Associate Professor and Associate Professor of Operations Research and Statistics at MIT Sloan School of Management. His research focuses on data-driven decision-making with applications in air traffic management, urban mobility, and decarbonization. He holds a PhD in Engineering and MS degrees from MIT and École Polytechnique. Education PhD in Engineering, MIT MS in Technology and Policy, MIT MS in Applied Mathematics, École Polytechnique Research Interests His work develops scalable optimization models for efficient, equitable, and sustainable operations. Key areas include stochastic optimization, large-scale systems design, and machine learning applications in transportation and public policy. Recent projects explore vertiport planning for urban aerial mobility and prescriptive analytics for pandemic response. Awards Harold W. Kuhn Award (2024) INFORMS Harvey Greenberg Research Award (2023) MIT Jamieson Prize for Excellence in Teaching (2023) Multiple INFORMS Best Paper Awards (2015-2023) Named Leading Academic Data Leader by Chief Data Officer Magazine (2021-2022) Teaching & Grants Teaches courses in optimization (15.093, 15.083) and analytics (15.072). His grants support work in robotic warehousing, air traffic scheduling, and disaster response logistics. Advises on transportation analytics for industry and government. Labs/Teams Leads MIT Sloan's operations research group, collaborating with industry partners like McKinsey & Co. and Booz Allen Hamilton on transportation analytics and optimization projects.

Fatma Kılınç-Karzanمشاهده پروفایل

دانشیار

• Mathematical Optimization
• Convex Optimization
• Non-convex Optimization
• +۷ مورد دیگر

Fatma Kılınç-Karzan is an Associate Professor of Operations Research at Carnegie Mellon University's Tepper School of Business, with a courtesy appointment as Associate Professor of Computer Science. She is also affiliated with the Algorithms Combinatorics and Optimization (ACO) PhD Program and was a Visiting Scientist at Berkeley's Simons Institute for the Theory of Computing during Fall 2017. Her educational background includes a PhD from Georgia Institute of Technology's H. Milton Stewart School of Industrial & Systems Engineering with a minor in Mathematics, supervised by Prof. Arkadi Nemirovski. She earned her B.S. and M.S. degrees from the Industrial Engineering Department of Middle East Technical University with a minor in Information Systems. Dr. Kılınç-Karzan's research spans mathematical optimization with emphasis on convex and non-convex optimization theory, algorithms, and applications. Her work bridges theoretical foundations with practical implementations in optimization under uncertainty (robust optimization, chance constraints, distributionally robust optimization), machine learning (preference learning from limited data), and business analytics. She develops foundational theory for large-scale optimization problems with applications in decision making under uncertainty and high-dimensional statistical inference. Analysis of her recent publications reveals a strong focus on convex hull characterizations, semidefinite programming relaxations, distributionally robust optimization, and online convex optimization frameworks. Her work demonstrates increasing integration of optimization theory with machine learning applications, particularly in developing data-driven approaches for decision making under uncertainty. NSF CAREER Award (2015) INFORMS Optimization Society Young Researcher Prize (2015) INFORMS Junior Faculty Interest Group (JFIG) Best Paper Award (2014) BP Junior Faculty Chair (2014-2015) Faculty Giving Chair (2012-2013) Wimmer Fellowship (2012-2013) Dr. Kılınç-Karzan has successfully mentored numerous PhD students who have received prestigious awards, including the 2021 INFORMS Optimization Society Best Student Paper (1st prize) and multiple honorable mentions. Her research has been supported by significant grants including an NSF CAREER Award, an ONR grant (with S. Küçükyavuz), and an AFOSR grant. She serves on editorial boards for Mathematical Programming, Operations Research, Mathematics of Operations Research, and other leading journals, and has held leadership positions in professional societies including the Mathematical Optimization Society and INFORMS Computing Society. Through her affiliations with CMU's Tepper School, Computer Science Department, and ACO Program, she collaborates across disciplines to advance optimization theory and its applications. Her professional service includes committee chair roles for major INFORMS competitions and program committee leadership for international optimization conferences.

Andrew Childsمشاهده پروفایل

استاد

• Quantum Algorithms
• Quantum Information Processing
• Quantum Simulation
• +۲ مورد دیگر

Andrew Childs is a Professor at the University of Maryland, affiliated with the Department of Computer Science and the Institute for Advanced Computer Studies (UMIACS). He serves as Director of the NSF Quantum Leap Challenge Institute for Robust Quantum Simulation (RQS) and is a Fellow at the Joint Center for Quantum Information and Computer Science (QuICS). His research focuses on quantum algorithms for simulating physical systems, algebraic problems, and quantum walk protocols, with applications in quantum computing and computational complexity. University of Maryland Institute for Advanced Computer Studies (UMIACS) Joint Center for Quantum Information and Computer Science (QuICS) NSF Quantum Leap Challenge Institute for Robust Quantum Simulation Childs' research spans quantum simulation, quantum Fourier transform, phase estimation, and Hamiltonian dynamics. He has developed techniques to reduce quantum computational resources for simulating quantum systems and explored limitations of quantum computers through hidden subgroup problems and non-unitary dynamics. His publications cover diverse areas including quantum walk optimization, Hamiltonian simulation methods, and applications to cryptography and condensed matter physics. Recent works address spatial search algorithms, product formulas for commutators, and quantum routing protocols. As an educator, Childs has taught courses on quantum algorithms and information processing at both the University of Maryland and University of Waterloo, with lecture notes and materials spanning multiple years. Contact: amchilds@umd.edu | Office: ATL 3359 | Affiliated with University of Maryland's quantum research institutes.

