Lucas Janson is an Associate Professor of Statistics and Affiliate in Computer Science at Harvard University. He leads the Harvard Statistical Consulting Service, supervising PhD students advising hundreds of researchers annually. His research focuses on high-dimensional inference, statistical machine learning, and applications in genetics, political science, and climatology. He teaches courses such as Statistical Inference I, Reinforcement Learning, and Statistical Machine Learning. His work bridges theoretical advancements with practical applications, including contributions to robotics motion planning and microbiome data analysis. Key research areas include variable importance inference, safe reinforcement learning, compositional data analysis, and robust paleoclimate reconstructions. His methodologies are implemented in software packages like Floodgate, EigenPrism, and Fast Marching Tree (FMT*). He advises a dynamic group of PhD students and has mentored alumni now in academia and industry roles. Notable contributions include the development of model-X knockoffs for controlled variable selection, conditional randomization tests, and optimization algorithms for adaptive control systems. His work emphasizes statistical rigor while addressing real-world challenges in healthcare, environmental science, and robotics.
Zhibo Pang is an Adjunct Professor at KTH Royal Institute of Technology's Department of Intelligent Systems (EECS) and Senior Principal Scientist at ABB Corporate Research Sweden. His work focuses on digital transformation in industry and healthcare, spanning robotics, AI, control systems, and wireless communication. He leads projects in embodied intelligence, Industry 4.0, and Healthcare 4.0, with 23 granted patents and over 120 journal papers. Education: PhD in Electronic and Computer Systems (KTH, 2013), MBA in Innovation & Growth (University of Turku, 2012). Key Roles: IEEE Technical Committee Chair, Editor of 6 IEEE journals, ABB Inventor of the Year (2016, 2018, 2021). Research Interests: Robotics safety, wireless automation, federated learning, digital twins, and IoT security. Recent Projects: Cloud-fog automation frameworks, robot skin systems for healthcare, and latency-aware industrial control. His work bridges academia and industry through cross-functional collaborations.
Behnaam Aazhang is the J.S. Abercrombie Professor of Electrical and Computer Engineering at Rice University and Director of the Rice Neuroengineering Initiative (NEI). He holds a B.S., M.S., and Ph.D. from the University of Illinois at Urbana-Champaign. His roles include leading the multi-university Rice Neuroengineering Initiative and directing the Center for Neuroengineering. He has held an Academy of Finland Distinguished Visiting Professorship (FiDiPro) at the University of Oulu (2006-2014) and received an Honorary Doctorate from the University of Oulu in 2017. Education: Ph.D. in Electrical Engineering, University of Illinois at Urbana-Champaign (1986) M.S. in Electrical Engineering, University of Illinois at Urbana-Champaign (1983) B.S. in Electrical Engineering, University of Illinois at Urbana-Champaign (1981) Research Interests: Dr. Aazhang’s work focuses on signal/data processing, information theory, and neuroengineering applications. Key areas include: Neuronal circuit connectivity and learning impacts Real-time closed-loop neuromodulation for neurological disorders (epilepsy, Parkinson’s, depression) Patient-specific cardiac pacing systems Cybersecurity in cloud computing Awards & Honors: 2022 Rice Outstanding Doctoral Thesis Advisor Award 2019 SIGMOBILE Test of Time Award 2017 Honorary Doctorate (University of Oulu) 2013 IEEE Communication Society Advances in Communication Award AAAS and IEEE Fellowships (2012 and 1999) Grants & Advising: His research is supported by multi-university collaborations and grants. He has advised numerous graduate students in electrical engineering and neuroengineering, though specific names are not listed here. Labs & Teams: Leads the Aazhang Lab and the Rice Neuroengineering Initiative, focusing on translational technologies for neurological and cardiac disorders, including non-invasive neuromodulation and cloud security systems.
