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BEGIN:VEVENT
SUMMARY:CSC4029Z Graphics [Patrick Marais]
DTSTART;VALUE=DATE-TIME:20260420T090000Z
DTEND;VALUE=DATE-TIME:20260420T110000Z
UID:191065535274
DESCRIPTION:CSC4029Z Graphics [Patrick Marais]
LOCATION:CS203
END:VEVENT
BEGIN:VEVENT
SUMMARY:CSC4029Z Graphics [Patrick Marais]
DTSTART;VALUE=DATE-TIME:20260422T090000Z
DTEND;VALUE=DATE-TIME:20260422T110000Z
UID:242548804778
DESCRIPTION:CSC4029Z Graphics [Patrick Marais]
LOCATION:CS203
END:VEVENT
BEGIN:VEVENT
SUMMARY:School of IT seminar slot (compulsory if scheduled)
DTSTART;VALUE=DATE-TIME:20260422T220000Z
DTEND;VALUE=DATE-TIME:20260422T220000Z
UID:180066411520
DESCRIPTION:Seminars do not happen every week. Check your UCT mail to find
  out if one is scheduled. This time is just block out on the calendar for 
 scheduling purposes
LOCATION:John Day LT1
END:VEVENT
BEGIN:VEVENT
SUMMARY:CSC4024Z - HCI [Sieera van Riel]
DTSTART;VALUE=DATE-TIME:20260416T220000Z
DTEND;VALUE=DATE-TIME:20260422T220000Z
UID:318696015699
DESCRIPTION:CSC4024Z - HCI [Sieera van Riel]
LOCATION:
END:VEVENT
BEGIN:VEVENT
SUMMARY:School of IT seminar slot (compulsory if scheduled)
DTSTART;VALUE=DATE-TIME:20260423T110000Z
DTEND;VALUE=DATE-TIME:20260423T115000Z
UID:169530431401
DESCRIPTION:Speaker: Sabre Didi Title: Co-evolutionary Adversarial Lear
 ning for Robust Malware DetectionAbstract: Adversarial attacks pose a gro
 wing challenge to machine learning systems used in malware detection\, par
 ticularly when models rely on complex\, high-dimensional feature represent
 ations such as those found in the EMBER dataset. In this talk\, I present 
 a co-evolutionary adversarial training framework designed to improve the r
 obustness of such systems. The approach combines gradient-based adversaria
 l attacks with evolutionary optimisation to create adaptive and increasing
 ly challenging adversaries. A hybrid CNN–MLP model is used to capture bo
 th structural patterns and metadata features within malware samples. Adver
 sarial examples are generated using Projected Gradient Descent (PGD)\; how
 ever\, rather than relying on fixed parameters\, an evolutionary controlle
 r dynamically adjusts the attack strategy based on gradient information. T
 his results in an online co-evolutionary process in which the classifier a
 nd adversary continuously adapt to one another\, mirroring the real-world 
 arms race in malware detection. Experimental results show that this method
  improves robustness against adversarial attacks while maintaining strong 
 performance on clean data. This work highlights how combining deep learnin
 g with evolutionary strategies can lead to more resilient cybersecurity sy
 stems and provides insight into the future of adaptive defence mechanisms 
 in adversarial environments.Biography:Dr. Sabre Z. Didi completed his PhD 
 (2018) in Computer Science and undertook postdoctoral research (2019–202
 2) in the evolutionary machine learning research group under the supervisi
 on of Professor Geoff Nitschke at the University of Cape Town (UCT)\, spec
 ialising in Evolutionary (Robotic) Controller Design. Between 2018 and 202
 5\, he worked in the software development industry across multiple organis
 ations\, holding roles of increasing responsibility\, including positions 
 in AI engineering and software development. Notably\, he worked as an AI a
 nd Software Engineer at Sedna\, a UK-based company specialising in the mar
 itime sector. He is currently a Lecturer at the Cape Peninsula University 
 of Technology in the Department of Information Technology.
