Find Academic Supervisors

Reza GhabchelooView profile

Professor

• Robotics
• Automation Engineering
• Control Systems
• +7 more

Reza Ghabcheloo is a Professor at Tampere University, affiliated with the Faculty of Engineering and Natural Sciences and the Department of Automation Technology and Mechanical Engineering. He leads the Robotics major and the international Automation Engineering program. His research focuses on autonomous mobile machines, robotics, control systems, and safety engineering, with specific interests in construction robotics, sensor fusion, and hydraulic systems. He co-leads the Autonomous Mobile Machines Group and is associated with the Robotics and Intelligent Machines Lab and the Innovative Hydraulics and Automation Lab. His research emphasizes developing autonomous systems for off-road machinery, safe control strategies, and energy-efficient automation. He has published extensively on topics such as reinforcement learning for crane control, radar-based perception, and safety architectures for autonomous systems. His work bridges robotics, control theory, and industrial automation, addressing challenges in heavy-duty machinery and real-world robotic applications. Research Group: Autonomous Mobile Machines Group Labs: Robotics and Intelligent Machines Lab, Innovative Hydraulics and Automation Lab Key Projects: Safety of automated off-road machinery, machine learning for autonomous loading, and trajectory optimization

Louis HickmanView profile

Assistant Professor

• Industrial-Organizational Psychology
• Algorithmic Fairness
• Machine Learning in Personnel Assessment
• +4 more

Louis Hickman is an Assistant Professor of Industrial-Organizational Psychology at Virginia Tech’s Department of Psychology. He also serves as a Visiting Academic at Amazon and holds a Senior Fellow position at Wharton People Analytics, University of Pennsylvania. His research bridges technology and work, focusing on machine learning applications in organizational science, particularly automated interviews and algorithmic fairness. He leads the Workplace Assessment and Social Perceptions (WASP) Lab, exploring how biases influence hiring and using AI to reduce algorithmic bias. Hickman holds a Ph.D. in Industrial-Organizational Psychology from Purdue University (2021), alongside advanced degrees in Computer Science and Creative Writing. His work emphasizes interdisciplinary collaboration, spanning psychology, computer science, and management. Research Interests: Automated personnel assessment via AI Algorithmic bias mitigation in hiring Machine learning applications in HR and education Interpersonal perception dynamics Unproctored testing in the AI era Publications: Recent work examines automated interview validity, LLM impacts on testing, and recruitment algorithm ethics. His 2025 studies highlight risks of unproctored testing and bias in automated systems. Earlier research (2023–2022) explores text mining for personality assessment and fairness in AI-driven selection. Awards: None explicitly mentioned, though his work has been widely cited in organizational psychology and AI ethics domains. Advising & Labs: Currently not accepting graduate students for 2026, but oversees the WASP Lab. Past research collaborations include projects on LLM competencies, bias simulation, and algorithmic fairness frameworks. Grants and funding sources are unspecified in provided text.

• Artificial Intelligence
• Natural Language Processing
• Multimodal Reasoning
• +4 more

Graham Neubig is an Associate Professor at the Language Technologies Institute (LTI) within Carnegie Mellon University (CMU). His research focuses on advancing artificial intelligence, particularly in natural language processing (NLP), multimodal reasoning, and large language models (LLMs). He explores topics such as AI safety, generative AI, and human-AI interaction, with an emphasis on practical applications like machine translation and web-agent systems. His work often involves developing frameworks for evaluating AI systems, such as OpenAgentSafety and BehaviorBox, which assess real-world agent performance and model behavior. Neubig's research also delves into improving LLM capabilities through reasoning analysis, hallucination detection (e.g., ZINA), and culturally aware systems (e.g., CAIRe). He has contributed to open-source projects like Pangea (a multilingual LLM) and frameworks such as Cmulab for model deployment. His recent work addresses challenges in agentic tasks, self-improving agents (Skillweaver), and benchmarking across domains like visual reasoning (VisualPuzzles) and software engineering. Notable achievements include advancing evaluation methodologies for LLMs, developing tools for ethical AI, and creating benchmark suites that test systems under realistic conditions. His lab collaborates on projects like the BrowserGym ecosystem and OpenHands platform, which aim to standardize web-agent research and AI-driven software development. Neubig's contributions span theoretical advancements and practical implementations, bridging the gap between cutting-edge research and real-world applications. He advises students such as Apurva Gandhi and actively publishes in top venues, addressing topics from instruction-following improvements to the societal impacts of AI. His work frequently emphasizes the importance of transparency, controllability, and cultural awareness in AI systems.