Monica Lam is the Kleiner Perkins, Mayfield, Sequoia Capital Professor in Stanford University's School of Engineering and holds a courtesy professorship in Electrical Engineering. She leads the Stanford Open Virtual Assistant Laboratory and has pioneered work in virtual assistants, privacy protection, and compiler design. Her research includes the Almond virtual assistant, privacy-preserving IoT systems, and the ThingTalk programming language. She co-authored the seminal 'dragon book' on compilers and co-founded Tensilica (now part of Cadence). Education: Bachelor of Science (Honors), Computer Science, University of British Columbia, 1980 Master of Science, Computer Science, Carnegie Mellon University, 1982 Doctor of Philosophy (PhD), Computer Science, Carnegie Mellon University, 1987 Research Interests: Dr. Lam's work focuses on conversational AI with privacy guarantees, compiler optimization for parallel computing, and open-source virtual assistant ecosystems. She is a leader in decentralized systems, having developed frameworks like SociaLite for large-scale graph analysis and Musubi for mobile social networking without centralized platforms. Article Trends: Recent publications emphasize multimodal interactions, multilingual dialogue systems, and LLM-driven applications in areas like question answering, persuasive chatbots, and adaptive assistants. Her work bridges foundational AI research (e.g., semantic parsing) with real-world deployments (e.g., privacy-compliant IoT). Awards & Honors: Member of the National Academy of Engineering ACM Fellow Popular Science's Best of What's New Award (Security, 2019) Advising & Grants: Lam oversees the Open Virtual Assistant Initiative, a collaborative project to build open-source semantic models. Her NSF CNS grant (CNS Core) focuses on federated privacy systems. She has advised over 50 students in AI and systems research, though specific names are not listed in the provided texts. Labs & Teams: Directs the Stanford Open Virtual Assistant Laboratory, collaborates with the Stanford NLP group, and maintains ties to industry through former startup Tensilica's legacy in embedded processors.
Mohammed Aledhari is an Assistant Professor at the University of North Texas, specializing in cybersecurity, machine learning, and data science. His research focuses on applications in computational medicine, bioinformatics, and autonomous systems. He holds a Ph.D. from Western Michigan University and degrees from the University of Basrah and the University of Anbar. His research interests include social cybersecurity techniques, federated learning in IoT, and AI-driven solutions for healthcare and transportation. Recent work explores blockchain-enabled digital twins, DDoS attack detection, and equitable ASD diagnostics using machine learning. His publications span cybersecurity frameworks, autonomous vehicle communication protocols, and biomedical IoT innovations. Notable contributions include optimizing intrusion detection in IoMT networks and developing interpretable machine learning models for healthcare. While no formal awards or grants are listed, his work emphasizes interdisciplinary applications of AI in healthcare, transportation, and energy markets. His email is Mohammed.Aledhari@unt.edu .
Ellie Pavlick is an Assistant Professor of Computer Science and Linguistics at Brown University, where she leads the Language Understanding and Representation (LUNAR) Lab . Her work bridges computational linguistics and cognitive science, focusing on grounded language learning and the structural dynamics of neural language models. PhD from University of Pennsylvania (2017), specializing in paraphrasing and lexical semantics Collaborates with Brown's Robotics, Visual Computing labs, and Cognitive, Linguistic, and Psychological Sciences department Research interests center on cognitively-inspired approaches to language acquisition, analyzing how LLMs and humans encode abstract reasoning and compositional structures. Her lab explores: Grounded language learning in multimodal systems Emergence of compositional behavior in neural networks Interpretability frameworks for black-box AI Key publication areas include: Transformer mechanisms and cognitive modeling Formality and complexity in language systems Relational reasoning and multimodal integration Ellie teaches courses on computational linguistics and human-machine language processing, with a focus on synergies between AI and psychological science.
Dr James Herbert-Read is an Associate Professor and Whitten Lecturer in Marine Biology at the Department of Zoology, University of Cambridge. He serves as Deputy Head of Department (Postgraduate Education) and leads the Marine Behavioural Ecology Group. His research focuses on understanding how animals, particularly marine organisms, collect and process information from their environments to make behavioral decisions, with emphasis on social interactions, adaptation mechanisms, and ecological constraints. His group employs theoretical frameworks, controlled experiments, and quantitative field studies to investigate behavioral diversity in marine species. Key themes include collective behavior, predator-prey dynamics, camouflage strategies, and the impacts of environmental stressors on animal decision-making. Recent publications highlight work on lionfish vocalization mechanisms, cuttlefish camouflage, citizen science applications in marine research, and behavioral responses to visual and acoustic noise. Scientific awards and affiliations include: Whitten Lecturer in Marine Biology Associate Professor, University of Cambridge He has supervised research projects on topics such as: Social attraction in invasive fish species Evolution of coordinated movement Neurophysiological basis for leadership in shoals Maternal effects on offspring exploration
Yuxin Chen is a Professor at the University of Pennsylvania , holding joint appointments in the Department of Statistics and Data Science and the Department of Electrical and Systems Engineering . Prior to UPenn, he was an Assistant Professor at Princeton University (2017-2021) and a Postdoctoral Researcher at Stanford University (2015-2017). His research spans statistics, optimization, reinforcement learning theory, diffusion models, and information theory , with a focus on theoretical foundations and practical algorithms for machine learning. Education : Ph.D. in Electrical Engineering (Stanford, 2015), M.S. in Statistics (Stanford, 2013), M.S. in Electrical and Computer Engineering (UT Austin, 2010), B.E. in Electrical/Microelectronics (Tsinghua, 2008). Research Interests encompass theoretical and applied aspects of machine learning, including nonconvex optimization , sample complexity analysis , low-dimensional adaptation , and generative modeling . His work bridges mathematical rigor with real-world applications, particularly in scientific imaging and high-dimensional data analysis. Scientific Awards include the SIAM Activity Group on Imaging Science Best Paper Prize (2024) Alfred P. Sloan Fellowship (2022) NSF Career Award (2022) Google Research Scholar Award (2022) IEEE Transactions on Power Electronics Prize Paper Award (2024) Advising and Grants : He has mentored numerous students who have transitioned to academic roles at institutions like UIUC and UW-Madison. His research is supported by grants from the NSF , Amazon , and Google , with recent projects focusing on controllable diffusion models and efficient reinforcement learning algorithms .
