Find Academic Supervisors

Wasiu O PopoolaView profile

Professor

• Optical Wireless Communications
• Visible Light Communication (VLC)
• Free-Space Optical Communications (FSO)
• +5 more

Professor Wasiu O Popoola is a Professor of Communications Engineering and Director of Electronics and Electrical Engineering at the School of Engineering, University of Edinburgh . With over 150 publications and a RAEng/Leverhulme Trust Research Fellowship (2022), his work focuses on optical wireless communication systems including VLC/LiFi, FSO, and underwater optical communications. BSc (First Class Hons), MSc (Distinction), PhD in optical communications (Northumbria University) Professional memberships: Fellow of Higher Education Academy (FHEA), Fellow of IET (FIET), Senior Member IEEE Research Interests span Indoor/Outdoor/Underwater Optical Wireless Communication , Modulation Techniques , and LiFi Applications . His 2025 articles explore underwater turbulence mitigation and hybrid RF-water communication systems . Earlier works include Best Poster Award at IEEE ICSAE 2016 and top-downloaded IEEE Xplore article (2008). Scientific Awards include: 2022 RAEng/Leverhulme Trust Research Fellowship 2016 IEEE ICSAE Best Poster Award 2009 'Xcel Best Engineering and Technology Student' (PhD) He contributes to editorial work as Associate Editor (IEEE Access) , Guest Editor (Optik Journal) , and peer reviewer for Physics World . Recent projects involve BOLD (Defence Science and Technology Laboratory) and TITAN Extension (University of Strathclyde) focusing on diffuse optical wireless systems and UAV swarm networks .

• Algorithms
• Computational Complexity
• Convex Optimization
• +4 more

James R. Lee is a Professor in the Department of Computer Science at the University of Washington. His research spans theoretical computer science, probability, and geometry. He has held visiting scientist roles at Microsoft Research (2023, 2018, 2017) and participated in programs at the Simons Institute (2023, 2020, 2018, 2017, 2014). Research Interests: Algorithms, complexity theory, convex optimization, metric embeddings, spectral graph theory, probability, stochastic processes, and the interplay between discrete and continuous analysis. Teaching: Courses on modern algorithms, quantum computing, optimization theory, and spectral methods in theoretical computer science. Scientific Contributions: Developed sparsification algorithms for generalized linear models and norms with near-linear size guarantees (STOC'24, FOCS'23). Extended Cheeger-type inequalities to higher eigenvalues (STOC'12, STOC'18). Proved super-polynomial lower bounds for LP/SDP relaxations in constraint satisfaction (STOC'15, FOCS'13). Disproved Benjamini-Papasoglou conjectures on annular separators (Discrete Comp. Geom. 2024). Advanced understanding of random walks in geometric and unimodular graphs (Israel J. Math. 2023, GAFA 2023). Scientific Awards: Best Paper Award, STOC 2015

Aravind MachiryView profile

Assistant Professor

• Software Security
• Embedded Systems
• Program Analysis
• +9 more

Aravind Machiry is an Assistant Professor at Purdue University's Electrical and Computer Engineering Department and a founding member of the Purdue Systems and Software Security (PurS3) Lab . His research focuses on system security, particularly vulnerability detection, prevention, and secure system development using static/dynamic program analysis, fuzzing, type systems, and machine learning. Designing practical solutions for software and embedded system security Recipient of NSF CAREER and Amazon Research awards Active participant in SPLASH 2025 as OOPSLA Review Committee member His recent work includes automated vulnerability detection in embedded software, spatial memory safety enhancements, and security analysis of GitHub workflows. He has received recognition for his research through multiple distinguished paper awards and industry funding. Selected scientific awards include NSF CAREER Award (2024) Amazon Research Award (2022) Test of Time Award at FSE 2023 for DynoDroid Distinguished Paper Award at OOPSLA 2022 for 3c Qualcomm Innovation Fellowship (2025) His research team has developed frameworks like ARGUS for taint analysis of CI/CD workflows and FuzzUEr for UEFI interface fuzzing, discovering hundreds of critical vulnerabilities in open-source projects and thousands of command injection flaws in GitHub repositories.

