جستجوی استاد راهنمای دانشگاهی

David R. Koesمشاهده پروفایل

دانشیار

• Computational Drug Discovery
• Machine Learning in Biology
• Open Source Software
• +۳ مورد دیگر

David R. Koes is an Associate Professor in the Department of Computational and Systems Biology at the University of Pittsburgh, affiliated with the School of Medicine. He holds roles such as Associate Director of the Joint CMU-Pitt Computational Biology PhD Program (CPCB) and is involved in multiple graduate programs including Intelligent Systems and Computational Biomedicine. His research focuses on developing computational algorithms and systems for drug discovery, emphasizing open-source software and machine learning applications in biomedical data. Koes teaches courses like MSCBIO2025 (Bioinformatics Programming in Python) and MSCBIO2065 (Scalable Machine Learning for Big Data Biology). He has secured NIH funding (R35GM140753) and collaborated on projects with institutions like NVIDIA and Google Cloud. His lab develops tools such as GNINA, Pharmit, and 3Dmol.js, and actively contributes to open drug discovery initiatives. Education: PhD in Computer Science from Carnegie Mellon University (CMU). Research Interests: Leveraging computation and AI for drug design, molecular docking, pharmacophore modeling, and open science. Specific areas include developing scalable machine learning pipelines, virtual screening systems, and tools for 3D molecular analysis. Grants and Funding: Current NIH R35 grant and prior support from NSF, Relay Therapeutics, and others. His work emphasizes translating computational methods into practical drug discovery solutions. Lab and Teams: Directs a lab focused on computational drug discovery, collaborating with multiple academic and industry partners. Supervises graduate students and postdocs in projects spanning AI-driven drug design, molecular modeling, and software development.

Knut Reinertمشاهده پروفایل

استاد

• Algorithmic Bioinformatics
• Genomic Sequence Analysis
• Proteomics
• +۷ مورد دیگر

Professor Knut Reinert is a leading figure in algorithmic bioinformatics at the Free University of Berlin, where he holds a professorship in the Department of Mathematics and Computer Science. He also maintains a significant affiliation with the Max Planck Institute for Molecular Genetics in Berlin, where he leads the Efficient Algorithms for Omics Data group. His research spans both institutions through the Reinert Lab, which focuses on developing novel computational approaches for biological data analysis. Reinert's educational background includes a Diploma in Computer Science (1994) and a Doctorate (Dr. Ing./Ph.D., 1999, with honors) from the Max-Planck-Institut for Computer Science and Universität des Saarlandes in Saarbrücken. Prior to his professorship, he worked as a computer scientist under Prof. Gene Myers at Celera Genomics in Rockville, USA (1999-2002). His primary research interests center on algorithmic bioinformatics with specific focus on developing novel algorithms and data structures for biomedical mass data analysis. This includes creating mathematical models for genomic sequence analysis and algorithms for mass spectrometry data to detect differential protein expression between normal and diseased samples. His work bridges the gap between computational tool development and practical biological applications, with particular emphasis on NGS and proteomics data. The publications and projects led by Prof. Reinert demonstrate a consistent focus on advancing computational methods in bioinformatics. His research spans genomic sequence analysis, RNA research (particularly long non-coding RNAs), parallel computing applications, and GPU acceleration for biological data processing. The work shows increasing sophistication in handling large-scale biological datasets through innovative algorithmic approaches. Intel® Parallel Computing Center designation for his lab CUDA Research Center status DFG funding of 530 thousand Euros for RNA research de.NBI funding of 2 million Euros BMBF funded projects 'LIVE-DREAM' and 'EssBar' Prof. Reinert leads multiple significant research projects and has established strong collaborations with international partners including Texas A&M, Kings College London, Eberhardt-Karls Universität Tübingen, Robert-Koch-Institute, and various Turkish institutions. His lab receives funding from major organizations including DFG, BMBF, and Intel. The Reinert Lab maintains active teaching responsibilities at FU Berlin, offering courses at BSc, MSc, and PhD levels using both traditional and innovative learning concepts like e-learning and inverted classrooms. The Reinert Lab consists of two interconnected research groups that work closely with experimental biologists and medical researchers to develop practical computational solutions for real-world biological problems. The lab has established itself as a key player in the German and international bioinformatics community through its development of the widely-used SeqAn library and participation in national infrastructure initiatives.