Immanuel TrummerView profile

Professor

• Database Systems
• Query Optimization
• Large Language Models
• +3 more

Immanuel Trummer is a Professor of Computer Science at Cornell University, specializing in database systems, query optimization, and applications of large language models (LLMs) and quantum computing. He leads research projects such as DB-BERT, UDO, and SkinnerDB, focusing on automated database tuning, adaptive query processing, and leveraging LLMs for code synthesis and system optimization. His research interests span quantum computing for database optimization, cost-efficient LLM utilization, and voice-based data exploration. Key contributions include developing systems like CEDAR for claim verification, CodexDB for LLM-driven code generation, and ThalamusDB for multimodal data querying. Trummer has received prestigious awards, including the NSF CAREER Award (2023-2028) and the Best Demonstration Award at BDA 2020. His work has been funded by NSF, Google, Huawei, and others, supporting projects like quantum-index selection and misinformation detection. He advises graduate students in database systems and teaches advanced courses such as CS 6320 (Advanced Database Systems) and CS 7390 (Seminar in Database Systems). His research lab hosts open-source tools like JoinGym and maintains extensive collaborations in industry and academia.

• Robotics
• Autonomous Systems
• Computer Vision
• +4 more

Dr. Stewart Worrall is a Senior Research Fellow at the Australian Centre for Field Robotics (ACFR) within the University of Sydney. His research focuses on autonomous systems, robotics, and intelligent transportation systems, particularly in the areas of autonomous vehicle perception, human-robot interaction, and sensor fusion. He has contributed to numerous high-impact publications on topics such as edge case testing for autonomous vehicles, collaborative perception frameworks, and context-aware human-robot interaction design. His work integrates robotics hardware, computer vision, and machine learning to address challenges in autonomous driving, crowd dynamics, and urban mobility scenarios. Current research students under his supervision explore topics ranging from light field imaging for autonomous driving to human-machine interfaces for vehicles. Worrall has pioneered datasets like the University of Sydney Campus Dataset and the ACFR Five Roundabouts Dataset, which are critical for evaluating autonomous systems. His contributions span academic conferences (e.g., IEEE IV, ICRA) and journals, emphasizing both technical innovation and societal impacts of autonomous technologies. Key labs/teams: Core member of the ACFR, collaborating across disciplines including robotics, computer science, and urban design.

Hector GeffnerView profile

Professor

• Artificial Intelligence
• Machine Learning
• Automated Planning
• +5 more

Hector Geffner is an Alexander von Humboldt Professor at RWTH Aachen University, leading the Chair of Machine Learning and Reasoning. He specializes in automated planning, machine learning, and reasoning, with a focus on representation learning for acting and planning. His work bridges symbolic and model-based AI, emphasizing general policies and subgoal decomposition. Education & Background : PhD from UCLA (1989), prior roles at IBM Watson Research Center and Universidad Simón Bolívar. Former ICREA researcher and professor at Universitat Pompeu Fabra (2001–2022). Research Interests : Classical and probabilistic planning, reinforcement learning, knowledge representation, and applications in robotics. His ERC-funded RLeap project explores learning generalized policies and symbolic representations for effective decision-making. Teaching : Courses include 'Actions and Planning in AI' and 'Social and Technological Change', emphasizing interdisciplinary AI applications. Awards & Recognition : Alexander von Humboldt Professorship (2023), AAAI/EurAI Fellowships, and editor of influential works on Judea Pearl’s contributions to AI. Grants & Projects : Advanced ERC grant (2020–2025), Humboldt Foundation support, and RWTH funding for research on planning and reasoning. Labs & Teams : Heads the Chair of Machine Learning and Reasoning at RWTH, focusing on interdisciplinary research in AI, robotics, and planning algorithms.

