Find Academic Supervisors

Dinesh JayaramanView profile

Assistant Professor

• Robotics
• Computer Vision
• Reinforcement Learning
• +3 more

Dinesh Jayaraman is an Assistant Professor at the University of Pennsylvania, with primary and secondary appointments in the Department of Computer and Information Science (CIS) and Electrical and Systems Engineering (ESE), respectively. He leads the Perception, Action, and Learning (PennPAL) Research Group at the GRASP Laboratory, focusing on interdisciplinary research at the intersection of robotics, machine learning, and computer vision. Research Interests: Robotics, computer vision, reinforcement learning, and autonomous systems. Recent Publications: His work explores vision-language models for robotic tool use, symmetry-based control acceleration, articulated object modeling, and in-context learning frameworks. Awards: Recipient of the 2022 NSF CAREER Award for innovative contributions to robotics and AI. Teaching: Co-teaching a robot-learning seminar (CIS 7000/ESE 6800) with Antonio Loquercio in Spring 2025. Students: Advising PhD candidates including Edward Hu, Arjun Krishna, and co-advised students with Osbert Bastani, Vijay Kumar, and Rajeev Alur.

• Autonomous Systems
• Robotics
• Motion Planning
• +3 more

Javier Alonso-Mora is a Professor in the Department of Mechanical Engineering at Delft University of Technology, specializing in Learning & Autonomous Control. His research focuses on autonomous systems, robotics, motion planning, and transportation logistics, with applications in mobile manipulation, dynamic environments, and urban mobility. He leads key projects such as INTERACT (Intuitive Interaction for Robots among Humans) and ACT (Perceptive Acting Under Uncertainty), exploring human-robot interaction, autonomous vehicles, and healthcare robotics. Notable achievements include an ERC Starting Grant (2022) and a Veni Grant (2017). His work addresses challenges in robot navigation, control systems, and fleet optimization, with contributions to both theoretical advancements and practical implementations. Projects like TRiLOGy focus on sustainable water transportation, while HARMONY advances assistive robotics in healthcare. Alonso-Mora’s research leverages geometric fabrics for motion planning, probabilistic modeling for dynamic environments, and multi-agent coordination. He collaborates internationally and contributes to open-source frameworks for robotics. His recent publications emphasize safety-aware control, instance-aware semantic mapping, and adaptive systems for cluttered environments.

David R. KoesView profile

Associate Professor

• Computational Drug Discovery
• Machine Learning in Biology
• Open Source Software
• +3 more

David R. Koes is an Associate Professor in the Department of Computational and Systems Biology at the University of Pittsburgh, affiliated with the School of Medicine. He holds roles such as Associate Director of the Joint CMU-Pitt Computational Biology PhD Program (CPCB) and is involved in multiple graduate programs including Intelligent Systems and Computational Biomedicine. His research focuses on developing computational algorithms and systems for drug discovery, emphasizing open-source software and machine learning applications in biomedical data. Koes teaches courses like MSCBIO2025 (Bioinformatics Programming in Python) and MSCBIO2065 (Scalable Machine Learning for Big Data Biology). He has secured NIH funding (R35GM140753) and collaborated on projects with institutions like NVIDIA and Google Cloud. His lab develops tools such as GNINA, Pharmit, and 3Dmol.js, and actively contributes to open drug discovery initiatives. Education: PhD in Computer Science from Carnegie Mellon University (CMU). Research Interests: Leveraging computation and AI for drug design, molecular docking, pharmacophore modeling, and open science. Specific areas include developing scalable machine learning pipelines, virtual screening systems, and tools for 3D molecular analysis. Grants and Funding: Current NIH R35 grant and prior support from NSF, Relay Therapeutics, and others. His work emphasizes translating computational methods into practical drug discovery solutions. Lab and Teams: Directs a lab focused on computational drug discovery, collaborating with multiple academic and industry partners. Supervises graduate students and postdocs in projects spanning AI-driven drug design, molecular modeling, and software development.

