Find Academic Supervisors

Kaiming HeView profile

Associate Professor

• Computer Vision
• Deep Learning
• Machine Learning
• +6 more

Kaiming He is an Associate Professor with tenure in the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT), holding the Douglas Ross (1954) Career Development Professor of Software Technology chair. He also works part-time as a Distinguished Scientist at Google DeepMind. Prior to joining MIT in 2024, he was a research scientist at Facebook AI Research (FAIR) from 2016 to 2024, and a researcher at Microsoft Research Asia (MSRA) from 2011 to 2016. Dr. He received his PhD from the Chinese University of Hong Kong in 2011 and his Bachelor of Science from Tsinghua University in 2007. His academic journey reflects a strong foundation in computer science and engineering that has led to transformative contributions in artificial intelligence. His research primarily focuses on computer vision and deep learning, with pioneering work on deep residual networks (ResNets), visual object detection and segmentation, and self-supervised learning. He is best known for his work on Deep Residual Networks (ResNets), recognized as the most-cited paper of the twenty-first century. The residual connections he pioneered are now fundamental components in modern deep learning architectures including Transformers, AlphaGo Zero, AlphaFold, and various generative AI models. His recent publications demonstrate continued innovation across generative models, transformer architectures, and cross-disciplinary AI applications. His work bridges theoretical advances in neural network design with practical implementations that address real-world challenges in physics, biology, and other scientific domains. PAMI Young Researcher Award (2018) Best Paper Award, CVPR (2009, 2016) Best Paper Award, ICCV (2017) Best Student Paper Award, ICCV (2017) Everingham Prize, ICCV (2021) Most-cited paper of the twenty-first century Dr. He advises graduate students including Jake Austin, Xingjian Bai, and Mingyang Deng, and teaches advanced courses such as "6.S978: Deep Generative Models" (Fall 2024) and "6.8300/6.8301: Advances in Computer Vision" (Spring 2024). His research group actively explores how AI can serve as a unifying framework across scientific disciplines, breaking down traditional barriers between fields through shared methodologies and tools.

• Machine Learning
• Probability
• Mathematical Physics
• +6 more

Boris Hanin is an Associate Professor at Princeton University's Department of Operations Research and Financial Engineering (ORFE) and Associated Faculty at the Program in Applied and Computational Mathematics (PACM). Prior to Princeton, he held academic positions at Texas A&M University (Assistant Professor of Mathematics), MIT (NSF Postdoctoral Fellow), and Northwestern University (PhD in Mathematics under Steve Zelditch). He works part-time at Foundry, an AI/computing startup, leading the Foundry Institute. PhD in Mathematics, Northwestern University NSF Postdoctoral Fellowship, MIT Mathematics Associate Professor, Princeton ORFE Part-Time Leader, Foundry Institute His research spans machine learning, probability theory, and mathematical physics, focusing on neural network theory (approximation power, optimization guarantees), random matrix theory, and spectral asymptotics. He has made foundational contributions to understanding gradient behavior, initialization, and infinite-width limits in deep learning. Boris has supervised numerous PhD students and postdocs, with former members securing prestigious positions at Harvard, MIT, and Huawei. He serves as Associate Editor for journals like Pure and Applied Analysis and Mathematics of Operations Research , and has taught short courses at Oxford, Luxembourg, and Tor Vergata on statistical physics of neural networks and deep learning theory. 2024 Sloan Fellowship in Mathematics NSF CAREER grant DMS-2143754 NSF grant DMS-2133806 His research group collaborates on topics including hyperparameter transfer, architecture-aware scaling, and spectral analysis of random waves and neural networks. He actively contributes to theoretical machine learning through foundational publications in venues like Probability Theory and Related Fields , Journal of Machine Learning Research , and Communications in Mathematical Physics .