LOCATION:John Day LT1
END:VEVENT
BEGIN:VEVENT
SUMMARY:CSC4024Z - HCI [Sieera van Riel]
DTSTART;VALUE=DATE-TIME:20260424T090000Z
DTEND;VALUE=DATE-TIME:20260424T110000Z
UID:824803426852
DESCRIPTION:CSC4024Z - HCI [Sieera van Riel]
LOCATION:d-school St 4 on middle campus
END:VEVENT
BEGIN:VEVENT
SUMMARY:Project Proposal Draft Due
DTSTART;VALUE=DATE:20260423
DTEND;VALUE=DATE:20260424
UID:479982008307
DESCRIPTION:Project Proposal Draft Due
LOCATION:
END:VEVENT
BEGIN:VEVENT
SUMMARY:UCT CS Honours: Block 2
DTSTART;VALUE=DATE:20260412
DTEND;VALUE=DATE:20260424
UID:130167014430
DESCRIPTION:UCT CS Honours: Block 2
LOCATION:
END:VEVENT
BEGIN:VEVENT
SUMMARY:freedom Day
DTSTART;VALUE=DATE:20260426
DTEND;VALUE=DATE:20260427
UID:148130328375
DESCRIPTION:freedom Day
LOCATION:
END:VEVENT
BEGIN:VEVENT
SUMMARY:School of IT seminar slot (compulsory if scheduled)
DTSTART;VALUE=DATE-TIME:20260430T110000Z
DTEND;VALUE=DATE-TIME:20260430T115000Z
UID:298665319401
DESCRIPTION:Title: Do Drug-Response Prediction Models Actually Use Molecu
 lar Features? A Systematic Benchmark and Ablation StudyAbstract: Numerous
  deep learning models have been proposed for drug-response prediction — 
 forecasting gene expression changes following drug treatment. While model 
 architectures have grown increasingly complex\, the lack of a unified eval
 uation framework has made fair cross-model comparison difficult\, and the 
 actual contribution of molecular features remains poorly understood. This
  study introduces a standardised evaluation framework with unified data sp
 lits\, cross-validation\, and metrics to systematically benchmark nine pub
 lished models. The framework centres on two key components: ablation via z
 ero-replacement and random shuffling of molecular feature inputs to quanti
 fy their true contribution\, and an MLP baseline using only cell-line feat
 ures to probe the relationship between architectural complexity and perfor
 mance. Preliminary results show that a simple three-layer MLP (DEG PCC 
 ≈ 0.637) matches or exceeds most complex models\, while several models s
 how little to no performance degradation when molecular features are remov
 ed. These findings highlight a widespread underutilisation of molecular in
 formation in current approaches and offer a reusable benchmark for guiding
  future model development in this field.Biography:Jinming Bai is a PhD can
 didate in the Department of Human Biology\, Faculty of Health Sciences\, U
 niversity of Cape Town\, having previously completed his MSc at UCT. His r
 esearch\, supervised by Prof Sharon Prince (cancer biology) and co-supervi
 sed by Prof Geoff Nitschke (computer science)\, applies machine learning t
 o predict post-treatment gene expression changes for drug repurposing and 
 targeted cancer therapy.
LOCATION:John Day LT1
END:VEVENT
BEGIN:VEVENT
SUMMARY:Project Proposal Presentations
DTSTART;VALUE=DATE:20260427
DTEND;VALUE=DATE:20260430
UID:106871237426
DESCRIPTION:Project Proposal Presentations
LOCATION:
END:VEVENT
BEGIN:VEVENT
SUMMARY:Worker's Day
DTSTART;VALUE=DATE-TIME:20260430T220000Z
DTEND;VALUE=DATE-TIME:20260501T220000Z
UID:757477336044
DESCRIPTION:Worker's Day
LOCATION:
END:VEVENT
BEGIN:VEVENT
SUMMARY:CSC2029Z Graphics [Patrick Marais]
DTSTART;VALUE=DATE-TIME:20260504T090000Z
DTEND;VALUE=DATE-TIME:20260504T110000Z
UID:128769967485
DESCRIPTION:CSC2029Z Graphics [Patrick Marais]
LOCATION:CS203
END:VEVENT
BEGIN:VEVENT
SUMMARY:Project Proposal Due
DTSTART;VALUE=DATE:20260504
DTEND;VALUE=DATE:20260505
UID:794373612993
DESCRIPTION:Project Proposal Due
LOCATION:
END:VEVENT
BEGIN:VEVENT
SUMMARY:CSC2029Z Graphics [Patrick Marais]
DTSTART;VALUE=DATE-TIME:20260503T220000Z
DTEND;VALUE=DATE-TIME:20260505T220000Z
UID:245531375817
DESCRIPTION:CSC2029Z Graphics [Patrick Marais]
LOCATION:CS203
END:VEVENT
BEGIN:VEVENT
SUMMARY:CSC2029Z Graphics [Patrick Marais]
DTSTART;VALUE=DATE-TIME:20260506T090000Z
DTEND;VALUE=DATE-TIME:20260506T110000Z
UID:321942299583
DESCRIPTION:CSC2029Z Graphics [Patrick Marais]
LOCATION:CS203
END:VEVENT
BEGIN:VEVENT
SUMMARY:STA4026S Analytics
DTSTART;VALUE=DATE-TIME:20260413T220000Z
DTEND;VALUE=DATE-TIME:20260506T220000Z
UID:305738810586
DESCRIPTION:Please note I have put this in the CSC Honours calendar for yo
 ur reference and for venue reference. However\, you should refer to the st
 ats department for the final word on scheduling of STA4026S.
LOCATION:HUM LT1B
END:VEVENT
BEGIN:VEVENT
SUMMARY:Ethics Application Deadline
DTSTART;VALUE=DATE:20260510
DTEND;VALUE=DATE:20260511
UID:168825705470
DESCRIPTION:Ethics Application Deadline
LOCATION:
END:VEVENT
END:VCALENDAR