Find Academic Supervisors

Han LiuView profile

Professor

• Artificial Intelligence
• Computer Systems
• Statistical Machine Learning
• +4 more

Han Liu is a Professor in the Department of Computer Science at Northwestern University's McCormick School of Engineering. He directs the MAGICS (Modern Artificial General Intelligible and Computer Systems) Lab and the Center for Foundation Models and Generative AI at Northwestern, with prior roles as director of the Deep Reinforcement Learning Center at Tencent AI Lab and professor at Princeton and Johns Hopkins Universities. PhD in Machine Learning and Statistics from Carnegie Mellon University (2012), advised by John Lafferty and Larry Wasserman Han Liu's research focuses on integrating artificial intelligence with computer systems, particularly through foundation models and probabilistic graphical models. His work aims to revolutionize science, engineering, and business by deploying statistical machine learning methods in edge and cloud computing environments. Recent research trends include transformer-based models, modern Hopfield networks, genomic foundation models, and theoretical analysis of attention mechanisms. His 2025 publications explore topics like species differentiation with DNA embeddings, universal approximation capabilities of transformers, and metaverse spatial reasoning. Alfred P Sloan Fellowship in Mathematics IMS Tweedie New Researcher Award ASA Noether Young Scholar Award NSF CAREER Award Presidential Early Career Awards for Scientists and Engineers Han Liu serves as associate editor for the Journal of American Statistical Association, Electronic Journal of Statistics, Technometrics, and the Journal of Portfolio Management. He has directed research centers at Northwestern and contributed to major conferences as area chair (NeurIPS, ICML, ICLR).

Henrik SandbergView profile

Professor

• Cyber-Physical Systems Security
• Power Systems
• Model Reduction
• +6 more

Henrik Sandberg is a Professor at the Division of Decision and Control Systems , KTH Royal Institute of Technology , Stockholm, Sweden. He holds the title of Deputy Head of Division and is affiliated with the School of Electrical Engineering and Computer Science . Education: MSc in Engineering Physics (1999) PhD in Automatic Control (2004) from Lund University Postdoctoral position at Caltech (pre-2007) Research Interests: Focus on cyber-physical systems security , power systems , model reduction , and fundamental limitations of control systems . Key sub-areas include attack detection , networked control , privacy-preserving estimation , and resilient control architectures . Publications: Over 150 papers across IEEE Transactions and Automatica , covering topics like stealthy attacks , distributed control , LQG optimization , and thermodynamic costs in filtering . Recent work includes LWE-based encrypted control and Bayesian deception mechanisms . Scientific Awards: Best Student Paper Award Finalist at IEEE CASE 2014; Best Student-Paper Award at IEEE CDC 2004. Grants & Projects: Leads the DYNACON project (WASP Cybersec cluster) and collaborates on CERCES (critical infrastructure resilience). Serves as examiner for multiple advanced courses in cybersecurity and control systems. Contact: Email: hsan@kth.se Phone: +46 (0)8 790 7294 Room: A:607, Malvinas Väg 10, Stockholm

CHAN Mun ChoonView profile

Professor

• Systems & Networking
• Computer Networking
• Mobile Computing
• +3 more

CHAN Mun Choon is a Professor at the School of Computing, National University of Singapore (NUS) , where he directs the NUS-NCS Joint Laboratory for Cyber Security . He previously worked at Bell Labs (1997-2003) and holds a PhD from Columbia University (1997). His research spans systems and networking with specific interests in mobile computing, software-defined networking, and cyber-physical systems . PhD, Electrical Engineering (1997), Columbia University M.Phil., Electrical Engineering (1993), Columbia University MS, Electrical Engineering (1993), Columbia University BS, Computer & Electrical Engineering (1990), Purdue University His recent work focuses on 5G network architecture , data center fault debugging , and energy-efficient mobile sensing . He has published over 100 papers and holds 7 US patents , including cache-based compaction techniques with 210+ citations. His projects include fronthaul slicing for 5G, network-wide packet history frameworks, and participatory indoor localization. Scientific recognition includes: Best Paper Awards: IEEE ICNP 2019, ACM SOSR 2019, ICDCN 2016 Best Demo: IPSN 2016 Distinguished Member, INFOCOM TPC (2016, 2020, 2021) He serves as Vice-Dean, Graduate Studies and Vice-Dean, Academic Affairs at NUS Computing, and has graduated 21 PhD students . His lab develops solutions for network security , latency-sensitive applications , and mobile sensing .

