Find Academic Supervisors

Sachi HoribataView profile

Assistant Professor

• Cancer Biology
• Drug Discovery
• Computational Genomics
• +3 more

Sachi Horibata is an Assistant Professor in the Department of Pharmacology & Toxicology at Michigan State University (MSU), affiliated with the College of Human Medicine. She is also associated with the Precision Health Program and the Neuroscience Program. Her research focuses on cancer biology, drug discovery, and computational genomics, with a particular emphasis on understanding mechanisms of drug resistance in cancers like acute myeloid leukemia (AML) and breast cancer. Dr. Horibata’s work integrates proteomics, transcriptomics, and cellular models to uncover therapeutic targets and biomarkers. She holds a PhD in Biological and Biomedical Sciences from Cornell University (2010–2016). Her research interests include genomic analysis of cancer heterogeneity, enzyme-driven cancer progression (e.g., PAD enzymes), and immune evasion mechanisms in tumors. Her recent studies highlight the role of protein citrullination in cancer cell migration and endocrine resistance. Dr. Horibata’s publications span topics in oncology, immunology, and molecular biology, with a focus on translational research. She teaches PHM 802: Cellular, Molecular and Integrated Systems Pharmacology. Her lab is located in the Interdisciplinary Science and Technology Building at MSU.

Nima Monshizadeh NainiView profile

Professor

• Cyber-Physical Human Systems (CPHS)
• Control Theory
• Optimization
• +5 more

Nima Monshizadeh Naini is a Professor in the Faculty of Science and Engineering at the University of Groningen. He holds the position of Chair of the IEM Program Committee and serves on the boards of ENTEG and YSEN. His academic journey includes a PhD in Control Systems from the University of Groningen (2013, Cum Laude), followed by postdoctoral research at the University of Cambridge (2016-2017) and the University of Groningen (2014). He has been an Assistant Professor since 2018 and was awarded the NWO Open Competition Grant in 2021. His research focuses on Cyber-Physical Human Systems (CPHS) , addressing two core areas: (1) Coordination of self-interested users in power systems and traffic networks using dynamic information design and game-theoretic mechanisms; and (2) Privacy-aware control and optimization with tailored encryption methods for dynamic systems. Applications include energy markets, microgrids, and smart grids. He has published extensively in top venues like IEEE CDC and Automatica, with key topics including privacy-preserving algorithms, distributed control, and optimal intervention design. His work contributes to UN Sustainable Development Goals related to affordable energy and responsible consumption. Education: PhD in Control Systems, University of Groningen (2013) Research Associate, University of Cambridge (2016-2017) Postdoctoral Researcher, University of Groningen (2014) Awards: Cum Laude distinction for PhD thesis (2013) NWO Open Competition Grant (2021) Advising & Grants: Supervised 5 PhD students Editorial roles in IEEE journals and conference proceedings Labs/Teams: Leading the Cyber-physical systems group within the Smart Manufacturing Systems institute.

Anna GautierView profile

Assistant Professor

• Multi-Agent Systems
• Robotics
• Artificial Intelligence
• +3 more

Anna Gautier is an Assistant Professor in the Department of Computer Science at Chalmers University of Technology, affiliated with the Division of Data Science and AI. Previously, she was a Digital Futures Postdoctoral Fellow at KTH Royal Institute of Technology (2023–2025), focusing on mechanism design for multi-robot systems. Her research emphasizes planning under uncertainty, multi-agent systems, and human-robot interaction. She holds a PhD from the University of Oxford (2023), an MSc from the London School of Economics, and dual undergraduate degrees from Washington University in St. Louis. Education Background: PhD in Computer Science, University of Oxford (2023) MSc in Applied Mathematics, London School of Economics BA in Mathematics and BS in Computer Science, Washington University in St. Louis Research Interests: Dr. Gautier explores planning algorithms for multi-agent systems, particularly in uncertain environments. She designs mechanisms to coordinate robots and humans, leveraging game theory and formal methods. Her work addresses challenges like resource allocation, risk-aware decision-making, and trust in autonomous systems. Recent projects include contingency planning for autonomous vehicles and auction-based resource distribution. Professional Activities: She co-chairs the ECAI 2025 Demonstration Track and teaches the course Safe Robot Planning and Control at KTH. Her projects include collaborations with WASP-Nest (PerCorSo) and TECoSA on trustworthy autonomy. She actively publishes in top venues like AAMAS and AAAI. Labs and Teams: Affiliated with Chalmers' Data Science and AI division, she leads research in multi-agent systems and human-AI collaboration.

