Find Academic Supervisors

Igor KadotaView profile

Assistant Professor

• Communication Networks
• Wireless Networks
• Full-Duplex Systems
• +6 more

Dr. Igor Kadota is an Assistant Professor of Electrical and Computer Engineering at Northwestern University's Robert R. McCormick School of Engineering and Applied Science. His research focuses on next-generation communication networks, blending rigorous theory, system implementation, and machine learning to address challenges in full-duplex systems, millimeter-wave networks, and spectrum sharing markets. He leads the Wireless Lab and is affiliated with the Communications and Networking Lab, SpectrumX, and the Trienens Institute for Sustainability and Energy. Educations: Ph.D. and S.M. in Communication Networks, MIT B.S. in Electrical Engineering, ITA, Brazil Affiliations: Communications and Networking Lab, SpectrumX, Trienens Institute Research Interests: Prof. Kadota’s work emphasizes Age of Information theory, spectrum sharing markets, full-duplex wireless systems, and drone networking. His projects include FPGA-based full-duplex prototypes, real-time adaptive algorithms, and machine learning pipelines for atmospheric sensing via millimeter-wave radar. Recent initiatives focus on bridging techno-economic gaps in spectrum zone management and deploying autonomous resource allocation strategies. Awards & Recognition: Best Student Paper Awards at WiOpt 2024 and 2025 NSF grants (SII-NRDZ-SBE, SII-NRDZ, and RINGS) Senior Member of IEEE (2024) ACM SIGCOMM 2023 SRC 1st Place Award (collaborative work) Grants & Service: Organized workshops at IFIP Performance and Asilomar Conferences Technical Program Committee member for ACM SIGMETRICS/IFIP Performance and IEEE INFOCOM Outreach initiatives include K-12 STEM programs and NSF PAWR COSMOS testbed demonstrations Labs & Teams: The Wireless Lab recruits Ph.D., Master’s, and undergraduate students to tackle interdisciplinary projects. Current openings exist in spectrum sharing algorithms, drone networks, and full-duplex hardware-software co-design.

Wing K. LiuView profile

Professor

• Computational Mechanics
• Multiscale Modeling
• Additive Manufacturing
• +5 more

Wing K. Liu is the Walter P. Murphy Professor of Mechanical Engineering, Civil and Environmental Engineering (by courtesy), and Materials Science and Engineering at Northwestern University. He leads the HiDeNN-AI research group, focusing on mechanistic data science, hierarchical deep learning neural networks (HiDeNN), and multiscale modeling for advanced manufacturing and materials innovation. His work integrates physics-based principles with machine learning to address challenges in additive manufacturing, computational mechanics, and materials design. Education : Ph.D., M.S., California Institute of Technology B.S. (Hons) in Engineering Science, University of Illinois at Chicago Research Interests : Liu’s research spans mechanistic data science, AI-driven materials innovation, and multiscale modeling. His HiDeNN framework enables fast design, optimization, and discovery in engineering systems. Key areas include laser powder bed fusion, image-based modeling, and predictive manufacturing processes. He emphasizes combining scientific principles with data to accelerate technology development. Awards and Recognition : Gauss-Newton Medal (2012) John von Neumann Medal (2007) ASME Robert Henry Thurston Lecture Award (2007) ISI Highly Cited Researcher in Engineering (2001) Teaching and Academic Leadership : Liu teaches advanced finite element methods, multiscale modeling, and computational mechanics. He has served as editor for Computational Mechanics , vice president of the International Association for Computational Mechanics, and past president of the U.S. Association for Computational Mechanics. Labs and Teams : His research group, Wing K. Liu Group , develops cutting-edge tools like JAX-FEM and C-HiDeNN, focusing on GPU-accelerated simulations and AI integration in engineering systems.