Robin JiaView profile

Assistant Professor

• Natural Language Processing (NLP)
• Deep Learning
• Artificial Intelligence
• +7 more

Robin Jia is an Assistant Professor in the Thomas Lord Department of Computer Science at the University of Southern California (USC) , where he leads the AI, Language, Learning, Generalization, and Robustness (Allegro) Lab . His research focuses on enhancing the reliability and robustness of large language models (LLMs) through mechanistic understanding, benchmarking under distribution shifts, and neurosymbolic integration. Key affiliations include collaborations with the USC Keck School of Medicine and contributions to legal frameworks like the EU's Digital Services Act. Robin's research spans multiple domains, including: Scientific analysis of LLM capabilities in in-context learning , data memorization , and numerical reasoning Advancements in robust NLP systems , emphasizing uncertainty estimation and calibration Development of methods combining LLMs with symbolic solvers for complex reasoning tasks Interdisciplinary applications in medicine and law , such as privacy-preserving synthetic data generation and medical misconception evaluation . His recent publications (2024-2025) address Fourier-based numerical embeddings (NeurIPS), neurosymbolic planning (NAACL), and multimodal benchmarking (COLM), with a strong emphasis on privacy , fairness , and transparency . Scientific awards include the Google Research Scholar Award (2023) , SoCalNLP Symposium Best Paper Awards , and ACL/EMNLP outstanding papers . He advises PhD students like Johnny Wei and Ameya Godbole, and has secured grants from the NSF , USC-Capital One , and USC-Amazon .

Olga Sorkine-HornungView profile

Professor

• Digital Content Creation
• Geometry Processing
• Shape Representation and Editing
• +5 more

Olga Sorkine-Hornung is a Professor of Computer Science at ETH Zurich and head of the Institute of Visual Computing. She leads the Interactive Geometry Lab, focusing on theoretical and practical advancements in digital content creation, geometry processing, and shape modeling. Current position: ETH Zurich, Department of Computer Science Previous roles: Courant Institute (NYU), Technical University of Berlin Education: BSc and PhD from Tel Aviv University, postdoc at TU Berlin Her research spans shape representation, digital fabrication, computer animation, and fundamental geometry processing. Key contributions include Laplacian surface editing, as-rigid-as-possible deformation, and generalized winding numbers. She works on applications in VR, AR, and autonomous systems. Awards include Test of Time Awards (2024), ACM Fellow (2020), ERC Consolidator Grant (2020), and EUROGRAPHICS Young Researcher Award (2008). She has supervised numerous students and co-developed software libraries like libigl and Instant Meshes . Co-chair roles for SIGGRAPH, Eurographics, and Pacific Graphics Editorial board member for ACM Transactions on Graphics and other journals Keynote speaker at VMV, CVPR, and SIAM conferences Her work bridges mathematical rigor with practical implementation, advancing computer graphics and geometry processing through intuitive algorithms that maintain surface detail while enabling efficient computation.

• Graph Algorithms
• Optimization
• High-Dimensional Geometry
• +3 more

Yang P. Liu is an Assistant Professor at Carnegie Mellon University 's Department of Computer Science . He received his PhD from Stanford University under the supervision of Aaron Sidford and previously studied at MIT . Fields of Interest : Graph Algorithms, Optimization, High-Dimensional Geometry, Additive Combinatorics, Theoretical Computer Science. His research focuses on algorithmic design and analysis for graph problems, optimization, and combinatorics, with applications in machine learning and complexity theory. Recent work includes advancements in parallel repetition games , combinatorial lines , and dynamic graph algorithms . In 2024, his research spanned FOCS , STOC , and RANDOM conferences, addressing problems in k-CSPs , min-cost flow , and hypergraph sparsification . Earlier contributions (2023) included deterministic flow algorithms and spectral hypergraph techniques. Scientific Awards : NDSEG Fellowship (2018-2021), Google PhD Fellowship (2022-2023), FOCS Best Paper (2022), STOC Best Student Paper (2022), FOCS Best Student Paper (2021). He teaches CS 15-759 , a graduate course on convex optimization theory and applications, covering gradient descent, interior point methods, and algorithmic sparsification techniques.