Glen TianView profile

Professor

• Big Data Computing
• Cloud Computing
• Computer Networks
• +11 more

Professor Glen Tian is a Professor at the School of Computer Science , Queensland University of Technology . He holds two PhDs: one in computer and software engineering from the University of Sydney (2009) and another in industrial automation from Zhejiang University (1993) . His academic career spans institutions including Hong Kong University of Science and Technology, Curtin University, and the University of Maryland at College Park. Editor-in-Chief of the Handbook of Real-Time Computing (Springer) Associate Editor for Information Sciences (Elsevier) and Asia-Pacific Journal of Chemical Engineering (Wiley) His research focuses on big data computing , cloud computing , computer networks , smart grid communication and control , networked control systems , and cyber-physical system security . Applications include power systems , medical big data , vehicular networks , and transport systems . Recent publications highlight advancements in smart grid communications , distributed optimization , secure multi-agent systems , and medical imaging analysis . He has led QUT's Big Data Lab and served as Leader of QUT's Networks and Communications Discipline . Scientific achievements include Over 20 research grants totaling >$6M 6 Australian Research Council (ARC) grants 1 MRFF-TTRA grant ($745,623) 1 ATN-DAAD Australia-Germany Collaborative Grant 1 DEST International Science Linkage grant He supervises PhD students in big data bioinformatics , smart grid optimization , and cyber-physical security , while mentoring 30+ postdocs and research fellows. Current projects include mitigating cyberattacks on power systems and developing AI-based atheroma diagnostic tools .

Do Own (Donna) KimView profile

Assistant Professor

• Digital Cultures
• Mediated Social Interactions
• Human-Machine Communication
• +4 more

Do Own (Donna) Kim is an Assistant Professor at the University of Illinois Chicago's Department of Communication. Her research bridges technology studies, cultural studies, and computer-mediated communication, focusing on boundary-crossing practices in human-technology interactions. She examines questions of authenticity, hybrid spaces, and mediated identities through case studies like virtual influencers and K-pop digital cultures. Donna holds a Ph.D. in Communication from USC Annenberg (2022), with earlier degrees from Korea University (BA, 2015) and USC (MA, 2020). Her book project Virtually Real explores virtual influencers' cultural integration through cross-cultural fieldwork. She teaches courses on emerging technologies and communication theory, emphasizing qualitative research methods. Donna serves on Pop Junctions' editorial board and has written for platforms like In Media Res and Civic Imagination Project . Key awards include the KFAS Fellowship and a 2021 Browne Award for her chapter on Korean feminist activism. Her work appears in New Media & Society , International Journal of Communication , and CHI PLAY . Recent presentations include talks on AI ethics at UIC and virtual influencers at Curtin University's symposium.