• Time Series Analysis
• Data Mining
• Machine Learning
• +۳ مورد دیگر

Eamonn Keogh is a Professor in the Computer Science and Engineering Department at the University of California, Riverside. His pioneering work centers on the Matrix Profile, a transformative approach to time series data mining enabling efficient solutions for motif discovery, anomaly detection, and similarity search. His algorithms (STAMP, STOMP, SCRIMP, DAMP, SCAMP) offer exact, parameter-free, and scalable solutions across domains like seismology, bioinformatics, and industrial IoT. Research areas include: Development of ultra-fast algorithms for time series joins and motif discovery at unprecedented scales (breaking the 100 million barrier) GPU acceleration for time series mining Domain-agnostic methods for semantic segmentation and anomaly detection Novel primitives like Time Series Chains, Snippets, and Consensus Motifs His work is highly cited and recognized by industry and academia, with applications ranging from NASA's Cassini mission to detecting BGP anomalies in computer networks.

• Computer Architecture
• Machine Learning
• Cloud Computing
• +۴ مورد دیگر

Chita R. Das is a Professor at Pennsylvania State University, known for extensive contributions in computer architecture, machine learning, and high-performance computing. Their research focuses on optimizing hardware-software co-design for edge computing, cloud infrastructure, and energy-efficient systems. Key areas include FPGA acceleration, GPU optimization, and serverless computing frameworks. Das collaborates frequently with institutions like AMD and Intel, addressing challenges in parallel computing and distributed systems. Their work bridges theoretical advancements with practical applications in recommendation systems, bioinformatics, and real-time video processing. Research interests span across hardware acceleration techniques, cloud resource management, and sustainable computing. Notable projects include adaptive training frameworks for intermittent power environments and neural-augmented game streaming for mobile platforms. Das's publications often address performance bottlenecks in modern architectures and propose novel solutions for latency and energy efficiency. Recent articles highlight innovations in serverless computing cost optimization, low-bandwidth VR streaming, and FPGA-based bioinformatics tools. Their contributions are characterized by interdisciplinary approaches combining computer architecture with machine learning and embedded systems.

Ozcan Ozturkمشاهده پروفایل

استاد

• Heterogeneous Computing
• Hardware Accelerators
• GPU Computing
• +۷ مورد دیگر

Ozcan Ozturk is a Professor in the Computer Science and Engineering and Electronics Engineering programs at Sabancı University's Faculty of Engineering and Natural Sciences. Previously, he held professorships at Bilkent University and adjunct roles at North Carolina State University. His expertise spans heterogeneous computing, parallel systems, processor architecture, and compiler optimization. He earned his Ph.D. from Penn State University, with prior academic roles at the University of Florida and internships at Intel and Marvell. Education: Ph.D. in Computer Science and Engineering (2007), Pennsylvania State University M.S. in Computer Engineering (2002), University of Florida B.Sc. in Computer Engineering (2000), Bogazici University Research interests include accelerator technologies, GPU-based systems, multicore processors, and compiler optimizations. His work focuses on improving parallelization efficiency, energy optimization, and reliability in heterogeneous architectures. He leads funded projects like 'Machine Learning for Compiler Flags' and 'Graph Accelerator Design'. Notable awards include the Bilkent Teaching Award (2019), BAGEP (2018), and HiPEAC Paper Award (2016). He serves on editorial boards of IEEE and ACM journals and chairs major conferences like ASPLOS and ICS. Grants and collaborations include partnerships with Huawei, Intel, NVIDIA, and TÜBİTAK. He advises over 20 students and has supervised projects in safety-critical systems, FPGA accelerators, and compiler-directed optimizations. His lab develops domain-specific architectures, including RISC-V extensions for graph processing.

