Find Academic Supervisors

James H. AndersonView profile

Professor

• Real-time systems
• Distributed computing
• Concurrent algorithms
• +3 more

James H. Anderson is the Kenan Distinguished Professor and Department Chair in the Department of Computer Science at the University of North Carolina at Chapel Hill, where he has been a faculty member since 1993. A leading researcher in real-time systems and distributed computing, he previously served at the University of Maryland (1990-1993). His educational background includes: B.S. in Computer Science from Michigan State University (1982) M.S. in Computer Science from Purdue University (1983) Ph.D. in Computer Sciences from the University of Texas at Austin (1990) Anderson's research focuses on real-time systems, distributed and concurrent algorithms, multicore computing, and operating systems. His work addresses fundamental challenges in scheduling, resource management, and reliability in time-critical environments, with recent emphasis on GPU acceleration for AI workloads and heterogeneous platforms. He investigates novel approaches to budget enforcement, processing graph scheduling, and timing predictability in complex systems. Analysis of his recent publications reveals a strong trend toward applying real-time scheduling principles to GPU and heterogeneous computing architectures. Key research thrusts include enabling predictable AI acceleration, managing timing uncertainties in autonomous systems, and developing composable resource partitioning techniques for safety-critical applications. His distinguished honors include: U.S. Army Research Office Young Investigator Award (1995) Alfred P. Sloan Research Fellowship (1996) Seven Computer Science Student Association Teaching Awards (1995-2019) Fellowships from IEEE (2012), ACM (2013), AAAS (2020), and AAIA (2022) IEEE TCRTS Outstanding Technical Achievement and Leadership Award (2018) Anderson actively mentors through the TOPICS Club, a reading group for undergraduate students that he co-leads with Cynthia Sturton and Danielle Szafir. His professional leadership includes chairing IEEE TCRTS (2016-2017) and ACM SIGBED (2019-2021), plus service as program/general chair for major conferences including RTSS, PODC, ECRTS, and RTAS. He directs ongoing research initiatives in real-time systems through collaborative projects and the TOPICS Club, which provides undergraduates with hands-on research experience in cutting-edge computer science topics.

Waël JaafarView profile

Professor

• Wireless Communications
• 5G/6G Networks
• Space Telecommunications
• +7 more

Waël Jaafar is a Professor in the Department of Software Engineering and IT at École de technologie supérieure (ETS), a constituent school of the Université du Québec system in Montreal, Canada. His research spans multiple critical domains in modern communications and computing infrastructure, with a particular focus on next-generation wireless networks and intelligent systems. Dr. Jaafar holds a B.Eng. from Sup'Com Tunisie, and both M.Sc.A. and Ph.D. degrees from Polytechnique Montréal. His academic background provides a strong foundation for his interdisciplinary research that bridges theoretical concepts with practical engineering solutions. His research interests center around wireless communications systems, with particular emphasis on 5G/6G networks, UAV communications, space telecommunications, and machine learning applications for networking. He has developed significant expertise in federated learning techniques for distributed networks, cybersecurity applications for next-generation mobile systems, and edge computing architectures. His work frequently explores the intersection of communication theory, artificial intelligence, and network security, with applications ranging from industrial IoT to public safety communications. Analysis of his recent publications reveals a strong trend toward integrating artificial intelligence with wireless networking infrastructure, particularly focusing on UAV-assisted communications, federated learning approaches for distributed networks, and security enhancements for 5G/6G systems. His research demonstrates increasing emphasis on practical implementation challenges including energy efficiency, communication overhead reduction, and reliability in non-ideal network conditions. As an academic supervisor, Dr. Jaafar actively mentors numerous graduate students across various projects. He currently supervises doctoral candidates working on blockchain-enhanced security for 5G networks, green network slice orchestration, and federated learning approaches for Open RAN architecture. His master's students are engaged in diverse topics including LiDAR-based power line monitoring, multimodal behavioral authentication, and 5G/6G security using AI techniques. Dr. Jaafar is affiliated with two prominent research laboratories at ETS: LASI (Computer System Architecture Research Laboratory) and LACIME (Communications and Microelectronic Integration Laboratory). At LASI, he contributes to research in AI-based systems engineering, resource orchestration in edge/cloud environments, and intelligent network design. Through LACIME, he engages with broader communications research spanning from microelectronic components to complex communication systems, with particular focus on wireless networks and signal processing applications.