Peter K. KitanidisView profile

Professor

• Groundwater Flow
• Hydrologic Modeling
• Inverse Problems
• +3 more

Peter K. Kitanidis is a Professor in the Department of Civil and Environmental Engineering and the Institute for Computational and Mathematical Engineering at Stanford University . His research focuses on groundwater flow , hydrologic forecasting , and stochastic inverse modeling , with applications to pollutant remediation and CO₂ storage monitoring . Education : Diploma, National Technical University of Athens (1974) M.S., MIT (1976) Ph.D., MIT (1978) Research Interests : Groundwater modeling and contaminant transport Hydraulic tomography and aquifer characterization Stochastic methods for uncertainty quantification Bioremediation and enhanced in-situ pollutant decay Dilution and mixing processes in heterogeneous media Real-time river flow forecasting Scientific Awards : L.G. Straub Award (1979) W.L. Huber Research Prize (1994) ISI Highly Cited Researcher (2001) AGU Hydrologic Sciences Award (2011) ASCE Pioneers in Groundwater Lecturer (2011) Advising and Grants : Advised 20+ PhD and MS students (1978–2018) Principal investigator on NSF, EPA, and DOE-funded projects Developed software for groundwater data analysis and CO₂ monitoring Contributed to bioremediation protocols and hydraulic tomography algorithms Labs and Teams : Kitanidis Laboratory for groundwater crisis solutions Collaborated with Oak Ridge National Laboratory and Stanford Hydrogeology Group Mentored postdocs (2000–2017) in reactive transport and inverse modeling

Rong ZhengView profile

Professor

• Mobile computing
• Wireless networking
• Cyber physical systems
• +4 more

Rong Zheng is a Professor in the Department of Computing and Software and a member of the School of Biomedical Engineering at McMaster University, Canada. She holds a Tier-1 Canada Research Chair in Mobile Computing and serves as Acting Chair of the Computing and Software department from July to December 2025. She is also an Associate Member of the Electrical & Computer Engineering department. Education: Ph.D. in Computer Science, University of Illinois, Urbana-Champaign, USA Master of Engineering (thesis) in Electrical Engineering, Tsinghua University, Beijing, China Bachelor of Engineering in Electrical Engineering, Tsinghua University, Beijing, China Dr. Zheng's research lies at the intersection of mobile computing, wireless networking, and machine learning, with a strong focus on applications for aging populations. She directs the NSERC Smart Mobility for the Aging Population CREATE program. Her work encompasses sensor development, wireless network design, and mobile data analytics to address real-world challenges in healthcare, mobility, and data center monitoring. She has developed innovative solutions like the MacQuest campus navigation app and has captured first prize in indoor localization competitions. Her recent publications demonstrate a clear trajectory toward applying wireless sensing technologies (particularly acoustic, Wi-Fi, and mmWave) to health monitoring and mobility assessment for older adults. There's a strong emphasis on developing efficient edge computing solutions that can process data in real-time on resource-constrained devices, as exemplified by her TeamNet framework for collaborative inference on the edge. Her work bridges theoretical advances with practical applications that have social impact. Scientific Awards: Tier-1 Canada Research Chair in Mobile Computing US National Science Foundation CAREER Award (2006) Joseph Ip Distinguished Engineering Fellow (2015-2018) Dr. Zheng leads the Wireless System Research Group (WiSeR) at McMaster University, which has secured significant funding including a $1.65M NSERC CREATE grant for smart mobility research for older adults. Her research has been supported by multiple funding agencies including NSERC, NSF, UH GEAR, and DURIP. She actively mentors graduate students and has developed specialized courses including CAS 772 (Mobile Data Analytics) and CAS 781 (Mobility in the Aging Population). The WiSeR group conducts impactful research on communication, networking, and data analytics issues in Cyber Physical Systems, with applications spanning healthcare, smart infrastructure, and data center monitoring. Their work on data center infrastructure monitoring networks has been featured in EurekAlert and Data Center Dynamics, and they've made significant contributions to indoor localization technology.

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.

