Find Academic Supervisors

Michael Mattarock IIView profile

Academic

• Artificial Intelligence
• Machine Learning
• Cybersecurity
• +2 more

Michael Mattarock II is an Assistant Teaching Professor and Associate Director at the Information Networking Institute (INI) at Carnegie Mellon University (CMU). He brings extensive expertise in national security, cybersecurity, and artificial intelligence from roles in government agencies, research leadership, and consulting. Current doctorate focus: Secure AI Prior roles: Executive Director of National Security Research at CMU, Technical Director for AI Mission and Engagement at Software Engineering Institute (SEI), Chief Enterprise Architect at Air Force Materiel Command Education: MS in Information Systems Engineering, summa cum laude (George Washington University) His research interests center on AI and ML for security , data privacy , and security of AI/ML , with teaching contributions in cybersecurity and AI ethics. He has collaborated with institutions like the Department of Defense, NASA, and NATO.

• Artificial Intelligence
• Conversational AI
• Large Language Models
• +4 more

Gokhan Tur is a Research Professor at the University of Illinois Urbana-Champaign's Siebel School of Computing and Data Science. He is a leading expert in conversational AI systems, having contributed to major virtual assistants like Apple Siri, Microsoft Cortana, Google Assistant, and Amazon Alexa. His research focuses on Large Language Models, Spoken Dialogue Systems, and Language Understanding. He holds over 250 publications and co-authored the influential book Spoken Language Understanding (Wiley, 2011). Education: PhD in Computer Science from Bilkent University, Turkey (2000). Visiting scholarships at CMU LTI, Johns Hopkins University, and SRI International (1997-1999). Research Contributions: Pioneered conversational systems at AT&T Research (2001-2006), worked on DARPA projects (GALE, CALO), and held key roles at Microsoft, Apple, Google, Uber, and Amazon. Current interests include LLMs and conversational AI evaluation frameworks like YourBench. Awards: IEEE Fellow (2020), IEEE SPS Best Paper Award (2020), and multiple best paper recognitions from ISCA and EURASIP. Labs & Teams: Leads the Conversational AI Lab at UIUC, collaborating with industry partners like Hugging Face. Advises PhD candidates specializing in NLP and ML.

Stefan BifflView profile

Associate Professor

• Software Engineering Process Improvement
• Cyber-Physical Production Systems
• Empirical Software Engineering
• +5 more

Stefan Biffl is an Associate Professor of Software Engineering at Technische Universität Wien's Institute of Information Systems Engineering. He holds MS and PhD degrees in Computer Science from Vienna University of Technology and an MS in Social and Economic Sciences from the University of Vienna. His research focuses on software-intensive systems engineering, particularly in process improvement, quality assurance, and cyber-physical production systems. He has led over €4M in research projects, including the 7-year Christian Doppler Laboratory for Software Engineering Integration. Biffl has authored/co-authored over 250 publications and serves on program committees for top conferences like ICSE and ESEC/FSE. His current roles include co-leading TU Wien's Center for AI and Machine Learning (CAIML) Business group and supervising the Doctoral College on Trustworthy Autonomous Cyber-Physical Systems. Key research areas span systems architecture, empirical software engineering, and digital humanism principles in automation. He has advised major automotive companies like Volkswagen and Neuman Aluminium through the Comet Center for Digital Production. Biffl's involvement in academic organizations includes steering committees for ECSA and SEAA, and他曾担任多个期刊的客座编辑。他的著作包括《基于价值的软件工程》等 Springer 出版的书籍。研究项目注重工业自动化与知识工程的交叉应用，特别是在集体智能系统和 Industry 4.0 领域。 近期项目包括探索 AI 在商业绩效提升中的应用（2025 年）、数字化工单的安全风险分析（与 Secure Business Austria 合作）以及基于数据的汽车生产优化。他的工作强调将人本主义原则融入数字化转型，确保技术进步与社会价值的结合。