• Wireless Communications
• Statistical Signal Processing
• Estimation and Equalization
• +3 more

Cihan Tepedelenlioglu is an Associate Professor at Arizona State University's School of Electrical, Computer and Energy Engineering. His work bridges wireless communications, statistical signal processing, and renewable energy systems, with a focus on photovoltaic array monitoring, fault detection, and optimization. PhD, MS, and BS in Electrical Engineering from University of Minnesota, University of Virginia, and Florida Institute of Technology 2001 NSF CAREER Award recipient Research interests span wireless communications , graph signal processing , stochastic optimization , and machine learning applications to solar energy systems . Key projects include quantum machine learning for PV topology optimization, consensus algorithms for distributed networks, and real-time fault detection using neural networks. Recent articles emphasize machine learning in energy systems (2023-2025), with 12 publications on photovoltaic monitoring and 3 on consensus algorithms. Earlier work focused on channel estimation in OFDM systems and fading models in wireless communications. Scientific awards : NSF CAREER Award (2001) Major grants include NSF funding for networked solar array management (2013-2016), nonlinear distributed consensus (2013-2016), and statistical processing of solar data (2009-2012). Teaching roles include EEE 350 Random Signal Analysis and graduate research supervision in signal processing and wireless communications. Collaborates extensively with Andreas Spanias, Mahesh Banavar, and other researchers on cyber-physical systems for energy applications.

Shuangshuang JinView profile

Associate Professor

• High-Performance Computing
• Parallel Computing
• Distributed Computing
• +6 more

Dr. Shuangshuang Jin is an Associate Professor in the School of Computing with a joint appointment in the Department of Electrical and Computer Engineering at Clemson University's College of Engineering, Computing and Applied Sciences. Previously, she served as a Senior Research Scientist at Pacific Northwest National Laboratory. Her educational background includes a Ph.D. in Computer Science (2007), M.S. in Computer Science (2003) from Washington State University, and a B.S. in Computer Science (2001) from Wuhan University. Ph.D., 2007 - Washington State University, Computer Science M.S., 2003 - Washington State University, Computer Science B.S., 2001 - Wuhan University, Computer Science Dr. Jin specializes in high-performance computing (HPC), distributed and parallel computing, general-purpose computation on graphical processing units (GPGPU), and HPC-based big data analysis, machine learning, scientific computation, and visualization. Her research focuses on applying these technologies to electrical engineering (power and energy systems, power electronics), automotive engineering, systems biology, and computer graphics. She leads the High-Performance Computing Enabled Science and Engineering (HPCeSE) Lab, where she supervises six PhD students working on HPC implementations for power system dynamic simulation, GridPACK application development, data-driven model-based smart control of power electronics converters, and other cutting-edge projects. Her recent publications demonstrate expertise in accelerating power system simulations, PV inverter reliability assessment, edge computing for power systems, and virtual prototyping of vehicle powertrain systems. The research trends show increasing focus on GPU acceleration, real-time simulation capabilities, and integration of HPC with emerging power system challenges. Junior Faculty Excellence in Teaching award (2021) Churchill Carter Fellowship (2022-2023) Zucker Graduate Education Center PhD Grant (2023) Doctoral Dissertation Completion Award (2023-2024) Outstanding Masters Student in Computer Science award (2022) Dr. Jin has successfully secured multiple grants from DOE, DOD, and other agencies for projects including 'Vehicle Propulsion Digital Twins', 'GridPACK-Wind', and 'Tool for Reliability Assessment of Critical Electronics in PV (TRACE-PV)'. She has advised numerous PhD and Master's students who have gone on to positions at national laboratories and industry. Her HPCeSE Lab maintains strong connections with Pacific Northwest National Laboratory, Fermi National Accelerator Laboratory, and other research institutions, providing students with valuable internship opportunities. Dr. Jin leads the High-Performance Computing Enabled Science and Engineering (HPCeSE) Lab at Clemson University, which focuses on developing optimized HPC-based parallel programming algorithms and architectures to solve complex scientific and engineering domain problems. The lab works on smart grid modeling and simulation, power electronics reliability assessment, ground vehicle systems prototyping, and advanced grid analytics, utilizing OpenMP, MPI, Pthreads, and CUDA/OpenCL on various computing platforms.