• Cloud Computing
• Operating Systems
• File/Storage Systems
• +۳ مورد دیگر

Haryadi S. Gunawi is a Professor in the Department of Computer Science at the University of Chicago where he leads the UCARE research group (UChicago systems research on Availability, Reliability, and Efficiency). His work focuses on improving the dependability of storage and cloud computing systems, with a particular emphasis on addressing performance stability, reliability, and scalability challenges in modern computing environments. Dr. Gunawi received his Ph.D. in Computer Science from the University of Wisconsin, Madison in 2009. Following his doctoral studies, he was a postdoctoral fellow at the University of California, Berkeley from 2010 to 2012 before joining the University of Chicago faculty. His research focuses on three main areas: (1) performance stability, where he builds storage and distributed systems robust to latency tails and "limping" hardware; (2) reliability and scalability, where he addresses concurrency and scalability bugs in cloud-scale distributed systems; and (3) the intersection of machine learning and systems, exploring how machine learning techniques can solve operating and storage system problems. His work often combines theoretical insights with practical system implementations that address real-world challenges in cloud and storage infrastructure. Dr. Gunawi's publication record shows a consistent focus on storage and cloud system reliability, with recent work increasingly incorporating machine learning techniques to address traditional systems challenges. His research spans the full stack from hardware interfaces to distributed system design, with a strong emphasis on practical solutions that can be deployed in production environments. His work often involves close collaboration with industry partners to ensure real-world relevance and impact. Dr. Gunawi has received numerous prestigious awards including the NSF CAREER award, NSF Computing Innovation Fellowship, Google Faculty Research Award, multiple NetApp Faculty Fellowships, and an Honorable Mention for the 2009 ACM Doctoral Dissertation Award. He has also received the Provost's Global Faculty Award and Facebook Faculty Research Award, highlighting the broad recognition of his contributions to the field. As an advisor, Dr. Gunawi has mentored several PhD students including Ruidan Li, Ray Andrew, Rani Ayu Putri, and William Nixon. His research has been supported by major grants from NSF, Google, Facebook, and NetApp, enabling his team to pursue ambitious research projects at the intersection of systems, storage, and machine learning. Dr. Gunawi leads the UCARE research group at UChicago, which focuses on improving the dependability of storage and cloud-scale distributed systems. He is also involved with the Chameleon cloud research infrastructure project and the broader Systems Group at UChicago, contributing to a vibrant research community focused on systems, programming languages, and software engineering.

Mithuna S. Thottethodiمشاهده پروفایل

استاد

• Computer Architecture
• Interconnection Networks
• Distributed Systems
• +۷ مورد دیگر

Mithuna S. Thottethodi is a Professor and Interim Associate Head of Teaching and Learning at the Elmore Family School of Electrical and Computer Engineering at Purdue University. He holds a B.Tech. from the Indian Institute of Technology, Kharagpur (1996), and a Ph.D. in Computer Science from Duke University (2002). His research focuses on computer architecture, interconnection networks, distributed systems, and security, with notable work on sparse tensor accelerators and processing-near-memory architectures. His work has been funded by the NSF, AT&T, and SK Hynix. Research interests include security, interconnection networks in multicores, storage performance optimization, and memory hierarchies. Notable contributions span hardware accelerators for machine learning, secure speculative execution, and datacenter network congestion control. He has advised over 20 graduate students, many of whom have joined top tech firms like Google, Microsoft, and Intel. Awards: NSF CAREER Award (2007) Wilfred Hesselberth Teaching Excellence Award (2021) Multiple Eta Kappa Nu Outstanding Professor Awards Teaching includes undergraduate courses like EE 437 (Computer Design) and graduate courses such as ECE 666 (Advanced Computer Architecture). He also advises VIP and EPICS student teams.

