Serena Booth is an incoming Assistant Professor in Computer Science at Brown University. Previously, she served as an AAAS AI Policy Fellow in the U.S. Senate, advising the Senate Banking Committee on AI policy. She holds a PhD from MIT CSAIL (2023) and a BA from Harvard College (2016). Her research focuses on human-AI interaction, specification design for AI systems, and ethical AI practices. She also worked as an Associate Product Manager at Google, scaling ARCore to 100 million devices. Her research explores how humans specify AI behaviors, assess system success, and mitigate misalignment risks. Key contributions include Bayes-TrEx (model transparency via Bayesian sampling) and RoCUS (robot controller understanding). Her work has been supported by NSF GRFP and MIT Presidential Fellowships. She advocates for science policy equity through MIT's Science Policy Initiative and co-founded initiatives to support women in computing (e.g., GW6 at MIT). Education: PhD MIT CSAIL (2023), BA Harvard College (2016) Awards: Rising Star in EECS, HRI Pioneer, NSF GRFP Key Areas: Reward design pitfalls, human-robot trust, ethical AI curriculum development Her recent publications analyze reward function misdesign (AAAI 2023), human-AI teaching frameworks (HRI 2022), and feature attribution reliability (AAAI 2022). She currently seeks PhD students/postdocs focusing on human-AI alignment, reinforcement learning, and policy implications.
Nura Aljaafari is a researcher in the Department of Computer Science, focusing on adversarial machine learning and text processing. Her work explores the robustness of machine learning models against adversarial attacks and defenses, particularly in federated learning and natural language processing contexts. Dr. Aljaafari holds a Doctor of Philosophy (Ph.D.) in Computer Science, providing expertise in advanced computing and machine learning methodologies. Her research interests center on adversarial machine learning, text processing, and enhancing the security of machine learning algorithms. Her recent studies include analyzing token compositionality in large language models and interpreting conceptual frameworks in GPT models, highlighting her focus on model interpretability and reliability. Earlier work includes investigating adversarial attacks in federated learning systems and developing defenses for license plate recognition systems. While her profile does not explicitly list awards or grants, her publications have garnered significant attention, with citations in cybersecurity and machine learning fields. Collaborations include developing bioinformatics tools using GANs and analyzing organizational security practices in Saudi Arabian contexts.
Dr. Rafeef Garbi is a Professor at the Department of Electrical and Computer Engineering, University of British Columbia, and the Founder/Director of the Biomedical Signal and Image Computing Laboratory (BiSICL). Her multidisciplinary research integrates artificial intelligence, computer vision, and medical imaging for clinical applications in pediatric orthopedics, oncology, and neurology. PhD (Chalmers University, Sweden), MSc (with distinction), Technical Licentiate Research Focus: Specializing in Medical Image Computing and Visual Computing , her lab develops AI-driven solutions for: Automated segmentation and analysis of multi-dimensional biomedical data Clinically-translatable biomarkers for disease assessment Computer-aided intervention systems in surgical contexts Scientific Leadership: UBC Killam Faculty Research Fellow Peter Wall Institute for Advanced Studies Early Career Scholar Senior IEEE Member & Founding IEEE EMBS Vancouver Section Member Key Collaborations: Active in the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society and CAIDA: UBC ICICS Centre for Artificial Intelligence Decision-making and Action. Her team bridges engineering, medicine, and computational biology through translational research.
Pedro Fonseca is an Assistant Professor at the Department of Computer Science, Purdue University. He leads the Reliable and Secure Systems Lab, focusing on building reliable and secure core software systems such as operating systems, hypervisors, and distributed systems. His research has been recognized with awards including the NSF CAREER Award and Google Faculty Research Awards. Before Purdue, he completed a postdoc at the University of Washington, working with Arvind Krishnamurthy, Hank Levy, and Xi Wang. He earned his PhD from MPI-SWS and the University of Saarland under Rodrigo Rodrigues. His academic contributions span over 30 peer-reviewed publications in top-tier conferences like SOSP, OSDI, EuroSys, and ASPLOS. He teaches courses including CS503 (Operating Systems), CS592 (Reliable and Secure Systems), and CS408 (Software Testing). He actively serves on program committees for major systems conferences including SOSP, OSDI, EuroSys, and ASPLOS.