Tülay AdaliView profile

Professor

• Statistical Signal Processing
• Machine Learning
• Medical Image Analysis
• +3 more

Tülay Adali is a Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). She has held this position since 1992 and was named a Distinguished University Professor in 2015 for her contributions to statistical signal processing and machine learning. Currently serving as Editor-in-Chief of the IEEE Signal Processing Magazine, she has also held leadership roles in IEEE committees and conferences. Her research focuses on statistical signal processing, machine learning, and their applications in medical imaging and data fusion. Dr. Adali earned her Ph.D. in Electrical Engineering from North Carolina State University in 1992. Her work integrates foundational signal processing techniques with biomedical applications, addressing challenges in neuroimaging analysis. She leads the Machine Learning for Signal Processing laboratory, supported by grants from NSF and NIH. Her lab develops algorithms for analyzing complex signals in medical contexts, emphasizing reproducibility and interdisciplinary collaboration. Recognition includes IEEE Fellow, AIMBE Fellow, AAIA Fellow, Humboldt Research Award, and NSF CAREER Award. She has authored numerous papers on fMRI analysis, independent component analysis, and multimodal data fusion. Her editorial leadership and service to technical communities reflect her commitment to advancing signal processing and education. Education: Ph.D. in Electrical Engineering, North Carolina State University (1992) Grants: NSF, NIH-funded projects on medical imaging and signal processing Awards: SPS Meritorious Service Award, SPIE Pioneer Award

Kazuhiro SaitouView profile

Professor

• Computational Design Synthesis
• Topology Optimization
• Manufacturing Process Optimization
• +7 more

Kazuhiro Saitou is a Professor of Mechanical Engineering at the University of Michigan, affiliated with the College of Engineering. His research focuses on computational design synthesis, topology optimization, and manufacturing process integration. He leads the Algorithmic Synthesis Laboratory (ASL), advancing algorithms for automated design and optimization of mechanical systems. Education: Ph.D. (1996), MIT; M.S. (1992), MIT; B.Eng. (1990), University of Tokyo. He has held tenured positions since 1997, including roles as Founding CEO of Comnext, Inc. (2007–2012) and visiting professorships at École Centrale Paris and Donghua University. Research interests include multi-material topology optimization (M^3 TO), AI-driven design, and sustainable manufacturing. Key projects address additive manufacturing, composite structures, and energy-efficient production systems. He has pioneered methods for manufacturability-driven design and assembly optimization. Notable awards include IEEE Fellow (2018), ASME Kos-Ishii Award (2015), and NSF CAREER Award (1999). He serves as Editor-in-Chief for IEEE Transactions on Automation Science and Engineering and holds leadership roles in ASME and IEEE societies. Teaching includes courses on design optimization, CAD, and global product development. His lab has advised over 30 students, with alumni in academia and industry. Current research explores biomechanical modeling, traffic flow optimization, and medical image registration algorithms.

• Sustainable Bioenergy
• Circular Economy
• Energy Conversion Systems
• +4 more

Mika Järvinen serves as Associate Professor in the Department of Energy and Mechanical Engineering at Aalto University, leveraging fundamental sciences—physics, thermodynamics, chemistry, and numerical modeling—to address cross-sector energy challenges. His research spans sustainable bioenergy and inter-linked circular economy systems integrating energy, materials, and environmental considerations across biomass, pulp/paper, metallurgy, and waste-to-energy industries. His expertise centers on sustainable bioenergy and circular economy frameworks, utilizing numerical modeling to optimize combustion engineering and metallurgical processes. By maintaining diverse industrial engagement, he ensures research continuity during economic fluctuations while advancing black liquor spraying technologies and energy-material-environment synergies. This approach enables flexible, physics-based solutions for complex industrial energy conversion problems. Professor Järvinen's 2025 publications reveal a cohesive focus on renewable energy systems operating within planetary boundaries, spanning carbon capture (calcium looping), low-temperature heat engines, and comprehensive textbook development. His work integrates solar, wind, bioenergy, and storage technologies into holistic frameworks that balance technical innovation with ecological constraints, emphasizing system-level sustainability over isolated component optimization. His scientific recognition includes: Best Dissertation Award (2002) from Helsinki University of Technology's Department of Machine Technology for research on black liquor droplet conversion Resonate Award (2015) from Caltech University's Resnick Institute As lead of the Energy Conversion and Systems research group, he directs projects bridging fundamental modeling with industrial applications while developing educational resources for next-generation energy engineers. His laboratory work emphasizes experimental validation of numerical models across combustion, metallurgical, and biomass conversion processes.