Kenneth Rossمشاهده پروفایل

استاد

• Database Systems
• Query Processing
• Query Language Design
• +۷ مورد دیگر

Kenneth Ross is a Professor in the Computer Science Department at Columbia University in New York City. His primary appointment is within the Department of Computer Science, with affiliations including the Foundations of Data Science Committee. His work bridges theoretical database research and practical system implementation. His research focuses on database systems with particular expertise in query processing, query language design, data warehousing, and architecture-sensitive database system design. Additional research spans computational biology, especially analysis of large genomic data sets. Current projects include Linear Algebra Operators in Databases for machine learning workloads and Repeats and Somatic Mutation analysis in genomics. His work consistently addresses the intersection of hardware capabilities and database system design. Ross leads the Database Research Lab at Columbia, which has produced significant work on query optimization, GPU database processing, and hardware-conscious database systems. His recent publications demonstrate strong focus on adapting database systems to modern hardware including GPUs, SIMD processors, and persistent memory. His scientific recognition includes: Packard Foundation Fellowship Sloan Foundation Fellowship NSF Young Investigator Award Distinguished Faculty Teaching Award (2008) Ross actively advises undergraduate engineering students (juniors with last names P-Z) and has taught foundational courses including Introduction to Databases and Programming and Problem Solving for over two decades. His teaching portfolio shows consistent engagement with both theoretical concepts and practical implementation challenges in computer science education.

Gabriele Schweikertمشاهده پروفایل

مدرس ارشد

• Computational Biology
• Machine Learning
• Epigenetics
• +۶ مورد دیگر

Dr. Gabriele Schweikert is a Senior Lecturer and Principal Investigator with a joint appointment between the Division of Computational Biology in the School of Life Sciences at University of Dundee and Cyber Valley in Tuebingen. Her research focuses on applying machine learning techniques to understand epigenetic mechanisms and molecular processes in living cells. Dr. Schweikert completed her PhD at the Max Planck Institute Tuebingen working with Schoelkopf, Weigel, and Raetsch labs on machine learning for computational gene finding. She subsequently joined Adrian Bird's lab at the Wellcome Trust Center for Cell Biology in Edinburgh, a pioneer in epigenomic research. Prior to her current position, she held prestigious Marie Curie and EMBO Fellowships at the School of Informatics, University of Edinburgh. Her research interests center on using machine learning to decode epigenetic mechanisms that determine cellular identity and function. She investigates how cells with identical DNA can differentiate into specialized cell types through epigenetic regulation, with particular focus on applications in understanding tumorigenesis where epigenetic machinery malfunctions. Her work combines high-throughput epigenomic data with advanced computational approaches to address complex biological questions. Analysis of her recent publications reveals a strong focus on epigenomic data analysis, machine learning applications in biology, and computational approaches to understanding gene regulation. Her work spans from fundamental epigenetic mechanisms to practical applications in disease research, with growing emphasis on individual-specific epigenomic analysis and explainable AI in biomedical contexts. UKRI Future Leaders Fellowship (2020, £1.6 million) Marie Curie Fellowship EMBO Fellowship Dr. Schweikert actively supervises PhD students and has received significant research funding for projects including 'Machine Learning Methods to Re-Annotate Histone Modifications,' 'Unlocking The Alternative Splicing Code,' and 'GPU-Based Machine Learning System For Fundamental Biological Research.' She is involved in multiple interdisciplinary collaborations and frequently presents her work at major conferences including ELLIS Health program retreat, Epigenetics Meetings, and RECOMB workshops. She maintains active research laboratories in both Dundee and Tuebingen, fostering international collaboration between computational biologists, machine learning experts, and experimental biologists to advance our understanding of epigenetic regulation in health and disease.