• Artificial Intelligence
• Privacy and Data Protection
• Trustworthy AI
• +4 more

Prof. Dr. Pinar Yolum Birbil is a leading researcher in Responsible AI at the Faculty of Science , Utrecht University . Her work bridges Artificial Intelligence , Privacy and Data Protection , and Software Agents , focusing on Human-Centered AI and Trustworthy Systems . She is part of the AI & Data Science and Responsible AI research groups. PhD in Computer Science, North Carolina State University (2003) MS in Computer Science, North Carolina State University (2000) BSc in Computer Engineering, Marmara University (1998) Her research explores Privacy Preservation in collaborative systems, Computational Theory of Mind for human-agent coordination, and Norm-Based AI Systems . Recent projects include the Hybrid Intelligence Center and tools like PANOLA for privacy support. She investigates how AI can balance user autonomy , ethical norms , and societal values in applications ranging from urban planning to healthcare. Her scientific publications (2023-2025) span topics like Explainable Privacy , Trust in Hybrid Teams , and AI for Diabetes Management , appearing in venues such as JAIR , AAMAS , and ACM TOIT . She emphasizes collaborative AI , with contributions to multiagent simulations and privacy-preserving mechanisms . Scientific Awards : NC State University Alumni Hall of Fame (2017) Woman Entrepreneur of the Year (Microsoft Turkey & KAGIDER, 2017) Bogazici University Academic Encouragement Award (multiple years) Best Paper Award at ESAW 2009 She supervises a dynamic research group with PhD students working on Hybrid Intelligence , Computational Ethics , and Privacy Modeling . Her projects often involve interdisciplinary collaboration with institutions like TNO and the Transforming Cities Hub .

Kaiyu HangView profile

Assistant Professor

• Robotics
• Robotic Manipulation
• Grasping
• +8 more

Kaiyu Hang is an Assistant Professor in the Department of Computer Science at Rice University, where he directs the Robotics and Physical Interactions Lab (RobotΠ Lab). His research spans multiple domains of robotics with a focus on physical interaction systems. Before joining Rice, he completed his postdoc at Yale University, earned his Ph.D./M.Sc. at KTH Royal Institute of Technology, and received his B.Eng. from Xi'an Jiaotong University. His research interests include robotic manipulation, grasping, in-hand manipulation, optimization, planning, learning, estimation, and control systems. He develops algorithms that enable robots to physically interact with other robots, people, and the world across scales from small grasping tasks to large-scale dual-arm and multi-robot manipulation systems. His work has practical applications in factories, kitchens, hospitals, warehouses, and construction sites. His recent publications demonstrate strong trends in in-hand manipulation techniques, energy-efficient drone operations, and benchmarking frameworks for robotic grasping. The 2025 IROS papers accepted highlight his leadership in developing standardized competition frameworks for evaluating robotic manipulation capabilities across diverse hardware platforms. ASME Rising Star of Mechanical Engineering (2024) NSF CAREER Award (2023) Multiple finalist awards at IEEE-RAS Humanoids and ICRA conferences Junior Fellowship Award from Institute for Advanced Study, HKUST (2017-2018) As an educator, he has taught multiple robotics courses including COMP 462/562: Introduction to Modern Robotics and COMP 461: Senior Design in A Robotized World. He serves as Faculty Advisor for the Rice Robotics Club and participates in graduate admissions. His lab actively recruits Ph.D. students and offers research opportunities for undergraduate and master's students who have completed core robotics courses.