• Wireless Communications (5G/6G)
• AI/Machine Learning Applications
• Cyber Security and Privacy
• +9 more

Dr. Diep N. Nguyen is an Associate Professor and Associate Dean (Teaching & Learning) at the University of Technology Sydney (UTS), leading the Agile Communications and Computing Group within the Faculty of Engineering and Information Technology. He holds adjunct positions at universities in the US, Vietnam, and Japan. His research focuses on wireless communications (5G/6G), AI-driven networking, cybersecurity, and edge computing, with over 200 publications and $7M in grants from NSF, ARC, and industry partners. He has supervised 15 PhD students, many now in senior academic and industry roles globally. Key roles include Director of Transnational Education, co-founder of Food Agility CRC, and leadership in international initiatives like APEC DARE. Education: Ph.D. and M.E. in Electrical and Computer Engineering from The University of Arizona and UC San Diego, respectively. Professional service includes editorial roles for IEEE Transactions and conference leadership (e.g., IEEE VTC, INFOCOM). Awards include UTS Research Excellence Awards (2021, 2024), DECRA (2015), and industry recognitions like the 2024 iAward and IoT Impact Award. Research emphasizes AI applications in communications, cybersecurity for Industry 4.0, and 6G security. Notable projects include Australia-Vietnam 5G/6G Centre ($2.1M) and DFAT-funded initiatives for disaster management. Books authored include Deep Reinforcement Learning for Wireless Communications and Generative AI for Communications Systems . Teaching focuses on integrating research into courses like Cloud Security and Industry 4.0 Technologies. His pedagogy emphasizes self-directed learning through authentic research tasks, supported by real-time feedback channels.

Geoffrey ChallenView profile

Teaching Professor

• Computers and Education Research
• Computer Science Education
• Educational Technology
• +2 more

Geoffrey Challen is a Teaching Professor in the Department of Computer Science at the University of Illinois. His focus is on scalable computer science education, particularly in introductory courses like CS 124 (formerly CS 125). He holds a PhD and AB in Computer Science and Physics from Harvard University. His research interests center on computers and education, emphasizing innovative teaching methodologies and EdTech tools. He has developed widely-used platforms like learncs.online and Jeed, which support interactive learning environments. His NSF CAREER Award (2017) recognizes his work in educational technology and course design. Challen’s articles explore topics including automated assignment grading, student preferences in large courses, and mobile systems optimization. His courses have enrolled over 8,962 students, with recent innovations like Kotlin language integration and diverse instructor profiles in course materials. Award Highlights: National Science Foundation CAREER Award (2017) Teaching & Mentoring: He has mentored over 2000 undergraduate/graduate staff, led curriculum revisions, and pioneered asynchronous online teaching formats. His lab focuses on training future computer scientists through project-based learning and collaborative environments.

Deepak PathakView profile

Assistant Professor

• Deep Learning
• Computer Vision
• Reinforcement Learning
• +12 more

Deepak Pathak is the Raj Reddy Assistant Professor in Robotics at Carnegie Mellon University's School of Computer Science, where he leads the Pathak Research Group in the Robotics Institute and is affiliated with the Machine Learning Department. His research focuses on building agents with human-like generalization capabilities through self-supervised learning from raw sensory data at the intersection of computer vision, machine learning, and robotics. PhD from UC Berkeley (advised by Alyosha Efros & Trevor Darrell) Bachelors in Computer Science from IIT Kanpur Research supported by Facebook, Nvidia, and Snapchat fellowships His work spans robot hands , legged robots , world modeling , 3D vision , and human-centered robotics , with key contributions in sim2real transfer , test-time adaptation , and structured world models . Recent articles focus on force-attending policies , zero-shot manipulation , and generative classifiers . Scientific accolades include: Sloan Research Fellowship 2025 MIT TR 35 under 35 Innovator 2024 Raj Reddy Chair at CMU 2024 Okawa Research Award 2022 Young Alumnus Award from IIT Kanpur 2022 He has advised 10+ PhD students and 15+ Masters students, with notable alumni now at top PhD programs (MIT, Stanford, Princeton) and industry (Tesla Optimus, Skild AI). His lab develops cost-effective hardware like LEAP Hand (2000 USD) and DASH (7000 USD) for dexterous manipulation research.