Rajeev AlurView profile

Professor

• Formal Methods
• Artificial Intelligence
• Cyber-Physical Systems
• +6 more

Rajeev Alur is the Zisman Family Professor in the Department of Computer and Information Science at the University of Pennsylvania, leading the School of Engineering and Applied Science. He is the Founding Director of the ASSET Center for Trustworthy AI and a member of the PRECISE Center. His research focuses on formal methods for system design, integrating AI, cyber-physical systems, and machine learning with logical reasoning to ensure safety in autonomous systems. Alur has held leadership roles in major NSF projects like ExCAPE and has directed the Embedded and Multi-Scale Systems (EMBS) program. His research interests span formal verification, temporal logics, programming abstractions, and synthesis techniques. Notable contributions include the development of Nested Words (visibly pushdown languages), streaming string transducers, and tools like AutomataTutor for education. He has advised over 60 PhD students and postdocs, many of whom now hold academic and industry leadership positions. Alur’s awards include the 2024 Knuth Prize and the 2016 Alonzo Church Award. His work on Verisig and compositional verification of neural networks has advanced safety-critical AI applications. He teaches foundational courses like CIS 2620 and develops educational tools, emphasizing both theoretical rigor and practical impact. Key projects include the ASSET Center’s focus on trustworthy AI, integration of logical specifications in reinforcement learning, and formal verification of closed-loop systems with neural components. His publications span over 350 papers, with recent work addressing neurosymbolic learning, security in large language models, and efficient neural network verification.

Gert CauwenberghsView profile

Professor

• Neuromorphic Engineering
• Biomedical Integrated Circuits
• Neural Interfaces
• +7 more

Gert Cauwenberghs is a Professor of Bioengineering at the University of California San Diego (UCSD), affiliated with the Jacobs School of Engineering. He co-directs the Institute for Neural Computation and holds a visiting professorship at MIT. His research focuses on neuromorphic engineering, energy-efficient neural interfaces, and wearable biosensors. Key contributions include silicon-based adaptive neural circuits, implantable neural recording systems, and in-ear biosensing devices. Education: M.Eng. in Applied Physics (University of Brussels, 1988), M.S. and Ph.D. in Electrical Engineering (Caltech, 1989–1994). Prior roles include Professorships at Johns Hopkins University and Visiting Professor at MIT. Research Interests: Biomedical integrated circuits, neuromorphic computing, brain-machine interfaces, and energy-efficient neural systems. His work bridges neuroengineering and clinical applications, emphasizing adaptive intelligence and low-power designs. Recent Work: Development of femtojoule-efficient neural chips, high-density neural interfaces, and closed-loop wearable systems. Projects include neurobench benchmarking frameworks and RRAM-based neuromorphic hardware. Awards: NSF Career Award (1997), ONR Young Investigator (1999), PECASE (2000), IEEE Distinguished Lecturer (2003–2004). Grants & Labs: Active in NIH and DoD-funded projects, co-directs the UCSD Institute for Neural Computation. Collaborates with industry on neural interface technologies. Labs/Teams: Cauwenberghs Lab at UCSD focuses on integrated neuroengineering systems, including neural recording systems and neuromorphic computing architectures.