William Yang WangView profile

Professor

• Artificial Intelligence
• Natural Language Processing
• Machine Learning
• +9 more

William Yang Wang serves as the Mellichamp Professor of Artificial Intelligence at the University of California, Santa Barbara (2019-present). He directs the UCSB Center for Responsible Machine Learning, the Mind and Machine Intelligence Initiative, and the UCSB NLP Group. His research focuses on theoretical foundations and practical algorithms for AI, particularly in NLP, LLMs, and neuro-symbolic reasoning. PhD in Computer Science from Carnegie Mellon University Active in AI theory and applications (2016-present) Research interests span multiple AI domains, with special emphasis on NLP and responsible machine learning. He has pioneered datasets like HybridQA, TabFact, and VaTeX, enabling advancements in multi-hop QA, fact verification, and video-language tasks. His work combines statistical relational learning with modern deep learning paradigms. Recent publications center around multimodal reasoning, knowledge graph integration, and responsible AI development. He has received numerous accolades including the IEEE SPS Pierre-Simon Laplace Award (2024) and NSF CAREER Award (2021). Karen Sparck Jones Award (2022) DARPA Young Faculty Award (2018) IBM Faculty Award Mentoring 15+ PhD and postdoc researchers who now hold positions at Microsoft Research, Amazon, Meta GenAI, and academic institutions like Arizona and Rutgers. His lab maintains active collaborations with industry partners through initiatives like ChipAgents.ai, which he founded as CEO.

• Natural Language Processing
• Knowledge Graphs
• Machine Learning
• +5 more

Dr. Sirui Li is a Lecturer at Murdoch University's School of Information Technology within the College of Science, Technology, Engineering and Mathematics. Her research focuses on Artificial Intelligence, Natural Language Processing (NLP), Machine Learning, Knowledge Graphs, Data Analysis, Temporal Data, and Multi-modal Models, with applications in medicine, agriculture, and mining. She collaborates with industry partners like BHP and has published in journals such as Food Chemistry and Knowledge and Information Systems , as well as conferences like ICSME and IJCNN. Education: Bachelor of Advanced Computing (Honours) in Computer Science at Australian National University Master of Computing (Specialising in AI) at ANU Ph.D. in Information Technology (AI) at Murdoch University Research interests include interdisciplinary applications of AI, such as clinical coding privacy solutions, disease spread modeling, and drug repurposing for pandemics. Her work emphasizes practical industry integration, demonstrated through awards like the 2024 EMNLP Best Demo Award and the 2023 Iron Ore Circuit Hackathon innovation prize. Professional roles include IEEE Western Australia Section committee membership, conference chair positions, and peer review for top journals. She actively mentors students pursuing Honours, Master's, or PhD projects in her areas of expertise.

Kristofer S.J. PisterView profile

Professor

• Micro/Nano Electro Mechanical Systems (MEMS)
• Control Systems
• Robotics
• +6 more

Kristofer Pister is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He co-directs the Berkeley Sensor and Actuator Center (BSAC) and the Ubiquitous Swarm Lab. His career spans groundbreaking innovations in Micro/Nano Electro Mechanical Systems (MEMS), Control Systems, and Low-Power Circuits, with a focus on Smart Dust and synthetic insects. Education: Ph.D. and M.S. in EECS from UC Berkeley (1992, 1989); B.A. in Applied Physics from UC San Diego (1986). His research areas include MEMS , Control Systems , Robotics , and Integrated Circuits , with recent work on self-powered micro-sensors, crystal-free radios, and interplanetary swarm networks. Key awards include the ISA Albert F. Sperry Founder Award (2009) , Alexander Schwarzkopf Prize (2006) , and the NSF CAREER Award (1996) . He has authored numerous influential publications in wireless sensor networks and microrobotics. His lab, Ubiquitous Swarm Lab , explores distributed robotics and swarm intelligence. Pister emphasizes open collaboration in research, ethical conduct in academia, and efficient resource utilization for graduate students.