Seetal PotluriView profile

Assistant Professor

• Trustworthy Machine Learning
• Hardware Security
• FPGA Virtualization
• +2 more

Seetal Potluri is Assistant Professor in the Department of Electrical & Computer Engineering at the University at Albany, SUNY's College of Nanotechnology, Science, and Engineering. He holds a Ph.D. in Electrical Engineering from IIT Madras and researches trustworthy ML systems, FPGA security, and hardware reverse engineering. Research addresses security challenges in hardware-accelerated AI systems, focusing on: 1) Model extraction vulnerabilities in neural network hardware; 2) Secure FPGA virtualization for cloud environments; 3) Side-channel attacks on cryptographic hardware; 4) Explainable ML for hardware security validation. Work bridges machine learning theory with hardware implementation security. Publications demonstrate consistent focus on hardware-level attack vectors (scan-chain exploitation, cache attacks) and defensive strategies (obfuscation, formal verification). Recent work increasingly targets cloud FPGA security and post-quantum cryptography implementations. Research contributions have been recognized with best paper awards at leading hardware security venues. Awards & Honors: Best Paper Recognition Award, IEEE HOST 2022 Runner-up, IEEE TTTC Doctoral Thesis Contest 2015 (Asia) Professional service includes technical program committees for DAC and ICCAD conferences. Teaching and research activities are based in CNSE Downtown 305E, with contact via spotluri@albany.edu or 518-956-8248.

Ioana BanicescuView profile

Professor

• Parallel Algorithms
• Scientific Computing
• Performance Modeling
• +3 more

Ioana Banicescu is a Professor at Mississippi State University's Department of Computer Science and Engineering. She holds a Ph.D. in Computer Science from New York University and previously directed the Center for Cloud and Autonomic Computing. Her research focuses on parallel algorithms, scientific computing, performance modeling, and autonomic systems. Her education includes: Ph.D. Computer Science - New York University M.S. Computer Science - New York University B.S./M.S. Electronics and Telecommunications - Polytechnic University of Bucharest Banicescu's research explores fundamental challenges in high-performance computing, including dynamic load balancing, performance optimization, and autonomic resource management. Her recent work emphasizes explainable AI for cybersecurity systems and robust resource allocation models for heterogeneous computing environments. Her publication record demonstrates consistent focus on performance optimization in parallel systems, with recent expansion into cybersecurity applications using machine learning approaches. She maintains collaborations with national laboratories and international research forums. Banicescu has received multiple National Science Foundation awards for research excellence and serves on numerous program committees for IEEE/ACM conferences. She contributes to the academic community through editorial roles for Cluster Computing and the International Journal on Computational Science and Engineering.

Shibiao WanView profile

Assistant Professor

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

Dr. Shibiao Wan is an Assistant Professor in the Department of Genetics, Cell Biology, and Anatomy at the University of Nebraska Medical Center (UNMC), and Assistant Director of the Bioinformatics and Systems Biology Core. With 14+ years of expertise in machine learning, bioinformatics, and computational biology, he has published over 50 articles in high-impact journals. His research focuses on AI-driven solutions for biomedical challenges, including cancer, neurological disorders, and precision medicine through multi-omics and clinical data integration. Education: Postdoctoral training at the University of Pennsylvania and Princeton University; PhD from The Hong Kong Polytechnic University; BEng from Wuhan University. Research interests include developing AI/ML methods for single-cell analysis, spatial transcriptomics, and clinical informatics. He collaborates with researchers in cancer biology, neuroscience, and immunology to address translational biomedical problems. Notable awards include the UNMC New Investigator Award (2024), Nebraska EPSCoR FIRST Award (2023), and Clarivate's Top Reviewer recognition (2019). He serves on editorial boards for journals like Briefings in Functional Genomics and as a conference program committee member. Labs/Teams: Wan Lab focuses on integrating omics data with AI for disease modeling and diagnostics. Collaborative projects span oncology, cardiology, and developmental biology.