Yannis ChronisView profile

Assistant Professor

• Database Systems
• Hardware-Software Co-design
• Data Processing Efficiency
• +3 more

Yannis Chronis is an incoming Assistant Professor at the Department of Computer Science , ETH Zurich (starting August 2025), where he will join the ETH Systems Group . Prior to this, he spent 3 years as a Systems Researcher at Google's Systems Research Group in Sunnyvale, USA. He holds a Ph.D. in Computer Science from the University of Wisconsin-Madison (advised by Prof. Jignesh Patel) and a Bachelor's/Master's from the University of Athens, Greece (advised by Prof. Yannis Ioannidis). His research focuses on optimizing databases and data processing for modern hardware through software-hardware co-design. Key areas include database efficiency in memory-centric architectures, learned query optimizers, and cloud resource management. His work is supported by a Facebook Fellowship and has been recognized with the EDBT 2016 Medal for best paper. Teaching: Taught CS 564 - Database Management Systems at UW-Madison (Spring 2022). Service: Served on program committees for SIGMOD, VLDB, CIDR, and others, and chairs the CIDR Proceedings Committee. Key Publications: His recent work includes studies on memory-centric computing for databases, cardinality estimation benchmarks, and adaptive query processing techniques.

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.

• Computer Science
• Networking
• Telecommunications
• +4 more

Prof. Liam Murphy is a Full Professor of Computer Science & Informatics at University College Dublin (UCD) and Director of the Performance Engineering Laboratory. He holds a B.E. from UCD, M.Sc. and Ph.D. from UC Berkeley. His research focuses on performance engineering of networks, software systems, and multimedia transmissions. He has published over 150 peer-reviewed papers and is an IEEE member and Fellow of the Irish Computer Society. Education: B.E. in Electrical Engineering, UCD (1985) M.Sc. & Ph.D. in Electrical Engineering & Computer Sciences, UC Berkeley (1988, 1992) Research Interests: Dynamic resource allocation in networks Cloud computing efficiency Software performance engineering Wireless multimedia systems Quality of Service (QoS) optimization Recent work emphasizes energy-efficient cloud workflows, multi-objective data center optimization, and decentralized traffic simulation. Grants & Awards: Fellow of the Irish Computer Society (2007) Conference Paper Awards (2004, 2002, 2001) Principal Investigator in multiple funded projects (e.g., EU-funded traffic simulation, cloud resource allocation) Advising & Labs: Directed 24 Ph.D. and 8 M.Sc. students. Leads the Performance Engineering Laboratory (PEL), focusing on distributed systems, cloud efficiency, and network performance. Collaborates on industry-relevant projects like crovan (UCD/DCU campus company). Teaching: Coordinates courses on computer science fundamentals, distributed systems performance, and software engineering at UCD.

Zhou Zhi-HuaView profile

Professor

• Artificial Intelligence
• Machine Learning
• Data Mining
• +3 more

Zhou Zhi-Hua is a Professor at Nanjing University's Department of Computer Science & Technology, serving as Standing Deputy Director of the National Key Lab for Novel Software Technology and Founding Director of LAMDA (Institute of Machine Learning and Data Mining). He holds simultaneous fellowships from ACM, AAAI, AAAS, IEEE, IAPR, IET/IEE, and CCF, reflecting his exceptional contributions to computational intelligence. His educational background includes: B.Sc. in Computer Science from Nanjing University (1996) M.Sc. in Computer Science from Nanjing University (1998) Ph.D. in Computer Science from Nanjing University (2000) Zhou's research pioneers fundamental advances in machine learning theory and applications. His seminal work on ensemble methods established new frameworks for classifier combination, while innovations in multi-label learning and anomaly detection addressed critical challenges in complex data analysis. His research bridges theoretical rigor with practical implementations across diverse domains including biometrics, data mining, and computer vision, resulting in over 150 publications and 18 patents. His textbooks "Ensemble Methods" (2012) and "Machine Learning" (2016) have become standard references in the field. Analysis of his publication trajectory reveals sustained leadership in core machine learning challenges: evolving from neural network ensembles (2002) through semi-supervised learning breakthroughs (2005) to foundational work on multi-instance learning (2012) and theoretical margin analysis (2013). His recent focus demonstrates increasing sophistication in handling complex data structures while maintaining theoretical soundness. His scientific excellence is recognized through: National Natural Science Award of China (2013) PAKDD Distinguished Contribution Award (2016) IEEE ICDM Outstanding Service Award (2016) IEEE CIS Outstanding Early Career Award (2013) Microsoft Professorship Award (2006) Simultaneous fellowships from 7 major international societies Zhou provides extraordinary service to the academic community as Executive Editor-in-Chief of Frontiers of Computer Science and Associate Editor-in-Chief of Science China Information Science. He founded the ACML conference and has chaired premier events including ICDM'16 and PAKDD'14. His leadership extends to serving as General Chair for ICDM'16, Program Chair for IJCAI'15 Machine Learning Track, and Area Chair for multiple top conferences. The available text does not specify student advising details or research grants. He directs LAMDA research group at Nanjing University, which has established itself as a global powerhouse in machine learning research, and contributes significantly to the National Key Lab for Novel Software Technology's mission of developing next-generation intelligent systems.