Steven SwansonView profile

Professor

• Non-Volatile Memory
• Persistent Memory Systems
• Storage Systems
• +12 more

Steven Swanson is a Professor in the Department of Computer Science and Engineering at the University of California, San Diego, within the Jacobs School of Engineering. He is the Director of the Non-Volatile Systems Laboratory (NVSL), where he leads cutting-edge research in non-volatile memory, storage systems, and hardware-software co-design. His work bridges computer architecture, systems, and software to develop efficient, reliable, and secure computing platforms. Ph.D., University of Washington, 2006 B.S., University of Puget Sound, 1999 Dr. Swanson's research centers on non-volatile and persistent memory systems , exploring how next-generation storage technologies can transform computing. His lab develops full-stack solutions including file systems like NOVA and Orion , programming models such as NV-Heaps , and hardware prototypes like Moneta and Onyx . The team also works on low-power co-processors (e.g., GreenDroid ) and tools for debugging and verifying persistent memory programs. Research spans system reliability, security, energy efficiency, and performance optimization. His recent publications reveal a strong focus on persistent memory safety , zero-copy I/O , RDMA-based distributed file systems , and real-world characterization of Intel Optane . These works appear in top venues including ASPLOS, FAST, MICRO, and USENIX ATC, demonstrating sustained innovation in storage and systems research. Scientific honors include: NSF CAREER Award Google Faculty Award Facebook Faculty Award NetApp Faculty Fellow Dr. Swanson has advised 15 PhD students and 4 postdocs , many now faculty or senior engineers at Google, Microsoft, Intel, and other leading tech firms. He has secured significant research funding and leads major community initiatives such as the annual Non-Volatile Memories Workshop and Persistent Programming In Real Life (PIRL) . His educational efforts include innovative courses on robotic system design, quadcopter building, and modern storage systems, emphasizing hands-on learning and real-world implementation. The Non-Volatile Systems Laboratory (NVSL) under his leadership fosters a collaborative, international research environment, hosting visitors and postdocs from around the world. The lab is recognized globally as a pioneer in storage systems research and a key contributor to the adoption of persistent memory technologies in industry.

Karen CochraneView profile

Assistant Professor

• Human-Computer Interaction (HCI)
• Mental Health Technologies
• Accessibility
• +6 more

Dr. Karen Cochrane is an Assistant Professor at the University of Waterloo, specializing in Human-Computer Interaction (HCI) with a focus on wearable and tangible computing for mental health and accessibility. Her research integrates soma design, autoethnography, and design fiction to address the needs of underrepresented communities, particularly those with disabilities. Key projects include developing assistive technologies for queer/crip identities, accessible gaming wearables, and tactile interfaces for sensory exploration. She explores embodied experiences through multidisciplinary approaches, blending traditional craft practices with modern computational tools. Her work prioritizes participatory design methodologies, collaborating with occupational therapists, disabled communities, and neurodivergent individuals to co-create inclusive technologies. Recent innovations include VARitouch (a haptic feedback device), Breathing Scarf (for emotional regulation), and adaptive switches for children with motor disabilities. Her research extends into sensory narratives, exploring how fabric and data can articulate bodily experiences through projects like Sensory Data Dialogues and Queer/Crip Body Mapping. While no formal grants or awards are listed, her publications reflect a strong focus on ethical technology, accessibility, and embodied interaction. Current projects emphasize the intersection of wearable tech with identity expression, mindfulness practices, and neuromotor rehabilitation. She actively designs prototypes that bridge clinical needs with creative technology solutions, though specific lab affiliations or team collaborations are not detailed in available texts.

Liang YingyuView profile

Associate Professor

• Machine Learning
• Optimization in Deep Learning
• Generalization in Neural Networks
• +3 more

Dr. Yingyu Liang is an Associate Professor at the University of Hong Kong (HKU), affiliated with the Department of Computer Science and the Musketeers Foundation Institute of Data Science. Previously, he held an Associate Professor position at the University of Wisconsin-Madison and was a postdoc at Princeton University. He earned his PhD from Georgia Tech and holds M.S. and B.S. degrees from Tsinghua University. His research focuses on theoretical foundations of machine learning, particularly optimization and generalization in deep learning, robust machine learning, and practical applications. Key contributions include analyzing neural network feature learning, adversarial robustness, and contrastive learning efficiency. Education: PhD (Georgia Tech, 2014), M.S. (Tsinghua, 2010), B.S. (Tsinghua, 2008) Awards: NSF CAREER Award Labs/Groups: Research Group focused on theoretical ML and algorithm design Publications span top venues like NeurIPS, ICML, and ICLR, addressing topics such as deep learning theory, adversarial robustness, and graph representation learning.