Elaine ShortView profile

Assistant Professor

• human-robot interaction
• accessibility
• robotics
• +4 more

Elaine Short is an Assistant Professor in the Department of Computer Science and a secondary appointment in Mechanical Engineering at Tufts University's School of Engineering. She leads the Assistive Agent Behavior and Learning (AABL) Lab, focusing on human-robot interaction, accessibility, and assistive technology. Education: PhD in Computer Science, University of Southern California (2017) MS in Computer Science, University of Southern California (2012) BS in Computer Science, Yale University (2010) Her research applies human-centered design and disability community values to AI/ML development in robotics, emphasizing robust human-robot interaction in natural environments, group/crowd dynamics, and inclusive design for non-normative users. Recent publications highlight trends in human-robot co-creative collaboration, shared control systems, policy modification through imagined actions, and accessibility challenges in robotics. Awards include NSF Graduate Research Fellowship, Google Anita Borg Scholarship, and multiple teaching/research recognitions. Scientific Awards: National Science Foundation Graduate Research Fellowship USC Provost's Fellowship Google Anita Borg Scholarship Viterbi School of Engineering Merit Award WiSE Merit Award Best Research/Teaching Assistant Awards Saybrook College Mary Casner Prize

Tiago Luís Pereira GalvãoView profile

Assistant Professor

• Materials Science
• Artificial Intelligence
• Corrosion Inhibitors
• +5 more

Tiago Luís Pereira Galvão is an Assistant Professor at the Department of Materials and Ceramic Engineering, University of Aveiro, Portugal. He is affiliated with CICECO – Aveiro Institute of Materials, where he leads research in the group G6 - Virtual Materials and Artificial Intelligence, focusing on computational materials science and corrosion prevention technologies. His work integrates artificial intelligence, machine learning, and multiscale modeling to address challenges in materials design and corrosion inhibition. Research interests include virtual materials development, corrosion inhibitors, nanomaterials, and computational approaches to materials characterization. His projects emphasize data-driven solutions, such as the DATACORTECH AI platform and CORDATA web application for corrosion inhibitor selection. Key contributions span over 14 journal articles and book chapters, with a focus on layered double hydroxides, molecular dynamics simulations, and corrosion monitoring systems. His work bridges theoretical chemistry and practical applications, such as corrosion-sensing coatings and early-stage corrosion detection using nanoadditives. Awards: Young Researchers Award 2021 Labs/Teams: CICECO – Aveiro Institute of Materials (Group G6)

Hany ElgalaView profile

Assistant Professor

• Telecommunications
• Digital Signal Processing
• Embedded Systems
• +6 more

Dr. Hany Elgala is an Assistant Professor in the Department of Electrical and Computer Engineering at the University at Albany, State University of New York, and Director of the Signals and Networks (SINE) Lab. Previously, he served as a Research Professor at Boston University, co-leading the Multimedia Communications Lab and acting as Communications Testbed leader at the NSF Smart Lighting ERC. His research focuses on telecommunications, digital signal processing, and embedded systems, with specialization in visible light communications (VLC), LiFi networks, IoT security, and AI-driven wireless solutions. He has coordinated industrial projects with Airbus and EADS on optical wireless networks in aircraft cabins. Elgala's work emphasizes heterogeneous wireless networks, backscatter communication, and security in IoT. He has authored/co-authored ~50 publications/patents, contributing to fields like spectral efficiency, energy optimization, and network coexistence. His leadership in the SINE Lab drives innovations in 6G networks, UAV-based systems, and hybrid LiFi-WiFi architectures. His research portfolio includes breakthroughs in VLC system design, machine learning applications for physical layer optimization, and jamming-resistant IoT networks. Recent efforts focus on indoor flying networks and low-complexity VLC frameworks leveraging multi-task learning and autoencoder-based modulation.