Sanat K. Sarkar serves as a Professor in the Department of Statistics, Operations, and Data Science at Temple University's Fox School of Business and Management. An internationally renowned expert, he has pioneered foundational work in multiple testing theory with applications spanning genomics, neuroimaging, and high-dimensional data analysis. His methodological innovations address critical challenges in false discovery rate control under complex dependency structures. Research Interests: Dr. Sarkar specializes in Multiple Testing, Statistical Methodologies, High-Dimensional Statistical Inference, and Multivariate Statistics. His work develops rigorous frameworks for hypothesis testing in modern scientific contexts where thousands of simultaneous tests are performed, ensuring reliable discoveries in fields like genetic association studies and brain connectivity mapping. Key contributions include adaptive FDR procedures and methods for structured hypothesis groups. Publication Trends: Over 2020-2025, his 11 publications demonstrate sustained leadership in refining false discovery rate methodologies. Recent work tackles correlated data (2025), knockoff variable selection (2022), and hierarchical hypothesis structures (2021-2024), reflecting his focus on real-world applicability in biomedical big data. His research bridges theoretical statistics with practical computational solutions. Honors and Awards: Fellow, Institute of Mathematical Statistics Fellow, American Statistical Association Elected Member, International Statistical Institute Musser Award for Research Excellence (Fox School) Multiple Dean's Research Honor Roll Inductions Research Support and Service: Funded continuously by NSF and NSA grants, Dr. Sarkar co-organized the NSF-CBMS conference on Multiple Comparisons and serves on editorial boards of Annals of Statistics , American Statistician , and Sankhya . He regularly delivers invited talks at international venues and mentors junior researchers in statistical methodology development.
He Zhu is an Assistant Professor at Rutgers, The State University of New Jersey, affiliated with the Department of Computer Science. His research focuses on programming languages, compilers, wireless communications, IoT systems, and 5G network protocols. He received the PLDI 2019 Distinguished Paper Award for his contributions to formal methods in programming systems. His work addresses challenges in vehicle-to-everything (V2X) communication, resource allocation in sidelink networks, network security, and dynamic authorization frameworks for IoT devices. Key research interests include optimizing 5G NR (New Radio) protocols for vehicular environments, developing efficient data aggregation techniques for user equipment, and enhancing service layer mechanisms for IoT systems. He leads projects funded by the NSF, such as 'Formal Symbolic Reasoning of Deep Reinforcement Learning Systems,' and has contributed to advancements in beam management, RACH protocols, and energy-efficient DRX configurations in wireless networks. His office is located in Core 315. Notable Awards: PLDI 2019 Distinguished Paper Award Grants: NSF Grant: Formal Symbolic Reasoning of Deep Reinforcement Learning Systems His research group explores intersections between compiler design, distributed systems, and network architecture, with applications in smart mobility, edge computing, and secure IoT communication. He actively contributes to standards development for 5G and future generations of wireless networks.
Shimeng Yu is a full professor at the Georgia Institute of Technology's School of Electrical and Computer Engineering, holding the Dean’s Professorship. He earned his B.S. from Peking University (2009) and M.S./Ph.D. from Stanford University (2011/2013). His research focuses on semiconductor devices, non-volatile memories, 3D integration, and AI hardware accelerators. Yu leads SRC/DARPA JUMP 2.0 centers on memory/storage and 3D integration, with over 400 publications and 30,000+ citations (H-index 82). He serves on flagship conference committees (e.g., IEDM, VLSI) and editorial boards (IEEE EDL, JETCAS). Education: B.S., Microelectronics, Peking University (2009) M.S./Ph.D., Electrical Engineering, Stanford University (2011/2013) Research Themes: Emerging non-volatile memories for AI Monolithic 3D integration Energy-efficient computing systems His work spans device fabrication, circuit design, and system-level co-optimization. Recent projects are funded by NSF, DARPA, DOE, and industry partners (TSMC, Intel, Samsung), totaling >$17M. His lab, located at the Pettit Microelectronics Research Center, develops prototypes with cleanroom access. Awards: IEEE Fellow (2024) ACM/IEEE DAC Under-40 Innovators Award (2020) NSF CAREER Award (2016) Multiple editorship roles and distinguished lecturer appointments (IEEE EDS/CASS) Grants & Funding: Lead of two SRC/DARPA JUMP 2.0 centers Total research funding exceeds $17M
Talal Shaikh is an Associate Professor at Heriot-Watt University's School of Mathematical and Computer Sciences in Dubai. He serves as Director of Undergraduate Studies and Programme Director for BSc Computer Science, BSc CS (AI), and MSc Software Engineering. With a decade of industry experience as a Chief Information Officer and Software Engineer, he bridges practical insights with academic research. Research Interests: Pervasive Computing, IoT/M2M, AI/ML, WiFi Sensing for Healthcare, Financial Machine Learning, Educational Technology Awards: Teaching Excellence Awards (2017/18), Fellow of the Higher Education Academy (FHEA), multiple Learning and Teaching Oscars (2016, 2017, 2018) His work spans Ubiquitous Computing and IoT , focusing on sensor networks and WiFi-based sensing for healthcare. In Artificial Intelligence , he applies ML to robotics, financial analytics, and educational innovation. Recent articles analyze Reinforcement Learning , Emotion Recognition , and WiFi Sensing applications. His teaching emphasizes student-centric learning, with over 100 supervised dissertations achieving distinctions. Collaborations include international conferences and interdisciplinary research in smart environments and adaptive systems.