Muhannad S BakirView profile

Professor

• Heterogeneous Integration
• 3D IC Packaging
• Advanced Cooling Systems
• +7 more

Muhannad S. Bakir is the Dan Fielder Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology and serves as the Director of the 3D Systems Packaging Research Center. His research focuses on heterogeneous integration of microsystems, including 2.5D and 3D ICs and packaging technologies, with significant contributions to advanced cooling systems, electrical and photonic interconnects, and biosensor integration with CMOS. Dr. Bakir's research interests span heterogeneous microsystem design and integration, advanced cooling and power delivery for emerging architectures, electrical and photonic interconnect technologies, biosensor technologies, and nanofabrication. His work addresses critical challenges in next-generation electronics, enabling polylithic integration that concatenates heterogeneous ICs of various functionalities while mimicking monolithic-like densities. His research particularly focuses on co-design of thermal technologies, power delivery networks, and signaling networks for silicon nanoelectronic systems. His recent publications demonstrate strong trends in fused-silica stitch-chip technology for heterogeneous integration, with particular emphasis on RF and mm-wave applications, power delivery for AI accelerators, and thermal management solutions. His work bridges electrical engineering, materials science, and thermal management to solve critical bottlenecks in computing performance and efficiency. 2013 Intel Early Career Faculty Honor Award 2012 DARPA Young Faculty Award 2011 IEEE CPMT Society Outstanding Young Engineer Award 2012 National Academy of Engineering Frontiers of Engineering Symposium Invited Participant 2015 IEEE CPMT Society Distinguished Lecturer 2014 Best Paper of the IEEE Transactions on Components Packaging and Manufacturing Technology More than 25 conference and student paper awards Twelve issued US Patents Dr. Bakir leads the Integrated 3D Systems Lab (I3DS) at Georgia Tech, which is actively researching advanced packaging, interconnects, electrical and thermal design, and system integration. His team has received significant recognition for their work, including multiple best paper awards from major conferences like ECTC, IITC, and CICC. The lab is currently seeking postdoctoral researchers and research faculty to advance next-generation electronics through collaborative research. His lab focuses on enabling the next phase of Moore's Law through polylithic integration, which concatenates heterogeneous ICs of various functionalities (digital, analog, photonic, and mm-wave) using advanced off-chip '2.5D' and '3D' heterogeneous interconnects and packaging. This work impacts applications in high-performance computing, machine learning, edge intelligence, autonomous vehicles, augmented/virtual reality, and healthcare.

Rafał JończykView profile

Assistant Professor

• Affective Neuroscience
• Experimental Pragmatics
• Psycholinguistics
• +5 more

Rafał Jończyk is an Assistant Professor at the Department of Pragmatics, Faculty of English, Adam Mickiewicz University in Poznań, Poland, where he conducts research at the intersection of psycholinguistics, affective neuroscience, and bilingualism. His work primarily investigates how emotional processing differs between native and second languages using neurophysiological methods, particularly EEG. With a Ph.D. in English Linguistics from Poznań (2015) and postdoctoral experience at Pennsylvania State University, he has established himself as a leading researcher in bilingual emotional processing. His research interests span Affective Neuroscience , Experimental Pragmatics , Psycholinguistics , Bilingualism and Emotion , and the Neurophysiology of Creativity . His methodology heavily relies on EEG to examine language processing, with particular focus on emotional language comprehension and production in bilingual contexts. His work has demonstrated how bilinguals process emotional content differently in their native versus second languages, revealing reduced sensitivity to negative content in the second language. Jończyk's research program has evolved from early work on hemispheric specialization for emotional words to sophisticated EEG studies of emotional anticipation and metaphor processing in bilinguals. His most recent work explores hyperscanning methodologies to study real-time bilingual communication and the neural correlates of creative thinking in bilingual contexts. His publications show a clear progression from foundational studies of emotional word processing to increasingly complex investigations of emotional communication in naturalistic contexts. Cognitive Neuroscience Society Post-doctoral Fellows Award (2018) START scholarship for most talented young Polish scientists (2018) Scholarship from the Minister of Science and Higher Education (2020) Travel Grant Award for International Symposium on Bilingualism (2013) As Principal Investigator, Jończyk leads two major National Science Center projects examining psychophysiological correlates of affective language processing in bilinguals (2016-2018) and neurophysiological correlates of emotional speech production in bilingualism (2021-2025). He also participates in multiple international collaborations including NSF-funded research on divergent thinking in engineering education. His editorial roles include Topic Editor for Brain Sciences and Associate Editor for Applied Psycholinguistics, reflecting his standing in the field. He teaches courses in Psycholinguistics, Emotions in Social Communication, and Creativity and Language at the university level.