Dr. Matloob Khushi serves as a Senior Lecturer in Computer Science at Brunel University London's College of Engineering, Design and Physical Sciences. With over 25 years of combined academic and industry experience, his work bridges theoretical AI advancements with practical applications in finance, healthcare, and public health domains. His research has established significant collaborations with international banks, healthcare institutions, and technology startups. Dr. Khushi earned his PhD in AI and Data Science from the University of Sydney, developing novel algorithms for genomic data analysis. His postdoctoral research at the Children's Medical Research Institute (2014-2017) pioneered AI-based diagnostic tools for medical condition detection. More recently, he developed bioinformatics tools for environmental assessment under a UKRI NEC grant. Research Focus FinTech Innovation : Creator of the SS Ratio (incorporating volatility and drawdown sensitivities), advanced portfolio optimization models, and synthetic data generation techniques for fraud detection and credit risk assessment Bioinformatics Leadership : Developer of AI tools for genomic analysis and early cancer detection, featured in SBS News and The Daily Telegraph Public Health NLP : Architect of systems for vaccine misinformation detection, mental health monitoring, and health surveillance on social media His publication portfolio shows consistent growth from foundational bioinformatics work to current multimodal AI applications, with increasing interdisciplinary collaboration across finance and healthcare sectors. Awards and Recognition Ranked among Stanford/Elsevier's top 2% of global AI scientists Recipient of Best Paper Awards from IEEE Transactions on Computational Social Systems and PeerJ Media recognition for cancer detection research by major news outlets Mentorship and Teaching Dr. Khushi has supervised six PhD candidates to completion and over 100 postgraduate dissertations. He teaches CS3002 Artificial Intelligence and mentors students in Final Year Projects. His supervision focuses on Deep Learning/NLP for FinTech prediction and Public Health Surveillance applications, emphasizing practical implementation of theoretical concepts.
Paul Pu Liang is an Assistant Professor at the Massachusetts Institute of Technology (MIT) Media Lab and Department of Electrical Engineering and Computer Science (EECS). He directs the Multisensory Intelligence research group, focusing on building AI systems that integrate diverse sensory inputs to enhance human-AI symbiosis. His work spans theoretical foundations, large-scale resources, and neural architectures for multisensory learning. Education: PhD in Machine Learning (Carnegie Mellon University), MS in Machine Learning (Carnegie Mellon), BS with University Honors in Computer Science and Neural Computation (Carnegie Mellon) Research Interests: Multimodal machine learning, human-AI interaction, clinical AI, generative models, and responsible deployment of AI systems Key Contributions: MultiBench, HEMM evaluation framework, CLIMB clinical data foundations, and multimodal transformer architectures Recent publications emphasize multimodal foundation models , clinical applications , and socially responsible AI . His work has been recognized with multiple best paper awards and fellowships from Siebel, Facebook, and other institutions. Scientific Awards Siebel Scholars Award Waibel Presidential Fellowship Facebook PhD Fellowship Center for ML and Health Fellowship Rising Stars in Data Science Four best paper awards Paul teaches courses on machine learning and multimodal AI at MIT and CMU. He mentors students across multiple programs including Media Arts & Sciences, EECS, and IDSS, with former advisees now at institutions like OpenAI, UC Berkeley, and Princeton.