Zhu HanView profile

Professor

• Game Theory
• Wireless Networking
• Security
• +4 more

Dr. Zhu Han is the John and Rebecca Moores Professor at the University of Houston's Cullen College of Engineering, Department of Electrical and Computer Engineering. His research focuses on game theory, wireless networking, security, data analysis, and smart grid applications. He holds doctoral and master's degrees from the University of Maryland and a bachelor's from Tsinghua University. Research interests span: Next-generation wireless systems (6G/7G) AI/ML integration in communications Reconfigurable intelligent surfaces Quantum machine learning applications Secure and efficient network architectures His recent publications demonstrate strong focus on generative AI integration in wireless systems, quantum networking, semantic communications, and security frameworks for future networks. Awards and honors include: IEEE/ACM/AAAS Fellow status IEEE Kiyo Tomiyasu Award (2021) Highly Cited Researcher since 2017 IEEE Distinguished Lecturer (2015-2018) Dr. Han leads research in wireless communications and networking, with extensive industry collaboration. His lab focuses on developing theoretical foundations and practical implementations for next-generation communication systems.

Jinqiu YangView profile

Associate Professor

• Software Engineering
• Automated Program Repair
• Software Testing
• +4 more

Jinqiu Yang is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University. His research focuses on software reliability, automated program repair, software testing, and quality assurance of machine learning systems, particularly in autonomous vehicles. He holds a PhD and MASc from the University of Waterloo and a B.Eng. from Nanjing University. Yang has been a tenure-track faculty member at Concordia since 2018, following research roles at IBM Watson and IBM CAS. Research Interests: Automated Program Repair Software Testing Machine Learning Systems Text Analytics of Software Artifacts Mining Software Repositories Autonomous Systems Quality Assurance Key Contributions: His recent work includes detecting concept drifts in ML systems (ICSE-25) and investigating social bias in LLM-generated code (AAAI-25). He leads the O-RISA Lab and has authored over 50 peer-reviewed publications, including distinguished papers at MSR-2018. Yang currently holds grants such as the NOVA FRQNT-NSERC Program (2024-2027) and the NSERC Discovery Grant (2019-2025). Service & Awards: Editorial Board Member of the Empirical Software Engineering Journal (EMSE) and active PC member in top venues like ICSE and FSE. Recipient of the IBM CAS Fellowship and ACM SIGSOFT Distinguished Paper Award. Teaching & Students: He mentors students in Master's and PhD programs, with funding available. His lab focuses on cutting-edge topics like secure code generation and autonomous vehicle reliability.

Patrik RydénView profile

Professor

• Mathematical Statistics
• Data Analysis
• Ambulance Care Optimization
• +3 more

Patrik Rydén is a Professor of Mathematical Statistics at the Department of Mathematics and Mathematical Statistics, Umeå University, where he applies statistical methods to complex datasets. He also directs the Industrial Doctoral School for Research and Innovation , fostering academia-industry collaboration. His research spans healthcare (ambulance logistics, cancer diagnostics), industrial optimization (automotive quality control), and bioinformatics. Research Focus Rydén develops data-driven methodologies for: Healthcare : Optimizing ambulance deployment, predicting patient care times, and modeling disease spread. Industry : Statistical process control for manufacturing and quality improvement. Bioinformatics : Cancer subtype identification and vaccine efficacy modeling. Publication Trends His recent work emphasizes applied statistics in healthcare (40%), industrial machine learning (30%), and genomic analysis (30%), with strong themes in predictive modeling, spatio-temporal analysis, and high-dimensional data interpretation. Leadership & Projects As director of the Industrial Doctoral School, he bridges academic research with industry needs. Key projects include: Predicting post-anesthesia care time using ML (2022–2027) Data-driven prehospital care optimization (2018–2028) Statistical learning for industrial defect detection (2017–2023)