John Haleمشاهده پروفایل

استاد

• Cyber security
• Bioinformatics
• High performance computing
• +۶ مورد دیگر

John Hale, Ph.D., is a Professor and Chair of Computer Science at The University of Tulsa's Tandy School of Computer Science, where he holds the Tandy Endowed Chair in Bioinformatics and Computational Biology. He is a founding member of the TU Institute of Bioinformatics and Computational Biology (IBCB) and a faculty research scholar in the Institute for Information Security (iSec). Education: Ph.D., Computer Science, The University of Tulsa (1997) M.S., Computer Science, The University of Tulsa (1992) B.S., Computer Science, The University of Tulsa (1990) Dr. Hale's research spans cybersecurity , bioinformatics , cyber-physical systems , and applied formal methods . His work focuses on neuroinformatics, cyber trust, attack modeling, secure software development, and information privacy. Recent publications highlight trends in large-scale graph analysis for cybersecurity, attack graph generation on high-performance computing clusters, and security frameworks for nuclear reactor control systems. His research also explores hybrid attack graph modeling, reflective deception strategies, and compliance methods for cyber-physical infrastructures. Scientific Awards: 2000 National Science Foundation CAREER Award Dr. Hale has advised numerous research projects and received funding from the U.S. Air Force, Army, NSF, NIH, DARPA, NSA, and NIJ. He has testified before Congress on cybersecurity and holds a patent for anti-piracy technology. His lab work includes developing cyber-physical testbeds and science DMZ security solutions.

Michela Becchiمشاهده پروفایل

دانشیار

• Computer Architecture
• Systems Software
• Heterogeneous Computing
• +۵ مورد دیگر

Michela Becchi is an Associate Professor in the Department of Electrical and Computer Engineering at North Carolina State University. She specializes in computer architecture, systems software, and applications, with a focus on heterogeneous systems, parallel algorithms, and acceleration techniques for bioinformatics, pattern recognition, and quantum computing. Her work spans multi-core CPUs, GPUs, FPGAs, and distributed clusters, emphasizing the boundary between hardware and software design. Dr. Becchi holds a Ph.D. and Master’s degree in Computer Engineering from Washington University in St. Louis (2009) and a Bachelor’s degree in Computer Engineering from Politecnico di Milano, Italy (2000). Her research has been recognized with prestigious awards, including the NSF CAREER Award (2015) and the University of Missouri System President Award for Early Career Excellence (2016). Her research interests include compiler and runtime techniques for heterogeneous systems, acceleration of bioinformatics algorithms, and high-speed networking applications. She has pioneered frameworks for efficient data transformation, GPU-accelerated compression, and memory-efficient graph algorithms for quantum computing. Her work also explores thread coarsening, mixed-precision auto-tuning, and secure multi-core processor design. Key contributions include the PILOT runtime system for GPU memory management, the GPU-FPtuner auto-tuner for floating-point applications, and innovative approaches to automata processors for genomic analysis. Her publications emphasize reproducible accuracy in scientific simulations and the optimization of irregular applications on many-core platforms.

Anshumali Shrivastavaمشاهده پروفایل

دانشیار

• Large Scale Machine Learning
• Randomized Algorithms for Big Data
• Graph Mining
• +۳ مورد دیگر

Anshumali Shrivastava is an Associate Professor of Computer Science, Electrical and Computer Engineering, and Statistics at Rice University, affiliated with the George R. Brown School of Engineering. His research focuses on large-scale machine learning, randomized algorithms for big data, and graph mining. He holds a PhD from Cornell University (2015) and an MSc from the Indian Institute of Technology Kharagpur (2008). His research interests span scalable deep learning, efficient neural network inference, and probabilistic algorithms. He has pioneered techniques in compressed learning, hashing-based search, and distributed optimization for handling massive datasets. Notable contributions include methods for accelerating LLM inference, memory-efficient quantization, and graph processing algorithms. Teaching: Probabilistic Algorithms, Large-Scale ML, and Machine Learning Seminars Awards: Charles W. Duncan Jr. Achievement Award (2023), Young Faculty Research Award (2021), NSF CAREER Award (2017), and multiple best paper awards His work bridges algorithm design with practical applications in recommendation systems, genomics, and edge computing. Current efforts focus on sustainable AI, hardware-aware compression, and efficient training/inference pipelines for large models.