Dana Brooks is a Research Professor in the Department of Electrical and Computer Engineering at Northeastern University, with affiliations in Bioengineering. He holds a PhD from Northeastern University (1991) and has received the Søren Buus Outstanding Research Award (2006). His primary research focuses on biomedical signal and image processing, medical imaging techniques (including MRI and electrocardiography), and neuromodulation technologies such as transcranial magnetic stimulation (TMS). He is also involved in protein conformation estimation using X-ray scattering and optimization algorithms for medical applications. Dr. Brooks leads the Biomedical Signals Processing Lab and collaborates with the Center for Integrative Biomedical Computing . His work bridges engineering and medicine, with recent grants including a $400K NSF MRI grant for advanced TMS systems and a $600K NSF grant for motor cortical organization studies. He has advised students like Setareh Ariafar (PhD’20) and contributed to innovations in image mosaicking for confocal microscopy and machine learning applications in dermatology. His publications span computational neuroscience, cardiac imaging, and uncertainty quantification in biomedical simulations. Notable achievements include developing algorithms for ECG imaging, optimizing TMS protocols, and creating tools like UncertainSCI for simulation reliability assessment.
Mauro Pezzè is a Full Professor of Software Engineering at the Università della Svizzera italiana (USI) and Università di Milano Bicocca, leading the STAR research group since 2006. He holds a laurea from the University of Pisa and a PhD from Politecnico di Milano. His research focuses on software testing, analysis, self-adaptive systems, and cloud systems. He has held editorial roles, including Editor-in-Chief of ACM Transactions on Software Engineering and Methodologies (TOSEM), and served on numerous program committees. Education: Laurea (Pisa), PhD (Politecnico di Milano). Professional roles include Dean of the Faculty of Informatics at USI (2009-2013), visiting scientist at UC Irvine and Edinburgh, and technical lead for international projects. He co-authored a seminal book on software testing (Wiley, 2007), with over 670 citations. Research Interests: Software Testing, Self-Adaptive Systems, Cloud Computing, AI in SE, Sustainable Software. Projects include work on field-based testing, failure prediction in distributed systems, and neuro-symbolic approaches for test oracles. Grants and Advising: Led STAR Lab projects in self-healing systems, GUI testing, and semantic matching. Advised numerous PhD/postdoc students (e.g., Ciniselli, Di Grazia, Qiu). Collaborations with European tech firms on R&D initiatives. Labs/Teams: STAR Group at USI/Constructor Institute, Bicocca, and Politecnico di Milano. Current members include postdocs and PhD students working on AI-driven testing and cloud reliability.