Carlos G. LeviView profile

Research Professor

• Structural Materials
• Thermal Barrier Coatings
• Ceramic Matrix Composites
• +3 more

Carlos G. Levi is a Research Professor in the Department of Materials at the University of California, Santa Barbara (UCSB), part of the College of Engineering. He holds the additional title of Professor Emeritus. His research focuses on microstructure evolution in inorganic materials, particularly in the design of advanced coatings, composites, and alloys for extreme environments. He has contributed significantly to thermal barrier coatings, ceramic matrix composites, and high-temperature alloys for aerospace and energy applications. Education: Ph.D. in Metallurgical Engineering, University of Illinois at Urbana-Champaign (1981) M.Sc. in Metallurgical Engineering, University of Illinois at Urbana-Champaign (1977) Degree in Chemical Engineering, Universidad Autonoma de Nuevo Leon, Mexico (1972) Research Interests: Levi’s work addresses challenges in structural materials, including advanced thermal/environmental barrier coatings resistant to molten silicates, ceramic matrix composites, hypersonic materials, and high-temperature alloys such as multi-principal element alloys. His studies explore degradation mechanisms, phase stability under extreme conditions, and novel synthesis techniques like vapor-mediated melt infiltration. Awards and Honors: 2014 TMS Morris Cohen Award 2012 Fellow of the American Ceramic Society 2008 NIMS Award (shared) for breakthroughs in materials science for energy/environment 2004 DLR Wissenschaftspreis (collaborative paper) 2002 Alexander von Humboldt Research Prize Advising and Grants: No formal student advisees are listed, though his research group likely includes graduate students/postdocs. Grant details are not specified in the provided texts. Labs/Teams: Levi’s research is conducted within UCSB’s Materials Department, leveraging advanced characterization tools (e.g., TriBeam tomography) and computational modeling to study material behavior under extreme conditions.

Afonso S. BandeiraView profile

Professor

• High Dimensional Probability
• Random Matrices
• Mathematical Statistics
• +3 more

Afonso S. Bandeira is a Professor in the Department of Mathematics (D-MATH) at ETH Zurich, where he conducts research at the intersection of mathematics, statistics, and computer science. He maintains strong affiliations with several interdisciplinary research centers including the Institute For Operations Research (IFOR), the Max Planck ETH Center for Learning Systems, the ETH Foundations of Data Science, and the ETH AI Center, with a courtesy appointment at D-ITET. Bandeira actively teaches courses including Mathematics of Signals, Networks, and Learning, and Mathematics of Data Science. His research focuses on High Dimensional Probability, Random Matrices, Mathematical Statistics, Theoretical Computer Science, Combinatorics, and Mathematical Optimization. Bandeira's work often explores the theoretical foundations of data science, examining phase transitions in statistical problems, computational barriers, and the geometry of high-dimensional spaces. He maintains a research group blog called Randomstrasse101 that emphasizes open problems in his field. Analysis of his recent publications reveals a strong focus on matrix and tensor concentration inequalities, synchronization problems, nonconvex optimization landscapes, and computational-statistical tradeoffs in high-dimensional inference. His work bridges theoretical mathematics with practical applications in machine learning and data analysis, particularly examining where computational limitations arise in statistical problems. Bandeira actively mentors students and researchers, currently supervising several doctoral candidates including Daniil Dmitriev, Konstantin Donhauser, Anastasia Kireeva, Kevin Lucca, Chiara Meroni, Gil Kur, Petar Nizic-Nikolac, and Almut Roedder. He emphasizes that students working with him should participate in the DACO seminar and group meetings, and encourages prospective students to have completed his Mathematics of Signals, Networks, and Learning or Mathematics of Data Science courses. He leads a research group focused on the mathematics of data science, with regular group meetings and seminars. Bandeira has developed comprehensive lecture notes including 'A Tour Through the Mathematics of Signals, Learning, and Networks' (2025) and 'Mathematics of Machine Learning' (2021), and previously authored 'Ten Lectures and Forty-Two Open Problems in the Mathematics of Data Science' (2015).