• Machine Learning
• Bioinformatics
• Computational Biology
• +۵ مورد دیگر

Jian Peng is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. His research focuses on computational biology, machine learning, and their applications to protein structure prediction, drug design, and molecular modeling. He has contributed to advancements in antibody engineering, protein-ligand docking, and generative models for biological systems. Key research areas include: Machine Learning for Molecular Modeling Protein Structure Prediction Antibody and Peptide Design Genomics and Single-Cell Analysis Structure-Based Drug Discovery His work emphasizes integrating deep learning techniques with biological datasets to address challenges in precision medicine, drug development, and systems biology. Notable achievements include developing the FastFold system to accelerate AlphaFold training and pioneering flow-based methods for antibody design. Awards include the Overton Prize (2020), recognizing contributions to computational biology. His research has been published in top journals and conferences, spanning topics from protein mutation prediction to geodesic-based immune complex modeling.

Michael Ferdmanمشاهده پروفایل

دانشیار

• Computer Architecture
• Operating Systems
• Machine Learning
• +۷ مورد دیگر

Michael Ferdman is an Associate Professor in the Department of Computer Science at Stony Brook University, where he leads research in computer architecture and systems. His office is located in Room 343 at Stony Brook, NY 11794-2424, and he can be contacted via phone (631-632-8449) or email. Ferdman directs the Computer Architecture and Systems Laboratory (compas.cs.stonybrook.edu), focusing on next-generation server infrastructure. Ferdman's research spans the entire computing stack with emphasis on: FPGA integration for server environments (Intel HARP, Microsoft Catapult) Machine learning accelerators for convolutional neural networks Server systems optimization in the post-Moore era Network processing and software-defined networking Programming models for emerging memory technologies (HBM, 3D XPoint) Reconfigurable hardware and high-level synthesis His work addresses both performance and security challenges in modern computing infrastructure. Analysis of his 15 most recent publications (2022-2025) reveals consistent focus on: Hardware acceleration techniques (FPGAs, specialized processors) Memory hierarchy optimization and cache management Security vulnerabilities in web applications and systems Post-Moore computing architectures Parallel processing and distributed systems His research shows strong emphasis on practical implementations bridging hardware and software layers. Awards recognizing his contributions include: Graduate Teaching Award (2014) Best Paper Award at ASPLOS XVII Best Paper Finalist at HPCA XVII Three IEEE Micro Top Picks selections (2009, 2012) He teaches advanced courses including CSE 502, CSE 602, and CSE 506 at Stony Brook University.

Melissa C. Smithمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +۵ مورد دیگر

Melissa C. Smith is a Professor of Electrical and Computer Engineering and Associate Dean for Graduate Studies at Clemson University. She holds a Ph.D. from the University of Tennessee and degrees from Florida State University. Her research focuses on machine learning, reconfigurable computing, and high-performance systems, with applications in embedded systems and interdisciplinary scientific advancements. Before joining Clemson in 2006, she was a research associate at Oak Ridge National Laboratory (ORNL), contributing to projects like the Spallation Neutron Source and PHENIX experiments. Education: Ph.D., Electrical and Computer Engineering, University of Tennessee M.S., Electrical Engineering, Florida State University B.S., Electrical Engineering, Florida State University Research Interests: Machine Learning and AI High-Performance and Reconfigurable Computing System Performance Modeling Embedded Systems Articles Summary: Her recent work spans machine learning applications, GPU/FPGA architectures, speech enhancement, and medical systems. Key themes include optimizing heterogeneous computing for real-time and scientific workloads, and advancing interdisciplinary solutions through architecture-application co-design. Lab & Collaborations: Leads the Future Computing Technologies Lab and collaborates with ORNL and national labs on projects like GEMmaker and HPC-enabled medical systems.