Khuzaima Daudjeeمشاهده پروفایل

استاد

• Distributed Systems
• Database Management
• Cloud Computing
• +۳ مورد دیگر

Khuzaima Daudjee is a Professor and David R. Cheriton Faculty Fellow in the Cheriton School of Computer Science at the University of Waterloo. His research focuses on systems-oriented problems at the intersection of systems and data management, particularly building large-scale systems, storage infrastructure in the cloud, and modern hardware applications. He leads projects in distributed database systems, elastic scaling, and resource optimization. His recent work includes Caerus (geo-replicated transactions), Tiresias (predictive storage), and MorphoSys (automatic physical design metamorphosis). Daudjee has chaired major conferences including ICDE 2026 and serves on editorial boards for VLDB, SIGMOD, and IEEE TKDE journals. His awards include ACM Distinguished Scientist and multiple best paper awards. Educational initiatives include developing distributed systems teaching materials and supervising graduate students across database and distributed systems domains. His industry collaborations involve cloud infrastructure optimization and scalable data processing frameworks.

Amadeus Gebauerمشاهده پروفایل

پژوهشگر

• Cardiac mechanics
• Cardiac growth and remodeling
• Computational mechanics
• +۳ مورد دیگر

Amadeus Gebauer is a Researcher at the Chair of Computational Mechanics within the Institute for Computational Mechanics at the Technical University of Munich (TUM), serving as a Research Associate since 2019. His work specializes in computational biomechanics with emphasis on cardiac mechanics modeling, growth and remodeling processes, and multi-physics simulation frameworks. Education: Master of Science (M.Sc.) in Mechanical Engineering, Technical University of Munich, 2019 Research Interests: Gebauer's research centers on cardiac mechanics modeling, including growth and remodeling of cardiac tissue, cardiac active tissue mechanics, and medical image processing. He develops advanced computational methods for parallel and high performance computing, particularly through the 4C multi-physics simulation framework. His work integrates constrained mixture models to simulate organ-scale biological processes, bridging computational mechanics with clinical cardiology applications and focusing on mechanobiological stability in cardiac systems. Publication Trends: Gebauer's publications (2018-2025) demonstrate consistent innovation in computational cardiology, primarily using constrained mixture models to address cardiac growth and remodeling. His recent work introduces adaptive integration techniques for history variables and homogenized modeling approaches, while expanding into software benchmarking for cardiac elastodynamics and gastric motility simulations. These contributions highlight his expertise in developing robust numerical methods for multi-physics biomedical problems, with increasing focus on patient-specific applications and high-performance computing solutions. Teaching and Advising: Gebauer teaches core computational mechanics courses including Finite Elemente and Numerische Festkörpermechanik across multiple semesters. He has supervised diverse student projects ranging from term papers to Master's theses, with notable collaborations including Maximilian Grill's shoulder biomechanics research (2020) and Janina Datz's artery geometry framework development (2021). His advising consistently focuses on cardiac mechanics, computational modeling, and medical device simulation. Research Environment: As part of Professor Wolfgang A. Wall's Institute for Computational Mechanics (LNM) at TUM, Gebauer contributes to a leading research group in computational solid/fluid mechanics. The LNM develops the 4C simulation framework for complex engineering and biomedical challenges, with current emphasis on cardiac growth modeling, multi-physics integration, and high-performance computing applications in personalized medicine.

Andrew Spakowitzمشاهده پروفایل

استاد

• Chromosomal organization and dynamics
• Protein self-assembly
• Polymer membranes
• +۷ مورد دیگر