Sherif KhattabView profile

Academic

• Cybersecurity
• Cloud Computing
• Internet of Things
• +3 more

Sherif Khattab is a Teaching Assistant Professor in the Department of Computer Science at the University of Pittsburgh's School of Computing and Information. With a Ph.D. in Computer Science from the University of Pittsburgh (2008), he brings extensive expertise in cybersecurity systems with applications across cloud computing, Internet of Things, electronic voting, and Big Data security. His research focuses on the systems aspects of cybersecurity, maintaining an h-index of 16 (Google Scholar) and 10 (Scopus) with over 60 publications. Khattab has successfully supervised more than 15 graduate students throughout his academic career. Prior to his position at Pitt, he served as an Associate Professor at Cairo University's Department of Computer Science, Faculty of Computers and Information. Professor Khattab teaches numerous undergraduate and graduate courses, with particular emphasis on hands-on ethical hacking and security education. His current teaching portfolio includes Algorithms and Data Structures (CS 0445) and Network Security (CS 1653), with extensive experience teaching operating systems, formal methods, and computer networks across multiple semesters. His research publications reveal consistent focus on practical security solutions for emerging technologies, with recent work addressing IoT security frameworks, blockchain-based voting systems, and cloud security challenges. The publication trend shows increasing emphasis on practical implementation aspects alongside theoretical security models. With industry experience from internships at Google Inc., Ericsson Data Networks, and Bosch Research, Khattab bridges academic research with real-world security challenges. His educational background includes a Bachelor's in Computer Engineering from Cairo University (1998) and both M.Sc. and Ph.D. in Computer Science from the University of Pittsburgh (2004 and 2008).

• Power Electronic Converters
• Embedded Systems
• Analog and Mixed-Signal Circuit Design
• +5 more

Dr. Anwar Ali is a Lecturer in the Department of Electronic and Electrical Engineering at Swansea University's Bay Campus, affiliated with the School of Aerospace, Civil, Electrical and Mechanical Engineering. He holds an M.S. in Electronic Engineering (2010) and a Ph.D. in Electronic and Communication Engineering (2014) from Politecnico di Torino, Italy. His research focuses on: Power electronic converters and conditioning systems Embedded systems for aerospace applications Analog/mixed-signal circuit design Satellite technologies including power management Attitude determination and control systems Thermal modeling of aerospace systems Dr. Ali has authored over 50 publications with recent works concentrated in satellite power systems, thermal analysis of spacecraft, machine learning applications in healthcare/robotics, and energy harvesting techniques. His research demonstrates consistent innovation in small satellite technologies and cross-disciplinary applications of electrical engineering principles. He currently supervises PhD projects on: Wireless power transfer for implantable medical devices Integrated power and attitude control optimization for small spacecraft and teaches modules including Analogue Design, Software Engineering, Embedded System Design, and Integrated Circuit Design.

Martin WellsView profile

Professor

• Statistical Inference
• Bayesian Methods
• Machine Learning
• +1 more

Martin Wells is the Charles A. Alexander Professor of Statistical Sciences at Cornell University, with joint appointments in Social Statistics, Clinical Epidemiology, and Industrial Labor Relations. Since joining Cornell in 1987, he has developed methodologies spanning Bayesian inference, tensor analysis, and machine learning applications in biomedicine and finance. His research integrates statistical theory with computational innovations, particularly in high-dimensional modeling and quantum-inspired algorithms. Recent work focuses on geometric approaches to tensor decomposition, misclassification correction methods, and phylodynamic models incorporating dormancy effects. Professor Wells teaches statistical methodology across disciplines including law, medicine, and biology, adapting analytical frameworks to diverse research contexts. His interdisciplinary collaborations extend to Weill Medical College and the School of Industrial and Labor Relations.

Peng LiView profile

Professor

• Integrated Circuits and Systems
• Brain-Inspired Computing
• Electronic Design Automation
• +6 more