Sandeep KumarView profile

Associate Professor

• Computer Security
• Computer Networking
• Operating Systems
• +3 more

Sandeep Kumar is an Associate Professor in the Department of Computer Science and Engineering at Texas A&M University, College Station. He holds a PhD in Computer Science from Purdue University (1995) and a B.Tech in Electrical Engineering from the Indian Institute of Technology, New Delhi (1985). His research focuses on computer security, networking, and system-level programming. Prior to academia, he worked in industry roles including at VMware in Palo Alto, CA. He currently teaches courses such as CSCE 313 (Introduction to Computer Systems) and CSCE 222 (Discrete Mathematics), emphasizing system software, networking, and cybersecurity. His teaching philosophy incorporates modern tools like GCP and Docker for practical learning. He advises students on technical projects but notes his non-tenure track role limits formal research supervision. Professional interests include curriculum design, educational technology, and bridging industry-academia gaps in cybersecurity. Education: Ph.D., Computer Science, Purdue University, 1995 M.S., Computer Science, University of Tennessee, 1987 B.Tech, Electrical Engineering, IIT Delhi, 1985 Research Interests: Computer Security, Networking, Operating Systems Teaching: CSCE 313 (Computer Systems), CSCE 222 (Discrete Math), CSCE 111 (Java Programming) Industry Experience: VMware (Networking & Security), Former Googler Awards: Hagler Fellow (2023), Google GCP Educational Grants Dr. Kumar’s work emphasizes practical system-level programming and security, with contributions to intrusion detection systems and secure enterprise networks. His courses integrate modern tools like RustRover and Docker, reflecting industry standards. He actively engages with educational technology, including LaTeX-based lecture materials and Gradescope integration.

Ryan MurrayView profile

Assistant Professor

• Calculus of variations
• Partial Differential Equations
• Machine Learning
• +5 more

Ryan Murray is an Assistant Professor in the Department of Mathematics at North Carolina State University (NC State). His research focuses on developing mathematical tools to address problems in applied analysis, including calculus of variations, partial differential equations (PDEs), and their applications to machine learning, fluid dynamics, and control theory. He holds a PhD in Mathematics from Carnegie Mellon University (2016). His expertise spans regularization methods for machine learning, singular perturbations in materials science, algorithms for distributed optimization, and singularity formation in fluid dynamics. His work is supported by the National Science Foundation (NSF) and the Simons Foundation. He actively collaborates with researchers in data science, PDE analysis, and optimization. Key research areas include adversarial training in classification, geometric data analysis via statistical depths, and the analysis of vortex sheet singularities. His teaching experience includes courses on partial differential equations, optimal control theory, and linear control systems. Ryan has published extensively in journals such as SIAM Journal on Mathematics of Data Science , Archive for Rational Mechanics and Analysis , and Journal of Machine Learning Research . His articles explore topics ranging from graph-based learning to fluid dynamics instabilities.

Parisa KordjamshidiView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +7 more

Parisa Kordjamshidi is an Associate Professor of Computer Science and Engineering at Michigan State University (MSU), leading the Heterogeneous Learning and Reasoning (HLR) Lab. Her research focuses on Neuro-Symbolic AI, spatial language understanding, and structured learning, with notable contributions to frameworks like Saul for declarative programming. She joined MSU in 2019 after roles at Tulane University and the Florida Institute for Human and Machine Cognition. Education: Ph.D. in Computer Science from KU Leuven (2013), postdoctoral research at UIUC's Cognitive Computation Group, and work in the KnowEng project. Research Interests: Artificial Intelligence, Machine Learning, Natural Language Processing, Neuro-Symbolic systems, spatial semantics extraction, structured output learning, and multimodal reasoning. Key projects include NSF CAREER awards for spatial language understanding and ONR grants for integrating domain knowledge into AI. Awards: NSF CAREER (2019), Amazon Faculty Research Award (2022), Fulbright Scholar (2025), and Rising Stars at MIT EECS (2015). Grants: Active projects on Neuro-Symbolic compositional generalization (ONR), spatial language learning (NSF), and collaborations with the Department of Media and Information for health misinformation management. Professional Activities: Editorial roles at JAIR, TACL, and Frontiers journals; service on program committees for ACL, EMNLP, and AAAI; organization of workshops like Spatial Language Understanding (SpLU) and CLeaR. Lab and Software: HLR Lab develops Saul (declarative learning-based programming framework) and tools for spatial role labeling. Her team emphasizes mentoring, with structured weekly meetings, reading groups, and conference participation for students.

