Dr. Feng Yan is an Associate Professor at the University of Houston's Computer Science Department, leading the Intelligent Data and Systems Lab (IDS Lab). He previously held an Associate Professor position at the University of Nevada, Reno. His research focuses on bridging Big Data, Machine Learning, and Systems, with interdisciplinary applications in wildfire science, materials engineering, and civil infrastructure. He has received prestigious awards such as the NSF CAREER Award and the Regents' Rising Researcher Award. Education: Ph.D. (2016) and M.S. (2011) in Computer Science from College of William and Mary; B.S. (2008) in Computer Science from Northeastern University. Research experience includes roles at Microsoft Research and HP Labs. Research Interests: Large Language Models (LLM), Distributed Deep Learning, AutoML, Serverless Computing, Federated Learning, and AI-driven domain sciences. His work emphasizes real-world impact through collaborations with industry and national labs. Publications: Over 60+ papers in top-tier venues like NeurIPS, ICLR, KDD, AAAI, SOSP, SC, and VLDB. Key contributions include ZeRO++ (collective communication optimization), Gradient Compression techniques, and Federated Learning frameworks like TiFL and HDFL. Awards: NSF EPSCoR Award ($20M), NSF CAREER Award, FAA BAKFAA Grant, and multiple best paper awards (IEEE CLOUD 2018, CLOUD 2019). Active in program committees for HPDC, ICAC, ICPE, and AAAI. Advising: Supervised over 30+ graduate/undergraduate students, with placements at Microsoft Research, IBM, Oak Ridge National Lab, Facebook, and MathWorks. Runs a vibrant lab with a focus on interdisciplinary AI/Systems research.
Paolo Papotti is an Associate Professor of Computer Science at EURECOM (France) since 2017, affiliated with the Data Science department. Previously, he was a senior scientist at QCRI (Qatar) and an assistant professor at Arizona State University (USA). He earned his PhD in Computer Science from the University of Roma Tre (Italy) in 2007, following an MEng in Computer Engineering from the same institution in 2003. His research focuses on scalable data management, data integration, data cleaning, and computational fact-checking. Notable contributions include work on knowledge graph rule discovery (Rudik), fact-checking frameworks (Scrutinizer), and data quality systems. His research has been supported by awards such as the 2020 Google Faculty Research Fellowship. Key publications include advancements in table representation learning, LLM-based data querying, and crowdsourced fact-checking validation. His work spans theoretical foundations and practical tools for improving data quality and information trustworthiness.
Emanuel Sallinger is a Full Professor at TU Wien's Databases and Artificial Intelligence Group and Vice Dean of Academic Affairs for Business Informatics and Data Science. He leads the Knowledge Graph Lab, focusing on scalable knowledge-based systems, reasoning in knowledge graphs, and AI integration. His research spans computational logic, database theory, and blockchain applications. Education: PhD in Computer Science (awarded 'sub auspiciis praesidentis rei publicae'), Master's degrees in Computational Intelligence and Informatics Management, and a Bachelor's in Software and Information Engineering. Research Interests: Knowledge graphs (construction, reasoning, scalability), logic-based systems, AI/ML integration with databases, enterprise architecture modeling, and financial knowledge systems. His work emphasizes practical applications like enterprise modeling, sustainable waste management, and regulatory compliance. Grants & Projects: Lead Vienna Science and Technology Fund (WWTF)-funded Knowledge Graph Lab. Involved in projects like 'Knowledge Graph-driven Tour Management' (sustainability), 'SustainGraph' (waste processing), and 'Enterprise Architecture Knowledge Graphs'. Teaching: Offers courses on Knowledge Graphs, Generative AI, Database Systems, and research methodology. Supervises doctoral and master's students in AI, databases, and knowledge representation. Labs/Teams: Knowledge Graph Lab at TU Wien, collaborating with industry on blockchain-based systems, financial AI, and enterprise architecture frameworks.
Professor Michael Keidar holds the A. James Clark Professorship at the George Washington University (GW) , School of Engineering and Applied Science, within the Mechanical and Aerospace Engineering department. He leads the Micropropulsion and Nanotechnology Lab , pioneering research in plasma medicine, micropropulsion systems, and plasma nanoscience. His lab collaborates with industry partners like Vector (licensed plasma thruster technology) and US Patent Innovations, LLC (a $5.3M grant for cold plasma cancer therapy). Key research areas include: Cold plasma applications in biomedical treatment Microthrusters for nanosatellites Synthesis of graphene and carbon nanotubes Multi-scale plasma simulations Scientific accolades include the 2017 Ronald C. Davidson Award and AIAA Engineer of the Year (2016-2017), alongside leadership in interdisciplinary projects with GW’s Global Food Institute .