• Machine Learning
• Artificial Intelligence
• Bioinformatics
• +7 more

Eric Poe Xing is a Professor at Carnegie Mellon University's School of Computer Science, holding joint affiliations with the Machine Learning Department, Language Technology Institute, and Computer Science Department. He also serves as President of the Mohamed bin Zayed University of Artificial Intelligence. His research focuses on machine learning methodology, statistical systems, and large-scale computational architectures, with recent work on foundation models for biology (AIDO), world/agent models (PAN), and open-source LLM initiatives (LLM360). He advises numerous students and postdocs in areas like AI, NLP, and computational biology. He teaches graduate courses in Machine Learning and Probabilistic Graphical Models, and actively contributes to academic leadership roles, including ICML program chairs and editorial boards. Research interests span automated reasoning, AI ethics, and scalable computing. His lab, SAILING, develops cutting-edge models for vision-language tasks, bioinformatics, and multi-modal learning. Notable projects include LLM360's open-source AGI efforts and innovations in distributed ML systems. His work emphasizes interdisciplinary applications, from healthcare decision-support (PetuumMed) to climate modeling (ClimSatDiff).

Dong Jin SongView profile

Professor

• Formal methods
• Safety and security systems
• Probabilistic reasoning
• +9 more

Dong Jin Song is a full Professor at the National University of Singapore's School of Computing, Department of Computer Science. He joined NUS in 1998 and was promoted to Professor in 2016 after serving as Associate Professor (2005) and Assistant Professor. He has held various leadership roles including Deputy Head of CS Department (2023-2024), NUS Senate Member (2020-current), and Assistant Dean (Graduate Office, SoC). PhD, University of Queensland, Australia (1993-1995) BInfTech with First Class Honours, University of Queensland, Australia (1989-1992) - Major in Software Engineering Professor Dong's research spans formal methods, safety and security systems, probabilistic reasoning, sports analytics, and trusted machine learning. He is best known for co-founding the PAT verification system which has attracted thousands of registered users from over 150 countries and won the 20-year ICFEM Most Influential System Award in 2018. He also co-founded 'Silas: Trusted Machine Learning' and the Dependable Intelligence company. His work bridges formal verification with practical applications in security, AI, and even sports analytics where he developed Markov Decision Process models for tennis strategy analysis. His recent publications show a strong trend toward integrating formal methods with modern AI systems, particularly focusing on trustworthy AI, LLM verification, and security applications. The research spans multiple high-impact venues including ICML, NeurIPS, IEEE Transactions, and top security conferences like USENIX Security, reflecting his interdisciplinary approach that combines formal verification with machine learning, security, and practical applications. Professor Dong has received numerous honors including the ACM SIGSOFT Distinguished Paper Award for ICSE 2020, the 20-Year ICFEM Most Influential System Award (2018), and being named a Fellow of the Institute of Engineers Australia (2018). His awards reflect both theoretical contributions to formal methods and practical impact on software engineering. ACM SIGSOFT Distinguished Paper Award for ICSE 2020 NUS Research Recognition Award (2020) Fellow of Institute of Engineers Australia (2018) 20-Year ICFEM Most Influential System Award (2018) Best Paper Award at ICECCS (2015 and 2012) Professor Dong has successfully supervised 33 PhD students, many of whom have become tenured faculty members at leading universities worldwide including The University of Auckland, Aston University, Singapore Management University, and Monash University. His students have gone on to successful careers in both academia and industry at organizations like Google, Apple, HP Research Lab, and IBM. He has served on the editorial boards of prestigious journals including ACM Transactions on Software Engineering and Methodology and has been active in numerous conference organizing committees. Through his research group and commercial ventures (Dependable Intelligence), Professor Dong has built a strong team focused on formal verification, trusted AI systems, and practical applications of model checking. His work has evolved from foundational formal methods research to cutting-edge applications in AI safety and security, maintaining a consistent thread of rigorous verification throughout his career.