Jalal EtesamiView profile

Assistant Professor

• Machine Learning
• Statistical Decision Making
• Causal Inference
• +4 more

Prof. Jalal Etesami is an Assistant Professor in the Department of Computer Science at Technical University of Munich (TUM), leading the Decision Sciences & Systems group. He holds a Ph.D. in Industrial and Systems Engineering from the University of Illinois at Urbana-Champaign and was a Postdoctoral Fellow at EPFL in Switzerland. His research focuses on machine learning, causal inference, multi-agent systems, and game theory, with applications to systemic risk modeling and market design. He teaches advanced courses such as Causal Inference in Time Series , Algorithmic Game Theory , and Optimization, Learning, and Market Design . Notable contributions include work on causal structure learning, stochastic optimization, and non-Gaussian causal models. Recent research explores causal effect identification under confounding, neural networks for market analysis, and optimal experiment design. Prof. Etesami’s work appears in top venues like NeurIPS, AAAI, and IEEE journals. He actively contributes to the academic community, organizing seminars and workshops on topics ranging from causal reasoning to computational social choice.

Bernadette BucherView profile

Assistant Professor

• Embodied AI
• Vision-Language Grounding
• Mobile Manipulation
• +3 more

Bernadette Bucher is an Assistant Professor in the Robotics Department (primary) and Computer Science and Engineering Department at the University of Michigan. Her research focuses on embodied AI, vision-language grounding, and mobile manipulation, with an emphasis on interpretable visual representations and uncertainty estimation for robotics tasks. She previously worked at Boston Dynamics AI Institute, NVIDIA Research, and Lockheed Martin Corporation. Her academic background includes a PhD in Computer Science from the University of Pennsylvania (GRASP Lab) under advisors Kostas Daniilidis and Nikolai Matni, alongside MA degrees in Mathematics and Economics from the University of Alabama (2014). Research interests include robotics, computer vision, and machine learning intersections, particularly autonomous mobile manipulation. Her work emphasizes uncertainty-aware systems and deployable learning-based methods. Notable achievements include the Best Paper in Cognitive Robotics at ICRA 2024. Her research spans projects like EVORA for off-road autonomy and ASHiTA for hierarchical task analysis. She has contributed to open-source projects like RoboNet and actively publishes in top conferences (CVPR, CoRL, ICRA). Key projects: EVORA, ASHiTA, Vision-Language Frontier Maps (VLFM) Grants and funding: Honda Research Institute (Curious Minded Machines project) Labs/Teams: Active participation in robotics labs at University of Michigan and prior collaborations with industry partners

Zhu-Tian ChenView profile

Assistant Professor

• Data Visualization
• Human-Computer Interaction
• Augmented Reality
• +7 more

Zhu-Tian Chen is an Assistant Professor in the Department of Computer Science and Engineering at the University of Minnesota, Twin Cities, where he leads research in data visualization, human-computer interaction, and augmented reality. Prior to this, he held postdoctoral positions at Harvard University and UC San Diego, working with leading researchers in visual computing and interactive design. Ph.D. in Computer Science, Hong Kong University of Science and Technology B.Eng. in Software Engineering, South China University of Technology His research focuses on augmenting human intelligence through hybrid human-AI systems, particularly in everyday and outdoor environments. He specializes in designing intelligent AR interfaces, embedded visualizations, and language-oriented interactions for applications in sports analytics, education, and data analysis. His work integrates human-centered design with applied machine learning to create intuitive and effective visualization tools. The recent trend in his publications shows a strong emphasis on intelligent AR systems for dynamic scenes, LLM-based code generation interfaces, and real-time augmentation of sports videos using natural language and gaze-based interactions. His work frequently appears in top-tier venues such as IEEE VIS, ACM CHI, and UIST. Best Paper Award, ACM CHI'23 Best Short Paper Honorable Mention, EuroVis'23 Best Paper Honorable Mention, IEEE VIS'22 (twice) Certificate of Distinction and Excellence in Teaching, Harvard University Hong Kong Ph.D. Fellowship Dr. Chen actively mentors undergraduate, master’s, and PhD students, as well as visiting scholars and interns, and is building a new research lab focused on visualization for intelligent AR systems. He has served on program committees for major conferences including ACM CHI, IEEE VIS, and EuroVis, and has been invited to speak at institutions such as Apple, JP Morgan, and multiple universities worldwide. He also contributes to the academic community through grant reviewing for NSF and the Department of Energy. He leads research projects in intelligent AR systems for sports, language-oriented interactions with LLMs, and immersive data visualization, often in collaboration with institutions like Harvard, UC San Diego, and HKUST. His lab welcomes students and collaborators interested in visualization, HCI, and applied AI.