• Legged locomotion
• Humanoid and quadrupedal robots
• Wearable robotics
• +5 more

Yan Gu is an Associate Professor of Mechanical Engineering at Purdue University, located in West Lafayette, Indiana. He is affiliated with the School of Mechanical Engineering within the College of Engineering. His research focuses on legged locomotion, humanoid and quadrupedal robots, wearable robotics, hybrid dynamical systems, control systems, state estimation, and dynamics. He leads the TRACE Lab and has been recognized with prestigious awards including the NSF CAREER Award (2021) and multiple teaching accolades. Gu holds a Ph.D. from Purdue University (2017) and a B.S. from Zhejiang University, China (2011). His work emphasizes robust control strategies for legged robots in dynamic environments, including adaptive ankle torque control, time-varying foot-placement algorithms, and state estimation techniques for non-inertial surfaces. His recent publications explore multimodal datasets in animal-robot interaction and the stabilization of quadrupedal locomotion on accelerating platforms. His research has been supported through grants such as the NSF CAREER Award, and his contributions span both theoretical advancements in hybrid control systems and practical applications in wearable robotics and exoskeleton design. Gu’s TRACE Lab serves as a hub for innovative robotics research, addressing challenges in robot-environment interaction and dynamic stability.

David LindlbauerView profile

Assistant Professor

• Mixed Reality
• Computational Interaction
• Extended Reality (XR)
• +5 more

David Lindlbauer is an Assistant Professor at Carnegie Mellon University's Human-Computer Interaction Institute (HCII), where he leads the Augmented Perception Lab and co-directs the CMU Extended Reality Technology Center. His research focuses on advancing Mixed Reality (MR) and Extended Reality (XR) interfaces through computational interaction methods that optimize spatial, temporal, and multimodal feedback.

• Spatio-temporal data modeling
• Mobile and web-based GIS
• Map personalisation
• +6 more

Michela Bertolotto is a Professor in the School of Computer Science at University College Dublin (UCD). Her research focuses on spatio-temporal data modeling, GIScience, and applications of geospatial technologies in fields like urban planning and health informatics. She leads a research group and has supervised 19 PhD and 8 MSc students. Her work includes innovations in LiDAR-based flood risk visualization, semantic web quality assurance, and open-source spatial data analysis. Bertolotto has held roles including College Lecturer at UCD (2000–2006) and postdoctoral research positions at the University of Maine and University of Genoa. Education: BSc and PhD in Computer Science from the University of Genoa (1993, 1998). Professional achievements include over 100 publications, 24 grants (e.g., Science Foundation Ireland-funded Urban ARK project), and editorial roles at journals like the International Journal of Geographical Information Science. Awards include the UCD President's Research Award (2001) and NATO Postdoc Fellowship (1998–1999). Research interests span map personalization, volunteered geographic information (VGI), and geospatial data quality. Her lab develops tools like the LAMSkyCam (low-cost sky imaging system) and dynamic flood risk viewers. She chairs international conferences and serves on program committees for GIScience events.

Minyi HuangView profile

Professor

• Mean Field Stochastic Control
• Stochastic Algorithms in Multi-Agent Systems
• Wireless Networks
• +4 more