Gautam Kamath is an Assistant Professor at the University of Waterloo's Cheriton School of Computer Science, a Faculty Member at the Vector Institute, and a Canada CIFAR AI Chair. He leads The Salon, a research group focused on statistics, algorithms, machine learning, and optimization. His work bridges theoretical foundations with practical applications in data privacy and robustness, contributing to both academic research and real-world deployments that impact millions of users. Dr. Kamath earned his PhD and SM degrees in Electrical Engineering and Computer Science at MIT, where he was advised by Costis Daskalakis. Prior to MIT, he graduated from Cornell University in May 2012 with a degree in Computer Science and Electrical and Computer Engineering, where he worked with Bobby Kleinberg. His academic journey reflects a strong foundation in both theoretical computer science and practical applications. His research focuses on developing solutions for trustworthy and reliable machine learning and statistics, with particular emphasis on data privacy and robustness. He addresses fundamental problems in these areas, revitalizing statistical toolkits for the modern data era where privacy preservation is paramount. His work spans theoretical foundations of differential privacy to practical applications that have been deployed at scale, including contributions to systems that protect the sensitive information of hundreds of millions of individuals. Analysis of his recent publications reveals a strong trend toward addressing the interplay between privacy, robustness, and machine learning performance. His work increasingly examines practical deployment challenges while maintaining theoretical rigor, with growing emphasis on diffusion models, generative AI, and the legal implications of AI systems. There's a clear trajectory from foundational privacy theory toward addressing real-world implementation challenges across diverse application domains. Canada CIFAR AI Chair Ontario Early Researcher Award 2024 Caspar Bowden Award for Outstanding Research in Privacy Enhancing Technologies STOC Best Student Presentation Award ICML 2024 Best Paper Award Microsoft Research Fellow at the Simons Institute for the Theory of Computing Dr. Kamath actively mentors students, with notable successes including Valentio Iverson winning the Germain-Erdős Undergraduate Award and Chris Trevisan receiving the CRA Outstanding Undergraduate Researcher Award. His service to the research community is extensive, serving as Editor-in-Chief of TMLR, on the Executive Committee of the Learning Theory Alliance, and on steering committees for major conferences including ICML, COLT, and ALT. He has organized numerous workshops focused on privacy-preserving machine learning and differential privacy. Through The Salon research group, Dr. Kamath fosters a collaborative environment where postdoctoral fellows, graduate students, and undergraduates work together on cutting-edge problems at the intersection of statistics, algorithms, machine learning, and optimization. The group maintains strong connections with industry partners and participates in major research initiatives, including the Vector Institute's privacy and security research efforts. Looking forward, Dr. Kamath will be moving to the Computer Science department at NYU's Courant Institute of Mathematical Sciences in September 2026, where he plans to expand his research program.
Vincent E. Price serves as President of Duke University and holds the Walter Hines Page Distinguished Professorship in Public Policy and Political Science. He is a Professor of Public Policy in the Sanford School of Public Policy and Professor of Political Science at Duke University. Dr. Price earned his Ph.D. from Stanford University in 1987, an M.A. from Stanford in 1985, and a B.A. from Santa Clara University in 1979. His academic background forms the foundation for his leadership in higher education policy and university administration. Price's research interests center on public policy, political science, and higher education leadership. His work examines university strategic planning, innovation policy, and the role of research universities in society. As president, he has focused on developing Duke's strategic vision for its second century, emphasizing a 'people-first' approach to institutional investment that prioritizes faculty, students, and staff over physical infrastructure. His recent publications and speeches address critical issues facing higher education, including innovation policy, research funding, university governance, and the future of academic institutions. Price has been particularly active in advocating for full funding of the CHIPS and Science Act to strengthen American leadership in critical technology fields. Member, American Academy of Arts & Sciences (2020) President Price has been actively involved in guiding Duke through significant challenges, including the pandemic, while advancing strategic initiatives in faculty recruitment, student financial aid, campus sustainability, and anti-racism efforts. He chairs the ACC Board of Directors and has overseen Duke's expansion of global partnerships, including Duke Kunshan University in China. His leadership emphasizes community building, strategic investment in people, and preparing Duke for its second century of impact.