• Federated Learning
• Optimization for Machine Learning
• Privacy-Preserving Machine Learning
• +12 more

Sai Praneeth Karimireddy is an Assistant Professor in the Thomas Lord Department of Computer Science at the University of Southern California (USC), with a courtesy appointment in the Ming Hsieh Department of Electrical and Computer Engineering. He previously held an SNSF postdoctoral fellowship at UC Berkeley under Michael I. Jordan and earned his PhD at EPFL advised by Martin Jaggi. He co-leads the Federated Learning and Data Quality working group at MONAI (NVIDIA) and collaborates with researchers at Apple Research. His research lies at the intersection of optimization, machine learning, statistics, and economics, with a strong focus on federated learning, privacy-preserving machine learning, data valuation, and AI for healthcare. He investigates how data quality, privacy, and incentives shape collaborative ML systems, especially in high-stakes domains like medicine. His work has been deployed at companies such as Meta, Google, OpenAI, and Owkin. His recent publications span top-tier venues including NeurIPS, ICML, ICLR, and JMLR, with influential contributions such as the SCAFFOLD algorithm for federated learning. His research shows a consistent trend toward building robust, private, and incentive-compatible collaborative learning systems, with increasing emphasis on real-world deployment in healthcare and decentralized data markets. 2023 SNSF Mobility Fellowship 2022 Patrick Denantes Memorial Prize for best thesis in computer science 2022 EPFL thesis distinction (top 8%) 2021 Chorafas Foundation Prize for exceptional applied research Capitol One Fellow (2025) He is actively mentoring PhD students and leads a research group focused on foundational and applied challenges in federated and privacy-preserving ML. He teaches graduate courses at USC, including CSCI 599 on Optimization for Machine Learning and CSCI 699 on Privacy-Preserving Machine Learning. He serves as an area chair for ICLR 2025 and co-organizes major workshops on incentives in data sharing and federated learning. His lab collaborates with institutions like NVIDIA, Apple, and Argonne National Laboratory, and he is building a research program centered on sustainable, equitable, and trustworthy AI ecosystems.