Minyi Huang is a Professor in the School of Mathematics and Statistics at Carleton University. His research focuses on Mean Field Stochastic Control, Stochastic Algorithms in Multi-Agent Systems, and Wireless Networks. He holds a Ph.D. from McGill University (2003) and has held postdoctoral positions at the University of Melbourne and the Australian National University. Dr. Huang is a Fellow of IEEE and a Member of SIAM. Education: Ph.D. in Electrical and Computer Engineering, McGill University (2003) M.Sc. in Systems and Control, Chinese Academy of Sciences (Beijing) B.Sc. in Mathematics, Shandong University (Jinan, China) Research Interests: Huang's work centers on stochastic control, mean field games, and multi-agent systems. His contributions include theoretical advancements in mean field social optimization, graphon-based control frameworks, and applications in wireless networks and economic models. He has organized workshops on Mathematical Cybernetics and Stochastic Processes, fostering interdisciplinary collaboration. Scientific Awards: Fellow of the IEEE Advising & Grants: Huang has advised numerous graduate students on topics in stochastic control and mean field theory. His grants include funding for international PhD students through Carleton's initiatives. He collaborates on projects involving mean field models for production output and social dynamics. Labs/Teams: Associated with the Ottawa-Carleton Institute for Mathematics and Statistics (OCIMS), contributing to collaborative research in control theory and applied mathematics.

Kaiyang LiuView profile

Assistant Professor

• Distributed Cloud/Edge Computing
• Data Center Networks
• Distributed Machine Learning
• +3 more

Kaiyang Liu is an Assistant Professor at the Department of Computer Science, Memorial University of Newfoundland. He holds a Ph.D. from Central South University (2019) and was a Postdoctoral Fellow at the University of Victoria, Canada. His research focuses on distributed cloud/edge computing, data center networks, and distributed machine learning, emphasizing optimization for data-intensive services. He is an IEEE Senior Member and has received prestigious awards, including the NSERC Discovery Grants and IEEE TCCLD Outstanding Ph.D. Thesis Award. Education: Ph.D. in Information Science and Technology, Central South University (2014–2019) M.Sc. in Information Science and Technology, Central South University (2012–2014) B.Eng. in Information Science and Technology, Central South University (2008–2012) Research Assistant at the University of Victoria (2016–2018) Research Interests: Kaiyang’s work bridges AI and cloud computing, exploring optimization strategies for next-generation systems. Key areas include learning-based congestion control, energy-efficient resource management, and scalable distributed storage solutions. His research has been published in top-tier journals like IEEE Transactions on Parallel and Distributed Systems and conferences such as IEEE ICDCS. Awards & Grants: NSERC Discovery Grants & Discovery Launch Supplement (2024) IEEE TCCLD Outstanding Ph.D. Thesis Award (2020) CSC-UVic Fellowship (2016–2018) Teaching: He teaches courses on Computer Networks, Advanced Computer Networks, and Operating Systems at Memorial University and previously at the University of Victoria. Labs & Teams: His research group focuses on Space Edge Computing, leveraging LEO satellites for resilient, low-latency networks. Ongoing projects include optimizing distributed systems for AI workloads and satellite-based data centers.

Miguel NacentaView profile

Professor

• Human-Computer Interaction
• Information Visualization
• Infotypography
• +5 more

Miguel Nacenta is a Professor in the Department of Computer Science at the University of Victoria (UVic), Canada, and a founding member of the Victoria Interactive eXperiences with Information (VIXI) research group. Previously affiliated with the University of St Andrews (UK), his work bridges Human-Computer Interaction (HCI), Information Visualization, and Cognitive Science. He specializes in designing interactive systems that enhance human cognition, with a focus on Infotypography (using typography to encode data), collaborative problem-solving tools, and perceptual input/output devices. Research Interests: His key areas include cognitive augmentation, visualization techniques for complex tasks, multi-display environments, and tools for constraint problem-solving. Notable projects include the WriteReason tool for essay writing, InfoTypography studies on perceptual typographic parameters, and Solvi for visual constraint modeling. Grants & Collaborations: He collaborates internationally, including with the University of St Andrews on PhD scholarship programs. His work is supported by grants focusing on HCI innovations and accessibility. He actively mentors students (e.g., Adam Binks, Johannes Lang) and supervises postdoctoral researchers. Affiliations: Member of the VIXI group,他曾是St Andrews计算机科学学院的教授, 并参与多个学术服务活动, including conference program committees and journal reviews. Labs & Teams: Leads the VIXI lab at UVic, focusing on interactive technologies for cognitive tasks. Collaborates with industry partners on projects like TypoCartographer for infoTypographic maps and HaptiQ for accessible graph exploration.