Christopher MetzlerView profile

Assistant Professor

• Computer Vision and Machine Perception
• AI and Robotics
• Machine Learning and Data Science
• +2 more

Christopher Metzler is an Assistant Professor in the Department of Computer Science at the University of Maryland (UMD), with appointments in the University of Maryland Institute for Advanced Computer Studies (UMIACS) and a courtesy appointment in the Electrical and Computer Engineering Department. He leads the UMD Intelligent Sensing Laboratory, focusing on computational imaging, machine learning, and wireless communications. His research develops novel systems and algorithms for imaging through scattering media, multimodal sensor fusion, and self-supervised AI techniques. Education: PhD in Electrical and Computer Engineering (Rice University, 2019), MSEE (2014), BSEE (2013). Postdoctoral Fellowship: Stanford Computational Imaging Lab (2020). Awards: AFOSR Young Investigator Program (2022), NSF CAREER (2023), ARO Early Career Award (2024), and multiple fellowships (NSF GRFP, NDSEG). Research interests include computational imaging, machine learning, statistical signal processing, and AI-driven sensor fusion. His work addresses challenges in non-line-of-sight imaging, turbulence mitigation, and hardware-aware algorithms. He advises 10 PhD students and collaborates on projects funded by the Air Force, NSF, and other agencies. Key contributions include neural wavefront shaping, adversarial sensing frameworks, and high-resolution non-line-of-sight imaging. His lab develops open-source software and datasets, such as the Transmission Matrix Dataset and learned compressive sensing tools.

Amitabh VarshneyView profile

Professor

• Virtual and Augmented Reality
• Scientific Visualization
• Interactive Computer Graphics
• +6 more

Amitabh Varshney is the Dean of the College of Computer, Mathematical, and Natural Sciences and Professor of Computer Science at the University of Maryland, College Park. He previously directed the UMD Institute for Advanced Computer Studies (2010–2018) and served as interim Vice President for Research (2016–2017 and 2021). His research focuses on virtual/augmented reality (VR/AR), scientific visualization, molecular graphics, and high-performance computing. Collaborations include NVIDIA, Honda, IBM, and the University of Maryland, Baltimore (UMB). Education: B.Tech. (IIT Delhi, 1989), M.S. and Ph.D. (UNC Chapel Hill, 1991 and 1994). Research highlights include molecular surface algorithms, GPU computing, and immersive technologies for healthcare and education. Awards include the NSF CAREER Award (1995), IEEE Visualization Technical Achievement Award (2004), and IEEE Fellow (2010). He leads the NVIDIA CUDA Center of Excellence and co-founded the Maryland Blended Reality Center. Recent work explores nanophotonics for AR/VR displays, VR medical training, and bias detection in AI systems. His interdisciplinary projects address challenges in personalized medicine, pain management, and implicit bias training through extended reality (XR). Key Projects: Augmentarium (immersive infrastructure), CHIB (healthcare bioinformatics), Immersive Media Design Program. Grants: NSF, NIH, industry partnerships. Awards: NSF CAREER, IEEE Technical Achievement Award, IEEE Fellow.

Haizhao YangView profile

Associate Professor

• AI for Science
• Machine Learning
• High-Performance Computing
• +4 more

Haizhao Yang is an Associate Professor of Mathematics and Computer Science at the University of Maryland College Park (UMCP). He holds affiliate appointments in the University of Maryland Institute for Advanced Computer Studies (UMIACS) and the Applied Mathematics and Scientific Computation (AMSC) program. Previously, he served as an Assistant Professor at Purdue University and the National University of Singapore, and as a Visiting Assistant Professor at Duke University (2015–2017). His education includes a B.Sc. from Shanghai Jiao Tong University (2010), M.Sc. from The University of Texas at Austin (2012), and Ph.D. from Stanford University (2015). Yang's research focuses on machine learning theory, scientific computing, and applied mathematics. Key areas include AI for scientific discovery, high-performance computing, and algorithm development for differential equations. His work bridges mathematical rigor with practical applications, such as developing neural network-based solvers for high-dimensional PDEs and quantum computing integration for uncertainty quantification. He leads a dynamic research group with over 30 students and postdocs across PhD, master's, and undergraduate levels. Notable achievements include the NSF CAREER Award (2020), ONR Young Investigator Award (2022), and DARPA Young Faculty Award (2024). His laboratory collaborates with institutions like Lawrence Berkeley National Lab and Duke University, emphasizing interdisciplinary projects in data science and computational physics. Recent publications highlight innovations in operator learning for PDEs, quantum algorithm integration, and adversarial reinforcement learning. His group actively explores AI-driven approaches to overcome computational bottlenecks in scientific simulations, with techniques like the Finite Expression Method and OptimAI framework for automated problem-solving.