Tianyi LinView profile

Assistant Professor

• Optimization
• Game Theory
• Machine Learning
• +3 more

Tianyi Lin serves as an Assistant Professor in the Department of Industrial Engineering and Operations Research (IEOR) at Columbia Engineering, Columbia University, a position he assumed in 2024. He holds dual affiliations as a verified Data Science Institute (DSI) Member and an Affiliated Member of both the Financial and Business Analytics Center and the Foundations of Data Science Center. His academic credentials include: Ph.D. in Electrical Engineering and Computer Science, UC Berkeley Postdoctoral Researcher, Laboratory for Information & Decision Systems (LIDS), MIT (2023-2024) M.S. in Operations Research, UC Berkeley M.S. in Pure Mathematics and Statistics, University of Cambridge B.S. in Mathematics, Nanjing University Dr. Lin's research spans optimization theory , game-theoretic models , and machine learning algorithms , with emphasis on nonconvex minimax problems , variational inequalities , and data science applications . His work bridges theoretical guarantees with practical implementations in high-dimensional settings, particularly focusing on convergence properties and computational efficiency in complex systems. Analysis of his 15 most recent publications (2022-2025) reveals dominant themes in high-order optimization methods , no-regret learning in games , and optimal transport algorithms . His contributions demonstrate consistent innovation in developing doubly optimal algorithms for monotone games, spectral regularization techniques for policy optimization, and structure-driven approaches for nonconvex problems, reflecting strong interdisciplinary connections between operations research, computer science, and applied mathematics. No scientific awards or honors were documented in the provided source material. Information regarding student advising and research grants remains unspecified in the current documentation, though his center affiliations suggest active participation in collaborative research initiatives. Dr. Lin maintains significant interdisciplinary engagement through his affiliations with Columbia's Data Science Institute and specialized research centers, positioning his work at the intersection of theoretical optimization and real-world data science applications.

• Midwifery
• Maternal Health
• Mindfulness in Healthcare
• +7 more

Louise Hunter is an Associate Lecturer at the Oxford School of Nursing and Midwifery, Oxford Brookes University, where she contributes to both undergraduate and postgraduate teaching in midwifery. She is actively involved in research, supervision, and professional development, with a strong focus on improving maternity care and the experiences of women and midwives. Education: PhD MA (Oxon) RM (Registered Midwife) Her research interests revolve around enhancing midwifery care through innovative models such as mindfulness training and group antenatal care. She has investigated mindfulness as a tool to improve workplace culture and support women during labor and early parenthood. Her work on the NIHR-funded REACH project explored group antenatal care, and she continues to research water birth feasibility, anemia in pregnancy, and equity in midwifery education. Her recent publications demonstrate a consistent focus on maternal and newborn health, midwifery education, and service user empowerment. Themes across her work include health equity, particularly for Black, Asian, and Minority Ethnic (BAME) students and birthing people, evidence-based practice, and the integration of psychological and social support into maternity care. Scientific Awards and Recognition: Fellow of the Higher Education Academy Louise Hunter is also a member of the Royal College of Midwives (RCM) and actively supervises research students, including Claire Litchfield, whose work examines water immersion in labor for obese women. She is involved in the Maternity Care (OxMater) research group and contributes to developing initiatives that involve service users in midwifery recruitment and training. Her work bridges clinical practice, education, and research, aiming to create more inclusive, effective, and compassionate maternity services.

Bernd KlauerView profile

Professor

• Water resources economics
• Ecological economics
• Environmental and resource economics
• +7 more

Prof. Dr. Bernd Klauer serves as Deputy Head of the Department of Economics at the Helmholtz Centre for Environmental Research (UFZ) since 2017 and holds an Honorary Professorship for Sustainability and Water Resources Management at the University of Leipzig since 2015. He leads the Working Group on Social Science Water Research and contributes to interdisciplinary projects spanning Germany, EU, Jordan, and India. Doctorate in Economics (1997), University of Heidelberg Diplom-Mathematiker (1992), University of Heidelberg His research integrates water economics with ecological economics and governance frameworks, focusing on hydro-economic modeling, water resource governance, decision support systems, and multi-criteria evaluation. Key application areas include Food-Water-Energy Nexus, Integrated Water Resource Management, and EU Water Framework Directive implementation. The 15 most recent publications reveal a strong emphasis on coupled human-natural systems modeling, climate change adaptation in agriculture, water scarcity governance, and socioeconomic analysis of informal water markets. His work consistently bridges technical hydrological models with economic decision-making frameworks across diverse geographic contexts. Current projects include FUSE (Food-Water-Energy Nexus) and NexusFootprints, while completed initiatives like the Jordan Water Project demonstrate his commitment to transboundary water challenges. He collaborates extensively with hydrologists, ecologists, and policy experts.