Peng Li is a Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara. His research focuses on integrated circuits, brain-inspired computing, electronic design automation, and hardware machine learning systems. He holds Fellow status in the Institute of Electrical and Electronics Engineers (IEEE). His work emphasizes neuromorphic engineering, spiking neural networks, and the intersection of machine learning with analog circuit design. Education includes a PhD in Electrical and Computer Engineering from Carnegie Mellon University, an MS in Systems Engineering from Xi'an Jiaotang University, and a BS in Information Science and Engineering from the same institution. His research has been recognized with prestigious awards including the ICCAD Ten-Year Retrospective Most Influential Paper Award and multiple Design Automation Conference Best Paper Awards. Key research trends in his articles include advancements in spiking neural networks (SNNs), hardware accelerators for neuromorphic computing, Bayesian optimization for analog circuit design, and robustness in machine learning systems. He explores topics like adversarial robustness, energy-efficient architectures, and data-efficient prediction techniques. His work bridges theoretical machine learning models with practical hardware implementations, particularly in 3D integration and systolic array acceleration. Notable contributions include pioneering hybrid approaches combining formal verification with machine learning for analog circuits (HFMV framework), and innovations in neuromorphic processors such as the 3D Liquid State Machine architecture. His research also addresses challenges in semiconductor manufacturing, including wafer map pattern recognition and failure detection through semi-supervised learning and contrastive methods. Awards highlight his impactful contributions to both design automation and neural computing. His grants and collaborations likely span industry partnerships in semiconductor technology and neuromorphic computing. He leads a lab focused on next-generation hardware-software co-design for intelligent systems, emphasizing energy efficiency and scalability.

Wenchao LiView profile

Assistant Professor

• Dependable Computing
• Cyber-Physical Systems
• Electronic Design Automation
• +4 more

Wenchao Li is an Assistant Professor in the Department of Electrical and Computer Engineering at Boston University, directing the Dependable Computing Laboratory. He holds a B.S., M.S., and Ph.D. in Electrical Engineering and Computer Sciences, along with a B.A. in Economics from UC Berkeley. His research focuses on dependable computing, applying formal verification, machine learning, and control theory to cyber-physical systems, electronic design automation, and AI safety. Key research interests include neural network verification, safe reinforcement learning, autonomous systems security, and resilient control strategies for connected vehicles. His work emphasizes provable safety guarantees and defense against adversarial attacks in critical infrastructure systems. Notable awards include the ACM Outstanding Ph.D. Dissertation Award and the Leon O. Chua Award. His lab investigates topics such as neural network repair, secure multi-robot coordination, and formal methods for autonomous systems. He advises students like Jiameng Fan and collaborates on projects funded by grants in AI safety and cyber-physical systems. Labs/Teams: Dependable Computing Laboratory Grants: Focus on formal verification, AI safety, and autonomous systems resilience

Manolis G.H. KatevenisView profile

Professor

• Computer Architecture
• VLSI Systems
• Interconnection Network Architecture
• +4 more

Manolis G.H. Katevenis is a Professor at the Department of Computer Science, University of Crete, and Deputy Director and Head of the Computer Architecture and VLSI Systems (CARV) Laboratory at the Institute of Computer Science (ICS), Foundation for Research & Technology - Hellas (FORTH). He co-founded the European Research Center on Computer Architecture (EuReCCA) and is a founding partner of the European Network of Excellence on High-Performance and Embedded Architecture and Compilation (HiPEAC). PhD in Computer Science from University of California, Berkeley (1983) Co-founder of EuReCCA (2011) Contributed to RISC architecture (1980-1983), interconnection networks (1985-2011), and parallel computing (1993-2010) His research spans Scalable Multicore Systems , Interconnection Network Architecture , Packet Switch Design , Computer Architecture , and VLSI Systems . He has made foundational contributions to per-flow queueing, backpressure mechanisms, and wormhole IP over ATM, with applications in internet routers, data centers, and supercomputers. His publications focus on high-radix crossbar switches, flow control algorithms, and explicit interprocessor communication. Notable scientific awards include: ACM Doctoral Dissertation Award (1984) David J. Sakrison Memorial Prize (1983) IBM PhD Fellowship (1981-1983) Greek State Fellowship (1973-1978) He has supervised 40 graduate theses and participated in 22 R&D projects totaling €9M, including HiPEAC (coordinator of interconnection networks), SARC (FPGA prototype design), and ENCORE (cache-optimized remote DMA). His work has received over 2000 citations, with an h-index of 23.