Andrew Spakowitz is a Professor of Chemical Engineering, Materials Science and Engineering, and by courtesy, Applied Physics and Chemistry at Stanford University. He currently serves as the Senior Associate Dean for Research and Faculty Affairs and holds the Tang Family Foundation Chair of the Department of Chemical Engineering. His academic career at Stanford spans from Assistant Professor (2006-2014) to Associate Professor (2014-2020) and now Professor since 2020. Dr. Spakowitz earned his PhD in 2004, MS in 2001 from the California Institute of Technology, and his BS in Chemical Engineering from the University of Wisconsin, Madison in 1999. He completed postdoctoral training in Molecular and Cell Biology and Biophysics at UC Berkeley from 2004-2006. His research focuses on theoretical and computational approaches to understanding biological processes and complex materials. The Spakowitz lab addresses fundamental chemical and physical phenomena through four main research themes: chromosomal organization and dynamics, protein self-assembly, polymer membranes, and charge transport in conducting polymers. His group employs diverse theoretical and computational methods including analytical theory of semiflexible polymers, polymer field theory, continuum elastic mechanics, Brownian dynamics simulation, equilibrium and dynamic Monte Carlo simulations, and reaction-diffusion modeling. Analysis of his recent publications reveals a strong emphasis on epigenetics and chromatin dynamics, with significant work on DNA methylation patterns, nucleosome clustering, and chromosome organization. His research also extends to polymer physics applications in biological systems, particularly in respiratory diseases, water purification membranes, and bacterial phage interactions with human mucus. Tang Family Foundation Chair of the Department of Chemical Engineering Professor Spakowitz mentors several graduate students and postdoctoral scholars in the Chemical Engineering and Materials Science departments. His lab members work on diverse projects spanning from chromatin dynamics to polymer membranes for water purification. He teaches multiple courses including CHEMENG 120B (Energy and Mass Transport), CHEMENG 340 (Molecular Thermodynamics), CHEMENG 466 (Polymer Physics), and CHEMENG 467 (Physics of Biomacromolecules). The Spakowitz lab operates from Clark S295 at Stanford University, conducting theoretical and computational research that bridges chemistry, physics, biology, and engineering disciplines to address complex problems across multiple length and time scales.

Felix Bottمشاهده پروفایل

پژوهشگر

• Computational Mechanics
• Meshfree Methods
• Peridynamics
• +۳ مورد دیگر

Felix Bott is a Researcher at Technische Universität München (TUM), currently affiliated with PainLabMunich at Rechts der Isar Hospital since September 2020. Previously, he served as a Research Associate and Teaching Assistant in the Mechanics & High Performance Computing Group at TUM from April 2018 to March 2020. His academic credentials include a Master of Science in Mechanical Engineering (TUM, 2018) and a Master de Sciences, Technologies, Santé specializing in multi-scale mechanics modeling (2019). Research Focus Bott's research specializes in computational mechanics, emphasizing mesh-free discretization techniques like Moving Kriging Collocation and Peridynamics. He develops probabilistic numerical methods for uncertainty quantification and inverse analyses, with applications in solid mechanics and engineering simulations. His work bridges theoretical computational frameworks with practical engineering challenges. Teaching & Advising As a teaching assistant, he led courses in Engineering Mechanics I (WS 2018/19) and Engineering Mechanics II (SS 2018, SS 2019). He supervised multiple student projects including Master's theses on peridynamics-based continuum modeling, Bachelor's theses on medical device mechanics, and research internships in dynamical systems visualization and impact phenomena simulation. Laboratory Affiliations Currently conducts research at PainLabMunich (Rechts der Isar Hospital), focusing on computational approaches for medical-mechanical problems. Previously contributed to the Mechanics & High Performance Computing Group at TUM, developing advanced numerical methods for engineering applications.

Nanpeng Yuمشاهده پروفایل

استاد

• Smart Grid
• Power Systems
• Data-Driven Optimization
• +۷ مورد دیگر

Dr. Nanpeng Yu is a Full Professor at the University of California, Riverside , serving as Vice Chair and Graduate Advisor in the Department of Electrical and Computer Engineering . He maintains cooperative faculty affiliations with the Department of Computer Science and Department of Statistics , and directs the Energy, Economics, and Environment Research Center at UCR. Education : B.S. in Electrical Engineering from Tsinghua University (2006), M.S. in Electrical Engineering and Economics, and Ph.D. from Iowa State University (2010) Prior Industry Experience : Senior Power System Planner and Project Manager at Southern California Edison (2011-2014) Dr. Yu’s research bridges smart grid technologies , data-driven optimization , and machine learning for energy systems. Key areas include voltage control , renewable energy integration , transportation electrification , and grid resilience . His recent work focuses on physics-informed graph learning for unit commitment problems and adversarial purification in power system classifiers. The AI-Energy Nexus Laboratory under Dr. Yu has produced impactful publications in Applied Energy , IEEE Transactions on Power Systems , and Transportation Research series, covering topics like heavy-duty EV charging infrastructure and dynamic distribution network reconfiguration . Lab members have won the American-Made Digitizing Utilities Grand Prize ($300,000) from the U.S. DOE. Leadership Roles : Chair, IEEE Power and Energy Society Distribution System Operation and Planning Subcommittee Chair, IEEE Power and Energy Society Working Group on Data-Driven Modeling for Power Distribution Networks Associate Editor, IEEE Transactions on Smart Grid and IEEE Power Engineering Letters Scientific Awards : Regents Faculty Fellowship Regents Faculty Development Award Multiple IEEE Best Paper Awards