• Model Checking
• Symbolic Execution
• Compositional Verification
• +7 more

Corina Pasareanu is an ACM Fellow and IEEE ASE Fellow serving as a Principal Scientist at Carnegie Mellon University's CyLab Security and Privacy Institute and Technical Professional Leader for Data Science at NASA Ames Research Center through KBR. Her work bridges formal methods, software verification, and artificial intelligence to ensure the safety and security of complex systems, particularly autonomous systems and machine learning applications. Dr. Pasareanu received her academic training at: Ph.D. in Computer Science, Kansas State University (2001) M.S. in Computer Science, University Politehcnica of Bucharest (1995) B.S. in Computer Science, University Politehcnica of Bucharest (1994) Her research focuses on developing formal verification techniques that can provide mathematical guarantees about the behavior of complex software systems. She specializes in applying model checking, symbolic execution, and compositional verification methods to challenges in autonomy, security, and AI safety. Her recent work addresses the verification of systems incorporating machine learning components, particularly neural networks used in safety-critical applications like autonomous vehicles. She investigates how to ensure these systems behave correctly even when their perception components have uncertainties or are subject to adversarial attacks. Analysis of her recent publications shows a strong trend toward verifying AI and machine learning systems, particularly focusing on neural networks in autonomous systems. Her work increasingly addresses the challenges of Large Language Models, examining both their vulnerabilities to attacks and methods to defend against them. She also continues to advance traditional software verification techniques while adapting them to modern programming languages and paradigms. Dr. Pasareanu has received numerous prestigious awards recognizing her contributions to the field: ACM Fellow IEEE ASE Fellow ETAPS Test of Time Award (2021) ASE Most Influential Paper Award (2018) ESEC/FSE Test of Time Award (2018) ISSTA Retrospective Impact Paper Award (2018) ACM Impact Paper Award (2010) ICSE 2010 Most Influential Paper Award (2010) As an advisor, Dr. Pasareanu mentors several PhD students and postdoctoral researchers, often in collaboration with other faculty members at CMU. Her students focus on cutting-edge research at the intersection of formal methods and AI safety. Her research is supported by substantial funding from diverse sources including NSF, DARPA, NASA, AWS, and industry partnerships. She leads multiple projects focused on AI security, formal verification of neural networks, and software analysis techniques. Dr. Pasareanu also plays a significant role in the broader research community, serving as Program/General Chair for major conferences including ICSE 2025, and as an associate editor for IEEE TSE and STTT. Dr. Pasareanu leads research teams working on projects like "Trinity: Neurosymbolic Learning and Reasoning" (DARPA) and "HUGS: Human-Guided Software Testing and Analysis" (NSF). Her work often involves interdisciplinary collaboration between computer scientists, formal methods experts, and domain specialists to address complex safety challenges in autonomous systems.

• Telecommunications
• Communication Systems
• Satellite Communications
• +7 more

Roberto Garello is a Tenured Associate Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino . He specializes in Communication Systems , Satellite Networks , and Channel Coding , with a focus on 5G/6G Technologies and Non-Terrestrial Networks . His work aligns with the School of Master’s Programmes and Lifelong Learning . Research Interests: Satellite communications systems, Direct-to-Satellite IoT constellations, Mega-constellation services in space, and physical layer advancements for 5G/6G. Teaching: Offers courses like Information Theory for Data Science , Communication and Network Systems , and Space Exploration and Resources , while supervising Applied Signal Processing Laboratory . Projects: Leads initiatives such as DitDSSS (satellite localization), RESTART (future telecommunications), and TESL@ (ICT energy efficiency). Scientific Awards: Received Best Paper Awards at CTRQ 2010 and COCORA 2013. Students: Supervises PhD candidates including Alessandro Compagnoni, Agbotiname Lucky Imoize, and Riccardo Tuninato, focusing on topics like Wireless Communication, Machine Learning, and Non-Terrestrial Networks. Publications: His recent work explores OTFS vs. OFDM, spectrum sensing algorithms, MIMO with cylindrical arrays, and 5G NTN synchronization, reflecting trends in satellite IoT and machine learning integration.