Tim OatesView profile

Professor

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

Dr. Tim Oates is a Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County . His research spans machine learning, artificial intelligence, and brain-machine interfaces, with a focus on weakly supervised methods, human-in-the-loop reinforcement learning, and grounded policy development for robotics. Ph.D., Computer Science, University of Massachusetts, Amherst, 2000 M.S., Computer Science, University of Massachusetts, Amherst, 1997 B.S., Computer Science and Electrical Engineering, 1989 Current research threads include: Developing non-invasive brain injury severity assessment via medical time series Modeling human brain development through computational frameworks Designing algorithms for autonomous robotic learning Recent publications highlight AI security mechanisms (backdoor detection via tensor decomposition, matrix factorization) Medical applications (3D artery reconstruction, skin lesion diagnosis, EEG denoising) Neuro-symbolic integration (holographic representations, language-guided reinforcement learning) Mathematical reasoning (schema-based problem solving, subitizing algorithms) Contact: oates@cs.umbc.edu | Office: 336 Information Technology and Engineering (ITE) Building

Lisa BeinbornView profile

Professor

• Natural Language Processing (NLP)
• Multilingual Language Models
• Cognitive Plausibility in NLP
• +5 more

Lisa Beinborn is a Professor for Human-Centered Data Science at the University of Göttingen, leading the Human-Centered Data Science group. Her research bridges natural language processing with cognitive science, focusing on multilingual models and interpretability. PhD in Computer Science (2016), Technische Universität Darmstadt MSc in Computational Linguistics (2010), Saarland University & Bolzano, Italy BSc in Computational Linguistics (2008), Saarland University & Barcelona, Spain Her research explores cognitive plausibility in NLP, analyzing how language models process language differently from humans. Key areas include multilingual model interpretability, semantic drift, eye-tracking, and readability prediction. Recent work examines input representation stability in neural models, cross-lingual transfer of complexity, and aligning language models with human cognitive patterns. Her team has presented findings at EMNLP, CoNLL, ACL, and CoLING. VENI Grant for "Interpretability of Transfer in Multilingual Models" Early Career Partnership by Royal Dutch Academy of Science "Most Interesting Paper" Award at BabyLM Challenge "Best Project Award" by Network Institute She has taught courses like Language as Data and Advanced NLP at University of Göttingen, VU Amsterdam, and TU Darmstadt. Her group collaborates with institutions like Gemeente Amsterdam and NT2 on multilingual text simplification and learner correction.

Leah MacfadyenView profile

Academic

• Critical Intercultural Studies
• Digital Literacies
• Digital Research Methods
• +6 more

Leah Macfadyen is an Associate Professor of Teaching in the Department of Language & Literacy Education at the University of British Columbia (UBC), Faculty of Education. She serves as Coordinator for Curriculum & Instruction in the Master of Educational Technology (MET) program. With interdisciplinary training in experimental sciences and humanities, she bridges analytical rigor with cultural theory in her research on digital education and learning analytics. Her research spans learning analytics , digital literacies , and critical intercultural communication . Key projects include developing UBC’s Introduction to Global Citizenship online course (2005) and co-authoring MET program courses like ETEC 542. She explores ethical dimensions of ‘big data’ in education and data literacy challenges. Recent publications (2020–2023) focus on institutional learning analytics frameworks, curriculum NLP analysis, and ethical codes for practitioners. Collaborative work with Shane Dawson and others has been published in journals like Journal of Learning Analytics , Computers & Education , and Journal of Genetic Counselling . Key Themes : Intercultural communication in digital spaces, systems thinking for educational analytics, ethical implications of data-driven learning Courses Developed : ETEC 500, 520, 542, 543, 581, and 590

Anuj PathaniaView profile

Assistant Professor

• Low Power Design
• Sustainable Systems
• Electronic Design Automation
• +4 more