Camillo J. Taylor is the Raymond S. Markowitz President’s Distinguished Professor in the Department of Computer and Information Science at the University of Pennsylvania , where he has been a faculty member since 1997. He also serves as Associate Dean for Diversity, Equity, and Inclusion at the School of Engineering and Applied Science. His research focuses on Computer Vision and Robotics , particularly in 3D reconstruction, semantic mapping, and autonomous navigation. Education: A.B. in Electrical Computer and Systems Engineering, Harvard College (1988) M.S. and Ph.D. in Computer Science, Yale University (1990, 1994) Research Interests: Dr. Taylor’s work bridges Computer Vision and Robotics to enable autonomous systems to perceive and navigate complex environments. Key themes include semantic SLAM, event camera applications, and meta-learning for adaptive controllers. His projects often integrate vision, physics, and multi-agent collaboration, as seen in systems like EvMAPPER and OCCAM . Recent Article Trends: His 2024–2025 publications focus on semantic mapping , event-based vision , and multi-agent LLM systems , reflecting his lab’s emphasis on real-time perception, physics-informed reconstruction, and rational decision-making in robotics. These works span applications from solar eclipse imaging to wildfire analysis and natural hazard resilience. Awards: NSF CAREER Award (1998) Lindback Minority Junior Faculty Award (2001) IEEE WACV Best Paper Award (2012) Lindback Distinguished Teaching Award (2012) Advising and Service: Dr. Taylor has advised numerous PhD students, including Jason Hughes and Bowen Jiang. He has served as a Program Chair for CVPR (2006, 2017) and General Chair for ICCV (2021). His contributions to the GRASP Laboratory have advanced autonomous micro-UAVs and semantic SLAM.
Gianni Franchi is an Assistant Professor at ENSTA Paris, part of Institut Polytechnique de Paris. His research focuses on robust computer vision, uncertainty quantification, and explainable AI (XAI). He has been teaching Deep Learning, Computer Vision, and Machine Learning courses since 2020 at ENSTA Paris and Télécom Paris. PhD in Fusion of Information, Machine Learning, and Image Processing (2016) from Mines de Paris Postdoctoral experience at Paris Saclay University (2018-2020) and Seigen University (2016-2018) Current PhD students: Rémi Kazmierczak, Olivier Laurent, Adrien Lafage, Mouïn Ben Ammar Alumni: Xuanlong Yu (2020-2023) Research interests include robust computer vision, anomaly detection, uncertainty quantification, out-of-distribution detection, certifiable AI, and explainable AI. He leads the development of the PyTorch library Torch Uncertainty for uncertainty quantification in deep learning. Recent publications span uncertainty quantification in foundation models, trajectory forecasting, vision-language adaptation, and explainability benchmarks. Gianni actively collaborates on multimodal autonomous driving datasets and uncertainty-aware systems for human-agent interaction.
Karl Henrik Johansson is a Professor at the School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology in Stockholm, Sweden, where he also serves as the Founding Director of Digital Futures. He is a Fellow of both IEEE and the Royal Swedish Academy of Engineering Sciences, and has held leadership positions including Immediate Past President of the European Control Association and IEEE Control Systems Society Vice President Diversity, Outreach & Development. Dr. Johansson earned his MSc in Electrical Engineering and PhD in Automatic Control from Lund University. His academic journey includes visiting positions at prestigious institutions such as UC Berkeley, Caltech, and NTU. His research focuses on networked control systems and cyber-physical systems with applications in transportation, energy, and automation networks. His work investigates fundamental challenges in connecting physical world systems through communication networks, exploring how wireless communication and sensor technology can enhance system robustness, reliability, energy efficiency, and safety. Current research directions include security of cyber-physical systems, distributed optimization, multi-agent systems, and applications to intelligent transportation and energy networks. Analysis of his recent publications reveals a strong focus on distributed optimization algorithms, secure networked control, multi-agent systems, and applications to transportation and energy networks. His work increasingly integrates machine learning techniques with traditional control theory, addressing challenges in privacy-preserving distributed computation, resilient state estimation, and resource allocation in complex networked systems. IEEE Control Systems Society Hendrik W. Bode Lecture Prize (2024) Swedish Research Council Distinguished Professor (2018-2027) Wallenberg Scholar (2009-2026) IFAC Young Author Prize IEEE CSS Distinguished Lecturer (2017-2019) IFAC Outstanding Service Award IEEE Fellow Dr. Johansson has supervised over 100 postdocs and PhD students, with many now holding prominent positions at institutions worldwide. His research has been supported by significant grants including the Swedish Research Council Distinguished Professor Grant (2018-2027), multiple Wallenberg Foundation grants, and numerous EU and national research projects. He has directed major research centers including ACCESS Linnaeus Centre (2009-2016) and Strategic Research Area ICT TNG (2013-2020). His research group operates within the Digital Futures initiative and maintains strong connections with industry partners through projects like the Integrated Transport Research Lab (supported by Scania and Ericsson) and Smart Mobility Lab. The group actively collaborates with international institutions and participates in major EU-funded projects addressing challenges in cyber-physical systems, transportation, and energy networks.