Chris F. KemererView profile

Professor

• Management of Information Systems
• Software Engineering
• Measurement and Modeling
• +2 more

Chris F. Kemerer is the David M. Roderick Professor of Information Systems and Professor of Business Administration at the University of Pittsburgh's Katz School. He previously served as an associate professor at MIT Sloan School of Management and as a principal at American Management Systems Inc. PhD in Systems Sciences (Information Systems), Carnegie Mellon University BS (magna cum laude) in Economics and Decision Sciences, Wharton School, University of Pennsylvania His research spans management and measurement issues in information systems and software engineering, including technical debt, software metrics, network effects, and technology adoption. He has made significant contributions to software design metrics and empirical analysis of IT markets. His publications over the last 15 years emphasize technical debt management, software reliability, standards competition, and economic dynamics in microcomputer software. These works bridge software engineering economics with real-world applications in enterprise systems and global markets. ISI/Thomson Reuters Highly Cited Researcher in Computer Science INFORMS Information Systems Society Distinguished Fellow He has secured grants from the National Science Foundation and IBM for software evolution research. His consulting includes expert testimony in landmark cases like Microsoft antitrust litigation and Oracle v. Google. He has taught courses in economics of information systems, software design, and technology diffusion at the PhD, Executive DBA, MBA, and EMBA levels.

Michihiro YasunagaView profile

Assistant Professor

• Large Language Models
• Multimodal Models
• Reasoning Systems
• +6 more

Michihiro Yasunaga is an Assistant Professor in the Department of Computer Science at Stanford University's School of Engineering. He received his PhD in Computer Science from Stanford, advised by Percy Liang, Jure Leskovec, and Chris Manning. Prior to his faculty position, he worked as a researcher at Google DeepMind and Meta. His research focuses on building LLMs and agents that assist humans in diverse tasks, with particular expertise in post-training techniques (RL, reward models, and evaluation), reasoning systems (AnalogicalReasoner), retrieval and tool use for LLMs (LinkBERT, QAGNN, DRAGON, REPLUG, HippoRAG), and multimodality (RA-CM3, Med-Flamingo, Transfusion). His work spans both theoretical foundations and practical applications of large language models. Yasunaga's publication record demonstrates significant contributions to the field of AI, with 15 recent articles (2023-2025) covering diverse aspects of language model development, evaluation, and application. His research shows a clear trajectory toward building more capable, efficient, and reliable multimodal AI systems, with particular emphasis on knowledge integration and robust evaluation frameworks. Among his notable achievements is the Best Paper Award at AAAI 2023 Deep Learning on Graphs Workshop for the DRAGON paper. He has also been deeply involved in major benchmarking efforts including HELM and HEIM, which provide comprehensive evaluation frameworks for language and vision-language models. Yasunaga actively contributes to the research community through service roles including Organizing Committee for the Workshop on Knowledge-Augmented Methods for NLP (ACL 2024), Workshop on Structured and Unstructured Knowledge Integration (NAACL 2022), and the Workshop on Scientific Document Summarization (SIGIR 2017-2020). He has also served on program committees for top conferences including NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, and ICCV from 2020-2025.