• Machine Learning
• Artificial Intelligence
• Medical AI
• +4 more

Dr. Matias Valdenegro Toro is an Assistant Professor of Machine Learning at the University of Groningen's Faculty of Science and Engineering, affiliated with the Artificial Intelligence department within the Bernoulli Institute. His research focuses on developing trustworthy machine learning models for medical AI and robotics applications, emphasizing uncertainty estimation, computer vision, and explainable AI. He holds a PhD from Heriot-Watt University (2019) and a Master of Science in Autonomous Systems from Bonn-Rhein-Sieg University (2014). His work contributes to UN Sustainable Development Goals related to quality education and industry innovation. Notable achievements include Best Reviewer awards at leading machine learning conferences and highlighted contributions to uncertainty quantification research. He teaches courses such as 'Introduction to Machine Learning' and 'Deep Learning' at the university's AI programs. Recent research explores adaptive prompt tuning for few-shot learning, Bayesian neural networks for uncertainty modeling, and neuromorphic approaches to robotics. His collaborations include the German Research Center for Artificial Intelligence and European summer schools on AI. Key datasets include the Japanese Uncertain Scenes Dataset.

Chun-Hung LiuView profile

Associate Professor

• Machine Learning Theory
• Stochastic Control and Optimization
• Data Science
• +4 more

Chun-Hung Liu is an Associate Professor in the Department of Electrical and Computer Engineering at Mississippi State University (MSU). He previously held positions at the University of Michigan, National Chiao Tung University, and Qualcomm Inc. His research focuses on machine learning theory, stochastic control, data science, wireless communication, and cyber-physical systems security. Prof. Liu has received prestigious awards including the Air Force Research Lab Faculty Fellowship (2023), Taiwan’s Young Scholar Award (2015), and an IEEE Globecom Best Paper Award (2014). Education: Ph.D., Electrical and Computer Engineering, University of Texas at Austin M.S., Mechanical Engineering, MIT M.S., Electrical Engineering, National Taiwan University B.S., Mechanical Engineering, National Taiwan University Research Interests: Dr. Liu’s work spans theoretical and applied domains, including deep learning topology analysis, edge computing optimization, federated learning architectures, and underwater acoustic communication systems. He explores practical solutions for energy-efficient computing, cyber-physical system security, and next-generation wireless networks (e.g., 5G/6G, RIS-enabled systems). Publications: His recent work emphasizes interdisciplinary approaches, with articles addressing topics like RIS-assisted MIMO systems, federated learning over UAV networks, and solar-powered edge computing. These studies highlight innovations in both algorithm design and system-level performance evaluation. Awards: Air Force Research Lab Faculty Fellowship (2023) Taiwan’s Young Scholar Award (2015) IEEE Globecom Best Paper Award (2014) Advising & Grants: While specific grant details are not provided, his publications indicate sustained research activity supported by military and academic collaborations. His lab focuses on experimental platforms for federated learning and wireless mesh networks, as evidenced by a mini-PC-based testbed described in 2022 work. Labs/Teams: Leads a research group at MSU investigating edge computing, federated learning, and reconfigurable intelligent surface technologies. Collaborates with industry partners (e.g., Qualcomm) and international institutions on communication systems and AI integration.