Terence BroadView profile

Senior Lecturer

• Generative AI
• Computational Creativity
• Artistic Applications of Machine Learning
• +5 more

Terence Broad is a Senior Lecturer at the Creative Computing Institute, University of the Arts London, and Acting Course Leader of the MSc Applied Machine Learning for Creatives. He is currently completing a PhD at Goldsmiths, University of London. His work bridges art and technology, focusing on generative AI, computational creativity, and the use of machine learning as artistic materials. His research has been exhibited globally at venues like The Whitney Museum of American Art and SIGGRAPH conferences. Education: MSci Creative Computing from Goldsmiths, University of London (2012–2016). Research interests include expressive manipulation of deep generative models, exploring the latent possibilities of 'black-box' systems, and ethical considerations in AI art. He has pioneered methods like 'Network Bending' for creative model customization. Key achievements include winning the ICCV Computer Vision Art Gallery Grand Prize (2019) and serving on the SIGGRAPH jury (2021). His work is part of Geneva's contemporary art collection and has been featured in media like The Independent and Vox. Professional activities include organizing conferences like IGGI 2020 and reviewing for journals like Leonardo and ACM Transactions on Image Processing. He actively participates in exhibitions, artist talks, and academic workshops.

Tze Meng LowView profile

Research Professor

• Parallel and High Performance Computing
• Formal Methods
• Graph Algorithms
• +3 more

Tze Meng Low is an Associate Research Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU), part of the College of Engineering. His research focuses on high-performance algorithms, formal methods, and hardware-software co-design, with an emphasis on achieving performance portability across architectures. He holds a Ph.D. and M.S. in Computer Science and dual B.A./B.S. degrees in Economics and Computer Science from the University of Texas at Austin. His research interests span parallel computing, graph algorithms, machine learning, and cyber-physical systems. He has contributed to projects like the DARPA BRASS initiative, collaborating on adaptive software systems for resource-challenged environments. His work also involves developing tools such as SMaLL and qLD, which address challenges in machine learning library instantiation and genomic analysis. Low has received the Dean’s Early Career Fellowship (2022) and led the $2.7M DARPA-funded BRASS project (2016–2020), supported by SpiralGen, Inc. and academic collaborators. His contributions include code generation frameworks like SPIRAL and advancements in linear algebra-based graph algorithms, emphasizing analytical models and automated code optimization. His research bridges theoretical formal methods with practical implementations, aiming to enhance software reliability and scalability in emerging domains. Collaborations include work on fault-tolerant coded computing and high-assurance systems for cyber-physical applications.

Shaofan LiView profile

Professor

• Artificial Intelligence & Machine Learning in Engineering
• Computational Mechanics
• Finite Element Methods
• +15 more

Shaofan Li is a Professor in the Department of Civil and Environmental Engineering at the University of California, Berkeley. His research spans interdisciplinary applications of computational mechanics, artificial intelligence, and multiscale modeling in civil, environmental, and biomedical engineering contexts. Education: Ph.D., Mechanical Engineering, Northwestern University (1997) M.S., Aerospace Engineering, University of Florida (1993) M.S., Computational Mechanics, Huazhong University of Science and Technology (1989) B.S., Mechanical Engineering, East China University of Science and Technology (1982) Research Interests: Li's work focuses on advanced computational methods for sustainable infrastructure and complex physical systems. Key areas include: Physics-based AI/ML for 3D printing, inverse problems, and autonomous systems Phase-field and peridynamics modeling of fracture mechanics Nonlocal continuum mechanics for pathogen transmission and environmental fluid dynamics Nano/mesoscale characterization of cementitious materials Soft matter mechanics and cellular mechanotransduction Machine learning for crystal plasticity and dislocation dynamics Scientific Awards: International Association of Computational Mechanics Fellows Award (2017) NSF CAREER Award (2003) Laboratory: Berkeley Computational Nano & Soft Material Lab