Mohamed-Slim Alouini is a Professor of Electrical Engineering and Associate Dean of the Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. He also serves as the Associate Vice President for Research and holds the UNESCO Chair in Education to Connect the Unconnected. With over 500 journal publications and more than 46,000 citations, he is a world-renowned expert in wireless communications who was elected IEEE Fellow in 2009 at the age of 39. Education: PhD in Electrical Engineering, California Institute of Technology (Caltech), 1998 MS in Electrical Engineering, Georgia Institute of Technology (Georgia Tech), 1995 Diplôme d'Etudes Approfondies (DEA) in Electronics, Université Pierre & Marie Curie (Sorbonne University), 1993 Diplôme d'Ingénieur, École Nationale Supérieure des Télécommunications (Télécom Paris Tech), 1993 Habilitation, Université Pierre & Marie Curie (Sorbonne University), 2003 Dr. Alouini is a world-renowned expert in wireless communication and networking with research interests spanning diversity combining techniques, MIMO systems, multi-hop/cooperative communications, optical wireless systems, cognitive radio, UAV communications, and advanced modulation schemes. His current focus addresses the technical challenges of uneven information and communication technology distribution, particularly targeting rural, low-income, disaster-prone, and hard-to-reach areas through integrated ground-airborne-space networks. His work bridges theoretical foundations with practical implementations to solve real-world connectivity problems. His recent publications (2020-2024) demonstrate a clear research trajectory toward integrated communication networks combining terrestrial, aerial, and space components. There's growing emphasis on UAV communications, satellite systems, optical wireless technologies, and rural connectivity solutions, with increasing integration of machine learning techniques for network optimization. His work shows consistent focus on addressing the digital divide, with several publications specifically targeting 6G challenges for connecting underserved populations and recycling existing infrastructure for enhanced rural connectivity. Scientific Awards: Member of the European Academy of Sciences and Arts (2019) Fellow of the African Academy of Sciences (2018) IEEE Fellow (2009) Abdul Hameed Shoman Award for Arab Researchers (2016) OIC Science & Technology Achievement Award (2017) Multiple recognitions as Highly Cited Researcher NSF CAREER Award (1999) Dr. Alouini has mentored numerous successful students and post-doctoral fellows who have secured positions at top institutions worldwide including Harvard, Caltech, Imperial College, and faculty positions at Korea University, Hanyang University, and universities across the Middle East. His December 2018 PhD graduate Qurrat-Ul-Ain Nadeem received the prestigious Marconi Society Paul Baran Young Scholars award, while post-doctoral fellows have won IEEE ComSoc Young Professionals Best Innovation Award and attended the Lindau Nobel Meeting. His Communication Theory Lab at KAUST drives significant research in wireless communications with funding supporting extensive publication output and innovative projects. Dr. Alouini leads the Communication Theory Lab at KAUST and holds the UNESCO Chair in Education to Connect the Unconnected, focusing specifically on technical solutions for connecting underserved communities. His lab works on integrated ground-airborne-space networks to bridge the digital divide, with particular emphasis on rural, low-income, and hard-to-reach areas. The team develops practical solutions using UAVs, satellite communications, and recycled infrastructure to provide cost-effective connectivity where traditional approaches fail.
Hamed Badihi is an Assistant Professor in Automation Technology and Dependable Systems at Tampere University , part of the Faculty of Engineering and Natural Sciences. He leads the Dependability and Automation Research in Cyber-Physical Systems (DARES) Group within the Dependable Systems Cyber Laboratories . His research focuses on critical aspects of condition monitoring, fault-tolerant control, and attack-resilient control to advance sustainable, dependable cyber-physical systems. Research Interests include: Cybersecurity for industrial control systems Fault-tolerant control mechanisms Resilient control strategies for renewable energy systems Condition monitoring of wind turbines and microgrids Recent Contributions emphasize hybrid approaches combining machine learning and control theory for cyber-attack detection and system resilience in wind farms and microgrids. His work addresses challenges like real-time fault diagnosis and adaptive control under adversarial or environmental perturbations. Awards & Roles : Senior Member of IEEE, editor for International Transactions on Electrical Energy Systems , Advances in Fuzzy Systems , and Processes journals. Active in EU projects like StreamSTEP . Labs & Teams : Directs the DARES Group, collaborating on initiatives like the Dependable Systems Cyber Laboratories to pioneer innovations in cyber-physical system dependability.