Zohreh PourzolfagharView profile

Assistant Professor

• Digital Transformation
• Smart Cities
• Information Management
• +3 more

Zohreh Pourzolfaghar is an Assistant Professor in Management Information Systems (MIS) at the School of Business, Maynooth University (MU). She serves as Director of the IT-Enable Innovation Programme and cluster lead for the Digital Construction cluster at the Innovation Value Institute (IVI). She is also a member of Lero, the SFI Research Centre for Software. Education: PhD in Project Management (University Putra Malaysia, 2011); MSc in Management Systems and Productivity; BSc in Software Engineering. Research Interests : Focuses on digital transformation for SMEs, information management in construction, and smart city architectures. Leads EU-funded projects like EnTrust (agri-data governance), DIGI+ (sustainable digital transformation), and DigiFABs (food & beverage SMEs). Co-PI for projects such as GDI-IE (genomic data infrastructure) and PERFORM (digital retail innovation). Grants & Awards : Secured funding from SFI, Enterprise Ireland, and EU. Notable awards include the BIM Hero Award (2022) and Research Engaged Award (2022). Teaching : Courses include Digital Business, Digital Technology Governance, and Enterprise Architecture. Advises PhD students in MIS and digital transformation. Industry Collaboration : Works with firms like ArcDox and Corballis Technologies on BIM standards and data governance. Active in national and EU policy networks, including NSAI committees.

Amit SingerView profile

Professor

• Computational Mathematics
• Structural Biology
• Cryo-Electron Microscopy
• +3 more

Amit Singer is a Professor of Mathematics at Princeton University, specializing in computational methods for structural biology and cryo-electron microscopy (cryo-EM). His work focuses on developing mathematical frameworks and algorithms for analyzing large-scale microscopy datasets, particularly in 3D reconstruction and heterogeneity analysis of molecular structures. He leads research in manifold learning, optimal transport, and harmonic analysis, with applications to cryo-EM, signal processing, and inverse problems. Research interests include: (1) Mathematical methods for cryo-EM, including particle alignment, density map analysis, and subspace-based reconstruction techniques; (2) Development of rotation-invariant representations for imaging problems; (3) Application of machine learning and optimization to biomedical imaging challenges. His contributions bridge pure mathematics (e.g., harmonic analysis, manifold theory) with applied computational techniques for real-world microscopy data. Key trends in his recent articles (2023–2025) include advancements in multi-reference alignment methods, Wasserstein distance-based image registration, and overcoming particle detection limitations in cryo-EM. He also explores sparsity constraints, autocorrelation analysis, and novel algorithms for handling heterogeneous datasets. These methods improve resolution and reduce computational costs in analyzing molecular structures at atomic scales. Notable contributions include the ASPiRE software package for steerable PCA, and foundational work on synchronization problems in cryo-EM orientation estimation. His research often addresses algorithmic scalability and robustness to noise in experimental setups.

Harish RavichandarView profile

Assistant Professor

• Robotics
• Machine Learning
• Human-Robot Interaction
• +2 more

Harish Ravichandar is an Assistant Professor at the School of Interactive Computing , Georgia Institute of Technology, and a core faculty member of the Institute for Robotics and Intelligent Machines (IRIM) . He leads the Structured Techniques for Algorithmic Robotics (STAR) Lab , focusing on structured computational frameworks and learning algorithms with inductive biases to enhance robot efficiency, reliability, and self-sufficiency in human-robot collaboration and complex applications like dexterous manipulation and multi-agent coordination. His research bridges robot learning , human-robot interaction , and multi-agent systems , emphasizing stable, frugal, and safe skill acquisition from human demonstrations. Key themes include intention inference , trajectory optimization , and heterogeneous team coordination , often leveraging Koopman operators , hypernetworks , and graph-based methods . Scientific recognition includes the NSF CAREER Award , IEEE MRS Best Paper Award , and Georgia Tech’s College of Computing Outstanding Post-Doctoral Research Award . His work also received the ASME DSCC Best Student Paper Award and P&W Institute Graduate Fellowship . Harish’s educational background includes a Ph.D. in Electrical and Computer Engineering from the University of Connecticut (2018) , an M.S. from the University of Florida (2014) , and a B.E. in Instrumentation and Control Engineering from Anna University (2012) . He previously held postdoctoral and research scientist roles at Georgia Tech before his current position.