Anuj Pathania serves as an Assistant Professor in the Parallel Computing Systems (PCS) group within the Informatics Institute at the University of Amsterdam's Faculty of Science. His research pioneers sustainable computing systems operating under severe power, thermal, and reliability constraints, with significant contributions to energy-efficient hardware design and embedded systems. Education: PhD in Computer Science (2018), Karlsruhe Institute of Technology MSc in Computer Science (2012), National University of Singapore B.Tech in Computer Science (2009), Maharaja Agrasen Institute of Technology Pathania's research centers on low-power design and sustainable systems for constrained environments, with particular expertise in thermal management of 3D-stacked architectures and energy-efficient machine learning inference . His work bridges electronic design automation with real-world reliability challenges, developing novel power budgeting techniques like T-TSP that incorporate transient temperature effects ignored by conventional methods. Current projects include EU-funded initiatives on energy labeling for digital services, addressing ecological impacts through technological, behavioral, and legal frameworks. His publication trajectory reveals a strategic evolution toward zero-waste computing , with recent work (2023-2025) focusing on hardware-software co-design for edge AI, energy modeling across computing continua, and parameter-efficient neural adaptation. Key themes include thermal-aware scheduling for S-NUCA many-cores, cooperative processor utilization in heterogeneous systems, and sustainability metrics for digital services. Scientific Recognition: Best Paper Award Nomination at IEEE Computer Society Annual Symposium on VLSI 2023 for 3D-TTP power budgeting technique Pathania actively mentors 4 PhD students (Ehsan Aghapour, Saeedeh Baneshi, Sudam Wasala, Yixian Shen) and has successfully supervised 5 Master's theses (including Cum Laude defenses by Joris op ten Berg and Jurre Wolff). His research is supported by major grants including Energy Labels for Ecologically Sustainable Digital Services (2023-2024) and Towards Zero-Waste Computing (2021-2025), developing simulation frameworks like HotSniper and CoMeT for thermal analysis. The PCS group maintains strong industry collaborations with ARM and NVIDIA, particularly through tools like ARM-CO-UP for heterogeneous processor utilization.

• Climate change
• Environmental research
• Human ecology
• +9 more

Tamara Artacker is a Researcher at the Institute of Social Ecology within the Department of Economics and Social Sciences at the University of Natural Resources and Life Sciences, Vienna (BOKU). Based in Vienna at Schottenfeldgasse 29, she has been actively contributing to the field since 2019, following the completion of her MA in International Development Studies from the University of Vienna in 2017. Her work bridges academic research with practical policy implications, particularly focused on Latin America and global environmental challenges. Artacker's educational background in International Development Studies has shaped her interdisciplinary approach to examining the complex relationships between society, environment, and economic systems. Her research spans multiple interconnected domains that address critical contemporary challenges facing communities worldwide. Her research interests center on political ecology, food sovereignty, ecofeminism, and sustainable agricultural systems. Artacker critically examines the structural inequalities embedded in global food systems, the impacts of climate change on agricultural communities, and the tensions between economic development models and ecological sustainability. Her work consistently highlights how extractive agricultural practices intersect with social injustices, particularly affecting vulnerable populations. She explores alternative models that prioritize community resilience, ecological balance, and social equity, with particular attention to the Global South and Latin American contexts. Artacker's extensive publication record reveals a strong focus on the intersections between environmental policy, agricultural practices, and social justice. Her research demonstrates consistent engagement with contemporary crises including the pandemic's impact on food systems, geopolitical conflicts affecting global food security, and the complex challenges of transitioning toward sustainable agricultural models. She has developed particular expertise in analyzing Ecuadorian agricultural policies and their implications for food sovereignty, while also examining broader regional and global patterns. As an active academic, Artacker regularly presents her research at international conferences and engages with diverse audiences through invited keynote speeches. Her work demonstrates a commitment to knowledge transfer beyond academic circles, with presentations addressing policymakers, practitioners, and community organizations working on food sovereignty and environmental justice issues.

Vincent RocaView profile

Researcher

• Privacy
• Robust Content Distribution
• Transport Protocols
• +6 more

Vincent Roca is a permanent researcher at Inria , a French national research institute for digital science and technology. Since 2019, he leads the Privatics research team focusing on privacy, previously part of the Planete team (2000-2012) for Internet protocols and applications. His work spans Privacy in smartphones, IoT, and web browsers Design of the ROBERT and CLEA protocols for the French TousAntiCovid app Standardization in IETF working groups, notably on FEC and network coding Co-author of Inria's cybersecurity white book (2019) Instructor of the popular Privacy in the digital world MOOC (14,006 participants) Research Interests include privacy risks in digital technologies, robust content distribution via FLUTE/ALC transport protocols and LDPC-Staircase FEC codes, and network coding for scalable services. His articles focus on privacy in LoRaWAN, contact tracing protocols, GDPR compliance, and FEC optimization. Scientific Awards include the Third Applied Research Price from FIEEC (2014) Knight Medal of the French Order of Merit (2020) Students supervised include PhD candidates in privacy-preserving IoT, secure authentication, and alumni like Samuel Pelissier (LoRaWAN privacy) and Michael Toth (GDPR compliance). Software Contributions include openfec.org (AL-FEC codes) and FLUTE/ALC implementations .