Sarath Chandar is an Associate Professor at Polytechnique Montréal and Core Faculty Member at Mila, the Quebec AI Institute. He holds a Canada CIFAR AI Chair and Canada Research Chair in Lifelong Machine Learning. His research focuses on developing interactive learning algorithms for continual and lifelong learning, with expertise in deep learning, reinforcement learning, and natural language processing. Education: Ph.D. in Computer Science, University of Montreal (advisor: Yoshua Bengio) M.S. in Computer Science, Indian Institute of Technology Madras (advisor: Balaraman Ravindran) Research Themes: Continual Learning and Lifelong Learning Deep Reinforcement Learning Optimization for Deep Networks Natural Language Processing AI for Scientific Discovery Notable Contributions: Founder of the Conference on Lifelong Learning Agents (CoLLAs) Developed Chandar Research Lab (CRL), focusing on adaptive learning algorithms Contributions to model-based reinforcement learning and bias mitigation in AI systems Awards & Grants: Canada CIFAR AI Chair Canada Research Chair Tier 2 MITACS-funded projects on reinforcement learning applications Lab & Collaboration: CRL collaborates with academic/industrial partners (e.g., IBM, Samsung) Hosts annual symposium showcasing research in AI, optimization, and multi-agent systems
Arya Mazumdar is a tenured Professor of Data Science and Computer Science at the Halıcıoğlu Data Science Institute (HDSI) , part of the School of Computing, Information and Data Sciences at the University of California San Diego (UCSD). He also holds affiliations with the Computer Science and Engineering and Electrical and Computer Engineering departments at UCSD. Previously, he was an Assistant and Associate Professor at the University of Massachusetts Amherst (2015–2021), and held a postdoctoral position at MIT (2011–2012). He is an IEEE Distinguished Lecturer (2023–2024) and recipient of the NSF CAREER Award (2015–2020). Education : PhD in 2011 from the University of Maryland, College Park (advisor: Alexander Barg) Postdoctoral scholar at MIT (2011–2012, advisor: Greg Wornell) Internships at IBM Almaden (2010) and HP Labs (2008) Research Interests : Focus on algorithmic and statistical aspects of machine learning, error-correcting codes, optimization, signal processing, and distributed systems. Key areas include clustering algorithms, compressed sensing, federated learning, and information-theoretic foundations of data science. He has contributed to theoretical guarantees for learning mixtures, distributed optimization, and robust coding schemes. Recent Articles Trends : Recent work emphasizes distributed optimization (e.g., vqSGD, Byzantine-resilient algorithms), sparse recovery in high-dimensional models, and theoretical foundations of learning (e.g., support recovery, parameter estimation). His research bridges information theory and machine learning, with applications in storage systems and large-scale data processing. Awards & Roles : 2020 EURASIP JASP Best Paper Award Co-PI and co-leader of the NSF AI Institute for Learning-Enabled Optimization at Scale Editorships: IEEE Transactions on Information Theory and Foundations and Trends in Communications Grants & Labs : Leads the EnCORE Institute as UCSD Site Lead, focusing on theoretical perspectives of large language models and computational-statistical gaps. Active in organizing workshops on topics like LLMs, clustering, and distributed optimization. His research is funded by NSF and industry collaborations. Teaching : Courses include Algorithms for Data Science , Probability and Statistics for Data Science , and Coding Theory , emphasizing foundational theory and scalable methods.
Viktor Prasanna is the Charles Lee Powell Chair in Engineering and Professor of Electrical and Computer Engineering and Computer Science at the University of Southern California. He holds courtesy appointments in Computer Science and leads the Center for Energy Informatics, focusing on interdisciplinary research linking energy technologies, computer science, and engineering. Education: BE in Electronics (Bangalore University), ME (Indian Institute of Science), PhD in Computer Science (Pennsylvania State University) His research spans reconfigurable computing, FPGA accelerators, parallel and distributed systems, and big data applications. He has pioneered high-performance architectures and algorithms using FPGAs, impacting domains like networking, security, HPC, and machine learning. Prasanna has published over 600 papers, received 22 best paper awards, and secured >$50M in grants. His work emphasizes energy-efficient computing, with recent grants totaling $12.9M (2016–2021). His h-index is 73, with 23,454 total citations. Scientific Awards: IEEE Fellow, ACM Fellow, AAAS Fellow, W. Wallace McDowell Award, multiple Distinguished Alumnus Awards He has advised over 70 doctoral students and led major centers including CiSoft (Big Data in oilfield tech) and CAST. His editorial roles include Editor-in-Chief of IEEE Transactions on Computers and Journal of Parallel and Distributed Computing.