Santiago GrijalvaView profile

Professor

• Decentralized Power Systems
• Renewable Energy Integration
• Grid Cybersecurity
• +5 more

Santiago Grijalva serves as Georgia Power Distinguished Professor and Director of the Advanced Computational Electricity Systems (ACES) Laboratory at Georgia Institute of Technology's School of Electrical and Computer Engineering. He also holds the position of Associate Director for Electricity Systems at the Strategic Energy Institute (SEI). His educational background includes an Electrical Engineer degree from EPN-Ecuador (1994), M.S. in Information Systems from ESPE-Ecuador (1997), and M.S./Ph.D. in Electrical and Computer Engineering from University of Illinois at Urbana-Champaign (1999/2002). Grijalva pioneers research in decentralized power system architectures, renewable energy integration, and grid cybersecurity. His work spans smart grid technologies, electricity markets design, AI applications for power systems, and ultra-reliable grid architectures. The ACES Laboratory under his direction focuses on real-time power system control, informatics, and economics. His recent publications demonstrate strong trends in photovoltaic integration, multi-agent control systems, voltage stability analysis, and prosumer-based grid architectures, reflecting the industry's shift toward distributed energy resources and decentralized control paradigms. Georgia Tech ECE Outstanding Faculty Award Great Minds in STEM National Achievement Award ARPA-E More Than Smart Grid Disruptor Award IBM Faculty Award As principal investigator for major research projects funded by U.S. Department of Energy, ARPA-E, EPRI, PSERC, NSF, and industry sponsors, Grijalva leads initiatives modernizing electrical infrastructure. His past roles include Senior Software Developer at PowerWorld Corporation and Department Head at Ecuador's National Center for Energy Control (CENACE). He serves as Associate Editor for IEEE Transactions on Industrial Informatics and has published over 250 peer-reviewed papers. The Advanced Computational Electricity Systems (ACES) Laboratory operates at the intersection of power systems engineering and computational science, developing next-generation grid technologies for renewable integration and cyber-physical security.

Aaron D. AmesView profile

Professor

• Bipedal Robotics
• Hybrid Systems
• Nonlinear Control
• +7 more

Aaron D. Ames is the Bren Professor of Mechanical and Civil Engineering, Control and Dynamical Systems, and Aerospace at the California Institute of Technology (Caltech). He serves as the Booth-Kresa Leadership Chair and Director of the Center for Autonomous Systems and Technologies since 2025. His academic journey includes a BS in Mechanical Engineering and BA in Mathematics from the University of St. Thomas (2001), followed by an MA in Mathematics and a PhD in Electrical Engineering and Computer Sciences from UC Berkeley (2006). Research Interests span theoretical and experimental work in hybrid systems, nonlinear control, and bipedal robotic locomotion. Specific focus areas include: Control Barrier Functions (CBFs) for safety-critical systems Powered prostheses and robotic assistive devices Legged locomotion for bipedal and quadrupedal robots Cyber-physical systems and autonomous control Model predictive control and real-time optimization Humanoid robotics and geometric safety filters Recent publications emphasize risk-aware control, safety verification, and locomotion on constrained environments, with applications to drones, exoskeletons, and quadrupedal robots. His work often integrates theoretical advancements with practical validation through the AMBER Lab. Scientific Awards & Honors : NSF CAREER Award (2010) Donald P. Eckman Award (2015) Leon O. Chua Award (2005) Bernard Friedman Prize (2006) Teaching includes graduate courses on nonlinear control, hybrid systems, and robotics at Caltech, alongside prior roles at Georgia Tech and Texas A&M. He leads the AMBER Lab (Bipedal Robotics) and has secured major grants from the National Science Foundation, NASA, and industry partners like Miso Robotics and SRI International.