Chen Binbin is an Associate Professor and Associate Head of Pillar (Innovation and Enterprise) in the Information Systems Technology and Design (ISTD) pillar at Singapore University of Technology and Design (SUTD). He serves as Deputy Director for the Future Communications Research and Development Programme (FCP), Singapore. Previously, he was a Principal Research Scientist at the Advanced Digital Sciences Center (now Illinois ARCS), affiliated with the University of Illinois. Education: PhD in Computer Science from National University of Singapore, and Bachelor's from Peking University. Research focuses on wireless networking, distributed systems, and cyber security for critical infrastructures like smart grids and industrial control systems. His work addresses secure communications, intrusion detection, and resilience against cyber-physical threats. Notable contributions include error-estimating coding, provenance verification in ICS, and AI-driven network security solutions. Key awards include the 2010 ACM SIGCOMM Best Paper Award for error-estimating coding research. His grants span agencies like Singapore's National Research Foundation (NRF), Cyber Security Agency (CSA), and Energy Market Authority (EMA). He leads projects on secure smart grid communication, industrial control system defense, and AI-enhanced cybersecurity tools. Technical leadership involves developing frameworks like CyberSAGE for security assessment and CMD for IoT malware detection. Active in collaborations with industry and government, his work bridges theory and practice in securing critical infrastructure systems.
Maozhen Li is a Professor in the Department of Electronic and Electrical Engineering at Brunel University of London , within the College of Engineering, Design and Physical Sciences . He serves as the Vice-Dean of the NCUT Transnational Education (TNE) programme, overseeing a joint school with North China University of Technology. He has been at Brunel since 2002, progressing from Lecturer to Professor in 2013. Education: PhD, Institute of Software, Chinese Academy of Sciences (1997) Postdoctoral Research, School of Computer Science and Informatics, Cardiff University (1999–2002) His primary research interests lie in high performance computing, big data analytics, and artificial intelligence, with applications in smart grids, smart manufacturing, and cybersecurity. He focuses on developing interpretable, robust, and lightweight AI models, including work in causal AI, parallel machine learning, and edge computing. His research integrates advanced techniques such as deep learning, reinforcement learning, and blockchain for real-world system optimization. An analysis of his recent publications reveals a strong and consistent research trajectory in AI-driven solutions for environmental monitoring (e.g., PM2.5 prediction), industrial defect detection, IoT security, and intelligent transportation. His work frequently combines deep learning with graph-based modeling and federated or reinforcement learning, emphasizing scalability, efficiency, and robustness in distributed and edge environments. Scientific Awards and Recognition: Fellow of the Institution of Engineering and Technology (IET) Fellow of the British Computer Society (BCS) Shortlisted for the Computing UK BIG DATA EXCELLENCE AWARDS 2018 in the category of Most Innovative Big Data Solution Maozhen Li has successfully supervised 25 PhD students and examined over 30 PhD theses externally. He has secured significant research funding from EPSRC, the European Union (Horizon 2020), Innovate UK, and the Royal Society , with projects including Z-BRE4K, IoRL, and TDX-ASSIST. He serves as an Associate Editor for journals such as the Journal of Cloud Computing and the International Journal of Grid and High Performance Computing . Research Groups and Teams: He is affiliated with the Intelligent Engineering Frameworks (IEF) research group at Brunel, contributing to collaborative efforts in AI, IoT, and smart systems. His leadership in transnational education also fosters international research collaboration between Brunel and Chinese institutions.
Elena A. Erosheva is a Professor in the Department of Statistics and Social Work at the University of Washington , where she also serves as the Associate Director of the Center for Statistics and the Social Sciences (CSSS). Her research focuses on hierarchical modeling , longitudinal data analysis , and latent variable models , with applications in social sciences, disability research, and health statistics. She has taught courses like Multivariate Data Analysis, Sample Survey Techniques, and Statistical Modeling with Latent Variables. Education : PhD in Statistics, University of Washington (2002) Current Research : Measuring gender-based homophily in collaborations, modeling life course transitions, partial mastery cognitive diagnosis models, and bias detection in grant review Scientific Awards : 2019 NIH Center for Scientific Review First Prize for Most Creative Idea for Detection of Bias in Peer Review 2013 Mitchell Prize, International Society for Bayesian Analysis 2015 ASA Student Paper Award (with Maryclare Griffin) Her students and postdoctoral researchers include Sheridan Grant, Y. Samuel Wang, and Jonathan Gruhl. She uses R software extensively in her teaching and research. Contact: erosheva@uw.edu | Personal Website