Ikjot Saini is a Professor at the University of Windsor’s Faculty of Engineering, co-leading the SHIELD Automotive Cybersecurity Centre of Excellence, Canada’s first organization addressing threats in connected transportation. Her research focuses on automotive cybersecurity, vehicular networks, and privacy-preserving technologies. She has supervised doctoral students Shiva Nejati and Kunj Dhonde, and contributed to courses in the University’s Continuing Education program, specializing in cybersecurity education for professionals. Her work includes pioneering studies on blockchain-based security for connected autonomous vehicles (CAVs), machine learning-driven intrusion detection systems, and privacy-enhancing mechanisms like pseudonym-changing strategies. She has been recognized with the K.W. Michael Siu Award from the APMA Institute for Automotive Cybersecurity (2020). Saini’s research bridges theoretical advancements with real-world applications, ensuring vehicles and infrastructure remain secure against evolving cyber threats. Her contributions span academic publications, industry partnerships, and policy recommendations, positioning her as a leader in vehicular cybersecurity. Ongoing projects emphasize eco-efficiency in cybersecurity solutions and adversarial modeling for privacy evaluation.
Andreas Grothey is a Senior Lecturer in the School of Mathematics at The University of Edinburgh, a position he has held since 2011. He completed his MSc in Numerical Algebra and Mathematical Computing at the University of Dundee (1995) and his PhD in Optimization at the University of Edinburgh (2001), supervised by Ken McKinnon. His research focuses on stochastic programming, interior point methods, decomposition approaches, high-performance computing, and energy systems optimization. He has contributed to energy planning, power grid reliability, and emergency response strategies for power networks. Grothey has advised seven PhD students, including work on unit commitment, top-percentile traffic routing, and power flow optimization. His projects include the OOPS solver, CESI energy integration center, and the Structured Modelling Language (SML). Recent work addresses pandemic policy optimization and exascale computational challenges. Education: MSc in Numerical Algebra and Mathematical Computing (University of Dundee, 1995) PhD in Optimization (University of Edinburgh, 2001) Research Interests: Stochastic Programming Interior Point Methods Decomposition Methods High-Performance Computing Energy Systems Optimization Advising & Projects: PhD Supervision (7 students, 2007–2022) OOPS Parallel Solver Development CESI Energy Systems Integration SML Structured Modelling Language Labs/Teams: Member of the Edinburgh Research Group on Optimization, leading projects in power grid stability and energy planning.
Professor David Taubman is a distinguished academic serving as Professor and Deputy Head of School (Research) at the School of Electrical Engineering and Telecommunications (EE&T) at the University of New South Wales (UNSW) in Sydney, Australia. He is also co-director of Kakadu Software Pty. Ltd. and its affiliates Kakadu R&D and Kakadu GPU. With a career spanning over three decades, Professor Taubman has made significant contributions to the field of image and video compression, most notably as the author of the EBCOT coding algorithm adopted in the JPEG2000 international standard. Professor Taubman earned his B.Sc. in Mathematics and Computer Science (1986) and B.E. (Medal) in Electrical Engineering (1988) from the University of Sydney, followed by an M.Sc. (1992) and Ph.D. (1994) in Electrical Engineering from the University of California at Berkeley. His professional journey includes engineering work at the Electricity Commission of N.S.W. (1988-1990), research positions at Hewlett-Packard Laboratories in Palo Alto (1994-1998), and an academic career at UNSW where he progressed from Senior Lecturer (1998-2003) to Associate Professor (2004-2009) and finally to Professor (2009-present). He has held various leadership roles including Head of the EE&T Telecommunications Research Group (2003-2014), Head of the EE&T Signal Processing Research Group (2014-present), Director of Research for the School of EE&T (2011-2016), and Deputy Head of School (Research) since 2017. Professor Taubman's research interests center on image and video compression, with particular expertise in JPEG2000 standards and implementations. His work spans signal processing, wavelet transforms, scalable video coding, motion modeling, and multimedia systems. He has pioneered numerous compression algorithms and frameworks, including the EBCOT coding algorithm that became central to the JPEG2000 standard. His recent research focuses on efficient motion modeling with cuboidal partitioning, learned lifting-based transform structures, and high-throughput implementations of JPEG2000 for video applications. His work bridges theoretical foundations with practical implementations, as evidenced by the commercially successful Kakadu Software tools that have garnered around 500 