Andrew D. WhiteView profile

Associate Professor

• Large language models
• Deep learning in chemistry
• Molecular dynamics
• +5 more

Andrew D. White is an Associate Professor of Chemical Engineering at the Hajim School of Engineering & Applied Sciences, University of Rochester. He holds a PhD from the University of Washington (2013). His research focuses on automating scientific discovery through AI, particularly leveraging large language models (LLMs) and deep learning techniques in chemistry. His lab develops agents that integrate literature analysis, hypothesis generation, and experimental design to advance fields like molecular dynamics and drug discovery. Education: PhD in Chemical Engineering, University of Washington, 2013 BS/MS (not explicitly stated in text, inferred from career timeline) Research Interests: Large language models for scientific automation Deep learning applications in chemistry and materials science Molecular dynamics simulations Scientific agents and autonomous systems Publications: His work includes groundbreaking studies on closed-loop AI systems for chemistry, federated learning in molecular property prediction, and multi-agent systems for drug discovery. Recent highlights include the Robin system and ChemCrow tools. Awards: Recipient of the NSF Career Award (2018), NIH Outstanding Investigator Award (2020), and the Curtis Teaching Award (2019). He also advises biotech companies and serves on the National Academy of Sciences' Chemical Sciences Roundtable. Grants & Funding: Supported by DOE, NSF (multiple grants including CBET-1751471), NIH (R35GM137966), and LLNL projects. Collaborates with institutions like Argonne National Lab and Qubit Pharmaceuticals. Labs & Teams: Leads the White Lab at Rochester and co-founded FutureHouse, a nonprofit advancing AI-driven scientific discovery. Supervises a multidisciplinary team of PhD students and postdocs in computational chemistry, AI, and biophysics.

Tiancheng ZhaoView profile

Researcher

• Multimodal Foundation Models
• AI Agents
• Vision-Language Models
• +3 more

Tiancheng Zhao is a principal researcher at the Binjiang Institute of Zhejiang University and founder of the Om Artificial Intelligence Laboratory (Om AI Lab), dedicated to frontier open multimodal AGI research for building next-generation agents that transform work and life through advanced human-machine interaction. His academic credentials include: Ph.D. in Computer Science from Carnegie Mellon University (2016-2019) under Prof. Maxine Eskenazi, Prof. Louis-Philippe Morency, Prof. William W. Cohen, and Dr. Dilek Hakkani-Tur, with pioneering dissertation “Learning to Converse With Latent Actions” in end-to-end generative conversational models M.S. in Computer Science from Carnegie Mellon University (2014-2016) B.S. in Electrical Engineering from UCLA (2010-2014) with Summa Cum Laude, focusing on speech signal processing under Prof. Abeer Alwan Dr. Zhao’s research centers on multimodal foundation models and agents, tackling three core challenges: Multimodal Models for cross-modal representation learning in high-dimensional data, Learning to Learn for effective skill acquisition from diverse signals (supervised labels, rewards, meta-learning), and AI Agents for open-world understanding and complex decision-making. His work bridges computer vision, natural language processing, and real-world applications including healthcare analytics and remote sensing. Analysis of his 50+ publications reveals accelerating innovation in multimodal large language models (2024-2025), with emphasis on stable vision-language architectures (VLM-R1), agent orchestration frameworks, and domain-specific applications in geospatial analysis and healthcare. Key trends include solving long-tail distribution challenges in satellite imagery, developing human-like zooming capabilities for multimodal LLMs, and creating unified benchmarks for autonomous GUI testing. His scientific recognition includes: National Breakthrough Technology Award by Ministry of Science and Technology (2021) Microsoft Research Best & Brightest PhD (2018) BEST PAPER AWARD at SIGDIAL 2018 Best Paper Nomination at SIGDIAL 2016 Top 1 Outstanding Bachelor of Science Award at UCLA (2014) As Om AI Lab founder, Dr. Zhao leads research teams developing computational building blocks for human-AI collaboration. While specific student mentorship details aren’t public, his extensive publication record with junior co-authors indicates active research supervision. Current projects focus on practical system implementations for real-world multimodal agent deployment across diverse domains.