Xuming HeView profile

Associate Professor

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +9 more

Xuming He is an Associate Professor at the School of Information Science and Technology (SIST), ShanghaiTech University, where he leads the PLUS Lab. His research spans computer vision and machine learning with a focus on developing algorithms that operate effectively under limited supervision and evolving data conditions. His core research interests include weakly-supervised and few-shot learning for scenarios with sparse annotations, continual learning frameworks for knowledge retention during sequential task acquisition, semantic segmentation techniques for scene understanding, and multimodal vision-language representations. He emphasizes interpretable machine learning to build transparent AI systems capable of human-understandable reasoning, addressing critical challenges in model trustworthiness and deployment reliability. Recent publications reveal strong trends toward novel class discovery in long-tailed recognition scenarios, physics-informed generative modeling for scientific applications, and robust segmentation under distribution shifts. His work increasingly integrates large language models for multimodal reasoning while maintaining focus on efficiency in resource-constrained environments like robotic grasping and medical imaging analysis. He actively mentors students, having supervised Qian He to PhD completion and Chuanyang Hu to Master's degree in 2023. He welcomes prospective graduate students through ShanghaiTech's Computer Science & Technology program and offers undergraduate research projects requiring minimum six-month commitments. The PLUS Lab under his direction drives innovation in learning under supervision constraints, with recent work spanning medical tumor analysis, cross-view geolocation, photonic computing, and semiconductor design verification. The lab's research bridges theoretical advances with practical applications across healthcare, robotics, and scientific discovery domains.

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.

Emmanuel LetierView profile

Associate Professor

• Software Engineering
• Requirements Engineering
• Software Architecture
• +8 more

Dr. Emmanuel Letier is an Associate Professor in Software Engineering at University College London (UCL), specializing in requirements engineering and software architecture. His research focuses on stakeholder goal analysis, formal reasoning techniques, and uncertainty management in software development. University College London, Department of Computer Science Research areas: Requirements Engineering, Software Architecture, Formal Methods, Uncertainty Analysis His work explores how to bridge stakeholder goals with software requirements using natural language, formal logic, and mathematical models. Key contributions include model checking, synthesis tools, and Bayesian analysis applications for managing technical debt and software evolution. Recent publications span AI-based systems requirements, app review mining for requirements elicitation, and uncertainty handling in self-adaptive systems. Notable awards include Best Paper Award (ACM SIGSOFT 2004) Best Paper Award (IEE 2003) Distinguished Paper Award (RE'11)

• Open Science
• Cognitive Neuroscience
• Reproducibility
• +5 more

Christopher Chambers is Professor of Cognitive Neuroscience at Cardiff University's School of Psychology. His research spans open science reform, cognitive neuroscience, and translational applications in behavior change. He founded numerous initiatives including Registered Reports, the UK Reproducibility Network (UKRN), and the Royal Society Replications initiative, while chairing the Registered Reports Committee at the Center for Open Science. Chambers' research focuses on: 1) Developing and promoting open research practices to enhance scientific reliability; 2) Investigating cognitive control mechanisms using brain stimulation (TMS, TES) and neuroimaging (fMRI, MEG); and 3) Translational applications in obesity and behavior change through response inhibition training paradigms. His work bridges cognitive neuroscience with metascience reform. His publications demonstrate strong trends in open science methodology (Registered Reports, reproducibility frameworks) and cognitive-behavioral interventions, particularly examining how inhibitory control training affects food valuation and eating behaviors. Recent work emphasizes robustness verification and implementation of open science practices across disciplines. Awards include: 2018 British Psychological Society Book Award for 'The Seven Deadly Sins of Psychology' 2018 PROSE Award from Association of American Publishers He leads major grants including a €1.99M ERC Consolidator Grant on cognitive control training and a £188k Arnold Foundation grant on Registered Reports. Chambers founded the Insciout research group (science communication) and UK Evidence Information Service (science-policy interface), and currently advises the Peer Community in Registered Reports.