commercial licensees. Analysis of Professor Taubman's recent publications reveals a consistent focus on advancing compression technologies with particular emphasis on scalability, efficiency, and adaptability. His work spans traditional image compression (JPEG2000 extensions), video coding (cuboid-based partitioning for UHD/360-degree video), and emerging applications (nanopore sequencing data compression). A notable trend is the integration of machine learning techniques with traditional compression frameworks, as seen in his work on learned lifting-based transform structures. His research maintains strong connections to real-world applications across diverse domains including medical imaging, astronomical data processing, and genomic sequencing. IEEE Fellow Engineers Australia Fellow (by invitation) Professor Taubman has served as Associate Editor for the IEEE Transactions on Image Processing for two four-year appointments (2003-2005 and 2010-2013). He has been actively involved in numerous research grants focused on image and video compression technologies, particularly those related to the JPEG2000 standard and its extensions. His work has received significant industry support, reflected in his consultancy with various U.S., Japanese, and Australian corporations. He has also contributed to international standards development as a member of Standards Australia Technical Committee MS-065 (mirroring ISO TC42 on Digital Photography) and as a constitutional member of Standards Australia Technical Committee IT-029 (Coded Representation of Picture, Audio and Multimedia/Hypermedia Information). Professor Taubman co-directs Kakadu Software Pty. Ltd. and its research affiliates Kakadu R&D and Kakadu GPU, which have developed the commercially successful Kakadu Software tools for JPEG2000. His research group at UNSW focuses on advanced image and video compression techniques, with particular expertise in wavelet-based methods, scalable coding, and motion modeling. The group maintains strong industry connections and has contributed significantly to the development and standardization of image compression technologies worldwide.
Sriram Subramanian is an Assistant Professor at the School of Computer Science in Carleton University since July 2025. He holds affiliations with the Vector Institute for Artificial Intelligence and the Schwartz Reisman Institute for Technology and Society in Toronto, and serves as a mentor in the Indigenous Black Engineering and Technology (IBET) PhD Project . Ph.D. in Electrical and Computer Engineering, University of Waterloo (2022) MASc in Electrical and Computer Engineering, University of Waterloo (2018) BE in Geomatics Engineering, Anna University (2016) His research focuses on advancing Multi-agent Systems and Reinforcement Learning through intersections with Game Theory , with applications in generative AI , robotics, finance, and autonomous driving. Recent work emphasizes cooperation mechanisms, constraint learning, and theoretical robustness in large-scale environments. Articles demonstrate cross-disciplinary impacts in chemistry (ChemGymRL) and societal systems. Notable awards include the MITACS Globalink Research Award , Pasupalak Fellowship in AI , and the CAIAC Best Doctoral Dissertation Award (2023) . Publications span top venues like AISTATS, ICML, AAAI, IJCAI, JAIR , and TMLR . He has collaborated with Microsoft, Royal Bank of Canada, Denso, ESRI, and Borealis AI. As a Distinguished Postdoctoral Fellow at the Vector Institute (2022-2025), he advanced algorithmic frameworks while maintaining active roles in conference reviewing and committee work. His advocacy for equity and diversity drives mentorship initiatives in Canadian institutions.
Agustín Zaballos Diego is an Assistant Professor in the Department of Computer Engineering at University Ramon Llull (URL), Barcelona, Spain, since 1999. He serves as Research Coordinator in the Department of Engineering at La Salle Campus Barcelona and leads the R&D Networking and Security Area since 2002. His academic background includes a PhD in Data Networks and Internet Technologies (2012), an International MBA (2014), and an M.S. in Electronic Engineering (2000). University: University Ramon Llull (URL) Department: Department of Computer Engineering Research Group: GRITS Research Focus: Real-time QoS-aware routing protocols in Smart Grids, Ubiquitous Sensor Networks, and IoT communications. His work bridges telecommunications, computer science, and energy systems through projects like OPERA (FP6), INTEGRIS (FP7), and FINESCE (FP7). Publication Trends: Recent articles highlight advancements in HF communications for Antarctic research, hybrid genetic algorithms for traffic engineering, IPv6 testing, and Industry 4.0-related networking solutions. Keywords span Smart Grids, IoT, Sensor Networks, and QoS optimization. Collaborative Projects: Key initiatives include the Antarctica Project , ATHIKA (ICT in healthcare), ENVISERA (environmental sensor networks), HOTSUP (online teaching innovation), PLANET4 (AI/ML in industry), and XIoT (IoT scalability challenges).