Di ShiView profile

Associate Professor

• Power System Data Analytics
• Energy Storage Systems
• Artificial Intelligence
• +3 more

Di Shi is an Associate Professor at the Klipsch School of Electrical and Computer Engineering , New Mexico State University (NMSU), holding the Paul W. and Valerie Klipsch Distinguished Professorship. He previously founded the AI energy startup AInergy, LLC and held leadership roles at GEIRI North America, NEC Laboratories America, and Arizona State University. Education: PhD in Electrical Engineering, Arizona State University (2012) MS in Electrical Engineering, Arizona State University (2009) BS in Electrical Engineering, Xi'an Jiaotong University (2007) His research focuses on power system data analytics , energy storage , artificial intelligence , and IoT applications for grid stability and renewable integration. His work bridges theoretical innovation with real-world deployment, including software adopted by 15 utility companies. Recent publications highlight his leadership in deep reinforcement learning for grid control, blockchain frameworks for energy management, and tensor decomposition for efficient load modeling. He has secured a $6M NSF grant for AI-driven digital twinning to address climate-aware energy resilience. Awards & Recognition: 2025 Paul W. and Valerie Klipsch Distinguished Professorship 2024 University Research Council Mid-Career Award 2024 IET Fellow Multiple IEEE Best Paper Awards (2019–2022) 2019 L2RPN AI Competition Championship He serves as an editor for IEEE Transactions on Power Systems , IET Generation, Transmission & Distribution , and other journals, and leads the IEEE Task Force on IoT for Power Systems . His team’s patents cover AI-driven load modeling , energy storage scheduling , and state estimation , with 42 granted or pending.

Sebastian SteinhorstView profile

Associate Professor

• Embedded Systems
• Internet of Things (IoT)
• Cyber-Physical Systems
• +5 more

Prof. Dr. Sebastian Steinhorst is an Associate Professor (W3-level with tenure) at the Technical University of Munich (TUM) within the Embedded Systems and Internet of Things group at the TUM School of Computation, Information and Technology . His research focuses on advancing the security, predictability, reliability, and interoperability of smart connected and autonomous systems, particularly for applications in Internet of Things (IoT) , Industry 4.0 , and automotive systems . PhD in Computer Science (2011) from Goethe University Frankfurt Postdoctoral roles at TUMCREATE Singapore (2011-2016) and Aarhus University (2016) Joined TUM in 2016 as Rudolf Moessbauer Tenure Track Professor His research areas include decentralized embedded systems, hardware/software co-design, modeling and verification of cyber-physical systems, security protocols for automotive networks, and time-sensitive networking (TSN) for industrial applications. Recent work explores blockchain-based data sovereignty, zero-knowledge proofs for vehicle authentication, and resilient architectures for autonomous systems. Key scientific contributions include the 2019 ACM TODAES Best Paper Award and pioneering work on CyberSecDome , LeapChain , and Simutack frameworks. He serves on editorial boards and conference committees, including co-organizing the Autonomous Systems Design initiative at DATE. His teaching portfolio spans lectures on System Design for IoT , Software Architecture for Distributed Systems , and IoT Security across multiple semesters. He also leads advanced seminars on embedded systems and IoT.

Emilio FrazzoliView profile

Professor

• Autonomous Robotics
• Control Systems
• Game Theory
• +4 more

Emilio Frazzoli is a Full Professor at ETH Zurich’s Department of Mechanical and Process Engineering. He leads the Institute for Dynamic Systems and Control and the Center for Sustainable Future Mobility, focusing on autonomous systems, robotics, and socio-technical control frameworks. Current affiliations: ETH Zurich (Dynamic Systems and Control, Sustainable Future Mobility) Research: Autonomous mobility-on-demand, game theory for resource allocation, and safety verification in multi-agent systems His work bridges robotics, control theory, and economics, with projects like the open-source AMoDeus simulation framework for autonomous taxis and karma-based resource allocation systems. Recent publications emphasize trustworthy AI, reproducibility in autonomous vehicle control, and human-robot interaction challenges. Notable projects include nuReality (VR-based pedestrian interaction studies) and CARSI II (context-driven vehicle interfaces).