Per Christian HansenView profile

Professor

• Numerical Analysis
• Numerical Linear Algebra
• Iterative Reconstruction Methods
• +8 more

Per Christian Hansen is a Professor at the Department of Applied Mathematics and Computer Science (DTU Compute), Technical University of Denmark (DTU), where he leads the Section for Scientific Computing. He is a VILLUM Investigator and heads the CUQI (Computational Uncertainty Quantification for Inverse Problems) research initiative, aiming to develop accessible computational platforms for uncertainty quantification in inverse problems. His expertise lies in numerical analysis, numerical linear algebra, iterative reconstruction methods, and computational inverse problems, with applications in tomography, signal analysis, and plasma physics. His research integrates theoretical analysis—such as perturbation and convergence analysis—with the development of robust, adaptive, and efficient computational methods. He has co-authored five books, over 100 scientific papers, and several widely used MATLAB software packages, including IR Tools and Regularization Tools. His recent work (2023–2025) emphasizes uncertainty quantification, Bayesian inversion, and high-dimensional tomography in fusion plasmas, reflecting a strong trend toward probabilistic and robust modeling in inverse problems. He is a SIAM Fellow (2015) for his contributions to computational methods for rank-deficient and discrete ill-posed problems and regularization techniques. His scientific leadership is evident in both theoretical advances and practical software implementations. He actively collaborates across disciplines, particularly in nuclear fusion and medical imaging, and continues to supervise PhD students and publish in top-tier journals such as Inverse Problems , SIAM Journal on Scientific Computing , and Nuclear Fusion . SIAM Fellow (2015) VILLUM Investigator He advises PhD and Master’s students in computational mathematics and inverse problems, and his research is supported by major grants, including the VILLUM Investigator award. He leads the CUQI team, which develops open-source tools for non-experts to apply uncertainty quantification in inverse problems. His lab focuses on creating modeling frameworks that bridge theory, computation, and real-world applications in materials science, imaging, and plasma diagnostics.

Miroslav PajicView profile

Professor

• Cyber-Physical Systems
• Embedded Systems
• Artificial Intelligence
• +6 more

Miroslav Pajic serves as a Professor in the Department of Electrical and Computer Engineering at Duke University's Pratt School of Engineering. He also holds joint appointments as Associate Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science and Associate Professor of Computer Science. As Director of Master's Studies, he oversees the graduate program in Electrical and Computer Engineering and teaches numerous courses spanning embedded systems, cyber-physical systems design, and robotics. Education: Ph.D. in Electrical and Computer Engineering from University of Pennsylvania (2012) Miroslav Pajic's research focuses on the design and analysis of cyber-physical systems (CPS) with varying levels of autonomy and human interaction. His work spans the intersection of embedded systems, artificial intelligence, machine learning, control theory, formal methods, and robotics. He specializes in developing high-assurance autonomous systems with applications in robotics, automotive systems, and medical devices, with particular emphasis on CPS security and resilient autonomy. His research addresses fundamental challenges in creating systems that can operate reliably in uncertain environments while maintaining security against potential cyber attacks. Analysis of Pajic's recent publications reveals a strong interdisciplinary research program bridging theoretical foundations with practical applications. His work spans secure sensor fusion for distributed autonomy, medical applications of CPS (particularly deep brain stimulation for neurological disorders), and innovative sensing technologies for autonomous vehicles. A significant portion of his research addresses security challenges in cyber-physical systems, including stealthy GPS attacks on UAVs and methods for attack-resilient state estimation. His publications increasingly integrate machine learning techniques with traditional control theory to create more adaptive and robust autonomous systems. Pajic actively mentors graduate students and leads research groups focused on cyber-physical systems security and high-assurance autonomy. His research is supported by multiple grants, including the NSF AI Institute for Edge Computing (Athena), which he co-leads. He has received funding from various sources to support his work on secure and resilient cyber-physical systems, medical device security, and autonomous vehicle technologies. Pajic collaborates extensively with medical researchers on applications of cyber-physical systems in healthcare, particularly in deep brain stimulation for neurological disorders. His work bridges the gap between theoretical control systems and practical implementations in safety-critical domains, with a growing emphasis on translating research into real-world applications that improve system security and reliability.