Dr. Keivan Ahmadi is an Associate Professor in the Department of Mechanical Engineering at the University of Victoria (UVic), serving as Graduate Program Director. He holds a PhD from the University of Waterloo (2012), followed by postdoctoral positions at UBC and Pratt & Whitney Canada. His research focuses on dynamics and vibrations in machining processes, robotic manufacturing, and advanced manufacturing systems. Education: BSc (Tehran Polytechnic), MSc (IUST), PhD (Waterloo) Affiliations: Dynamics and Digital Manufacturing Lab (DDML), UVic Mechanical Engineering Research interests include vibration suppression in machining, chatter prediction, robotic milling dynamics, and high-speed manufacturing systems. His work combines experimental modal analysis, Bayesian modeling, and data-driven approaches to enhance manufacturing precision and sustainability. Key projects include vibration compensation in 3D printing, dynamic modeling of robotic arms for milling, and optimization of thin-walled structure machining. Over 20 peer-reviewed articles showcase his contributions to machining stability, FRF estimation, and additive manufacturing. Advised 19 graduate students (9 alumni, 10 current) Collaborations with industries like GM, Linamar, and CanEV Labs/Teams: Leads the Dynamics and Digital Manufacturing Lab (DDML), focused on sustainable manufacturing through dynamic systems innovation. Hosts a diverse team prioritizing underrepresented groups in engineering.
Rafael Guedez Mata is a Researcher at the Energy Department of KTH Royal Institute of Technology , specializing in techno-economic modeling for energy conversion and storage technologies. He holds a PhD from KTH and completed an executive program in Management and Leadership at MIT Sloan. His research focuses on optimizing solar power plants, thermal energy storage, and supercritical CO₂ systems, with projects funded by the EU, Swedish Energy Agency, and industry partners. As a Principal Investigator (PI), he leads high-impact projects such as SOLARSCO2OL and SHARP-sCO₂, advancing hybrid solar and supercritical CO₂ technologies. He has supervised over 60 MSc theses and received the PhD Supervisor of the Year 2021 award. He teaches courses like Large Scale Solar Power (MJ2500) and Energy Storage Technology (MJ2386) , and is Deputy Director of the Sustainable Energy Engineering MSc program. His professional roles extend beyond academia: he co-founded Europe Power Solutions , advises firms like Naventus Corporate Finance and Odqa Renewable Energy Technologies , and previously worked at SolarReserve and Total’s Solar Energy division. His work bridges academia and industry, emphasizing sustainable energy solutions. Key technical contributions include optimizing hybrid solar-PV plants, power-to-ammonia systems, and thermal storage integration. His publications (80+ papers) address system design, economic modeling, and climate resilience. Current projects include decarbonizing industrial sectors via power-to-heat systems and assessing environmental impacts of stationary batteries.
Martin Berzins is a Professor of Computer Science at the University of Utah, affiliated with the School of Computing and the Scientific Computing and Imaging (SCI) Institute. His research focuses on parallel scientific computing, numerical methods for partial differential equations, and high-performance computing frameworks. He is a leading developer of the Uintah framework, a scalable simulation tool used for large-scale engineering and scientific problems. Research Interests : Parallel algorithms, adaptive mesh refinement, material point method (MPM), exascale computing, computational fluid dynamics, and performance portability. His work emphasizes scalable software solutions for complex multiscale and multiphysics simulations, with applications in environmental modeling, explosive detonation analysis, and computational mechanics. Recent articles highlight advancements in Uintah's portability to exascale systems, error estimation in MPM, and high-order numerical methods. Berzins has contributed significantly to the development of task-based parallelism strategies and heterogeneous computing optimizations. His research bridges theoretical numerical analysis with practical large-scale computational challenges. Collaborations include DOE projects on hazard analysis and exascale computing. He has pioneered the integration of runtime systems like Hedgehog with Uintah to enhance scalability on modern architectures. His work ensures computational frameworks remain viable for emerging hardware trends, emphasizing both algorithmic innovation and software engineering rigor.