• Neuroscience
• Neuroimaging
• Magnetoencephalography (MEG)
• +7 more

Krish Singh is a Professor and Head of Human Electrophysiology at Cardiff University's School of Psychology. He holds a BSc from Durham University and a PhD from the Open University. As a leader at the Cardiff University Brain Research Imaging Centre (CUBRIC), he drives multimodal neuroimaging research, focusing on MEG/EEG methodologies for clinical and cognitive neuroscience. His work spans epilepsy, schizophrenia, and Alzheimer’s disease, including studies on electrophysiological genetic risk markers. His research bridges physics and neuroscience, emphasizing non-invasive brain imaging (MRI/MEG) to explore neural mechanisms in health and disease. Key interests include: Cortical oscillations and neurophysiological biomarkers Genetic influences on brain networks in psychiatric disorders Multimodal integration of MEG with MRI/PET data Clinical applications in neurodegeneration and epilepsy Publications demonstrate consistent focus on neurophysiology, schizophrenia, Alzheimer’s, and methodological innovations in MEG analysis. Trends include genetic risk modeling, pharmacological modulation of neural oscillations, and longitudinal biomarker discovery. He leads the undergraduate module Introduction to Neuroimaging Methods and Applications , covering MRI, fMRI, EEG/MEG, TMS, and clinical applications. As a supervisor, he supports postgraduate projects in neuroimaging and collaborates internationally via the MEG-UK Partnership to standardize MEG methodologies. He directs research at CUBRIC and collaborates with Cardiff's Neuroscience and Mental Health Innovation Institute, fostering cross-disciplinary studies with medicine and biosciences.

Ernesto Elias Vidal RosasView profile

Lecturer

• Neuroimaging
• Brain-computer interfaces
• Medical optics imaging
• +2 more

Dr. Ernesto Elias Vidal Rosas is a Lecturer at the School of Electronics and Computer Science, University of Southampton. Previously, he was a Postdoctoral Researcher at UCL Medical Physics and Biomedical Engineering Department. He specializes in neuroimaging and optical technologies with extensive expertise in near-infrared spectroscopy (NIRS), diffuse optical tomography (DOT), and wearable brain imaging systems. He is currently accepting PhD students. Research Focus: Dr. Vidal-Rosas leads research in neuroimaging methodologies and brain-computer interfaces. His work emphasizes wearable technologies for clinical and home-based monitoring, with applications spanning from neonatal care to motor rehabilitation. Key research domains include: Development of high-density diffuse optical tomography (HD-DOT) systems Integration of EEG and fNIRS for brain-computer interfaces Real-time processing algorithms for neurofeedback applications Non-invasive monitoring of therapeutic responses in oncology He is currently involved in the Horizon Europe PUREMIND project focused on preventing mental illness through multimodal biomarker analysis. Academic Activities: Actively advises PhD candidates in Computer Science and teaches undergraduate courses including Healthcare Technology Design (ELEC2231) and Digital Control System Design (ELEC3206). His pedagogical research includes developing virtual tools for engineering education. Affiliations: Member of the Digital Health and Biomedical Engineering research group and the Institute for Life Sciences at Southampton.

Jessica HullmanView profile

Professor

• Artificial Intelligence
• Human-Computer Interaction
• Uncertainty Quantification
• +4 more

Jessica Hullman is the Ginni Rometty Professor of Computer Science at Northwestern University's McCormick School of Engineering and a Faculty Fellow at the Institute for Policy Research. Her research develops theoretical frameworks and interfaces for human-AI collaboration, focusing on uncertainty quantification, statistical modeling, and decision-making in domains like scientific research and AI-assisted analysis. Education: PhD in Information (Visualization), University of Michigan (2013) MS in Information Analysis, University of Michigan (2008) BA in Comparative Studies, Ohio State University (2003) Tableau Postdoctoral Fellowship, UC Berkeley (2015) Research Focus: Hullman's work bridges formal models of rational inference (e.g., Bayesian decision theory) with real-world applications. Key areas include: human-AI complementarity in decision-making, visualization of uncertainty, statistical reform, and LLM applications in behavioral science. Her research consistently addresses the alignment of data-driven interfaces with human cognitive capabilities. Publication Trends: Recent work demonstrates a strong emphasis on human-AI collaboration frameworks, decision-theoretic evaluation of visualizations, and methodological rigor in machine learning and social science. Key themes include uncertainty quantification (conformal prediction, privacy tradeoffs), behavioral experiments in AI-assisted tasks, and critical analyses of scientific practices. Awards & Honors: Microsoft Faculty Fellow (2019) Google Faculty Award NSF CAREER, Medium, and Small Awards Multiple best paper/honorable mention awards at top HCI/visualization venues (CHI, VIS) Funding & Labs: Principal Investigator for NSF-funded projects including HCC: Medium on visualization tools. Previously affiliated with University of Washington's Interactive Data Lab and DataLab. Current research includes NSF-supported work on improving data visualization for reasoning about analytical assumptions.