Christoph KeplingerView profile

Professor

• Soft Robotics
• Functional Polymers
• Energy Capture
• +4 more

Christoph Keplinger serves as Managing Director of the Max Planck Institute for Intelligent Systems (MPI-IS) in Stuttgart, Germany, leading the Robotic Materials Department since 2020 and assuming overall institute leadership in 2023. He holds dual academic appointments as Honorary Professor at the University of Stuttgart and Eminent Visiting Professor of Soft Robotics at the University of Colorado Boulder, establishing him as a pivotal figure in bridging fundamental materials science with advanced robotics. His interdisciplinary approach integrates physics, chemistry, and engineering to pioneer breakthroughs in soft robotic systems. Keplinger's academic foundation includes a PhD in Soft Matter Physics from Johannes Kepler University Linz, Austria, followed by postdoctoral research at Harvard University focusing on mechanics and chemistry of soft materials. This unique background enabled his transition into robotics innovation, particularly in electrohydraulic actuation systems. His research program centers on three synergistic pillars: (I) soft robotics development through novel actuator technologies like HASEL artificial muscles; (II) energy capture mechanisms using soft materials; and (III) functional polymers engineered for robotic applications. This work produces transformative hardware that mimics biological functionality, with significant implications for human-robot interaction, medical devices, and sustainable robotics systems. His group employs cutting-edge materials synthesis and characterization techniques to create lifelike robotic components. Analysis of recent publications reveals dominant trends in wearable haptic interfaces, electrohydraulic actuation systems, and tremor-suppression technologies. The research consistently leverages HASEL (Hydraulically Amplified Self-healing Electrostatic) technology to achieve muscle-like performance in soft actuators, with applications spanning from fingertip haptic feedback to underwater manipulation systems. This trajectory demonstrates a clear progression from fundamental material properties toward practical implementations in medical rehabilitation and human augmentation. His exceptional contributions have earned prestigious recognition: 2017 Packard Fellowship for Science and Engineering, awarded for high-impact interdisciplinary research 2021 Alexander von Humboldt Professorship (declined to remain at MPI-IS), Germany's most valuable international research award 2013 EAPromising European Researcher Award from the European Scientific Network for Artificial Muscles As principal investigator, Keplinger leads a dynamic interdisciplinary research group while securing competitive funding for frontier projects. His entrepreneurial vision materialized in 2018 through co-founding Artimus Robotics, where he serves as Chief Science Officer to commercialize HASEL technology. This dual commitment to academic research and industry translation exemplifies his dedication to real-world impact, particularly in creating biodegradable and sustainable soft robotic solutions. The Robotic Materials Department operates state-of-the-art facilities for materials fabrication, robotic integration, and haptic interface development. The team maintains strong collaborations across MPI-IS departments and external institutions including the University of Colorado Boulder, fostering innovation in sustainable robotics through initiatives like biodegradable electrohydraulic actuators. Current projects focus on wearable tremor suppression systems, electrohydraulic locomotion platforms, and energy-autonomous soft robots that address critical challenges in medical rehabilitation and human augmentation.