Gabriel GhinitaView profile

Associate Professor

• Databases
• Information Security
• Privacy
• +5 more

Gabriel Ghinita is an Associate Professor in the Department of Computer Science at the University of Massachusetts at Boston. He holds a Ph.D. in Computer Science from the National University of Singapore (2008) and a B.S. from the 'Politehnica' University of Bucharest (2003). His research focuses on databases, information security, privacy, and spatio-temporal data management. During the 2018/19 academic year, he was a Visiting Associate Professor at the University of Southern California. Key research interests include privacy-preserving sharing of location data, secure data provenance, and privacy-aware network measurement. He teaches courses such as Database Management Systems and IT Network Security. Professional activities include serving on program committees for top venues like SIGMOD, ICDE, and ACM GIS, and as a PC Chair for CODASPY 2016. His work emphasizes balancing utility and privacy in data-driven systems, with contributions to differential privacy, secure spatial queries, and IoT security.

Riccardo CantiniView profile

Assistant Professor

• Deep Learning
• Large Language Models
• Sustainable AI
• +3 more

Riccardo Cantini is an Assistant Professor (RTDA) at the Department of Computer Science, Modeling, Electronics and Systems Engineering (DIMES), University of Calabria. He holds a European Ph.D. in Information and Communication Technologies (2023) and has been a visiting researcher at the Barcelona Supercomputing Center (BSC-CNS, 2021-2022). His research focuses on deep learning (Large Language Models, sustainable AI) and big social data analysis targeting politically polarized data and high-performance distributed systems. Education: B.Sc. (2016), M.Sc. (2019), and Ph.D. (2023) in Computer Engineering from the University of Calabria. Research interests include: Large Language Models and their ethical deployment Sustainable AI and energy-efficient edge computing Political polarization analysis using social media data Optimization of data-intensive workflows in distributed environments Key projects include the FAIR initiative (Green-Aware AI), eFlows4HPC (HPC workflows), and ASPIDE (Exascale data processing). He has authored/co-authored over 30 publications, including works on bias detection in LLMs and explainable AI in healthcare. Awards: 2024 Top 3 Best PhD Thesis in Big Data & Data Science (CINI), 2022 Editor's Choice article in Big Data and Cognitive Computing . Teaching roles include courses on Business Intelligence, High-Performance Computing, and Operating Systems. He has advised over 40 theses in AI, NLP, and big data. Professional services: Guest Editor for Big Data and Cognitive Computing , Program Chair of Green-Aware AI workshops, and reviewer for top journals/conferences (ICLR, IEEE BigData, etc.).

Raju RangaswamiView profile

Professor

• Operating Systems
• Storage Systems
• Virtualization
• +7 more

Raju Rangaswami is an Eminent Scholar Chaired Professor in the Knight Foundation School of Computing and Information Sciences at Florida International University (FIU), where he directs the Systems Research Laboratory. His academic roles include serving as Associate Professor (2009-2019), Assistant Professor (2004-2009), and a visiting role at IIT Bombay (2015). He holds a Ph.D. in Computer Science from the University of California, Santa Barbara, an M.S. from the same institution, and a B.S. in Computer Science and Engineering from the Indian Institute of Technology, Kharagpur. His research focuses on operating systems , storage systems , virtualization , real-time systems , and computer security , with applications in cloud computing, distributed systems, and mobile computing. Key contributions include work on caching strategies, persistent memory, and energy-efficient storage architectures. Rangaswami has received prestigious awards such as the NSF CAREER Award (2008-2013), DOE ECPI Award (2006-2010), and multiple NetApp Faculty Fellowships. He actively contributes to the field through roles like Program Chair for the USENIX File and Storage Technologies Conference, editorial work for the Springer Journal of Distributed and Parallel Databases , and NSF panel service. His research spans over 50 publications in top venues like FAST, USENIX ATC, and IEEE conferences, focusing on topics like cache replacement policies, storage tiering, and secure data management. Current projects explore AI-driven caching systems and energy-efficient hybrid memory architectures.